esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Impact of Process Variations (PV) on Different Operation Modes  
The block structure of a complete SPUF is shown in Figure 1. The core of the SPUF is a two-dimensional array of storage cells, where the horizontal rows are referred to as word-lines (\(WLs\)) and the two vertical lines that run through each cell are called the bit-line (\(BL\)) and complement bit-line (\(BLB\)). An individual cell can be selected by the row and column decoder circuit with an external input address, addr. One sense amplifier is attached to each bit-line pair of the column to accelerate read operation while the write driver circuit improves the access time in write operation. When the SPUF is set to PUF mode by the mode_sel input, the SPUF is reset and powered up. Due to mismatches and noise between the cross-coupled inverters formed by the transistor pairs, M1-M2 and M4-M5, a _response_ bit is generated in each of the SRAM cells. An input _challenge_ is applied to the \(WL\) of the addressed cells to turn on the access transistors, M3 and M6. The _response_ bits are then read from the bit-lines [2]. In the memory mode, regular read and write operations are performed on the same SRAM array after the SPUF has been fully powered up. For a read operation, the small voltage difference between the true and complement bit-lines is amplified by the sense amplifier and the data stored in the addressed cell is transferred to the output. For a write operation, the bit-lines are initialized with the data that is to be written into the cell with its word-line asserted. These two different modes of operation on a 6-transistor (6T) SRAM cell are shown in Figure 1.  
Figure 1: **(a)** Architectural diagram of an SRAM-based PUF; **(b)** PUF and memory modes of operation of a 6-transistor Static Random Access Memory (SRAM) cell in an SPUF.  
While the two modes of operation seem independent, they cannot be designed independently because the PV influences on them occur simultaneously. As shown in Figure 2, when the threshold voltage variation \(\sigma_{V_{th}}\) increases, the failure rate of memory operations increases, whereas the reliability of response bits for PUF operation improves.
## 3 Results and Discussion
### SEM Analysis of NiO and ZnNb\({}_{2}\)O\({}_{6}\)  
To delve into the microstructures and morphology of the NiO samples sintered at 1100 \({}^{\circ}\)C for 4 h and the ZnNb\({}_{2}\)O\({}_{6}\) samples subjected to the same sintering conditions, scanning electron microscopy (SEM) analysis was performed. The SEM examination confirmed that the NiO samples revealed a single-phase cubic crystalline structure, supporting the results of the XRD investigation. Similarly, the ZnNb\({}_{2}\)O\({}_{6}\) samples exhibited single-phase structures characterized by an orthorhombic crystal structure. Figure 3a,b show that there were no signals of secondary phases or microstructural impurities. It is worth noting that NiO possesses a centrosymmetric cubic crystal structure, while ZnNb\({}_{2}\)O\({}_{6}\) adopts a centrosymmetric orthorhombic crystal structure.  
The average EDS analysis of the NiO and ZnNb\({}_{2}\)O\({}_{6}\) samples, presented in Figure 3c,d, confirms their elemental composition. NiO is primarily composed of nickel (Ni) and oxygen (O), while ZnNb\({}_{2}\)O\({}_{6}\) consists mainly of niobium (Nb), zinc (Zn), and oxygen (O). These findings align well with the quantitative XRD measurements, further validating the phase composition of the samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **a (Å)** & **b (Å)** & **c (Å)** & **Volume (Å3)** & **SYS** \\ \hline NiO & 4.177 & 4.177 & 4.177 & 72.88 & Cubic \\ \hline ZnNb\({}_{2}\)O\({}_{6}\) & 5.726 & 14.208 & 5.04 & 410.03 & Orthorhombic \\ \hline PANI & 7.65 & 10.22 & 5.75 & 449.55 & Orthorhombic \\ \hline \end{tabular}
\end{table}
Table 1: Lattice characteristics of NiO, ZnNb\({}_{2}\)O\({}_{6}\) specimens (sintered 4 h at 1100 \({}^{\circ}\)C) and PANI were calculated using FullProf.  
Figure 2: XRD patterns for NiO, ZnNb\({}_{2}\)O\({}_{6}\) (single-phase sintered at 1100 \({}^{\circ}\)C for 4 h) and polyaniline [18; 25].  
The microstructure indicated a formation of complementary-morphology grains. As a result of internal reflections inside the material, the multiple reflection effect has a substantial impact on electromagnetic shielding. Large surface areas or interface zones in materials are frequently the sites of this phenomenon. Single-phase sintered NiO and ZnNb\({}_{2}\)O\({}_{6}\), which have porous structures, probably have significant specific surface areas and multiple internal grain boundaries. These materials, which probably have a porous structure, have large specific surface area and large internal boundary grains (large number of internal grain boundaries), which further contribute to the shielding of electromagnetic waves.
## 3 Results
### Fatigue Effects (Pre vs. Post Fatigue Test)  
Before and after fatigue test descriptive results are shown in Table 1 for angular kinematics modification and Table 2 for spatiotemporal parameters.  
The effect of fatigue (pre vs. post) was shown at the initial contact, maximum knee flexion, and toe-off phases, whereas peripheral or central fatigue did not modify the angular kinematics during the maximum oscillation phase.  
Specifically, during the initial contact, the shank angle increased (\(p<0.05\)), only after central fatigue protocol, between pre and post evaluation (95%CI = \(-1.204/-0.054^{\circ}\), ES = 0.908, \(p\) = 0.034), and ankle plantar flexion increased after fatigue protocol (95%CI = 0.698/1.918^{\circ}\), ES = 0.316, \(p\) = 0.035). At maximum knee flexion instant, knee flexion increased (\(p\) = 0.007) only after central fatigue protocol (95%CI = 1.033/2.302^{\circ}\), ES = 0.979, \(p\) = 0.000). At the toe-off instant, knee flexion was increased by the effect of fatigue (pre vs. post) (\(p\) = 0.010) only in peripheral fatigue (95%CI = \(-2.415/-0.602^{\circ}\), ES = 1.036, \(p\) = 0.003). Shank angle also increased after the fatigue test (\(p\) = 0.010) in central (95%CI = 0.536/1.444^{\circ}\), ES = \(-1.497\), \(p\) = 0.002) and peripheral (95%CI = 0.105/1.035^{\circ}\), ES = \(-0.844\), \(p\) = 0.020) fatigue protocols.  
Stride frequency and stride length were not significantly altered by central and peripheral fatigue (\(p>0.05\)). Stance time was significantly higher (\(p\) = 0.044), only after central fatigue protocol, at post evaluation instant compared with pre-evaluation (95%CI = \(-0.009/-0.003\) s, ES = 1.325, \(p\) = 0.025).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{Peripheral Fatigue} & \multicolumn{3}{c}{Central Fatigue} & \multicolumn{3}{c}{\(p\) Values} \\ \cline{2-10}  & **Pre-Fatigue** & **Post-Fatigue** & **\(\Delta\)** & **Pre-Fatigue** & **Post-Fatigue** & \(\Delta\) & **Fatigue Effect** & **Fatigue Type** & **Interaction** \\ \hline  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **(Pre-Post)** & **(Pre-Cent)  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{Peripheral Fatigue} & \multicolumn{3}{c}{Central Fatigue} \\ \cline{2-7}  & **Pre-Fatigue** & **Post-Fatigue** & **A** & **Pre-Fatigue** & **Post-Fatigue** & **A** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline \hline  & & & & & & \\ \end{tabular}
\end{table}
Table 2: Results of spatiotemporal parameters and spring-mass model pre- and post-fatigue.
###### Abstract  
Earth Observation services guarantee continuous land cover mapping and are becoming of great interest worldwide. The Google Earth Engine Dynamic World represents a planetary example. This work aims to develop a land cover mapping service in geomorphological complex areas in the Aosta Valley in NW Italy, according to the newest European EAGLE legend starting in the year 2020. Sentinel-2 data were processed in the Google Earth Engine, particularly the summer yearly median composite for each band and their standard deviation with multispectral indexes, which were used to perform a k-nearest neighbor classification. To better map some classes, a minimum distance classification involving NDVI and NDRE yearly filtered and regularized stacks were computed to map the agronomical classes. Furthermore, SAR Sentinel-1 SLC data were processed in the SNAP to map urban and water surfaces to improve optical classification. Additionally, deep learning and GIS updated datasets involving urban components were adopted beginning with an aerial orthophoto. GNSS ground truth data were used to define the training and the validation sets. In order to test the effectiveness of the implemented service and its methodology, the overall accuracy was compared to other approaches. A mixed hierarchical approach represented the best solution to effectively map geomorphological complex areas to overcome the remote sensing limitations. In conclusion, this service may help in the implementation of European and local policies concerning land cover surveys both at high spatial and temporal resolutions, empowering the technological transfer in alpine realities.  
land cover; Sentinel-1 SAR; Sentinel-2; deep learning; Google Earth Engine; SAGA GIS; ESRI ArcGIS Pro; ESA SNAP; mountains; EAGLE; geomorphological complex areas Article  
A Scalable Earth Observation Service to Map Land Cover in Geomorphological Complex Areas beyond the Dynamic World: An Application in Aosta  
1
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 5 Measurement and Analysis  
### Statistical Analysis Method  
According to the accuracy evaluation standard, this experiment uses five independent measurements to evaluate the repeatability of the system. The calculation formulas for the standard deviation (\(\sigma\)) and coefficient of variation (CV) are as follows:  
\[\sigma\ =\ \sqrt{\frac{1}{n-1}\sum\limits_{i\ =\ 1}^{n}(x_{i}-\mu)^{2}} \tag{11}\]  
\[CV\ =\ \left(\frac{\sigma}{\mu}\right)*100\% \tag{12}\]  
In the formula: \(x_{i}\) is a single measurement value, \(\mu\) is the sample mean value, \(n=5\). The Shapiro-Wilk test is used to verify the normality of the data. According to the critical value table of the Shapiro-Wilk test (sample size \(n=5\), significance level \(\alpha=0.05\)), when the statistic W \(>0.76\), the assumption that the data obeys the normal distribution is accepted. The calculation formula of the W statistic is as follows [31]:  
\[W\ =\ \frac{\left(\sum\limits_{i\ =\ 1}^{n}a_{i}x_{(i)}\right)^{2}}{\sum \limits_{i\ =\ 1}^{n}(x_{i}-\overline{x})^{2}} \tag{13}\]  
In the formula: \(a_{i}\) is a constant coefficient, \(x_{(i)}\) is the order statistic, and \(\overline{x}\) is the sample mean.
## 3 Method
### Multiple Endmember Spectral Mixture Analysis  
As stated above, LSMA assumes that the spectrum measured by a sensor is a linear combination of the spectra of all endmembers within the pixel [13]. The mathematical model of LSMA can be expressed as Equation (1),  
\[R_{i}=\sum_{k=1}^{N}f_{k}R_{ik}+\varepsilon_{i} \tag{1}\]  
where \(R_{i}\) is the reflectance of band \(i\) of a pixel, \(k\) is the number of endmembers, \(f_{k}\) is the proportion of the \(k\)th endmember within the pixel, \(R_{ik}\) is the reflectance of the \(k\)th endmember within the pixel in band \(i\), \(\varepsilon_{i}\) is the residual of band \(i\) which indicates the unmodeled portions of the spectrum.  
However, two major drawbacks exist in the LSMA method. Firstly, the number of endmembers is invariant when modeling the entire landscape using LSMA, regardless of whether the ground components represented by the endmembers are present within the pixel [20; 21; 22; 23]. This may result in a decreased accuracy of the fractional abundance. Secondly, each endmember has only one fixed spectrum, neglecting that the same ground component may have different spectral characteristics [21; 22; 23]. Hence, LSMA is unable to account for with-in class spectral separability.  
To address the defects of LSMA, MESMA allows the number and type of endmembers to vary on a per pixel basis to better represent the spectral variability of the complex landscapes [20], which can increase the accuracy of the estimated fractions. To be specific, MESMA consists of endmember selection and spectral unmixing modeling which is documented in the following sections.
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 2 Proposed Method
### Disparity Map Extraction Using Stereo Matching  
The disparity map was extracted from the rectified stereo image using [PERSON]'s method [14], i.e., stereo matching based on Semi-Global Matching (SGM) [23]. This method uses the census transform [24] in the matching cost process to determine the matching block. The census transform is more accurate than block matching using only color information because it determines structurally similar blocks as matching blocks. In the cost aggregation process, optimal path disparity is searched for by combining the path costs for eight directions. Equation (2) shows the calculation of the path cost:  
\[L_{r}(p,d)=C(p,d)+\min\left[\begin{array}{c}L_{r}(p-r,d)\\ L_{r}(p-r,d-1)+P_{1}\\ L_{r}(p-r,d+1)+P_{1}\\ minL_{r}(p-r,i)+P_{2}\end{array}\right]-minL_{r}(p-r,l) \tag{2}\]  
where \(r\) is the direction, \(p\) is a pixel, \(d\) is the disparity, and \(C\) is the matching cost. \(P_{1}\) is a penalty when the disparity difference is 1, and \(P_{2}\) is a penalty for the disparity difference is greater than 1.  
SGM can acquire disparity with high accuracy and the algorithm structure is suitable for parallel processing. The [PERSON]'s method runs in real-time at an HD resolution through CUDA parallel programming. Figure 7b shows the disparity map extracted using [PERSON]'s method [24]. The object closer to the camera had a larger disparity, so the brightness of the image became brighter. The disparity map was transformed into a depth map through Equation (3) obtained through a pinhole camera model after stereo rectification [25]:  
\[z=\frac{b\cdot f}{d} \tag{3}\]  
where \(b\) is the distance between cameras, \(f\) is the focal length, \(d\) is the disparity, and \(z\) is the depth value. Unlike the disparity map, the closer the object to the camera, the darker the image.  
Figure 6: Example of counting line.  
Figure 7: Depth map extraction: (**a**) color image, and (**b**) disparity map.
## 3 Experimental Results of Static Collapse Tests
### Strain Responses of BFRP Bars  
The strain of BFRP bars was monitored during the tests as shown in Figure 8, where S-Top and S-Bottom represent the top and bottom BFRP bars in the BESCs respectively, M-Top and M-Bottom mean the top and bottom BFRP bars in the BEMCs correspondingly. It can be seen that in most cases the S-Top and the M-Bottom BFRP bars kept tensile before the final failure occurred. While strain of the S-bottom and M-Top BFRP bars usually underwent fluctuation, commonly, they were firstly subjected to compression and then to tension. In general, the strain of the S-Bottom and M-Top BFRP bars was lower than that of the S-Top and the M-Bottom BFRP bars. It is noted that the strain of the S-Top and the M-Bottom BFRP bars increased fast in the early loading stages, this is related to the severe cracking of concrete at the beam ends due to low elastic modulus of BFRP bars.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Identifier** & **The First Peak Load P\({}_{\textbf{P}}\) (kN)** & **Load at the Final Failure Stage P\({}_{\textbf{u}}\) (kN)** \\ \hline S1 & 30.2 & 71.3 \\ S2 & 32.2 & 89.4 \\ S3 & 36.9 & 112.1 \\ S4 & 38.8 & 67.6 \\ S5 & 34.2 & 67.0 \\ S6 & 25.0 & 44.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristic load values.  
Figure 7: Relation of load-vertical displacement at the failed middle column: **(a)** Influence of prestress, **(b)** Influence of span to depth ratio, **(c)** Influence of material types of stirrups.
###### Abstract  
Arid and semi-arid environments are characterized by highly variable and unpredictable rainfall patterns, which significantly affect the structure and function of natural ecosystems. Understanding the interconnected relationship between climate variability, forage availability, and livestock dynamics in these regions is crucial to ensure sustainable management. This study provides novel insights into the effects of rainfall variability on natural forage resources and livestock populations in Botswana. In this arid region, traditional livestock farming remains a key economic and food security pillar. By employing a mathematical model based on plant-herbivore interactions, this article quantitatively evaluates the impact of changes in rainfall timing and intensity on forage biomass and, subsequently, livestock populations. A robust analysis of critical threshold values for ecosystem sustainability is possible when real-world climate data are incorporated. This study examines the effects of harvesting and rainfall variability on livestock dynamics across different locations in Botswana. Delayed rainfall leads to a sharp decline in livestock, while Sehitlwa sees biomass loss without a notable reduction in herd size. In Kgagodi, for example, livestock numbers decline by 37% without harvesting, but they remain stable with controlled harvesting. Conversely, Letlhakeng experiences a 6% increase in livestock numbers despite delayed rainfall, which results in a biomass decline. Both Mabutsane and Letlhakeng maintain stable livestock numbers. The findings confirm that early and intense rainfall enhances livestock productivity, while delayed or reduced rainfall leads to population decline, aligning with observed trends in historical data. Additionally, the study underscores the potential of adaptive livestock harvesting strategies as a viable approach to mitigating climate-related risks in grazing systems. As this work integrates theoretical modeling with empirical climate data, it contributes to understanding arid land dynamics, providing a predictive method for assessing ecosystem responses to climate variability. These insights are invaluable for policymakers, conservationists, and local farmers seeking sustainable livestock management practices in the face of changing climatic conditions.  
livestock production; non-autonomous plant-livestock model; climate variability; natural forage; prey-predator model +
Footnote †: journal: Applied sciences  
Article
## 5 Conclusions and Future Work
### Limitations  
However, significant drawbacks of the proposed system include:  
1. The use of a single data set;
2. The use of a single statistical technique, the chi-squared measure, to identify important features (predictors);
3. The use of embeddings rather than a pre-trained CNN model; and
4. In this paper, we employed a binary classification system to categorize input traffic as normal or malicious.
## 4 Proposed Efficient Design for Transmit Waveform and UAV Altitude  
In this section, we investigate the proposed efficient low-complexity design to jointly optimize the transmit waveform and UAV altitude by devising a novel transmit waveform and jointly optimizing the design parameters, the scaling factor, and the UAV altitude. To decrease the computational complexity derived from the SDP solver, based on the insights from MRT beamforming in (33), we propose a novel waveform for UAV-enabled integrated RadWPT systems, whose structure is as follows:  
\[\mathbf{X}_{\text{Prop}}=\Big{(}1-\sum_{k=1}^{U}w_{k}\Big{)}\mathbf{X}_{\text{ Rad}}+P_{T}\sum_{k=1}^{U}w_{k}\mathbf{\hat{a}}(\phi_{k})\mathbf{\hat{a}}^{H}( \phi_{k}), \tag{37}\]  
where \(w_{k}\in[0,1]\) (\(\forall k\in\mathcal{U}\)), satisfying \(\sum_{k=1}^{U}w_{k}\leq 1\), is the weighting factor to be optimized; it controls the priority of the \(k\)-th EHD's optimal beamforming. \(\mathbf{X}_{\text{Rad}}\) is the optimal waveform when considering only MIMO radar sensing, i.e., \(\rho=1\), and it can be obtained by solving the following problem:  
\[\mathbf{P}_{5}:\min_{\mathbf{X}_{\text{Rad}},\gamma} \rho L_{\text{Rad}}(\mathbf{X}_{\text{Rad}},\gamma)\] s.t. \[[\mathbf{X}_{\text{Rad}}]_{n,n}=\frac{P_{T}}{N},\,\forall n\in \mathcal{N}, \tag{38}\] \[\mathbf{X}_{\text{Rad}}\succeq\mathbf{0}. \tag{39}\]  
Here, it can be readily verified that (37) satisfies the per-antenna power constraints and the positive semidefinite constraint:  
\[[\mathbf{X}_{\text{Prop}}]_{n,n} =\frac{P_{T}}{N},\,\forall n\in\mathcal{N},\] \[\mathbf{X}_{\text{Prop}} \succeq\mathbf{0}, \tag{40}\]  
because \([\mathbf{X}_{\text{Rad}}]_{n,n}=\frac{P_{T}}{N}\) and \(\mathbf{X}_{\text{Rad}}\succeq\mathbf{0}\), \(\big{[}P_{T}\mathbf{\hat{a}}(\phi_{k})\mathbf{\hat{a}}^{H}(\phi_{k})\big{]}_{n, n}=\frac{P_{T}}{N}\) and \(\mathbf{\hat{a}}(\phi_{k})\mathbf{\hat{a}}^{H}(\phi_{k})\succeq\mathbf{0}\), and \(w_{k}\in[0,1]\) (\(\forall k\in\mathcal{U}\)), with \(\sum_{k=1}^{U}w_{k}\leq 1\). In our waveform design, the weighting factors should be determined to minimize the objective function of problem \(\mathbf{P}_{1}\).
## 3 Results  
### Data Descriptive Statistics  
In this study, there were 33,785 PM\({}_{2.5}\)-AOT sample pairs in total for the 35 monitoring sites and 1301 days (239 days for 2013, 261 days for 2014, 268 days for 2015, 269 days for 2016, and 264 days for 2017), which were used for model fitting and model validation. The number of PM\({}_{2.5}\)-AOT sample pairs varies from 666 to 1015 by site. The data statistics demonstrate that the overall mean of the PM\({}_{2.5}\) 24 h average is 72.15 \(\mu\)g/m\({}^{3}\), varying from 44.95 \(\mu\)g/m\({}^{3}\) (SD = 46.89 \(\mu\)g/m\({}^{3}\)) to 105.02 \(\mu\)g/m(SD = 87.29 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) by site (Table S2), and the overall mean of the PM\({}_{2.5}\) period average between 10:00 a.m. and 2:00 p.m. is 65.82 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\), varying from 39.39 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (SD = 47.49 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) to 92.33 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) (SD = 87.36 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) by site (Table S3). The overall mean MAIAC AOT is 0.49, varying from 0.30 (SD = 0.32) to 0.61 (SD = 0.66) by site (Table S4). As shown in Figure 1, the study area is surrounded by mountains in the west, north, and northeast, while it is quite flat in the south and southeast. PM\({}_{2.5}\) levels have an increasing gradient trend from north to south, and the spatial distribution of the MAIAC AOT average and PM\({}_{2.5}\) levels for the whole study time period has a similar spatial pattern. The consistency of the spatial trends between the MAIACT AOT average and PM\({}_{2.5}\) levels indicates the correlation of AOT and PM\({}_{2.5}\).
# Cooperative Path Planning for Multiple Mobile Robots via HAFSA and an Expansion Logic Strategy
###### Abstract  
The cooperative path planning problem of multiple mobile robots in an unknown indoor environment is considered in this article. We presented a novel obstacle avoidance and real-time navigation algorithm. The proposed approach consisted of global path planning and local path planning via HAFSA (hybrid artificial fish swarm algorithm) and an expansion logic strategy. Meanwhile, a kind of scoring function was developed, which shortened the time of local path planning and improved the decision-making ability of the path planning algorithm. Finally, using STDR (simple two dimensional robot simulator) and RVIZ (robot operating system visualizer), a multiple mobile robot simulation platform was designed to verify the presented real-time navigation algorithm. Simulation experiments were performed to validate the effectiveness of the proposed path planning method for multiple mobile robots.  
Keywords:path planning; multiple mobile robots; artificial fish swarm algorithm; expansion logic strategy +
Footnote †: journal: Applied Sciences