esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Lateral Bearing Behavior of Spliced Helical Piles with the SPH Method
## 3 Finite Element Simulation
### Lateral Loading
The displacement of the top of the pile in the central axis of the reference position is measured, and the whole loading process of the two piles (Figure 5). The displacement of both of the piles rises exponentially with the load within the limits of the plastic region of the soil, **I** for the initial loading region, **II** for the plastic region, and **III** for the soil stiffness enhancement region. The pile driving can cause significant perturbation to the surrounding soil and change the soil stress state. The excess pore water pressure will resist the external load with the soil particles at **I** and dissipate at **II**, where the soil will exhibit viscoplasticity, and the displacement will show a steady upward trend. As the stresses in the soil extend to the boundary in **III**, the region of the effect of the pile-soil interaction gradually approaches the boundary, the soil is further compacted, the strength and stiffness of the surrounding soil increase with the increasing load, and the pile displacement in this region grows slowly and reaches the ultimate load-carrying capacity of this simulation model. As 7\({}^{\circ}\) is specified as the ultimate load-carrying capacity for the deflection angle of the helical pile, the displacement of the original pile is 25.2 mm, and the load is 10,000 N. The displacement of the spliced pile is 40.27 mm, and the load is 3625 N, both within **II**, which does not influence the values obtained for the results. The displacement-soil resistance is based on the pile-soil stresses in the active and passive regions under horizontal tension (Figure 6). The two piles are loaded with a relatively dense number of fetching points at the beginning of the loading. With increasing loads, the displacements are smaller, but the soil resistance has reached higher values, the soil is squeezed dense and gradually reaches the soil's primary stiffness stability value. The soil particles will experience stable viscoplasticity, with a greater increase in the displacement and smoother curves, corresponding to **I** and **II** (Figure 6). After that, the slope of the soil resistance changes, and the displacement grows slowly. This is due to the soil particles being squeezed and the soil displacement region having reached the boundary, while the high-stress region is approaching the boundary of the soil model and the soil reaches a stable value of secondary stiffness. The secondary stiffness arises because the soil model is small, but the number of cells in the whole pile-soil model have reached 500,000, and the simulation is more time-consuming; thus, the boundary effects have an impact on the soil, but the main object of the study is within the area of primary stiffness. The tilt angle and displacement are minor and do not affect the analysis of the pile soil. Figure 5: Load-displacement relationship curve of helical pile under lateral load.
# Review of Control Technologies for Quiet Operations of Advanced Air-Mobility
## 4 Active Noise Control (ANC) Technology
### Calculation of Distributed Array of Loading Actuators  
A distributed array of acoustic actuators is used for imposing the required loading noise needed to reduce the noise at a set of azimuthal observers. From the Farassat's formulation 1A [56], the total acoustic pressure at an observer point is given as:  
\[p^{\prime}(x,t)={p^{\prime}}_{T}(x,t)+{p^{\prime}}_{L}(x,t), \tag{6}\]  
To find the loading solution, we set \(p^{\prime}_{L}(x,t)=-p^{\prime}_{T}(x,t)\). If we assume the blade surface to be a compact source, the loading noise formulation becomes:  
\[p^{\prime}_{L,tail}(x,t)= \frac{1}{4\pi c_{0}}\sum_{i=1}^{nB*nR}\left[\frac{1}{r_{i}(1-M_{ r_{i}})^{2}}\frac{\partial L_{r_{i}}}{\partial\tau}+\frac{c_{0}L_{r_{i}}}{r_{i}^{2} (1-M_{r_{i}})}+\frac{L_{r_{i}}\left(r_{i}M_{ri_{i}}\ +c_{0}M_{r_{i}}-c_{0}M^{2}\right)}{r^{2}(1-M_{r_{i}})^{3}} \right]_{rt} \tag{7}\]  
where \(L_{r}\) is the component of the loading solution in the radiation direction, \(nB\) and \(nR\) are the number of blades and number of rotors, respectively. The anti-noise loading \(L_{r}\) needed is then solved by feeding the values of the thickness noise, \(p^{\prime}_{T}\), and getting values of the observer distance \(r\), the Mach number in the radiation direction \(M_{r}\), and the flight Mach number \(M\) from the computation of the thickness noise into the ordinary differential equation shown in Equations (8) and (9). A fourth order Runge-Kutta algorithm is employed to solve for the loading \"anti-noise\" solution, which is then fed into Equation (7) to find the corresponding anti-noise pressure signal.  
\[\frac{dL_{r}}{d\tau}+p(\tau)L_{r}=r(\tau) \tag{8}\]  
where  
\[p(\tau)=\sum_{i=1}^{nB*nR}\left[\frac{c_{0}(1-M_{r_{i}})^{2}+r_{i}M_{r_{i}}+c_ {0}\big{(}M_{r_{i}}-M^{2}\big{)}}{r_{i}(1-M_{r_{i}})}\right] \tag{9}\]  
\[r(\tau)=-4\pi c_{0}\frac{p^{\prime}_{T}}{\varepsilon}\sum_{i=1}^{nB*nR}r_{i}(1 -M_{r_{i}})^{2} \tag{10}\]  
Target Zone: For a zone of target observers, a new loading term, \(L_{rA}\), is introduced such that:  
\[L_{rA,i}=\sum_{j=1}^{nObs*nR*nB}B_{j}K_{j}L_{r,j} \tag{11}\]  
where \(K_{j}\) and \(B_{j}\) are the coefficients and tuning parameters that needs to be solved. This function is looped over each blade and each rotor, and thus the number of tuning parameters increase. The resulting optimized loading solution, \(L_{r}\) as well as far-field noise signal are shown in Figure 13. The far-field pressure field is also computed and shown in Figure 14. We see a slightly imperfect total cancellation. This occurs because the loading actuator must account for each observer peak, which is offset, and is not at the same temporal location. For the specific geometry, this produced about 9 dB reduction in the in-plane thickness noise during forward flight of the two rotors. Note that although the method works well for thickness noise control, broadband noise might prove to be important for AAM, and thus the method would need to be extended for BBN control.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 5 Logarithmic Weight Update  
When performing the particle weight update, the numerical values can be too large to be evaluated by a computer. The numerical issues at the weight update when evaluating Equation (27) can be solved by shifting the equations to the logarithmic scale and performing an additional normalization step. As a first step, let  
\[I_{a}(z)\approx\frac{e^{z}}{\sqrt{2\pi z}}\Big{(}1-\frac{4\alpha^{2}-1}{8z}+ \frac{(4\alpha^{2}-1)(4\alpha^{2}-9)}{2!(8z)^{2}}+...\Big{)} \tag{28}\]  
be an approximation of the Bessel function of first kind where only the first term is used to approximate the function with order zero as  
\[I_{0}(z)\approx\frac{e^{z}}{\sqrt{2\pi z}}. \tag{29}\]  
With Equation (29), Equation (27) can be rewritten to  
\[p(\mathbf{s}|\boldsymbol{\tau},\boldsymbol{\omega})\approx\underbrace{\frac{ 1}{(2\pi)^{L}}\exp\{-L\}N\sqrt{\frac{\pi}{2L}}}_{H_{const}}\prod_{n=1}^{N} \left(\frac{\exp\left\{\frac{|P_{n}(\tau_{n},\omega_{n})|^{2}}{2}\right\}}{ \sqrt{2\pi\frac{|P_{n}(\tau_{n},\omega_{n})|^{2}}{4}}}\right). \tag{30}\]  
Based on the reason that a later introduced normalization step is performed in Equation (35), the constant term \(H_{const}\) can be neglected. With that, the weight update Equation in (9) can be expressed as  
\[w_{k}^{i}\propto w_{k-1}^{i}\prod_{n=1}^{N}\left(\frac{\exp\left\{\frac{|P_{n} (\tau_{n},\omega_{n})|^{2}}{2}\right\}}{\sqrt{2\pi\frac{|P_{n}(\tau_{n},\omega _{n})|^{2}}{4}}}\right). \tag{31}\]  
Let us define \(\widetilde{w}_{k}^{i}=\log(w_{k}^{i})\) and \(\widetilde{w}_{k-1}^{i}=\log(w_{k-1}^{i})\); then, the update can be written in logarithmic scale as \[\widetilde{w}_{k}^{i}\propto\widetilde{w}_{k-1}^{i}+\sum_{n=1}^{N}\Bigg{(}\frac{|P _{n}(\tau_{n},\omega_{n})|^{2}}{2}-\log\Bigg{(}\sqrt{2\pi\frac{|P_{n}(\tau_{n}, \omega_{n})|^{2}}{4}}\Bigg{)}\Bigg{)}. \tag{32}\]  
The weight update from Equation (9) can now be done in five steps from Equation (33) to Equation (37) using  
\[\widetilde{w}_{k-1}^{i} = \log(w_{k-1}^{i}), \tag{33}\] \[\widetilde{w}_{k}^{i} = \widetilde{w}_{k-1}^{i}+\sum_{n=1}^{N}\Bigg{(}\frac{|P_{n}(\tau_{n },\omega_{n})|^{2}}{2}-\log\Bigg{(}\sqrt{2\pi\frac{|P_{n}(\tau_{n},\omega_{n})| ^{2}}{4}}\Bigg{)}\Bigg{)},\] (34) \[\widetilde{w}_{k}^{i} = \widetilde{w}_{k}^{i}-\max(\widetilde{w}_{k}^{i}),\] (35) \[w_{k}^{i} = \exp(\widetilde{w}_{k}^{i}),\] (36) \[w_{k}^{i} = \frac{w_{k}^{i}}{\sum_{i=1}^{N}w_{k}^{i}}. \tag{37}\]  
This series of equations allows for performing the weight update within numerical boundaries (e.g., double precision) and can generally be used to implement the optimal particle weight update in any Bayesian filter working with correlator values.
## 3 Results
### Real-Time Application and Device Performance  
The edge sensor node has been utilized in agricultural applications for analyzing milk components. Its capabilities for real-time data collection and processing play a crucial role in determining critical components such as protein and fat content in milk. The device has been tested for energy efficiency under field conditions and found suitable for long-duration operations. Its ability to deliver maximum performance with minimal energy is particularly advantageous in remote areas lacking electrical infrastructure.  
Data transmission has been implemented using Bluetooth and MQTT protocols, which ensure low energy consumption and high data transmission reliability. The use of the MQTT protocol facilitates the rapid and reliable transfer of data to central systems, allowing for instantaneous monitoring of milk quality and rapid interventions.  
Figure 9 demonstrates the data collection process from the multi-spectral sensor in real-time. While the device captures data across a wide range of wavelengths (410 nm to 940 nm, as shown in the table), our analysis focuses specifically on key wavelengths--610 nm, 680 nm, 730 nm, 760 nm, 810 nm, and 860 nm. These wavelengths were selected based on their relevance and effectiveness in predicting milk components, allowing us to optimize model performance without unnecessary data redundancy. Figure 9 illustrates the comprehensive raw data collection, even though only selected wavelengths were processed for the analysis presented in this study.  
Figure 10 illustrates how data transmission is carried out using the MQTT protocol, showing the configuration of data packets and the technical details of the transmission process. It also demonstrates the role of this protocol in system integration and mechanisms for ensuring data security. The visual is composed of three sections:  
Sensor edge sensor node: Real-time spectral measurements are taken and processed according to a machine learning model, then they are transmitted wirelessly.  
Edge gateway: The collected data are processed here and prepared for the next stage. MQTT screen: The processed data are displayed on the MQTT interface.  
This configuration ensures effective integration and security in both the data collection and transmission processes.  
These visuals and descriptions scientifically frame the performance of the device in the field, its energy efficiency, and its data transmission capabilities, enhancing the depth and applicability of this study. This comprehensive presentation helps stakeholders understand the operational effectiveness and practical benefits of using the edge sensor node in dairy quality management.
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 4 System Implementation
### SNR and BER Analysis  
In this section an experimental procedure was performed to analyze the data transmission error as a function of the signal-to-noise ratio (SNR). The quantity of error in the reception was acquired by comparing the raw data before the transmission through the acoustic channel, and the received signal. Both raw and received data were bit compared one-by-one, which gives the bit error rate (BER), where the data rate was fixed at 64 bps. The SNR was measured by separating the energy of the signal and the energy of the noise. The signal, measured after the envelope detector circuit, contains the sum of the data signal and noise; however, as the raw data acquired on the transmitter mostly has the energy of the data signal, the noise energy is isolated through a fast Fourier transform (FFT) operation, removing the raw data frequency bins of the data signal. The missed frequency bins, from the noise signal, are therefore interpolated to have all of the frequency components. The amplitude levels of the signal data are summed, where the DC component is removed, and divided by the sum of the noise amplitudes, approximately estimating the SNR.  
For a fixed SNR, the quantity of the bit error was acquired. The SNR was varied over different values of frequency instead of varying the input voltage of the power amplifier, and so maintaining almost the same noise level. One can notice that slightly changing the operating frequency does not interrupt the functionality of the system, but changes the input voltage variation as shown in Figure 5b. The frequency variation, therefore, increases or decreases the energy of the signal data in the reception.  
Figure 10 shows the experimental and theoretical results of BER versus SNR. The theoretical curve, resulting from Equation (2), represents the probability of a bit error rate for a BASK modulation when the variation between the high level and the low level, bit 1 and 0, respectively, is 18% [26]. This variation represents the best value in our experimental results, approximately at 13.5 dB with \(10^{-6}\) of bit error rate. The experimental analysis also shows the results when the voltage variation between the high and low level is 5%, at approximately 3 dB with \(10^{-1}\) of bit error rate. Both signals are shown in Figure 11. In any case, it should be noted that, for our system, the error data rate is lower than the theoretical curve due to Manchester coding implementation.  
Figure 10: Experimental and theoretical BER curves.
## 3 Proposed Model  
The main focus of this research is to develop a new path loss prediction model, which we will call the TYM model (see Equation (7)). The success of the TYM model depends on new parameters, as it works in one type of environment like urban areas with a frequency ranging from 28 to 38 GHz. The proposed approach is comprised of two main steps: dataset gathering and coefficient calculation using a linear regression technique. The details of these main steps will be explained, in addition to the effects of the new parameters on the proposed model.
## 7 Challenges and Limitations  
In the following section, the challenges encountered when collecting and preprocessing the Arabic corpora are outlined, and potential solutions are proposed. The primary challenge was consolidating all available corpora into a single location on the Google Drive storage platform, a task that spanned approximately six months. The duration of this process was influenced by various factors, especially network disconnections, which impacted the download and upload processes for large text files. This problem was exacerbated by the need to restart downloads in the case of network disruptions. To mitigate this issue and effectively overcome the challenges associated with network interruptions, the BigTextFileSplitter tool was employed to break down large files into 100 MB segments during the upload and corpus creation processes.  
Another significant challenge revolved around the availability of data. Despite our comprehensive efforts to include all freely accessible Arabic corpora on the Internet, the accessibility and diversity of the Arabic corpora remained limited. Notably, there is a scarcity of publicly available large-scale Arabic text collections, especially when compared to languages such as English [80], and many publicly available Arabic corpora are either unclean or exist in formats such as JSON, CSV, and HTML rather than plain text. The rich dialectal variations in Arabic further complicated the task of gathering representative and balanced MSA corpora.  
The limited availability of Arabic books in text format posed another challenge. The Hindawi organization's books were a notable exception, as these were available in PDF and EBOOK formats. Moreover, Arabic corpora lack coverage in specialized domains and specific subject areas, hindering the development of domain-specific NLP applications. Many corpora focus on general language usage, but do not adequately cover the medical, legal, and scientific domains or the technical literature, which poses challenges for research in specialized areas.  
Addressing the above challenges requires collaborative efforts to collect and share diverse, cleaned, domain-specific Arabic corpora. Moreover, investing in linguistic resources and tools specific to Arabic is crucial to overcoming these limitations and driving advancements in the area of Arabic language processing. This collaborative and resource-centric approach is pivotal for the development of comprehensive and representative corpora, and for advancements in various domains of Arabic research and applications.
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 3 Results  
### Joint Angles  
The lower-extremity joint angles during the gait cycle of the three gait patterns are presented in Figure 3, and statistical analysis of maximum values are listed in Table 1. The results in Figure 3 and the following figures will be qualitatively described. Three gait patterns showed similar trends for joint angles in the sagittal plane (hip flexion, knee flexion, and ankle plantar flexion). Compared to the natural gait, a post hoc test revealed that the toe-in and toe-out gaits decreased the max angle of knee flexion by 8.8 and 12.18 degrees respectively (\(p<0.05\)) and the max angle of hip adduction by 1.28 and 0.99 degrees respectively (\(p<0.05\)). The toe-in gait reduced the max angle of hip abduction by 2.19 degrees (\(p<0.05\)), while the toe-out gait increased it by 2.34 degrees (\(p<0.05\)). The toe-out gait reduced the hip abduction angles and ankle adduction angles (stance phase cycle) in the frontal plane. The toe-in gait pattern increased hip adduction angles during the early stance and swing phases; however, the ankle adduction angles were decreased during the stance phase. Change of angles in the transverse plane (hip rotation and ankle rotation) was directly linked to the angle differences caused by the FPA gait modifications. More information on the joint angle changes are shown in Figure 3 and Table 1.  
Figure 3: Measured joint angles (mean and standard deviation) of a gait cycle for the natural, toe-in, and toe-out gait patterns (12 participants and 5 trials for each gait pattern).
# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
## 4 Analysis of Examples
### Result Analysis
In addition, the calculations based on the above overview also lead to:  
(1) Safety stock and transshipment costs are negatively correlated, i.e., an increase in safety stock will reduce transshipment costs. The ratio of safety stock and trans-shipment fee unit costs affects the setting of the safety stock strategy. The larger the proportion of safety stock costs, the greater the impact on total costs. The smaller the transit fee balance, the greater the impact on total costs. In general, transshipment costs mainly affect the total cost of emergency logistics. Therefore, a higher safety stock factor in emergency-prone areas can reduce the transshipment cost more effectively, and it is reasonable and feasible to assign a value to the safety stock factor according to the statistical level of emergency events. (2) The deployment of the Emergency Logistics Center has been effective. Contingency logistics centers can only be deployed in smaller quantities because of higher deployment costs and inventory levels. Transit costs mainly affect the total cost of emergency logistics. Emergency logistics centers cover several warehouses. The more the logistics transfer effect plays, the stronger it is, and warehouses can still move the transfer. Therefore, from the perspective of the mobile emergency logistics system, the actual coverage of the emergency logistics center has been significantly improved under the safety stock strategy. (3) There is a lower limit on the cost of mobile emergency logistics. The total cost of emergency logistics is limited by population distribution, transshipment costs, and inventory costs and cannot be further reduced after it reaches a certain level. The removal of restrictions on the number of emergency logistics centers and warehouses to be deployed has resulted in an overall leveling off of the total cost of emergency logistics. The total cost of contingency logistics will not decrease as a result of an increase in the number of contingency logistics centers and warehouses. Non-core counties and districts with sparse population distribution, remote distance, and low safety stock coefficients covering their emergency needs mainly rely on the neighboring warehouses for transshipment. This suggests that adopting a hierarchical coverage of mobile emergency logistics is a more effective strategy in emergencies. In summary, we give the following management suggestions: 1. In areas where emergencies occur more frequently, setting up a higher level of safety stock can effectively reduce the total cost of emergency logistics; 2. Emergency logistics centers should be built in areas where the population is more concentrated so that they can more effectively respond to the emergency needs of many people during emergencies; 3. In areas where the population distribution is more sporadic, the number of distribution warehouses should be increased and their coverage expanded to meet emergency needs more effectively.