esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
### 5G Services and Their Economic Value  
The International Telecommunication Union (ITU) Radio-communication sector defines the minimum standards for 5G within three main services, i.e., enhanced Mobile BroadBand (eMBB), massive Machine Type Communication (mMTC) and URLLC (Ultra Reliable and Low Latency Communications) [4]. The most deployed service continues to be eMBB [28], focused on improving data transmission rates for smartphone users. Although in 2021 5G enabled smartphones already constitute 43% of new shipped units worldwide [29], they still represent a tiny segment within the current deployed stock of smartphones. The other two services, mMTC and URLLC, are yet in their infancy. The economic value that 5G is expected to provide to the world economy is in the order of USD 12 trillion [30]. Table 2 introduces the technologies that 5G uses [4; 31; 32]:
## 5 Adaptive Algorithm
### Adaptivity Algorithm  
The adaptivity algorithm for the reduced element approach can be summarized as:  
1. Specify tolerance \(T_{l}\), the degree \(m\) of the reduced element and the initial element size \(h_{0}\);
2. Solve for the FE solution \(u^{h}\) on the current element by using conventional element of degree \(m+1\);
3. Extract the reduced solution \(u^{h}_{\mathrm{R}}\) and calculate the maximum error ratio \(\tilde{\varepsilon}^{*h}_{\mathrm{max}}\);
4. If Equation (22) is satisfied, then move to the next element with current element \(h\) as the initial \(h_{0}\), and return to Step 2;
5. If Equation (22) is not satisfied, calculate a new element size \(h\) by using Equation (23), and return to Step (2);
6. The above procedure is repeated until reaching the end of the time domain \(\tilde{T}\).  
Note that the present paper employed the more stringent maximum norm rather than the more commonly used energy norm used in most adaptive analyses. Numerous numerical experiments show that the satisfaction of Equation (20) would indeed make Equation (19) also satisfied.
## 4 Case Studies  
This section describes two use cases that demonstrate the method developed in the previous section. The first example, the exchange of a sketch, can be classified as moderate. The postprocessor of the target system used here is capable of importing the sketch. However, certain necessary information is lost. We rank the second example, the transfer of a retaining ring model, as severe because the target system cannot import the object. We want to highlight that the described examples are relatively simple compatibility use cases. In other words, they are trivial enough to show that the approach is developed yet complex enough to address all relevant aspects. Nevertheless, the method presented in this paper is also suitable for more complex use cases and other exchange formats.
# Power Distribution IoT Tasks Online Scheduling Algorithm Based on Cloud-Edge Dependent Microservice
## 4 Cloud-Edge Collaborative Online Scheduling Algorithm
### Prioritization Strategy
#### 4.1.2 Prioritization of Tasks  
The previous section defines the microservice prioritization relationships for specific DAG graphs, but priority relationships among multiple power tasks need to be addressed  
Figure 1: Cloud-edge scheduling mechanism for power tasks under microservice architecture.  
as well. When the edge computing system is resource constrained, important tasks should be prioritized.  
The fuzzy logic based approach has low computational complexity and can be computed with uncertain information [20], so fuzzy logic is used to prioritize different tasks. The steps are as follows.  
1. Fuzzification. We use the triangular affiliation function with high computational efficiency, which is calculated as follows. \[\text{f(x)}=\begin{cases}0&\text{x}<\text{a}\\ \frac{\text{b}-\text{a}}{\text{b}-\text{a}}&\text{a}\leq\text{x}\leq\text{b} \\ \frac{\text{c}-\text{a}}{\text{c}-\text{b}}&\text{b}<\text{x}\leq\text{c}\\ 0&\text{c}<\text{x}\end{cases}\] (3) x is an input or output variable, a, b, c are given real numbers, and a \(<\text{b}<\text{c}\). We select three variables that have a significant impact on task execution performance as inputs: time delay, task type value, and input data volume. For time delay and input data volume, _low_, _medium_, and _high_ are used as linguistic variables. For task type values, _general_, _alarm_, and _fault_ are used as linguistic variables. The output is task priority and _very low_, _low_, _medium_, _high_, and _very high_ are used as linguistic variables. The affiliation functions of the input and output variables are shown in Figure 2.
2. Fuzzy inference. Inference is performed using fuzzy rules, and the result of the inference is a fuzzy variable for defuzzification. A fuzzy rule is a simple if-then rule with a condition and a conclusion. Since there are 3 affiliation functions and 3 linguistic variables for each affiliation function, there are 27 fuzzy rules. The rules are shown in Table 1.
3. Deblurring. The center-of-mass method is used to perform the deblurring calculation with the following formula. \[\text{P}_{\text{m}}=\frac{\int\text{xf(x)dx}}{\int\text{f(x)dx}}\] (4) \[\text{P}_{\text{m}}\] in the formula is the priority of each task.  
Figure 2: Diagram of affiliation degree. (**a**) Input data volume affiliation function. (**b**) Time delay affiliation function. (**c**) Task type value affiliation function. (**d**) Task priority affiliation function.
## 4 LNSSCL Algorithm
### Determining Particle Initialization Positions  
The Stochastic Competitive Learning algorithm stipulates that each particle randomly selects a different node in the network as its starting position for walking. The random uncertainty in particle initialization can lead to unstable community detection outcomes. Additionally, this initialization approach might result in particles' starting positions clustering within a single community, intensifying the competitive relationships among particles during their walks. This situation requires a considerable amount of time for convergence. Addressing these concerns, the random placement for initialization is abandoned. Instead, each particle's initial position is determined based on the node's degree and the Salton similarity index between nodes. This approach aims to distribute particles across different communities as much as possible, accelerating the convergence rate of particle walks and enhancing the stability of community detection outcomes.  
Node degree is commonly used to measure the importance of a node within the entire network, while the Salton similarity index is often employed to gauge the similarity between a node and its neighboring nodes. It is defined as follows:  
\[Salton(i,j)=\frac{|N(v_{i})\cap N(v_{j})|}{\sqrt{d(v_{i})\times d(v_{j})}} \tag{9}\]  
Combining the two aforementioned metrics, the rules for determining particle initialization positions are as follows. Firstly, arrange all nodes in the network in descending order based on their degree values. Select the node with the highest degree value as the starting position for the first particle's walk. Next, calculate the average Salton similarity index between the node where the already determined starting-position particle is located and all other nodes in the network. Choose the node with the smallest average Salton similarity index as the starting position for the next particle. This process is then repeated iteratively to progressively determine the starting positions for the remaining particles. Finally, when the starting position for each particle is determined, the particle initialization process is completed. Figure 1 depicts the particle initialization position under the condition of three particles. As can be seen from the figure, particles 1, 2, and 3 are dispersed and placed in the network after the position initialization step. \(p^{(k)}(0)\) denotes the starting position of particle \(k\). The rules for determining particle initialization positions can be expressed as follows:  
\[p^{(k)}(0)=\left\{\begin{aligned} \operatorname*{argmax}(d(v_{i})),& k=1\\ \operatorname*{argmin}\left(\frac{\sum\limits_{k=1}^{k-1}Salton (j,p^{(u)}(0))}{k}\right),& 2\leq k\end{aligned}\right. \tag{10}\]
## 4 Discussion
Future studies should emphasize the clinical context of the presented parameters when assessing surface characteristics and applications. PMMA exhibits relatively low hardness, and its surface is prone to daily wear. Roughness parameters undergo continuous changes over time. The functional performance of a surface is closely linked to the surface finish, including the surface layer's texture and mechanical properties. These properties accompany daily use and consequent wear in the oral cavity [8]. As studied in other materials [8, 9, 17], skewness and kurtosis in surface roughness are critical parameters for clinically assessing retention and fit, fatigue life, corrosion, resistance, wear resistance, tissue response, aesthetics, cleaning, and maintenance of dental prostheses made of acrylic resins. For example, [PERSON] et al. [17] emphasize the importance of selecting abrasive grain sizes, as they can directly modify surface roughness and functionality, such as susceptibility to microbial contamination or the accumulation of fluid/debris. [PERSON] (2016) [8] exhibited a strong correlation between skewness and kurtosis with the grit size of abrasive tools. With the additional influence of the primary profile, professionals can make informed decisions to optimize the surface finishing process of acrylic resins. \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Ra:** & **Pa:** & \\  • Result of a filtering operation that excludes information from a surface; & • Direct extraction of the primary profile. \\  • Only addresses surface irregularities in a vertical direction; & **Skewness and Kurtosis** & \\  • Height average deviation to the mean line (does not differentiate peaks and valleys); & • Height distribution and tip geometry of peaks and valleys; & • Describes how the roughness is distributed along the profile; \\  • Same Ra value with different tribological performances. & • Differentiates between different tribological performances. & • Differentiates between different tribological performances. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between the conventional and proposed approach.
## 4 Results  
After the harvesting of the medicinal plants _C. schoenanthus_ and _N. azurea_, the subject of this research in the two regions of Djibouti, extractions by hydrodistillation using a Clevenger apparatus, was carried out to obtain, respectively, the two essential oils CSEO and NAEO. The yields of these two essential oils obtained were, respectively, 1.16% and 1.25% (Table 3). The chemical compositions of the CSEO and NAEO were determined by gas chromatography (GC) coupled with a flame ionization detector (FID); all the data found are mentioned in Table 4. The analyses made allowed us to identify 47 compounds for CSEO with a total percentage of 99.9%; among them, two (2) major compounds were visualized: 3-isopropyl-5-methyl-1-cyclohexene (32.3%) and D-limonene (11.3%) and seven (7) compounds with moderate concentrations between 2% and 8%, such as: \(\beta\)-elemol (7.8%), 2-p-menthen-1-ol (5.1%), geraniol acetate (3.7%), terpineol, cis-beta.- (3.5%), 10-epi-\(\beta\)-eudesmol (3.0%), 4-Nonanone (2.1%) and cis-piperid (2.1%). In addition, the NAEO analysis identified 20 compounds with a percentage of 100%, hence a single major compound, methyl (2E)-2-nonenoate (53.2%), and seven (7) compounds with concentrations moderate between 2% and 9%, such as: phellandral (8.6%), 1-methyl-2-decalone (6.1%), cis-caryophyllene (5.7%), cyclohexanol acetate (5.0%), boschialactone (3.6%), cyanophyllene oxide (2.8%) and L-calamenene (2.0%). The two oils have compounds in common with different concentrations; we noted: 1-tetradecyne, elemene, cis-caryophyllene, methyl (2E)-2-nonenoate, cyanophyllene oxide and viridiflorl. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pic** & **RT (*)** & **Compounds** & **CSEO** & **NAEO** \\ \hline
1 & 6.35 & 3-Isopropenyl-5-methyl-1-cyclohexene & **32.3** & - \\ \hline
2 & 6.51 & \(\alpha\)-Phellandrene & 0.2 & - \\ \hline
3 & 7 & \(\beta\)-Phellandrene & 0.5 & - \\ \hline
4 & 7.1 & D-Limonene & **11.3** & - \\ \hline
5 & 7.3 & \(\beta\)-trans-Ocimene & 1.7 & - \\ \hline
6 & 7.54 & Ocimene & 1.1 & - \\ \hline
7 & 8.15 & 4-Nonanone & 2.1 & - \\ \hline
8 & 8.49 & L-Fenchone & 0.5 & - \\ \hline
9 & 8.75 & Linalol & - & 0.4 \\ \hline
10 & 9.24 & 2-p-Menthen-1-ol & 5.1 & - \\ \hline
11 & 9.49 & cis-p-menth-2-en-1-ol & 0.2 & - \\ \hline
12 & 9.6 & Terpineol, cis-beta.- & 3.5 & - \\ \hline
13 & 10.21 & Cyclohexene, 3-acetoxy-4-(1-hydroxy-1-methyl)-1-methyl- & 0.8 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chemical compositions of CSEO and NAEO. \begin{table}
\begin{tabular}{c c} \hline \hline
**Essential Oil** & **Yield (\%)** \\ \hline CSEO & \(1.16\pm 0.22\) \\ \hline NAEO & \(1.25\pm 0.25\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Yields of both essential oils.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis
### Optimization of Actuation Speed Parameters  
#### 3.5.1 Experimental Conditions  
As discussed in the previous sections, not only does the applied strain on the kirigami skin play a critical role in deformation behavior, but dynamic actuation parameters also significantly influence the effectiveness of directional anchoring and overall locomotion performance. To investigate this, the present study evaluates the impact of actuation speed--specifically extension and retraction rates--on the crawling efficiency of a soft robotic system powered by a PM linear actuator. Experiments were conducted using a triangular kirigami pattern with a 4 \(\times\) 20 cut density fabricated on a 100 \(\upmu\)m PET sheet over a 51 mm \(\times\) 140 mm contact area. The actuator was programmed to perform five extension-retraction cycles at a constant stroke length of 30 mm across four speed conditions defined by matched extension and retraction rates: 10/10, 15/15, 20/20, and 30/30 mm/s. These conditions were selected to evaluate how increasing the frequency of cyclic deformation affects the ability of the kirigami pop-ups to effectively anchor and re-engage with the substrate during locomotion. The goal was to determine whether higher actuation speeds reduce surface interaction time and compromise traction or whether they can maintain or even enhance performance through faster cycling.
## 2 Materials and Experimental Methodology
### Asphalt High Temperature Aging: Mechanisms of Short-Term (RTFO) and Extended Aging (PAV)
#### 2.4.1 Short-Term Thermal Aging  
This stage addresses the first aging brought on by bituminous mixture manufacturing. The Rolling Thin Film Oven (RTFO) of ASTM D2872-19 [33] mimics the conditions bitumen comes across when aggregates are heated. The RTFO test heats bitumen at 163 \({}^{\circ}\)C under a constant airflow for 85 min. This arrangement replicates the volatilization of smaller hydrocarbons and oxidation reactions typical of hot-mix asphalt manufacturing. This test offers an understanding of bitumen's performance right after application by analyzing variations in viscosity and other physical characteristics.
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 3 Methodology
### Anomaly Trajectory Recognition Based on Trajectory Rules
#### 3.3.1 Mathematical Description of Vehicle Information  
The vehicle trajectory information obtained based on the tracking boxes under a certain surveillance camera includes the following main aspects: the time duration \(T\) for which the trajectory exists, the total number of frames \(f\) in the trajectory over time \(T\), and the world coordinates of the vehicle's position \(s_{i}=(x_{i},y_{i})\) in the \(i-th\) frame sequence. Generally, \(z\) is assumed to be either 0 or a constant. The description of model parameters can be as follows:  
1. Set of vehicle positions \(S=\{s_{1},s_{2},\ldots,s_{f}\}\), where \(s_{f}=(x_{f},y_{f})\);
2. Set of vehicle movement paths \(L=\{l_{1},l_{2},\ldots,l_{f}\}\), where \(l_{i}\) represents the change in vehicle position, or the path length the vehicle has traveled from the \((i-1)-th\) frame to the \(i-th\) frame. It is calculated as shown in Equation (1): \[l_{i}=\sqrt{(y_{i}-y_{i-1})^{2}+(x_{i}-x_{i-1})^{2}},i>1\] (1)
3. Set of vehicle movement angles \(A=\{\theta_{1},\theta_{2},\ldots,\theta_{f}\}\), where \(\theta_{i}\) represents the angle of vehicle movement between the \((i-1)-th\) frame and the \(i-th\) frame in a coordinate system defined by the coordinates. \(\theta_{i}\in(-\pi,\pi)\) and is calculated as shown in Equation (2): \[\theta_{i}=\arctan\frac{y_{i}-y_{i-1}}{x_{i}-x_{i-1}},i>1,\text{i}>1\] (2)  
Figure 2: Architecture diagram of vehicle recognition network based on improved attribute information.  
Set of slopes of vehicle movement angles \(K=\{k_{1},k_{2},\dots,k_{f}\}\), where \(k_{i}\) represents the slope of the vehicle trajectory from the \((i-1)-th\) frame to the \(i-th\) frame. It is calculated as shown in Equation (3): \[k_{i}=\frac{y_{i}-y_{i-1}}{x_{i}-x_{i-1}},i>1\] (3)
5. Set of average vehicle movement speeds \(V_{k}=\{\overline{v}_{1},\overline{v}_{2},\dots,\overline{v}_{f-k}\}\), where \(\overline{v}_{i}\) represents the average speed of the vehicle over the past \(k\) frames, from \(i-k\) frame to the \(i-th\) frame. It is calculated as shown in Equation (4): \[\overline{v}_{i}=\{\begin{array}{l}\sqrt{(y_{i}-y_{i-k})^{2}+(x_{i}-x_{i-k}) ^{2}}\cdot\frac{f}{\overline{\cdot}K}\quad i\geq k\\ \sqrt{(y_{i}-y_{1})^{2}+(x_{i}-x_{1})^{2}}\cdot\frac{f}{\overline{\cdot}K}\quad i <k\end{array}\,.\] (4)  
In the aforementioned mathematical descriptions, the parameter \(L\) can be employed to assess whether the vehicle is in motion, while parameters \(A\) and \(K\) are utilized to evaluate whether the vehicle's travel direction is accurate. Additionally, parameter \(V_{k}\) serves to evaluate whether the vehicle's speed adheres to road regulations.
## 5 DUS-CS Data Acquisition and Analysis
### Data Processing and Analysis  
Firstly, the Nyquist sampling data is compressed, observed, and reconstructed to verify the correctness of the method. Figure 9 shows the Nyquist sampling data (sampling rate of 500 kHz) and the reconstruction results of the DCS-OMP method and the DUS-CS method.  
To show the reconstruction effect more clearly, we cut off part of the original signal and compared it to the under-sampling observation and reconstruction performed on the original signal (Figure 10).  
The amount of the data is 20% of the original signal after direct under-sampling. Noise is added to generate signals with the different SNRs. Then, the original signal is reconstructed using DCS-OMP and DUS-CS (Figure 11).  
Figure 10: The original signal.  
Figure 9: Nyquist sampling and reconstruction results.  
Figure 12 is a compressive sensing signal with a compression ratio of 10% with different SNRs.  
Figure 11: Reconstruction results with compression ratio of 20%. (**a**) SNR = 3 dB; (**b**) SNR = 0 dB; (**c**) SNR = \(-\)3 dB; (**d**) SNR = \(-\)8 dB.  
Figure 12: Reconstruction results with compression ratio of 10%. (**a**) SNR = 3 dB; (**b**) SNR = 0 dB; (**c**) SNR = \(-\)3 dB; (**d**) SNR = \(-\)8 dB.  
Figure 11 shows the compressive sensing signal with a compression ratio of 10% with different SNRs. As for the processing result of the compressive sensing signal, it can be seen from Figure 11 that when the compression ratio is 20% and SNR = 3 dB, the reconstruction effect of the DCS-OMP method is poor, but the prior information method works well. As the SNR continues to decrease, the DUS-CS still reconstructs the original signal very well. When the compression ratio is 10%, the DCS-OMP method has a poor processing effect, as seen from Figure 12. For the DUS-CS method, as the signal-to-noise ratio decreases, the amplitude of the reconstructed signal deviates from the original, but the overall reconstruction effect is still significant. This result shows that the compressive signal obtained by the direct under-sampling system can recover the original signal, which confirms the correctness and engineering feasibility of the direct under-sampling compressive sensing system.
## 3 Model Formulation
### Objectives
The schedule-based passenger flow assignment simultaneously considers the desired departure times and the ticket-booking order of passengers, searches the optimal travel schemes for time-varying demand, and assigns the passenger flow to trains under the capacity constraint, where the main procedure includes transfer network design, optimal transfer scheme calculation, optimal partition of departure time, and passenger flow assignment based on the ticket-booking order and capacity constraint. The two levels would be executed sequentially until the end condition. In the model, we further relax constraint (12) to improve the solving efficiency due to the imbalanced distribution of passenger demand on the large-scale HSR network and the rigid capacity constraint in the passenger flow assignment [1; 8]. Constraint (12) is transformed into the following penalty function to measure the transit capacity allocation insufficiency. \[Z_{3}=\sum_{(r,s)\in RS}q^{0}_{rs}l_{rs}/\sum_{(r,s)\in RS}q_{rs}l_{rs} \tag{16}\]  
We introduce objective weights to balance the proposed objectives that can be determined by the change magnitude of each objective in the searching process. Based on the objective weights, we construct the bi-level programming model. \[\min Z=\sum_{i=1}^{3}w_{i}Z_{i} \tag{17}\]  
s.t. Formulas (1)-(11) and (13). where \(T(r,s),T_{0}(r,s),q_{rs}^{0}\) are obtained by passenger flow assignment on the lower level.
## 2 Principles and Technical Architecture of the Satellite Navigation Signal Authentication
### Technical Architecture  
The satellite navigation system consists of the space segment, ground segment, and user segment. Based on the existing satellite navigation system, the satellite navigation signal authentication will be extended to the space segment, the ground segment, the user terminal, and the network auxiliary segment. The space segment adds the authentication spreading code/authentication messages to the broadcast downlink satellite navigation signal, the user segment authenticates the received satellite navigation signal, and the network auxiliary segment uses the communication base station (terrestrial communication/satellite communication) to provide network auxiliary authentication information. If there is a GNSS spoofing signal in the actual environment, the user segment can identify whether the current signal is a spoofing signal through the authentication of the message/spreading spectrum code. The architecture of the satellite navigation signal authentication is shown in Figure 6.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.17 Message Queuing Telemetry Transport Protocol (MQTT)  
The Message Queuing Telemetry Transport Protocol (MQTT) is a publish/subscribe messaging protocol used for lightweight machine-to-machine (M2M) communications in constrained networks. MQTT uses the Transmission Control Protocol (TCP) as its transport protocol that guarantees the delivery of messages. MQTT also uses TLS/SSL for security, which encrypts messages to protect them from unauthorized access. MQTT supports three levels of QoS, making it more reliable when delivering messages. It uses a small amount of bandwidth and processing power. This makes it ideal for use with small devices that have limited resources. MQTT is also suited for large networks because it can efficiently handle a large number of devices. This is because it uses a publish/subscribe model, which allows devices to receive only messages that are relevant to them [52].