esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Malicious Certificate Detection Algorithm  
There are two main types of detection algorithms: one is based on blacklisting malicious digital certificates and the other is based on machine learning [13; 17] for malicious digital certificate detection. [PERSON] et al. [18] proposed a malicious SSL certificate detection module (MSSLD) in response to APT attacks. It detects APT command and control (C&C) communication of malicious SSL certificate blacklists by matching the blacklist with a certificate or IP. This method is easier and less time-consuming, but the blacklist update is time-consuming and tedious. To solve this problem, it has been proposed that machine learning [19; 20; 21] be applied for malicious certificate detection. [PERSON] et al. [22] proposed a system for automatically detecting phishing website systems using key attributes of SSL certificates. It uses different machine learning algorithms to do research utilizing extracted SSL certificate features and found that the decision-tree algorithm achieved better classification accuracy than others. Deep neural networks are also widely used in the field of network security, such as malicious certificate detection, backdoor attacks [23; 24; 25; 26], and Android malware detection. [PERSON] et al. [27] proposed a method that uses deep neural networks to identify web-based malicious certificates. It successfully identifies legitimate certificates and the malicious patterns used by attackers through the contents of the TLS certificate. The features of SubjectPrincipal and IssuerPrincipal were encoded with one-hot and trained by LSTM, and the results were fused with the other features extracted after training in the Dense/Relu layer. The system had an accuracy of 94.87% for the identification of malware certificates and 88.64% for the identification of phishing certificates.  
Although all these models achieve good classification results, they all require feature engineering to extract the features of the certificate. Almost all features need to be identified by industry experts and then the features are manually coded. Eventually, the model can be trained to identify malicious certificates. The effectiveness of the model depends heavily on how well the features describe the data, which leads to the limitations of traditional machine learning methods. This problem is addressed by GCNs [28], which attempt to learn features in the data by themselves, significantly reducing the cost of feature discovery. GCNs achieve the best performance available for the node classification task, and they are also able to effectively take advantage of structural information of neighbors while retaining low-frequency signals. [PERSON] et al. [29] applied graph attention mechanism networks to website fingerprinting, using a GCN to learn intra-process and inter-process features. His work further demonstrates the advantages of GCNs over traditional machine learning methods.
## 4 Examples and Results  
In this example, there were two units, among which there were twenty pieces of equipment, respectively. Each task required four pieces of equipment and consumed their 3 h working life. The upper limit \(g\) of the quantity of equipment for each rotation was three. The four fitness functions were optimized under different circumstances by using enumeration method (1), improved genetic algorithm (2) and I-NSGA-III-VLC (3). A total of five experiments were carried out. The comparison of the time and the average value of the optimal solution of each fitness function is shown in Table 3, The population number of 2 and 3 was set to 600, the crossover probability was set to 0.4, the mutation probability was set to 0.8, the diversity maintenance parameter was set to 6 and the number of iterations was set to 100 generations. \(MWL\) and \(MCL\) were set to 180 and 36, respectively. When the enumeration method had the problem of combination explosion, the best combination was selected from the first 100,000 combinations.  
The two units \(u_{1}\) and \(u_{2}\) and the equipment data of \(u_{1}\) and \(u_{2}\) are shown in Tables 1 and 2. The average monthly task intensities were \(U_{1}=\)6 and \(U_{2}=\)4. The iteration results of the 3-month equipment-echelon-usage problem using I-NSGA-III-VLC are shown in Figure 8.  
By the weight formula \(\beta=\alpha_{1}F_{1}+\alpha_{2}F_{2}+\alpha_{3}F_{3}+\alpha_{4}F_{4}\), where \(\alpha_{1}=0.3\), \(\alpha_{2}=0.1\), \(\alpha_{3}=0.58\), \(\alpha_{4}=0.02\), the acquired maximum value of \(\beta\) was 94.7  
The remaining working life and remaining calendar life of each equipment after the task was completed are shown in Figures 9 and 10.  
Equipment \(U\) and \(F\) in \(u_{1}\) were rotated in \(u_{2}\), \(u_{1}\) and formed a good echelon status.  
By rotation, the equipment \(U\) reduced the life waste of \(u_{2}\), and \(u_{2}\) had a good echelon status.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task Number** & **Equipment Number** & **Task Number** & **Equipment Number** \\ \hline
1 & ‘U’, ‘SS’, ‘JI’, ‘KK’ & 7 & ‘DD’, ‘GG’, ‘CC’, ‘U’ \\
2 & ‘DD’, ‘FF’, ‘IT’, ‘LL’ & 8 & ‘QQ’, ‘PP’, ‘CC’, ‘U’ \\
3 & ‘QQ’, ‘BB’, ‘AA’, ‘RR’ & 9 & ‘U’,’CO’, ‘HH’, ‘BB’ \\
4 & ‘LL’, ‘GG’, ‘KK’, ‘HH’ & 10 & ‘U’, ‘VS’, ‘J’, ‘KK’ \\
5 & ‘LL’, ‘GG’, ‘KK’, ‘HH’ & 11 & ‘GG’, ‘BB’, ‘AA’, ‘QQ’ \\
6 & ‘LL’, ‘MM’, ‘GG’, ‘U’ & 12 & ‘MM’, ‘RR’, ‘J’, ‘GG’ \\ \hline \hline \end{tabular}
\end{table}
Table 6: The equipment number performing the task of \(u_{2}\).  
Figure 8: Iterative results of I-NSGA-III-VLC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotation Stage** & **Equipment Number** \\ \hline \(1\) & ‘F’, ‘U’ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equipment number of rotation in 3 months.
## 3 Results and Discussion
### Simulations for Hierarchical Tree Structured Network
#### 3.2.3 Network Lifetime  
In this section, we consider a network that consists of 38 nodes to analyze the network lifetime. The BER of a link is approximately equal to 0.05. The heterogeneous measurement noise variances are generated by setting \(b_{0}=0.3\) and \(a_{0}=0.1\). When AVG is used as fusion function, we allow maximum 3 retransmission attempts for each hop per data gathering cycle and when EBDKF is employed, 2 maximum retransmission attempts are set as justified in the previous section on retransmission allowed tree structure. The initial energy levels for nodes are assigned randomly between 10 mJ and 50 mJ using a uniform distribution. The total energy consumption of a link is determined using the Equation (10). All the parameters in Equation (10) are similar to the values we specified for the chain-based network simulations. Here, we monitor the number of nodes active in the network for different fusion techniques in order to achieve similar accuracy of the network. The information retransmission is therefore employed to improve the accuracy in EBDKF and AVG methods to achieve accuracy just higher than the proposed method. Figure 18 depicts the number of active nodes in the network when we use the proposed statistical, Diffusion Kalman Filtering and AVG methods in tree-based network at BER of 0.05. We run extensive simulations and record number of total active nodes at each time step for 100 data gathering cycles and replicate for 50 times.  
As it can be clearly identified in Figure 18, with the proposed statistical method, the network can have 34 data gathering cycles using all nodes. This value goes down to 9 and 1 in AVG and EBDKF fusion techniques, respectively. The energy of the information and acknowledgment retransmissions becomes the dominant factor when deciding the network life time. The proposed method does not need to use any retransmissions to achieve better accuracy such that it can save energy on additional data transmission and reception, and acknowledgment transmission and reception. Therefore, the network lifetime is much longer than AVG and EBDKF methods. Further, it can be observed that the rate of losing active nodes in EBDKF method is higher than both AVG and proposed methods. It can be observed that after 42 data gathering cycles when network using EBDKF fusion loses all of the nodes, network with proposed method and AVG will lose only 2.6% and 15.8% of nodes respectively. Therefore, the network lifetime in decreasing order is proposed method, AVG and EBDKF respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fusion Function** & **CPU Cycles** & **Time ((\(T_{A}\))(\(\mu\)s))** \\ \hline Average & 48 & 3 \\ Proposed method & 156 & 9.75 \\ EBDKF & 252 & 15.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CPU overhead of different information fusion functions for hierarchical network.
# Novel Resistive Sensor Design Utilizing the Geometric Freedom of Additive Manufacturing
## 3 Results
### Resistive Behavior  
Mean values and standard deviations of the extremes in resistance for each sensor measurement cycle are presented in Table 4. In this case, the values shown are the lowest values reached, as the resistive path shortening under load leads to a reduction in resistance. A decrease in gap size led to stronger resistive response, i.e., lower extreme values. This was to be expected, as with a reduced gap size more windings will contact under identical compression. The effect of velocity in the 0.3 mm gap size group was not immediately apparent, as different geometric variants appear to exhibit different trends. Closer inspection revealed that the trend observed aligned with the order in which measurements were performed, which is indicated by the arrows in Table 4. Measurements were not performed in identical order on each sensor, specifically to allow for the detection of any unwanted behaviour related to the test order rather than the variable being tested. In this case, sequential measurements showed a reduction in resistance. This reduction in resistance is possibly the result of insufficient wait time between the measurements, rather than an influence of velocity. The effect of this is minor relative to the total resistance change, never exceeding 1% of the total resistance change, and thus was not considered to be problematic. A similar reduction in resistance in the sequential order was not present for the samples with gap size of 0.5 mm. Figure 4: Force and resistance plots for the measurement with the highest standard deviation: variant 4 (Figure 1e), gap of 0.3 mm at 10 mm/min velocity. A decrease in peak force is visible with repeated actuation. Figure 5: Force–distance relationship for variants 1 to 4 (Figure 1b–e), all with gaps of 0.3 mm and velocities of 10 mm/min. All variants show hysteresis, and the stiffer geometries show a decrease in peak force with repeated actuations. Despite the minor variances observed in peak resistance, the actuation velocity did not influence the sensor behavior, as can be seen in Figure 6. To ensure this was not related to the limited range of 5 to 20 mm/min, an additional measurement at 100 mm/min was performed which elicited no further change in the sensor response. Table 5 demonstrates the high sensitivity of these sensors under the applied load, showing the percentage of the initial resistance R0 that remains at the highest compression levels. Values range from 17.43% to 32.08%, inversely, this means R0 is in the range of 3.1 to 5.7 times higher than the sensors' extreme output value. The relationship between the distance the part is compressed and its resistive response, for parts with a gap size of 0.5 mm, is displayed in Figure 7. The initial part of the deformation caused no resistive response, which can be attributed to the gap that must first be bridged before the resistive path shortening mechanism has an effect. Once the gap was
# Improving Safety in Food Allergen-Specific Oral Immunotherapy
## 3 Food Allergen Immunotherapy
### Adjuants in Oral Immunotherapy
#### 3.4.1 Monoclonal Antibodies  
(a) Anti-immunoglobulin E monoclonal antibodies  
The rationale behind talizumab and omalizumab (OMB) usage as an adjuvant therapy of OIT is the role of IgE in allergic reactions. These humanized anti-IgE monoclonal antibodies (mabs) prevent IgE binding to receptors on basophils and mast cells. They also downregulate the expression of IgE receptors on mast cells, basophils, and dendritic cells [53]. Administered as a monotherapy, anti-IgE mabs showed a significant effect in increasing the threshold sensitivity to food allergens [54; 55; 56]. Additionally, most research data have shown the synergistic effect of mab as an adjuvant to OIT. OMB as an adjuvant therapy had a superior effect compared to mab monotherapy in achieving desensitization in CMA [57]. OMB makes OIT more effective in achieving desensitization, and it is also safer due to the lower frequency of ARs [58; 59; 60]. It also makes the up-dosing process faster with higher maintenance OIT doses [48; 57; 60; 61]. The introduction of OMB to OIT in high-risk patients with cow's milk allergy facilitated desensitization. In the same research, the discontinuation of OMB resulted in ARs [62]. In the studied egg OIT, OMB helped desensitization, but ending the treatment resulted in the recurrence of symptoms; once adjuvant therapy was reinstated, the symptoms disappeared [63]. In peanut allergies, OMB facilitated rapid oral desensitization and improved the quality of the desensitization process, but it had no effect on SU [58; 59]. Participants with multiple food allergies subjected to combined OIT and OMB therapy developed long-term desensitization faster and more safely [61; 64; 65].  
Dupilumab  
Dupilumab is a human monoclonal antibody that binds to interleukin (IL) 4R alpha receptor [66; 67]. IL4R\(\alpha\) is a shared receptor for IL-4 and IL-13, and dupliumab inhibits the signaling of both IL-4 and IL-13, key components of type 2 inflammation [68; 69]. Dupliumab is currently approved for the treatment of atopic dermatitis, asthma, chronic rhinosiusitis with nasal polyps, eosinophil esophagitigits (EoE), and prurtigo nodularis [70; 71; 72; 73]. Dupilumab as an adjunct to OIT moderately improved desensitization to peanut protein in children and adolescents. It had no significant effect on the frequency of ARs [74].
# A Novel Multi-Task Performance Prediction Model for Spark
## 3 Proposed Model
### Data Collection  
Data collection is the first key step in building our MHAC model, aiming to accumulate a large amount of historical data from Spark run activities. Our data collection is primarily based on the Spark History Server. The Spark History Server functions as a log processing tool for Spark clusters, enabling the collection of historical runtime information of Spark jobs. We define the set of all collected Spark log data as \(Data=\{data_{1},data_{2},data_{3},\ldots,data_{i},\ldots,data_{n}\}\), where \(n,i\in\mathbb{Z}^{+}\), \(data_{i}\) represents a single historical data entry. The data include start and end times, job execution status, resource utilization, task runtime, and CPU and memory usage. In addition, these data record detailed information about job failures, log errors, and performance bottlenecks. We refer to this dataset as the raw Spark log data.
# Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior
## 5 Conclusions  
The dark channel prior is invalid when the scene brightness is similar to that of atmospheric light, and there is no shadow projection. Therefore, this paper improves the dehazing algorithm based on a dark channel prior. First, we estimate the atmospheric light using the method of quad-tree subdivision and then combine the dark channel prior to introduce a value of adaptive tolerance K to obtain the correct transmission. To prevent the occurrence of halo artifacts at the edges of the restored image, the guided filtering technique is used to optimize the transmission. Finally, these parameters are brought into the atmospheric scattering imaging model to complete the dehazing treatment. The experimental results show that our dehazing algorithm not only better recovers images  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **[PERSON]’s Method** & **[PERSON]’s Method** & **[PERSON]’s Method** & **Introducing Fixed Tolerance** & **Our Method** \\ \hline Image 1 & 0.7867 & 0.9152 & 0.9463 & 0.9043 & 0.9222 \\ Image 2 & 0.7984 & 0.8570 & 0.9094 & 0.8617 & 0.8902 \\ Image 3 & 0.7322 & 0.8062 & 0.9115 & 0.7859 & 0.8578 \\ Image 4 & 0.7619 & 0.8691 & 0.9163 & 0.8589 & 0.8755 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural similarity (SSIM) of different algorithms in Figure 7.  
Figure 9: False edges caused by serious color distortion in the dehazing results. (**a**) [PERSON]’s result. (**b**) Our result.  
with large areas of bright areas but also better preserves image details compared with other methods. In future work, we will extend our work to the issue of video dehazing.  
Methodology, F.Y.; Supervision, S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 High Voltage Direct Current
### HVDC Benefits  
Since the first commercial HVDC project was completed between the Swedish island of Gotland and mainland Sweden in 1954 [47], this energy transmission technology has been increasingly used worldwide due to its operational and economic advantages over conventional AC transmission methods. Furthermore, a direct current transmission line serves the same purpose as its AC counterpart, with added capabilities that make it ideal for applications such as asynchronous interconnections between grids, underwater cables, long-distance power transmission, and stabilization of power flows [48]. Some operational and economic advantages can be listed as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Description** \\ \hline \multirow{4}{*}{Point to Point} & Connection between two substations (point to point) when the HVDC alternative is better than HVAC. Each of the substations works as a rectifier or inverter, depending on the direction of the power flow. This is the most common type of HVDC link. \\ \cline{1-1}  & Connection of two different frequency systems inside a substation. \\ \cline{1-1}  & Used when three or more substations are connected to a HVDC system. \\ \cline{1-1}  & Used when the DC transmission is performed directly at the power generation point. Mainly used in hydroelectric or wind power plants. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different configurations for HVDC systems.  
Figure 3: HVDC configurations: (**a**) Monopolar, (**b**) Bipolar, (**c**) Homopolar.  
1. Operational advantages 1. The power carrying capability of a DC line is unaffected by its length due to the capacitance phenomena in cable only appearing when the cable is energized for the first time or when the value of voltage changes. 2. As a DC line requires no reactive power, controlling the voltage at both ends is easier. 3. Their technology does not need voltage compensation to overcome problems, such as line charging and stability limitations. 4. A DC line can interconnect two grids as an asynchronous link rather easily thanks to its fast controllability of power flow. 5. Whereas AC lines cannot operate on unbalanced conditions for more than a second, bipolar DC lines can operate on single pole configuration for extended periods of time. 2. Economic advantages 1. Right of Way (RoW) incorporates small towers in comparison with the towers used in AC transmission and requires less land for its installation, which translates into a reduction cost for the tower and its installation. 2. DC lines can carry, with two conductors, as much power as an AC line using three conductors, which represents a reduction in cost for end users. 3. The line cost is reduced since it considers less conductors and insulators per pole. 4. As it has fewer conductors, power transmission losses along a DC line are inferior to those of an AC equivalent after a certain length, as shown in Figure 4.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 1 Introduction
constructed a radar differential reflectivity calibration method based on the UAV with a metal ball and verified that the negative deviations of \(6^{\prime\prime}\) and \(12^{\prime\prime}\) metal balls on the KOUN WSR-88D radar were \(-0.56\) and \(-0.52\) dB, respectively [11]. [PERSON] et al. implemented a differential reflectivity calibration of a dual-polarization weather radar system using a UAV and a metal ball, and the results show that the mean Zdr value of a 40 cm metal ball is \(-0.265\) dB [12]. In radar radial velocity measurement, [PERSON] et al. realized the velocity calibration of a vertically pointing millimeter-wave radar system by using a UAV and a metal ball, and determined the absolute deviation between the GPS- and radar-measured results to be a maximum of 0.014 m/s and a minimum of 0.002 m/s [13]. [PERSON] et al. carried out calibration of the weather radar reflectivity factor, differential reflectivity, and radial velocity using three types of metal balls suspended from a UAV, and the results showed that the deviation of the measured velocity of a 30 cm metal ball from the GPS-calculated velocity was less than 0.1 m/s at a sampling point number of 128 [14]. Existing methods of weather radar radial velocity external measurement mostly measure the velocity of the metal ball mounted on the UAV in the stationary state. However, the rope suspending the metal ball can be affected by the ambient wind field to produce a pendulum effect, which prevents the metal ball from achieving the ideal stationary state. Moreover, this method can only obtain radar measurements when the target is stationary and cannot simulate a moving target such as a rapidly developing severe convective weather system. Therefore, this paper proposes a weather radar radial velocity validation method based on an RTK UAV, which complements the static calibration of internal and external instrumentation with dynamic validation based on the RTK UAV and realizes systematic weather radial velocity calibration and verification. At the same time, based on the evaluation parametric system of validation for point targets [15], the evaluation parameter of optimal absolute accuracy is proposed. It effectively reduces the validation error caused by the attitude instability of the UAV and improves the robustness of the method and the credibility of the evaluation results. In the following sections, we provide a detailed introduction to the research methodology, experimental design, and analysis of experimental results, to comprehensively demonstrate our in-depth discussion and scientific research results on weather radar radial velocity validation. Through this series of research work, we expect to provide strong support for improving the quality and credibility of weather radar data and to promote scientific research and technological innovation in the field of meteorology.