esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 4 Secrecy Sum-Rate Scaling Analysis
: _For an \(\epsilon\in(0,1)\), \(\eta_{Q}^{\star}=\epsilon\log N\), and \(\eta_{I}^{\star}=\eta_{L}^{\star}=\rho^{-1}\), the proposed scheduling scheme achieves a secrecy sum-rate scaling of \(\Theta(M\log(\rho\log N))\) with high probability when_  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]1  
_where \(\epsilon_{0}\in(\epsilon,1)\) is a constant._  
Proof. : We consider the modified scheduling scheme instead of the proposed scheduling scheme for the proof. First, we focus on the probability that at least one transmitter satisfying all criteria in (13) for \(l^{*}\)-th beam, i.e., \(p^{[l^{*}]}\). Using the probability \(\Pr(\mathbf{C1})\), \(\Pr(\mathbf{C2})\), and \(\Pr(\mathbf{C3})\), \(p^{[l^{*}]}\) is lower-bounded by  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]2 \[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]3  
where we define \(F_{\mathrm{C2}}(\eta_{\mathrm{L}}^{\star})\triangleq\Pr(\mathbf{C2})\) and \(F_{\mathrm{C3}}(\eta_{\mathrm{L}}^{\star})\triangleq\Pr(\mathbf{C3})\). From Lemma 1 with \(0<e^{-\eta_{\mathrm{Q}}^{\star}}F_{\mathrm{C2}}(\eta_{\mathrm{I}}^{\star})F_{ \mathrm{C3}}(\eta_{\mathrm{L}}^{\star})\leq 1\), it follows that \(p^{[t^{\tau}]}\) converges to one as \(N\) tends to infinity if  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]4  
Using Lemma 2, \(F_{\mathrm{C2}}(\eta_{\mathrm{I}}^{\star})\) and \(F_{\mathrm{C3}}(\eta_{\mathrm{L}}^{\star})\) are lower bounded as follows, respectively:  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]5 \[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]6  
where \(c_{1}=\frac{e^{-1}2^{-(S-1)}}{(S-1)\Gamma(S-1)}\) and \(c_{2}=\left(\frac{e^{-1}2^{-M}}{M\Gamma(M)}\right)^{K}\). Thus, by using (20), the term in (19) can be lower-bounded by  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]7  
Substituting \(S=M\), \(\eta_{\mathrm{Q}}^{\star}=\epsilon\log N\), and \(\eta_{\mathrm{I}}^{\star}=\eta_{\mathrm{L}}^{\star}=\rho^{-1}\), it is further reduced to  
\[\lim\limits_{x\to\infty}(1-f(x))^{x}=\lim\limits_{x\to\infty}\left(1-\frac{c_ {3}}{x}\right)^{x}=e^{-c_{3}}>0,\]8  
which tends to infinity when \(N\) scales as \(\rho^{\frac{M(K-1)+1}{1-\epsilon_{0}}}\). Therefore, the probability \(p^{[t^{\tau}]}\) converges to one when \(N=\Theta\bigg{(}\rho^{\frac{M(K-1)+1}{1-\epsilon_{0}}}\bigg{)}\). It remains to be shown that the achievable secrecy sum-rate scales \(\Theta(M\log(\rho\log N))\).
# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
## 4 Results
### Influence of Amount of Waste Loaded into the Bioboxes on Drying Efficiency
The \"_Month_\" variable showed a small negative impact (\(-0.36\)), suggesting that drying efficiency slightly decreases in later months, potentially due to seasonal variations. The biobox number (\"_Biobox no._) had a negligible negative influence (\(-0.034\)), demonstrating minimal variation in performance across different bioboxes. This model highlights the importance of operational factors such as input tonnage and seasonality in influencing biodrying efficiency. However, the relatively low R2 value underscores the need to incorporate additional predictors, such as ambient temperature, humidity, and waste composition, to improve the model's accuracy and provide a more comprehensive understanding of the biodrying process.
# A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications
## 3 The Proposed CNN-MPSK Architecture
### BER Comparison of CNN-MPSK and Coherent Demodulation  
This part presents the demodulation performance of CNN-MPSK. In this experiment, the SNR is \(snr\_db=[-5,\,-4,\,-3,\,-2,\,-1,\,0,\,1,\,2,\,3,\,4,\,5,\,6,\,7,\,8,\,9]\) in decibels. We need to restore the \(snr\_db\) to the initial value and perform \(10^{(snr\_db/10)}\) conversion. We utilize additive white Gaussian noise (AWGN) to simulate channel interference. The tested data for each SNR is 5 million bits, so the total number of noised data flows to CNN-MPSK network is 100 million. With the trained parameters, we predict the noised data and thus obtain the demodulated curve for BPSK, 4 PSK, 8 PSK and 16 PSK, as shown in Figure 7. The blue BER curve is obtained by coherent demodulation. The horizontal axis represents the SNR in dB, while the vertical axis is the BER. The BER curves of the four demodulations from the CNN structure overlap heavily with those based on the conventional coherent demodulation.  
Figure 6: A plot of accuracy and loss for the CNN-BPSK network.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Topology Management
#### 5.2.3 Communication Management  
Integrating SDN in the WSN topology enables management and operation of a network with sensors of different architectures and manufacturers with little or no challenge with compatibility issues. These nodes can be densely located with neighbouring sensor nodes appearing very close to each other and hence the need for an efficient communication scheme to be deployed. The communication scheme should take into account energy consumption and signal quality however, the primary focus should be on conservation of energy to improve network lifetime. Multi-hop techniques based on short range radio communication such Meshlogic and ZigBee [46] have been proposed [108] for dense WSNs enabling consumption of less energy compared to direct communication. In SDN applications employing clustered hierarchical topologies, long range radio can be used for node to cluster head communication. This would allow for continuity in network operation even when a few nodes in a cluster fall out due to the direct communication between the cluster head (SDN local controller) and each node. The proposed radio technology to be implemented in such scenarios should also require less power such as the LoRa technology intended for Low Power Wide Area Networks (LPWAN) [109; 110]. A hybrid of multi-hop techniques and long range radio can be used to manage a large low power WSN based on SDN where multi-hop communication is implemented between nodes in a cluster and long range radio is used between cluster heads and the global controller as shown in Figure 4.
## 3 Results  
### sPLS-DA Modeling for All Timeframes  
Following the pre-processing procedure detailed in Section 2.6, the sample set contained 40 features of interest. This sample set was log10-transformed and used to conduct modeling of the SARS-CoV-2-positive and -negative hand odor samples. The sPLS-DA model of the hand odor samples demonstrates the class grouping of the positive and negative samples (Figure 1). The loading contributions of each feature of interest can be found in Figures 15 and 16.  
The sPLS-DA model shown in Figure 1 depicts the individual human hand odor samples collected from SARS-CoV-2-positive (\(n=56\)) and SARS-CoV-2-negative (\(n=46\)) individuals. These samples were collected across a timespan that included the dominant presence of both the Delta and Omicron variants of the SARS-CoV-2 virus. The model depicted (Figure 1) was cross-validated using a five-fold cross-validation, which was repeated 200 times; this procedure randomly sampled the positive samples to construct models informed by \(n=46\) positive and negative samples. Following this cross-validation, the model was seen to yield an accuracy of 75.8% (\(\pm 0.4\)); the model correctly predicted the SARS-CoV-2 infection status of a sample in 75.8% (\(\pm 0.4\)) of attempts (95% CI). This performance breaks down to a sensitivity/true positive rate (TPR) = 81.8% (\(\pm 0.5\)), specificity/true negative rate (TNR) = 69.7% (\(\pm 0.6\)), false positive rate (FPR) = 30.3% (\(\pm 0.6\)), false negative rate (FNR) = 18.2% (\(\pm 0.5\)), positive predictive value (PPV) = 73.0% (\(\pm 0.4\)), and negative predictive value (NPV) = 79.4% (\(\pm 0.5\)).
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods  
### Diseases and Pests Multi-Source Image Dataset of Apple Canopy  
#### 2.1.1 Image Data Collection  
The canopy multi-source image data of apple diseases and insect pests were collected at the Baishui Apple Test Demonstration Station of Northwest A& F University in Tongji Village, Du Kang Town, Baishui County, Weinan City, Shaanxi Province (Figure 1). Two data acquisitions were conducted on the orchard on 11 June and 31 August 2023. The data were captured at noon on sunny and windless days to minimize interference from factors such as light intensity and angle on the prediction results. The data acquisition equipment used was the DJI Phantom 4 Pro multispectral UAV. During the image capturing process, the aircraft was manually controlled to designate a cruise area and encompass the boundaries of the orchard to ensure data availability. The shooting angle of the camera was \(-90^{\circ}\) (directly downwards), the shooting height was 40 FT, and the horizontal and lateral overlap rate was 75%. And the UAV kept hovering during shooting. In addition, at the same time as the UAV shooting, the areas where diseases and insect pests occur were manually recorded or marked. The visible light and multispectral image information obtained by UAV shooting is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Camera Category** & **Band Name** & **Wavelength/nm** & **Minimum** & **Mean** & **Maximum** & **Standard Deviation** & **Variance** \\ \hline \multirow{3}{*}{RGB} & Red & 660 & 5.07 & 109.74 & 255.99 & 33.32 & 1110.50 \\  & Green & 550 & 14.84 & 115.87 & 254.94 & 27.86 & 776.29 \\  & Blue & 470 & 0.00 & 76.97 & 254.80 & 26.20 & 686.36 \\ \hline \multirow{3}{*}{Multispectral} & \(R_{blue}\) & 450 & 0.20 & 2.42 & 87.85 & 1.07 & 1.15 \\  & \(R_{green}\) & 560 & 0.62 & 12.18 & 359.16 & 4.85 & 23.56 \\ \cline{1-1}  & \(R_{red}\) & 650 & 0.21 & 11.01 & 457.65 & 7.49 & 56.06 \\ \cline{1-1}  & \(R_{red}\) & 730 & 1.27 & 35.67 & 397.91 & 11.09 & 122.95 \\ \cline{1-1}  & \(R_{nir}\) & 840 & 1.77 & 50.89 & 458.85 & 16.35 & 267.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the multispectral image.  
Figure 1: Data collection locations: Baishui, Weinan, Shaanxi, China.
## 2 Model and Experimental Research
### Dust Settler Model Tests  
#### 2.3.1 Model Research Purpose and Subject  
The research aim was to determine the height of the suction channels formed by the horizontal partitions in the segment's dust settling chamber. The subject of the research was a model of the segment from MT multi-cyclone dust settler, including 18 cyclones arranged in three identical columns.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## 2 Bench Conception
### Dimensioning Methodology
#### 2.2.3 Speed Scaling  
Considering the third criterion, the speed \(v\) of the wheel carriage was chosen to maintain the same fraction of renewed contact area (denoted \(A_{relative\leavevmode\
obreak\ renewed\leavevmode\
obreak\ contact}\)) during a track circuit signal period \(\Delta t\) at both scales. The calculation of this relative area is developed in Appendix A. It is expressed as:  
\[\begin{split} A_{relative\leavevmode\
obreak\ renewed\leavevmode\
obreak\ contact}&(t_{0}+\Delta t)=1-\frac{2}{\pi}\pi arctan \big{(}\frac{a}{b}\tan(\alpha)\big{)}+\frac{4S}{\pi ab}\\ \text{where}&\leavevmode\
obreak\ \leavevmode\
obreak\ \alpha=\arctan \left(\frac{2b\sqrt{1-\frac{v^{2}\Delta t^{2}}{4\alpha^{2}}}}{v\Delta t} \right)\\ \text{and}&\leavevmode\
obreak\ \leavevmode\
obreak\ S=\frac{1}{2}v\cdot b \cdot\Delta t\sqrt{-\frac{v^{2}\Delta t^{2}}{4\alpha^{2}}}\end{split} \tag{6}\]  
The extreme case of this criterion corresponds to the minimum displacement speed at which the contact ellipse will be completely renewed within a track circuit signal period \(\Delta t\). In this case, the contact interface comprises a \"new\" oxide layer at each current alternation. The minimum speed required to obtain a fully renewed contact ellipse is denoted by \(v_{2a}\). It can be calculated using the following equation:  
\[v_{2a}=2*a*f_{cdv} \tag{7}\]  
where \(f_{cdv}\) is the frequency of the track circuit considered in our study (2.3 kHz).  
Considering the example shown in Table 1, one can find that \(v_{2a\leavevmode\
obreak\ scale1}=30.56\leavevmode\
obreak\ \text{m}/\text{s}\) and \(v_{2a\leavevmode\
obreak\ reduced\leavevmode\
obreak\ scale}=2.31\leavevmode \
obreak\ \text{m}/\text{s}\), illustrating a 13.2 factor on the speed. This result is very important and convenient since it allows us to work at a reduced speed on the bench while reproducing the conditions of higher real-life speeds.
# Hierarchical Mobile Edge Computing Architecture Based on Context Awareness
## 2 Problems of the Existing Cloud Computing Architecture  
### Propagation Distance and Multi-User Access Problem  
In order to deliver real-time multimedia content and the results of computing operations to users quickly, the content-propagation distance is critical to the transmission latency. However, in the existing mobile cloud computing environment, mobile consumer devices need to deliver data of interest to the data center, which is located a long distance from the location of the data. Also, this central mobile cloud computing brings about long latency, because various types of data are transmitted  
Figure 1: The existing cloud computing environment.  
across multiple networks, including wireless access networks, back-haul networks, and the Internet, which require their own traffic control, routing, and network management tasks. There have been a lot of attempts to solve these problems, for example, by using a cloudlet that integrates several cloud servers and MECS located near the user [11; 12; 13]. However, these alternatives also require a large number of computing resources to be shared when lots of users send requests to a server are located at the edge and the cloudlet, resulting in increased processing latency for the computing requests and high delivery latency for the content requests due to system resource sharing [14; 15]. Thus, low service latency and fast computing operations are not guaranteed if the content requester is far away from the content source, or when there is a large number of users connect to the MECS or the cloud data center.
## 5 Problems and Development Trends
### Future Prospects and Trends  
#### 5.2.1 Widespread Application of Genomics and Genome Editing Technologies  
In the future, the selection of large yellow croaker fry will increasingly rely on the deep integration of genomics and genome editing (GE) technologies. With the advancement of genomic research, researchers can use whole-genome data to predict the growth potential, disease resistance, and environmental adaptability of fry at an early stage, enabling precise selection [137]. The combination of genomics and genome editing technologies can not only significantly improve breeding efficiency and accuracy but also effectively reduce the risk of genetic degeneration, providing molecular-level technical support for sustainable aquaculture [138; 139].
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 1 Introduction
### Extension  
This paper is an extended version of the authors' conference paper [13]. In this extended paper, we present more examples and a more extensive analysis demonstrating the influence of the censored PL on positioning in a wide range of radio channel conditions. The conference paper is extended as follows: (1) more examples with realistic PL distributions, one with the model parameters taken from a measurement campaign and one using the 3 GPP path loss model for urban microcellular scenarios at 2 and 28 GHz; (2) a selection criterion for the BSs with censored PL; (3) the reduced amount of information needed for a unique positioning solution is noted; (4) examples with different LOS and NLOS PL distributions; (5) examination of the influences of the noise threshold value and LOS detection probability.
## 1 Introduction
When integrated, these two data streams may enable a more accurate prediction of winter cover crop biomass beyond the previously identified saturation points. Thus, our first objective was to determine which optical index from Sentinel-2 was best-suited to estimating the cover crop biomass. Secondly, we evaluated whether the addition of SAR backscatter intensities, polarimetric parameters, and InSAR coherence to optical data increased our ability to estimate cover crop biomass, especially over 1500 kg ha\({}^{-1}\)[14]. We hypothesized that: (1) non-normalized, red-edge indices would outperform other VIs for estimating winter cover crop biomass and (2) that the integration of optical and SAR metrics would result in better biomass estimation in winter cover crops. To test these hypotheses, we collected destructive biomass samples over three winter-spring cover crop growing seasons from cover crop fields planted with cereal grass species at the U.S. Department of Agriculture (USDA), Beltsville Agricultural Research Center (BARC) facility located in Beltsville, Maryland, United States, and regressed a suite of remote sensing measures to biomass.
# MAC-Based Compression Ratio Improvement for CAN Security
## 2 Existing CAN Data Authentication Methods  
### Practical Security Algorithm  
In the Practical Security algorithm, AES-128 and hash-based message authentication (HMAC) are used for CAN data encryption and authentication, respectively [12]. The ciphertext \(C\) is obtained from the plaintext \(M\) as  
\[C=E_{EK_{k}}(CTR_{ECU_{s}})\oplus M, \tag{1}\]  
where \(E_{EK_{k}}(\cdot)\) is the AES-128 encryption function using the \(k\)-th session encryption key \(EK_{k}\), \(CTR_{ECU_{s}}\) is the message counter value of the transmitting ECU (\(ECU_{s}\)), and \(\oplus\) is the XOR operation. Each ECU must manage data frame counter values for all relevant incoming and outgoing data.  
AES-128 encryption produces an output of 128 bits, but since the maximum size of the CAN data field is 64 bits, only the first 64 bits are used to generate the ciphertext in (1).  
The MAC data of the Practical Security algorithm is generated as follows:  
\[MAC_{PS}=H_{AK_{k}}(ID_{s}\|C\|CTR_{ECU_{s}}), \tag{2}\]  
where \(H_{AK_{k}}(\cdot)\) denotes the keyed hash function using the \(k\)-th session authentication key \(AK_{k}\), \(ID_{s}\) denotes the ID of the sending ECU, and \(\|\) denotes a concatenation operation.  
In the Practical Security algorithm, 32-bit MAC is used, and the 32-bit MAC provides sufficient authentication for CAN systems. Since there is no room for MAC in the data field, the 16-bit extended ID field and 16-bit CRC field of CAN 2.0B are used for MAC transmission as shown in Figure 1. However, variations in the CAN protocol can cause issues such as compatibility.
## 5 Experimental Validations
By substituting the \(\overline{SRC}\) in the proposed damage quantification algorithm, a damage image is constructed in Figure 12b. Through searching the largest \(DI\), the defect size is identified as 28 mm in width and 0.85 mm in depth, which is very close to the actual defect size. Following the same procedure, damage quantification is conducted for the second case where the defect size is 40 mm in width and 1.5 mm in depth. The center of the defect locates at 590 mm from the left end. The captured signal and its corresponding complex Morlet wavelet transform result are illustrated in Figure 13a,b, respectively. The variation patterns of the reflections from the defect and end are different from the first damage case. After extracting the \(\overline{SRC}\) at different frequencies in Figure 14a, the damage image is constructed in Figure 14b. Accordingly, the defect is identified as 38.5 mm in width and 1.4 mm in depth, which also shows high accuracy. To sum up, the experimental results confirm the efficacy of the proposed damage quantification method. In addition, the use of the DPC transducer significantly enhances the efficiency of the proposed method and simplifies the measurement system, thus showing great potential for engineering applications. As a final remark, the position of the DPC transducer can be tactically tuned for wave excitation and sensing, which offers significant flexibility in practical applications. Figure 12: (**a**) Variation of the \(\overline{SRC}\) with respect to frequency; (**b**) damage image for the 30 mm-wide and 1 mm-deep defect.
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### Further Optimization of the DSCNN-GRU Surrogate Model
#### 3.3.2 Optimization of Optimizers  
Following the decision to use five convolutional layers, this study further investigates the impact of different optimizers on model efficiency and accuracy. The results, presented in Table 1, reveal that when using Adam, the model's training time is 2858 s, with the lowest MSE value during testing recorded at 0.0062. In contrast, when using RMSprop and Nadam, the MSE values are 0.043 and 0.068, respectively, representing an order of magnitude higher compared to using Adam. This difference in performance may be attributed to RMSprop having a high learning rate, causing the optimization process to oscillate around the optimal solution without convergence. However, it is noteworthy that the training time for RMSprop is 2598 s, while for Nadam it is 3212 s. Overall, Nadam yields a stable MSE value of 0.0031 after the 50 th cycle during model training, yet the MSE value on the test set increases to 0.068, and the R\({}^{2}\) value reaches 0.853, which is inferior to RMSprop. This implies that Nadam, due to its accelerated convergence speed without appropriate regularization or early stopping strategies, may lead to overfitting problems. Consequently, even though the MSE value during model training is low, the model might assimilate a significant amount of noise information, resulting in diminished prediction accuracy.  
The experimental data clearly demonstrate that the choice of optimizer significantly influences the model's training efficiency and prediction accuracy. Based on the experimental data, it is evident that the choice of optimizer has a notable influence on the training efficiency and prediction accuracy of the model. Although Adam prolongs the training time by 260 s compared to RMSprop, the substantial disparity in prediction accuracy favors Adam for this study. However, in the case of Nadam, its performance was unsatisfactory in this experiment.