esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of FOPID Controller
### Description of Fractional Order Controller Based on Bode Envelope  
The transfer functions given in (15) and (16), respectively, offer the minimum and maximum plots of the gain.  
\[\widehat{G}_{1a}\ (s)=\frac{e_{0}^{-}}{f_{1}^{+}s+f_{0}^{+}} \tag{15}\]  
\[\widehat{G}_{1b}\ (s)=\frac{e_{0}^{+}}{f_{1}^{-}s+f_{0}^{-}} \tag{16}\]  
Figure 4: Closed loop ROCIP with FOPID controller.  
Figure 5: Closed loop HOCIP with FOPID controller.  
Similarly, the transfer functions given by (17) and (18), respectively, provide the minimum and maximum plots of the phase.  
\[\widehat{G}_{1c}\ (s)=\frac{e_{0}^{+}}{f_{1}^{-}s+f_{0}^{+}} \tag{17}\]  
\[\widehat{G}_{1d}\ (s)=\frac{e_{0}^{-}}{f_{1}^{+}s+f_{0}^{-}} \tag{18}\]  
Designing of \(PI^{\kappa}D^{\beta}\) controller for \(\widehat{G}_{1b}\left(s\right)\) and \(\widehat{G}_{1d}\left(s\right)\) should satisfy (13) and (14), respectively. Thus, (13) and (14) become  
\[20*\log_{10}\bigg{|}\overset{\wedge}{C}\left(j\omega_{gc}\right)\ \widehat{G}_{1b}\ (j \omega_{gc})H(j\omega_{gc})\bigg{|}=0 \tag{19}\]  
\[Arg\Big{(}\overset{\wedge}{C}\left(j\omega_{gc}\right)\ \widehat{G}_{1d}\ (j \omega_{gc})H(j\omega_{gc})\Big{)}=-\pi+\phi_{pm} \tag{20}\]  
Using the value of \(\overset{\wedge}{C}\ (s)\) from (9), the expressions given in (19) and (20) become (21) and (22), respectively.  
\[20*\log_{10}\left|\left(\frac{e_{0}^{+}}{\sqrt{(f_{1}^{-}\omega_{gc})^{2}+(f_ {0}^{-})^{2}}}\right)*\left(\sqrt{x^{2}+y^{2}}\right)*\left(\frac{1}{\omega_{ gc}}\right)\right|=0 \tag{21}\]  
\[tan^{-1}\Big{[}\frac{y}{x}\Big{]}-\frac{\pi}{2}-tan^{-1}\Bigg{(}\frac{f_{1}^{ +}\omega_{gc}}{f_{0}^{-}}\Bigg{)}=-\pi+\phi_{pm} \tag{22}\]  
where  
\(x=K_{a}+K_{b}\omega_{gc}^{-\alpha}cos\Big{(}\frac{\pi}{2}\alpha\Big{)}+K_{c} \omega_{gc}^{\beta}cos\Big{(}\frac{\pi}{2}\beta\Big{)}\)  
\(y=-K_{b}\omega_{gc}^{-\alpha}sin\Big{(}\frac{\pi}{2}\alpha\Big{)}+K_{c}\omega _{gc}^{\beta}sin\Big{(}\frac{\pi}{2}\beta\Big{)}\)  
The SCA is utilized for solving (21) and (22) to determine the unknown parameters of \(\overset{\wedge}{C}\left(s\right)\).
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion
### Rotating Disk System Error
#### 5.3.2 Pitch Angle \(\alpha_{x}\)  
The pitch angle \(\alpha_{x}\) is synthesized by the projections \(\theta_{x}\) and \(\varphi_{x}\) of \(\theta\) and \(\varphi\) on OY\({}_{2}\)Z\({}_{2}\). When \(\theta_{x}=\theta\) and \(\varphi_{x}=\varphi\), the pitch angle \(\alpha_{x}\) in OY\({}_{2}\)Z\({}_{2}\) can reach the maximum. In this state, the coordinate axes \(X,X_{1}\), and \(X_{2}\) coincide, and \(Z,Z_{1}\), and \(Z_{2}\) are coplanar. The schematic diagram of the positional relationship between the rotating disk and the LDV is shown in Figure 15.  
Since axes \(Z\) and \(Z_{1}\) do not coincide, when the rotating disk rotates at an angular velocity \(\omega\), the angle \(\alpha_{x}\) will also change with a period of \(2\pi/\omega\), that is, \(\alpha_{x}=\theta+\varphi\cdot\sin(\omega t)\). This expression shows that the rotating disk will produce a periodic pitching motion, which will cause the actual measured value \(V_{\rm mx}\) to change. Since \(V_{\rm mx}\) will be averaged during subsequent data processing, the influence of \(\varphi\) on \(V_{\rm mx}\) can be ignored. However, for \(\theta\), it always exists and will not be eliminated due to averaging. Therefore, when discussing the influence of pitch angle \(\alpha_{x}\) on \(V_{\rm mx}\), it can be considered that \(\alpha_{x}=\theta\).  
Figure 14: Waveform curves of velocity components \(V_{\rm m1}\) and \(V_{\rm m2}\) changing with time \(t\).  
Figure 15: When the pitch angle is the largest, the positional relationship between the rotating disk and the LDV.  
Combined with the influence of the position error of the laser spot, when \(\theta\) is small, it is reasonable to ignore the position change in the measuring point caused by the pitching motion. In this case, the actual laser spot \(\mathrm{P}^{\prime}\) can always be considered at the desired point P. Then, according to the projection relationship in Figure 15, the measured value \(V_{\mathrm{mx}}\) can be obtained by Equation (27).  
\[V_{\mathrm{mx}}=V_{\mathrm{T}}\sin(\beta-\alpha_{x})=\omega r\cdot\sin(\beta- \alpha_{x}) \tag{27}\]  
Finally, the laser beam angle \(\beta_{\mathrm{mx}}\) is reversed according to Equation (27), and \(\beta_{\mathrm{mx}}=\beta-\alpha_{x}=\beta-\theta\). In this case, the error \(\Delta\beta_{\mathrm{mx}}\), due to installation accuracy, is shown in Equation (28).  
\[\Delta\beta_{\mathrm{mx}}=-\theta \tag{28}\]  
According to Equation (28), angle \(\theta\) between the normal line \(Z_{1}\) of the rotating disk and the vertical direction \(Z_{2}\) greatly influences the dynamic measurement result of \(\beta\). Therefore, when performing angle measurement based on the newly proposed measuring method and device, a high-precision inclinometer can be used to measure the inclination angle of the rotating disk to correct the measuring results, or an angle fine-tuning device can be used to make the rotating disk as horizontal as possible.
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
## 4 Attempted Improvements of AlN on Sapphire to Obtain TDD Less Than 10\({}^{9}\)/cm\({}^{2}\)
### Growth of Thick AlN Layer  
Compared with GaN, AlN hardly decomposes or undergoes mass transport [104; 105]. Therefore, the techniques that can be used to improve crystal quality are limited. Since the quality of crystals naturally tends to improve during their growth, a method of improving the crystal quality of AlN is to grow a thick layer with a thickness of about 5 \(\upmu\)m [109; 32]. CHT growth at a temperature exceeding 1300 \({}^{\circ}\)C can reduce the growth time due to its growth rate of more than 5 \(\upmu\)m/h. The growth of thick layer often results in cracks due to the limitation of critical film thickness, limiting the maximum AlN thickness; thus, the thickness of AlN is considered to be limited at around 5 \(\upmu\)m in practice. The achieved FWHM values of \(\upomega\)-scans of (0002) and (10-10) XRCs for an AlN thickness of 4.5 \(\upmu\)m were 150 and 700 arcsec, respectively, when using planar sapphire with a miscut of 0.3\({}^{\circ}\) at a growth rate of 5.5 \(\upmu\)m/h [109]. In addition, by modulating the V/III ratio for CHT growth, (0002) and (10-10) XRCs with FWHMs of 220 and 475 arcsec were obtained [110] for an AlN thickness of 3-4 \(\upmu\)m. Meanwhile, the groups employing EM have reported that thick AlN layers to obtain the AlN with a TDD of about \(2\times 10^{8}\)/cm\({}^{2}\)[24], which enables a high IQE of 49% for smooth and flat MQWs. A TDD of \(2\times 10^{8}\)/cm\({}^{2}\) was also reported by another EM group to have a high IQE [41]. Despite the growth rate of about 0.5 \(\upmu\)m/h for EM, an attempt to grow thick AlN has also been reported [70] to obtain AlN with a low TDD. From the viewpoint of cost-feasible production, the growth of thick AlN layers by EM is not suitable because of the long growth time. Instead, in the early stage of development of CHTgrowth, the short lifetime of consumables, such as the heater and wafer tray, due to the high growth temperature (>1300 \({}^{\circ}\)C) were problems to be solved.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 2 State of the Art
### Mobile Laser Scanning for Vegetation Analysis  
The main applications of MLS have remained restricted to city mapping or autonomous driving in urban areas, while relatively few studies of mobile mapping for vegetation sampling exist. A basic task in these applications is object recognition in point clouds. Through the detection of vertical cylinder-shaped objects, trees along roads can be detected amongst other objects such as road signs and street lights [16; 17; 18; 19]. [PERSON] et al. [20] use MLS data in combination with multi-spectral data of a park and achieve a tree species classification of 10 species with an accuracy of 84%. [PERSON] et al. [21] present the first study on the use of MLS in a large-scale forest area. The extracted tree positions and DBH show accuracies that are close to those of plot-based TLS studies (RMSE of 28 cm and 2.36 cm, respectively). The positioning accuracy of an MLS system is always reduced in forest environments due to the shadowing of the satellites by the canopy and the multipath effects. [PERSON] et al. [22] use the heading angle from GNSS/INS and the velocity data in an IMLE-SLAM algorithm to improve the horizontal accuracy of the trajectory (H-aided IMLE-SLAM with 0.13 m and HV-aided IMLE-SLAM with 0.06 m horizontal accuracy).  
In a recent study, backpacks have been used as mobile platforms (personal laser scanning--PLS) [23]. [PERSON] et al. [24] compare TLS data (single and multiple scans) with hand-held mobile laser scanner data (HMLS) and present a RMSE of 1.11 cm for the DBHs determined with HMLS. [PERSON] et al. [25] show similar results for the DBHs comparing a wearable laser scanner (WLS) with TLS data (RMSE of 1.1 cm). However, the RMSE for tree height was much higher and amounted of 9.44 m, which is due to the low range of the WLS. Compared to the vehicle-supported MLS platforms, PLS, WLS and HMLS are cheaper, more flexible and independent of the forest path and the terrain. Unfortunately, the low measurement range and the large beam divergence of the systems studied so far [24; 25] are strictly limiting the use in forestry applications. The higher acquisition costs and lower flexibility of MLS systems is balanced by the capturing of corridor-like scan areas within a very short time, which makes it potentially attractive for commercial large-scale data evaluation and forest inventories in large forest stands. A motorized platform also has the advantage that the point cloud is recorded with constant point density.
# Nanomechanical, Structural and Antioxidant Characterization of Nixtamalized Popcorn Pericarp
## 3 Results and Discussion
### Antioxidant Activity
#### 3.4.2 Reducing Power  
The results of reducing power are shown in Figure 3. The activity of the pericarp was compared with the activity shown by Trolox, caffeic acid, ferulic acid, and coumaric acid (0.1 M), as the pericarp contains these last three acids. The pericarp exerts a reducing power of 29% compared to the Trolox (0.1 M), 30% compared to caffeic acid (0.1 M), 33% compared to ferulic acid, and 88% compared to coumaric acid (0.1 M), being the coumaric acid, the control with a highest reducing power; high percentages are related to a bioactive component performance. The values of reducing power were not significantly different (\(p<0.05\)) between caffeic acid and Trolox. [PERSON] et al. (2018) reported values of around 85% to 40% in different corn types, while [PERSON] et al. (2018) studied reducing power in milling cereals such as barley, and they obtained values of around 32-38% expressed as mg of Trolox equivalent. Another study found the lowest values in reducing power in yellow and white maize from Nigeria at 2.27 d \(\pm\) 0.06 and 0.94 \(\pm\) 0.03 (mg AAE g\({}^{-1}\)), respectively (2018). Therefore, we concluded that the pericarp has an actual charge of reducing power.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Total** & **ABTS** & **DPPH** \\  & **Phenolic Content** & **ABTS** & **DPPH** \\ \hline
**Péricarp** & 0.21 \(\pm\) 0.008 & 550.1 \(\pm\) 2.9 & 44.2 \(\pm\) 1.6 \\ \hline \hline \end{tabular} The content of total phenols will be expressed in ug gallic acid equivalents/mL of sample (mg GAE/mL), ABTS and DPPH assays reported in mM Trolox equivalents (TE)/mL.  
\end{table}
Table 2: Antioxidant activity of pericarp (total phenolic content, ABTS and DPPH).
## 4 Discussion
### Reliability and Merits of the SWH Model
In addition to ET partitioning, the SWH model also estimates GPP, another key variable in ecological processes, which helps to address the coupling relationship between carbon and hydrological processes (e.g., ecosystem WUE) at multiple spatiotemporal scales [54]. The third merit of the SWH model is that relatively few input variables are required. One remote sensing product (i.e., NDVI) and six meteorological variables (i.e., air temperature, precipitation, relative humidity, wind speed, \(R_{\text{n}}\) and PAR) are the input driving variables for the model (Figure 1). Most of the variables are accessible from public data sources, facilitating regional and global applications.
# Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches
## 5 Discussion
### Limitations  
While the proposed method for brain tumour classification using deep learning and ensemble algorithms shows promising results, several limitations must be acknowledged. The model's performance relies heavily on the training datasets used, which may not fully represent the diversity of real-world clinical data, affecting the generalisation to unseen data. Brain tumour datasets often exhibit class imbalances, leading to biassed model training and suboptimal performance for less common tumour types. Additionally, the training and deployment of deep learning models require significant computational resources, which may not be accessible in all clinical settings. Furthermore, the interpretability of these models remains a challenge, as clinicians require clear and understandable explanations for their predictions to aid in decision-making, which the current models may lack. These limitations highlight the need for continued research and development to address dataset bias, class imbalance, computational constraints, and model interpretability, ensuring the deployment of robust and clinically relevant brain tumour classification models.  
Robustness and generalisation are critical factors for successfully translating advanced deep-learning models for brain tumour classification from research to clinical practise. Ensuring robustness across different imaging conditions and scanners is crucial, as variations in MRI protocols, scanner types, and patient populations can impact the model's performance. To ensure generalisation, the models must be validated extensively across diverse clinical settings. Additionally, despite applying regularisation techniques and early stopping, the risk of overfitting persists, especially when models are trained on limited datasets, which can lead to poor performance on new, unseen data. Furthermore, integrating these complex models into clinical workflows poses practical challenges, requiring the development of user-friendly interfaces and seamless integration with hospital information systems to facilitate widespread adoption. Addressing these limitations, including those relating to robustness, generalisation, overfitting, and clinical integration, is crucial for the reliable and effective deployment of advanced deep learning models in real-world medical applications.
## 3 Method
### Traffic Classification--Cluster of Classifiers  
To flexibly classify features under different numbers of packets, we set up a classifier cluster in SwiftSession to ensure more accurate classification when different numbers of data packets were captured. As shown in the structure Figure 1, the cluster classifier consists of \(K\) models trained with the data of different numbers of packets, where the value of \(K\) depends on the maximum number of packets.  
During the training phase, SwiftSession obtained \(K\) different feature sets with \(k=1\), \(2...\)\(K\) from the same training set and trained them, respectively, to obtain \(K\) classification models. Each model classifies the features of the first \(k\) data packets of flow, which means our models can achieve better performance during the classification phase.  
During the classification phase, we set up a model selector in the classifier cluster. This simple structure can match the features obtained from the flow feature buffer pool to the corresponding model, selecting the appropriate model for classification, without consuming additional computational performance. For each classifier, the method utilizes an incremental computation method, extracting features from the first \(k\) packets upon receiving the \(k\)-th packet. The system dynamically determines whether to continue packet collection or output classification results based on classifier confidence, optimizing accuracy, and computational resource utilization.  
SwiftSession employs a confidence-based decision mechanism for classification. Each classifier outputs a probability distribution vector \([p_{1},p_{2},\ldots,p_{n}]\), where \(p_{i}\) represents the probability of the \(i\)-th traffic category, with \(\sum p_{i}=1\). When the maximum probability exceeds a predefined threshold, the system terminates packet collection and outputs the classification result; otherwise, it continues collecting packets for further analysis. In a binary-classification environment with two classes, A and B, the probability threshold is set at 0.8. When an unclassified flow has its initial 3 rd data packet captured, the classifier outputs a probability distribution of [0.6 (A), 0.4 (B)]. Given that the maximum probability of 0.6 is less than 0.8, the system proceeds to capture the 4 th data packet. After processing this packet, the classifier generates a new distribution of [0.9 (A), 0.1 (B)]. Since the classification probability of 0.9 is greater than 0.8, once the maximum probability exceeds the predefined threshold, the system halts the collection of data packets and outputs the classification result as Class A.
# Galileo Single Point Positioning Assessment Including FOC Satellites in Eccentric Orbits
## Materials and Methods
### Experimental Setup  
The experiment was carried out on the E1, E5a, E5b, and E5 Galileo signals. In detail, we processed the measurements captured by the YEL2 (Canada) station of the International GNSS Service (IGS) network [19]. It is a low-multipath site in conformity with IGS site guidelines [20]. The experiment was carried out using the RINEX3.0 file of measurements acquired by the LEICA GR10 receiver (E5 filter bandwidth of 51 MHz), fed by the LEIAR25.R3 choke ring antenna and the LEITradome. IGS is a voluntary association made up of universities, space agencies, and geodetic agencies whose mission is \"to provide the highest-quality GNSS data and products in support of the terrestrial reference frame, Earth rotation, Earth observation and research, positioning, navigation and timing and other applications that benefit society\" [19]. IGS products are generated at several Analysis Centers (ACs) with strict quality control and proper weighting, thus having the highest quality and internal consistency. The dataset consists of the data received in the days from 21 February 2018 (DOY 52) to 12 March 2018 (DOY 71). Data were obtained from the Federal Agency for Cartography and Geodesy (BKG) GNSS data center web-site ([[https://igs.bkg.bund.de/dataandproducts/rinexsearch](https://igs.bkg.bund.de/dataandproducts/rinexsearch)]([https://igs.bkg.bund.de/dataandproducts/rinexsearch](https://igs.bkg.bund.de/dataandproducts/rinexsearch))). It is a regional IGS data center that collects observational and navigational data from several operational centers and stations, maintains a local archive of the data received, and provides on-line access to these data to the user community. We used a dataset of twenty days because the ground track of the Galileo FOC satellites E14 and E18 repeats every 20 sidereal days, and therefore, the Milena (E14) and Doresa (E18) satellites do not reach the whole range of elevations during one single day. All the analyses were carried out by discarding the satellites with an elevation angle lower than 5 degrees and with a Carrier-to-Noise density ratio C/N\({}_{0}\) below 30 dB/Hz.
## 2 Materials and Methods  
### Sample Prototyping Using SLM  
A total of eight tensile samples of Inconel 718 were produced at the NASA Marshall Space Flight Center, in Huntsville, AL, USA, using the SLM process. The argon atomized Micro-Melt 718 AM metal powder was provided by Carpenter Powder Products, Bridgeville, PA, USA. The SLM process involves the use of metallic powder and targeted laser patterns to generate successive layers of a part designed with computer aided design (CAD) software such as Creo View or Solid Edge.  
As shown in Table 1, tensile bars 1-4 were generated using a Concept Laser M1 SLM printer while 5-8 were generated using a Concept Laser M2 SLM printer.  
M1 and M2 are both powder bed SLM printers, with the primary difference being that the M2 was designed with built-in gloves to allow manipulation of the as-built part while maintaining the inert build environment. All eight tensile bars were printed vertically as cylinders with a diameter of 12.7 mm. Samples 1-8 were all printed using the same SLM process parameters and they all underwent a stress relief after being removed from the SLM build plate. Samples 4 and 8 underwent additional post-build heat treatment using hot isostatic press (HIP) to further increase their density, with the goal of increasing their intrinsic mechanical properties through the reduction in microstructural porosity. This sample set was designed to capture the natural variance in SLM processing for one selected material composition, which can produce a corresponding variance in the mechanical properties.
## 4 Simulation Verifications  
To verify the validity of the proposed control strategies for HESS shown in Figure 12, a simulation model of DC microgrids constituted of HESS is constructed based on Matlab R2020a/Simulink software, and the schematic diagram is shown in Figure 13. The PV generation is represented by a current source with a cascaded boost converter. The variable load is a closed-loop-controlled resistor. HESS involves 2 battery converters and 1 supercapacitor converter. The simulation parameters are shown in Table 3.  
First, the conduction signals of switches are compared when traditional MPC and improved MPC are utilized to verify whether the number of conduction switches is decreased  
Figure 13: The schematic diagram of DC microgrids constituted 2 battery converters, 1 supercapacitor converter, a current source, and a variable load.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline DC bus voltage \(v_{dc}\) & 400 V \\ Battery voltage \(v_{br1}\) & 160 V \\ Battery voltage \(v_{br2}\) & 160 V \\ Supercapacitor voltage \(v_{sc}\) & 160 V \\ Variable load voltage \(v_{load}\) & 200 V \\ Droop coefficient of the first battery converter \(K_{t2}\) & 0.5 \\ Droop coefficient of the second battery converter \(K_{t2}\) & 0.5 \\ Bus capacitor & 3000 \(\mu\)F \\ The PV generation power & 4100 W \\ Initial power of the variable load & 3100 W \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters of DC microgrids constituted 2 battery converters, 1 supercapacitor converter, a PV generation, and a variable load.  
the DC bus voltage drop caused by the droop control is eliminated by the proposed DC bus voltage compensator.
# The Use of Radio and Telemedicine by TMAS Centers in
## 4 Discussion
### What Needs to Be Done?  
Telemedicine systems have advanced to include high-resolution video conferencing capabilities, which make it possible for diagnosis, treatment, and consultation to be conducted remotely [32; 33]. By using these technologies, onshore medical practitioners can provide direction and guidance to onboard medical officers. Telemedicine ensures that crew members and passengers have access to qualified health care with accurate medical advice and support [34; 35]. Additionally, it reduces unnecessary rescue operations and costs. Hence, telemedicine is an efficient, cost-effective, and beneficial method to deliver medical assistance to ships.