esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Impact Damage Monitoring Framework  
Considering that the traditional damage monitoring method based on Lamb waves needs prior structural information and manual experience, an impact damage monitoring framework based on a multi-task 2D-CNN is established. For the proposed impact damage monitoring framework, the data processing flow and model structure are designed as shown in Figure 1. Figure 1 indicates that the workflow of the method roughly includes the original data acquisition stage, signal processing stage, and multi-task network damage detection stage. Detailed instructions are as follows.  
1. Original data acquisition stage: The impact damage monitoring method that is data-driven needs a lot of data, and Lamb wave data can be used to reflect the impact damage information. PZT sensors are pasted on the tested structure to receive Lamb wave signals. Spring impact hammers are used to apply impact loads to simulate impact damage. Lamb wave signals with different impact locations and different impact energies are collected.
2. Signal processing stage: Lamb wave signals collected by each sensor are one-dimensional time domain signals. In order to achieve multi-sensor data fusion, a three-dimensional surface maps method is applied to integrate the information of all the sensor data. Vertical projection transformation converts a three-dimensional image to a two-dimensional color image. The gray image algorithm is used to convert color images into gray images and is used for model training and diagnosis.
3. Impact damage detection stage: Input the gray image data into the multi-task 2D-CNN model to train the model and adjust the parameters of the multi-task 2D-CNN model through verification. The damage feature information is extracted from the gray image. The two classification tasks of the model are used to monitor the impact damage location and energy.  
In this paper, a multi-task 2D-CNN is built in the PyTorch framework. The quantitative proportions of the training set, validation set, and test set are set to 8:1:1 by the holding out method. The training set is used to train the network, the validation set is used to verify the network performance, and the test set is used to test the network performance.
## 3 Coverage Calculation Based on the Line of Sight  
The concept of coverage reflects how well an area is monitored by a set of sensors [22]. The coverage problem has been investigated in the scientific literature based on several approaches, depending on the nature of the application. One example is the art-gallery problem that aims to define the required number of cameras where each point in the gallery is covered by at least one observer [22]. In the art-gallery problem, the coverage is defined by the direct visibility between the observer and the target [23]. Another example related to coverage issues is extending the lifetime of sensors through reducing energy consumption ([24, 25, 26]). These approaches try to identify and turn off the redundant sensors that cover the same zone when their use is not gainful.  
A general definition of the coverage problem was proposed by [22], it stipulates that for a given set of sensors deployed in a target area, every point in the zone of interest is covered by at least k sensors, where k is the number of sensors that monitor simultaneously the target. In Figure 1, the white circle corresponds to the area covered by one sensor, the dotted zones correspond to areas covered by two sensors, the gray zone corresponds to the area covered by three sensors, and the dashed zone corresponds to a non-covered area.  
The coverage is mainly related to the sensing range of each sensor, which is related to the monitored phenomenon, and the obstacles that exist in the target zone. Hence, developing a model that allows calculating the coverage must take into consideration the nature of the application and the presence of obstacles. In the case of camera network deployment, the line of sight may be considered as an appropriate method to model the coverage of a camera network ([19,21,27,28]). In fact, the \"Line of Sight\" model calculates the visibility based on a visual line from a viewpoint \(O\) (or the observer) to another point \(T\) that belongs to the target area. If this visual line intersects with any obstacle before reaching \(T\), then \(T\) is hidden and not visible from \(O\), otherwise, \(T\) is visible from \(O\)[27] (Figure 2).  
When the visibility analysis process is applied on all points that belong to the target area, we talk about \"viewshed analysis\". It results from the movement of the line of sight across the entire surface of interest [28]. The viewshed analysis can be performed based on one or many viewpoints. The result of viewshed analysis consists of determining the set of points on the surface that are visible (and that are not visible) from the set of viewpoints [27].
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 2 Methodology and Materials
### Experimental Setup
#### 2.4.2 Receiving End  
As explained in the Section 2.4.1, the surface waves which reach the receiving end carry charges from the spark gap as well as the atmospheric electric field. In this work, we harvested the energy from these waves using a device based on a modified live-wire detector (see the right side of Figure 2). The circuit contains a 6 M ohm resistor, a neon bulb, another 1 ohm resistor, and a monopole antenna connected in series. The neon bulb requires 90 volts to turn on. The monopole antenna was vertically mounted in a suspended place, with the top of the antenna being at least 1.5 m above the experimental setup.  
The monopole antenna serves two purposes. In the AC sense, its capacitive end forms an AC ground of the circuit. In the DC sense, it forms a DC biasing node. Our Earth is enclosed in an electric field of 100 V/m [38; 39; 40; 41]. The electrostatic voltage at a height of 1.5 m is equivalent to a DC voltage of 150 volts. Once the antenna top reaches this altitude, an atmospheric current will flow through the body of the modified live-wire detector circuit. This atmospheric current is too minute to do real work. However, the neon bulb requires only 70 volts to light, and it can operate with virtually no current. By biasing the neon bulb with the atmospheric field, the negative resistance inside the neon bulb will ring and self-generate a very large AC current, typically in excess of 1A. In the presence of a vertically mounted monopole antenna, this AC current will continue to oscillate and spill onto the resistors and the rest of the circuit. Typically, this current was found to be anywhere between 1 mA and 200 A.  
The modified live-wire detector at the receiving end was used not only for detection, but also for measurement of the received power on the water surface. In this work, this device was used to measure the received power at the transmitting end (\(P_{T}\)) and the receiving end (\(P_{R}\)). The transmission efficiencies under different salt-concentrations can be calculated using the following equation:  
\[Eff_{T}=\frac{P_{R}}{P_{T}} \tag{26}\]
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
###### Abstract  
This paper proposed implementing a water and air monitoring system using sensor development and a LoRa Network. To transmit data, a self-made PCB board integrates the terminal sensors with Renesas RX64M MCU and LoRa. There are 16 monitoring point stations for the media experiment. The sensors were used to measure the water and air parameters such as PM\({}_{2.5}\), CO\({}_{2}\), DO concentration, pH level, temperature, and humidity. In addition, the Grafana system was implemented to present the status and variation in the monitoring parameters in the environmental area. To evaluate the monitoring system, we also collected public information provided by the environmental protection department of the Taiwan government at the same monitoring point for comparison.  
LoRa; internet of things; low-power network; environmental monitoring; air pollution; water monitoring +
Footnote †: journal: Applied  
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(MCC), cloudlet computing, mobile clouds, fog computing, and edge computing, to name a few [5,6]. LoRaWAN, according to the LoRa Alliance, addresses important Internet of Things (IoT) criteria such as secure bi-directional communication, mobility, and location services. The LoRaWAN standard enables the rollout of IoT applications by providing seamless interoperability among smart items without the need for complicated local installations. Low-power, wide-area networks, according to experts, will account for a large share of the expansion of IoT devices in the next years [7].  
In this paper, we adopted two strategies. First, we use a low-power LoRaWAN network (low-power, wide-area network technology), which is capable of long-distance transmission [8,9]. Second, we use solar cell charging equipment to achieve self-contained power without constraints on the power supply issue. In this way, we solve the power supply problem, and the logging data can be transmitted remotely [10,11,12]. In this case, this work sets up six sets of monitoring stations in Tunghai University. In each station, we use PM\({}_{2.5}\), CO\({}_{2}\) concentration, temperature, and humidity sensors. Every minute after processes logging data, the data were sent back to the database system based on the OSM map (Open Street Map) [13,14]. Therefore, the objectives of this paper are listed as follows:  
* Implement the LoRaWAN network in an air quality and water monitoring system;
* Evaluate the performance of the LoRaWAN receiver in terms of sensitivity and throughput.
## 1 Introduction
### Set of Spectrum-Consistent Records  
A set of severe ground motions compatible with the seismic conditions at the building's location (Messina, Italy) was considered in the present investigation. Related to the building location, the design response spectrum for the no-collapse requirement (horizontal component) was considered according to Eurocode 8 [35] and Italian code [36]. Figure 4 plots this design spectrum (black curve) and the parameters adopted to obtain it. Consistent with this reference design response spectrum, 10 pairs of accelerograms were considered, some characteristics of which are listed in Table 3. In particular, the earthquake name, the date, the station identifier, the moment magnitude \(M_{w}\), the epicentre distance and the peak ground acceleration (PGA) are provided in the table. Note that seven of the selected accelerograms are taken from a European database and correspond to near-source earthquakes with a moderate moment magnitude \(M_{w}\); they are obtained by means of REXEL software [37] according to EC8 [36]. The remaining three ground motions listed in Table 3 are near-intermediate source Chilean records obtained through SeismoSpect [38]. The response spectra of the 10 couples of records (light blue curves) with their average spectrum (blue curve) are plotted in Figure 4 together with the reference design spectrum (black).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **Type** & **Period (s)** & **Frequency (Hz)** & **Mass x (\%)** & **Mass y (\%)** \\ \hline
1 & 1\({}^{\circ}\) flexional x & 0.292 & 3.423 & 0.00 & 91.44 \\
2 & 1\({}^{\circ}\) flexional x & 0.288 & 3.467 & 92.00 & 0.00 \\
3 & 1\({}^{\circ}\) rotational & 0.257 & 3.888 & 0.00 & 0.31 \\
4 & 2\({}^{\circ}\) flexional y & 0.107 & 9.305 & 0.00 & 8.22 \\
5 & 2\({}^{\circ}\) flexional x & 0.106 & 9.372 & 8.00 & 0.00 \\
6 & 2\({}^{\circ}\) rotational & 0.097 & 10.339 & 0.00 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: First six modal properties of the building.  
Figure 4: Response spectra of the 10 pairs of spectrum-consistent records (Table 2), average spectrum, and reference design spectrum.  
In the present investigation, the seismic records have been scaled through non-negative scale factors \(SF\), according to the approach proposed in [PERSON] and [PERSON] [19]. Starting from 0.3, the scale factors have been progressively increased every 0.3 until the dynamic response reached the collapse threshold defined in [19].
###### Abstract  
Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to understand and trust the results. In order to address this, it is necessary to incorporate explainability into the detection model. There is insufficient research to provide reasons why applications are detected as malicious or explain their behavior. In this paper, we propose a method of a Vision Transformer(ViT)-based malware detection model and malicious behavior extraction using an attention map to achieve high detection accuracy and high interpretability. Malware detection uses a ViT-based model, which takes an image as input. ViT offers a significant advantage for image detection tasks by leveraging attention mechanisms, enabling robust interpretation and understanding of the intricate patterns within the images. The image is converted from an application. An attention map is generated with attention values generated during the detection process. The attention map is used to identify factors that the model deems important. Class and method names are extracted and provided based on the identified factors. The performance of the detection was validated using real-world datasets. The malware detection accuracy was 80.27%, which is a high level of accuracy compared to other models used for image-based malware detection. The interpretability was measured in the same way as the F1-score, resulting in an interpretability score of 0.70. This score is superior to existing interpretable machine learning (ML)-based methods, such as Drebin, LIME, and XMal. By analyzing malicious applications, we also confirmed that the extracted classes and methods are related to malicious behavior. With the proposed method, security experts can understand the reason behind the model's detection and the behavior of malicious applications. Given the growing importance of explainable artificial intelligence in cybersecurity, this method is expected to make a significant contribution to this field.  
Keywords:explainable artificial intelligence (XAI); deep learning; cybersecurity; mobile malware; malware detection; visualization +
Footnote †: journal: Applied sciences
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 3 Materials and Methods
### Model Flow Chart
#### 3.3.8 EfficientNetB0  
Within the EfficientNet family of convolutional neural networks (ConvNets), EfficientNet-B0 is a specific model. It aims to strike a good balance between model accuracy and computational efficiency. As the lowest version of the EfficientNet series, EfficientNet-B0 provides a standard architecture for comparison and scalability. Despite its compact size, EfficientNet-B0 delivers competitive performance. It has been trained and evaluated on various image classification tasks, including benchmark datasets like ImageNet. The model demonstrates strong accuracy while maintaining a relatively low computational cost, making it suitable for applications and scenarios with limited resources [35]. It was then fine-tuned by adding one flatten layer, and three dense layers. The first dense layer has 32 neurons, with LeakyReLU activation function, while the second dense layer has 16 neurons with LeakyReLU activation function and an output dense layer with one neuron and activation function of sigmoid, as shown in Table 9. The pretrained Inception-v3 extracted features from the input image and transferred them to the layers to classify the skin lesion as either Benign or Malignant. The model parameters are shown in Table 9.  
The fine-tuned EfficientNetB0 model underwent training for 20 epochs using the Adam optimizer with a batch size of 64. The activation function employed for the output dense layer was sigmoid, and binary cross-entropy served as the designated loss function.
## 4 Results and Discussion
### Time Complexity for Selected Algorithms  
The \"Big O\" notation was used to check the complexity of each of the L(h,k) algorithms studied, with h = 1,2,3 and k = 1, for different input sizes. Table 3 indicates the running times of the algorithms for different input sizes.  
Figure 15 shows a comparison of the running times of the three L(h,k) algorithms for different input sizes. The figure shows that, given a large amount of subscribers, the L(3,1) algorithm outperforms the L(2,1) algorithm (i.e., the L(3,1) algorithm has a smaller running time). This indicates that when there is a large number of users (e.g., in a densely populated area), the L(3,1) algorithm will perform optimally for channel allocation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm/Input Size** & **L (1,1)** & **L (2,1)** & **L (3,1)** \\ \hline
0 & 2 & 3 & 5 \\
1 & 5 & 8 & 8 \\
2 & 12 & 19 & 15 \\
3 & 23 & 36 & 26 \\
4 & 38 & 59 & 41 \\
10 & 212 & 323 & 215 \\
20 & 822 & 1243 & 825 \\
40 & 3242 & 4883 & 3245 \\
80 & 12,882 & 19,363 & 12,885 \\
100 & 20,108 & 30,303 & 20,111 \\
200 & 80,202 & 120,403 & 80,205 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of running times (in milliseconds) for each of the coloring algorithms for different input sizes (number of subscribers).  
Figure 15: Comparison of the running times of the three algorithms for different input sizes.
## 3 Mathematical Model  
### Assumptions  
During this experiment, a 3D transient mathematical model was established by the computational fluid dynamics software Fluent based on the characteristics of continuous laser-MIG hybrid welding. The heat and mass transfer between the heat source and the material, as well as the effects of surface tension, recoil pressure, buoyancy, electromagnetic forces, evaporative condensation, evaporative heat transfer, and gravity of the molten droplets, were mainly considered in the model. Post-processing analysis was performed using CFD-post.  
Because of the complex flow behavior of the laser-arc hybrid welding pool, the surface of the molten pool fluctuated dramatically in the actual welding process. It was difficult to consider all the conditions in numerical simulation. Therefore, to simplify the model, we made the following assumptions:  
1. The effect of protective gas on melt flow is neglected.
2. The initial temperature of the material is 300 K.
3. Specific heat capacity, thermal conductivity, viscosity as a function of temperature, and other material thermophysical properties are constants.
4. The liquid phase of the melt pool cannot be compressed, and the flow pattern is laminar flow.
5. The absorption rate of the welded workpiece to the arc and laser energy is constant.
6. Thermal buoyancy is assumed using the Boussinesq approximation.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 2 Overview of the FRS
### Architecture of FRS  
The architecture of an FRS can be divided into the following four components, as shown in Figure 3:  
1. **Data aggregator**: This component is responsible for collecting and processing data from multiple sources (e.g., websites and mobile apps). The data aggregator then stores the data in a unified format, allowing the recommendation system to access the data from all sources in one place [35]. It can be used to  
Figure 3: Architecture of the FRS, divided into four components used for user’s data sources.  
determine user preferences, as well as to enable recommendations to be made across multiple platforms [36].
2. **Feature extractor**: This component is a critical part of the FRS architecture. It is responsible for extracting key features from data sources in order to create a unified representation of the data [37]. This unified representation enables the system to compare data from different sources and make recommendations accurately. Furthermore, it enables the system to generalize the data, reducing the amount of data that need to be processed [38]. The feature extractor component can extract features such as user preferences, user demographics, item attributes, and item ratings.
3. **Recommendation system**: A recommendation system is a key component of an FRS. It generates user recommendations based on the data gathered from the different federated sources [39]. The system uses algorithms to analyze the data and create personalized recommendations for the users. The goal is to provide the most-relevant and accurate recommendations tailored to users' interests and preferences [40]. The recommender system is also responsible for updating the recommendations as new data are added to the federated sources. This helps ensure that the recommendations remain relevant and up-to-date [41].
4. **Evaluation engine**: This component measures the system's performance, such as user engagement, user satisfaction, and other metrics [42]. The evaluation engine collects and analyzes data from different sources and uses machine learning algorithms to identify patterns in the data and evaluate the system's performance. It also provides feedback to the other components in the system, such as the content providers, to enable further optimization [43; 44]. The evaluation engine ensures fairness and accuracy in the system.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 4 Discussion
### DTEC Model Evaluation  
The results of this study show that the accuracy and applicability of the DTEC model in China were greater than those of the GO-SIF and MOD17 products. The model parameters in this paper do not use the parameters given in previous studies but were adjusted and modified for the Chinese region on the basis of the original parameters to conform to the characteristics of vegetation in China, which is one of the reasons why the accuracy of the DTEC model is higher than that of the other two models in China. Second, it distinguishes the LUE of C4 and C3 vegetation, which can be seen in the accuracy validation of the farmland site. The simulation accuracy of the DTEC model is significantly better than that of the GO-SIF and MOD17 products. As farmland ecosystems are the most active carbon reservoirs in terrestrial ecosystems, the high-accuracy estimation of the GPP of farmland ecosystems will be effective in reducing the error in the simulation of the total amount of China's terrestrial GPP. Finally, the effect of scattered radiation on vegetation photosynthesis was considered. In the past, the LUE of low-canopy vegetation leaves was often underestimated [67], which made the simulation error larger in areas with complex vegetation structures and dense canopies, such as tropical rainforests, while the DTEC model considered this problem and effectively improved the simulation accuracy in these  
Figure 10: The distribution of GPP changes in China’s major afforestation program from 2001–2020.  
areas. In summary, the DTEC model has good applicability in China and can meet the needs of subsequent studies.