esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Finger Type Models: Blood Vessel and Whole Finger Models
#### 2.2.2 Whole Finger Model  
The whole finger model considers the lumped fingertip constituents as a homogeneous mixture. The blood entering this model will increase the fractional volume of arterial blood. Considering only the increase in the arterial fraction, the equation for the absorption coefficient becomes [14]:  
\[C_{a}=\Big{(}V_{a}\mu_{a}^{art}(\lambda)+V_{v}\mu_{a}^{nein}(\lambda)+V_{w}\mu_ {a}^{water}(\lambda)+(1-(V_{a}+V_{v}+V_{w})\Big{)}\times\mu_{a}^{baseline}). \tag{24}\]  
Equations (25) and (26) can be easily obtained [14] after replacing the values of \(P_{HbO}\) and \(P_{HHb}\) from Equations (20) and (21), as follows:  
\[\mu_{a}^{art}=\Big{\{}P_{HbA1c}\Big{(}\mu_{a}^{HbA1c}(\lambda)-\mu_{a}^{HbO}( \lambda)SpO_{2}-\mu_{a}^{HHb}(\lambda)(1-SpO_{2})\Big{)}+\mu_{a}^{HbO}(\lambda )SpO_{2}+\mu_{a}^{HHb}(\lambda)(1-SpO_{2})\Big{\}}\text{ and } \tag{25}\]  
\[\mu_{a}^{nein}=\Big{\{}P_{HbA1c}\Big{(}\mu_{a}^{HbA1c}(\lambda)-\mu_{a}^{HbO}( \lambda)SpO_{2}-\mu_{a}^{HHb}(\lambda)(1-SpO_{2})\Big{)}+\mu_{a}^{HbO}(\lambda )SpO_{2}+\mu_{a}^{HHb}(\lambda)(1-SpO_{2})\Big{\}}. \tag{26}\]  
Now, considering the arterial fraction increment, the absorption coefficient equation is as shown in Equation (27). Here, \(\Delta C_{a}\) represents the change in the absorption coefficient due to a change in the arterial blood volume:  
\[C_{a}+\;\Delta C_{a}=\Big{(}(V_{a}+\Delta V_{a})\mu_{a}^{art}(\lambda)+V_{v} \mu_{a}^{nein}(\lambda)+V_{w}\mu_{a}^{water}(\lambda)+(1-(V_{a}+\Delta V_{a}+V_ {v}+V_{w})\Big{)}\times\mu_{a}^{baseline}). \tag{27}\]  
After subtracting Equation (24) from (27), we obtain  
\[\Delta C_{a}=\Delta V_{a}(\mu_{a}^{art}(\lambda)-\mu_{a}^{baseline}(\lambda)). \tag{28}\]  
From the Beer-Lambert law, we obtain  
\[I=I_{o}\times 10^{-C_{a}d}. \tag{29}\] \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Molar Absorption Coefficient (cm\({}^{-1}\).M\({}^{-1}\))**} \\ \cline{2-3}  & **\(\mathbf{\Lambda 1}\) = 525 nm** & \(\mathbf{\lambda 2}\) = 615 nm \\ \hline \(HbA1c\) & 455,139.5677 & 170,555.4218 \\ \hline \(HbO\) & 30,882.8 & 1166.4 \\ \hline \(HHb\) & 35,170.8 & 7553.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Molar absorption coefficients for the blood vessel model. Equation (29) must be differentiated in terms of \(C_{a}\) to find the relationship between the physical light intensity and Equation (28):  
\[\frac{dI}{dC_{a}}=-\ln(10)I_{a}d\times 10^{-C_{a}d}\text{ and } \tag{30}\]  
\[\frac{dI}{dC_{a}}\approx\frac{\Delta I}{\Delta C_{a}}\;. \tag{31}\]  
From Equations (30) and (31), we obtain  
\[\Delta I\;\approx-\ln(10)I_{a}\Delta C_{a}d\times 10^{-C_{a}d}. \tag{32}\]  
Now, the AC-DC intensity ratio is generated by the assumption \(\frac{I_{AC}}{I_{DC}}=\frac{\Delta I}{I}\).
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Table Management
#### 3.2.2 GCS-Routing Operations in the Drone  
Algorithm 2 shows that operations in a drone can be divided into route update and link monitoring. In the route update phase (Lines 1-15), the drone parses the routing message, \(m\), addressed to itself from the received aggregated route message, \(M\). It then compares new routes in \(m\) with old routes and updates only changed route entries for efficiency. The drone deletes its routing table from \(M\) and classifies the remaining \(M\) into \(M_{1}\), \(M_{2}\), \(\cdots\), \(M_{n}\) for its subtrees to generate new aggregated route messages. The subtree root is the received node's one-hop child node. The newly generated aggregated messages are then issued, and the process is repeated until \(M\) reaches the leaf nodes. Consequently, routing tables of all drones can be maintained up-to-date. Figure 3 shows an example of distributing the routing table in GCS-routing. Each drone extracts routing table information from corresponding data in the aggregated route message, and then sends the remaining data to the child node.  
In the link monitoring phase (Lines 16-21), a drone scans for link failures between itself and neighbors periodically based on the reception rate of beacons from neighbors. As discussed above, beacon broadcasts conducted by AP interfaces do not affect routing performance in ad-hoc networks. If a link failure is detected, the drone broadcasts a link failure message. Neighboring drones receive the message and forward it to the GCS.
## 3 Control Design for Transition Phase
### Control Law Design
#### 3.2.1 Pitch Angle Control  
The pitch angle control structure adopts the pitch rate control as the inner loop, and the outer loop is designed using the dynamic inversion control method, which enables the pitch command to track the pitch rate command accurately. Figure 6 shows its control structure. The dynamic equation of the pitch angle is given by:  
\[\dot{\theta}=q\cos\phi-r\sin\phi \tag{12}\]  
\(\dot{\theta}\) can be regarded as the desired change rate of the pitch angle. The NDI controller ensures that the pitch rate controller can track the commands quickly without steady-state errors in the presence of model uncertainty and external bounded unsteady disturbances. In order to ensure that the pitch angle can track the commands rapidly, the pitch angle control adopts the proportional control structure, and the pitch angle rate is expressed as:  
\[\dot{\theta}=k_{\theta}(\theta_{c}-\theta) \tag{13}\]  
where \(k_{\theta}\) is the bandwidth of pitch angle control loop. Substitute Equation (13) into Equation (12):  
\[k_{\theta}(\theta_{c}-\theta)=q\cos\phi-r\sin\phi \tag{14}\]  
The rolling angle is limited within \(-1\)-\(1\) rad, and the control law of the pitch angle loop can be obtained using Equation (14):  
\[q_{c}=\frac{k_{\theta}(\theta_{c}-\theta)+r\sin\phi}{\cos\phi} \tag{15}\]  
where, \(\theta_{c}\) is the pitch angle command, \(\theta\) is the pitch angle feedback value, \(\phi\) is the roll angle, \(r\) is the yaw angle rate, and \(q_{c}\) is the pitch angle rate command generated by the pitch angle loop.  
Figure 6: Control structure of pitch angle.
# Compliance of Distribution System Reactive Flows with Transmission System Requirements
## 1 Introduction
### Contribution and Organisation  
This paper addresses the problem of adherence to the reactive power exchange limits between the distribution and transmission systems under cases of complex requirements,by utilising DER reactive power control capabilities. The case study of the Finnish TSO reactive power exchange requirements with the distribution network is taken as a real-life application. Expanding from the works in [11; 14; 15; 16; 17; 18; 19], this work proposes a novel method with the following features:  
* It introduces complex TSO requirements to a quadratically constrained quadratic programming optimisation problem that can be efficiently and timely solved.
* It considers the constraints of the distribution system to ensure that DER compensation does not compromise network currents and voltages.
* It cakes use of a short-term MPC framework that ensures control actions do not negatively affect the compliance with the requirement within the specified time frame set by the regulation.  
The rest of the paper is organised as follows. In Section 2, the developed methodology is described, and in Section 3, the considered case studies are presented. Finally, Section 4 concludes the paper.
# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
## 4 Experiments  
### Dataset  
* **Training datasets:** To train MDNCL for AWGN removal, we utilized the Berkeley segmentation dataset (BSD) [27], which consists of 432 original natural images with dimensions of \(481\times 321\). In order to enhance the training process and increase the number of training patches, each image was divided into 128 patches with dimensions of \(58\times 58\), resulting in a total of 55,296 image patches. Subsequently, random data augmentation techniques were applied to the training patches to enhance the generalization ability of MDNCL. These techniques included vertical flipping, rotation, etc. For denoising in grayscale images, we trained two versions of MDNCL, namely MDNCL, which utilizes known noise levels, and MDNCL-B, which performs denoising with blind noise levels. Similarly, for color images, we conducted experiments using CMDNCL and CMDNCL-B. Known noise levels were set to \(\sigma=15\), 25, and 50, where \(\sigma\) represents the standard deviation. The blind noise level was set within the range of \(\sigma\in[0,55]\). To train MDNCL for real-world noise removal, we used the Smartphone Image Denoising DATA (SIDD) sRGB track [28] with 160 scene instances, where each instance includes two pairs of high-resolution images. Each pair contained one noisy image and the corresponding clean image. In all, 320 pairs of images were used to train for real-world denoising.
* **Test datasets:** For grayscale testing of MDNCL, we evaluated its denoising performance using two datasets, namely the Berkeley segmentation dataset (BSD68) [29] and Set12 [29]. BSD68 consists of 68 images, while Set12 contains 12 images. These datasets were specifically used for AWGN removal. Regarding color image testing, we employed the CBSD68 [29], McMaster [30], and Kodak24 [31] datasets. CBSD68 consists of 68 images, the McMaster dataset consists of 18 images, and Kodak24 consists of 24 images. To assess real-world denoising performance, we utilized sets from the SIDD validation dataset and the Darmstadt Noise Data (DND) dataset [32]. The DND dataset comprises 50 pairs of images but does not provide ground-truth images. To evaluate the denoising results, we uploaded the denoised images to the official DND website, where we obtained the PSNR [33] and SSIM [34] measurements.
## 2 Materials and Methods
### Experimental Procedure  
In this study, the factor examined among the printing parameters is nozzle temperature. A total of thirty-six tensile test samples were fabricated in groups of six according to the determined factors. The printing nozzle temperatures ranged from 220 to 270 \({}^{\circ}\)C. If the nozzle temperature changes in FFF fabrication, the printing speed must also change to ensure good mechanical properties. Low printing speed is desired at high nozzle temperatures [30; 31; 32; 33]. Thus, samples were fabricated with printing speeds of 90, 80, 70, 60, 50, and 40 mm/s. The table containing the observed parameters to be used in the ANOVA is as follows (Table 2).  
The ISO527-type1A standard [34] was preferred for sizing tensile test samples. The technical drawing and dimensions of the samples are given in Figure 2a. The samples prepared according to this standard have dimensions of 150 mm \(\times\) 20 mm \(\times\) 5 mm for length, width, and thickness. Tensile test studies were performed on a 50 kN force transducer capacity machine (AGS-X, Shimadzu, Kyoto, Japan) using the ISO527-type1A standard. The device has a reading range of \(\pm\)0.1% precision between 50 kN and 50 N according to ISO 7500/1 [35], ASTM E4 [36], and DIN51221 [37] standards. The test speed range is 0.0005 mm/min-1000 mm/min. Experimental studies were performed at a drawing speed of 0.8 mm/s. Ultimate tensile strength, strain, and force data measured during the experiment were recorded with the software. The image of the samples as a result of the tensile test is given in Figure 2b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Nozzle Temperature (\({}^{\circ}\)C) & 220 & 230 & 240 & 250 & 260 & 270 \\ \hline Printing Speed (mm/s) & 90 & 80 & 70 & 60 & 50 & 40 \\ \hline \end{tabular}
\end{table}
Table 2: Observed factors and levels.  
Figure 1: Fused filament fabrication printing process.  
During the tensile test, the ABS specimen was prepared in a rectangular shape and placed into the testing machine. The loading direction was determined parallel to the length of the material (i.e., parallel to the long axis of the specimen). This orientation is crucial for assessing the material's strength and flexibility based on stress and deformation across its cross-sectional area. Throughout the test, a constant load was applied to the material at a fixed rate, while changes in elongation and force of the specimen were recorded.  
The measurements necessary to observe the dimensional change of all fabricated samples were made with a caliper. Length (L), width (W), and thickness (T) parameters of the samples were measured. In addition, the mass of the samples was measured with the help of precision scales. Finally, statistical analysis was performed with the ANOVA method to examine the effect of the nozzle temperature parameter on these features.
## 2 Optimal Device Design Consideration and Simulations
### COMSOL Simulation of SAW UV-C Sensor  
COMSOL simulations were conducted to assess the response of a SAW-based UV sensor using HfO\({}_{2}\) as the sensing material. This analysis focused on examining frequency and amplitude variations in the propagating SAW. A two-port SAW delay line was designed on a 128\({}^{\circ}\) YX LiNbO\({}_{3}\) piezoelectric substrate with a simplified geometry, incorporating four pairs of IDTs (Figure 3a,b). Intensive meshes were formed along the top surface. A 30 nm HfO\({}_{2}\) layer was deposited in the cavity region between the two IDTs (Figure 3c), where mechanical displacements along the \(Y\)-axis were measured before and after UV exposure. As illustrated in Figure 3d, the SAW stabilized after approximately 50 ns, reaching a maximum amplitude of 1.09 nm prior to UV exposure.  
UV exposure induced conductivity changes in the HfO\({}_{2}\) layer. These changes were simulated by modeling HfO\({}_{2}\) as a semiconductor within the component selection, correlating UV exposure to a 5% increase in carrier concentration, a 10% enhancement in electron-hole mobility, a 0.05 eV reduction in bandgap, and a 5% increase in the effective density of states (N\({}_{\text{c}}\) and N\({}_{\text{v}}\)) to replicate UV effects accurately. Following UV exposure, a delay of up to 0.96 ns was observed, along with slight variations in the frequency, phase, and amplitude of the SAW, as shown in Figure 3d. These shifts were attributed to mechanisms such as direct recombination and trap-assisted recombination within the HfO\({}_{2}\) layer, which were incorporated into the semiconductor model to accurately mimic the effects of UV exposure. For standard UV exposure (resulting in a 1-5% increase in conductivity), the measured SAW amplitude was approximately 0.75 nm, with a frequency shift of up to 10 kHz, closely aligning with experimental results. Based on these findings, we suggest that HfO\({}_{2}\) can effectively alter its conductivity upon UV application, leading to measurable shifts in SAW characteristics.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of input IDT pairs & 120 \\ Number of output IDT pairs & 65 \\ Aperture length (\(\lambda\)) & 110 \\ Cavity length (\(\upmu\)m) & 2200 \\ Wavelength \(\lambda\) (\(\upmu\)m) & 17.6 \\ IDT Ti/Pt thickness (nm) & 10/100 \\ Acoustic velocity (m/s) of 128\({}^{\circ}\) YX & 3890 \\ LiNbO\({}_{3}\) & 5.5 \\ Electromechanical coupling factor, \(K^{2}\) (\%) & 55 \\ Effective dielectric coefficient \(\varepsilon_{\text{p}}/\varepsilon_{0}\) & 55 \\ Sheet resistance (\(\Omega\)-m) & 0.939 \(\times\) 10\({}^{8}\) \\ \hline \end{tabular}
\end{table}
Table 1: The parameters of the two-port SAW delay line determined through COM modeling.
## 4 Simulation Verification
### Analysis of Simulation Results
In addition, references [16; 43] indicate that the change of damping will affect the performance of the algorithm in identifying modal parameters. From the Figure 15, damping ratio of time-varying structure constantly changing, which leads to low precision of modal parameter identification in some time. In addition, References [28; 30] indicate that the window length \(L\) will also affect the identification accuracy of modal parameters. From Equation (28), the window length \(L\) is proportional to the sample frequency \(f_{s}\) and frequency resolution \(\triangle f\). Frequency resolution \(\triangle f\) cannot be too small, because it cannot reflect the change of frequency, and cannot be too large, because the average frequency variation \(\triangle f_{L}(i)\) cannot be identified. At the same time, \(\triangle f_{L}(i)\) cannot be larger too much than \(\triangle f\), otherwise, time-varying structure cannot be regarded a time-invariant structure in a window. According to the noise simulation results of 3-DOF structure, the MWLPP algorithm turns out to be robust to noise. This is probably LPP algorithm computes a transformation matrix which maps the data points to a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense [36]. This feature helps retain information about the response signal.
## 2 Materials and Methods
### TVE Quality Parameter  
The tests were defined based on the requirements of the IEEE C37.118.1a-2014 synchrophasor standard [13], which includes a number of modifications to lower the technological requirements of the classic IEEE Std C37.118.11M-2011 [16]. The standard defines the concept of total vector error (TVE) as a quality parameter that determines the measurement of the error between the theoretical phasor value of the signal under study and the actual value measured periodically. This standard has been widely accepted as the method that defines synchronised phasor measurements in power applications and provides methods that allow these measurements to be quantified, representing the tests that guarantee the measurements and error limits for these tests.  
Subsequently, the specifications that determine the quality of measurements with PMUs (set out in section \"4.3 Measurement time synchronisation\" of the standard [16]) are analysed to establish an uncertainty level for distributed measurement systems.  
The standard specifies two performance classes: Class P and Class M. Class P is proposed for applications requiring fast response without strict accuracy criteria. Class M is defined for those applications that require precise measurements (with high bandwidth) and where speed of response is not a critical factor. As far as the present work is concerned, since we are dealing with medium-access protocols, the timing specifications should be the most restrictive ones that guarantee the two performance classes.  
The standard details a time error of 1 us, corresponding to a phase error in the synchrophasor of 0.018 degrees for a 50 Hz system. It is stated that a phase error of 0.57 degrees (0.01 radians) will cause an error of 1 % TVE (the maximum allowed), corresponding inthe time scale to an error of \(\pm\) 31 \(\upmu\)s for a 50 Hz system. A time source with a frequency error (FE) of 0.1 MHz in a 50 Hz system will cause the maximum allowed steady state FE of 0.005 Hz. For a TSN master type time source that reliably provides a reference, it is recommended that the frequency and frequency stability be at least 10 times better than the values corresponding to 1% TVE. This implies a temporal error of less than \(\pm\)3.1 \(\upmu\)s for 50 Hz systems. In this work, \(\pm\)3.1 \(\upmu\)s is taken as a reference to ensure that the quality of the TSN synchronising system is at the same level as that required for the design of a PMU.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 3 Results
### Tests against S3 Compatible Systems
#### 3.2.4 Tests against Cloud Systems  
Different S3 vendors were contacted, which either offered a testing account or explicitly allowed us to execute the IO500 benchmark against their endpoints. We follow the guidelines depicted by the performance design patterns for Amazon S3 [33], especially regarding the request parallelization and horizontal scaling to help distribute the load over multiple network paths. Since all the providers offer multi-region storage, we choose the closest storage location to Mistral (located in the EU) to ensure the lowest latency. Due to the limited number of nodes with Internet connectivity on Mistral, the benchmarks are launched on only two nodes with PPN = 1. The results are summarized in Table 2; we scaled down the units by 1000 to better visualize the differences. The results of MinIO launched in local-gw mode--with the same number of nodes and tasks per node--are displayed in the last column.  
The IBM Cloud Storage provided the best performance in our tests; however, this is far from the performance expected in HPC. Although MinIO in gateway mode provides better performance, this is also below our HPC experience since the network latency, in this case, is minimal compared to the other scenarios. A better solution is needed to leverage existing file systems found either inside or outside the HPC environment, as can be seen in Section 4. Note that some of the mentioned providers might provide better performance when using their native interface instead of S3; however, this is outside the scope of this work. Furthermore, the network interconnection between DKRZ and the cloud provider bears additional challenges.
## 3 Results
### Historical Trend Analysis of NDVI
Specifically, the NDVI for forest land was 0.40551 in 2001 and fluctuated to 0.40167 by 2023, with a relatively minor change, indicating stable vegetation coverage and growth status. Cropland NDVI showed a noticeable upward trend from 2001 to 2009, followed by greater variability after 2010, yet without significant changes, potentially associated with climate change, advancements in agricultural technology, or shifts in farming practices. Although the NDVI values for shrubland fluctuated throughout the period, there were no dramatic changes overall, possibly due to the greater influence of natural factors on shrubland vegetation cover and growth status. Grassland NDVI demonstrated variability from 2001 to 2023, notably with a markedly lower value in 2007 (0.18703), which may have been influenced by drought, climate change, or human activities. Generally, grassland NDVI showed larger fluctuations and periods of lower values, potentially related to grassland degradation or inadequate management. Furthermore, seasonal analysis reveals heterogeneous trends in NDVI across different vegetation types. Overall, from 2001 to 2023, vegetation in China exhibited varying trends across seasons. In spring, the NDVI values of forest, cropland, and grassland experienced a significant downward trend between 2001 and 2005, followed by a gradual recovery, reaching a notable peak in 2014. However, between 2016 and 2023, the NDVI values of these vegetation types fluctuated again, particularly remaining at lower levels from 2016 to 2019, but they started to gradually rise again from 2020 onwards. In contrast, the NDVI of shrub vegetation continued to show a steady upward trend during this period. In summer, the NDVI values of forest, cropland, and grassland showed a fluctuating upward trend from 2001 to 2012, peaking in 2012. After that, the NDVI values gradually declined, reaching a low point in 2017, before starting to rise again. By 2021, the values were close to the previous peak, although there was a slight decline by 2023. In contrast, the NDVI of shrub vegetation remained relatively stable, with a slow upward trend throughout the entire observation period. In autumn, the NDVI values of all four vegetation types (forest, cropland, grassland, and shrub) generally increased before entering a phase of fluctuating decline. Particularly between 2007 and 2021, the changes in NDVI were especially dramatic, with peaks in 2008 and 2009, followed by a downward trend. In contrast, the NDVI of shrub vegetation showed a relatively stable upward trend during this period. In winter, there was no clear trend of increase or decrease overall, but rather a fluctuating pattern. The NDVI values of forest, cropland, and grassland experienced significant fluctuations between 2006 and 2012, with a noticeable trough in 2008. Similarly, the NDVI of shrub vegetation also encountered a trough in 2008, but then gradually recovered and maintained a relatively stable upward trend.