esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 5 Fundamental Issues
### Safety and Risks
#### 5.3.5 Advancements in Safety Technologies  
Recent advancements in safety protocols and technologies for high-pressure hydrogen tanks have significantly improved safety measures. One notable development is the enhancement of leak detection systems, which now utilize advanced sensors to detect hydrogen concentrations at much lower levels than previous technologies. [PERSON] et al. [39] analyze hydrogen sensing technologies and their performances. Commercial hydrogen sensors are primarily electrochemical or catalytic, with significant differences in performance across manufacturers, despite similar designs. Electrochemical sensors are widely used due to their high sensitivity and fast response times, typically detecting hydrogen at concentrations as low as 10 ppm. Catalytic sensors are better suited for detecting combustible gases at lower explosion limits, but may lack specificity for hydrogen detection alone. Resistance-based sensors offer an alternative using solid-state or semiconductive materials, providing robust hydrogen detection with low cross-sensitivity to other gases. The choice of the appropriate sensor depends on the specific application requirements and environmental conditions [39; 197].
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 3 Results
### Simulation  
We modeled the energy consumption for a 128 kB firmware image update under both the secure and insecure versions of the proposed system. The performance was modeled using MATLAB because existing LoRa simulator tools were insufficient to quantify total power consumption on the cattle monitoring device. The LoRa transmit and receive characteristics used in the modeling were validated in [19].  
The results are shown in Figure 8. The insecure version is similar to the secure version but omits the encryption and MAC operations to authenticate each packet. The orange line indicates the full capacity of a 200 mAh battery which possesses 2.8 kJ. The parameters used in the simulations are given as follows: (1) LoRa Bandwidth = 62.5 kHz, (2) 12-symbol preamble, (3) CRC enabled, (4)implicit header disabled, (5) error coding rate = 5/4, (6) \(\eta\) = 90%, (7) \(V_{s}\) = 2.8 V, (8) \(I_{transmit}\) = 100 mA, and (9) \(I_{receive}\) = 10 mA. Note that our model here excludes the ADR operations, which were covered previously in [19] and the initial exchange because its contribution is negligible.  
These results show that it is not even possible to update a firmware image of this size with LoRa settings 11 and 12 on a single charge. The power consumption decreases dramatically with each setting however, motivating the need for ADR. The power consumption for the insecure implementation is similar to the secure implementation, which is justified by the relative energy breakdown shown in Figure 9. The energy consumption of the AES operation is very small because it is executed efficiently in hardware. Despite the LoRa receive time being much longer for the higher settings, it is less significant because the transmit current is 10x the receive current. Therefore, most of the energy expenditure comes from the packet acknowledgment operation under the high LoRa settings. As the settings decrease toward setting 1, the memory write operation begins to dominate because the RF communications become very small in duration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Time** & **Average Current** \\ \hline AES Encode & 200 μs & 6.5 mA \\ AES Decode & 196 μs & 6.5 mA \\ Flash Read & 170 μs & 6.5 mA \\ Flash Write & 3.735 ms & 25.5 mA \\ LoRa Transmit & varies & 100 mA \\ LoRa Receive & varies & 10 mA \\ \hline \hline \end{tabular}
\end{table}
Table 3: The settings used for modeling the firmware update parameters.  
Figure 7: Timing characterization for Advanced Encryption Standard (AES) and flash read and writes.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 4 5G Communication Status  
When discussing 5G, which is the marketing name for IMT-2020, demystifying the misconception that technology is an upgrade to its predecessor 4G/IMT-Advanced, which depicts 5G as an enhanced mobile broadband communication technology [18; 19]. Additionally, 5G is an ecosystem that encompasses several applications and services that are expected to empower authorities as well as companies to be able to transform their cities and industries, respectively. In order to accommodate new and future applications, 5G's capacity has to be improved in many dimensions. In Reference [20], the authors go as far as suggesting that 5G needs to be supplemented with other communication technologies such as Wi-Fi. Figure 1 shows the enhancements expected of 5G in comparison to its predecessor, 4G.  
In addition to technical enhancements, there are about 11 general requirements to be addressed before the rollout of the technology [2; 18]; 5G is expected to: [18; 21]  
Figure 1: IMT-2020 (5G) vs. IMT-Advanced (4G) [2].  
Support service diversity in terms of QoS, traffic patterns, mobility and/or data types;
* Some of these services are incompatible and require 5G adaptable via programmability;
* Needs to serve new radio technologies and work with the old CN;
* Allow distributed network architecture unlike its centralized predecessors;
* Have separation of control and user plane functions;
* Process and store data to reduce congestion and response time;
* Possess intelligence such that automation and interoperability can be implemented;
* Serve a user demand with optimal capacity;
* Guarantee reliability with sufficient performance;
* Provide security and privacy to devices as well as data;
* Be energy efficient.  
With such a meticulous list of requirements together with enhancements in all directions, 5G can maintain several applications and services. However, evaluating the 5G services with uniform metrics does not reflect whether the quality of the service is acceptable or not. Hence, metrics suitable for each service must be devised. Another issue that needs to be addressed is the effort of countries to promote and facilitate the move towards 5G. These and related issues are discussed in the following subsections.
## 4 Results
### Machine Learning-Based IDS on the Cloud without FS Method
#### 4.1.6 Comparison of Machine Learning-Based IDS on the Cloud without Using FS Techniques  
We deployed and compared the LSTM, Conv1d, RF, MLP, and XGBoost algorithms for IDS implementation on the cloud. From the results, it is evident that XGBoost presented the best results with a detection accuracy of 99.50%. After XGBoost, the second-best results are presented by the RF algorithm with a detection accuracy of 99.4% on attacks. The third-best results are presented by the LSTM algorithm with a detection accuracy of 94.2% followed by MLP with 93.9%. Overall XGBoost and RF are suitable for the implementation of an IDS in terms of accuracy, and Conv1d is not feasible for the implementation of an IDS on the cloud.
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 2 Methods
### Multiscale Convolution Module  
The convolution kernels in the traditional residual cell are the same sizes, which makes it impossible for the convolution layer to \"observe\" the load data from multiple scales, and it is difficult to obtain richer input features. To solve this problem, this paper adopts multi-scale convolution. The multi-scale structure of the backbone network is shown in Figure 2.  
The multi-scale convolution module has four branches in total. The first branch uses \(1\times 1\) convolution. The \(1\times 1\) convolution can increase the dimension and reduce the number of feature channels and uses a small amount of computation to increase the linear transformation and nonlinear transformation of features, so as to improve the expression ability of the network and improve the accuracy of load prediction. In the second branch, the original input and the input of the first branch are added and then convolved by \(3\times 1\) convolution, which is equivalent to two feature changes. In the third branch, the original input and the input of the second branch are added and then convolved by \(5\times 1\), and then the outputs of the three branches are spliced on the channel dimension. After that, the concatenated result is dimensionally reduced through the \(1\times 1\) convolution module, so that it has the same number of channels as the original input. Finally, we add the output and the original input to obtain the final output. The specific calculation formula is as follows:  
\[f_{1}=f^{1\times 1}(f_{in}), \tag{1}\]  
\[f_{2}=f^{3\times 1}(f_{in}+f_{1}), \tag{2}\]  
Figure 1: Structure diagram of the proposed model.  
Figure 2: Structure diagram of multi-scale convolution.  
\[f_{3}=f^{5\times 1}(f_{in}+f_{2}), \tag{3}\]  
\[f_{out}=f_{in}+f^{1\times 1}(C(f_{1}+f_{2}+f_{3})), \tag{4}\]  
where \(f_{in}\) is the initial input data, \(f_{out}\) is the output feature, \(f^{1\times 1}(\cdot)\) a one-dimensional convolution with \(1\times 1\), \(f^{3\times 1}(\cdot)\) a one-dimensional convolution with \(3\times 1\), \(f^{5\times 1}(\cdot)\) a one-dimensional convolution with of \(5\times 1\), \(C(\cdot)\) refers to the operation of stacking data on the channel dimension. The multi-scale convolution obtains multi-scale features of the load series and multi-scale features of other relevant information (including temperature, humidity, rainfall, etc.).
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 3 AE Impact on Network Traffic Classifiers
### Generating AEs for Non-Gradient-Based Models  
Due to their simplicity, interpretability, and efficiency, non-gradient-based models such as the target models employed in this study are often used for classifying network traffic. However, these models cannot be directly used to craft AEs using gradient-based attacks, as they do not have a continuous and differentiable loss function. To overcome this limitation, we developed a neural network model called AdverNet trained on the baseline dataset using the JSMA technique to generate AEs that can be transferred to non-gradient-based models to evaluate their robustness against adversarial attacks. To use AdverNet to attack non-gradient-based models, we first generate AEs for AdverNet and then exploit the transferability property of AEs to attack the target models. This transferability property allows the AEs generated for one model to be effective in fooling other models even if they are trained on different datasets or using different architectures [51]. Because the target models and AdverNet are known to be similar and trained with portions of the same dataset, the approach is intended to be illustrative of black-box attacks. Nevertheless, because we inevitably have some knowledge of the underlying models, this may well be called a gray-box attack. AdverNet was trained using the same procedures and architecture as in [PERSON] et al. [33], except that 50 training epochs were used to train AdverNet, whereas 300 epochs were used in their study, with an early stoppage time of 100 epochs.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception
#### 3.2.3 Mapping
For example, simultaneous localization and composition (SLAM) allows the robot to get information about the image while localizing it, and this technique is currently used more often for indoor multi-robots, especially for catastrophic search and rescue where high responsiveness and mobility are required [76]. In the literature [56] it was mentioned that this technique was used on a ground robot for heterogeneous multi-robot in a greenhouse and that the robot was motion controlled using an Augmented Monte Carlo Localization (AMCL) methods. The experimental results showed that the UGV using SLAM takes less time to construct the map than the remotely operated UAV. And based on the map, the ground robot can complete collision-free autonomous movement between the initial and target points. It is obvious that SLAM technology has great potential for multi-robot mapping [77], and the application of multi-SLAM on agricultural multi-robots will be one of the future research directions.
# NiO-Based Electronic Flexible Devices
## 3 NiO-Based Flexible Devices
### Temperature Devices  
Flexible, resistive temperature detectors (RTDs) allow temperature measurements in wearable devices. In addition, the coupling to RFID converts them into wireless sensors which can be used for checking the body temperature, short-life span food and medications. The main requirements of such devices are a high and linear temperature coefficient of resistance (TCR), high resistance to bending and deformation and simplicity of design and measurements. A Ni/NiO based resistive temperature detector (RTDs) was proposed with such characteristics, based on the layered deposition on a PET fiber [107]. The device was fabricated by depositing a Ni/NiO bilayer on a bare cylindrical PET fiber pre-treated by sonication in acetone for 10 min, followed by sonication in methanol for 10 more min, washing and drying. NiO and Ni were deposited on the rotating PET fiber substrate by Radio Frequency magnetron sputtering, ensuring simultaneous argon and oxygen flow for NiO deposition and argon flow for the Ni thin film second coat. Resistance variation measurements with the temperature indicated that the device had a TCR of 3.8 \(\times\) 10\({}^{-3}\)\({}^{\circ}\)C\({}^{-1}\) and a linearity of 0.9852. The bending process simulated movements through daily activities. After 16,000 bends the TCR of the device was 1.0 \(\times\) 10\({}^{-4}\)\({}^{\circ}\)C\({}^{-1}\) with a linearity of 0.9065. Additional tests were performed washing with tap water and laundry detergent and exposing to acidic and basic environment. The electrical properties including resistance, TCR and linearity of slope remained unaffected when subjected to the different conditions. Moreover, the device maintained about 75% of the original TCR after being exposed to an acidic medium.
## 3 Kinematics Analysis
### Forward Kinematics  
#### 3.3.1 Modeling and Solution of Forward Kinematics  
The problem with determining the forward solution for the platform is that the displacements of the six leg-driven oblique blocks must be known to solve for the pose of the platform. The forward kinematics problem of the parallel mechanism has not yet found an analytical solution and it is difficult to solve using the closed-loop vector method. The solution process involves a problem composed of nonlinear equations, which are usually solved via numerical methods. After the displacements of the six leg-driven oblique blocks are given, the equations for the relationship between the displacements of the oblique block and the pose of the mobile platform are established using the D-H method.  
For a given slider displacement \(S_{G}=[S_{1\text{G}},S_{2\text{G}},\ \ldots,S_{6\text{G}}]^{T}\), the function \(F(p)\) is defined as follows:  
\[F(p)=IKM(p)-S_{G} \tag{11}\]  
\(IKM(p)\) is the inverse kinematic solution \([S_{1},S_{2},\ \ldots,S_{6}]^{T}\), which corresponds to the platform pose \(p=[X,Y,Z,\alpha,\beta,\gamma]^{T}\). The vector \([\alpha\ \beta\ \gamma]\) indicates the orientation of the mobile platform. According to RPY rules, the mobile platform rotates \(\alpha\) angle around \(X_{B}\) axis, \(\beta\) angle around \(Y_{B}\) axis, and \(\gamma\) angle around \(Z_{B}\) axis. The Newton-Raphson numerical iterative method can be used to solve Equation (11), so the following numerical iteration format is obtained:  
\[p_{n+1}=p_{n}-(\partial F(p_{n})/\partial p)^{-1}[IKM(p_{n})-S_{G}] \tag{12}\]  
where:  
\[\frac{\partial F}{\partial p}=\left[\begin{array}{ccccc}\frac{\partial S_{ 1}}{\partial X}&\frac{\partial S_{1}}{\partial Y}&\frac{\partial S_{1}}{ \partial Z}&\frac{\partial S_{1}}{\partial x}&\frac{\partial S_{1}}{\partial y }&\frac{\partial S_{1}}{\partial Y}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \frac{\partial S_{6}}{\partial X}&\frac{\partial S_{6}}{\partial Y}&\frac{ \partial S_{6}}{\partial Z}&\frac{\partial S_{6}}{\partial x}&\frac{\partial S _{6}}{\partial x}&\frac{\partial S_{6}}{\partial y}&\frac{\partial S_{6}}{ \partial Y}\end{array}\right] \tag{13}\]  
By selecting the initial value appropriately, the algorithm can converge quickly to the theoretical pose. After the initial value \(p_{0}\) is given, the iterative format given in Equation (12) is used to perform the numerical iterative calculations. When the mobile platform pose is satisfied with \(|p_{n+1}-p_{n}|\leq 1\times 10^{-9}\), the iteration converges, and \(p_{n+1}\) is the final solution for the forward kinematics. The forward kinematics calculation flow when using the D-H parameter method is shown in Figure 9.
## 5 Signal Acquisition Approaches
### Ultrasound Sensors  
Employing ultrasound waves, these sensors measure the brain's electrical activity and are often used in medical settings to detect seizure activity. Ultrasound sensors work by using sound waves to detect changes in brain activity, a method known as echoencephalography. They are particularly useful in medical diagnostics for detecting abnormalities in brain activity, such as seizures, and in monitoring cerebral blood flow. Recent advancements in ultrasound technology have improved the efficacy and applications of these sensors in neurological research and diagnostics.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Evaluation Metrics  
We evaluate the performance of each model with the Dice score, which measures the overlap between the segmentation predictions of voxels and their corresponding ground truths. It can be defined as:  
\[Dice(A,B)=\frac{2|A\cap B|}{|A|+|B|}=\frac{2 AB}{A^{2}+B^{2}}. \tag{7}\]  
where \(A\) denotes the segmentation of model prediction and \(B\) denotes the ground truth from manual annotation.  
In addition, we use the 95% Hausdorff Distance (\(HD_{95}\)) as a boundary-based metric to measure the similarity between two sets of points. It is defined as:  
\[HD_{95}(A,B)=max\{d_{95}(A,B),d_{95}(B,A)\}. \tag{8}\]  
where \(A\) denotes the set of points from the ground truth, and \(B\) denotes the set of points from the predicted labels. \(d_{95}(A,B)\) denotes the 95 th percentile of the distances from each point in \(A\) to its nearest neighbor in \(B\), and \(d_{95}(B,A)\) denotes the 95 th percentile of the distances from each point in \(B\) to its nearest neighbor in \(A\).
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 3 Transmitter Performance  
Figure 7 below shows the optical eye diagram obtained through Keysight's digital communication analyzer (DCA). The CDAUI-16 electrical signal is generated by a Viavi optical network tester (ONT) and passed through a pluggable connector. A clock signal derived from the ONT drives the DCA to obtain these optical eye diagrams. As seen, a clear 4-level eye diagram with wide eye opening indicates a good signal to noise ratio (SNR). One can also notice that there is a misalignment in the center of the eye, due to the nonlinear response of the modulator.  
Figure 8 shows the optical spectrum from the CFP8-LR8 modules. The optical wavelengths are around 1310 nm so that the influence of chromatic dispersion is minimized. The wavelength assignment of these eight optical channels follows the CWDM designation. This facilitates the interoperation between the CFP8 modules. Also, the measured side mode suppression ratio (SMSR) is well above 40 dB, indicating the excellent jitter performance for the optical modules.  
Figure 6: (**a**) Experimental demonstration of the interoperation of CFP8-LR8 modules; (**b**) two CFP8-LR8 modules in Juniper’s PIC and PTX5000 chassis.  
Figure 7: Optical eye diagrams of eight wavelength division multiplexing (WDM) channels using PAM4 modulation format. Top: channels 1 to 4; bottom: channels 5 to 8.
## 3 Network Loss Function
### Image Decomposition Module Loss Function  
According to Retinex theory, an object's reflectance remains invariant under different lighting conditions. Therefore, the reflectance components extracted by the decomposition network from both high- and low-illumination images should be consistent. To enforce this property, a reflection consistency loss is introduced to measure the discrepancy between the reflectance maps of the two images. The loss is defined as follows:  
\[l_{rs}=\|R_{l}-R_{h}\|_{2}^{2} \tag{6}\]  
where \(R_{l}\),\(R_{h}\) denotes the decomposition to obtain low- and high-illumination reflectance maps, respectively, and \(\|\cdot\|_{2}\) denotes the L2 paradigm. This loss function is smaller when a smaller gap between the reflection maps is obtained by decomposition. The light image obtained by decomposition should be smooth and continuous, so the light smoothing loss is also needed:  
\[l_{is}=\left\|\frac{\
abla L_{l}}{\max(|\
abla I_{l}|,\varepsilon)}\right\|_{1 }+\left\|\frac{\
abla L_{h}}{\max(|\
abla I_{h}|,\varepsilon)}\right\|_{1} \tag{7}\]  
where \(\bigtriangledown\) denotes the first-order derivative operator in both horizontal and vertical directions; \(L_{l}\) represents the luminance maps obtained from the decomposition of low- and high-illumination images; \(L_{h}\) denotes the original low- and high-illumination input images; and \(\varepsilon\) is a positive constant introduced to prevent division by zero, which is set to 0.01. The notation \(\left|\cdot\right|_{1}\) refers to the L1 norm. When the gradient magnitude is large--such as at image edges--the constraint on illumination smoothness is relaxed. In contrast, for regions with small gradients (i.e., structural interiors), stronger smoothing is enforced to reduce the loss. This ensures that the illumination map maintains local smoothness while preserving the overall structural consistency of the scene. The corresponding loss function is formulated as follows:  
\[l_{mc}=\|M\cdot\exp(-c\cdot M)\|_{1} \tag{8}\]  
where \(M=|\bigtriangledown L_{l}|+|\bigtriangledown L_{h}|\) denotes the constraint constant, and \(c\) denotes the constraint constant; here \(c\) is taken as 10. When \(M\) is close to 0 or infinity, then the light map is in the smooth stage, when \(M\) is in the more intermediate range, then the light map is in the edge stage and the low-light map is similar to the high-light map, ensuring that the light map is in the same overall structure. Finally, since the decomposition obtains the reflection and light maps according to Retinex theory, the result after multiplication should be consistent with the original image, so it is also necessary to reconstruct the loss as follows:  
\[l_{rec}=\|I_{l}-R_{l}\bullet L_{l}\|_{1}+\|I_{h}-R_{h}\bullet L_{h}\|_{1} \tag{9}\]  
Finally, these losses form the loss function of the decomposition network, which can be combined according to certain weights as follows:  
\[l_{LD}=l_{rec}+0.02l_{rs}+0.15l_{is}+0.2l_{mc}l \tag{10}\]