esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL  
### Simulation Setup  
Table 1 shows the details of the simulation setup used in our research. A custom simulation model of HRS, M-Nearest, M-Nearest_MaxTx, MRS-ST and MRS-SD was developed in Octave [29]. One thousand devices are distributed uniformly at random across a square network area, with widths of 100 and 1000 m. Random Waypoint mobility model is used to emulate movement patterns of people as close as possible. The mobility model is implemented using BonnMotion [34].  
We are considering a scenario where people carrying mobile devices are walking within a certain area. Therefore, the speed of devices is varied between 0-2 m/s and have maximum pause time of 5 s. The social trust value between two devices follow Pareto distribution. Different social trust scenarios among devices in the network are achieved by varying the Shape and Scale parameters. Source and destination are selected randomly from the 1000 devices. We set the transmission power of a source device \(P_{s,d}\) = 20 dBm, noise power \(N\) = \(-\)114 dBm, pathloss exponent \(\theta\) = 4, bandwidth of channel \(B=1\) MHz as in References [12; 35]. We assume the duration of a time slot for D2D communications \(T=1\) s and that for a probe \(\tau=0.01\) s [5]. The mobile devices are considered to have 10 different battery percentage levels with the difference of 10 between them. The battery percentage of a device is selected randomly. Each simulation runs for 1000 s. In this analysis, the maximum allowable transmission power for a relay is considered to be equal to transmission power of source. The results reported for each of the schemes are time average of the throughput.  
Section 5.3 presents the results various relay selection schemes analyzed based on this simulation setup.
# Development of Korea Airport Pavement Condition Index for Panel Rating
## 2 Materials and Methods  
### Background  
Assuming a condition without distresses as a maximum of 100 [11; 12], the PCI is calculated by subtracting the deduct value (DV), which is a function of the distress type, severity, and density on the pavement surface using Equation (1). In case of multiple distresses, a corrected value is calculated [13; 14].  
\[\mathrm{PCI}\ =\mathrm{C}-\sum_{i=1}^{p}\sum_{i=1}^{m_{i}}a\big{(}T_{i},\ S_{j},D_{ij} \big{)}\ \mathrm{F}(t,d) \tag{1}\]  
Here, PCI is the pavement condition index, C is a constant depending on the desired maximum scale value (100), and \(a\big{(}T_{i},\ S_{j},D_{ij}\big{)}\) is the deduct weighing value depending on the distress type (\(T_{i}\)), level of severity (\(S_{j}\)), and density of distress (\(D_{ij}\)). i, j, and p are the counter for the distress types (i), counter for the severity levels (j), and total number of distress types for the pavement type under consideration (p), respectively. The number of severity levels for the i th type of distress are represented by \(m_{i}\), and \(F(t,d)\) is an adjustment factor for multiple distresses that vary with the total summed deduct value (t) and number of deducts (d).  
The PCI calculation is carried out in sample units. The size of an asphalt pavement is defined to be 464.5 \(\pm\) 185.8 m\({}^{2}\), and a concrete pavement is assumed to consist of 20 \(\pm\) 8 slabs [14]. If a given sample unit contains different distress types or different severity levels but of the same type, they are treated as multiple distresses and an adjustment factor is applied. A corrected deduct value (CDV) calculation process using the total deduct value (TDV) and the number (q) of distresses to be considered is then carried out [15; 16]. The TDV is determined by adding all deduct values from each distress condition observed. And CDV is determined based on the TDV and the number of distress conditions observed with individual deduct values over five points. The PCI is calculated as in Equation (2).  
\[\mathrm{PCI}=100-\mathrm{CDV} \tag{2}\]  
This PCI calculation enables automatic road analysis, such as ARAN systems [17]. The system is based on the data on the pavement deterioration obtained during the measurement. The data is collected in the form of photos of the pavement made with cameras and a three-dimensional image of the pavement obtained as a result of three-dimensional scanning [18]. Unfortunately, the automatically obtained data are then processed manually by humans, which still affects the quality of information about the actual technical condition of the assessed pavement. ARAN was also used to collect distress data to find potential relations with pavement roughness and pavement condition [19].  
To overcome the limitations of these analytical techniques, Iranian scientists in their study [20] presented attempts to develop an alternative method for determining the PCI using optimization techniques based on artificial neural networks and a genetic algorithm. The proposed approach may help in the future to reliably extrapolate the PCI index.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 2 Global Market/Bibliographic Data Analysis and Standards
### Standards
#### 2.1.2. ISO 21217 (ITS--Communications Access for Land Mobiles (CALM))  
The standard ISO 21217 sets up a framework that improves connectivity and interoperability in the transportation field by framing its architecture and communication standards. Through its comprehensive framework for vehicle-infrastructure communication, this standard boosts the effectiveness of traffic control and emergency support and enables the incorporation of new technologies. Adopting this standard develops a well-connected network where ITS applications function seamlessly and cooperate to benefit the safety of transportation infrastructures [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standards** & **Technology Used** & & **Description** & **Merits** & **Demerits** \\ \hline ISO 15118 & Road Vehicles—Vehicle-to-Grid Communication Interface &  
1.3 International Organization for Standardization (ISO) 15118 (Road Vehicles--Vehicle-to-Grid Communication Interface)  
The development of smart charging solutions requires ISO 15118 for integrating EVs into the energy grid. ISO 15118 adds value beyond typical charging methods; it is necessary for building a reliable and regular framework for two-way exchanges. This standard equips EVs to take energy from the grid while allowing them to the energy effectively. ISO 15118 is crucial for allowing smooth interaction between vehicles and the energy network. This situation improves energy consumption while providing routes for enhanced stability and effective demand management [45].
# Analytical and Graphical Profiling of Thread-Milling Cutters for Forming Internal Threads
## 1 Introduction  
Thread milling is among the common processes of internal thread manufacturing in industrial applications due to its flexibility, surface finish parameters and suitability in CNC machine centers. Among the various categories of tools that are available, some special thread-milling cutters are more applicable for cutting internal threads in complex or non-standard parts.  
Despite their advantages, the biggest problem in their conception is precise profiling of the cutter's working areas. The cutter must exactly model the thread shape without inducing undercutting of flanks. Analytical profiling methods, although precise, are prone to be heavily mathematical and computation-intensive. Graphical methods, however, are easier to carry out but are hardly ever verified to design high-precision tools.  
The graphical profiling technique developed by the authors, known as the Tangent Circles Method, is applied in this work to define the geometry of cutting tools with complex profiles. This graphical method was originally developed by the authors as a practical approach to tool profiling and is presented here along with its validation against analytical results. To the best of the authors' knowledge, this is the first time the Tangent Circles Method is formally described and tested in the context of internal thread-milling tool design.  
Several thread-milling cutters are designed, and the tool profiles obtained using the graphical method are directly compared with those generated analytically. This comparison allows us to assess the mathematical accuracy of the graphical approach. Additionally, CAD-based simulations are used to evaluate the influence of tool design parameters such as rake angle and relief height on the final shape of the tool profile. The aim of this study is to validate the Tangent Circles Method as a reliable and precise alternative to analytical profiling for internal thread-milling cutters and to examine how design variables affect tool geometry through simulation.
## 3 Methodologies
### SAE-DNN-Based HI Constructor
To prevent the AE from just learning the identity of the input and make the learnt features more robust, noise can be added to the input data for training. The AE is forced to reconstruct a corrupted version of the input. This method is called a stacked denoising autoencoder (SDA) and it is chosen for our study. Following the unsupervised pretraining, the output layer is added to the top of the SAE and the weights and biases are fine-tuned. Each hidden layer's weights are initialized by the pretrained weights \(\{W_{k},b_{k}\}_{k=1,2,\ldots,K}\). The output layer's parameters \(\{W_{0},b_{0}\}\) can be set by random initialization. Afterwards, back-propagation is utilized to achieve improved weights \(\left\{W_{k}^{\prime},b_{k}^{\prime}\right\}_{k=1,2,\ldots,K}\) by fine-tuning the entire network. The predicted error is minimized as follows:  
\[J_{o}=\sum_{j=1}^{N}{\left\|y_{j}-\hat{y_{j}}\right\|}^{2}\Bigg{/}2N \tag{4}\]  
with \(y_{j}\) and \(\hat{y_{j}}\) being the \(j^{th}\) data sample's label and predicted output, respectively. The SAE training procedure is displayed in Figure 5.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 4 Secure and Verifiable Cloud Computing for Mobile Systems
### Construction and Evaluation of Garbled Circuits
#### 4.1.3 Evaluation of the Garbled Circuit, \(GC\)  
The garbled table for each gate, \(A\), in the circuit is an ordered list of four values, \([A_{00},A_{01},A_{10},A_{11}]\).  
During evaluation, let \(\alpha,\ \beta\) denote the garbled values for the two input wires of gate \(A\). Let \(a=LSB(\alpha)\), \(b=LSB(\beta)\). Then, the garbled value for the output wire, \(\gamma\), is computed using \(\alpha,\ \beta,\ A_{ab}\), as described in the two steps below:  
1. split the most significant \(nk\) bits of \(\alpha\) into \(n\) parts, \(\alpha_{1},\ \alpha_{2},\ \alpha_{3},\ \ldots,\ \alpha_{n}\); each part has \(k\) bits; similarly, split the most significant \(nk\) bits of \(\beta\) into \(n\) parts, \(\beta_{1},\ \beta_{2},\ \beta_{3},\ \ldots,\ \beta_{n}\); each part has \(k\) bits; _i.e._, \(|\alpha_{i}|=|\beta_{i}|=k\), where \(1\leq i\leq n\).  
compute \(\gamma=[G_{b}(\alpha_{1})\oplus G_{b}(\alpha_{2})\oplus\ldots\oplus G_{b}(\alpha_{n} )]\oplus[G_{a}(\beta_{1})\oplus G_{a}(\beta_{2})\oplus\ldots\oplus G_{a}(\beta_{ n})]\oplus A_{ab}\).  
In this manner, the garbled output for any gate in the circuit can be computed using the garbled table and the two garbled inputs to the gate. Note that while the construction of the garbled circuit requires interaction among all the \(n\) parties, \(p_{i}\) (\(1\leq i\leq n\)), the server \(p_{e}\) can perform the evaluation of the garbled circuit independently.
## 8 Challenges and Future Directions
### Future Directions  
* There is no way to evaluate something without experimentation, but we may draw some conclusions from the experiment's owners. These defense strategies, for example, had been used against white-box attacks, but what about black-box attacks? Thus, there is a need for techniques to counter the black-box attack in the future. In contrast, in [103], the authors presented an approach to address transferable adversarial attacks. We believe it to be a promising defense approach with excellent efficiency against black-box attacks, although it has been examined using a white-box attack.
* In the future, there will be a demand for a solution that handles all types of adversaries that affect the robustness of an IDS.
* Various models may necessitate several defenses [9]. Thus, they need to measure their effectiveness in protecting ML and DL based on IDS.
* Some researchers have stated that their technique may be used in other ML/DL models or is available online for experimentation. Therefore, we suggest increasing the effectiveness of the dropout strategy to make it more reliable and suitable for additional domains such as IDS.
* In this paper, we focus on IDSs; generally, we think that protecting ML/DL-based IDSs is easier to preserve since it is difficult to deceive IDSs because the features contain discrete and non-continuous values [107]. Therefore, we believe that enhancing the GAN defense strategies such as APE-GAN++ will make them more reliable for IDSs, which will be a valuable technique for handling adversaries in the future. Moreover, Table 5 demonstrates a comparison between these strategies.  
Despite the threats that face the ML and DL when using them as an engine for IDS, it is a powerful technique that has served cybersecurity in general and IDSs in particular. Therefore, this paper highlighted various attacks and defense techniques to improve the precision of ML-based IDSs and the IDSs' robustness.
# Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
## 2 Background
### Clinical Text Summarization
#### 2.1.3 Hybrid Text Summarization  
Hybrid text summarization combines both abstractive and extractive methods, with the aim of combining the strengths of each [16]. Initially, the extractive phase selects key sentences from the input text that are deemed essential to convey its overall meaning. Following this, the abstractive phase uses these extracted sentences to generate a concise and coherent summary using abstractive techniques.
## 1 Introduction
In the present attempt, three well-proven research methods, Interpretive structural modeling (ISM), Matrice d'impacts croises-multiplication appliquee a un classement (MICMAC) analysis, and Interpretive ranking processes (IRP), are used; these can deliver relationship modeling, categorization of LBs, and ranking of LBs to help in successful lean implementation. Thus, the paper provides LB relationship modeling and ranking based on ISM, MICMAC, and IRP, which will help practicing managers and researchers in LB evaluation. Based on the above, the present research poses the following research questions:  
* What are the various LBs for SMEs in the manufacturing sector? * How may the LBs be classified using MICMAC analysis? * How can the LBs be modeled using ISM and ranked employing IRP? The paper is organized as follows: Section 2 documents a literature review to provide the stock of lean implementation barriers for SMEs in the manufacturing sector; Section 3 documents the various research methodologies of ISM, MICMAC, and IRP applied in the present research. Section 4 provides ISM model development and Section 5 provides the discussion of the present research. Section 6 states the limits of the research and potential future directions. Finally, a conclusion is drawn.
# Medical Robotic Bed to Prevent Pressure Sores
## 4 Discussion  
In this study, we proposed a new robotic bed to prevent pressure sores. To prevent pressure sores, it is important to ensure that the pressure applied to the patient's body is not excessive and does not last for a long time. Thus far, the most commonly used commercial products are bubble air mattresses, which have no function to control body pressure. Although these mattresses may reduce the likelihood of pressure sore formation, the risk of excessive pressure on the body remains, depending on the patient. An active mattress with pneumatic actuators can regulate body pressure, but the pneumatic pump is excessively noisy, which can cause patient discomfort. In this study, we proposed a new medical robotic bed that controls body pressure by using a closed-loop control method. The developed robot bed is composed of 17 independently driven segments and uses body pressure information for feedback control. Each segment is driven by a four-bar mechanism actuated by a BLDC motor. A new belt-shaped pressure sensor array was developed for this bed by using FSR technology. Electrodes and resistors were printed on  
Figure 14: Fuzzy pressure control performance.  
Figure 15: Open-loop control experiment result.  
a PET substrate, and the developed sensor was found to be capable of measuring body pressure values of up to 80 mmHg. The control system was implemented as a multi-mode controller with closed- and open-loop modes. In the closed-loop control mode, the segment height is controlled to ensure that the body pressure is lower than the reference value by using the body pressure sensor information and the fuzzy controller. The reference body pressure was set to 32 mmHg, which is known as the pressure that can cause pressure sores. If excessive body pressure is applied for a long time, the controller switches to the open-loop control mode, in which the positions of the bed segments are periodically and forcefully repositioned to ensure that excessive pressure is not applied to the body for a long period. Body pressure control experiments were performed with the test subject lying on the developed bed. The experiments were conducted for two postures, and in both cases, it was proven that the body pressure error converged at zero and was controlled to a value lower than the reference pressure. Through actual experiments, we verified that the proposed bed is effective at preventing pressure sores. In the future, through long-term clinical trials, we plan to prove the effectiveness of the proposed robotic bed for various body types and patient groups. Moreover, improving patient comfort is another future goal.  
Software, M.S.; conceptualization and project leading, Y.L.; pressure sensor validation, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the 2020 Technology Development Program (S2926521) funded by the Ministry of SMEs and Startups of Korea.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Definition of GS-MR Scheme and Security Model
### Security Model  
In the GS-MR scheme, three properties are required: correctness, anonymity, and traceability. Correctness includes validation correctness, open correctness, and message recoverability, where validation correctness means that the group signature output by the signature algorithm can be successfully verified, recoverability means that the complete signed message can be recovered when the group signature is successfully verified, and open correctness denotes the ability to acquire the right signer's identity from a valid signature. We will describe the strong anonymity of the GS-MR scheme through the CCA (Chosen Ciphertext Attack) security model, as detailed in Definition 5. We will describe the traceability of the GS-MR scheme using Definition 6. To describe the security model of GS-MR, the present paper leverages the security definitions for group signatures of varying strengths provided by [PERSON] et al. [36]. Through a corresponding game between a challenger \(\mathcal{S}\) and an adversary \(\mathcal{A}\), the anonymity and traceability guaranteed by the GS-MR scheme will be depicted.  
We summarize three distinct query types that an adversary \(\mathcal{A}\) can ask in the corresponding games, as well as the possible responses that the challenger \(\mathcal{S}\) can give to those queries.  
1. Corrupt query: \(\mathcal{A}\) makes a corrupt query on a member's index \(i\in[N]\) and \(\mathcal{S}\) returns a corresponding signing key \(gsk_{i}\).
2. Signing query: \(\mathcal{A}\) makes a signing query on an index \(i\) and a message \(M\), and \(\mathcal{S}\) runs the algorithm \(Sign(gpk,M,gsk_{i})\rightarrow\text{SIG}\), and returns the signature \(SIG\) to \(\mathcal{A}\).
3. Opening query: \(\mathcal{A}\) makes an opening query on a signature \(SIG\), and \(\mathcal{S}\) calls the algorithm \(Open(gpk,SIG,gtk)\) to output a member identity \(\mathbf{ID}_{i}\), and returns the member's identity \(\mathbf{ID}_{i}\) to \(\mathcal{A}\); otherwise, it returns to \(\bot\).  
**Definition 5**.: _(Full Anonymity) The property of anonymity in the GS-MR scheme implies that signatures produced by any two distinct signers are computationally indistinguishable. The GS-MR scheme meets full anonymity if for any PPT adversary \(\mathcal{A}\), \(\mathcal{A}\)'s advantage in GAME I in Figure 2 can be negligible._  
**Definition 6**.: _(Traceability) The property of traceability in the GS-MR scheme implies that the advantage of generating a non-openable signature or blaming for other members is negligible. The GS-MR scheme meets traceability if for any PPT adversary \(\mathcal{A}\), \(\mathcal{A}\)'s advantage in GAME II in Figure 2 can be negligible._
# Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method
## 3 Experimental Study
### Water Content Alteration  
Soils with three water contents were tested. For each water content, both non-void (case W1, W2 and W3) and void (cases W1v, W2v and W3v) conditions were considered, as listed in Table 1. For case W1, the soil was hammered to retain a soil height of 0.8 m in the box when the density was \(\rho_{3}=1656\) kg/m\({}^{3}\) and the natural water content was \(\omega_{1}=0.3\%\). In order to increase the water content, the soil was watered; then, the sand box was sealed with watertight tarpaulin. After 24 h, the water content was measured with a constant temperature oven for five soil samples using 200 g aluminium boxes. Finally, soil with averaged water contents of \(\omega_{2}=8.2\%\) (W2) and \(\omega_{3}=15.3\%\) (W3) were obtained. Following the dynamic test for each soil water content case, the box with dimensions of 150 mm \(\times\) 150 mm \(\times\) 100 mm was embedded behind the concrete plate to model the void conditions. Thereafter, the dynamic tests were repeated.
## 3 Methodology
### Classification Methods
#### 3.3.2 Support Vector Machine (SVM)  
The SVM was invented in 1960 [47]. Following its invention, it was presented in 1998 by [PERSON] [48]. Initally, SVM was presented as a form of initial binary classification [49]. Then, two types appeared: linear and multi-class SVM [50].  
SVM is used to classify data on the basis of two distinct categories and to find the best hyperplane [51]. The primary objective is to find a hyperplane that maximizes the distance with the nearest data points [52,53]. To maximize the minimum distance between the hyperplane and the training data, an algorithmically selected hyperplane is used. The minimum distance is usually referred to as the margin [54]. The structure of the SVM is shown in Figure 5.  
The hyperplane formed by SVM can be formulated according to Equation (2) [12]:  
\[\mathrm{f(x)}=w^{T}\cdot x+b \tag{2}\]  
The optimization problem of SVM can be summarized as shown in Equation (3):  
\[\mathrm{min}\frac{1}{2}\;||x||^{2} \tag{3}\]  
subject to Equation (4):  
\[y_{i}(wTxi+b)\geq 1,\;\forall i_{1}=1,\;2,\ldots,\;N \tag{4}\]  
Figure 4: General architecture of RF [46].  
Figure 5: SVM structure.