esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
## 2 Experiments  
The pressure measurement test was carried out in a wind tunnel [15]. Simultaneous acquisition of wind pressure data from each pressure tap on the model was performed using an electronic pressure scanning module, and the sampling frequency was 333 Hz. The sampling time of each collection is set as 60 s, indicating that the sampling length is 20,000 at each pressure tap. According to the Chinese design code [16], spires and roughness elements were combined to simulate suburban terrain. The test was conducted at a geometric scale of 1/300. The mean wind speed profile along height is shown in Figure 1a, and the turbulence intensity profile along height in Figure 1b. A good agreement between the simulated profiles and the Chinese design code is observed. The mean wind speed is approximately 9.5 m/s at reference height of 0.609 m (reference height is the top of the models). The Reynolds number Re calculated in terms of \(U_{\text{H}}\) and the width of the models is 9.65 \(\times\) 10\({}^{4}\). In Figure 2, the longitudinal wind speed spectrum at reference height is well in accordance with the von Karman spectrum.  
Figure 1: The distribution of mean wind speed and turbulence intensity along height. (**a**) The distribution of mean wind speed along height. (**b**) The distribution of turbulence intensity along height.  
The dimension of benchmark model after scaling is 609.6 mm \(\times\) 152.4 mm \(\times\) 101.6 mm (Height \(\times\) Breadth \(\times\) Depth), and other models are the benchmark model with different corner set-backs (5%, 10%, 15%, 20%). The rigid models are all made by 5 mm thick ABS (Acrylonitrile Butadiene Styrene) plate, and Figure 3 shows the test model. There are seven measurement layers in each rigid model, and the relative height of each measurement layer is 0.17, 0.33, 0.5, 0.67, 0.80, 0.90 and 0.98, respectively. There are 20 pressure taps in each measurement layer, and a total of 140 pressure taps in a model. Figures 4 and 5 show in detail the arrangement of the pressure taps. The 700 mm length PVC (Poly Vinyl Chloride) tube is used to connect each pressure tap and the scanning module to record the time history of wind pressure. Prior to data processing, the effect of tube is rectified by numerical compensation [17]. The sampling frequency and record length were 333 HZ and 60 s, respectively.  
Assessing the effect of different corner set-backs of rectangular high-rise buildings on wind loads and wind induced response, rate of corner set-back \(\gamma\) is defined by the following equation:  
\[\gamma=2b/B \tag{1}\]  
In which, \(b\) is the corner set-back length and \(B\) is the breadth of the benchmark model on windward. The detailed definition of the corner set-back rate is show in Figure 6.  
Figure 3: The test model.  
Figure 2: The longitudinal wind speed spectrum at the top of the models.  
Figure 4: The models and pressure tap distributions (Unit: mm).
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 4 Traffic Measurement with SDN
### Measurements with Network Parameters  
The network parameters allow us to see the status of the network in various conditions, applications, etc. Despite many network parameters such as topology, traffic, and performance, we focused on network traffic parameters. Designing such parameters with an SDN-based MA improves the network measurement and network traffic monitoring, which encourages service providers to measure the status of traffic flow continuously. Network traffic parameters depend on the number of packets per second when packets pass through the network components such as network ports. The parameters used in the network traffic allow us to investigate the behavior of network users who use different types of services and applications. Regarding the SDN, data and control traffic are essential for the configuration of the measurements. Although the data traffic of the SDN is between the switches, the traffic flow's characteristics depend on the statistical information collected from the switch port. Control traffic of SDN occurs between a specific SDN controller and switches or many SDN controllers and switches. To measure traffic flow between any two nodes, statistical information such as packet size and packet rate is used with the delay of the end-to-end traffic matrix considered between the two nodes assumed as the entire network.  
As shown in Figure 4, the solutions for transportation services provide the limitations of the energy consumptions, which depend on the users. Although these measurements give predictions of the services and reliability of the infrastructure, data traffic can be simplified through the F5 TS, which depends on the version of the emerging technologies. All applications and services rely on traffic flows, which may be categorized to manage the traffic. For instance, priority is one of the traffic management procedures, so the priority of the traffic can be categorized according to the time and situation. Here, transportation deals with efficient data traffic management, which handles the network data traffic intelligently and dynamically.  
Transportation services and infrastructure involved with 5G provide better measurements depending on the latency, bandwidth, connectivity, EE, and throughput. Solutions for the transportation system depend on these measurements. These 5G requirements are considered to be measurements for improving the solutions of the transportation system.  
Figure 4: Solutions for transportation services.
# Analytical and Measured Effects of Short and Heavy Rail Cars on Railway Bridges in the USA
## 1 Introduction  
The increase in shipments in short railcars in North America increases the loading on railway bridges. The 12.8-m (42-ft) railcars commonly used to ship sand and cement are approximately a 25% increase in load per unit length compared to 16.2-m (53-ft) coal cars. This may cause overloading of the structure and require weight restrictions on railway bridges--especially on longer spans that accommodate multiple short railcars [1]. For example, on a 64-m (210-ft) span, five 12.8-m (42-ft) railcars can fit instead of only four 16.2-m (53-ft) railcars [2,3].  
In the last years in North America, the number of railcars increased consistently; from 2010 to 2015 it increased about 5%, and the number of all HAL railcars increased 19% [4]. Figure 1 shows that the number of HAL railcars in the North American railcar fleet shorter than 14.6 m (48 ft) in length (primarily covered hopper cars) grew from \(\sim\)73,000 in 2010 to more than 125,000 in 2015. Further, the number of railcars shorter than 12.8 m (42 ft) in length increased from \(\sim\)40,000 in 2010 to more than 85,000 in 2015.  
The other aspect of this research was to compare stresses calculated from measured strains and stresses calculated using analytical models. Strain measurements were performed in the field under regular train service. Load measurements can enhance bridge assessment because they better define the stress levels at considered locations [5]. The actual live load distribution is often significantly different than expected from conventional analytical approaches that were most likely used in the original design. The study by [PERSON] [6] shows the necessity of using field monitoring to better understand the behavior of older bridges prior to the design of renovation approaches. Other researchers concluded that the average ratio of measured static to simple calculated static values was 78% [7].  
This paper presents comparisons between analytical calculations and field measurements of the bridge performance under short 12.8-m (42-ft) railcars and typical 16.2-m (53-ft) coal railcars. The results of finite element method (FEM) models match the stresses calculated from measured strains with small discrepancies.
## 3 Fixed Wireless Access
Furthermore, high-efficiency data compression techniques permit high-resolution video delivery with less bandwidth. Moreover, adaptive bitrate streaming permits the best feasible multimedia viewing experience. This is due to its capability to automatically adapt to any network condition changes like available bandwidth fluctuations [27]. As a result of the aforementioned advantages of the 5G FWA scheme, there have been renewed research interests in investigating novel and cutting-edge radio techniques that can facilitate its deployment. Although there are considerable research interests in the wireless parts of 5G FWA solutions, much concern has been given to associated transport requirements. Noted that for effective fixed connectivity across various cell sites, the FWA transport requirements impose different challenges. For instance, based on the specified operating frequency and service, denser small cell sites may be demanded by the FWA deployment, compared to the traditional macro cell sites. Consequently, the required dense or ultra-dense network imposes more pressure regarding the number of fronthaul connections and the associated traffic. Note that the transport network capacity requirements are not the only contingent on the RAN deployment, but also on the adopted RAN FSOn as explained in Section 8[8]. In this section, we consider different aspects of 5G-based FWA, ranging from its prospect to the transport network requirements for effective system deployment.
# Design and Analysis of a Novel Microspeaker with Enhanced Low-Frequency SPL and Size Reduction
## 1 Introduction
The design changes of the smartphones are shown in Figure 1.  
Figure 1: Schematics of the change in smartphone design and microspeaker location: (**a**) design with thick bezels and (**b**) design with a bezel-less display and a digital stylus pen. For this reason, the allowable size of microsp speakers has decreased, and prototype microsp speakers can no longer be used in newer smartphones. Owing to this design shift, smartphones require smaller speakers, especially in terms of length and height. However, size reduction necessitates a reduction in the magnetic circuit structure, resulting in low acoustic performance over all frequencies. Low-frequency sound is important for providing immersive experience when consuming multimedia content. Thus, in this study, a novel microspeker with reduced exterior dimensions was designed to improve the low-frequency SPL. For enhancement of the low-frequency SPL for microsp speakers, the magnetic circuit structure must change to increase the force. In this paper, a novel magnetic circuit structure is proposed. The introduction of a new magnetic circuit accompanied many design changes, including the coil, top plates, yoke, and permanent magnet. To improve the electromagnetic force, we designed a ring-shaped permanent magnet, and the distance between the top plates and the coil was reduced. In addition, a new type of coil was used to increase the force. These design elements can be applied in many future microspeker products requiring smaller size but with improved low-frequency SPL. The prototype speaker, termed 1610_SPK, mainly consists of a coil, three neodymium permanent magnets, a center diaphragm, a side diaphragm, a yoke, and top plates. The prototype 1610_SPK has a length of 16.0 mm, a width of 10.0 mm, a height of 2.11 mm, and a coil resistance of 6.0 \(\Omega\). The coil and diaphragms are the moving components in 1610_SPK. The coil is attached to the diaphragms, and the vibration from the coil vibrates the diaphragms, which, in turn, generates sound. The remaining components are fixed and stationary. The design of the prototype 1610_SPK, its components, and manufactured samples are presented in Figure 2.
## 2 Materials and Methods
### Compilation of Experimental Conditions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Variables** & **Head Markers** & **Trunk Markers** & **COM Markers** & **Trajectory** & **Time** \\  & & & & & **Estimation** & **Difference** \\ \hline
[12] & HY, TJH & 2: Frontal and & & & Head midpoint & CC \\  & & occipital (antenna) & & & displacement & CC \\
[21] & HY, TY, TJH & 2: Frontal and & & & Head midpoint & CC \\  & & occipital (antenna) & & & displacement & CC \\
[22] & GY, HY, TJH & 2: Frontal and & & & Head midpoint & CC \\  & & occipital (antenna) & & & displacement & CC \\
[23] & HY, TY, TR & 3: Eyes border & 3: acromions + & & & TO \\  & & and chin & & sternal notch & & 28: 14-segments & & TO \\
[24] & HY, TY, TR, & 3: Eyes border & 3: acromions + & & & TO \\  & MLC & (antenna) and chin & & & airbloed process & anthropometric model & & TO \\
[25] & HY, TLC & 3: Eyes border + chin & 3: acromions + & By using trunk & & TO \\  & & & & & airbloed process & markers & & TO \\
[26] & GY, HY & & & & airbloed process & 9: 3-segments (head, & & TO \\
[16] & HY, TY, TR, & 3: not specified & 3: acromions + & & & Head midpoint & TO \\  & MLC & & & airbloed process & & & displacement \\
[28] & HY, TY, TR, & 3: not specified & 3: sternal notch & By using trunk & & & TO \\  & MLC & & & & markers & & \\
[29] & GY, HY, TY & 4: back and front & 2: acromio-clavicular & & Head midpoint & CC \\  & & & & joints & displacement & \\
[19] & HY, TJH & 3: temples + glabella & 3: acromions + & & Trunk markers & CC \\  & & & & sternum & midpoint & \\
[20] & GY, HY, TJH & 43: VICON® plugin & 43: VICON® plugin & Isobarycenter of & & \\  & & gait model & gait model & the pelvis & CC \\
[30] & HY & 3: helmet & & & & Projection of & \\
[31] & HY, TY & 4: helmet & 4: backpack & & & TO \\
[17] & GY, HY, TY & 3: not specified & 3: sternal notch & & TO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Kinematical variables and estimation details in the studies based on optical motion capture systems. Gaze Yaw (GY), Head Yaw (HY), Trunk Yaw (TY), Trunk Roll (TR), Trajectory Heading (TJH) and Body Center Of Mass Medio-Lateral Displacement (MLC).
# Bone Temperature Variation Using a 3D-Printed Surgical Guide with Internal Irrigation
## 2 Materials and Methods
### Surgical Guide Production  
Each bone specimen was subjected to cone beam computed tomography (CBCT) (Orthophos XG 3D unit; Dentsply Sirona, Bensheim, Germany) with the following parameters: 85 kV, 4 mA, 14.4 s. From the CBCT data, the implant planning was carried out using a dedicated software (NaviMAX(r), Biomax S.p.a., Vicenza, Italy). For each specimen, four 4.1 \(\times\) 13 mm T3 Certain(r) Parallel Walled Non-Platform Switched Implants (Zimmer Biomet Dental, Palm Beach Gardens, FL, USA) were planned. The implants were intended to be perfectly parallel to each other, perpendicular to the bone crest with an inter-implant distance of 3 mm. The fixation pins were planned one on each lateral side of the rib. Drill holes of 1.8 mm in diameter for the insertion of the thermocouples were also designed laterally and perpendicularly to the osteotomies (Figure 1a).  
An acrylic (MED620) 3D-printed surgical guide (NaviGUIDE(r), Biomax S.p.a., Vicenza, Italy) was then produced for each rib, according to the individual planning using an Objet30 OrthoDesk (Stratasys, Ltd., Eden Prairie, MN, USA) printer. The axes resolution was 600 dpi, 600 dpi and 900 dpi for X-, Y- and Z-axes respectively.  
The guide was provided with a built-in tube system for internal irrigation, with an aperture in the proximity of each polyetheretherketone (PEEK) sleeve (Figure 1b,c). The average diameter of the internal irrigation tube system was 2.2 mm (range 3.6 mm to 1 mm).
# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 2 Materials and Methods
### ISSA-ELM Risk Probability Prediction Model  
The Extreme Learning Machine (ELM) is better at generalization, has a faster convergence rate, a simpler structure, and fewer training parameters than regular gradient descent backpropagation neural networks. Nevertheless, in real-world applications, augmenting the number of nodes within the hidden layers to enhance the model precision frequently diminishes the training samples' capacity for generalization. The Improved Sparrow Search Algorithm (ISSA) optimizes the input layer weights and hidden layer biases of the ELM to solve this problem. We used the ISSA-ELM primary indicator evaluation model to assess the operational safety status of terminal area control systems.  
The ELM model, improved by the ISSA, was initially used for network security situation prediction [24]. The ELM can quickly train samples thanks to the ISSA's optimization of the initial weights, which improves the accuracy of network security predictions. This model is able to predict the next network security situation with high accuracy based on time series. This article predicts terminal area control system safety situations with the ISSA-ELM model. We slightly modified the Sparrow Search Algorithm's (SSA) optimization technique to immediately use the second indicator as an input vector. We used the output of the risk probability prediction for the key indicators to calculate the overall safety situation value of the terminal area control system.  
The ISSA enhances the performance of ELM models relative to the standard ELM in terms of the parameter optimization capability, global search capability, convergence speed, and model stability. The parameters of the standard ELM are usually randomly initialized or determined by the trial-and-error method, while the ISSA is able to search for the optimal parameter combinations of the ELM more efficiently, which improves the model's performance and generalization ability. The ISSA expands the search space and reduces the risk of falling into the local optimal solution by introducing improvements such as [PERSON]'s flight strategy, which enables the ELM model to explore the global optimum in a better way. The improvement of the ISSA makes it converge faster in the process of searching for the optimal solution, allowing it to find a better combination of parameters in a shorter time, which improves the training efficiency of the ELM model and makes it more suitable for the terminal area security assessment scenarios with higher real-time requirements. Finally, after the optimization of the ELM model by the ISSA, the ELM model has better stability and robustness, and can maintain a more consistent performance in different datasets and assessment tasks, which provides a more reliable basis for the security assessment of the terminal area, and more timely and effective support for security management.
## 2 Overview of Fake News and Fake Profiles
### Fake Profile Detection
#### 2.3.3 Behavior and Interaction Patterns  
In the realm of behavior and interaction patterns, various methods are utilized to detect and analyze fake accounts on social media platforms. These methods focus on examining the frequency of posts, the content of tweets, and the interaction patterns of users, employing machine learning algorithms to identify anomalies and ensure the authenticity of user profiles.  
_Posting Frequency/Tweets_ are critical indicators in detecting fake accounts. A study presents a classification method for detecting fake accounts on Twitter using the Naive Bayes algorithm [19]. The data include features provided by the Twitter API, such as description, profile image, number of followers, users, and public lists in which the user participates. Additionally, they consider the number of hashtags, mentions, and URL links used in the last 20 tweets, as well as the number of tweets liked by the user. The dataset was preprocessed using a supervised discretization technique named Entropy Minimization Discretization (EMD) on numerical features. In the Naive Bayes learning algorithm, preprocessing the data with EMD without any feature selection increases the success rate, as Naive Bayes performs better with discrete values than continuous values. The success rate of the Naive Bayes algorithm is 90.9% after discretization.  
In another study, Facebook user feeds were collected using the Facebook Graph API, which was fine-grained based on privacy settings [49]. The most commonly used supervised machine learning classification techniques were applied to a basic dataset comprising the user's node, their friends in their social neighborhood, and a set of manually identified spam accounts. The performance of the classifiers was evaluated to determine their ability to detect fake accounts on Facebook. Results show that the best-performing classifiers achieved a high classification accuracy of 79%. Furthermore, the study investigated the feature set to determine the attributes that most successfully detect fake profiles on Facebook. It was found that user activities related to likes, comments, and, to some extent, shares on Facebook contribute the most to detecting fake accounts. Therefore, the study represents a significant step towards a profile-feature-based detection of fake accounts on Facebook.
## 4 Results and Discussion
### WBVE and Pain  
CP management during the COVID-19 pandemic is a challenging process, especially with growing evidence that COVID-19 infection is associated with mylagias, referred pain, and widespread hyperalgesia [71]. The benefits of the WBV exercise on pain level have been reported by [PERSON] et al., 2017 [72] in individuals with fibromyalgia, [PERSON] et al., 2018 [73] individuals with Metabolic syndrome (MSy) and by [PERSON] et al., 2018 [74] in individuals with patellofemoral pain (PFP). These studies used a different model of VP and vibration protocol. The number of sessions ranged from 12 to 24 sessions, the time of exposure to vibration ranged from 30 to 60 s and the frequency used ranged from 30 and 35 Hz and 2 to 4 mm in amplitude. [PERSON] et al., 2018 [59], studying individuals with MSy on an alternating oscillating VP, frequency-5 Hz, peak-to-peak displacement (ranging from 2.5 to 7.5) demonstrated the ability of WBV exercise in the decrease of pain level. In relation to the number of participants, there were 98 individuals.  
[PERSON] et al., 2018 [74] showed the superiority of eight weeks of WBV training plus home exercise over home exercises alone in patients with PFP. It was investigated three days a week with at least one day between each session for eight weeks (total of 24 sessions), for 20-30 min per session. Four different types of WBV exercises in three sets were applied for 30 or 45 or 60 s/set with a 30 s rest period between sets and a 60 s rest period between exercises. WBV training is a feasible and efficient exercise intervention for the management of patients with PFP. However, patients should be assessed with etiologic factors causing their knee pain for the most appropriate treatment of PFP due to its multifactorial nature. It is believed that the study contributes to evidence in WBV research by providing an efficient protocol. [PERSON] et al., 2017 [58] considered that individuals that performed WBV plus exercise presented significant symptomatic improvement at the sixth month (chronic training effect), after a protocol performed in four weeks, twice a week. There were six different kinds of exercises, with 30 s each and six repeats with a recovery of 3 min between repetitions. The exercise protocol consisted of dynamic (isotonic) and static (isometric) muscular contractions. [PERSON] et al., 2018 [73] pointed out that WBV exercise with low frequency-5 Hz promoted changes in physiologic parameters that contribute to reduce the PL and to improve the flexibility and to maintain to cardiovascular responses, considering heart rate (HR) and blood pressure in MSy individuals. The protocol consisted of three bouts (1 min each) of WBV exercise, followed by 1 min of interpesed restThe total time of intervention was 17 min (Table 4).  
In addition, there are already systematic reviews emphasizing the benefits of WBV in CP in different clinical conditions [75; 76; 77].