esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Elevation may also play a role in the health of vegetation, as elevation can constrain the growth of plants due to topographic climate effects [34]. In this study, we focus on the 79 national parks in southern Africa, all of which are some variation of savannas, varying from sparse grassland to deciduous woodland cover types. These parks are of critical importance, both socioeconomically and ecologically, as they support large human populations though tourism revenue and natural resource extraction, and support high levels of biodiversity [35; 36]. Parks are important conservation features on the landscape because they tend to focus on maintaining biodiversity in vegetation and wildlife while minimizing human impacts. Theories in landscape ecology suggest that larger parks tend to be more successful in achieving their conservation goals [37], but the land matrix surrounding the park is also important. A way to better understand this matrix is to develop a comparison between the park and the surrounding matrix, or buffer zone. Buffer zones are important in their role of alleviating human impact on national parks [38]. More recently, multiple country, cross-border parks have also been developed, which are known as transfrontier conservation areas. The creation of transfrontier conservation areas is based on the landscape ecology principles of connectivity--the more connected, the better for conserving biodiversity (positive impact; [39]). It is now an important conservation question as to whether such transfrontier conservation areas are actually working and, as such, to address the landscape ecology theory that the size and connectivity of a protected area impacts the overall quality [37]. Currently, conservation development via parks and protected areas is a global phenomenon. However, such landscapes have rarely been studied on a larger scale explicitly to determine the factors of vegetation greenness or park success. This research examines factors of spatial heterogeneity in NDVI, specifically addressing the role of parks versus buffer zones or the surrounding land matrix. Such potential factors of savanna vegetation within these protected areas and their surrounding buffers include precipitation, human population density, transfrontier conservation areas, elevation, park size and area, land cover, and country. The research objectives of this study are as follows: (1) to understand the factors of vegetation health in national parks and their associated buffer zones in southern Africa; and (2) to determine how savanna vegetation persistence within these national parks and buffer zones of southern Africa varied from 2000 to 2016.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 2 Indoor Standard Device for Large-Scale Length Measurement
### Long-Scale Guide Rail System  
The guide rail spanned a length of 57 m, boasting an operational noise level of below 40 dB. The guide rail system should be easily expandable and accommodate additional mobile platforms, and the positioning accuracy of each of these platforms stood at 50 \(\upmu\)m. A fixed instrument platform was in place at both ends of the rail, while the starting end was fitted with four pre-set manual sliding tables (two with four sliders and two with two sliders). Furthermore, a pre-set automated mobile platform complemented the system's capabilities.  
To ensure accurate control of the long-scale moving target, specific control error thresholds were set. The straightness error should not exceed 0.03 mm/m, 0.05 mm/4 m, 0.10 mm/20 m, and 0.25 mm/57 m. Similarly, the flatness error must stay within 0.03 mm/m, 0.08 mm/4 m, 0.12 mm/20 m, and 0.30 mm/57 m. Operating at noise levels below 40 dB, the guide rail boasted a robust load-bearing capacity of at least 150 kg. Its foundation, spanning 57 m, required careful pouring based on stable rock layers, taking into account necessary allowances. The granite-made guide rail was supported by adjustable frames on a concrete foundation, enabling adjustments in six directions. Quality standards dictated that the guide rail's foundation surface remained free from any scratches, cracks, delamination, or rust. Overall, the guide rail system was assembled by no more than 14 pieces of granite rail, each measuring at least 600 mm \(\times\) 500 mm. To overcome the limitations of the friction-driven air-floating platform, characterized by low operational speed and insufficient stability, we incorporated linear guides and transmission rates into the granite guide rail system.
## 3 Results and Discussion
### Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES)  
Fifteen elements were measured in the marine collagen by ICP (Table 1) to ensure that the extracted collagens were free of heavy metals or toxic elements. Elements such as calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), strontium (Sr), and zinc (Zn) were absent in all collagen samples. Apart from that, silver (Ag), arsenic (As), and phosphorus (P) were detected in low amounts in _COLRp_I_ compared to the other evaluated collagens. Otherwise, cadmium (Cd) was only identified in _COLRp_I_. Interestingly, aluminum (Al), gold (Au), and lead (Pb) were absent in _COLRp_II_. However, the Pb element was detected with the highest value in _COLRp_III_ compared to _COLRp_I_. In summary, the presence of some target elements was identified in the produced collagens, but in all cases in small amounts and well below the limits for elemental impurities recommended by the European Medicines Agency (ICH guideline Q3D (R1) on elemental impurities). These elements can be naturally present in marine byproducts due to accumulation in fish tissues, but the determined quantities show that the respective products do not raise any concerns regarding security for further use.
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
## 3 Problematic RF Impairments in a Narrowband  
### Carrier Frequency Offset  
CFO occurs when the local oscillator frequency for down conversion in the receiver does not synchronize with the carrier frequency contained in the received signal [15]. The fundamental causes of the CFO are crystal inaccuracies and variations in the transmitter as well as the receiver side, and the Doppler effect when the mobile stations are moving. Frequency errors are typically corrected in the digital receiver side using the correlation properties of training sequences [15; 16]. After the digital correction, very little frequency error exists. For an orthogonal frequency division multiplexing (OFDM) system, the orthogonality among subcarriers is maintained only if the receiver uses a local oscillator that is synchronous with the carrier frequency contained in the received signal [15]. Otherwise, carrier frequency mismatch between transmitter and receiver can result in inter-carrier interference (ICI).  
In the IEEE 802.11 wireless LANs for 5 GHz ISM bands, the oscillator precision tolerance is specified to be less than 20 ppm, so that the CFO is in a range from 40 ppm to \(-\)40 ppm [2]. For example, if the TX oscillator is operated at 20 ppm below the nominal frequency and if the RX oscillator is operated at 20 ppm above, then the received baseband signal will have a carrier frequency error of 40 ppm. With a carrier frequency of 5.4 GHz in this standard, the CFO is up to 216 KHz. In addition, if the mobile station is moving, the Doppler effect adds some hundreds of Hz in frequency spreading.  
Even though the carrier frequency of the transmitter is usually known to a receiver, due to RF tolerance, the frequency at which the receiver operates may not exactly match the transmitter frequency. Additionally, in systems in which the transmitter or receiver is mobile, Doppler effects may also give rise to carrier frequency offsets. This offset causes a time-dependent phase shift in the received signal after conversion to the baseband, which can cause errors in the reconstruction of the transmitted signal.
## 7 Conclusions  
Embedded systems applied in safety-critical equipment require a high quality of security to guarantee program execution security, especially for remote-controlled hardware platforms. This paper presents a CPU-SPU collaborative SoC that integrates the proposed SPU to monitor the program execution and instruction fault recovery in real time. The hardware-implemented SPU architecture employs an instruction monitor to verify the BB integrity signature for detecting malicious instruction tampering behaviors caused by hardware Trojans and artificial modification.  
The instruction fault self-repairing module (FSRM) was integrated into the SPU to provide a low-cost instruction recovery for BB re-execution. The CPU-SPU collaborative SoC was implemented and validated on the Virtex-5 FPGA development board. The evaluation results after executing different benchmarks indicate that the SPU can provide both high-efficiency instruction execution monitoring and fast instruction fault self-repairing while maintaining a low performance overhead. Its average performance overhead lowered to the 1.92% at typical 8-KB I/D caches.  
Moreover, the security capability evaluation and practicality comparison of the SPU confirmed its superiority in detecting instruction tampering behaviors and transplanting different ISA platforms. According to the hardware implementation of SPU, its hardware complexity is acceptable for embedded systems. Ultimately, the SPU satisfies a good balance between security capability and resource consumption.  
Conceptualization, Z.Z. and X.W.; methodology, Z.Z. and X.W.; software, Z.Z. and Q.H.; validation, Z.Z. and D.X.; formal analysis, J.W.; investigation, Z.Z. and Q.H.; resources, J.L.; data curation, Z.Z., J.M. and J.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z.; supervision, X.W.; project administration, X.W.; and funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grants No. 60973106 and No. 81571142), the Key Project of National Natural Science Foundation of China (Grant No. 61232009), and the Open Foundation of Space-Trusted Computing and Electronic Information Technology Laboratory under grant OBCandETL-2019-02.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Platform** & **Resource Utilization** & **SoC** & **SPU** \\ \hline \multirow{4}{*}{FPGA} & Slice Registers & 2572 & 958 \\  & Slice LUTs & 16,754 & 2074 \\  & Occupied Slices & 6835 & 672 \\  & BlockRAM/FIFO & 57 & 52 \\ \hline \multirow{2}{*}{ASIC} & Chip Area & 3.42 mm\({}^{2}\) & 1.26 mm\({}^{2}\) \\  & Power Consumption & 56.6 mW & 7.4 mW \\ \hline \hline \end{tabular}
\end{table}
Table 6: The CPU–SPU collaborative SoC hardware implementation on FPGA and ASIC.
## 5 Numerical Simulations
### Simulation Results and Analysis
#### 5.2.1 Case 1  
In the first case, the position and the attitude of UAV base are controlled. Therefore, the initial and the final poses of UAV base are not considered. The kinematic equation of the AEROM system is shown in Equation (8). The cost function in (24) considers angular variations of joints (22), i.e. \(\Gamma=\Gamma_{1}\). The weight matrix \(\mathbf{M}_{2}\) is assigned as \(\mathbf{M}=\frac{1}{\sin\left(\frac{\mathbf{x}}{\delta\mathbf{0}}\right)}\mathbf{E}_{6}\), where \(\mathbf{E}_{6}\) is a sixth-order unit matrix. The AEROM system is commanded to move from initial state \(\mathbf{q}^{0}=[0,30,30,20,50,30](\text{deg})\) to desired state \(\mathbf{q}^{f}=[10,0,60,10,30,20](\text{deg})\). Figure 6 shows the variations of cost function. The proposed HPSO algorithm stops when \(k_{\text{eq}}=101>100=k_{\max}\), where \(N=267\) and the final cost value is 0.7362. Figure 7 shows the time histories of joint angles, meeting aforementioned conditions. Figure 8 shows variations of joint velocities, where the components are all 0 at initial time and final time. According to the simulation results, the joints move smoothly and the robotic manipulator successfully arrives at the desired state. The error vector \(\delta\mathbf{q}\) in Equation (18) is \(\delta\mathbf{q}=[-6.07,1.01,1.07,0.311,2.49,0.1]\times 10^{-8}(\text{deg})\). Actually, multiple solutions exist due to the geometric construction of robotic manipulator. The selected optimal solution is just one of the multiple solutions. In addition, the represented solution can be employed for further purpose of subsequent optimization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Mass (kg)** & \(\mathbf{I_{xx}}\) (**kg\(\cdot\)m2**) & \(\mathbf{I_{xy}}\) (**kg\(\cdot\)m2**) & \(\mathbf{I_{xz}}\) (**kg\(\cdot\)m2**) & \(\mathbf{I_{yy}}\) (**kg\(\cdot\)m2**) & \(\mathbf{I_{yz}}\) (**kg\(\cdot\)m2**) & \(\mathbf{I_{zz}}\) (**kg\(\cdot\)m2**) \\ \hline UAV base & 80 & 44 & 0.3 & 0.4 & 43 & 0.3 & 26 \\ Link 1 & 1.5 & \(5.2\times 10^{-3}\) & \(1.9\times 10^{-5}\) & \(2.3\times 10^{-4}\) & \(7.6\times 10^{-3}\) & \(2.8\times 10^{-5}\) & \(3.5\times 10^{-3}\) \\ Link 2 & 1.1 & \(3.6\times 10^{-3}\) & \(1.1\times 10^{-3}\) & \(1.2\times 10^{-2}\) & \(1.2\times 10^{-1}\) & \(3.0\times 10^{-4}\) & \(1.1\times 10^{-1}\) \\ Link 3 & 1.5 & \(9.1\times 10^{-3}\) & \(3.1\times 10^{-5}\) & \(1.2\times 10^{-3}\) & \(1.1\times 10^{-2}\) & \(8.1\times 10^{-5}\) & \(2.5\times 10^{-3}\) \\ Link 4 & 1.0 & \(5.4\times 10^{-2}\) & \(5.6\times 10^{-4}\) & \(7.4\times 10^{-4}\) & \(4.5\times 10^{-2}\) & \(0.1\times 10^{-2}\) & \(0.1\times 10^{-2}\
## 3 Results
### Cost Analysis  
The average embolic material cost per procedure (AEMC) was analyzed using the institutional cost for all embolic materials used in each case (Table 2). The AEMC for the Caterpillar group was 1050 \(\pm\) 0 USD compared to 2312.75 \(\pm\) 1382.84 USD in the coil group (\(p\) = 0.000532), representing a 120% increase in cost. This cost difference was even higher in the larger vessel subgroup. AEMC in the Caterpillar group in vessels >2 mm was 1050 \(\pm\) 0 USD compared to 2622.64 \(\pm\) 1446.52 USD in the coil group (\(p\) = 0.000601), representing a 150% increase in cost. In vessels <2 mm, the AEMC in the Caterpillar group was 1050 \(\pm\) 0 USD compared to 1442.25 \(\pm\) 721.67 USD in the coil group (\(p\) = 0.001), representing a 43% increase in cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Caterpillars** & **Fibered Coils** & \multirow{2}{*}{_p_**-Value**} \\  & **(_n = 16_)** & **(_n = 32_)** & \\ \hline
**Relative time to** & & & \\
**occlusion (RTO)** & & & \\
**Overall RTO** & 0 min 57 s \(\pm\) 0 min 34 s & 11 min 44 s \(\pm\) 8 min 13 s & **0.000016** \\ \hline RTO for \textless{}2 mm & 0 min 50 s \(\pm\) 0 min 34 s & 12 min 54 s \(\pm\) 8 min 46 s & **0.000011** \\ RTO for \textless{}2 mm & 1 min 16 s \(\pm\) 0 min 29 s & 9 min 49 s \(\pm\) 7 min 09 s & **0.000825** \\ \hline
**Embolization related** & & & \\
**time** & & & \\
**Radiation time** & 19.2 \(\pm\) 14 min & 30.7 \(\pm\) 15 min & **0.017** \\
**Procedure time** & 81.0 \(\pm\) 36 min & 111.5 \(\pm\) 49 min & **0.015** \\ \hline
**Embolic material cost in** & & & \\
**USD** & & & \\
**Overall** & 1050 \(\pm\) 0 & 2312.75 \(\pm\) 1382.84 & **0.000532** \\
**Arteries \textgreater{}2 mm** & 1050 \(\pm\) 0 & 2622.64 \(\pm\) 1446.52 & **0.000601** \\
**Arteries \textless{}2 mm** & 1050 \(\pm\) 0 & 1442.25 \(\pm\) 721.67 & **0.001** \\ \hline \hline \end{tabular} Data are presented as the mean \(\pm\) SD as appropriate.  
\end{table}
Table 2: Comparison of embolization outcomes.
# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 5 Conclusions  
In this study, we examined the utilization of a discrete wavelet transform (DWT) and quantum cat swarm optimization (QCSO) to extract features from 5G signals. This innovative approach exploits the combined potential of the DWT based CNN and QCSO to enhance the discriminative value of features derived from 5G signals. Furthermore, an innovative wavelet-based convolutional neural network (WCNN) architecture is presented, seeking to maximize the use of the extracted features for enhanced signal classification. The results show the efficacy of the proposed methodology, achieving exceptional performance in categorizing 5G signals. The amalgamation of the DWT and QCSO enabled us to capture the temporal and spectral characteristics of the signals, providing a holistic representation for subsequent classification efforts. The WCNN, tailored with wavelet-based feature extraction insights, further enhanced the accuracy of the classification outcomes. Our research contributes to the expanding knowledge repository in signal processing for 5G communication systems, highlighting the potential to integrate cutting-edge techniques and tools such as the WCNN and QCSO into the feature extraction process. The proposed WCNN framework demonstrates promising results, underscoring the importance of customizing neural network architectures to the distinct attributes of 5G signals.  
As we move toward the realm of 5G and beyond, the precise classification and differentiation of signals will become increasingly crucial. Fusion of DWT, QCSO, and WCNN, as described in this study, paves the way for future investigations aimed at optimizing and adapting these methodologies for real-world 5G communication scenarios.  
Conceptualization, A.R. and S.S.: methodology, A.R.: software, A.R.: validation, A.R. and S.S.: formal analysis, S.S.: investigation, A.R.: resources, A.R.: data curation, A.R.: writing--original draft preparation, A.R.: writing--review and editing, A.R.: visualization, A.R.: supervision, S.S.: project administration, A.R.: funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available via Matlab 2022b, India ([[https://in.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-1](https://in.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-1) te-signals.html]([https://in.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-1](https://in.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-1) te-signals.html), accessed on 16 April 2025).  
The authors declare no conflicts of interest.
# Research on Side-Channel Analysis Based on Deep Learning with Different Sample Data
###### Abstract
By comparison with a traditional template attack (TA), MLP, and CNN, the results show that this model helps to improve training accuracy and attack effect, but its training time and attack time are much higher than other models. Aiming at the inability of deep learning models to effectively learn the internal features of the data when the size of the training set is limited, [PERSON] et al. [22] proposed a cross-subkey training method for enhancing power traces, using multiple subkeys to construct a model with a better fit. The accuracy of the subkey combination training model is 28.20% higher than that of the single subkey training model. At the same time, the number of power traces that need to be captured when training the neural network model is greatly reduced. However, the existing research lacks the quantitative research and experimentation of each deep learning model in SCA, so it cannot deeply evaluate the effectiveness of various deep learning models. In this paper, the CW308T-STM demo board is used on the ChipWhisperer experimental platform [23] to convert the extracted power trace during the encryption process of the unmasked AES-128 cipher algorithm into an experimental data set. It analyses the data set in combination with Correlation Power Analysis. After determining the interesting interval, the established MLP, CNN, RNN, and LSTM models are used to train and test small data sets, sufficient data sets, and samples of different scales. Finally, the side-channel attack capability of each model is evaluated through the indicators of classifier accuracy, network loss, training time, and rank of the side-channel attack. This paper only focuses on the AES-128 algorithm and studies the experiments under the condition of balanced samples and good data signal-to-noise ratio, and it lacks validation on public datasets and protected datasets. The actual test environment will have low signal-to-noise ratio datasets or other more complex interference situations. The remainder of this paper is divided into five sections. Section 2 describes the four deep learning models built in the experiments, including multilayer perceptions, convolutional neural networks, recurrent neural networks, and long short-term memory networks. In Section 3, we introduce the relevant information on the AES-128 target encryption algorithm. Section 4 studies the CPA side channel analysis based on deep learning, includingCPA-related principles, the CPA experimental environment configuration, and the CPA experimental analysis process. The specific experimental process and the model evaluation is provided Section 5.
## 5 Feasibility Study Through System Simulations
### Software and Hardware Details  
The simulator was implemented in the Python 3 programming language, using the machine learning library TensorFlow [228], version 2.4.2, and the Keras library, version 2.4.0. Each simulation was executed in a single core of a multi-core CPU (Central Processing Unit) with a 3.2 GHz clock frequency, a 512 KiB cache size, and 128 GiB of CPU RAM (Random-Access Memory). A GPU (Graphics Processing Unit) with 24 GiB of memory, a 1395 MHz clock frequency, and 10496 shading units was used to process the NN models.
# Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse
## 4 Discussion  
An airborne lidar simulator was developed using a rigorous radiative transfer theory of a laser pulse. The simulator allows the realistic simulation of a laser pulse with a realistic beam divergence angle. Thus, it can reproduce the various interaction conditions between the laser pulse and a target. The simulator also computes the TPU associated to each lidar point. The lidar simulator also has the capability to perform target detectability research. Additionally, the requirement conditions for the 3D accuracy assessment can be investigated using this simulator, as demonstrated in the example showing the intensity point cloud from the painted line over the pavement. To the best of the author's knowledge, an airborne lidar simulator combining these rigorous concepts has not been available previously. Further lidar applications research will be performed in the future.  
**Funding:** This research was funded by USGS National Geospatial Program (NGP) 3D Elevation Program (3 DEP).  
**Acknowledgments:** The author acknowledges the support from [PERSON] and [PERSON] from 3 DEP, [PERSON] from the USGS Earth Resource Observation and Science (EROS) Center. Seonkyung Park, United Support Services, a contractor to EROS, contributed to the software development. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.  
Figure 12: The point location uncertainty using airborne lidar simulator.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.