esa-sceva/satcom-chunk-collection huggingface dataset:
# Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism
## 2 Correlation Technique
### BiGRU
#### 2.2.3 Hidden State  
After calculating the candidate hidden state \(\widetilde{h_{t}}\), it must be combined with the update gate \(Z_{t}\) to further determine the weight of the hidden state at the previous moment \(h_{t-1}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbolic** & **Meaning** \\ \hline \(\sigma\) & Activation function \\ \(W_{xx},W_{hr},W_{xz},W_{bz}\) & weight parameter \\ \(b_{r},b_{z}\) & offset parameters \\ \(h_{t-1}\) & Hidden state of the previous time \\ \(x_{t}\) & Current input \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbolic meaning.  
Figure 3: Structure of candidate hidden states.  
Figure 2: Reset gate and update gate structure of GRU.  
and the new candidate hidden state \(\widetilde{h_{t}}\) in the new hidden state \(h_{t}\). The calculation formula is as follows.  
\[h_{t}=h_{t-1}\odot Z_{t}+(1-Z_{t})\odot\widetilde{h_{t}}. \tag{4}\]  
It can be seen from (4) that when the update gate \(Z_{t}\) approaches 1, most of the model retains the information of the hidden state at the previous moment \(h_{t-1}\), and the information from \(x_{t}\) will be ignored. When the update gate \(Z_{t}\) approaches 0, most of the information in the candidate hidden states \(\widetilde{h_{t}}\) is extracted from the model. This can solve the problem of gradient disappearance in recurrent neural networks. Figure 4 demonstrates the entire process framework for GRU.  
BiGRU is a variant of GRU. Its output depends on the dual effects of forward and backward states, which makes the final output more accurate. The structure of the BiGRU model is shown in Figure 5, where \(x_{t}\) denotes the input word at \(t\) time, \(e_{t}\) denotes the embedding layer word vector, \(\overrightarrow{h_{t}}\) denotes the hidden state of the forward GRU, \(\overleftarrow{h_{t}}\) denotes the hidden state of the reverse GRU, and \(h_{t}\) denotes the hidden state after the forward and reverse splicing.
## 4 Experiments
### Dataset Splitting  
The k-fold cross-validation method is employed to divide the dataset into subsets for training and testing during the experimental phase.\(k\)-fold cross-validation is a popular statistical technique that splits the whole dataset into k folds, which are equal-sized (or nearly equal) subgroups.  
This approach enhances the robustness of model evaluation by minimizing reliance on a single train-test split. For each iteration, one fold serves as the test set, while the remaining k-1 folds are used for training. Each fold is used as the test set once during the k repetitions of the procedure. After completing all k iterations, the performance metrics are averaged across all folds to provide a comprehensive assessment [83; 84]. Figure 6 illustrates the k-fold cross-validation process. By mitigating the influence of specific data splits, this method ensures a more reliable evaluation of the model's performance on unseen data. The present investigation employs 5-fold cross-validation to produce accurate results and enable a thorough examination and comparison of experimental results.
## 3 Results
### Color Analysis  
Color parameters, including lightness [L*], a*, b* values, whiteness index [WI], yellowish index [YI], C* (chroma), and H* (hue angle) values, are shown in Table 3. Native potato starch and spray-dried starch powders showed similar L* values and yellowness index, indicating that the spray-drying process did not significantly affect these color parameters. Native potato starch exhibited significantly lower b* values than the SD_3% powder, implying that the native starch has a less yellow hue. A similar trend was also found in the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Span**} & \multirow{2}{*}{**Uniformity**} & **Specific** & **D(4.3)** & **D(3.2)** & **D(0.1)** & **D(0.5)** & **D(0.9)** \\  & & & **Surface** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** \\ \hline Native & 1.31 & 0.40 & 0.18 & 45.25 & 34.18 & 20.56 & 41.87 & 75.42 \\ potato starch & (\(\pm\)0.09) c & (\(\pm\)0.03) c & (\(\pm\)0.00) c & (\(\pm\)1.32) a & (\(\pm\)0.42) a & (\(\pm\)1.31) a & (\(\pm\)1.12) a & (\(\pm\)4.12) a \\ \hline \multirow{2}{*}{SD\_3\%} & 2.85 & 0.85 & 0.73 & 20.98 & 8.22 & 4.97 & 13.82 & 44.43 \\  & (\(\pm\)0.45) a & (\(\pm\)0.09) a & (\(\pm\)0.03) a & (\(\pm\)0.97) c & (\(\pm\)0.25) c & (\(\pm\)0.04) c & (\(\pm\)0.61) c & (\(\pm\)7.79) b \\ \hline \multirow{2}{*}{SD\_5\%} & 1.87 & 0.58 & 0.43 & 26.41 & 13.88 & 9.05 & 23.02 & 52.06 \\  & (\(\pm\)0.08) b & (\(\pm\)0.03) b & (\(\pm\)0.00) b & (\(\pm\)0.23) b & (\(\pm\)0.06) b & (\(\pm\)0.04) b & (\(\pm\)0.24) b & (\(\pm\)2.42) b \\ \hline \hline \end{tabular}  
* Different superscript letters within the same column signify significant differences (\(p<0.05\)) amongst the means, based on [PERSON]’s multiple comparison test.  
\end{table}
Table 2: Particle size distribution parameters [span, uniformity, specific surface, D(4.3), D(3.2), D(0.1), D(0.5), D(0.5), D(0.9)] of native potato starch, spray-dried potato starch powders at concentrations of 3% [SD_3%] and 5% [SD_5%] *.  
chroma [C*] values. Hue angle values for all samples varied from 83 to 92\({}^{\circ}\). Native starch had a yellow-orange hue angle, the SD_3% had a yellow hue angle, while the SD_5% had a light-yellow hue angle, close to yellow-green [58]. These differences could be related to the moisture content in the spray-dried samples, which may affect the color properties of the starch, causing them to appear more greenish. The effect of moisture content on the color of spray-dried and freeze-dried powders has also been reported by [PERSON] et al. [59].
## 3 System Architecture and Test Environment
### Object Detection, YOLOv7  
To select the best algorithm for our vehicle detection and classification task, we needed to consider several criteria, including real-time detection with high reliability and deployment ability on the Nvidia Jetson Nano card. After careful consideration, we chose the YOLOv7 as the most suitable detection algorithm for our application as it has been successfully tested in several real-world applications [8; 14; 15]. YOLOv7 is characterized by a unique CNN architecture that processes multiple networks separately [8].  
In a YOLO model (the general structure [8] of which is represented in Figure 4), the back part is the part that groups the pixels of the image. The representations of the layers of the convolutional network are combined and blended in the neck; then, they are passed to the prediction head. The head is the final part of the CNN, in which our YOLO model predicts the object classes and locations.  
We carried out a comparative analysis of YOLOv7 with other YOLO versions, including YOLOv4 and YOLOv5. Our study revealed that YOLOv7 outperforms both YOLOv4 and YOLOv5 in terms of accuracy, parameters, and computation. Specifically, YOLOv7 showed a 1.5% higher average precision (AP) than YOLOv4-tiny, while reducing the number of parameters by 75% and computation by 36%. Moreover, the more edge-optimized version of YOLOv7, YOLOv7-tiny, achieved the same AP as YOLOv4-tiny, while using 39% fewer parameters and requiring 49% less computation. When compared to YOLOv5-N, YOLOv7-tiny demonstrated a 10.7% higher accuracy on AP and was 127 FPS faster [16].  
Our custom model for vehicle detection and classification was trained using the transfer learning approach, and it was based on the pretrained weight file of YOLOv7-tiny [17] and our annotated dataset. We ran YOLOv7-tiny on the Nvidia Jetson Nano as this model can work faster on this type of device. Overall, our comparative study and choice of YOLOv7 demonstrate that it is a highly effective and efficient algorithm for real-time vehicle detection and classification.  
Figure 4: YOLO network architecture.  
Figure 3: The architecture of the system used for the dataset collection.
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 5 Conclusions  
An improved ant colony algorithm that can calculate the optimal path in real time according to the ocean current environment is proposed for energy saving in this paper. Through the simulation test of the current model of the dynamic change process of the ocean current in the sea area of Lianyungang, it is shown that the improved ant colony algorithm inferred that the current environment can well adapt to the complex and changeable marine environment. Based on this path planning method, the ship can adjust the course according  
Figure 6: The customary route by ship master.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Sailing Time** & **Path Planning Distance** & **Energy Consumption** \\ \hline Improved & 9171.0 s & 46,491.7 m & 102,931,597,865.8 J \\ Customary route & 9103.8 s & 46,013.4 m & 105,797,842,971.5 J \\ \hline \hline \end{tabular}
\end{table}
Table 3: Index comparison between improved ant colony algorithm and ship master’s customary route.  
to the calculation results of the model at the time of departure to meet the requirement of saving energy during sailing at sea. The path adjustment algorithm has achieved an energy-saving rate of 3.326% in the simulation test in Lianyungang. In experiment 2, we compared the energy consumption of the master's customary route with the energy consumption optimized by the ant colony algorithm to verify the practical significance of the energy-saving route. The energy-saving rate of ocean current energy consumption reached 2.709%, which has certain practical significance. In the future, the optimized ant colony algorithm can be further combined with dynamic obstacle avoidance, which has a wide range of application values. Afterwards, more environmental factors would be taken account in this study to stimulate a more realistic application in the navigation field.  
Conceptualization, Y.D. and R.L.; methodology, R.L.; chart data extraction, H.S.; validation, L.C.; customary route plan design, H.S.; algorithm programming, H.S.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, R.L. and J.L.; supervision, R.L.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52171346), the Natural Science Foundation of Guangdong Province (2021A1515012618), the special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province (2019 KZDZX1035), the scientific research program for university of Guangzhou Education Bureau (202032779), and program for scientific research start-up funds of Guangdong Ocean University (R19055).  
Not applicable.  
The authors declare no conflict of interest.
# Layered Graphs: Applications and Algorithms
## 3 Algorithm
### Input  
The input consists of \(LG_{k}^{n,q}\) which is specified in terms of \(M_{1},\ldots,M_{q}\) and \(I_{1},\ldots,I_{q-1}\), where \(M_{i}\) is the 0-1 adjacency matrix for layer \(i\), i.e., \(G_{i}\). \(I_{i}\) is the 0-1 adjacency matrix for \(E_{i,i+1}\). Rows \(1,2,\ldots,k\) of \(I_{i}\) correspond to vertices \(V_{i1},V_{i2},\ldots,V_{ik}\), and columns \(1,2,\ldots,k\) of \(I_{i}\) are vertices \(V_{i+1}\), \(V_{i+1}\)\({}_{2},\ldots,V_{i+1}\)\({}_{k}\). It must be noted that for a linear graph, \(I_{i}\) can just be a \(k\) dimensional vector and the corresponding computation is less expensive where \(I_{i}[a]=1\iff\text{an edge between }a\in V_{i},a\in V_{i+1}\) exists. The adjacency matrix \(M_{i}\), for layer \(i\), is a matrix of dimensions \(k\times k\), which means it requires \(O(k^{2})\) space. Similarly, each \(G_{i}\) also requires \(O(k^{2})\) space. Therefore, the total space required for the input graph is \(O(nk)\), since each layer requires \(O(k^{2})\) space, and there are \(O(n/k)\) layers.  
The Boolean valued function _compatible_ (please see Algorithm 1) determines whether the candidate sub-solutions (of the current layer and the subgraph induced by vertices of all previous layers) can be combined; here, the layer number, \(i\), is implicit. For each mask, \(j\), of a given layer, \(i\), the function \(valid(i,j)\) determines if \(j\) is a feasible option for layer \(i\). The helper function, \(cardinality(j)\), returns the number of bits that are set in the binary representation of mask \(j\).  
All algorithms consist of the following sequence of computational tasks:  
* Repeat (i) and (ii) for all layers \(1,\ldots,q-1\).
* (i) Feasible: \(\forall_{j}\) (if \(valid(i,j)\)), then go to step(ii).
* (ii) Extension: If \(j\) and \(l\) are compatible, then store the cardinality of \(cs_{i,j}\bigcup ccs_{i-1,I}\) in \(sol_{i,j}\) and the count of \(ccs_{i,j}\) in \(count_{i,j}\).
* (iii) Summarize: At layer \(q\) :, execute (i) and (ii). Identify the optimum cardinality among \(\forall_{j}sol_{q,j}\) and the corresponding count.  
A particular problem has specific characteristics. In the sequel, where a problem is dealt with in detail, the corresponding validity/compatibility and other specifics are elucidated.
## 3 Energy and Energy Spread Characterization
This confirms that the beam energy spread of 0.5% at 220 MeV satisfies the acceptance criteria, indicating a high level of agreement between the simulated and measured dose distributions. Furthermore, the optimal energy spreads determined for other investigated proton energies are 1.5%, 1.25%, and 1% for beams of 70 MeV, 100 MeV, and 150 MeV, respectively. To investigate the depth-dose distribution, Bragg peak position, and peak LETd position for each proton energy, the resulting energy spreads for the four energies were collected and used as the initial proton beam energy spreads for the MC simulation. The Bragg peak positions, R80, peak LETd positions, and distal fall-off were monitored and are summarized in Tables 4 and 5. Typically, R80 is considered and defined as the target position [6,19]. The variation in peak position and R80 is less than 1% compared to the measured data for all the considered energies. The peak position of the LETd value shifts approximately 1 mm away from R80, as illustrated in Table 5. This information will be utilized as a guide for predicting the position of the desired peak LETd for each proton beam energy, using the R80 depth position, which is crucial for effectively killing cancer cells. \begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Energy (MeV)**} & **Measured** & **Measured** & **R80 (mm)** & **R80 (mm)** & **Diff R80** \\  & **R80** (mm) & **R80** (mm) & **R80** (mm) & **Diff R80** (mm) & **Diff R80** (mm) \\ \hline
70 & 39.6 & 40.5 & 39.5 & 40.5 & 0.1 & 0 \\
100 & 75.3 & 77.0 & 75.2 & 77.3 & 0.1 & 0.3 \\
150 & 154.3 & 157.7 & 155.0 & 158.1 & \(-\)0.7 & \(-\)0.4 \\
220 & 301.0 & 305.4 & 303.4 & 306.6 & \(-\)0.24 & \(-\)1.2 \\ \hline \end{tabular}
\end{table} 
Table 4: The simulated depth of the Bragg peak (RBP) and R80 were analyzed for proton energies of 70 MeV, 100 MeV, 150 MeV, and 220 MeV and compared against the measurement data. Figure 8: The relative depth-dose distribution comparing the measured depth dose and the simulated depth dose (**left**). The results of the \(\gamma\)-index for \(\delta r\) and \(\delta D\), set to 3 mm and 3%, respectively, are shown in the right plot for a proton beam energy of 220 MeV (**right**).
## 2 Proposed Method
### Missing Value Imputation
#### 2.2.1 GRUD  
GRU has been widely proved to have an excellent ability to capture dependencies between time series data [37]. However, the traditional GRU cannot handle sequences with missing values. Based on the GRU model, GRUD adds the decay mechanism to estimate the missing values according to the previous sequence [24]. The schematic diagram of the GRUD model is shown in Figure 3. The trainable decay coefficient \(\gamma\) is defined as:  
\[\gamma_{t}=\exp(-max(0,W_{\gamma}\delta_{t}+b_{\gamma})) \tag{2}\]  
Figure 2: Schematic of the DBSCAN.  
where \(\delta_{t}\) is the time interval between the current moment \(t\) and the last non-missing value, \(W_{\gamma}\) and \(b_{\gamma}\) represent the weight and bias of a neural network so that that \(\gamma\) can be updated during the training process.  
The missing values of the input data \(x\) are replaced by \(\hat{x}\), expressed as:  
\[\hat{x}_{t}=m_{t}x_{t}+(1-m_{t})(\gamma_{x}x_{t^{\prime}}+(1-\gamma_{x}) \overline{x}) \tag{3}\]  
\[m_{t}=\left\{\begin{array}{c}0,\text{ if }x_{t}\text{ is missing}\\ 1,\text{ otherwise}\end{array}\right. \tag{4}\]  
where \(m_{t}\) is the mask code, \(x_{t^{\prime}}\) is the last non-missing value, and \(\overline{x}\) indicates the mean value of \(x\).  
The decay mechanism is also applied to the hidden state \(h\) to enhance the learning of missing value patterns:  
\[\hat{h}_{t}=\gamma_{h}\odot h_{t} \tag{5}\]  
where \(\odot\) represents element multiplication.  
Moreover, the mask code \(m\) is fed into the GRU cell directly. Finally, the update functions of GRUD are as follows:  
\[r_{t}=\sigma\Big{(}W_{r}\Big{[}s_{t},\hat{h}_{t-1},m_{t}\Big{]}+b_{r}\Big{)} \tag{6}\]  
\[z_{t}=\sigma\Big{(}W_{z}\Big{[}s_{t},\hat{h}_{t-1},m_{t}\Big{]}+b_{z}\Big{)} \tag{7}\]  
\[\widetilde{h}_{t}=\tanh\Big{(}W\Big{[}\hat{x}_{t},r_{t}\odot\hat{h}_{t-1},m_{ t}\Big{]}+b\Big{)} \tag{8}\]  
\[h_{t}=(1-z_{t})\odot\hat{h}_{t-1}+z_{t}\odot\widetilde{h}_{t} \tag{9}\]  
where \(r\) and \(z\) represent the reset gate and the update gate, and \(\sigma()\) and \(tanh()\) indicate the sigmoid and tanh activation function.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Log-Based Attack Detection
#### 4.4.1 Rule and Machine Learning-Based Hybrid Methods  
Hybrid methods combine rule-based detection and machine learning, which together achieve better performances than do single detection systems. Many rule-based detection systems (e.g., Snort) generate masses of alerts; however, most of the alerts involve only operations that do not match the rules; therefore, these are often not real intrusion behaviors. The hybrid methods take the log output of the rule-based systems as inputs; then, machine learning models are used to filter out the meaningless alerts.  
Many IDSs suffer from high false alarm rates, which cause real attacks to be embedded among many meaningless alerts. Ranking alerts via machine learning models forms a possible solution. To reduce the false alarm rate, [PERSON] et al. (2009) proposed a KNN method to filter alarms. They conducted experiments in a real network environment and generated alerts using Snort. Then, they trained a KNN model to rank the alerts. There were 5 threat levels in total in their experiment, and the results showed that the KNN model reduced the number of alerts by 89%.  
Some IDSs perform a function similar to human interaction, in which alerts are ranked by machine learning to reduce analyst workloads. [PERSON] et al. (2009) proposed an alert filtering method based on a DNN. They first collected the log generated by McAfee. Then, they trained a DNN model to find important security events in the logs. Next, the extracted important events were analyzed by security experts. Then, the analysis results were used as training data to enhance the DNN model, forming an interaction and promotion cycle. The proposed hybrid system can reduce analyst workloads and accelerate security analyses.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 5 Prototype
### Charging Time  
The goal of the first experiment was to demonstrate the performance of a simple RF energy harvesting application, the impact of capacitor size and the distance on the charging time of a sensor node running a simple application, where a sensor node simply boots and transmits a packet to a base station. The interval between packets is taken as the charging time of the supercapacitor. The upper and lower threshold voltages were set to 1.23 V and 1.02 V, respectively. Figure 9 compares the charging time as a function of distance for 50-mF and 200-mF capacitors. As expected, the amount of time to charge a large capacitor was significantly longer. The amount of time to fully charge a large capacitor at a 3-m distance was 110 s, whereas it took only 29 s for a small capacitor. The figures also compare the experimental results with the theoretical values obtained through the following model [17].  
Figure 8: Oscilloscope screenshot for the energy profile of a simple application and the prototype. The oscilloscope was set at 20 mV/div with a timebase of 10 ms/div.  
Figure 7: Measurement setup.
## 3 Methodology  
In our previous work [27], we outlined the critical states for the Modbus protocol, and the implications for the protocol's operation from the lack of defined timeout requirements for any communication errors. With that, however, we must also consider these issues for the underlying layers of TCP/IP that transport our Modbus data. For this method, we carefully reviewed the specifications and investigated the implementation guides for both Modbus TCP and DNP3, and found differing rules for connection loss and connection re-establishment between the two protocols, which would lead to differing performance with communication outages and time-sensitive data.  
To lend context to our findings within the broader OT landscape, we also consider the behavior of protocols such as IEC-61850's [28] GOOSE and SMV, and the IEEE C37 series. Unlike Modbus TCP and DNP3, GOOSE and SMV are designed for high-speed, event-driven, or time-critical communication directly over Ethernet, bypassing all higher-layer aspects of the TCP/IP stack entirely. SMV, in particular, supports continuous streaming of time-synchronized measurement data with strict timing constraints, making it suitable for protection and control applications that demand deterministic performance. The IEEE C37.118 protocol suite, widely used in synchrophasor communications, also emphasizes low-latency, high-reliability data transfers with explicit handling for error states and quality indicators to maintain operational continuity. These more modern or specialized alternatives highlight the limitations inherent to legacy protocols and underscore the need for well-defined recovery mechanisms in time-sensitive OT environments. This broader perspective emphasizes the relevance of our work for evaluating protocol robustness across a spectrum of industrial communication standards.