esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement and Analysis on Magnetic Field Influence of Substation for Magnetic Shielding Device
## 4 Test Results and Discussion
### Discussion  
According to the test results, it can be preliminarily concluded that:  
1. The substation has an influence on both the DC and AC magnetic fields, so a certain distance from the substation should be considered when building magnetic shielding devices;
2. For test point 3 of substation 1 (15 m away from the substation), test point 6 of substation 2 (60 m away from the substation), and test point 2 of substation 3 (5 m away from the substation), the magnetic field attenuates to be equivalent to the Earth's magnetic field and is close to the simulation results. It can be used as the construction site of the magnetic shielding device;
3. The magnetic field fluctuation caused by the substation also has a great impact on the magnetic shielding device, especially when close to substation 2, the AC magnetic field is large (about two to three orders of magnitude higher), so it is necessary to consider the impact of its working magnetic field on other equipment. After the distance reaches 60 m, the amplitude of the AC magnetic field decreases significantly, which indicates that the safe distance can effectively reduce the influence of the substation magnetic field;
4. The magnetic field of substations is changed to be based on the magnetic field attenuation of magnetic dipole (\(1/d^{3}\)). The formulas of three different substations are analyzed in combination with power, and the general formula including different power is obtained as follows: \[B=50,400+\frac{120\mu_{0}\times w}{4\pi*d^{3}}\times 10^{9}\] (5) where \(B\) is the magnetic field strength, \(d\) is the shortest distance between the calculation point and the substation, \(w\) is the equivalent power, and \(\mu_{0}\) is vacuum permeability, \(4\pi\times 10^{-7}\).
## 4 Performance Evaluation and Comparison
### Results and Comparison
#### 4.2.1 Cicids2017 Dataset  
For the CICIDS2017 dataset, our LSTM-based IDS achieves an overall accuracy of 99.34%. The precision for the \"BENIGN\" class is 99.94%, while the recall reaches 99.24%, resulting in an F1-score of 99.59%. For the \"attack\" class, the precision remains high at 96.99%, and the recall is outstanding at 99.74%, contributing to an F1-score of 98.35% as showing in Table 2, Confusion matrix for CICIDS2017 dataset showing in Figure 3, and ROC curve for CICIDS2017 dataset Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted** \\ \hline Actual & True Positive (TP) & False Negative (FN) \\ \cline{2-3}  & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
Figure 4: ROC curve for CICIDS2017 dataset.  
Figure 3: Confusion matrix for CICIDS2017 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **Fl-Score** & **Support** \\ \hline BENIGN & 1.00 & 0.99 & 1.00 & 567,830 \\ \hline Attack & 0.97 & 1.00 & 0.98 & 139,139 \\ \hline Accuracy & & & 0.99 & 706,969 \\ \hline Macro avg & 0.98 & 0.99 & 0.99 & 706,969 \\ \hline Weighted avg & 0.99 & 0.99 & 0.99 & 706,969 \\ \hline \end{tabular}
\end{table}
Table 2: Classification report for CICIDS2017 dataset.
## 3 Results and Discussion
### Performance of Studied Asphal Mixtures
#### 3.4.1. Volumetric Characteristics and Water Sensitivity of Studied Asphalt Mixtures  
Before assessing the water sensitivity of the mixtures, the test specimens were volumetrically characterized. Thus, the maximum density of the mixtures was determined according to the procedure described in EN 12697-5, and the bulk density of all specimens was determined according to EN 12697-6, Procedure B. After that, the air void content of each specimen was calculated (EN 12697-8), and the average values of the five asphalt mixtures were assessed (Table 4).  
From the results obtained it was found that the volumetric characteristics of asphalt mixtures with PEX are different from those of mixtures with conventional aggregates. This is essentially related to the maximum density of the mixtures, which is much lower in the former due to the low density of PEX. This can be considered as one of the advantages of these mixtures, reducing its transportation costs and offering other advantages when applied on bridges or other structures intended to be lighter.  
The results of the indirect tensile strength and water sensitivity tests obtained for the five mixtures are shown in Figure 9.  
Comparing the performance of the mixtures with the same binder type (50/70) and binder content (5.0%), but with and without PEX as aggregate, i.e., M1 and M3, the introduction of PEX slightly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Specifications**} & \multicolumn{5}{c}{**Mixture**} \\ \cline{3-7}  & & **M1** & **M2** & **M3** & **M4** & **M5** \\ \hline Binder content (\%) & \(\geq\)4.0 & 5.0 & 5.0 & 5.0 & 5.5 & 5.5 \\ Air voids content (\%) & [3; 4; 5] & 2.8 & 4.8 & 5.0 & 2.9 & 5.3 \\ Bulk density (kg/m\({}^{3}\)) & – & 2374 & 2369 & 2254 & 2285 & 2242 \\ \hline \end{tabular}
\end{table}
Table 4: Volumetric characteristics of the studied mixtures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**Aggregate Volumetric Composition**} & \multicolumn{2}{c}{**Asphalt Binder**} \\ \cline{2-5}  & **Mineral Aggregate** & **PEX Waste** & **Type** & **Content** \\ \hline M1 & 100\% & 0\% & 50/70 bitumen & 5.0\% \\ M2 & 100\% & 0\% & PMB 45/80-60 & 5.0\% \\ M3 & 95\% & 5\% & 50/70 bitumen & 5.0\% \\ M4 & 95\% & 5\% & 50/70 bitumen & 5.5\% \\ M5 & 95\% & 5\% & PMB 45/80-60 & 5.5\% \\ \hline \end{tabular}
\end{table}
Table 3: Composition of the studied mixtures.  
Figure 9: Water sensitivity test results of the studied mixtures.  
reduces the water sensitivity performance. However, when the binder content is increased by 0.5% (M4 or M5) in the mixtures with PEX, the situation is reversed, being the mixtures with the best performance in terms of water sensitivity.
# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 5 Discussion
### Evaluation of Restored Images  
Since the inconsistency between the common non-reference image quality metrics and the visual observation has been observed [1; 8; 46], color-card-based evaluation methods are more reliable. However, it is not accurate enough only using the grayscale patches [7; 13]. Taking advantage of the color patches on color charts could evaluate the color accuracy better [11].
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 5 Implementation
### Implementation of Key Parameters
#### 5.3.1 Graph Parameters  
The graph topologies that can be created using the _graph generator_ have already been shown in Section 4.1. We implemented the graphs using adjacency lists, i.e., lists of lists of indices. As mentioned before, the graph generator keeps generating new graphs until the last generated graph is connected. This is guaranteed for all given topologies except for RANDOM_P and CONECTED_STARS, which are not created deterministically. A simple breadth-first search is performed to check if the graph is connected, starting with the first node with index 0. If the maximum number of attempts is exceeded, the program terminates immediately. This number can be passed as a command-line argument.
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 3 Model and Problem Formulation
### Problem Formulation  
In this paper, we consider achieving the dynamic service placement by minimizing the total delay of multiple mobile users under the physical resource and cost constraints. We suppose that the cost during the dynamic service placement process is produced by the migration of services across edge servers. In order to satisfy the quality-of-service (QoS) requirements of users under the erratic movement, the service should be dynamically migrated to adapt to the users' mobility; however, the resulting cost for the operators will be excessive. Let \(\rho\) denote the unit cost of \(v_{i}\) during the service migration, and the cost is defined as  
\[C^{h}_{m_{i},m_{j}}(t)=\rho\cdot D^{u}_{u_{i}}(t) \tag{5}\]  
Moreover, we use \(\Gamma\) to represent the higher bound of the maximum total cost that is afforded by the operators. The problem formulation is shown as follows:  
\[\text{minimize }\sum_{t=0}^{T}\sum_{j=1}^{|M|}\sum_{i=1}^{|U|}D^{c }_{u_{i}}(t)+D^{l}_{u_{i}}(t)+D^{u}_{u_{i}}(t)\] (6) s.t. \[\sum_{t=0}^{T}\sum_{h=1}^{|V|}C^{h}_{m_{i},m_{j}}(t)\leq\Gamma, \tag{7}\] \[\sum_{v_{h}\in\
abla_{m_{j}}}\lvert v_{h}\rvert\leq R^{s}_{m_{i} ^{\prime}}\sum_{u_{i}\in\mathbb{U}(\
abla_{m_{j}})}\!\!\!\!\!\!\!\!\!\!\!\!A_{i }(t)\leq R^{c}_{m_{i}^{\prime}},\forall j\in M,\] (8) \[x^{j}_{ih}\in\{0,1\},\forall i\in U,\forall h\in V. \tag{9}\]  
Our objective is to minimize the total delay of users in set \(U\) during a continuous time period in Equation (6). Equations (7)-(9) are the constraints. Equation (7) states the cost constraint, which means that the total cost of the provided services cannot exceed the threshold \(\Gamma\). Equation (8) states the constraint on the physical resources, where the services that are placed on edge sever \(m_{i}\) cannot exceed the storage \(R^{s}_{m_{i}}\), and the amount of computing resources required by the service request of \(u_{i}\) cannot exceed the computing capacity \(R^{c}_{m_{i}}\). Equation (9) states whether user \(u_{i}\) uses service \(v_{h}\) at time slot \(t\).
## 6 Experimental Results  
The experimental results performed to assess effectiveness and performance of ESCAPE are discussed in this section. We tested ESCAPE through different real datasets (dataset descriptions are reported in Section 6.1). The experimental settings are described in Section 6.2.  
Experiments have been designed to address three main issues: (i) the ability of ESCAPE to perform all the functions of the textual analytics pipeline to support the analyst with setting the parameters; (ii) the effectiveness of ESCAPE in discovering good document partitions; and (iii) a comparison with state-of-the-art techniques.
###### Abstract  
This study presents a novel method for the development of TiO\({}_{2}\)/reduced graphene oxide (rGO) nanocomposites for photocatalytic degradation of dyes in an aqueous solution. The synergistic integration of rGO and TiO\({}_{2}\), through the formation of Ti-O-C bonds, offers an interesting opportunity to design photocatalyst nanocomposite materials with the maximum absorption shift to the visible region of the spectra, where photodegradation can be activated not only with UV but also with the visible part of natural solar irradiation. TiO\({}_{2}\)@rGO nanocomposites with different content of rGO have been self-assembled by the hydrothermal method followed by calcination treatment. The morphological and structural analysis of the synthesized photocatalysts was performed by FTIR, XRD, XPS, UV-Vis DRS, SEM/EDX, and Raman spectroscopy. The effectiveness of the synthesized nanocomposites as photocatalysts was examined through the photodegradation of methylene blue (MB) and rhodamine B (RhB) dye under artificial solar-like radiation. The influence of rGO concentration (5 and 15 wt.%) on TiO\({}_{2}\) performance for photodegradation of the different dyes was monitored by UV-Vis spectroscopy. The obtained results showed that the synthesized TiO\({}_{2}\)@rGO nanocomposites significantly increased the decomposition of RhB and MB compared to the synthesized TiO\({}_{2}\) photocatalyst. Furthermore, TiO\({}_{2}\)@rGO nanocomposite with high contents of rGO (15 wt.%) presented an improved performance in photodegradation of MB (98.1%) and RhB (99.8%) after 120 min of exposition to solar-like radiation. These results could be mainly attributed to the decrease of the bandgap of synthesized TiO\({}_{2}\)@rGO nanocomposites with the increased contents of rGO. Energy gap (\(F_{\text{g}}\)) values of nanocomposites are 2.71 eV and 3.03 eV, when pure TiO\({}_{2}\) particles have 3.15 eV. These results show the potential of graphene-based TiO\({}_{2}\) nanocomposite to be explored as a highly efficient solar light-driven photocatalyst for water purification.  
graphene oxide; TiO\({}_{2}\)@rGO nanocomposite; photocatalysis; solar-like irradiation; Methyl-lene blue; Rhodamine B +
Footnote †: journal: Applied Sciences
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming  
The problem definition in this research was modified from a standard module Single-Depot MTSP (SD-MTSP) [16]. The object and subject are as follows:  
\[x_{ijk}=\left\{\begin{array}{ll}1&\text{the k path goes from city i to city j}\\ 0&\text{otherwise}\end{array}\right. \tag{2}\]  
\[\forall(i,j)\in\text{A},k=1,\ldots m \tag{3}\]  
Object:  
\[min\sum_{k=1}^{m}\sum_{(i,j)\in A}c_{ij}x_{ijk} \tag{4}\]  
Subject to:  
\[\sum_{(i,j)\in A}c_{ij}x_{ijk}\leq D_{lmt},k=1,\ldots m \tag{5}\]  
\[\sum_{k=1}^{m}\sum_{j=2}^{n}\ x_{1 jk}=m \tag{6}\]  
\[\sum_{k=1}^{m}\sum_{j=2}^{n}\ x_{jlk}=m \tag{7}\]  
\[\sum_{k=1}^{m}\sum_{i=1}^{n}\ x_{ijk}=1,\ j=2,\ldots,n \tag{8}\]  
\[\sum_{k=1}^{m}\sum_{j=1}^{n}\ x_{ijk}=1,i=2,\ldots,n \tag{9}\]  
\[\sum_{k=1}^{m}x_{i1k}+x_{1 ik}\leq 1,i=2,\ldots,n \tag{10}\]  
\[u_{i}\geq 1,\ i=2,\ldots,n \tag{11}\]  
where \(c_{ij}\) is the distance array of \(A\), \(m\) is the number of robot cars, \(D_{lmt}\) is the maximum distance limit value, \(n\) is the number of target points, and \(u_{i}\) is the number of target points to be visited along one car's path from its original point to a specific target \(i\). (2) is the variable integer, which determines whether targets \(i\) to \(j\) have been traveled by \(k\) car. In (3), \(A\) is the set of all specified paths, including the alternatives that do not bypass the no-travel zone, while (4) is the object function of this problem.  
Figure 5: Experimental robot car.  
Per (5), the subject \(D_{lmt}\) cannot be exceeded by all the cars, and (6,7) ensure a start from the original point and a return to that same point. Per (8,9), all the targets are entered and exited by the car, and per (10), each car must visit at least one target. Finally, (11) prevents the problem of a sub-path that does not include the original point. This is an NP-Hard problem, and heuristic algorithms are always adopted to solve this type of complicated optimization problem. While there are many types of heuristic algorithms, the A\({}^{*}\) algorithm and the TS heuristic algorithm were primarily adopted.
## 4 Simulation Results Analysis
### ECM Simulation Results  
#### 4.2.1 ECM Simulated Scenario  
An ECM simulated scenario is shown in Figure 6. Each UAV carries reconnaissance and jamming payloads. When the radar radiation source is detected, it will perform jamming, covering the target. The relevant parameters with their values are shown in Table 1.  
The jamming pattern of this paper is noise convolutional jamming signal. The jamming signal \(S_{j}\) is formed by the convolution of the received radar signal \(S_{r}\) and the Gaussian white noise signal \(S_{n}\). The jamming signal can be represented as follows:  
\[S_{j}=conv(S_{r},S_{n}) \tag{16}\]  
\[S_{r}=exp(j\pi Kt^{2}) \tag{17}\]  
where \(exp()\) is the exponential function with the constant \(e\) as the base, and \(K=B_{w}/T_{r}\) is the frequency modulation slope.  
The time domain and frequency domain of jamming signal are shown in Figure 7.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline \(F_{r}\) & The radar center frequency & 3.1 GHz \\ \(T_{r}\) & The pulse width & 20 \(\upmu\)s \\ \(B_{w}\) & The chirp bandwidth & 30 MHz \\ \(B_{r}\) & The bandwidth of receiver & 100 MHz \\ \(G_{r}\) & The antenna gain of radar in the jammer direction & 38.5 dB \\ \(B_{j}\) & The bandwidth of jammer & 50 MHz \\ \(P_{j}\) & The jammer transmitting power & 3 W \\ \(G_{j}\) & The jammer transmitting antenna gain & 20 dB \\ \(L_{j}\) & The comprehensive loss of jammer & 5 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters with their values.  
Figure 6: The ECM scenario based on UAV swarm.
## 4 Simulation Results and Analysis
### Impedance Matching  
The antenna array uses a microstrip line feeding technique. Microstrip lines are designed with controlled width and dielectric height to achieve a 50 ohm characteristic impedance. Each patch or radiating element is fed from the microstrip at a point where the input impedance is matched to 50 \(\Omega\). The trace width (W), substrate height (h), and dielectric constant (\(\epsilon r\)) are all tailored in the layout to achieve this. Evidence of matching is clear from the S11 plot, the simulated minimum S11 \(\approx-\)23.5 dB at \(-\)28.1 GHz, and the measured minimum S11 \(\approx-\)20.7 dB at \(\sim\)28.3 GHz.  
These values indicate excellent impedance matching--typically, anything below \(-\)10 dB implies less than 10% reflection, meaning that over 99% power transfer efficiency at resonance is achieved. A low return loss value proves that the microstrip-fed antenna array is well matched to 50 \(\Omega\), with negligible mismatch losses.  
The design includes a symmetric microstrip-based power divider, likely a Wilkinson or T-junction structure integrated into the same substrate. Each branch of the power divider splits power equally. A quarter-wave transformer or matching stubs may be used (common in Wilkinson designs) to maintain 50 \(\Omega\) at the input while ensuring proper isolation between outputs. The traces feeding into the antenna array maintain the same characteristic impedance to ensure seamless transition. Evidence of matching is noticeable from the simulated and measured S11 values of the complete setup (antenna + power divider). The low S11 value (\(\sim\)\(-\)20 dB to \(-\)23 dB) confirms minimal mismatch at the input port. If the power divider was poorly matched, a higher return loss or significant resonance shift could be observed.
## 4 Numerical Results and Discussion
### BER Performance over a UWA Channel
The reason is that when the shift step size is too small, the inter-symbol interference (ISI) of the superimposed sequences is greatly increased. On the other side, when the shift step size is too large, the accuracy of the channel estimation will be reduced by the influence of \(L\). Therefore, the shift step size not only affects the BER performance, but also affects the data rate. Figure 5: BER performances of the two systems with \(J=5,4,3\) over a UWA channel. Figure 6: Influence of parameter \(L\) on BER performance of the MSSS-I/Q-IM system over UWA channels.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
### Summary of Network Development Status  
The emergency communication network is a heterogeneous network involving multiple communication technologies. According to the time, place, and damage degree of the infrastructure network, its network composition is uncertain, diverse, and dynamic. Traditional emergency communication is mainly based on satellite communication, cluster communication, and ad hoc networks. With the emergence of broadband, wireless, heterogeneous, and other new technologies, emergency communication has led to faster, more convenient, and more robust solutions. In this section, the applications of 370M narrowband private networks, mobile satellite communication networks, hybrid networks, and 5G communication networks are introduced.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal
### Sink Placement  
As the nodes are clustered, the fundamental goal is to locate multiple sinks to achieve the longest lifespan, more energy efficiency, and the fastest data delivery to each sink. As a result, a sink placement strategy was required to accomplish the predicted goals for each cluster, where all of these clusters would collectively achieve the large-scale WSN goals. If the sink is situated in a location with a small number of neighbour nodes, they would be regularly used to relay packets to the sink. As a result, these nodes consume more energy,causing the WSN's lifetime to be shortened. Figure 3 represents a scenario in which the sink has a small number of communication neighbours.  
For this situation, we propose a strategy that places the sinks in the appropriate locations so that the number of nodes surrounding the sink is as high as possible. Therefore, we suggest placing the sink in the barycentre of the cluster with a number of nodes greater than a predetermined number (N\({}_{\text{threshold}}\)) and in the barycentre of multiple groups that have the fewest number of nodes. In Figure 4, two possible sink locations are in the barycentre of a cluster with more nodes than the threshold using Equation (15) or in the barycentre of the most neighbour clusters (MNC) using Equation (16). The details are formally described as a flowchart in Figure 5, which depicts the network construction in addition to two possible sink locations.  
The coordinates of sink k are denoted as \(\text{sink}_{\text{k}}(\text{x}_{\text{k}},\text{y}_{\text{k}})\) and determined according to the possible location as follows:  
Figure 4: WSN construction with our proposed strategy.  
Figure 5: Flow chart of sink placement.  
Figure 3: Sink location in low-neighbour area.  
When sink k is located in the barycentre of cluster j:  
\[\begin{cases}x_{k}=\frac{\sum_{i=1}^{N_{i}}C_{\text{range}_{i}}x_{i}}{\sum_{i=1}^ {N_{i}}C_{\text{range}_{i}}}\\ y_{k}=\frac{\sum_{i=1}^{N_{i}}C_{\text{range}_{i}}y_{i}}{\sum_{i=1}^{N_{i}}C_{ \text{range}_{i}}}\end{cases} \tag{15}\]  
where \(x_{i}\) and \(y_{i}\) are the coordinates of sensor i, which exists in cluster j, \(C_{\text{range}_{i}}\) is the range communication of sensor i, and \(N_{j}\) is the number of sensors in cluster j.
2. When sink k is located in the barycentre of p neighbour clusters:  
\[\begin{cases}x_{k}=\frac{\sum_{i=1}^{P}C_{\text{range}_{i}}x_{\text{CH}_{i}}}{ \sum_{i=1}^{P}C_{\text{range}_{i}}}\\ y_{k}=\frac{\sum_{i=1}^{P}C_{\text{range}_{i}}y_{\text{CH}_{i}}}{\sum_{i=1}^{P}C_{ \text{range}_{i}}}\end{cases} \tag{16}\]  
where \(\left(CH_{1},\ldots,CH_{i},\ldots,CH_{P}\right)\) is the set of p cluster heads of these neighbours' clusters with communication range \(\left(r_{1},\ldots,r_{i}\ldots,r_{P}\right)\), \(x_{CH_{i}}\) and \(y_{CH_{i}}\) are the coordinates of cluster head \(CH_{i}\) that exists in cluster i, and p is the number of clusters.
## 2 Research Methodology
### Adaptive Pulse Width Frequency Shifter
By phase-aligning the two generated square waves with different duty cycles and then applying the XOR operation, we obtain the adaptive pulse-width square wave, as illustrated in Figure 5b, where wave3 is derived from the XOR of wave1 and wave2. Figure 6 demonstrates that, in MATLAB simulations, adjusting the two different pulse widths of the adaptive pulse-width square wave allows for precise control over the amplitudes of certain harmonic components. Furthermore, by varying the frequency of the adaptive pulse-width square wave, we can flexibly adjust the range of frequency shifting. Figure 4: Overview of the adaptive pulse-width square wave. Figure 5: Illustration of the square wave generation. (**a**) Adjustable duty cycle square wave generation. (**b**) Adaptive pulse-width square wave generation.