esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experimental Result Evaluation
#### 4.2.3 Decoding Accuracy Rate Performance  
The performance of the positioning information depends on the decoding success rate of the receiver. The decoding rate error of the receiver side may result in wrong positioning information, and hence it, requires re-positioning, which causes the system to exhibit high latency and unstable positioning information. The decoding success rate depends on the vertical distance and horizontal distance from the transmitter of the LEDs and the receiver of the image sensor. In the normal case, the distance from the ceiling to the user is nearly 1.5 m. The decoding rate in accordance with the vertical distance from LEDs to image sensor is shown in Figure 12a. The decoding performance of our system is as follows: it  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters Name** & **Value** \\ \hline Image Sensor & Rolling shutter CMOS sensor \\ Shutter speed & 16 kHz \\ ISO & 100 \\ Frame rate & 6 \\ MCU & 20 fps \\ Image processing library & OpenCV \\ Smartphone model & Samsung Galaxy S8 \\ Camera & Front Camera with 8 megapixels \\ Focal length & 24 mm \\ Aperture & 1.7 \\ Camera API & Camera 2 with API Level 23 \\ Camera image resolution & 600 \(\times\) 800 pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Receiver parameters configuration.  
Figure 11: (**a**) Data rate vs. camera frame rate; (**b**) image processing performance vs. image sensor resolution.  
reached the highest 100% when the vertical distance of the transmitter and receiver was less than 1.5 m, and it remained at 80% until the distance was 1.78 m. To observe the decoding rate of the system with respect to the horizontal distance from the LED luminaries, we tested the success accuracy rate from the LED lights to the image sensor to the horizontal distance, as shown in Figure 12b, which was sufficient to cover the region of under the single LED luminaires. The decoding accuracy of the horizontal distance from directly below the LED was 100%, and it was reduced when the image sensor moved far away from the direct incident place of the LED to the edge of the LED luminaire. The decoding accuracy was calculated by counting how many successive receiving data frames were received on the receiver side by the camera image sensor to extract the ID messages, i.e., decoding accuracy = ((received frame)/(total frame)) \(\times\) 100%.
## 3 Proposed Model
### Automatic Relevance Determination Network  
Given the variability in engine component failure probabilities, the likelihood and magnitude of responses from engine sensors differ. Some sensors demonstrate more pronounced changes, necessitating greater scrutiny. This study utilizes an automatic relevance determination (ARD) network to allocate priority weights to each sensor. These weights may be randomly initialized or determined based on the engine's historical failure rates. Employing gradient descent within the network yields the sensors' final posterior weights, indicative of the significance of each sensor's data [26; 27]. Conventional ARD approaches regard variables as governed by sparsity-induced priors. To circumvent intricate weight inference, constant inputs are parameterized via a neural network, and a Softmax function ensures their aggregate equals one [28].  
\[\omega=softmax(\text{NN}_{ARD}(I)) \tag{10}\]  
where \(\text{SoftMax}(\text{x}_{i})=\exp(x_{i})/\sum_{j}\exp(x_{j})\). The input vector \(I\in\mathbb{R}^{L}\), which corresponds to the dimensionality of the number of sensors, can be defined randomly or based on the engine's prior probability of failure. As depicted in Figure 3, where \(s\) denotes input sensor data, \(i\) represents the number of sensors, and \(t\) indicates the time step. The ARD network's posterior weights \(\omega\) can be efficiently determined through gradient descent on the parameters by training sensor data thereby avoiding the posterior inference issues of traditional Bayesian methods.
# Enhanced Eco-Friendly Concrete Nano-Change with Eggshell Powder
## 4 Discussion  
For a more complete analysis of the resulting concrete, changes in strength and deformation characteristics were determined, expressed as percentages (Table 11).  
According to the data on changes in the strength and deformation characteristics of concretes modified with ESP, presented in Table 11, it was found that the optimal dosage of eggshell powder, introduced instead of part of the cement, is a dosage of 10%. This recipe solution achieves an increase in strength characteristics up to 11% and a decrease in deformations during axial compression and tension by 10%.  
These results are in good agreement with studies [16; 19]. In this case, the optimal dosage of eggshell powder as a replacement for part of the cement is 10%, as in [13; 16; 19; 21], but the performance gains achieved in these works differ. The optimal dosages of ESP according to [10; 13; 16; 18; 20; 21; 22] are from 5 to 15%. However, sometimes there are also more optimal amounts of added ESP, for example, 20% [15]. There are quite a few studies in which the strength characteristics of samples modified with eggshell powder in the optimal dosage, replacing part of the cement, do not reach the values of control samples (without ESP) [16; 18; 20].  
The most complete range of factors affecting the final physical and mechanical properties of concrete modified with the ESP additive is shown in Figure 15 in the form of an Ishikawa diagram.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Concrete Characteristics**} & \multicolumn{6}{c}{**\(\Delta\)\% for Content of Eggshell Powder in the Amount**} \\ \cline{2-7}  & **0** & **5** & **10** & **15** & **20** & **25** & **30** \\ \hline \(R_{b}\), _cub_, MPa & 0 & +3 & +9 & +1 & \(-3\) & \(-17\) & \(-22\) \\ \(R_{bb}\), MPa & 0  
The factors that have the greatest influence on the characteristics of concrete, nano-modified ESP, were divided into four main groups: prescription factors (branch 1), technological factors (2), rheological characteristics of the concrete mix (3), raw materials (4). Within each branch, subfactors were identified that specify the main factors. Thus, we have grouped and analyzed the most important and significant factors influencing the quality of concrete with the addition of eggshell powder.  
The use of eggshell powder in an amount of 10% instead of a part of cement makes it possible to obtain concrete without loss of strength characteristics. Thus, the use of this additive makes it possible to reduce the consumption of cement, which will lead to a reduction in the cost of concrete, as well as solve the environmental problem of eggshell waste disposal.
## 1 Introduction
Though sensitivity to minor components is not a strength of the technique (sub 5-50 mg/L depending on the molecule), the major components of a food or beverage can be observed with little sample preparation, and a single experiment observes multiple component types at the same time. Organic acids, carbohydrates, amino acids, alcohols, esters, aldehydes, lipids, botanicals, terpenes, etc., can all be observed in the context of the entirety of the sample. This means that the technique identifies the components that are expected to be observed but also readily identifies unexpected components. This allows fingerprint and database approaches to be developed for food and beverage markets to authenticate products and identify fraud. \({}^{1}\)H metabolomics and quantitative qNMR approaches can be used to screen for authenticity and quality control at any step in the production, transportation, and sales process [19; 20; 21; 22; 23; 24]. In this work, we investigate portable SPME-GCMS, Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FTIR) Spectroscopy, and quantitative Nuclear Magnetic Resonance (qNMR) as potential methods for authenticating baijiu samples. FTIR was considered, as it is a common analytical instrument and is capable of providing data quickly with little or no sample preparation. It can also provide information on the chemicals present in the sample. FTIR data have been used previously to classify vegetable oils, fruit juices, and coffee when combined with a Principal Component Analysis (PCA) [25; 26; 27; 28]. SPME combined with portable GCMS was investigated, as it known to be capable of classifying beverages based on aroma compounds and has the potential to be used to test suspect samples onsite.
## 2 Data Collection and Analysis
### Data Analysis
#### 2.2.2 Time Headway  
A study has shown that the negative exponential distribution, Erlang distribution, and log-normal distribution can effectively describe the distribution of the time headway [28]. Therefore, the negative exponential distribution, 2-stage Erlang distribution, and 3-stage Erlang distribution were used to fit the measured sample data of the time headway. The adequacy of the fit for each distribution outcome is presented in Table 2.  
Table 2 demonstrates that the time headway observed in the exit section conforms solely to both the 2-stage Erlang distribution and the negative exponential distribution. Due to the relatively challenging nature of estimating parameters within the negative exponential distribution, the utilization of the 2-stage Erlang distribution is preferred to model the time headway within the exit section of RTRL within the interchange, as illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of** & **Mainline Design** & **Mainline Curb Lane Limited** & **Average Vehicle** & **\(V_{\text{SS}}\)(km/h)** \\
**Interchange** & **Vehicle Speed (km/h)** & **Vehicle Speed (km/h)** & **Passing Vehicle Speed (km/h)** & **\(V_{\text{SS}}\)(km/h)** \\ \hline Interchange X & 120 & 100 & 85.3 & 93.0 \\ Interchange L & 100 & 80 & 68.2 & 82.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic values of vehicle speed at the initial section of the taper of the deceleration lane.  
Figure 4: The vehicle speed of the initial section of the taper.
## 3 Results, Analysis, and Discussions  
### CFD Simulation of IBS 2D Frame  
The pressure was used as the substitute to assign the flooding load forces. The water was released, and then the pressure directly impacted the IBS structure, as shown in Figure 12a. It was found that the maximum pressure occurs at the impact area on the column-footing connection and the bottom side beam as well. The other part of the structure suffered from the vibration impact, which is part of the investigation to observe the resistance of vibration by the structure. Additionally, Figure 12a shows the pressure and the velocity around the IBS frame from the contour tables as 20,000 Pa and 1500 cm/s, respectively, while Figure 12b,c show the graphs of velocity and pressure at the time of running the simulation model. The maximum velocity and pressure from the graphs were recorded as 8.96 \(\times\) 10\({}^{3}\) cm/s and 2.59 \(\times\) 10\({}^{7}\) Pa, respectively. The maximum velocity and pressure from the graphs are not necessarily from the surrounding area of the 2D frame. The graphs captured the maximum rate, while the contour (Figure 12a) provides the maximum values and the exact velocities and pressures around the structures. The software simulations served as the guideline for the experimental work. The numerical simulations worked very well, provided a good result, and showed the performance behavior of the structures. This is usually the case when the components of the CFD simulation, fluid domain, meshing, and boundary condition are properly controlled; hence, leading to accurate results from the CFD [53]. Table 9 presents the pressure and velocity results of the simulated CFD for both graphs and contour.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 5 Experiments
### Reconstruction Error Threshold  
As explained in Section 3.1, the distribution of the reconstruction error of SPIT messages has a mean value larger than the reconstruction error distribution mean value of normal SIP messages. Therefore, a threshold value should be chosen, above which an SIP message is considered a SPIT message.  
For the D1-DCAE model, the values from 6 to 28 are tested for the reconstruction error threshold. Figures 4 and 5 show the reconstruction error threshold against the model detection performance for the INRIA and RIT validation datasets. In the case of the INRIA dataset, the best F1 score and AUC were achieved when the reconstruction error threshold equals 16, while the best F1 score and AUC for the RIT dataset were achieved at a value of 20.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 5 Performance Evaluation
### Spectral Efficiency  
Figure 4 shows the spectral efficiency for the proposed scheme at various blockage densities. The proposed scheme aims to enhance the spectral efficiency once beam blockage is in effect. Figure 4 shows the spectral efficiency for the proposed scheme versus conventional recovery methods that test neighboring beam directions or reset beam scanning procedures. When the direct propagation link between the MS and BS is free of obstacles (LoS operation), i.e., \(\mathbb{I}(x)=0\), high spectral efficiency is observed here. As a result, conventional methods and the proposed bundle-beam scheme yield high spectral efficiency. However, when obstacles start to appear in the propagation path of the primary beam, then the received signal level degrades, affecting capacity levels, and thereby reducing the spectral efficiency, as observed for the conventional schemes. For example, the work in [8] testing neighboring beams, requires beam scanning. Moreover, the beam aggregation method enhances the spectral efficiency if the density of the obstacles is low. However, this method results in a small number of scattering paths, attributed to the limited spatial coverage provided by the recovery narrow beams, which has a HPBW that is smaller than the dimensions of the obstacles. Meanwhile, the proposed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline System & \(f_{\alpha}\) (GHz), \(b\) (MHz), \(P_{\text{tr}}\)(dBm) & 28, 700, 30 \\ Arrays & \(a\), \(g_{n}\), \(N_{\text{MS}}\), \(N_{\text{BS}}\), & 1, 5, 256, 256 \\ Channel & \(\alpha_{w}^{2}\) (dB) \(\zeta\), \(d\) (dB), \(\rho\) & 1, 3, 5, 250, 0–1 \\ Path loss & \(d_{ref}\) (m), \(\delta_{\text{LoS}}\), \(\delta_{\text{NLoS}}\) & 5, 2, 6, 4 \\ Spectral efficiency & \(\Omega\), \(\delta_{\text{max}}\) & 2, 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
Figure 3: Proposed multi-backup beam recovery scheme.  
scheme achieves high spectral efficiency if blockage parameters are dense. This is attributed to the wide spatial space covered by the back-up beam-bundle, which also yields in a high scattering profile that is leveraged using MRC.
## 3 Results and Discussion
### Robustness and Method Control
#### 3.4.2 Chromatographic Analysis  
The FFD for GC-MS robustness screening was based on the \(3^{3-1}\)-level factors model, around the optimal point, with the gas flow rate (0.8, 1.0, and 1.2 L/min), oven temperature (75, 80, and 85 \({}^{\circ}\)C), and ramp gradient (45, 50, and 55 \({}^{\circ}\)C/min) being evaluated (Table S9, Supplementary Material). A desirability plot was performed by overlaying all CMPs. For the selection of values to be used in the desirability analysis, the TPA, RSD, PR, TF, and SF data were considered at the optimum point, where 1% was defined as desirable (\(7.5\times 10^{8}\), 2.0, 3.0, 1.0, 1.0), 0.5% as acceptable (\(6.0\times 10^{8}\), 7.0, 2.0, 0.6, 2.3), and 0% as unacceptable (\(5.2\times 10^{8}\), 10.0, 2, 0.5, 2.9). The desirability plot is shown in Figure 3b. The highest desirability index was reached with flow rates between 0.9 and 1.1 L/min, oven temperatures of 75-80 \({}^{\circ}\)C, and ramp temperature of 50-55 \({}^{\circ}\)C/min. In Table S11 (Supplementary Material), the agreement analysis between the predicted and the observed values for robustness  
Figure 3: Robustness analysis results of all CMPs through the desirability plots. (**a**) Desirability analysis results for HS-SPME(DVB/CAR/PDMS), simultaneous analysis of TPA, and IP robustness results. (**b**) The desirability analysis for GC-MS(BP20), simultaneous analysis of TPA, IP, PR, TF, and SF robustness results.  
verification is shown. From the results, it is possible to verify that all the observed values are within the range of the predicted values, within a confidence interval of 95%, confirming the good robustness of the method. However, the sensitivity of the CMPs influences the robustness analysis, thus it is necessary to establish the system suitability limits (see Section 3.4.3).
###### Abstract
Besides, an analytical modeling structure is proposed to give a closed-form expression on the average delay for one constructed routing path. This is achieved by modeling LBRP routing initialization as a semi-Markov process, where the relay selection process is represented as the states transition and the transition interval denotes the beacon-less relay selection delay. The main contributions of this paper are summarized as follows:  
* To the author's best knowledge, LBRP is the first beacon-less routing protocol designed for large volume content delivery in VANETs. To better describe the dynamic topology in the vehicular network, the path lifetime is taken into consideration in the protocol design. Apart from choosing the neighboring node nearest to the destination node, LBRP also takes the link stability factor into consideration. With this design, the constructed routing protocol can achieve a more stable routing path, with which the network throughput and the end-to-end delay performance can be enhanced. * Based on the proposed LBRP protocol, a Markov process-based theoretical analysis structure is proposed. Compared with the centralized routing policy, the routing initialization for the beacon-less routing should be highlighted since it is one of the main sources of the delay, which is important to evaluate the performance of the proposed LBRP protocol. The proposed analytical model can estimate the transmission delay for one routing path construction. The remainder of this paper is organized as follows. Section 2 summarizes the existing literature. Section 3 presents the system model and hypotheses. A lifetime-aware beacon-less multimedia routing protocol is proposed in Section 4. The analysis is outlined in Section 5, including the one-hop delay/distance model, the discrete Markov chain-based relay transition model and the path delay model. In Section 6, simulations are conducted to verify the proposed LBRP protocol with two other protocols. Moreover, the performance of the derived analytical results is also verified. Section 7 concludes this paper and gives some possible extension as future work.
## 7 Therapies Supporting the Management of Kinesiopbobia
### Modern Technologies Supporting Patients with Kinesiophobia  
Modern technologies such as virtual reality (VR), mobile applications, and rehabilitation robotics are becoming pivotal tools in solving the problem of kinesiophobia. Virtual reality enables the creation of controlled environments where patients can gradually acclimate to movements that induce fear. Research by [PERSON] et al. [93] demonstrated that VR therapy reduced kinesiophobia levels in 65% of patients with spinal pain while also improving their range of motion and confidence in daily activities. Mobile applications, including myMove and Kaia Health, allow for personalized rehabilitation plans and real-time monitoring of patient progress. [PERSON] et al. [94] found that patients using such technologies showed a reduction in pain (mean 4.4, SD 1.5 vs. mean 3.48, SD 2.1). Rehabilitation robots, such as Lokomat, support patients in performing precise motor exercises. Studies conducted by [PERSON] et al. [95] showed that robotic-assisted therapy reduced movement-related fear by 50% within just six weeks of treatment. Innovative technologies are effective tools in the treatment of kinesiophobia. Their integration with traditional therapeutic approaches enhances rehabilitation outcomes and improves patients' quality of life, as evidenced by numerous studies.  
This review examines therapeutic approaches for managing kinesiophobia, consolidating key strategies and evidence. Pain Neuroscience Education (PNE) fosters fear-free attitudes and reduces TSK scores, while Cognitive-Behavioral Therapy (CBT) improves mood and reduces avoidance behaviors. Distraction techniques, particularly virtual reality (VR), show potential in reducing fear of movement, though results vary by condition and implementation. Modern technologies such as VR, mobile applications, and robotics enhance outcomes by creating controlled environments, personalizing rehabilitation, and reducing fear effectively. Future research should focus on standardizing therapeutic protocols, conducting meta-analyses on VR interventions, and validating innovative technologies across diverse chronic conditions to optimize kinesiophobia management strategies.
## 3 The Proposed Model
### System Model  
The network scenarios include N dynamic nodes that are sparsely and randomly distributed throughout a L \(\times\) L \(\times\) L. The data sources are the water medium's sensed data. The data are collected using underwater sensors. Pressure, temperature, and current flow are all detected parameters. Underwater sensors are equipped with acoustic modems that allow them to communicate with another node submerged in the water [24]. The SN is located on the surface landmass and is equipped with RF and acoustic modems; the SN acoustic modem collects data from underwater sensors, while the RF modems transfer data to the BS. They assumed this network scenario was connected to the network. Due to the fact that the underwater sensors are movable due to the water current's velocity of around 1-3 m/s, the topology changes rapidly [25]. The network's assumption might be characterized as follows:  
1. The nodes know its position and the position of SN on initial placement;
2. Nodes might become the CH, and clusters member/relay;
3. The CH is rotated among the sensors for conserving energy.  
Due to the fact that the properties of acoustic waves in an underwater broadcast medium differ from those of radio waves, the power consumption of WSN cannot be utilized for UWSN. They apply the underwater acoustic channel's power consumption strategy for the present investigation [26]. The energy used while transferring \(k\) bits of data across a distance \(d\) at a data rate \(R\) is calculated as follows.  
\[E_{Tx}(k,d)=k\times E_{elec}+\frac{k}{R}P_{tx} \tag{1}\]  
where \(E_{elec}\) represents the power consumptions to route one bit of data and \(P_{Tx}\) means the transferred power [27].  
For receiving \(k\) bit of data, the receiver radio's power consumptions are given below  
\[E_{Rx}(k)=kP_{r} \tag{2}\]  
Let \(P_{r}\) denote a constant based on the device. For fusing \(k\) bit of data, the power consumptions are determined by  
\[E_{DA}(k)=k\times E_{DA0} \tag{3}\]  
In which \(E_{DA0}\) indicates the energy expended by fusing 1 bit of data, i.e., taken as five nJ/bit. As nodes are mobile because of the water currents, they place arbitrary motion for nodes during operational time. The present velocities are 1-3 m/s.