esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 5 Conclusions  
We suggest a novel method for controlling the traffic of AVs at merging lanes while maintaining a high traffic flow rate. The method is based on a large preparation section and platoon merging.  
The main idea is to disperse the congestion along the road, to enable driving at predefined moderated speeds and to merge at a high speed.  
The merge percentage results during rush hour that were achieved by the suggested method were as high as 23%, and flow remained high. Higher flow rates were obtained when the preparation distance--between the road entrance and the merging point--was long, where large platoon sizes were set, and the onset merging velocity was reduced to 75% of the road speed.  
The paper assumes 100% AV, although this is still far from being realized. In future work, it will be interesting to study the impact of the strategy under market mixes of AV and regular vehicles. Within this context, one should pay attention to the associated computational costs for obtaining these optimized gap profiles. Other research directions can explore parameters for other speed limits (30-100 km/h) or of bigger networks (with more intersections), and they can optimize the specific parameters to adjust for passenger comfort.  
**Author Contributions:** Conceptualization, N.S., S.G. and I.A.; methodology, N.S., S.G. and I.A.; software, S.G.; formal analysis, N.S. and S.G.; writing N.S. and I.A.; visualization, N.S., S.G. and I.A.; supervision, N.S. and I.A.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** [PERSON] was supported by a scholarship provided by Ariel University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Program
### General Conditions of the Tests  
To remove any factors that might have additional influence on the combination of static and cyclic stresses in the behavior of the soil, the following conditions were set for all the tests:  
* Undisturbed samples.
* Specimens with 7 cm diameter and 1.91 cm height.
* Consolidation stress equal to the effective consolidation stress \"in situ\" (\(\sigma^{\prime}_{\text{ov}}\)). Since the samples tested were extracted by drilling on the port caissons of the Prat dock, the effective consolidation stress, applied on the samples, was estimated by the following expression: \[\sigma^{\prime}_{\text{ov}}=\sigma^{\prime}_{\text{ovz}}\ +\ \Delta\sigma^{\prime}_{\text{z}}\] (1) where \(\sigma^{\prime}_{\text{ovz}}\) is the effective consolidation stress \"in situ\" by own soil weight, \(\Delta\sigma^{\prime}_{\text{z}}\) is the increase in effective stress for port caisson action, and z the depth at which the sample was taken.
* Undrained conditions and evaluation of the generated pore water pressure. According to the criteria of [PERSON] and [PERSON] [36], simple shear tests at constant volume are equivalent to undrained tests, and the change in vertical stress applied to the sample is equivalent to the change in pore water pressure, which would be generated within a sample subjected to simple shear test with restricted drainage and constant axial stress. This was verified by [PERSON] and [PERSON] [35] by a really undrained test in which they measured the pore water pressure generated at the base of the specimen.
* End of the test for permanent shear strain (\(\gamma_{\text{p}}\)) equal to 15%, or cyclic shear strain (\(\gamma_{\text{c}}\)) equal to 15%, or number of cycles (N) equal to 1300, or evaluated pore pressure equal to 95% of the effective consolidation stress.
* Sinusoidal wave with amplitude equal to the cyclic stress (+/- \(\tau_{\text{c}}\)) and period equal to 10 s. A period equal to 10 s was chosen so that the results of this investigation could eventually be used in design previous stages related to port structures in areas near the site where the samples were extracted, that is, near the Prat pier.
* Controlled deformation during the monotonic stage. The tangential deformation rate was equal to 0.015 mm per minute, or equal to 4% per hour.
# Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
## 3 Experiment and Discussion
### Effect of Parameter on Corner Error
#### 3.1.2 Effect of Pulse-off Time (OFF) on Corner Error  
The OFF parameter was significant for different corner degrees, which was indicated by a \(p\)-value less than 0.05. Additionally, the results showed that the OFF\({}^{2}\) term provides secondary contribution to the 30\({}^{\circ}\) and 60\({}^{\circ}\) corners. Table 7 shows the experimental results of corner machining for different pulse-off time (OFF) parameter settings. It can be seen that the corner error decreased when the pulse-off time (OFF) increased from 8 to 12 for the 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) corners. However, the corner error increase was steep when the corner degree decreased from 60 to 30 degrees. Meanwhile, the corner error slightly increased when the corner degree decreased from 90 to 60 degrees. The machining time increased with the increase in pulse-off time (OFF).
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 3 UFBS Placement Procedure
### Computational Complexity of k-Means and k-Medoids Algorithms  
Another crucial aspect is to estimate the computational complexity of the examined k-means and k-medoids algorithms based on their respective methods as described in Algorithms 1 and 2, respectively. K-means is a centroid-based algorithm, and k-medoids is a medoid-based algorithm.  
The computational complexity of the k-means algorithm has been proven to be \(O(nkId)\), where n is the number of data points, k is the number of clusters, I is the number of iterations, and d is the number of dimensions [33]. It uses the mean of the data points to calculate the cluster centroid and updates the assignment of the data points to the closest cluster centroid. The algorithm requires multiple iterations until convergence. The time complexity of the k-means algorithm is affected by the number of data points, the number of clusters and the number of dimensions.  
The computational complexity of the k-medoids algorithm has been proven to be \(O(k(n-k)^{2}I)\), where n is the number of data points, k is the number of clusters, and I is the number of iterations [34]. K-medoids selects a single data point as the representative of a cluster, known as the medoid, and updates the assignment of the data points to the closest medoid. The algorithm requires multiple iterations until convergence. The time complexity of the k-medoids algorithm is affected by the number of data points, clusters, iterations, and the distance metric used.  
In summary, both algorithms have a polynomial time complexity, and the main difference is that k-means use centroids, and k-medoids use medoids as the center of the cluster. As a result, the k-means is sensitive to the initial choice of centroids, while k-medoids is less sensitive and tends to find the global optimum more quickly.
## 4 Results and Discussion
### Spatial TDR Waveform Variation along the Sand Column in the Drainage Test  
The TDR traces for the gravitational drainage test are shown in Figure 7. Two TDR pulses are separately sent to the sensor in the column from the top and bottom. A pair of TDR traces measured from both ends of the sensor at the same time can be used to conduct the inverse analysis. According to Figure 6(a) (Terminal Top) and b (TerminalBottom), it is clear that the reflection coefficient of TDR traces increases with the falling water table and water draining out of the soil column. Since air enters the pore space at the top end of the column with the dropping of the water table, TDR traces measured from the top and end show an increase in the reflection coefficient up to 0.3. Meanwhile, the second reflection points in these TDR traces, indicating the transition between the sensor and the second coaxial cable, decrease from 110 to 100 ns. The TDR traces measured from the bottom display significant changes at the end of the TDR trace, showing a more pronounced transition from the sensor to a coaxial cable (drop after the second rise in the signal). The comparison of the signals demonstrates the strong variation in the TDR traces, corresponding to the change in the moisture profile. It further proves the capability of the spatial TDR technique for characterizing information on moisture content dynamics in large-scale unsaturated soil experiments.
# Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data
## 3 Experiments and Results
### Analysis of Color Indicators and Emotional Perception
#### 3.3.2 OLS Regression Analysis Versus Geographically Weighted Regression Analysis  
The emotional experiences of residents and tourists are significantly influenced by color complexity and coordination. However, OLS is unable to fully account for regional differences in response to color features. GWR analysis addresses this limitation and elucidates the spatial heterogeneity of color features on emotional scores. To gain a deeper understanding of the specific effects of color complexity and coordination on emotional perceptions and their spatial differences, this study employed both OLS and GWR models to conduct regression analyses with color complexity and coordination as independent variables, respectively (Table 3). After accounting for spatial heterogeneity, the R\({}^{2}\) values of the GWR model were significantly higher than those of the OLS model. Additionally, the AICc values were all smaller than those of the OLS model. The GWR model demonstrated a superior performance to the OLS model in terms of the interrelationship between color and emotion, as well as in terms of the fitting effect. While the Koenker (BP) statistic was not significant for some emotional scores, this does not preclude the existence of spatial heterogeneity. The GWR model, which allows regression coefficients to vary spatially, was better able to capture this spatial heterogeneity and more effectively explain the role and spatial
## 4 Results and Discussions  
### Partial Discharge Inception Voltage  
The PDIVs of SF\({}_{6}\) and g3 with 4% NOVECTM 4710 in the POC and the POE at different pressures are shown in Figure 3. It can be observed that PDIVs almost increased linearly with the gas pressure in both gases in two types of electrode systems. In addition, SF\({}_{6}\) had a steep increase in PDIV with gas pressure compared with g3. At the same gas pressure, the PDIVs of g3 were about 76%-81% of that of SF\({}_{6}\) in the POC, and were about 78%-84% of that of SF\({}_{6}\) in the POE. In two types of electrode  
Figure 1: Configuration of experimental setup. PD: partial discharge; DSO: digital oscilloscope; PXI: peripheral component interconnect eXtensions for instrumentation.  
Figure 2: Electrode system. (**a**) Protrusion on conductor (POC); (**b**) Protrusion on enclosure (POE).  
systems, the PDIVs in g3 gas at 0.5 MPa were equivalent to SF\({}_{6}\) at 0.3 MPa. To be specific, the PDIV of 0.5 MPa g3 was 12.8 kV in the POC, whereas it was 12.7 kV in 0.3 MPa SF\({}_{6}\). In the POE, the PDIVs of 0.5 MPa g3 and 0.3 MPa SF\({}_{6}\) were 12.1 kV and 11.8 kV, respectively. Therefore, the use of g3 with 4% NOVEC(tm) 4710 as an alternative gas to SF\({}_{6}\) requires an increase in gas pressure in regards to PDIV.
## 7 Simulation Results
Power consumptions of three actuators used in above-knee prosthesis are also illustrated as a table (Table 6). Power consumptions of the three actuators are computed for minimum, maximum and optimized mass values. The optimized stiffness values produce acceptable displacement errors [34] along the \(x\), \(y\) and \(z\) axes. The maximum displacement errors obtained from this study are as follows: 0.7394 mm along the \(x\) axis, 0.7392 mm along the \(y\) axis and 0.0274 mm along the \(z\) axis. The displacement errors along the whole walking cycle are shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Consumed Powers**} & **Actuator 1** & **Actuator 2** & **Actuator 3** & **Total** \\  & **(Watt)** & **(Watt)** & **(Watt)** & **(Watt)** \\ \hline Minimum power & 16.25 & 14.03 & 0.02 & 30.30 \\ Maximum power & 23.78 & 25.21 & 0.03 & 49.02 \\ Optimized power & 17 & 15.07 & 0.02 & 32.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Power consumption of three actuators of above-knee prosthesis. Figure 11: Power consumptions of the actuators for a gait cycle. Figure 12: Displacement errors along (**a**) \(x\) axis (**b**) \(y\) axis (**c**) \(z\) axis.
## 2 Materials and Methods
### Data Acquisition  
UAV imagery was collected using a DJI Mavic 3E (DJI, Shenzhen, China) platform equipped with a 20 MP 4/3-inch CMOS sensor and a wide-angle mechanical-shutter camera. The survey was conducted from 15 to 25 October 2024, in four major orchard counties of Hotan Prefecture--Pishan, Minfeng, Cele, and Moyu. All flights were carried out between 12:00 and 14:00 local time under predominantly clear-sky conditions to ensure uniform illumination and minimize shadow artifacts.  
The UAV followed a pre-planned flight path at a fixed altitude of 108.4 m and a ground speed of 8 m/s, with 75% forward and side overlap. The gimbal maintained a nadir orientation (82\({}^{\circ}\) field of view). Each image had a resolution of 5280 \(\times\) 3956 pixels, corresponding to a ground sampling distance (GSD) of about 5 cm. Centimeter-level geolocation accuracy was achieved using Real-Time Kinematic (RTK) positioning.  
The survey focused on six representative fruit tree species: walnut (_Juglans regia_), prune (_Prunus domestica_), apricot (_Prunus armenica_), pomegranate (_Punica granatum_), saxaul (_Haloxylon ammodendron_), and cherry (_Prunus avium_). Approximately 50,000 aerial images were acquired, covering three orchard types, namely regularly spaced plantations, irregularly clustered stands, and heterogeneous mixed-species zones.  
All images were processed through aerial triangulation and orthorectification to generate high-resolution, geo-referenced orthophotos, which served as the basis for manual annotation, model training, and performance evaluation.
## 4 Numerical Model
### Influence of Water-Cement Ratio
#### 4.5.1 Ground Settlement Analysis  
Figure 19 shows the settlement of each monitoring point on the ground when the water-cement ratio was 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00. The position corresponding to x = 0 was the central axis of the left tunnel, the negative direction was away from the tunnel, and the positive direction was close to the right tunnel. When the water-cement ratio \(\eta\) was 0.90, 0.95, and 1.00, the ground settlement curve was compared. The maximum settlement varied greatly within the range, indicating that the reinforcement effect changed greatly with the increase or decrease of the water-cement ratio \(\eta\). When the water-cement ratio \(\eta\) = 0.75, 0.80, and 0.85, the variation range of the maximum settlement was small. When the water-cement ratio varied from 0.75 to 1.00, the maximum ground settlement continued to increase with the increase of the water-cement ratio, and the increase amplitude gradually becomes larger. The result indicated that when the water cement ratio was high, it had a great influence on the reinforcement effect.  
Figure 18: Relationship between the maximum displacement of the pipeline and grouting reinforcement layer thickness.  
Figure 20 shows the relationship between the maximum ground settlement and the slurry water-cement ratio \(\eta\). When the slurry water-cement ratio \(\eta\) = 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00, the maximum surface settlement was 12.5 mm, 13.5 mm, 16.1 mm, 23.8 mm, 56.9 mm, and 125.1 mm, respectively. The variation range of the maximum settlement was 1.0 mm, 2.6 mm, 7.7 mm, 33.1 mm, and 68.2 mm, respectively. The variation range of ground settlement shows that the maximum change in the settlement amount between \(\eta\) = 1.00 and \(\eta\) = 0.95 was the largest, indicating that when the water-cement ratio changed from 0.90 to 1.00, the reinforcement effect obtained was very significant. When the water-cement ratio \(\eta\) changed from 0.75 to 0.80, the variation range of maximum settlement was only 1.0 mm and the reinforcement effect was not obvious. In general, when the slurry water cement ratio \(\eta\) = 0.90 or \(\eta\) = 0.85, the maximum ground settlement caused by excavation was small and the reduction was large, the reinforcement effect obtained was better and the benefit was higher.  
Figure 19: Ground settlement of monitoring points.  
Figure 20: The relationship between the maximum ground settlement and the slurry water–cement ratio \(\eta\).
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 4 Employment Scam Detection & Relevant Problems
### Opinion Fraud  
Opinion fraud, also known as review spamming, is the deliberate posting of deceptive and misleading fake reviews to promote or discredit target products and services such as hotels, restaurants, publications and SaaS (2016) products. The main obstacle while designing countermeasures is the unpredictability of human reviewing methods. Popular review hosting sites such as Yelp.com have built proprietary domain-specific review spamming filters (2016). According to [PERSON] et al. the great mass of the proposed methodologies focus on spam review detection compared to spammer and spammer groups detection (2016).  
Supervised learning is the dominant technique in opinion fraud detection. Most of the employed features fall into three groups namely (a) linguistic features; (b) behavioral features and (c) network features. Such features derive from the content of the review, the metadata information and the information about the targeted product or service. Previous work in the field pieced together content based features, genre identification, POS analysis, psycholinguistic deception detection, n-gram-based text categorization techniques (2016; 2016; 2017) as well as deep syntactic stylometry patterns based on context free grammar parse trees (2017). [PERSON] et al. (2017) analysed a real-world dataset provided by Dianping.com and generated features based on spatial and temporal patterns by leveraging the dependencies among reviews, users and network characteristics such as IP addresses. [PERSON] et al. studied the burst patterns of review scammers and employed Markov random fields to model such behaviors (2017).  
Other researchers focused on identifying novel detection techniques that can be generalized across domains. They also tried to overcome the main obstacle in opinion fraud detection, that is the lack of ground trust information by employing unsupervised classification models and co-training algorithms with unlabelled data. To this direction, [PERSON] et al. (2017) proposed FRAUDEAGLE, a framework where fake review detection is modeled as a network classification task on a signed bipartite network containing three type of nodes, namely users, reviews and products. [PERSON] et al. used collective Positive-Unlabeled Learning (PU learning) trained on language independent features (2016). For a systematic review of the opinion fraud detection techniques the reader should refer to the work of [PERSON] et al. (2016).
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### Experimental Evaluation  
The power circuit of the developed EV SRM drive in conducting the grid-connected G2V/V2G operations is shown in Figure 18, which consists of a CLLC resonant DC/DC converter and a six-switch inverter. In a G2V charging operation, the three-phase inverter acts as a PFC front-end SMR and charges the battery through the CLLC resonant converter. Regarding the V2G operation, the battery discharges power to the utility grid through the same power circuits. Reactive and harmonic power compensations can also be achievable.
## 6 Machine Learning and 5G Security
### Machine Learning Applications in 5G Security
#### 6.2.1 Anomaly Detection  
Anomaly detection is a paramount task in maintaining the security of 5G networks. This involves the identification of unusual patterns or deviations from the expected behavior, which could indicate potential threats or network malfunctions. Several machine learning techniques have been employed in anomaly detection tasks. For instance, clustering algorithms like k-means can group network traffic data based on similar characteristics, allowing for the detection of outliers or anomalies [125]. Classification algorithms, on the other hand, can be trained to distinguish between normal and anomalous network behaviors. In particular, SVMs and decision trees have shown effectiveness in this regard. Moreover, advancements in machine learning have brought forth more sophisticated techniques such as deep learning, which are capable of detecting more complex and subtle anomalies. Techniques such as autoencoders, a type of artificial neural network, are able to learn a compressed representation of the input data and then reconstruct the original data. When trained on normal data, the model would generate high reconstruction errors for anomalous data, thereby identifying the anomalies [125]. These machine learning-driven anomaly detection techniques not only enhance the accuracy of detection but also reduce the time taken to detect and mitigate potential security threats, significantly boosting the resilience of 5G networks.