esa-sceva/satcom-chunk-collection huggingface dataset:
# Implications of Metamaterial on Ultra-Wide Band Microstrip Antenna Performance
## 4 Antennas Implemented with Metamaterials  
### Implementation of Circular-SRR  
The UWB circular microstrip antenna with elliptical rings was combined with C-SRR to improve the antenna return loss and increase the bandwidth. The experiment started with implementing the same C-SRR with dimensions detailed in Table 3, on the back of the antenna (ground side of the substrate). Beginning with one C-SRR at the center (origin (0, 0)), then the number of C-SRRs are increased gradually; by this addition of C-SRRs, arrays of \(3\times 2\) and \(3\times 3\) are created. Figure 20 shows the implementations of different numbers of C-SRR, and the results are presented in Table 4 and Figure 21.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **C-SRR No.** & **Frequency Bandwidth (GHz)** & **Return Loss (dB)** \\ \hline
**a** & \(1\) & \(3.5\)–\(9\) & \(-42\) \\
**b** & \(2\) on y-axis & \(3.5\)–\(8\) & \(-49.65\) \\
**c** & \(3\) on y-axis & \(3.2\)–\(8.2\) & \(-40\) \\
**d** & \(3\) on x-axis & \(2.2\)–\(8\) & \(-34.8\) \\
**e** & \(3\times 2\) & \(2.2\)–\(8\) & \(-33.74\) \\
**f** & \(3\times 3\) & \(2.8\)–\(8\) & \(-45.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of C-SRR implementations with the antenna.  
Figure 20: Different number of C-SRR on the back of the antenna. Arrays of (**a**) \(1\times 1\) (**b**) \(1\times 2\) (**c**) \(1\times 3\) (**d**) \(3\times 1\) (**e**) \(3\times 2\) (**f**) \(3\times 3\).  
The return loss (S\({}_{11}\)) of the circular antenna affected by increasing C-SRRs number was noticeable for \(3\times 3\) array while the lower numbers of C-SRRs have the no effect or small change enhancement occurs by implementing \(3\times 3\) C-SRR on the back of the antenna with a one-millimeter separation between each SRR unit, Figure 22. The effect of C-SRR on antennas return loss is shown in Figure 23. The introduction of the SRR resulted in a shift of the UWB start at 2.8 GHz and improved return loss over the entire band. The antenna's maximum gain is presented in Figure 24; it is almost 5 dB. The antenna's radiation pattern with implemented C-SRR is examined for different frequencies of 4, 6, 8 and 10 GHz with \(\varphi=0^{\circ}\) and \(90^{\circ}\); it is noticeable that its radiation pattern has maintained omnidirectional behavior, Figure 25.  
Figure 21: S\({}_{11}\) for antenna with different number of C-SRR.  
Figure 23: Simulated S\({}_{11}\) for circular antenna and the antenna with \(3\times 3\) C-SRR.  
Figure 22: (a) Antenna front side (b) antenna back.  
The proposed circular microstrip antenna with an array of \(3\times 3\) C-SRR is fabricated in the lab and presented in Figure 26. The frequency bandwidth of the antenna is between 2.9 GHz to 8.8 GHz, Figure 27, with VSWR less than 2 for antenna's impedance bandwidth. The simulated and measured results are in good agreement.  
Figure 24: Simulated maximum gain for circular antenna with \(3\times 3\) C-SRR.  
Figure 25: (a) Radiation pattern at Phi = \(0^{\circ}\) (b) radiation pattern at Phi = \(90^{\circ}\).
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Baseline Soil Erosion by Soil and Landform Types  
Soils in areas with a mean annual rainfall erosivity greater than 4500 presented high mean annual soil loss rates above 10 t ha\({}^{-1}\) yr\({}^{-1}\) (Table 12). Nitosols had the highest erosion rate of 22.69 t ha\({}^{-1}\) yr\({}^{-1}\), followed by Andosols and Planosols. Yermosols, which are predominant in the north-eastern parts of the country, recorded the lowest mean soil loss rates. The steep escerment landforms of the Great Rift Valley presented the highest annual erosion rates, followed by mountainous landforms (Table 13).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Slope (\%)**} & **Area** & **MARE (Baseline)** & **MASLR (Baseline)** \\  & **(10\({}^{4}\) ha)** & **(MJ) mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline
0–7 & 4444.5 & 1438.7 & 1.88 \\
7–11.3 & 593.7 & 2673.8 & 7.57 \\
11.3–17.6 & 310.8 & 3438.9 & 14.36 \\
17.6–26.8 & 205.2 & 3670.4 & 19.91 \\ \(>\)26.8 & 272.3 & 3146.5 & 21.45 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Baseline soil erosion rates in different slope zones of Kenya.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Kenya Regions**} & **Area** & **MARE (Baseline)** & **MASLR (Baseline)** \\  & **(10\({}^{4}\) ha)** & **(MJ) mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline Lake Victoria & 248.5 & 7430.0 & 25.25 \\ Rift Valley & 1762.6 & 2077.6 & 6.86 \\ Central \& Eastern & 1717.5 & 1562.3 & 4.97 \\ North Eastern & 1262.7 & 644.8 & 0.34 \\ Coastal & 838.2 & 1983.0 & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Baseline soil erosion rates in Kenya regions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Kenya Basins**} & **Area** & **MARE (Baseline)** & **MASLR (Baseline)** \\  & **(10\({}^{4}\) ha)** & **(MJ) mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline Lake Victoria & 510.0 & 6480.8 & 21.90 \\ Great Rift Valley & 1285.2 & 1335.0 & 4.80 \\ Ewaso Nyiro & 2092.9 & 769.8 & 1.12 \\ Tana River & 1265.7 & 1955.6 & 4.22 \\ Athi River & 672.7 & 2303.2 & 4.87 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Baseline soil erosion rates by river basins in Kenya.
## 4 Experimental Results  
In this paper, the OGRU-CSO model's performance is simulated using MATLAB 2020a software tool on a system configuration with 16 GB random access memory, an Intel Core i9 processor, and Windows 10 operating system. The developed OGRU-CSO model's efficiency is investigated in terms of MCC, precision, f-measure, specificity, recall, and accuracy. The mathematical formula of the undertaken performance measures is represented in Equations (29)-(34). Where, _FP_, _FN_, _TP_, and _TN_ indicate false positive, false negative, true positive, and true negative.  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{29}\]  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{30}\]  
\[F-measure=\frac{2 TP}{FP+2 TP+FN}\times 100 \tag{31}\]  
\[Accuracy=\frac{TP+TN}{TN+TP+FN+FP}\times 100 \tag{32}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\times 100 \tag{33}\]  
\[Specificity=\frac{TN}{TN+FP}\times 100 \tag{34}\]
###### Abstract  
Road extraction is a sub-domain of remote sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. Convolutional neural networks (CNNs), especially the DeepLab series known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects' features, address some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in the place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP with a CNN backbone network and a Squeeze-and-Excitation block will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from remote sensing images. The Results Section presents a comparison of our model's performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach.  
DeepLabV3+; ASPP; deep learning; semantic segmentation; satellite imagery; Xception; remote sensing; Squeeze-and-Excitation; road extraction Article
## 2 Materials and Methods
### Data Collection  
In this study, a FEIMA D2000 multi-rotor UAV (Figure 3a) was used to collect data. This UAV included a fuselage, power motor, quick removal wing, differential antenna, magnetometer, and data transmission antenna. The 24.3-megapixel camera installed on the D2000 UAV provided a ground sampling distance (GSD) of 1.7 cm/pixel at an altitude of 110 m relative to the ground. The camera captured images at fixed intervals and stored them in JPG format. Detailed information about the UAV and camera are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**UAV Body** & \multicolumn{2}{c}{**D-CAM2000 Aerial Module**} \\ \hline System standard takeoff weight & 2.8 kg & Camera & SONY a6000 \\ Standard load & 200 g & Effective pixels & 24.3 million \\ Endurance & 74 min & Sensor & 23.5 \(\times\) 15.6 mm (aps-c) \\ Remote control distance & 20 km (max) & Focal length & 25 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the UAV platform.  
Figure 3: Experimental settings. (**a**) FEIMA D2000 UAV. (**b**) Distribution of GCPs and CPs in the bundle adjustment. (**c**) Measurement of field control points. (**d**) Display of control points in the image. (**e**) The number of overlapping images used in each pixel calculation. Red and yellow areas indicate low overlap, while green areas indicate more than 5 images per pixel.  
By pre-setting flight parameters in the UAV Manager software, the UAV flew autonomously from takeoff to landing. The ground control station consisted of computers, a ground-based data transmission radio, and antennas that communicated with the UAV to continuously monitor its flight status and allowed users to interrupt the flight if the UAV was in danger. The aerial survey was conducted on 10 August 2021 with clear weather and light wind.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 3 The Proposed CSS Method  
The design of the proposed CSS method is presented in this section, which consists of two key components: BEISTA-Net and BSWSS-Net. BEISTA-Net is designed for reconstructing compressed signals, while BSWSS-Net is designed for WSS.
###### Abstract  
The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete.  
Keywords:path algebra; composite metric; path enumeration; virtual network embeddings +
Footnote †: journal: _etlecom_
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 1 Introduction
The Libmodbus library provides an implementation of the Modbus/TCP protocol in C and the ECCLIB provides the necessary functionality to implement cryptographic protocols based on Elliptic Curve Cryptography of ECCLIB. The ECCLIB is a library in C++ that obtain algebraic operations and a rich variety of cryptographic protocols for Elliptic Curve Cryptography in fields of the form \(F_{p}\), where \(p\) is an odd prime based on the _Complex Multiplication_ method. In our work, Libmodbus and ECCLIB were modified and combined in such way in order to enhance Modbus/TCP protocol leading to a modified version of it, focusing on the security of the data exchanged among the devices and the supervisory servers. The mechanisms we implemented include key generation, key exchange/sharing, message authentication, data integrity check, and encryption/decryption of data. The key generation and key exchange protocols we implemented using Elliptic Curve Cryptography functionality. The encryption/decryption keys created by the SCADA devices are stored in their local memory and are consider \"alive\" only for the duration of the current communication session. These keys are used in encryption/decryption of the exchanged messages, in establishing the integrity of the messages and in the authentication process of the involved entities. The modified library was compiled for the Android operating system, to implement the server side in Android. The client side can be installed on any computer. The communication between the entities of client and server implements an example of the successful establishment of sessions using Elliptic Curve Cryptography, based on secure sessions of Modbus wireless communication between a portable device acting as a supervisor station and a monitoring and control workstation. Our first performance measurements are, also, very promising and show the feasibility of embedding Elliptic Curve Cryptography into SCADA systems, giving a significant approach to an area that had not been studied. Our approach has two goals. The first was to embed a key generation and exchange mechanism in Modbus, while keeping the basic Modbus functionality unchanged. The second was to assess the result of the new Modbus considering its performance in comparison with the original Modbus implementation. In other words, our approach aims at exploring the feasibility of integrating encryption functionality in industrial SCADA protocols as well as the interoperability of this integration with existing SCADA installations based on Modbus.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 2 XPCS Concepts
### Higher-Order Correlations  
If the sample dynamics change during the XPCS series, the \(g_{2}\)-function in Equation (1) will not detect the dynamics of the sample accurately because of the averaging over time \(t\). In order to quantify such dynamical heterogeneities, the two-time correlation \[C(q,t_{1},t_{2})=\frac{\langle I(q,t_{1})I(q,t_{2})\rangle_{\text{pix}}}{\langle I (q,t_{1})\rangle_{\text{pix}}\langle I(q,t_{2})\rangle_{\text{pix}}} \tag{7}\]  
can be used [45; 46]. It calculates the correlation between two patterns of the series at times \(t_{1}\) and \(t_{2}\) averaged over all detector pixels of same \(q\) without any time averaging. Therefore, it is mirror symmetric along the \(t_{1}=t_{2}\) axis, i.e., \(C(q,t_{1},t_{2})=C(q,t_{2},t_{1})\). Furthermore, the \(g_{2}\) function can be obtained by averaging over \(t_{1}\) as \(g_{2}(q,\Delta t)=\langle C(q,t_{1},\Delta t)\rangle_{t_{1}}\), with \(\Delta t=t_{2}-t_{1}\). Figure 4a shows an exemplary two-time correlation matrix of an aging system. It was calculated from diffusing disks whose diffusion constant decreased with every speckle pattern. In this way the correlated region in the vicinity of the \(t_{1}\)-\(t_{2}\) axis broadens with experimental time. This becomes obvious from Figure 4b showing cuts through \(C\) for fixed \(t_{2}=t^{*}\) that highlight the continuous slowing down. The degree and time scale of the dynamical heterogeneities are quantified by the variance [47]  
\[\chi(\Delta t)=\frac{\langle C^{2}(q,t_{1},\Delta t)\rangle_{t_{1}}-\langle C (q,t_{1},\Delta t)\rangle_{t_{1}}^{2}}{\langle C(q,t_{1},\Delta t=0)_{t_{1}}}, \tag{8}\]  
whose peak marks the characteristic time scale and degree of dynamical heterogeneities (see Figure 4c). Besides aging phenomena as shown in Figure 4, this procedure can be used to detect avalanche dynamics, e.g., during phase transitions or sudden rearrangements of structures, as well as intrinsic heterogeneous processes such as gelation or glass transitions.  
Figure 3: (**a**) XPCS scheme in small-angle X-ray scattering (SAXS) geometry. The speckle patterns are recorded with repetition rate of \(1/\Delta t_{\text{min}}\) several meters downstream of the sample. Adapted from [48]. (**b**) Calculated \(g_{2}\) functions for nanoparticles with radius of 50 nm dispersed in water at room temperature. The speckle contrast is set to \(\beta=0.3\). The \(q^{2}\)-dependence of the relaxation time is visible by the non-equidistant functions. (**c**) \(g_{2}\) showing different shapes: stretched with \(\gamma=0.5\), single exponential, and double exponential with 1:1 contribution of the two decays. The first decay is compressed (\(\gamma=2\)) and is additionally shown as single curve as dashed green line. The dashed black lines mark the corresponding relaxation times found at \(\beta\exp(-2)\).
## Evaluation and Results
### Random Network Formation
#### 5.5.2 Results--Average Energy Consumption  
Figures 14-17 show the average energy consumption across significant nodes. As with the grid topology, these nodes now take on the role of hotspot. The comparisons shown are between the evaluation results, where the standard duty cycling with CCA and check interval is in use, and results where the MADCAL algorithm is applied, such that a dynamic threshold is created within significant nodes for communication with the MSN.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interference Range** & **Significant Nodes** \\ \hline
77.52 m & 1, 2, 3, 4, 5, 6, 9, 11, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25 \\
69.13 m & 1, 2, 3, 4, 5, 11, 15, 16, 17, 19, 20, 21, 24, 25 \\
62.02 m & 1, 2, 3, 4, 5, 11, 15, 16, 17, 19, 21, 24, 25 \\
55.94 m & 1, 2, 3, 4, 5, 11, 15, 16, 17, 21, 24, 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Significant nodes—random topology.  
Figure 14: Average energy consumption (mWs), random topology, significant nodes. Interference range 77.52 m.  
Figure 14 illustrates reduced energy consumption when the MADCAL algorithm is in use. However, in comparison to the grid topology it can be seen that energy levels are lower even before MADCAL is applied. This is likely due to the large gaps between groups of significant nodes in this more random topology. Allowing for less overlap of communication over the network as a whole. This is then improved upon still by the application of MADCAL. As such we can begin to see that MADCAL is again of benefit, even in a less controlled environment such as this.  
In Figure 15 interference range is reduced to 69.13 m. The results shown are now a significant improvement as the interference range decreases. However, it can be seen also that energy consumption has risen considerably in comparison to Figure 14. In this test there are now three fewer significant nodes, therefore as each takes on a greater role, more energy is consumed overall as a result.  
Figure 16: Average energy consumption (mWs), random topology, significant nodes. Interference range 62.02 m.  
Figure 15: Average energy consumption (mWs), random topology, significant nodes. Interference range 69.13 m.  
In Figure 16 there is again benefit shown when using the MADCAL algorithm as interference range decreases again. Even more so than when a controlled, grid topology was in use.  
Figure 17 is significant in that the interference range is now strained to a great extent. However, benefits can still be seen with MADCAL in use. As with the grid topology, this is less evident when the sink is moving faster. However, unlike in the equivalent test, shown in Figure 9, the benefits in energy consumption are greater overall now.
## 4 Theoretical Analysis of Different Detection Schemes
### BER Analysis of D-QR Detection  
Applying QR decomposition:  
\[\mathbf{H}=\mathbf{Q}\mathbf{R} \tag{35}\]  
Multiplying both sides by \(\mathbf{Q}^{H}\):  
\[\tilde{\mathbf{y}}=\mathbf{Q}^{H}\mathbf{y}=\mathbf{R}\mathbf{x}+\tilde{\mathbf{ n}},\quad\tilde{\mathbf{n}}=\mathbf{Q}^{H}\mathbf{n} \tag{36}\]  
The pairwise error probability (PEP) for transmitted \(\mathbf{x}_{i}\) and erroneous \(\mathbf{x}_{j}\) is:  
\[P(\mathbf{x}_{i}\rightarrow\mathbf{x}_{j})=\mathbb{E}_{\mathbf{H}}\Bigg{[}Q \Bigg{(}\sqrt{\frac{\|\mathbf{R}(\mathbf{x}_{i}-\mathbf{x}_{j})\|^{2}}{2 \sigma^{2}}}\Bigg{)}\Bigg{]} \tag{37}\]  
Under [PERSON]-\(m\) fading, using [PERSON]'s formula:  
\[Q(\sqrt{a})=\frac{1}{\pi}\int_{0}^{\pi/2}\exp\biggl{(}-\frac{a}{2\sin^{2}\theta }\biggr{)}d\theta \tag{38}\]  
\[P(\mathbf{x}_{i}\rightarrow\mathbf{x}_{j})=\frac{1}{\pi}\int_{0}^{\pi/2} \Biggl{(}\frac{m}{m+\frac{\|\mathbf{R}(\mathbf{x}_{i}-\mathbf{x}_{j})\|^{2}} {4\sigma^{2}\sin^{2}\theta}}\Biggr{)}^{m}d\theta \tag{39}\]  
The average bit error rate is bounded as:  
\[P_{b}^{\text{D-QR}}\leq\frac{1}{\log_{2}M}\sum_{\mathbf{x}_{j}}\sum_{\mathbf{ x}_{j}\
eq\mathbf{x}_{i}}d_{H}(\mathbf{x}_{i},\mathbf{x}_{j})\cdot P(\mathbf{x}_ {i}\rightarrow\mathbf{x}_{j}) \tag{40}\]
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### RNN, LSTM and GRU Model Training Comparison
#### 3.3.3 Learning Rate Comparison  
This section chooses linear interpolation plus relative position and relative position data sets for comparison, because there is a slight overfitting problem in Loss during the training and verification stages. In this paper, we test when the neuron is 512 under the learning rate of 0.1, 0.01 and 0.001 to find the most suitable learning parameters.  
From Table 6, we can see that RNN has the highest accuracy of 89% when LR = 0.1, while LSTM and GRU achieve higher accuracy when LR = 0.01. The accuracy is 98.3% and 96.6%, respectively.  
From Table 7, we can see that all three models achieve the highest accuracy when the learning rate is 0.1. The accuracy is 88.63%, 93.6%, and 96.3%, respectively. It is observed that the accuracy has not improved after interpolation, which may indicate that interpolation method does not have the correct compensation effect for the missing values.