esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 5 The Proposed Security Scheme
### Setup Phase
The first feature is a Location Feature \(LF\) to guarantee that the drones stick to their paths. For instance, the drone \(D_{r}\) mission is to join group \(G_{y}\) at the subarea \(\mathbb{A}_{s}\) to take photos. Thus, the location feature \(LF_{r}\) for \(D_{r}\) is to be within the \(\mathbb{A}_{s}\) subarea. If it moves outside \(\mathbb{A}_{s}\), this means that it is controlled by a malicious adversary or a malfunction. Then, the operator does not depend on the \(D_{r}\)'s information. The second feature, which is the Data Feature \(DF\), is utilized to detect the compromised drones. The operator assigns a certain protocol that the drone should follow before encrypting the collected data; if the drone does not apply that protocol, it is declared as a compromised node. The data feature could be a specific padding data that were added in certain bits, repeating the data in the packet in a pre-determined sequence, or adding the drone's ID in different location. For example, the operator assigns a certain padding data \(DF_{r}\) for \(D_{r}\) to add in specific bits in the message before encryption:  
\[CS\xrightleftharpoons{\mathbb{E}_{k_{y}}(Mission_{r},LF_{r},DF_{r})}D_{r}.\]  
In addition, the features are changed every time that the drone returns to the station for recharging its battery, i.e., the drone has a new mission in a different subarea and assigned other new features.
# Unraveling Convolution Neural Networks: A Topological Exploration of Kernel Evolution
## 3 Experiments
### Betti Curves on Diverse Datasets
#### 3.2.1 Study on Grayscale Images (Category A)  
In the grayscale image category (A), we trained the networks on datasets such as MNIST, Kuzushiji-MNIST, and Fashion-MNIST. The analysis primarily targeted the second and third convolutional layers over 15,000 training iterations. Betti curves, representing the topological structure of 3 \(\times\) 3 convolutional kernels, were computed at regular intervals to observe the evolution patterns. For networks with an additional fourth layer (A-II), the analysis was extended to cover this layer as well.  
Figure 3 illustrates the Betti curve results for experiments A-I and A-II, conducted on grayscale image datasets. Each panel, corresponding to the MNIST, Kuzushiji-MNIST, and Fashion-MNIST datasets, shows the behavior of Betti curves over successive training iterations. A consistent trend is observed across both experimental conditions: the Betti-0 curve shifts rightward as the number of iterations increases, a pattern that becomes more apparent when early iterations are compared to later ones, indicating rapid initial changes in the topological structure that stabilize as training progresses. For the Betti-1 curve, as the number of iterations increases, the peak decreases, and the curve as a whole shifts rightward as well, indicating a reduction in the number of 1-dimensional loops within the kernel space as training progresses.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Feature Oversampling Based on SMOTE  
The data for this study were obtained from multiple sea trials conducted using UAHWs. The data quantities are presented in Table 1. Specifically, the acoustic decoy dataset comprises 349 data samples collected from 10 different sea trials, while the submarine dataset comprises 889 samples from 50 distinct sea trials.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Type** & **Sea Trial Number** & **Data Number** \\ \hline Acoustic decoy & 10 & 349 \\ Submarine & 50 & 889 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data source and quantity description.  
Figure 9: Signal frequency-domain correlation detection.  
As shown in Table 1, it is clear that the sea trial data display an imbalance in the quantity of the two target classes. For ease of representation, both target types were encoded, with 0 representing acoustic decoys and 1 representing submarines. To prevent information leakage and in consideration of the operational characteristics of UAHWs, we implemented a 5-fold cross-validation with data stratification. For each training dataset in the 5-fold cross-validation, the ratios of acoustic decoy samples to submarine samples in both the training and test datasets are 287/731, 295/714, 289/701, 276/701, and 249/709, which are approximately 2/5, 2/5, 2/5, 2/5, and 1/3, respectively. To analyze the impact of imbalanced data on our algorithm, we performed oversampling, using SMOTE, on the features extracted from the original data. It is crucial to emphasize that this oversampling was exclusively applied to the training set. We doubled the number of acoustic decoy features in the training set while keeping the number of submarine features unchanged. During this process, the \(k\) in the SMOTE algorithm was set to 5. The t-SNE [32] visualization results for the original 5-fold cross-validation of features are presented in Figure 10. Figure 11 illustrates the corresponding t-SNE visualization results for the 5-fold cross-validation after oversampling.
# IPv6 Convergence for IoT Cyber-Physical Systems
## 4 Proposed Approach
### Protocol Packing
#### 4.1.1 Fragmentation Strategy  
We extended the baseline fragmentation strategy set out in Internet Engineering Task Force (IETF) 6 LoWPAN. In the baseline strategy, if a datagram does not fit within the size of a single IEEE 802.15.4 frame, it is broken into link fragments. The fragment offset can only express multiples of eight bytes, and therefore, all link fragments for a datagram except the last one must be multiples of eight bytes in length. The first link fragment contains the first fragment header defined, as shown in Figure 9.  
The second and subsequent fragmentation header is shown in Figure 10.  
The frame fields are as follows:  
* **Datagram_size:** This 11-bit field encodes the size of the entire IP packet before link-layer fragmentation (but after IP-layer fragmentation).
* **Datagram_tag:** The value of datagram_tag (datagram tag) shall be the same for all link fragments of a payload.
* **Datagram_offset:** This field is present only in the second and subsequent link fragments and specifies the offset, in increments of 8 octets of the fragment from the beginning of the payload datagram.
## 2 Machine Structure and Force Operation Principle
### Function of Bridge and Magnetic Circuit  
It is well known that the back EMF \(E_{0}\) and average electromagnetic torque \(T_{e}\) of PMV Machines are mainly sourced by 2 air gap magnetic field harmonic, i.e., the \(P_{w}\)-pole-pair field \(B_{(1,-1)}\) and \(P_{r}\)-pole-pair field \(B_{(1,0)}\). \(E_{0}\) and \(T_{e}\) can be expressed as:  
\[E_{0}=\sqrt{2}k_{w}N_{s}r_{g}L_{st}\left(\frac{P_{r}}{P_{w}}B_{(1,-1)}+B_{(1,0 )}\right)\Omega_{r} \tag{7}\]  
\[T_{e}=3\sqrt{2}k_{w}I_{phase}N_{g}r_{g}L_{st}\left(\frac{P_{r}}{P_{w}}B_{(1,-1) }+B_{(1,0)}\right) \tag{8}\]  
where \(k_{w}\) is the winding factor, \(N_{s}\) is the series coil turns per phase, \(r_{g}\) is the air gap radius, \(L_{st}\) is the stack length, and \(I_{phase}\) is the RMS value of phase current.  
As a PMV machine, \(P_{r}\) is always much larger than \(P_{w}\)[21]. The field \(B_{(1,0)}\) is a conventional component existing in regular PMV machines. But the filed \(B_{(1,-1)}\) is depending on amplified by the pole ratio in torque generation. In fact, this additional modulation component is an important factor in enabling the PMV machine to achieve a high torque density. Thus, in the design of the PMV machine, to make better use of the flux modulation effect to obtain better performance, it is necessary to obtain considerable amplitude with a small pole pair number of \(P_{w}\) in modulating the magnetic field.  
The magnetic circuit of machine I (spoke type) is shown in Figure 3a. The reluctance of this main magnetic circuit is very large because it needs to pass through 5 PMs and bypass the PMs four times, which would weaken the field and decrease the torque density [22; 23]. However, from the flux bridges and rotor core of machines II, III, and IV (proposed) as shown in Figure 3b-d respectively, they provide a magnetic circuit for the low-pole-pair working harmonic and enhance the field which contributes to enhanced torque capability [23].  
The magnetic circuit of Halbach-array permanent magnets on the rotor is shown in Figure 4. The magnetic fields of the three permanent magnets are concentrated, achieving a better magnetic focus effect. Alongside, there is an air magnetic barrier between the permanent magnet and an inner yoke to prevent a short circuit.
# Could _Gnomoniopsis castaneae_ Be Used as a Biological Control Agent against Insect Pests?
## 3 Results  
### Virulence of the Tested Fungi on Larvae of P. interpunctella  
Significant differences in larval mortality were detected among treatments (F = 30.784, df = 1.1832, \(p<0.001\)), exposure time (F = 7.022, df = 6.1832, \(p<0.001\)), and used EF concentrations (F = 23.536, df = 6.1832, \(p<0.001\)). The main effects and interactions for all factors proved to be significant (Treatment * Exposure time: F = 4.123, df = 6, \(p<0.001\); Treatment*Concentration: F = 2.570, df = 5, \(p=0.028\); Exposure time * Concentration: F = 8.464, df = 30, \(p<0.001\); Treatment * Exposure time * Concentration: F = 3.660, df = 30, \(p<0.001\)).  
The mortality percentage depended on the dose of the used fungi. Final mortality percentages (7 days after exposure) of _P. interpunctella_ larvae ranged from 10 to 86.5% with _I. fumosorsea_ and from 6.7 to 50% with _G. castaneae_. Control treatment (using only ddH\({}_{2}\)O) demonstrated minor mortality (3.3%) at the end of the experiment (Figure 1). The survival time of _P. interpunctella_ larvae treated with _I. fumosorsea_ and _G. castaneae_ was significantly reduced in comparison to the survival time of the control treatment. More specifically, after exposure to _I. fumosorsea_, the survival time of the larvae ranged from 6.56 to 4.6 days, and after exposure to _G. castaneae_, from 6.73 to 5.96 days. For control larvae, the survival time was 6.9 days (F: 3.667; df: 12; \(p=0.030\)).
## Case Study
### Strategic Verification
#### 5.2.3 Simulation Results and Discussion  
The comparison of the system frequency curves between UFLS and ALS is shown in Figure 13. The blue line is the original curve of the system with no load participation, and the yellow line and green line are the 30-MW and 40-MW load shedding via UFLS respectively, whereas the red one is load shedding via ALS.  
With no customer-side participation, the frequency would drop rapidly to 48.5 Hz in a duration shorter than 10 s, which far exceeds the permits of safe grid operation and has a high potential of causing a black out. After adding UFLS, the frequency is able to recover to the normal state with an offset of 0.2 Hz. Considering the recovery speed and over-correction, this is approximately the best performance that the traditional strategy could achieve.  
However, the frequency recovery of ALS has better performance. After triggering the third-grade response loads (49.3 Hz in Table 2), the frequency rose gradually to a normal state with a higher bottom and lower peak of frequency.  
Hence, from the quantity of over-correction and frequency drop, the ALS approach has better performance than the traditional method. This is because, in UFLS, loads are cut off regionally at the bus, which leads to a relatively conservative control strategy to prevent important loads from being affected as much as possible. In addition, in the proposed DR system, the interruptible loads and the important ones are separated to achieve more precise classification of load shedding. It means that the  
Figure 13: Comparison of control performances between under-frequency load shedding (UFLS) and ALS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Value** \\ \hline Frequency threshold & 49.0 \\ Circles of load shedding & 1 \\ Quantity of decreasing loads & 30 MW, 40 MW \\ Delay of communication and action & 0.2 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Settings of traditional UFLS approach.  
control strategy can be adjusted to perform a quicker response to prevent accidents from spreading, with no significant impact on customers' normal life.  
As a conclusion, the proposed DR system is affordable for undertaking quick DR tasks through the method of \"local detection and action\". Moreover, the configuration mechanisms of parameters like the threshold and quantity are from existing projects and researches on power systems. In other words, the strategies applied and researched for traditional power systems could be applied easily in the proposed system to gain more flexible and selective control. It shows that, with feasible configuration and strategies, the proposed DR system can be easily integrated into the existing power system.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**95\% Confidence Interval Mean**} \\ \cline{3-6}  & & **Mean** & **Upper** & **Lower** & **Std. Deviation** \\ \hline \multirow{4}{*}{Distance Zone 1 *} & CD & 3634.46 & 3882.38 & 3386.54 & 587.13 \\  & CM & 3158.08 & 3430.69 & 2885.48 & 703.02 \\  & F & 3725.59 & 4030.97 & 3420.20 & 528.90 \\  & SD & 3232.52 & 3530.27 & 2934.77 & 598.75 \\  & SM & 3706.06 & 4008.86 & 3403.26 & 646.99 \\ \hline \multirow{4}{*}{Distance Zone 2 **\(\text{ }^{\text{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{ }}}}}}}}}}}}}}\) & CD & 3157.37 & 3378.41 & 2936.32 & 523.48 \\  & CM & 3616.92 & 3870.14 & 3363.70 & 653.03 \\  & F & 2716.50 & 3105.36 & 2327.65 & 673.47 \\  & SD & 3061.41 & 3344.23 & 2778.59 & 568.72 \\  & SM & 2811.88 & 3056.24 & 2567.53 & 522.10 \\ \hline \multirow{4}{*}{Distance Zone 3 **\(\text{ }^{\text{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ }}}}}}}}}}}}}}\) & CD & 1520.94 & 1654.33 & 1387.55 & 315.89 \\  & CM & 2143.78 & 2273.13 & 2014.43 & 333.57 \\  & F & 1507.76 & 1681.80 & 1333.73 & 301.41 \\  & SD & 1597.58 & 1759.45 & 1435.71 & 325.50 \\  & SM & 1486.44 & 1631.69 & 1341.19 & 310.35 \\ \hline \multirow{4}{*}{Distance Zone 4 **\(\text{ }^{\text{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ }}}}}}}}}}}}}}\) & CD & 1135.58 & 1228.93 & 1042.23 & 221.07 \\  & CM & 1553.20 & 1661.75 & 1444.64 & 279.96 \\  & F & 1185.61 & 1325.68 & 1045.53 & 242.60 \\  & SD & 1230.87 & 1383.62 & 1078.13 & 307.14 \\  & SM & 1116.83 & 1259.65 & 974.01 & 305.15 \\ \hline \multirow{4}{*}{Distance Zone 5 \(\text{ }^{\text{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{}}}}}}}}}}}}}\) }} & CD & 437.59 & 557.07 & 318.12 & 282.94 \\  & CM & 458.40 & 532.34 & 384.45 & 190.70 \\ \cline{1-1}  & F & 554.61 & 664.82 & 444.40 & 190.87 \\ \cline{1-1}  & SD & 558.63 & 633.11 & 484.16 & 149.76 \\ \cline{1-1}  & SM & 505.10 & 558.82 & 451.38 & 114.78 \\ \hline \multirow{4}{*}{Distance Zone 6 \(\text{ }^{\text{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{{\tiny{\tiny{\tiny{\tiny{\tiny{{}}}}}}}}}}}}}}\) }} & CD & 99.09 & 141.98 & 56.19 & 101.58 \\ \cline{1-1}  & CM & 48.71 & 63.48 & 33.95 & 38.08 \\ \cline{1-1}  & F & 146.47 & 183.40 & 109.53 & 63.96 \\ \cline{1-1}  & SD & 140.53 & 172.54 & 108.52 & 64.36 \\ \cline{1-1}  & SM & 152.61 & 187.11 & 118.11 & 73.72 \\ \hline \hline \end{tabular} Note: The following symbols denote significant pairwise differences: CM vs. SM (*), CM vs. CD (#), CM vs. SD (#), CM vs. F (#), SM vs. CD ($), CD vs. SD (#).  
\end{table} 
Table 3: Detailed distance zones. and offensive transitions. Center defenders (CDs) showed lower mean values across most zones, except for Distance Zone 1, where they recorded the highest mean (3634.46 m, 95% CI: 3386.54-3882.38), significantly differing from CMs. These results emphasize the varied physical demands of playing positions, with central midfielders excelling in moderate-intensity zones and forwards and side players dominating high-intensity zones.
## 1 Introduction
In this work, however, we enlarge our earlier work in several aspects. This work's specific contributions are as follow:  
* We design a tree-based technique to solve link asymmetry between the sender to receiver, receiver to sender or both. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Tx Power** & \begin{tabular}{l} **Symmetric** \\ **(<10\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Asymmetric** \\ **(10–90\%)** \\ \end{tabular} &
\begin{tabular}{l} **Unidirectional** \\ **(>90\%)** \\ \end{tabular} \\ \hline \(-19\) dBm & 50\% & 43\% & 7\% \\ \hline \(-14\) dBm & 65\% & 22\% & 13\% \\ \hline \(-5\) dBm & 88\% & 6\% & 6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Link asymmetry in different power level [13]. * In addition to the tree formation information, we develop an efficient algorithm to select an efficient helper node. Unlike ASYM-MAC, this scheme can deal with the asymmetric link scenario more strictly in low power WSNs. * For effective helper selection, we propose a wake-up schedule algorithm for each node by which the most efficient node among sender's neighbors wakes up early to send its beacon. * The performance of the proposed protocol was evaluated through a simulation model based on the OMNeT++ simulation framework. * Performance comparisons with state-of-the-art MAC protocol ASYM-MAC showed that the proposed protocol outperforms the ASYM-MAC protocol in terms of energy consumption, PRR, Packet Delivery Ratio (PDR), and end-to-end delay. The organization of the remainder of this paper is as follows: In Section 2, the related works are elaborated. Section 3 discusses problem statements and network architectures. Section 4 presents COASYM-MAC, this paper's proposed protocol. The experimental results are described in Section 5, and finally the conclusion and some future scope of this work are discussed in Section 6.
## 4 FPGA Implementation of PID Controller and BB-BC
### FPGA-In-The-Loop (FIL)  
MATLAB/Simulink is a high-level language and interactive environment highly popular among system and algorithm designers for many different real-world applications and research topics. To accommodate developers to interface with real hardware (e.g., components implemented in FPGA devices), MathWorks(r) presented the FPGA-in-the-loop (FIL) concept. Based on this idea, data can be exchanged between the MATLAB/Simulink modules and the FPGA device. FIL scheme can be used for multiple tasks while designing, evaluating, and verifying designs. For example, it can be used to configure and tune FPGA design parameters at runtime, such as tuning data pipe gains and modifying filter parameters [29], or to monitor hardware components by reading specific status registers at runtime. Another powerful capability of the FIL approach is that allows the generation of complex stimulus datasets on the host PC and rapid download to the FPGA target in real time [29]. By using the FIL approach and connecting the hardware target via some common protocol interface (e.g., Ethernet or JTAG), one can obtain significant improvements in simulation runtime compared to cycle-accurate host simulation.  
As shown in Section 5, the proposed approach in conjunction with the FIL technique achieves a speedup of around 253X compared to the MATLAB 2015b software simulation. Moreover, when design complexity increases, the advantages of the FIL methodology over software simulation at runtime will be more evident. Figure 7 presents the structure of the FIL acceleration scheme used in our system, where MATLAB/Simulink provides the user with a working environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Logic Utilization** & **Used** & **Available** & **Utilization** \\ \hline Number of Slice Registers & 306 & 54,576 & 0\% \\ Number of Slice LUTs & 16,998 & 27,288 & 62\% \\ Number of Block RAM/FIFO & 2 & 116 & 1\% \\ Number of DSP48A1s & 54 & 58 & 93\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Device utilization summary of the PID controller on a Xilinx Zynq FPGA device  
Figure 7: FPGA-in-the-loop (FIL) acceleration scheme.
## 4 Experimental Results and Analysis
### Selection of the Model Hyperparameters  
This study uses the PyTorch deep learning framework, with Python as the programming language and PyCharm as the development environment. The system operates on Windows 64-bit and utilizes an NVIDIA GeForce RTX3050 GPU for training. The experimental environment is equipped with 8 GB of RAM. The optimization algorithm used in this study is the adaptive momentum method (Adam), which minimizes the loss function to achieve adaptive optimization of the model parameters. The learning rate was set to 0.001, the batch size was 64, and the maximum number of epochs was 200. The activation function used is ReLU, which sparsifies the neuron output and enhances the processing speed. The pooling method used is max pooling. The ECA kernel size is set to 3 or 5, and the number of input and output channels in the ECA module matches those of the corresponding bound residual layer, as provided in Table 1.
## 2 Results and Discussion
### Floc Growth and Breakup/Rearrengement Stages
#### 2.2.2 Evaluation of Breakup/Rearrangement Stage  
In Figure 2, the breakup tendency was more clearly shown at 160 rpm than at 120 rpm. Also, when compared at the same \(GT\) value, the ALT ratio was less likely to breakup at the optimum value (near neutralization). Flocs that grow and nearly reach their maximum diameter are unable to keep their maximum diameter and become smaller by flow shear forces. It is expected that separative breakup will predominate during the growth stage, followed by erosive breakup during the breakup and rearrangement stage after the maximum diameter. Densification occurs as the small fragments generated by erosive breakup become incorporated into the surrounding larger flocs. In addition, erosion breakup is more frequent, and circularity is expected to increase. As circularity increases, the floc is expected to become denser under constant laminar shear flow due to periodic tension and compression associated with rotation. With appropriate control of the ALT ratio and \(G\) value, the flocs become granulated. This phenomenon was more pronounced at the 160 rpm condition, which is the optimum ALT ratio and had a higher shear force.  
Figure 4 shows the relationship between \(D_{90}\) and the \(GT\) values in the breakup and rearrangement stage after 180 s from the start of the test. The power of \(D_{90}\) and of the \(GT\) value, \(\beta\), is the breakup/rearrangement rate at which the particle size decreases in the breakup/rearrangement stage. In this study, \(D_{90}\) decreased monotonically with respect to \(GT\) values, and the rate of decrease differed according to the ALT ratio and \(G\) values. The relationship between \(\beta\) and the ALT ratio was not as clear as in the growth stage, but, at ALT ratios of 0.03 and 0.05, \(\beta\) was more affected by \(G\) values than by the ALT ratio. Under the condition of an ALT ratio of 0.10, the difference in \(\beta\) by \(G\) value was small, and the ALT ratio was the rate-limiting factor for the aggregation reaction.  
Figure 3: The power of \(D_{90}\) and \(GT\) values in the growth stage, \(\alpha\), represents the rate of flocculation. \(\alpha\) increases with the ALT ratio. At the same ALT ratio, a lager \(G\) value is greater.