esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Volume of Changes  
To understand the extent of changes induced by perturbations, it is valuable to not only compare the affected areas, but also assess their magnitude by comparing the peak values (Table 2) observed in each curve with those documented during unperturbed gait.  
In analysing extreme values for gait with perturbations compared to gait without them, it becomes evident that most substitutions occurred sequentially during the perturbation in the Initial Contact phase (six significant for kinematic parameters and four for kinetic parameters), Pre-Swing (five significant for kinematic parameters and two for kinetic parameters), and Mid Stance phase (two for kinematic parameters and three for kinetic parameters). Furthermore, in most cases, there were significant increases in extreme values in the presence of perturbations.  
It is worth highlighting that the most substantial increases in kinematic parameters are for ankle plantar flexors, which exceed those achieved during treadmill gait by 382.06%, 321.98%, and 241.13% for perturbations in the IC, PS, and MS phases, respectively. A less marked but consistent trend is evident for ankle dorsiflexors, with values for IC, PS, and MS perturbations exceeding those recorded for treadmill gait by 36.61%, 23.32%, and 12.84%, respectively. The hip joint tends to reduce its maximal extension compared to treadmill gait by 36.62%, 1.97%, and 22.96% for perturbations in the IC, PS, and MS phases, respectively. Simultaneously, flexion values increased by 8.8% for gait with perturbation in the MS phase and by 25.43% and 29.71% during perturbation in the PS and IC phases, respectively. The knee joint remains in flexion, with maximum values increasing by 12.26% during perturbations in the IC phase and by 10.13% during PS phase perturbations. Notably, knee extension was observed during treadmill gait, but this phenomenon was not repeated during any perturbation (Table 2).  
The response to changes in kinematic parameters is also evident for kinetic parameters. Notably, the alterations in ankle joint torques are relatively minor compared to the previously discussed kinematic values. This phenomenon can probably be attributed to the involvement of the bi-articular muscles, in this case directing their influence mainly to the knee joint. Within the knee joint, a significant increase of 248.48% is observed in the flexion torque when perturbation is introduced during the IC phase, in contrast to gait without perturbation. Meanwhile, the hip joint experiences a substantial increase in the activation of hip flexor muscles, particularly during perturbations in the IC and PS phases, with increases of 78.69% and 14.75%, respectively (Table 2).
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 5 Conclusions  
In conclusion, the comprehensive analysis conducted in this paper sheds light on a comparison between the performances of WiFi and mmWaves-based V2V communication systems, using realistic stochastic geometry tools. Through rigorous examination encompassing LoS and NLoS scenarios in both 2D and 3D modeling contexts, the corresponding STP and AT expressions for both considered technologies have been derived. Then, numerical results have been provided and analyzed to validate the derived expressions and to investigate the corresponding transmission performances. The results show that the transmission distance and the vehicles' density have significant impacts on the overall system performances of both technologies. In fact, for a short transmission distance and a low vehicle density, the mmWaves offers a higher transmission performance than that of the WiFi system, However, the WiFi communication systems is more efficient for long transmission distances. Furthermore, the findings underscore that in V2V communications, 3D modeling consistently outperforms 2D simulations, highlighting the crucial role of vertical space in optimizing system performance.  
Methodology, M.R. and A.O.; Software, M.R.; Validation, A.O., S.B., H.C. and R.B.; Investigation, M.R.; Writing--original draft, M.R.; Writing--review & editing, A.O.; Supervision, A.O.; Project administration, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
# The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings
### Performance Evaluation
#### 2.3.3 Results and Discussion
Comparing the two approaches, the use of an already classified dataset with the appropriate degradation states gives better results. These results are evidenced by the fact that this approach does not have difficulties in determining the degradation state of the crosswalks, even when the images submitted for classification have regions with shadows or excessive brightness. Figure 15 shows some comparisons between the two approaches. It is important to note that both were applied in the same context and on the same crosswalks to ensure the same evaluation conditions. Figure 15a(1,2) shows the detection using the first approach considered--YOLOv4-tiny and Adaptive Threshold. In this case, the crosswalk is classified with moderate wear due to the presence of shadows. Figure 15a(3) shows the result obtained under the same conditions but using the second approach--YOLOv4-tiny with three classes. In this case, the classification obtained corresponds to the true state of wear of this walkway, i.e., no wear. In Figure 15b, both techniques show the same classification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **mAP (\%)** & **Overall (\%)** \\ \hline No wear & 82.13 & \\ Moderate wear & 54.72 & 71.21 \\ Severe wear & 76.96 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the YOLOv4-tiny model for the 3-class dataset. Figure 14: Examples of Adaptive Threshold detection in scenes without shadows: (**a**) Example 1: detection (1), identification (2), and classification (3) process; (**b**) Example 2: detection (1), identification (2), and classification (3) process. Upon examination of the outcomes, it is proposed that the second approach, as detailed in the subsequent section, be employed for the development of the prototype. This approach entails the classification of a dataset into three distinct wear classes. As evidence, this approach demonstrates efficacy in a multitude of conditions, including those with varying lighting and the presence of shadow projections on crosswalks.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 2 Locata Core Technologies
### Locata Rover  
A Locata Rover is a mobile or user receiver whose output is a PNT solution normally expected of a GNSS receiver. Currently, the Locata Rover (like the LocataLite) uses Field Programmable Gate Array (FPGA) technology in a modular hardware design with separate receiver and RF transmission boards. It is approximately half the size of the LocataLite, since the transmitter board is not required. Raw measurement data (code pseudorange and carrier phase) from the receiver can be obtained. Real-time positioning rates of up to 25 Hz are possible by streaming the receiver data to the Locata Integrated Navigation Engine (LINE) application, which runs on either an external computer or an internal processor.  
The Locata Rover and LINE use a Direct Carrier Ranging (DCR) algorithm to determine its position and timing from measurements that were made to at least four (for three-dimensional (3D) positioning) or three (for two-dimensional (2D) positioning) LocataLites. This algorithm is similar to that of standard GNSS Single Point Positioning (SPP), but it uses carrier phase measurements instead [17]. In order to perform DCR, the carrier phase ambiguities must first be resolved. Currently, this is done either via the Known Point Initialisation (KPI) method or an On-the-Fly (OTF) procedure [16].
## 3 Perceptual Evaluation
### BRIR Synthesis
#### 3.2.3 Sound Source Directivity  
The influence of adding a directional characteristic to the sound source was investigated in a listening test. Thereby, a data set of measured BRIRs was adjusted to correspond to a different position in the room. The BRIR synthesis from Section with the enhancement described in Section 2.2.3 was used for this purpose. The measured position corresponds to position \"D1\" in Figure 13. The loudspeaker used for the measurements was a Geithain MO-2. The directivity characteristic of the loudspeaker was simulated with the software VituixCAD [46] using data from the technical specifications of the loudspeaker. Of course, this is just a coarse approximation of the real speaker.  
Eighteen listeners (14 male and four female) participated in a comparison test to rate the overall difference on a 6-point scale. The new synthesized positions \"A2\", \"C2\", \"C5\", and \"D2\" with and without directivity correction had to be compared with a reference, which was the real BRIR measurement at this position. The listening test was conducted without tracking of the head orientation, and therefore the participants had to stand on a specified position and were requested to listen without rotation of the head. A music and speech sample were used as audio content to auralize the measured and synthesized BRIRs for evaluation purposes. An analysis of the ratings using a Wilcoxon test showed no difference (\(p<0.05\)) between the audio content, which were therefore evaluated together. Figure 17 displays the results for an incidence angle of 30\({}^{\circ}\) and the positions \"A2\", \"C5\", and \"D2\". A value of \"0\" indicates that there was no perceptual difference. The higher the value, the higher was the difference between the auralizations of the synthesized and the measured BRIRs. The ratings of the test participants show a clear decrease of the perceived overall difference when the SSD is included (D-1M) compared to the results without the  
Figure 16: Box-plots for Localization Stability (LS) rated on the scale shown in Figure 12; Differences between distributions with \(p<0.2\) using a Wilcox-test are indicated; scale labels: \(1=\) instable, \(5=\) stable (data from the work in [43]).  
Figure 15: Boxplots for Overall Impression (OI) rated on the scale shown in Figure 12; Differences between distributions with \(p<0.2\) using a Wilcox-test are indicated; scale labels: \(0=\) extremely bad, \(1=\) bad, \(2=\) poor, \(3=\) fair, \(4=\) good, \(5=\) excellent, \(6=\) ideal (data from the work in [43]).  
inclusion of the SSD (1M). The stimuli without directivity correction reach a median of 3.5 to 2 scale points compared to a median of 1 to 0 for the stimuli with directivity.  
The evaluation showed that the integration of the SSD results in an auralization, which differs to a lesser degree from the original BRIR and therefore provides an enhanced listening experience. A more detailed description of the experiments and their results is discussed in [33].
## 3 Calculation of Groundwater Drawdown Difference  
### Methods and Assumptions  
The dewatering process inside a foundation pit with a suspended waterproof curtain can be generally divided into two stages. In the first stage, the groundwater in the phreatic aquifer flows into the foundation pit through the opening between the end of the curtain and the impermeable layer. In the second stage, the groundwater flows upwards to the bottom of the pit as shown in Figure 2.  
According to Groundwater Hydraulics [45; 46], groundwater flows into the filtered screen of partially penetrating wells during the process of pumping. During the dewatering process, the deep foundation pit with a suspended waterproof curtain can be regarded  
Figure 1: Schematic diagrams of foundation pit dewatering with waterproof curtain: (**a**) closed waterproof curtain, (**b**) suspended waterproof curtain.  
Figure 2: Diagram of foundation pit dewatering with a waterproof curtain and flow channels.  
as a partially penetrating well with a large diameter. Therefore, the dewatering process of a foundation pit with the suspended waterproof curtain can be described with the formula of a partially penetrating well with a large diameter, corresponding to the first stage in dewatering.  
In order to describe the dewatering process in a foundation pit with a suspended waterproof curtain, an analytical solution to the unsteady flow model is established [30; 31], and the following assumptions are made: (1) The phreatic aquifer soil is homogeneous and isotropic, and extends indefinitely with uniform thickness. (2) Vertical flow is negligible. (3) The head within the well does not vary spatially. (4) The porous medium and fluid are slightly compressible. (5) The groundwater seepage follows [PERSON]'s law. (6) The waterproof curtain and bottom of phreatic aquifer are impermeable. (7) Ignore the fluctuation of strata and the leakage from other aquifers. (8) The suspended waterproof curtain of the foundation pit can be regarded as a partially penetrating well with a large diameter.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 3 Methodology
### Method 3: Lossless Remote Video Online Measurement
The test accuracy is similar to that of Method 1, and the remote measurement function of Method 2 can be realized and is not limited to the scenario with a GPS signal. It can be used as a convenient, continuous, and reliable method for popularization and application.
# A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning
###### Abstract  
Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory.  
machine learning; judicial sentencing; abductive learning; artificial intelligence +
Footnote †: journal: Applied Sciences
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 4 Testbed and Evaluations
### Deployment Approaches
#### 4.1.2 Centralized Approach  
In this approach (see Figure 5), there is one _Central Control Platform_ that handles attack definitions from all the connected networks. Upon successful verification, the platform then forwards the attack information to the connected SDN controllers. The SDN controllers upon receiving the attackdefinitions install the flows defined in the policy after verification check. This approach is helpful in preventing hop-by-hop dissemination of attack definition, specially in scenarios where a huge amount of traffic is being handled by SDN controllers. Moreover, in this approach the flows installed can be targeted depending upon the destination address. As a result, not every SDN network has to install all the flows. The central approach only forwards the relevant flows to the intended SDN controller hence saving the TCAM memory of OpenFlow switches.  
Figure 5: Centralized Policy Distribution.  
Figure 6: Mesh Connectivity for Direct Policy Distribution.
## 9 Fundamental Concepts Underlying Adaptive Filters
### Exploring the Dynamics of Least Mean Squares (LMS) Algorithm  
Suppose we have a reference signal, \(n(t)\), which represents an estimate of the artifact present in the EEG signal, \(x(t)\). The filtered output, \(y(t)\), is the result of the convolution of the filter weight vector, \(\mathbf{w}\), with the reference signal, \(n(t)\), represented as [107, 108]:  
\[y(t)=\sum_{i=1}^{M-1}w_{i}(t)\cdot n(t-1) \tag{17}\]  
where \(M\) is the number of filter coefficients, and \(w_{i}(t)\) is the i-th weight of the filter at a time \(t\). The error, \(e(t)\), is therefore the difference  
\[e(t)=x(t)y(t). \tag{18}\]  
The weight update formula in the LMS algorithm is used to minimize the mean square error between the desired signal and the filter output. Weight updates are given by:  
\[w_{i}(t+1)=w_{i}(t)+\mu\cdot e(t)\cdot n(t-1), \tag{19}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **PCA Approach** \\ \hline
**Strengths** & Reduction in data dimensionality favoring the removal of artifacts. Simplifying data into core components. \\ \hline
**Weaknesses** & PCA assumes that artifacts are the components with the greatest variance in the data. Loss of temporal information. Difficulty in processing data affected by uncertainties and/or inaccuracies. \\ \hline
**Opportunities** & Combining PCA with other techniques. Development of improved algorithms. \\ \hline
**Threats** & Evolving recording techniques introduce new types of artifacts. High computational complexity. \\ \hline \hline \end{tabular}
\end{table}
Table 5: SWOT analysis for PCA.  
where \(\mu\) is the learning rate, a key parameter that affects the speed of convergence of the algorithm towards optimal error minimization. This parameter must be chosen carefully to ensure that the algorithm converges without becoming unstable.  
By iterating over \(t\), LMS dynamically adapts the filter weights based on the calculated error at each step, thus optimizing the weights to minimize the error between the desired EEG signal and the filter output. This adaptive process allows the artifact \(x(t)\) to be effectively isolated and removed, without the need for exact a priori knowledge of the nature of the artifact.
# Vibration Isolation of a Surveillance System Equipped in a Drone with Mode Decoupling
## 2 Theories for System Identification
### Mathematical Model of a Multi-DOF System
Therefore, responses can be predicted by solving the second order ordinary differential equation for each coordinate. Coordinates are coupled--dependent--to each other in a coupled system (Figure 1b). Therefore, \(k_{ij}=c_{ij}\
eq 0\), if \(i\
eq j\). Hence, the EOM for a coupled two-DOF system is derived as  
\[\left[ \begin{array}{cc}m&0\\ 0&m\end{array} \right]\left[ \begin{array}{cc}\ddot{q}_{1}\\ \ddot{q}_{2}\end{array} \right]+\left[ \begin{array}{cc}c_{11}&c_{12}\\ c_{12}&c_{22}\end{array} \right]\left[ \begin{array}{cc}\dot{q}_{1}\\ \dot{q}_{2}\end{array} \right]+\left[ \begin{array}{cc}k_{11}&k_{12}\\ k_{22}&k_{22}\end{array} \right]\left[ \begin{array}{cc}q_{1}\\ q_{2}\end{array} \right]=\left[ \begin{array}{cc}c_{11}&c_{12}\\ c_{12}&c_{22}\end{array} \right]\left[ \begin{array}{cc}\dot{q}_{1b}\\ \dot{q}_{2b}\end{array} \right]+\left[ \begin{array}{cc}k_{11}&k_{12}\\ k_{12}&k_{22}\end{array} \right]\left[ \begin{array}{cc}q_{1b}\\ q_{2b}\end{array} \right]. \tag{13}\]  
Hence, this problem can be solved by transforming the original coordinate into a modal coordinate using the modal matrix. One can solve the decoupled EOM for each modal coordinate; after that, the modal coordinate responses are transformed into the original coordinate again using the modal matrix [15]. This EOM can be extended to a three-DOF system as follows:  
\[\left[ \begin{array}{cc}m&0&0\\ 0&m&0\\ 0&0&m\end{array} \right]\left[ \begin{array}{cc}\dot{q}_{1}\\ \ddot{q}_{2}\\ \ddot{q}_{3}\end{array} \right]+\left[ \begin{array}{cc}c_{11}&c_{12}&c_{13} \\ c_{12}&c_{22}&c_{23}\\ c_{13}&c_{23}&c_{33}\end{array} \right]\left[ \begin{array}{cc}\dot{q}_{1}\\ \dot{q}_{2}\\ \dot{q}_{3}\end{array} \right]+\left[ \begin{array}{cc}k_{11}&k_{12}&k_{13} \\ k_{12}&k_{22}&k_{23}\\ k_{13}&k_{23}&k_{33}\end{array} \right]\left[ \begin{array}{cc}q_{1}\\ q_{2}\\ q_{3}\end{array} \right]=F(t) \tag{14}\]  
where \(F(t)\) denotes an external force. However, the assumption to solve the multi-DOF system using a modal matrix in a modal coordinate considers that parameters, including equivalent mass, damping coefficients, and stiffness coefficients, are known. It suggests that a method to identify these parameters is indispensable. The following two subsections demonstrate how to identify the system parameters in Equations (12)-(14) based on the modal testing, i.e., a frequency response function.
## 1 Introduction
* To address security and efficiency concerns in data transmission among traditional enterprises, we introduce an adaptive encryption strategy tailored for cloud-edge and edge-edge scenarios. This approach balances secure data transmission with low latency, improving communication efficiency across industrial collaborations. * Recognizing the importance of enterprise credibility, we propose a mechanism for assessing corporate integrity. This involves periodic collaborative supervision by third-party trusted institutions to ensure quality control and other critical aspects of industrial collaboration.
## 2 Materials and Methods
### Traffic Condition Prediction Model
#### 2.4.1 Correlation Analysis  
Undoubtedly, having a large dataset is a bonus for predicting accident targets. However, it also brings up the issue of variable selection. This includes a large number of variables directly in classification or regression models that may lead to overfitting, which in turn may affect the interpretation of the relationships between variables and, more importantly, the use of the model in the prediction phase. Therefore, it is important to analyze our data to determine which variables are the main influencing factors of the dependent variable. In order to achieve this, Pearson correlation analysis was used in this paper to study the correlation between independent and dependent variables, with the specific formula as follows [27]:  
\[\rho_{xy}=\frac{COV(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}} \tag{6}\]  
The correlation coefficient \(\rho_{xy}\) of independent variable \(X\) and dependent variable \(Y\), \(COV(X,Y)\) is the covariance between variables \(X\) and \(Y\), and \(D(X)\) is the variance of the variable.