esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 3 Methodology
### Simulators and Frameworks
#### 3.2.2 OMNeT++  
OMNeT++ is a discrete-event simulation library and platform developed to perform network simulations. These network simulations can include anything from wired to wireless communication to domain-specific networks, such as wireless sensor networks, ad hoc networks, and so on. These networks are modeled using external framework projects and imported into the OMNeT++ simulator. INET [52] is a popular framework that contains models for a diverse range of network protocols, such as IPv6, Border Gateway Protocol (BGP), and many Ad hoc routing protocols.  
OMNeT++ employs a modular architecture for models, referred to as components. These components are written in C++ language and then integrated using higher-level language NF network Description (NED) into larger components or models. This modular architecture enables researchers to easily import and embed external models into the applications for simulation.  
In this research, OMNeT++ is the core simulation platform, INET provides the communication libraries such as routing protocols and wireless technologies, while Vehicles in network simulation (Veins) [53; 54] provides cars and road network libraries to create VANETs. All of this is performed within the specific traffic models generated by SUMO on the imported OSM map, as described by the system design in Figure 3.  
Figure 3: Simulationdesign of the experiments and a subset of the modules utilized from each component.
## 5 Discussion
### Relationship between Stratographic Lithology and Landslides  
The landslide surface is dominated by Upper Pleistocene aeolian loess (Q\({}_{S}\)\({}^{ext}\)) (Figure 8a). The overall structure of loess is loose, with large porosity, low strength, vertical joints, and developed fissures. The shear and compressive strength are sensitive to water, and the strength when in contact with water decreases significantly. The loess is underlain by Neoproterozoic mudstone, which is poorly lithogenic and broken. These two sets of strata not only have low strength but also are extremely sensitive to changes in water content. They are highly susceptible to softening when they encounter water, which makes it easy to form a weak sliding surface and create a slippery layer. Concerning water from rainfall, landslides in such stratigraphic environments are susceptible to sliding due to the weak slippery layer [45]. Moreover, other studies have confirmed that the stratigraphic lithology can control and facilitate the progression of a landslide [46; 47].  
According to the site investigation and drilling engineering exposure (Figure 8b-d), the topsoil layer consists of mainly arable soil or slope deposit loess. The middle and lower soil layers are mainly aeolian loess with a relatively greater depth of approximately 2 to 30 m. This layer is loose in texture, with large pores and developed vertical joints, and it is characterized by wettability, low strength, poor engineering geological properties, and susceptibility to water saturation. The underlying bedrock is of the Mining Formation, and the exposed strata are highly weathered or even almost fully weathered mudstone, with extremely developed fissures. The porosity of the upper loess is large, which makes it easy for surface water to penetrate. The weathered mudstone is softer in texture, and it can  
Figure 7: Relationship between slope deformation and rainfall during landslide development. (**a**) Annual deformation rate from July 2021 to September 2022; (**b**) time-series deformation of point P1 correlated with monthly rainfall; (**c**) time-series deformation of point P2 correlated with monthly rainfall.  
easily form an impermeable layer when it is softened by water, resulting in the long-term saturated state of the slope. Slopes are highly susceptible to sliding-deformation damage over time.  
In summary, this study determined the deformation characteristics of the Hongya Village landslide and analyzed the deformation mechanism, considering the factors of rainfall and the stratigraphic lithology. Building on this information, this study conducted a detailed analysis of the natural factors influencing the stability of the landslide, showing an advancement from other published material which only considers one of these factors.
### Contributions  
mmWave channel models do not exist for a residential environment, which differs significantly from other indoor environments such as an office room. However, high-throughput communication in residential environments is the main use case for mmWave wireless communication using the IEEE 802.11 ad standard. In this paper, we performed indoor radio channel measurements in a living room environment, at frequencies ranging from 50 to 75 GHz, using a spectrum analyzer-based channel sounder. We also compared them to double-directional PL measurements using the Terragraph (TG) platform. Next to providing a residential channel model, it is the first time that network performance, i.e., packet error rate (PER) and throughput, is analyzed via measurements and channel model-based simulations. The channel model is implemented in a network coverage prediction algorithm that estimates received power and throughput for every location in an indoor environment, using a ray-launching algorithm. The network performance simulations combine environmental layout information with stochastic channel models. Therefore, coverage prediction for a specific environment is possible, and it will be shown that no cm-level accuracy of the environment model is required.  
The outline of this paper is as follows. In Section 2, we first present the measurement equipment that we used for the channel measurement campaign and system-level measurements, followed by a description of the measurement setup. We present the prediction tool that we designed to analyze network performance of mmWave wireless systems for indoor environments in Section 3, In Section 4, we provide the angular PL measurement results and link the results to the PER and throughput measurements. We then compare the results to simulations using the coverage prediction tool. Section 5 concludes this paper.
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 4 Numerical Experiments
### Example 2: Simulated AR(1) Processes with a Single Change-Point
#### 4.2.1 Example 2.1  
In scenario 1, we generated three sets of 100 AR(1) sequences of length 201 with a fixed autocorrelation \(\rho=0.5\) and one abrupt change-point at position \(\tau_{1}=100\), which partitions a time series into two segments. The mean of first segment, \(\delta_{1}\), equal 0, and then jumps from 0 to \(\delta_{2}=1\) or \(\delta_{2}=2\) or \(\delta_{2}=3\) at the second segment. For each set, three sequences of simulated piecewise AR(1) process are graphically demonstrated by Figure 2.  
Table 2 shows that the detection accuracies of all the methods strucchange, AR1 seg, and CE.AR improved while the value of \(\delta\) increased. Generally, the best performing method was the strucchange package.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**AR parameter** & \multicolumn{3}{c}{\(\rho=0.1\)} & \multicolumn{3}{c}{\(\rho=0.5\)} & \multicolumn{3}{c}{\(\rho=0.9\)} \\ \hline  & & \(\bar{N}\) & & & \(\bar{N}\) & & & \(\bar{N}\) & \\
**Algorithm** & \(0^{*}\) & \(1\) & \(\geq 2\) & \(0^{*}\) & \(1\) & \(\geq 2\) & \(0^{*}\) & \(1\) & \(\geq 2\) \\ \hline AR1 seg & 95\% & 4\% & 1\% & 89\% & 3\% & 8\% & 36\% & 17\% & 47\% \\ strucchange & 97\% & 3\% & 0\% & 100\% & 0\% & 0\% & 94\% & 5\% & 1\% \\ CE.AR & 100\% & 0\% & 0\% & 100\% & 0\% & 0\% & 100\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The estimated number of change-points in an AR(1) with no change points.  
Figure 2: **(a)** The three samples of the simulated AR(1) process whose mean varies from \(\delta_{1}=0\) to \(\delta_{2}=1\).  
**(b)** The sample with a shift from \(\delta_{1}=0\) to \(\delta_{2}=2\). (c) The simulated sample from \(\delta_{1}=0\) to \(\delta_{2}=3\).  
We use the histogram (see Figure 3) to illustrate the performance of the estimated single change-point location under three levels of mean shifts by looking at the plot vertically to compare three methods (a,b,c) under the same settings. Overall, all methods tend to more accurately identify the change-point's location as the mean shifts become more significant. By examining the case of \(\delta_{2}=2\) or 3, we can see that AR1 seg identified a few change-points located at the beginning of the process, while the CE.AR method tended to seek locations around 100. Under the case of \(\delta_{2}=1\), all the methods perform poorly; the CE.AR method especially is insensitive to the small mean change.
## 1 Introduction
Hence, the influence of NC on cement hydration has been studied extensively. It is found that the addition of NC can accelerate cement hydration by shortening the induction period and promoting the occurrence of the main hydration peak [20]. Besides, NC can adsorb calcium ions in the pore solution and reduce the concentration of calcium ions around cement particles, thereby promoting the dissolution of cement particles and increasing the formation of hydration products [21,22]. In addition, NC can react with aluminate-contained phases to form hydrated calcium carboaluminate, which can improve the early strength of the cement paste [23,24]. [PERSON] [25] investigated the effect of NC on the setting behavior of OPC concrete. With the addition of NC, a decrease in setting time was found. However, the setting time was increased along with the increased NC dosage, which may be attributed to the insufficient dispersion of NC and the formation of NC agglomeration. In addition, the improved mechanical strength of HCP by the nucleation effect of NC has also been reported by many researchers [26, 27, 28]. However, most of the researchers conducted the studies in the OPC system, and little attention was paid to the impact of NC on the setting and hardening performance of OPC-CSA binary blended cement. Based on the considerations above, in order to produce a new type of fast repair material with the synergistic properties of fast setting and high strength development, NC was employed as the nanomaterial to promote early strength, and AS was used to shorten the setting time of the OPC and OPC-CSA binary blended cement in this study. Subsequently, the impact of NC and AS on the setting and hardening performance of OPC and OPC-CSA binary blended cement was investigated first. Moreover, the underlying mechanism was thoroughly explored by the combined analysis of the hydration process, hydration products, and microstructure of HCP. This study, on the one hand, providesus with a reliable basis for the preparation of low-carbon cementitious material used for the fast repair of pavement. On the other hand, it can deepen our understanding of the synergistic impact of NC and AS on the setting and hardening performance of cement paste from a scientific viewpoint.
## 4 Modeling and Validation
### Individual Modeling
#### 4.1.3 Decision Tree (DTree)  
A decision tree is a classification method that generates a tree-like structure that consists of a root node, branches, internal nodes, and leaf nodes. The algorithm starts at the root node and recursively splits the data into smaller subsets based on the feature tests until it reaches a leaf node. More precisely, each one of the internal nodes represents a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) denotes a class prediction [25]. For the sklearn DecisionTreeClassifier model, the Gini index was found to be the most optimum test criterion for branching with the gene dataset.
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## 1 Introduction  
As a fast, flexible and economical means of transportation, cars are generally favored by people over other forms of transportation. Although cars bring convenience to people, the following problems are also obvious: with the increase of vehicles, traffic accidents happen more frequently, causing an alarming number of casualties and property losses. Rear-end collisions caused by inaccurate spacing and improper control between vehicles traveling at high speed on expressways has become one of the main forms of traffic accidents in China in recent years. The analysis of highway rear-end collisions shows that more than 80% of the accidents are caused by the driver's slow response, and more than 65% of the car collisions are rear-end collisions. The main reason for this kind of accident is that the speed of the driving vehicle is too high, and the driver fails to maintain the corresponding safe distance. According to the research of Mercedes-Benz on various traffic accidents, if drivers can realize the danger of accidents in advance and take appropriate measures, the vast majority of traffic accidents can still be avoided [1]. In the same direction of the highway, there are complete following, incomplete following and other driving conditions. As far as driving safety is concerned, different following states have different requirements for safe driving distance. If the distance between vehicles in longitudinal alignment on the expressway is kept completely in line with the requirements of stopping sight distance, rear-end collision accidents can be completely avoided, but road capacity will be greatly reduced, resulting in a serious waste of road capacity. As an important part of the vehicle active collision avoidance system, improvement of the minimum safe following distance model has aroused the interest of researchers at home and abroad. In the collisionavoidance system, the determination of safe following distance completely depends on the safety distance model adopted. Therefore, whether the safe following distance model is reasonable is of great significance to improve the reliability and effectiveness of the collision avoidance system. In order to ensure traffic safety between vehicles on the expressway and bring the road capacity into play, it is particularly important to discuss the determination and internal relationship of the appropriate value of the safe distance between vehicles running in the same direction on the expressway in different following states, different speeds and different slope sections. Study into the highway safety following distance has far-reaching significance for improving traffic safety level and service quality.
## 2 Point Cloud Registration Algorithm
### Sample Consensus Initial Registration  
The sample consensus initial alignment is used for initial registration. Assuming that there exists a source cloud \(O_{s}=\{P_{i}\}\) and a target cloud \(O_{t}=\{Q_{j}\}\), then the specific steps are as follows:  
Step 1: Based on the FPFH feature descriptor of each sample point, greedy projection triangulation is performed on the target point cloud to establish local correlation of the scattered point cloud data.  
Step 2: A number of sampling points are selected in the source point cloud \(O_{s}\). In order to ensure that the sampling points are representative, the distance between two sampling points must be greater than the preset minimum distance threshold \(d\).  
Step 3: Search for the feature points in the target point cloud \(O_{t}\), whose feature value are close to the sample points in the source point cloud \(O_{s}\). Given that the greedy projection triangulation establishes a reasonable data structure for the target point cloud and then performs feature matching, it directly locates the tetrahedron with a large correlation and searches for the corresponding point pairs within the local scope.  
Figure 2: Greedy projection triangulation schematic.  
Step 4: The transformation matrix between the corresponding points is obtained. The performance of registration is evaluated according to the total distance error function by solving the corresponding point transformation, which is expressed as follows:  
\[H(I_{i})=\left\{\begin{array}{ll}\frac{1}{2}I_{i}^{2}&\|l_{i}\|<m_{i}\\ \frac{1}{2}m_{i}(2\|l_{i}\|-m_{i})&\|l_{i}\|>m_{i}\end{array}\right., \tag{4}\]  
in which, \(m_{i}\) is the specified value and \(l_{i}\) is the distance difference after the corresponding point transformation. When the registration process is completed, the one with the smallest error in all the transformations is considered as the optimal transformation matrix for initial registration.
## 1 Introduction  
Dysphonia describes an impairment of the speaking voice [1] which may occur due to a variety of reasons including those secondary to neurological disorders of the central or peripheral nervous system. Spasmodic dysphonia is a neurogenic voice disorder characterized by sudden, involuntary spasms of laryngeal musculature, either adductory, abductory, or in combination. Adductor spasmodic dysphonia (AdSD) is the most common diagnostic subtype which involves abnormal adduction of the vocal folds during voicing that may result in intermittent phonatory breaks that negatively impact the perceived voice quality [2]. The speech produced by those with AdSD can be intelligible but may still be poorly rated by listeners due to distracting auditory-perceptual features [3]. As such, evaluating the severity of AdSD and its impact on listeners' perception are of significant clinical and research interest [3; 4; 5; 6].  
The process of voice evaluation is complex, multidimensional, and frequently comprises the use of aerodynamic, acoustic, and auditory-perceptual assessments. Because of the typical characteristics of AdSD, auditory-perceptual evaluation often provides the most important means of monitoring the response to treatment. The vocal feature of \"strain\", defined as the listener's perception of excessive vocal fold closure, best characterizes AdSD [5]. Vocal strain is measured both clinically and experimentally to assess AdSD severity and to monitor the success of treatment such as Botox(r) injections [3; 6; 7]. However, multiple factors can impact auditory-perceptual evaluations of vocal strain including the training level and/or experience of raters (naive vs. experienced); choice of the stimuli (sustained vowels, sentences, or running speech); and/or evaluation procedures such as equal appearing interval (EAI), visual analog (VA), or direct magnitude estimation (DME) methods [8]. In addition, vocal strain does not capture the additional attention or cognitive \"effort\" expended by the listener when communicating with an AdSD talker. For this reason, attempts to assess demands from the perspective of listeners may enhance our understanding of the nature of AdSD and its influence on the listener.
## 3 Test and Verification
### Comparison of Experimental and Simulation Results  
The same test and simulation conditions are set in this study; when the applied voltage is 48.5 kV and the background pressure is standard atmospheric pressure, the radius of the wire is 5 mm, the height of the tip defect is 6 mm, and the radius of curvature at the top of the tip is 0.1 mm. Under these conditions, the corona discharge current at the tip defect of  
Figure 5: Wires and the electrode configuration.  
the wire can be output stably. The simulation and test results of corona discharge current under the above conditions are obtained and compared.  
Figure 6 shows the obtained current waveform. It can be seen that the two current curves exhibit pulse characteristics: the rise time of the pulse current is about 10 ns and the decay time is about 300-500 ns; the current amplitude is between 2.0 A and 2.5 A. The current results are basically consistent after comparison, indicating that the simulation model has certain rationality. In addition, there is a phenomenon (shown in Figure 6) in that the amplitude of the test current is slightly smaller than that of the simulated current; the reason is that the conical spike used in the test has slight passivation, which makes the intensity of corona discharge slightly weakened.  
Furthermore, the results of corona inception voltage under the same simulation and test conditions are obtained. When the air pressure is 600 hPa, 700 hPa, 800 hPa, 900 hPa, and 1013 hPa, the wire's radius is 5 mm, the tip's height is 2 mm, and the curvature radius of the tip is 0.1 mm. The comparison of corona inception voltage is shown in Figure 7.  
It can be seen from Figure 7 that the corona inception voltage increases with an increase in air pressure. Compared with the experimental results, the error of voltage gained by simulation is within 0.2-0.5 kV and the maximum error is less than 0.2%, which is considered to be acceptable and shows that the simulation model is basically reasonable.  
Figure 6: Comparison of current waveform.  
Figure 7: Comparison of voltage.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 4 Forecasting Download and Upload Traffic
### Upload Traffic  
For upload traffic, the parameters for the SARIMA model are as follows:  
\[\text{SARIMA }(3,0,1)(2,0,2)_{24}\]  
For the Holt-Winters model, the trend appears also to be multiplicative and the seasonality additive.  
The forecast for upload traffic is shown in Figures 6 and 7 using the SARIMA and Holt-Winters models, respectively. By comparing the test and forecast data, we can conclude that the dynamics of the forecast peaks for upload traffic do not repeat the test values. Additionally, the fluctuations exhibit pronounced general seasonality. However, if we consider day 24, the SARIMA model shows better results as it accurately predicts the peak in the data. On the other hand, the Holt-Winters model assumes peaks in days after day 24, resulting in fluctuations that have pronounced overall seasonality and are more similar to the test data. Based on the results in Table 10, it can be concluded that the SARIMA model is more accurate in predicting upload traffic in our case.  
Figure 4: Download traffic forecast using SARIMA model.  
Figure 5: Download traffic forecast using Holt-Winters model.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 5 Tabu-Search-Based Enhanced Rosenbrock Algorithm
### Boundary Problems  
#### 5.3.1 Neighborhood Boundary Description  
With the neighborhood definition in (14), the neighborhood may lay beyond the index interval near the boundary, i.e., \(v_{t-1}<1\) or \(v_{t+1}>V_{t}\) and \(h_{t-1}<1\) or \(h_{t+1}>H_{t}\). To remedy the boundary problem of the neighborhood, we suppose the vertical direction in the neighborhood as \(v^{*}\) and the horizontal direction in the neighborhood as \(h^{*}\), when \(v^{*}<1\) or \(v^{*}>V\), we have  
\[v^{*}=\text{mod}(v^{*},\;V) \tag{16}\]  
when \(h^{*}<1\) or \(h^{*}>H\), we have  
\[h^{*}=\text{mod}(h^{*},\;H) \tag{17}\]