esa-sceva/satcom-chunk-collection huggingface dataset:
## Calibration Procedures
### Internal Calibration  
The calibration constant (\(C_{radar}\)) is required for retrieving reflectivity (see Equation (3)). \(C_{radar}\) can be measured precisely with an already characterized reflector. Since none were available, we performed an internal calibration. This calibration is performed by characterizing each radar element and cascading their effect into the parameters of Equation (7), which is an adaptation of [20] for FMCW radars. Each device signature is found using either a vector network analyzer, a spectrum analyzer, or a finite element method solver as for the radar antennas. The principal parameters are summarized in Table 2. Using those values, the calibration constant for reflectivity is \(-109\) dB [\(\frac{nm^{6}}{mW^{2}}\)]:  
\[C_{radar}=10\cdot log_{10}\Bigg{(}\frac{512\cdot ln(2)\lambda^{2}10^{18}L_{sys }}{P_{t}G_{a}^{2}\Delta r\pi^{3}\phi^{2}\left|K\right|^{2}}\Bigg{)}. \tag{7}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Distance from Radar to UAV [m]** & **Backscatter Signal IF [MHz]** \\ \hline
200 & 3.8235 \\ \hline
300 & 5.2941 \\ \hline
400 & 6.7647 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the distance calibration experiment.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Variable** & **Value** \\ \hline Wavelength & \(\lambda\) & 7.77 mm \\ \hline Transmitted power & \(P_{t}\) & 12 dBm \\ \hline System losses & \(L_{sys}\) & \(-67.5\) dB \\ \hline Antenna gain & \(G_{a}\) & 22.7 dBi \\ \hline Beamwidth (3 dB) & \(\phi\) & 15 degrees \\ \hline Range resolution & \(\Delta r\) & 20 m \\ \hline Index of refraction of water sphere & \(\left|K\right|^{2}\) & 0.93 [17] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radar parameters for internal calibration. The system losses are negative because they include the receiver gain.
## Materials and Methods
### Characterization of LVL Panels  
The mechanical properties of LVL panels containing the various binder compositions were investigated according to the standard EN 14279 [33]. Under EN 14279, the bending properties (bending strength (MOR) and modulus of elasticity (MOE)) are determined according to EN 310 [34] in both major and minor axis (face and edge bending). Bending tests were performed using a Zwick universal testing machine (Zwick Roell Group, Ulm, Germany) with a constant cross-head displacement rate of 8 mm/min. The dimensions of bending samples were \(290\times 50\times 12\) mm\({}^{3}\) (length \(\times\) width \(\times\) thickness). Nine samples were prepared for each of the LVL samples.  
Under EN 14279 [33], the shear strength of the LVL specimen (\(130\times 25\) mm\({}^{2}\)) are recommended to be measured according to EN 314 in connection with Annex A [35; 36] for the bonding class 1 (interior conditions) and class 2 (humid conditions). For interior conditions, the samples were soaked in distilled water (\(20\pm 2\)\({}^{\circ}\)C) pretreatment for 24 h. For humid conditions, the samples were first soaked in boiled water for 6 h, removed from the water, and then immersed in water with a temperatureof 20 \(\pm\) 2 \({}^{\circ}\)C for at least 1 h prior to shear strength measurement. In addition, the samples with no pre-treatment (reference samples) were also tested for comparison. Shear tests were conducted using a Zwick universal testing machine (Zwick Roell Group, Ulm, Germany) with a constant cross-head displacement rate of 0.8 mm/min. The reported mean of the shear strength value represented the average of 15 samples for each LVL panel.  
The axial compression strength parallel to fiber direction of the LVL specimens were performed following the provision in EN 408 [37]. The tests were conducted with a constant rate of cross-head motion of 0.3 mm/min in a computer-controlled mechanical test machine (Zwick Roell Group, Ulm, Germany). The modulus of elasticity was not measured. The dimensions of the compression samples were 72 \(\times\) 12 \(\times\) 12 mm\({}^{3}\) (length \(\times\) width \(\times\) thickness). Figure 1 shows the structure of the samples prepared for mechanical tests.  
A one-way analysis of variance (ANOVA) of the LVL properties was performed with the SPSS software (IBM, USA). Statistical differences between variations were evaluated by multiple comparisons on the basis of Duncan test. The statistical significance was set at \(p\) < 0.05.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Empirical Distribution and Actual Average Delay  
Figure 7 illustrates the empirical Cumulative Distribution Function (CDF) of the total number \(N_{t}\) of RRs required for the typical cell with \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\).  
Figure 5: Mean number of required Radio Resources (RRs) in a typical cell considering \(\lambda_{b}\) ranges from 0.3 to 0.9 nodes/km\({}^{2}\) with \(\lambda_{a}=5.5\) nodes/km\({}^{2}\), \(\tau_{max}=1\) ms.  
Figure 6: Total number of required RRs in a typical cell versus the collector intensity \(\lambda_{b}\) with \(\lambda_{a}=5.5\) nodes/km\({}^{2}\), \(\tau_{max}=1\) ms.  
For example, according to Figure 6, for the same value of \(\lambda_{a}\), \(\lambda_{b}\), we find that the \(N_{t}\) value required for the SISO system is 19. In Figure 7, we can see that \(N_{t}=19\) corresponds to a CDF value of 0.88. This means that there is a network outage probability of \(1-0.88=0.12\) meaning that the network cannot attributed RR to 12% of nodes in the typical cell. The corresponding average delay can be then computed using (19).  
Figure 8 show the actual average access delay corresponding to different antenna configurations and transmission modes for given intensities of active sensors and collectors \(\lambda_{a}\), \(\lambda_{b}\) and the maximum access delay \(\tau_{max}=1\) ms. It can be seen from the figure that the average access delay \(\tau\) is less than the maximum access delay \(\tau_{max}\). SIMO with MRC transmission mode requires the longest access time because it has the lowest individual off probability, i.e., it serves the largest percentage of active nodes. The second highest access delay is the SIMO with antenna selection scheme. For the SISO configuration, the average access delay is relatively lower because in this mode, many nodes are in off state and not served. For the MU-MIMO system, each time there are \(n_{u}\) nodes transmitting information simultaneously and being processed by the collector. The larger the \(n_{u}\) is, the shorter the average access delay required.  
Figure 8: Low access delay \(\tau\) with statistical dimensioning in which the maximal delay \(\tau_{max}=1\) ms, \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\).  
Figure 7: The empirical Cumulative Distribution Function (CDF) of \(N_{t}\) total number of RRs required for the typical cell in which the maximal delay \(\tau_{max}=1\) ms, \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\).
###### Abstract
Machine learning (ML) has emerged as a vital tool in medical image analysis, playing a crucial role in tasks like segmentation, classification, and ultimately automation of renal function diagnosis [8]. Traditional machine learning models, such as decision trees (DT), random forest (RF), and simple neural networks (NNs) are extensively used in research related to processing electronic health record (EHR) tabular data such as demographics,  
Figure 1: Visualizations of one renal scan for a patient from dynamic renal scintigraphy study database [6]. biological, or laboratory data. When it comes to image analysis, due to the lack of annotated data, researchers reach out beyond traditional supervised learning to approaches with less supervision, such as semi-supervised learning, multiple instance learning, deep learning, and transfer learning [9]. Preliminary literature screening reveals that scintigraphy images are not widely utilized in medical image analysis. PET has seen more extensive research interest lately but SPECT and planar scans are typically more affordable and more widely available in clinical practice, especially in smaller hospitals or regions with limited resources. Also, if the clinical question involves functional assessment specifically (such as evaluating kidney filtration or perfusion), SPECT and planar imaging are sufficient and potentially more suitable. Therefore, the objective of this scoping review is to determine an overview of published research:  
1. Aassess ML applications for renal SPECT and planar scintigraphy;
2. Identify gaps and challenges in ML applications for CKD detection and prognosis;
3. Explore related research efforts in ML applications to other scintigraphy domains;
4. Set grounds for potential future research pathways in the medical image analysis for SPECT and planar scintigraphy images. To achieve our objectives, we proposed the following research questions (RQs) that guide our scoping review:  
* RQ1: What ML methods are currently utilized for detecting, predicting, and diagnosing CKD using PLANAR and SPECT images in renal scintigraphy? * RQ2: What ML methods are currently utilized for processing PLANAR and SPECT images across various scintigraphy domains? * RQ3: What are the challenges and limitations associated with applying ML methods to PLANAR and SPECT images in scintigraphy? We begin by reviewing the use of ML methods for evaluating renal dysfunction, with a particular focus on CKD and scintigraphy through techniques like PLANAR and SPECT. Then, we explore the use of ML methods across various recent applications in scintigraphy for images of all organs and structures. Finally, we aim to discuss the potential and the limitations of ML and artificial intelligence (AI) in detecting, diagnosing, and predicting CKD and high-risk groups with potential renal dysfunctions. Figure 2: Comparison of renal planar (**a**–**f**) and SPECT (**g**–**l**) images [7].
## 5 Results
### Genetic Algorithm Convergence Analysis
This graph demonstrates that, as the generations progress, the number of satellites selected in the optimal constellation steadily decreases and eventually stabilizes, reflecting a cost-effective configuration. This trend not only corroborates the convergence of the GA in terms of fitness but also highlights that the algorithm effectively identifies a satellite deployment strategy that minimizes the number of satellites--thereby reducing both deployment and operational costs--without compromising performance. Such a trade-off between performance and economic efficiency is central to our study's objective, as it aligns with our goal of designing satellite constellations that are both technically robust and economically viable. The inclusion of this graph provides compelling visual evidence that our optimization approach successfully balances these critical aspects, reinforcing the overall direction and impact of our research. Table 6 summarizes the final genetic algorithm optimization results for satellite constellation deployment by detailing the optimized configuration parameters for each communication technology and orbital combination. The table presents the optimized inclination in a dual-value format (e.g., 59.70/63.89\({}^{\circ}\)) to reflect the independent optimization of two inclination groups, the number of planes, and the number of satellites per plane to indicate the constellation structure, the phase difference representing the relative phase offset between satellites, the total number of satellites required, and the final GA fitness value (where lower values indicate a more optimal configuration). These results demonstrate that distinct optimal parameters are obtained for different technologies and orbital configurations, effectively balancing trade-offs among coverage, revisit time, and economic efficiency and thereby providing a robust basis for the final satellite deployment strategy.
## 4 Results and Discussion  
In order to evaluate the performance of the coACO algorithm in solving the BN structure-learning problem, a series of test experiments is performed and a comparison of the proposed coACO with the basic ACO is carried out. All the tested algorithms are implemented using the Matlab-2009a, and the Bayes Net Toolbox (BNT) developed by [PERSON] [28] is used to evaluate the BIC score. The experimental platform is a personal computer with an Intel(R) Core(TM) i3, 3.07 GHz CPU, 4 GB memory, and Windows 7. The parameters of the two ACO algorithms are set as \(\alpha=1\), \(\beta=2\), \(q_{0}=0.8\), \(\rho=\psi=0.4\), \(M_{\text{ant}}=10\), \(n_{S}=5\), \(cr=0.9\), \(F\) is set as a random value that is uniformly distributed in [0.2,0.9], and the maximum number of iterations is set as \(T_{\text{max}}=100\). The initial pheromone level placed on each arc is \(\tau_{0}=\frac{1}{n|f_{\text{BIC}}(G_{\text{K2}},D)|}\), where \(n\) is the number of variables and \(G_{\text{K2}}\) is the network obtained by the K2 algorithm using the BNT. Moreover, two traditional algorithms, based on the score+searchframework, K2 and B [2], are also tested for comparison. Different from the ACO-based methods, both the K2 and B algorithms are not population-based. The learning datasets are generated from the well-known benchmarks of BNs, including the ASIA and the ALARM networks. The ASIA network consists of 8 nodes and 8 arcs, while the ALARM network consists of 37 nodes and 46 arcs. Using the BNT, we generated a dataset of 10,000 cases for the ASIA network and 5000 cases for the ALARM network. For the ASIA network, the subsets consisting of the first 1000, 3000, 5000, 8000, and 10,000 cases are considered. For the ALARM network, the subsets consisting of the first 1000 and 5000 cases are considered. Table 1 lists the summary of the datasets used herein. We run the stochastic algorithms, including ACO, coACO,
## 5 Application of the OW-Fxlms Algorithm to ANC of Interior Noise
### Performance of the ANC System with OW-FxLMS  
Figure 17 shows the sound pressure of the interior noise in the test vehicle while driving on the road. The upper solid line is the SPL of the interior noise, and the lower dotted line is the actively controlled SPL. Figure 17a shows the overall sound pressure, and Figure 17b shows the sound pressure of the second-order component of the engine rotating frequency. The second order is generally related to the booming noise. The engine was under full load for this test while driving the car on the road and the engine speed increased rapidly from 1000 to 4500 rpm during the course of 10 s. In this case, the higher orders were not controlled because the calculation speed of the ANC system was beyond the capacity of dSPACE, and the noise of the second order was dominant. From these results, we suggest that the new ANC system using the OW-FxLMS algorithm performed well in reducing the sound pressure of the interior noise related to the harmonic components of engine rotating speed. Figure 18 shows the performance of the ANC system with the new algorithm for full load condition of the engine. Figure 18a,b the results of active noise cancellation at left seat (driver seat) and at right seat (passenger seat) respectively.  
Figure 16: Comparison of sound pressure level (SPL) controlled actively by the FxLMS-algorithm-based adaptive notch filter and the OW-FxLMS algorithm at the driver seat. The driving conditions are a no-load condition and fast run-up. (**a**) Time history; (**b**) PSD; (**c**) Overall level of SPL; (**d**) SPL of the second order.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 4 Discussion
### Response of Phenology to Climatic Factors
High VPD in the fall accelerates water loss, prompting plants to enter dormancy and leaf drop rapidly [87]. Increased radiation in spring accelerates soil heating, the activity of root systems, and earlier shoot growth, leading to earlier SOS [88]. A lower solar altitude angle in the fall shortens sunlight and reduces radiation [89], limiting photosynthesis and contributing to earlier EOS [90]. An in-depth understanding of the changes in soil temperature and other elements, and their effects on plant growth, is of vital significance for the scientific management of forestry resources and the maintenance of ecological balance.
## 1 Introduction
If a sine excitation or a narrowband chirp excitation is used, the nonlinear higher harmonic components can easily be differentiated using standard fast Fourier transformation (FFT). However, in the case of a broadband chirp excitation, this is no longer possible (see also explanation in Section 3). One possible solution is to use a combination of inverse filtering and phase symmetry analysis for extraction of the second and third higher harmonic components [35; 36]. Here, a novel method is proposed using short-time Fourier transformation (STFT) and time-frequency bandpass filtering. This proposed method does not require multiple phase coded excitations and advanced data handling which is needed in the case of phase symmetry analysis and inverse filtering. Also it allows harmonic components of any specified order to be extracted. The different steps of the damage detection procedure discussed in this paper are schematically shown in Figure 1. For each step, the corresponding section number is indicated.
## 3 The Receiver Acquisition Chain and the Ad Hoc Back-End of the BIRALET Radar
### Transmitter Architecture of the TRF Antenna Based on the USRP-2954R Board
A sampling frequency of 5 MS/s is used and the maximum RMS transmitting power is, as mentioned above, equal to 10 kW, with a 100% duty cycle. Using a 100% duty cycle allows to use the whole energy of the transmitter (there is no coupling between transmitter and receiver) and the 50 Hz PRF permits to measure the range without ambiguities (the Doppler information, to compensate the range error, is extracted from the CW tone). The choice of a 3.6 MHz chirp allows to exploit all the available bandwidth, maximizing the range resolution and reserving a small portion of the band for the CW tone useful for Doppler extraction. This guarantees to measure a maximum Doppler shift of more than 100 kHz, which can be considered more than sufficient, given the expected target speed in LEO with the used carrier at about 410 MHz. In order to maximize the signal-to-noise ratio (SNR) for the range extraction, a matched filter has been implemented with the aim of performing a pulse compression. The interpulse integration is also used by the addition of four chirps (see Figure 6). This aspect means that a measurement of the target range and Doppler shift is performed every 80 ms (the step between each measure is four pulses: 20 ms \(\times\) 4 = 80 ms). Finally, both range and Doppler shift measurements are time stamped with a millisecond accuracy, allowing precise target trajectory prediction.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Random Topologies' Generation
#### 5.1.2 Generation Process Automation  
A key aspect of this project was the automation of topology generation, which is essential for conducting large-scale simulations and enabling a comprehensive evaluation of ML and DL models for error detection. By using customized scripts, BRITE topology generation was automated, producing a total of 180 topologies that could then be used for different simulation executions in DEN2 DE. The total number of topologies generated was determined by the product of several configured parameters:  
* Topology models: two models, Router Waxman and Router Barabasi-Albert.
* Node counts: topologies were generated with 100, 150, and 200 nodes.  
Figure 9: Comparison of Router Waxman and Barabasi-Albert topology examples. (**a**) Router Waxman model topology example. (**b**) Router Barabasi–Albert topology example.  
* Connectivity levels: three different degrees of connectivity were specified, with each topology having a degree of 2 m (due to bidirectional links).
* Random seeds: 10 different seed files were applied to ensure variety in the generated topologies.
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
## 2 Materials and Methods
### Calculation of Gait Parameters Using UWB
#### 2.3.2 Foot Trajectory Estimation  
The UWB sensor, after compensation, was installed on the shoe, as shown in Figure 2. The location coordinates of the tag were estimated using the gait experiment. In general, the location coordinates are estimated from the relative location of the tag, based on fixed anchor coordinates. However, the coordinates of the anchor cannot be fixed in mobile-type smart insoles. Therefore, for each step in this study, the location of the foot tag in the swing phase was calculated based on the anchor on the foot in the stance phase.  
To determine the location of the foot, the coordinate system was first defined based on the anchor located on the big toe (\(Anchor_{loe}\)) and that located at the heel of the foot (\(Anchor_{hel}\)) in the stance phase. Here, the location coordinates of the tag, defined as (\(x\), \(y\), \(z\)), were \(Anchor_{hel}\) as (0, 0, 0), and that of the anchor located at the three-fourths point of the shoe length (\(Anchor_{quater}\)) as (0, \(y_{Q}\), \(z_{Q}\)), and that of X as (0, \(y_{T}\), 0). Then, the distance between each anchor and tag can be expressed as follows:  
\[d_{T}{}^{2}=x^{2}+\left(y-y_{T}\right)^{2}+z^{2} \tag{5}\]  
\[d_{Q}{}^{2}=x^{2}+\left(y-y_{Q}\right)^{2}+\left(z-z_{Q}\right)^{2} \tag{6}\]\[d_{H}{}^{2}=x^{2}+y^{2}+z^{2} \tag{7}\]  
The above equations can be evaluated to find the tag coordinates (\(x,y,z\)), which are expressed as follows:  
\[y=\frac{d_{H}{}^{2}-d_{T}{}^{2}+y_{T}{}^{2}}{2y_{T}} \tag{8}\]  
\[z=\frac{d_{H}{}^{2}-d_{Q}{}^{2}+y_{Q}{}^{2}+z_{Q}{}^{2}-2 yy_{Q}}{2z_{Q}} \tag{9}\]  
\[x=\pm\sqrt{d_{H}{}^{2}-y^{2}-z^{2}} \tag{10}\]  
In Equation (10), the positive and negative signs are obtained when the location coordinates of the right and left feet are estimated according to the reference coordinate.