esa-sceva/satcom-chunk-collection huggingface dataset:
# Unequal Interval Dynamic Traffic Flow Prediction with Singular Point Detection
## 5 Multiscale Traffic Flow Prediction via Vasicek Model
### Unequal Element Solution via Matrix Decomposition
Correspondingly, the unequal size element \(X_{k}\) can be obtained via the inverse operation of Equation (26) to locate the position in the dynamic interval. Therefore, the result of \((X^{\text{T}}WX)^{-1}\) can be obtained via Equation (32), and the result of \(X^{\text{T}}WY\) can be obtained via Equation (33). Then the parameter \(\alpha\) is calculated via Equation (24). The increment of the traffic flow \(\triangle_{i}X\) is presented as Equation (17), which decides the traffic flow prediction result before the next singular point as  
\[\widetilde{X}_{i+1}=X_{i}+\triangle_{i}X. \tag{34}\]  
Algorithm 2 presents the pseudo code of the proposed dynamic unequal interval traffic flow prediction via the Vasicek model (DTFP-vm). Lines 1-5 present the initialization for the traffic data and the optimization. Lines 6-13 are the process of dynamic interval division based on the singular points. The optimal result for the Vasicek model is calculated via mixed-integer linear programming (MILP) in lines 14-24. Lines 25-37 present the results for the different requested time. ```
1:/*Initialization*/
2:Input: Observation process: \(X\), Current moment: \(t\),
3:Input: Singular Points set: \(\{T_{1},T_{2},\ldots,T_{i}\}\),
4:Input: The predicted next singular point: \(\widetilde{T}_{i+1}\)
5:error threshold: \(c_{i}\), iteration threshold: \(in_{max}\)
6:/*Dynamic intervals division*/
7:for\(k=1:1:i\)do
8: Divide \(X_{k}\) from \(T_{k-1}\) to \(T_{k}\)
9: Calculate the duration of interval: \(TS_{k}=T_{k}-T_{k-1}\)
10: Determine the sampled duration: \(\triangle_{k}t\)
11: Add \(k\)th observed value into matrix \(X\): \(X\bigcup\{X_{k}\}\)
12:endfor
13:Set up \(X\), \(Y\), \(\alpha\), and \(W\)
14:/*Traffic flow prediction via Vasicek model*/
15:Set up optimization objective as Equation (19)
16:while\(in\triangle_{i}n_{max}\)do
17: Do MILP to solve the Equation (19)
18: Attain \(W\) as Equation (22); \(Y\) as Equation (23), \(\alpha\) as Equation (24), and \(X\) as Equation (25)
19:if\(|p-p^{\prime}|\geq e\)then
20: adjust \(\alpha\)
21:\(in++\)
22:endif
23:endwhile
24:Output \(W\), \(Y\), \(X\), and \(\alpha\)
25:Set up traffic flow via Vasicek model as Equation (16)
26:Input the time for prediction: \(t^{\prime}\)
27:if\(t^{\prime}\leq t\)then
28: Output the observation value of \(X_{t^{\prime}}\)
29:else
30:if\(t^{\prime}>\widetilde{T}_{i+1}\)then
31: Waiting for the next singular point via Algorithm 1
32:endif
33:else
34: Attain the increment \(\triangle_{r}X\) via Equation (18)
35:\(X_{t^{\prime}}=X_{t}+\triangle_{\mu^{\prime}}X\)
36:endif
37:Respend the traffic flow results to the request
```  
**Algorithm 2** Dynamic unequal interval traffic flow prediction via the Vasicek model (DTFP-vm).
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 3 the Proposed Method
### Optimization  
As described in Section 3.1, we optimize the proposed architecture by enforcing similarity of a video \(x_{i}\), and its counterpart sentence \(y_{i}\) will be greater than similarities of the video \(x_{i}\) and other sentence \(y_{j}\), such as \(\mathrm{s}(x_{i},y_{i})\geq\mathrm{s}(x_{i},y_{j})\) or \(\mathrm{s}(x_{i},y_{i})\). We achieve this by using the triplet ranking loss [38; 39; 40], where \(\alpha\) is a margin.  
\[\mathcal{L}_{\mathrm{s}}(x_{i},y_{i},y_{j}) =\max\bigl{\{}0,\alpha-\mathrm{s}(x_{i},y_{i})+\mathrm{s}(x_{i},y _{j})\bigr{\}} \tag{13}\] \[\mathcal{L}_{\mathrm{v}}(x_{i},y_{i},x_{j}) =\max\bigl{\{}0,\alpha-\mathrm{s}(x_{i},y_{i})+\mathrm{s}(x_{j}, y_{i})\bigr{\}} \tag{14}\]  
Given a dataset \(\mathcal{D}=(x_{i},y_{i})_{i=1}^{N}\), with \(N\) pairs, we optimize the following equation by stochastic gradient descent [41; 42].  
\[\mathcal{L}=\sum_{i=1}^{N}\sum_{j=1}^{N}\bigl{(}\mathcal{L}_{\mathrm{s}}(x_{i },y_{i},y_{j})+\mathcal{L}_{\mathrm{v}}(x_{i},y_{i},x_{j})\bigr{)} \tag{15}\]  
The training time was less than 3 h using a GTX 1080 Ti GPU. We fixed the parameters of ResNet and GloVe during training. Thus, training time was relatively short. We note that the proposed method is worse than the other methods in the computational complexity due to the number of embedding spaces. Addintionally, the complexity of the proposed method increases linearly according to the number of spaces.
# Key Agreement and Authentication Protocols in the Internet of Things: A Survey
## 2 Materials and Methods
### Cyberattacks on IoT Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack** & **Description** & **References** \\ \hline Replay attack & The attacker intercepts traffic and sends correspondence to its original target, duplicate packets can be sent many times to the recipients. & [63; 64] \\ \hline Spoofing attack & The attacker tries to hide the communication or identity so that it appears to be associated with a trusted, authorized source. & [65] \\ \hline Stolen (smart card, verifier) attack & The attacker can guess or steals the password; for example, when the smart card is lost, also he can use the stolen verifier directly to impersonate the authorized participant of the communication. & [66] \\ \hline Man in the Middle attack (MITM) & The attacker disrupts communication between two nodes by injecting a malicious node between legitimate nodes. & [64; 67] \\ \hline Impersonation attack & The attacker uses the identity of another user (user, server, gateway, node, IoT device). & [68; 69; 70] \\ \hline (Privileged)-insider attack. & The attacker, or insider, is authorized to access the system, then the insider can use his access to the data breach. & [64] \\ \hline Known session-specific temporary information (KSSTI) attack & The attacker can calculate the session key based on temporary information. & [71] \\ \hline (Offline password) guessing attack & The attacker tries iteratively to guess a password or other login details to impersonate the user. & [72] \\ \hline Denial of Service (DoS) attack & The attacker floods the network with signals, which results in network closure. & [64; 67; 73] \\ \hline Sinkhole attack & The attacker announces updates of routing information, thus attracting network traffic, and as a consequence, it may launch further attacks. & [64; 67] \\ \hline Desynchronisation attack & The attacker tries to destroy synchronization between the nodes. & [74] \\ \hline (Sensor node, IoT device) capture attack, cloning attack & The attacker hijacks a sensor node or IoT device to take over the network, remove the node from the network, and redeploys it as a malicious node. & [64; 75; 76] \\ \hline key compromise impersonation (KCI) attack & The attacker installs the client’s certificate on the device and can then impersonate it. & [77] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Typical attacks in IoT.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 4 Simulation Cases
### Basic Parameters  
The EVs simulated are supposed to be private vehicles with unified capacity of 24 kWh and nominal charging power of 3.3 kW. The charging and discharging efficiency are all set to 0.98.  
Figure 6: Treatment for control variables of a single EV.  
Figure 7: Optimization process for different V2A applications.  
Furthermore, as EV users may arrive and depart the charging piles at random times, their charging behaviors should be predicted. Based on existing studies, the charging pattern of EVs can be captured through some stochastic methods [23; 24]. In this work, Truncated Gaussian Distribution [23; 24] is used to simulate the charging activities of EV customers, with the ranges of initial and expected SOC values given in Table 1. Moreover, for the household case, the EV charging is assumed to be performed during the night due to home user working/living habits [8]; for the building and charging station scenarios, EVs are supposed to park during the daytime, with ranges of plugging-in and unplugging times displayed in Table 1. In addition, the penalty coefficients \(\rho_{i}\) and \(\lambda_{i}\) are uniformly set to 1.5 and 0.15 for the test cases in Section 4.2 to Section 4.4. For the evaluation of battery degradation, Section 4.5 is given to simulate the influences of penalty coefficients \(\rho_{i}\) and \(\lambda_{i}\) on EV charging/discharging.  
The simulated EV numbers for the test building and charging station are set at 5 and 10, respectively. The test signals for the power load price \(\theta_{L,t}\) and the V2G power price \(\vartheta_{V2G,t}\) are given in Figure 8. As stated before, the two price signals are differentiated for incentive purposes of EV charging/discharging activity in response to power load profiles [17]. Meanwhile, the two price vectors are just presupposed for testing purpose, which means the prices are fixed values. As can be seen, there are two high-power price periods (11:00-13:00 and 18:00-20:00) for \(\theta_{L,t}\) to mitigate the morning and evening peak loads; there is one period (17:00-21:00) with high \(\vartheta_{V2G,t}\) to motivate EV discharging since the power demand in the evening can be heavy due to increased residential loads (cooking, lighting, air conditioning, etc. when people return home after work) [25]. In addition, the basic loads for the household, the building, and the charging station are presented in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(t_{start}\) range of EV & [8:00, 15:00] & \(Soc_{EV,max}^{i}\) & 0.9 \\ \(t_{end}\) range of EV & [13:00, 22:00] & \(P_{EV}^{i}\) (kW) & 3.3 \\ \(Soc_{EV,t_{start}}^{i}\) (range) & [0.2, 0.65] & \(C_{EV}^{i}\) (kWh) & 24 \\ \(Soc_{EV,exp}^{i}\) (range) & [0.6, 0.95] & \(\eta_{c}^{i}\) & 0.98 \\ \(Soc_{EV,min}^{i}\) & 0.2 & \(\eta_{d}^{i}\) & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 8: Price signals for simulation.
## 9 Conclusions
**Abbreviations**  
The following abbreviations are used in this manuscript:  
A2A Air-to-air  
A2G Air-to-ground  
ADC Analog-to-digital converter  
AGC Automatic gain control  
APNT Alternative positioning, navigation, and timing  
ATM Air traffic management  
BPSK Binary phase-shift keying  
CHP2 Communications and High-Precision Positioning  
CNS Communications, navigation, and surveillance  
COTS Commercial off-the-shelf  
DME Distance measuring equipment  
DSP Digital signal processing  
G2G Ground-to-ground  
GNSS Global Navigation Satellite System  
GPS Global Positioning System  
ISNR Integrated signal-to-noise ratio  
LDACS1 L-band Digital Aeronautical Communication System 1  
LiDAR Light detection and ranging  
LNA Low-noise amplifier  
LoS Line of sight  
LUT Lookup table  
MIMO Multiple-input, multiple-output  
MSK Minimum-shift keying  
NTP Network Timing Protocol  
OCXO Oven-controlled crystal oscillator  
OSI Open Systems Interconnection  
P-WAM Passive wide area multilateration  
PL Pseudolite  
PNT Positioning, navigation, and timing  
RF Radio-frequency  
RFID Radio-frequency identification  
RFSoC RF system-on-chip  
RTK Real-time kinematic  
SDR Software-defined radio  
SWaP Size, weight, and power  
SWaP-C Size, weight, power, and cost  
ToA Time-of-arrival
## 3 Results and Discussion
### Time-Averaged Results
#### 3.1.2 Q-Criterion Distribution  
In order to get more details of vortex identification, the Q-criterion was calculated based on time-averaged results (Equation (1)). When Q was positive, vortex regions occurred, and the magnitude of vorticity was greater than that of the rate of strain. Then, the circulation (Equation (2)), was calculated over an area with a positive value of the Q-criterion, as this value was located in the vortex area.  
\[\mathrm{Q}\ =\ \Big{(}|\Omega|^{2}-|\mathrm{S}|^{2}\Big{)}/2 \tag{1}\]  
\[\Gamma=\iint_{\mathrm{A}}\Omega\mathrm{.d}\mathrm{A} \tag{2}\]  
where \(\Omega\) is vorticity, S is strain rate, \(\Gamma\) is circulation, and A is the area of the positive Q-criterion.  
The mean magnitude of circulations was calculated and listed in Table 1. For the free external domain, the vortices cancelled out each other, and the circulation for the whole domain was almost zero. However, for the fully confined case, the circulation was increased about 10 times, compared to the free external domain. In addition, a linear relation between the Reynolds number and non-dimensional circulation ratio \((\mathrm{DU_{j}}/\Gamma)\) was found. In contrast, this ratio for the free external domain case was almost constant and independent from the Re number.  
Time-averaged Q-criterion contours for a better understanding of the large coherent structure distribution are shown in Figure 9. For the free external domain, the maximum concentration of the Q was located at the maximum deflection of the jet. However, for the fully confined external domain, this behavior could be found in the vortex position, as was illustrated in Figure 7d.  
Figure 8: Instantaneous non-dimensional velocity magnitude for Re = 6440 in the free external domain.
## 4 Emergency Maneuver Strategies and Blind 60 Spot Management
### Advanced Algorithmic Approaches  
Recent advancements in artificial intelligence and machine learning have led to more sophisticated approaches for emergency handling and blind spot management.  
Machine learning techniques can help classify and respond to various emergency scenarios. [PERSON] et al. [89] developed a deep reinforcement learning framework for emergency decision-making in autonomous vehicles, capable of handling a wide range of critical situations.  
Predicting the movement of objects in blind spots is crucial for proactive safety measures. [PERSON] et al. [85] introduced a probabilistic approach for predicting the intentions and trajectories of surrounding vehicles, including those in blind spots, to enable more informed decision-making.  
Combining data from multiple sensor types can provide a more complete picture of the vehicle's surroundings, including blind spots. [PERSON] et al. [90] proposed an adaptive sensor fusion algorithm that dynamically adjusts the weighting of different sensors based on environmental conditions and sensor reliability.  
Quick and accurate decision-making is essential in emergency scenarios. [PERSON] et al. [91] developed a hierarchical decision-making framework that combines rule-based systems with deep learning to enable rapid responses to critical situations while maintaining an overall driving strategy.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 5 Delay and Power Analysis
### Delay Analysis
In MU transmissions, the contention delay is also different from that of single-user transmissions because of the larger collision probability of the TF, which increases as the number of STAs in the MU transmission procedure increases. If the extended inter-frame space (EIFS) parameter operation is applied, the contention collision probability of the MU TF is significantly reduced, which affects the contention delay of the MU trigger delay. Therefore, the average delay for the multicast-MAC transmission procedure is:  
\[ \begin{split} E[T_{\textit{MUL\_MAC}}]&=\left\lceil \frac{N}{9}\right\rceil(E[T_{\textit{contention-supkt}}]+E[T_{\textit{ contention-MUltrig}}]+T_{\textit{MUIWUpkt}}+T_{\textit{WUldelay}}+\\ T_{\textit{MUITrigger}}+T_{\textit{MUL\_data+ack}}+T_{\textit{ MU-ack}}+2 SIFS)\end{split}  \tag{3}\]  
where \(T_{\textit{MUIWUpkt}}\), \(T_{\textit{MUltrigger}}\), \(T_{\textit{MUL\_Data+ack}}\), and \(T_{\textit{MUL\_ack}}\) denote the transmission delay of the multicast wake-up frame, the TF, the data frame aggregated with the acknowledgement frame in uplink multi-user transmission, and the multi-STA blockade frame respectively. In the _multicast-scheduled_ procedure, because data frame transmissions and the wake-up packet transmission are operated, the delay for gathering the data from all STAs is further reduced. Therefore, the average delay is a modified form of the multicast-MAC procedure, which is:  
\[ \begin{split} E[T_{\textit{MUL\_Scheduled}}]=(E[T_{\textit{ contention-wupkt}}]+T_{\textit{WULdelay}}+E[T_{\textit{contention-MUltrig}}]+T_{\textit{MUIWUpkt}}+\\ T_{\textit{MUTrigger}}+T_{\textit{MUL\_data+ack}}+T_{\textit{ MU\_ack}}+2 SIFS)+(\left\lceil\frac{N}{9}\right\rceil-1)E[T_{\textit{int\_transmission}}]\end{split}  \tag{4}\]  
with the following condition:  
\[E[T_{\textit{contention-wupkt}}]+E[T_{\textit{contention-MUltrig}}]+T_{ \textit{MUIWUpkt}}+T_{\textit{MUltrigger}}+T_{\textit{MUL\_data+ack}}+T_{ \textit{MUI\_ack}}<T_{\textit{wudelay}} \tag{5}\]  
where \(E[T_{\textit{int\_transmission}}]\) is the average interval of the transmission procedure. \(E[T_{\textit{int\_transmission}}]\) depends on the scheduling procedure in AP, where each transmission interval \(T_{\textit{int\_transmission}}\) is smaller than \(T_{\textit{wudelay}}\).
## 6 Conclusions  
1. In the conventional triaxial compression deformation process, the input energy density of sandstone continued to increase, while the elastic strain energy density gradually increased in the pre-peak stage. When the energy storage limit of sandstone was exceeded, the sandstone was damaged, the elastic strain energy density decreased sharply, and the dissipative energy density increased rapidly.
2. The peak elastic strain energy density increased linearly with the confining pressure; based on this, the rock energy density failure criterion under conventional triaxial compression was established. This criterion is simple in form, contains only a few parameters, and the physical meaning of each parameter is clear. The energy density failure criterion can be transformed into the Hoek-Brown failure criterion through parameter transformation, indicating that the Hoek-Brown failure criterion is a special case of the energy density failure criterion.
3. The Mohr-Coulomb, Drucker-Prager, and energy-density criteria were used to calculate the strengths of the seven types of rocks under different confining pressures, and the energy-density criterion was the closest to the experimental results, indicating that the feasibility and accuracy of the energy-density criterion were higher than those of the Mohr-Coulomb and Drucker-Prager criteria.  
Conceptualization, L.Z.; methodology and validation, Y.C.; formal analysis, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Research Project in the Universities of Anhui Province, grant number KJ2021A1056.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.  
Figure 11: Error analysis: (**a**) the average relative error; (**b**) the root mean square error.
# Simulation Analysis of Knee Ligaments in the Landing Phase of Freestyle Skiing Aerial
## 3 Results  
### Results of Kinematic Analysis  
Using SIMI Motion, 30 groups of motions were parsed. Mean velocity (\(\overline{v}\)) and standard deviation (\(SD\)) at takeoff were calculated as: \(\overline{v}\pm SD=16.62\pm 2.51\) (m/s). In addition, the mean duration of the balancing phase (\(\overline{t}\)) and \(SD\) of landing were measured as: \(\overline{t}\pm SD=0.16\pm 0.04\). The values of \(\alpha_{L}\), \(\alpha_{R}\), \(\beta_{L}\), \(\beta_{R}\), and \(\gamma\) for the landing balancing phase are shown in Figure 3.  
Figure 3: Variation trend of human body angle during the balancing stage, where \(\alpha_{L}\) represents the angle of the left knee, \(\alpha_{R}\) the angle of right knee, \(\beta_{L}\) the angle of left ankle. \(\beta_{R}\) the angle of right ankle. \(\gamma\) represents the angle between the torso and landing slope.  
Figure 2: Mesh generation and material properties of knee models. The right figure represents assembled knee model. In the left figure, different colors define the different material properties. ACL: anterior cruciate ligament; PCL: posterior cruciate ligament; FC: femoral cartilage; MM: medial meniscus; LM: lateral meniscus; MTC: medial tibial cartilage; LTC: lateral tibial cartilage; MCL: medial collateral ligament; LCL: lateral collateral ligament.  
From videos of successful landings, a successful landing can be approximately divided into three variations, as shown in Figure 4. The first can be denoted forward landing due to the front of the skis touching the snow first with the athlete's center of gravity being in a forward orientation. This requires an aerialist to adjust their center of gravity backward to ensure a successful landing. The second is denoted neutral landing, where the front and rear of the skis touch the snow simultaneously with the athlete's center of gravity mid-way of all the variations. This requires maintenance of knee joint stability and center of gravity to ensure a successful landing. The third is described as a backward landing, where the rear of the skis touch the snow first with the center of gravity of the athlete rearward. This requires an adjustment of the center of gravity forward to ensure a successful landing.  
More apparent differences can be observed if the three are combined into one image. An integrated image can be obtained when the center points of the knee and ankle joints from the three are overlain, as shown in Figure 5. Vertical lines from the center of body weight demonstrate the different positional features. In forward landing, this line is forward of the knee joint, essentially located within the boundaries of the knee joint in neutral landing and behind the knee joint in backward landing.
# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 3 Materials and Methods
### Experimental Dataset and Setup  
A large Arabic dataset consisting of thousands of sentences, words, and social media comments was compiled from various sources and labeled accordingly into different classes. The dataset in itself is novel because it identifies words, phrases, and sentences in Arabic and divides them into seven distinct classes, making it unique among the currently published datasets for Arabic hate speech that usually contain two to four classes only. This new dataset will open the door for broad research from a wide audience of researchers in the AI field. The dataset contains a total of 4203 comments collected from various sources  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Year** & **Method Name** & **Dataset Name** & **Accuracy (\%)** \\ \hline
[8] & 2017 & CNN & Twitter hate speech & 77.75\% \\
[9] & 2017 & SVM & Hate speech detection & 77.5\% \\
[10] & 2017 & SVM & Strong hate, weak hate, and no hate & 64.61\% \\
[10] & 2017 & LSTM & Strong hate, weak hate, and no hate & 60.50\% \\
[11] & 2018 & CNN & Summary of datasets, totals for each class & 84\% \\
[14] & 2018 & Mazajak/SVM & Distribution of hate speech types & 88.6\% \\
[16] & 2020 & CNN & YouTube dataset & 79\% \\
[16] & 2020 & Deep Learning & Twitter dataset & 86\% \\
[17] & 2020 & TF-IDF & Test sets & 86.5\% \\
[18] & 2019 & SVM-nGram & nGram with reduced typed dependencies and & 89\% \\  & & hateful terms & & 84.7\% \\
[19] & 2014 & Soft Voting & Hate speech detection performance using & 87.84\% \\  & & balanced dataset & & 79\% \\
[20] & 2019 & DL-CNN & YouTube comments & 87.84\% \\
[21] & 2020 & CNN + Word2 Vec & Sunnah Shia Arabic comments dataset & 79\% \\
[22] & 2017 & CNN & Fox News user’s Comments & 77.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recent studies on text classification.  
on social media. The comments were annotated and labeled by a group of undergraduate students whose mother tongue was Arabic and who were proficient in the dialect, as shown in Table 2. Each comment was annotated and labeled by at least three students that agreed 100% on the class; otherwise, the comment was discarded. The experiments were performed using a Windows computer with i7-8700K (3.70 GHz) CPU, 32 GB memory, and NVIDIA GeForce GTX1080 GPU; the software code was written in Python.