esa-sceva/satcom-chunk-collection huggingface dataset:
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Experimental Condition
#### 5.1.2 Experimental Setup  
We employed a Mavic 2 Pro drone, launched by DJI Co., Ltd. Shenzhen, China The resolution of the receiving camera was \(1080\times 1920\), the pixel count was 2 mega pixels, and the frame rate was 30 fps. The focal length was \(f=28\) mm, and the zoom magnification was set to \(M=1\).  
Figure 9 shows a sensor node. A sensor node was equipped with five sensors and an LED panel. It collected temperature, humidity, illumination, soil water content, and infrared sensor data. We employed WS2812B serial LED panels with 64 LED lights arranged  
Figure 8: Experimental setup.  
in a square. An Arduino UNO microcontroller was connected to the sensors and the LED panel to control them.  
The battery supplies power to the sensors, the Arduino UNO microcontroller, and the LED panel.  
In this experiment, On-Off Keying (OOK) was used as the modulation scheme. The Arduino UNO microcontroller encoded and modulated the sensor data as optical signals to be transmitted by the LED panel. A sensor node transmitted optical signals when a drone flew over it by detecting the drone with an infrared sensor to reduce energy consumption. An example image of the sensor node taken by the drone is shown in Figure 10.  
The study area was a leaf mustard farm in Kamitonda-cho, Wakayama, Japan. We deployed eight sensor nodes in an area of \(50\times 40\) m\({}^{2}\), which is shown in Figure 11. The flight trajectory of the drone was calculated using the proposed algorithm.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model
### Data Preprocessing
#### 3.2.4 Segmented RUL Labeling  
Taking into account that the operation data of the aero-engine manifest as a long time series, during the initial stage of operation, the engine performance remains relatively stable, and the degradation trend of the performance is not overly evident. Hence, it can be postulated that the value of the engine's remaining useful life (RUL) will be maintained as a constant value within this specific period of time. When the operating time continuously increases and reaches the critical value \(R\), the wear and tear of the components can no longer be overlooked. At this juncture, the remaining service life of the engine gradually diminishes along with the wear and aging of the hardware. Its RUL objective function is depicted in Figure 9.  
To prevent the overestimation of the value of \(R\), it is typically selected to be within the range of 55% to 65% of the average value of the engine running cycle. In this particular study, 65% was chosen, that is, \(R=130\). When the number of engine running cycles amounted to fewer than 130, the remaining life of the engine gradually decreased as the engine operated. When the number of engine running cycles was greater than 130, the remaining life was set to a fixed value of \(R\).  
Figure 8: Sensors of the third type. (**a**) The variation in the values of four representative sensors with respect to the engine running time. (**b**) The specific values of sensor #7 as the running time of engines #1–#4 increases.
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 3 Results
Specifically, the southern PEJs were higher in the dusk and daytime than the northern PEJs, while on the downside and nightside, the northern PEJs were higher than the southern PEJs. PEJs at dusk appeared at lower latitudes when compared to those at dawn. At night, PEJs occurred \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**NH (SH)**} & \multicolumn{2}{c}{**FAC Density [\(\upmu\)A/m\({}^{2}\)]**} & \multicolumn{2}{c}{**FAC MLat [\({}^{\circ}\)]**} \\  & **R1** & **R2** & **R1** & **R2** \\ \hline Dusk & 2.72 (2.99) & 1.60 (2.17) & 64.2 (\(-\)64.9) & 60.8 (\(-\)67.2) \\ Dawn & 2.43 (3.12) & 1.79 (2.29) & 67.1 (\(-\)67.9) & 63.0 (\(-\)67.5) \\ \hline  & Upward & Downward & Upward & Downward \\ \hline Daytime & 1.36 (1.90) & 2.10 (2.44) & 69.5 (\(-\)68.2) & 70.0 (\(-\)70.0) \\ Nighttime & 2.17 (2.92) & 1.89 (1.28) & 63.5 (\(-\)63.2) & 61.8 (\(-\)63.9) \\ \hline \end{tabular}
\end{table} 
Table 1: The average FAC density and MLat during the 12 to 48 storm-time period for four local time sectors. In the dawn and dusk sectors, R1 and R2 FACs are provided, and in the daytime and nighttime sectors, upward and downward FACs are presented. Values outside (inside) parentheses correspond to the Northern (Southern) Hemisphere (NH, SH). at lower latitudes than PEJs in the daytime. The northern PEJs were found at lower latitudes compared to the southern PEJs, with the exception of the daytime.
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 5 Experiments
### Visualization with Grad-CAM  
To intuitively understand the adaptive RF sizes of neurons through the additive effects of feature-selective and spatial attention of FSRFNet, we use the Grad-CAM method [60] to visualize the class activation mapping (CAM) of SKNet-50 and our proposed FSRFNet-50 backbone networks. In the visualization examples shown in Figure 8, the areas with light colors indicate that the current area has great influence on the classification result. SKNet achieves good results in multi-scale information selection. However, Since FSRFNet has stronger ability to adaptively select the appropriate convolution kernel size, the FSRFNet has activation maps that tend to cover the whole object. Finally, compared with SKNet, our FSRFNet has a better class activation maps.
## 5 Results and Discussion
### Network Longevity  
Figure 4 illustrates the network longevity by running different sets of nodes. The simulation was conducted using three sets of nodes: 100, 200, and 300. In the 100-node scenario, the performances of the EECHS-ARO, EECHS-ISSADE, PDU-SLno, and EACH-COA approaches in terms of dead nodes were 1500, 1600, 1700, and 1800, respectively. For 200 nodes, the performances of the EECHS-ARO, EECHS-ISSADE, PDU-SLn0, and EACH-COA approaches in terms of dead nodes were 1600, 1500, 1750, and 1850, respectively. In the 300-node scenario, the performances of the EECHS-ARO, EECHS-ISSADE, PDU-SLn0, and EACH-COA approaches in terms of dead nodes were 1750, 1700, 1850, and 2000, respectively. The overall network lifetime was improved by adopting the EACH-COA approach. It also reduces the convergence time when selecting a CH in the network.  
Table 3 presents the network longevity based on the number of sensor nodes. The network performance in terms of lifetime was extended in different scenarios when the network sizes were 100, 200, and 300. The COA outperformed similar methods by reducing the convergence period during CH selection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network area & \(500\times 500\) m\({}^{2}\) \\ Sink location & (250, 250) \\ Number of sensor nodes & 100, 200, 300 \\ CH percentage & 5–10\% \\ Control packet size & 200 bits \\ Data packet size & 4000 bits \\ Free space energy \(\in_{fs}\) & 10 PJ/bit/m\({}^{2}\) \\ Multipath energy \(\in_{mp}\) & 0.0013 PJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation environment.  
Figure 4: Network longevity vs. number of nodes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Network Longevity**} \\ \cline{2-5}  & **EECHS-ARO** & **EECHS-ISSADE** & **PDU-SLn0** & **EACH-COA** \\ \hline
100 & 1500 & 100 & 1500 & 100 \\ \hline
200 & 1600 & 200 & 1600 & 200 \\ \hline
300 & 1750 & 300 & 1750 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network longevity vs. number of nodes.
## 1 Introduction  
Three-dimensional (3D) printing was first introduced in the 1980s, mainly for industrial use. However, since early 2000, it gained popularity in different medical fields, such as maxillofacial, dental, orthopedic and oncologic surgery [1, 2].  
In orthopedic surgeries 3D-printed models can be used for both educational purposes, pre-operative planning and surgery simulation. Furthermore, it can be used to create custom guides or implants [1, 3, 4]. A hands-on model can increase the visualization for bone pathology and deformity, especially for less skilled surgeons. Different studies focused on 3D printing technologies' utility in orthopedic surgeries, with a dramatic increase in publications between 2014 and 2020 [5].  
Patient-specific 3D-printed models can be obtained from both Computer Tomography (CT) scans and Magnetic Resonance Imaging (MRI) slices, with the latter being less used [6].  
There are two 3D printing techniques in orthopedic surgery. In the subtractive technique, the model is milled from a foam (e.g., polyurethane), with low costs and poor geometric accuracy, and with no possibility to be sterilized for intra-operative use. Conversely in additive techniques the models are produced through a layer-by-layer process, with a powder, liquid, or plastic material. With this technique, the 3D-printed model can reproduce more complex structures and cavities, with higher accuracy compared to subtractive techniques [7]. Three additive technologies are mainly used in orthopedic fields: stereolithography, selective laser sintering (SLS) and fused deposition modelling (FDM). Stereolithography is based on an optical light energy source scanning over a resin,which turns solid specific areas of the liquid's surface. SLS is based on a high-power laser which fuses plastic, metal, ceramic, or glass powder into a physical model designed on 3D images by a computer-aided design. This type of manufacturing can provide high geometry accuracy, but with high costs. FDM is based on extraction and solidification of materials in layers made by the deposition of a polymer. As this technique allows the use of different materials (and colors), it is probably the most versatile, and also provides a high geometric accuracy and the possibility to use sterilizable materials [7; 8].  
Three-dimensionally-printed models have been used in different orthopedic fields, including traumatology, shoulder surgeries, oncology, spine and, mainly, complex hip arthroplasty and acetabular fractures [9]. However, there are no studies or case reports on the application of 3D printing in the pre-operative planning of primary and revision Total Knee Arthroplasty (TKA). The aim of this manuscript was to summarize the application of 3D-printed models in the pre-operative planning for both primary and revision TKA. For this purpose, two cases from our institution will be described as an example.
## 3 Materials and Methods
### Apply PCA with Derived AHP-QFD Matrices
#### 3.3.1 Dataset Description  
* Dataset X1: In this study, Dataset X1, as shown in Table 6, was employed, extracted from the AHP-QFD matrix presented in Table 3. Dataset X1 encompasses customer requirements, denoted as X1 CRi, alongside their corresponding feature requirements, denoted as X1 FRi. These elements are pivotal in evaluating the most suitable locations for cargo logistics centers. * Dataset X2: In this study, Dataset X2, as shown in Table 7 was utilized, sourced from the AHP-QFD matrix presented in Table 4. Dataset X2 consists of customer requirements, identified as X2 CRi, and their corresponding feature requirements, denoted as X2 FRi. The focus of Dataset X2 is on the enhancement of the current product utilized at Songklanagarind Hospital, particularly in the design of a temperature-controlled medicine bag. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline CR1 & 2 & & & & 3 & & & & 0.239 \\ CR2 & 2 & 3 & & & & & & & 0.143 \\ CR3 & & & 3 & & & & & 3 & 0.022 \\ CR4 & & 1 & & & & & & 3 & 0.058 \\ CR5 & & & & & & & & 3 & 0.057 \\ CR6 & 2 & 2 & & 3 & & & & & 0.026 \\ CR7 & 2 & 3 & & & & & 1 & 2 & 0.099 \\ CR8 & & 3 & & & & 2 & 2 & 2 & 0.037 \\ CR9 & & & & & 3 & 2 & & & 0.077 \\ CR10 & 2 & 2 & & & & 3 & & & 0.242 \\ FRs & & & & & & & & & & \\ Weight & & & & & & & & & & \\ \hline \hline \end{tabular}  
* The list of customer requirements associated with this AHP-QFD include: (CR1) lightweight, (CR2) hold position, (CR3) wireless, (CR4) open ear for health, (CR5) open ear for safety, (CR6) waterproof, (CR7) comfort, (CR8) easy to carry, (CR9) continuous usage, (CR10) cool appearance.  
\end{table} 
Table 5: Original matrix from AHP-QFD of new earphone development [24]. * Dataset X3: In this study, Dataset X3, as shown in Table 8 was employed, derived from the AHP-QFD matrix presented in Table 5. Dataset X3 includes customer requirements, denoted as X3 CRi, and their associated feature requirements, labeled as X3 FRi. Notably, Dataset X3 introduces an approach to new product development and showcases its application through a case study centered on the development of new sports earphones. The AHP-QFD matrix defines the input dataset, \(X\in R_{m\times n}\), for the feature selection algorithm. It should be noted that the customer requirements are the \(m\) observations of \(X\), and the feature requirements are the \(n\) observations of \(X\). The list of customer requirements and feature requirements associated with these AHP-QFD matrices are provided in Tables 9 and 10. Table 9 provides a detailed list of customer requirements across the three datasets. Each customer requirement represents a specific need or expectation that must be addressed in the context of the respective dataset.
## 4 M2M-Based Division of MAC Layer Protocols
### Adaptive Multichannel Protocol for Large-Scale M2M  
This protocol divides the available bandwidth into control channels and communication channels [31]. The time slots are also divided into estimation, negotiation, and data transmission phases. The estimation phase consists of a number of time slots in which the nodes send busy messages on the control channel if they either have data to send or if they hear a busy tone from other participating nodes. Based on the number of sent and received busy tones, statistical estimation is done for finding the number of active nodes on the network. The negotiation phase then assigns channels to the intended nodes for data communication and thus assigns channels based on the probability of a free channel calculated in the estimation phase. The transmission phase thus utilizes the assigned channel without any collision. The protocol uses mathematical estimations that might take extra time and cause extra delays. If nodes are not aware of each other, the number of active nodes may vary from device to device, which can cause extra hidden node issues.
## 2 UAV Communication
### Security in UAV Communication
One of the major hazards, named the Jelly Fish Attack, has been investigated by [PERSON] et al. [60] by utilizing MANET in combination with UAV networks. They have devised a multicast protocol-based system that provides resistance to such attacks. On the basis of most stable and secure paths, the routing algorithm determines trustworthy nodes [61]. Cryptography is another foundation to ensure protection of information in vehicles. In their study [60], [PERSON] et al. have offered a data collection mechanism that is based on optical code word. Some others have suggested layered unmanned aerial vehicle network topology with drone nodes, sensor and data collection nodes at different levels. In such scenarios, network-related concerns are catered through two approaches, firstly by utilizing the above mentioned idea of optical code words to classify network nodes and secondly, transportation of designated data from UAV node to root node for mandatory required supplementary processing and subsequent decision formulation. Homomorphic cryptography controller-based security appraisal has been investigated by [PERSON] et al. [62] by devising a realistic linearly homomorphic Authenticated Encryption (LinHAE) for implementation of the controller. The Advanced Encryption Standard (AES) encryption key [63] extracted from the operator's EEG (electroencephalogram) signal was utilized by [64]. In the study [65], [PERSON] et al. utilized a quantum key allocation procedure to generate secret keys, in order to encrypt video. The keys are distributed and their identity  
Figure 7: Security challenges in UAV communication. is known by two persons on opposite channel end. Without being detected, it is difficult for any eavesdropper to capture messages, as each photon is altered instantaneously once read and corresponds to a qubit. In the study [66], [PERSON] et al. describe that, by utilizing an encryption key negotiation method, protection and validation of data carried out on UAVs in partitions and transferred in between ground station can be ensured.
## 3 A flexible Approach to a Reachable Network Topology with a Minimum Connection Degree
### Network Model Design
By contrast, for less node degree, the needed transmission power and the number of connected nodes are less due to shorter transmission range. The network topology is changed by adjusting transmission range with required node degree. In different densities, the needed transmission range is diverse in the same node degree. Thus, the node density influences the transmission range in the same node distribution. For example, the distance between nodes in low node density is longer than that between nodes in high node density. As a result, the node degree and node density are significant factors for the determination of the transmission range. For this reason, an important issue is how to determine a proper node degree by adjusting the transmission range for network optimization with different node densities. The network lifetime and operation efficiency are affected by network topology. Therefore, in this study, node degree and node density are considered in designing an adjustment model of the transmission range to establish an optimal, energy-efficient network with full reachability.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 3 Proposed Evaluation Methodology
### Selection Strategy  
The threat intelligence scenario is very extensive and includes a wide range of systems, platforms, tools, standards and formats. In order to perform an accurate evaluation, it is necessary to define a strategy of inclusion and exclusion to select some of them. Thereby, in this work, we focus on the most popular and open source standards and platforms.  
A literature review was conducted aiming to obtain a complete overview of the CTI panorama. Some web research engines were used in this task, including Google Scholar, IEEE Digital Library, Springer and ScienceDirect. The following terms were merged to find suitable results: (Threat Intelligence OR Cyber Threat Intelligence) AND (platform OR tools OR standards). The searching process resulted in a large number of works and some of them were filtered based on their relevance to the topic and number of citations.  
For the filtering methods applied to the searching process, a considerable amount of initiatives of TI standards and platforms were collected. Thus, to reduce the number of results found, based on brief readings of the official web sites and documentation, standards or platforms that were not able to address two or more stages from the threat intelligence process flow, presented in Figure 1, were excluded and not considered for the selection process.  
Then, in order to select for evaluation the most relevant standards and platforms in the TI field, the results found with the searching process were described in terms of popularity and license model:* **Popularity criteria**: In the context of this work, the popularity was estimated according to the number of times the standard or platform was mentioned in reliable works and sources, combined with collected statistics about the percentage of utilization among organizations.
* **License model criteria**: The type of license was analyzed aiming to limit the work to standards or platforms available to all interested communities by including only free or open source solutions and initiatives.  
Finally, based on the criteria aforementioned, a minor data set of standards and platforms, composed with the most popular ones and those with permissive licenses or free versions, were chosen for evaluation.
## 3 Model and Analysis
### Construction of a Four-Way Evolutionary Game Model
#### 3.2.1 Construction of the Payoff Matrix
The contractor must pay liquidated damages under the contract if it fails to perform._  
Figure 2 depicts the framework for constructing a model payoff matrix based on parameters and assumptions. Each agent of the game has two strategies: \"abatement\"and \"no-abatement\". The four agents form 16 strategy sets. The payoff of each agent in each strategy set is \(L_{ni}(n\in[1,16];i\in\{X,Y,Z,U\})\). Each strategy set, denoted by (x, y, z, u), comprises four strategies, where 0 signifies \"no-abatement\" and 1 signifies \"abatement\". The mapping of strategy sets to payoffs is presented in Table 2. The payoff matrix is shown in Tables 3 and 4. Consider the strategy set (1, 1, 1, 1) in Table 3, where all four players implement abatement measures. The corresponding payoffs for the four parties are \(L_{1X},L_{1Y},L_{1Z}\), and \(L_{1U}\), respectively. All agents incur the cost \(C_{i}\) and receive the benefit \(E_{i}\). All agents receive incentives \(J_{i}\), assistance \(\Delta C_{i}\), and process emission reduction information to gain benefits \(D\gamma i\lambda i\). The total external benefits are \(\Pi i=J_{i}+\Delta C_{i}+D\gamma i\lambda i\). Since all four agents choose to reduce emissions, each agent can obtain value-added benefits from the other three agents. For instance, the value-added benefits of collaborative emission reduction obtained by X are \((\beta_{XY}+\beta_{XZ}+\beta_{XU})E_{X}\). The payoff of X is \(L_{1X}=E_{X}-C_{X}+\Pi_{X}+(\beta_{XY}+\beta_{XZ}+\beta_{XU})E_{X}\). The detailed composition of the payoffs is presented in Table 3.