esa-sceva/satcom-chunk-collection huggingface dataset:
The following security requirements are important for any WSN based system such as VANETs:  
* Non-repudiation: Any electric vehicle transaction has economic value and this can motivate fraudulent acts by the entities selling or buying electricity. Therefore, this measure of non-repudiation ensures that any electricity transaction can be accounted for, to the involved parties and any modification cannot be denied by the party. * Message integrity and authentication: In a similar manner, any network transaction once completed cannot be modified by any malicious entity and once there is an attempt to tamper with the transaction, then it should be detectable by any legal entity of the system. * Privacy: The actual identity of a consumer nor the information of a transaction in the network should not be known by any malicious party eavesdropping on the communications involving a particular targeted entity. * Unlinkability: By observing the transactions in the VANETs network the entity's activities should still not be analysed and be associated with a particular RSU or vehicle. Thus to say messages lying on the network for any participant should still look random to an attacker and nothing associated with the participant should be determined. * Traceability: However, for the undesirable conduct of an entity in the network such acts should be traced and be accounted for, against the individual. On the other hand the vehicle should be hidden or inaccessible from other unauthorized entities. * Resistance to Attacks: Due to communication over a public channel, V2G security scheme must withstand various general attacks such as an impersonation attack, replay attack, modification attack, man-in-the-middle-attack and stolen verifier table attack in VANETs. Therefore, we propose a novel anonymous certificate-less aggregate signature scheme for VANETs with conditional privacy-preservation in a smart grid system, that addresses common weaknesses of most existing certificate-less aggregate signature schemes. The main contribution of the paper can be summarized as follows:  
* The proposed scheme achieves user anonymity with conditional privacy, such that each domain stores a Certificate Revocation List (CRL) in all road sign units located in that particular domain. * The proposed scheme achieves optimal efficiency for certificate-less aggregate signature while precluding complex cryptographic operations like bilinear pairings and map-to-point hash operations. * The proposed scheme withstand escrow property powers of the KGC but use of partial private key and user generated full private key for signature signing. The rest of the paper is organized according to the outline given as follows--Section 2 reviews most relevant related works of CLAS schemes for VANETs. Section 3 provides the mathematical building blocks for the proposed scheme. Section 4 gives the detailed steps of the proposed work. Section 5, presents an indepth analysis of the scheme in terms of security, privacy and performance assessment. Finally, in Section 6 we give concluding remarks about the proposed scheme.
## 4 Discussion
#### Strengths and Limitations  
Among the main strengths of this study is the use of a well-structured protocol based on PRISMA guidelines, along with registration in PROSPERO, which ensures a robust and transparent methodology. Additionally, the literature search covered multiple recognized databases and grey literature sources, increasing the comprehensiveness of the data collected. Advanced tools such as the Rayyan QCRI program were also used for article selection and evaluation, improving the efficiency of the review process and minimizing biases in study selection.  
However, an important limitation was the exclusion of articles published in languages other than English, which may have restricted the generalization of findings and increased the risk of systematic bias. Additionally, the limited use of advanced imaging techniques, such as magnetic resonance imaging, and the lack of integration of more detailed psychological assessments represent significant methodological gaps. Additional limitations of the manuscript include the absence of forest plots, attributed to the inconsistency and lack of comparable data across studies, which made it unfeasible to generate meaningful results without risking misinterpretation. Finally, the high heterogeneity in the reported data and selection bias in some included studies underscore the need for more standardized and consistent approaches in future research.
## 3 Results and Discussion
### Characterization of the Materials
#### 3.1.3 Pore Structure  
The textural properties of the XDA-1G resin were investigated using nitrogen adsorption-desorption isotherms measured at 77.35 K. The isotherm profile (Figure 4a) corresponds to combined features of Type I and Type IV behavior of a type IV isotherm with a pronounced H4 hysteresis loop, characteristic of materials with mesoporous structures and slit-like pores [30]. The sharp uptake at low relative pressures (P/P\({}_{0}\) < 0.1) indicates the presence of a substantial proportion of micropores, while the gradual increase and hysteresis loop at intermediate pressures (P/P\({}_{0}\) = 0.4-0.9) suggest capillary condensation within mesopores [30; 31].  
The BJH pore size distribution curve (Figure 4b) shows a dominant peak in the microporous region (<2 nm), along with broader contributions extending into the mesoporous range (2-10 nm) [32]. The inset highlights a dense population of narrow micropores, which play a critical role in improving adsorbate-adsorbent interactions by providing a high surface area and confinement effects.  
Figure 4: Porous structural characterization of the XDA-1G resin. (**a**) Nitrogen adsorption–desorption isotherms; (**b**) pore size distribution curve derived from BJH analysis. The isotherm reveals predominant microporosity, as evidenced by steep nitrogen uptake at low relative pressures. Meanwhile, the pore size distribution suggests the presence of minor mesopores.  
Quantitative textural parameters derived from BET, t-plot, BJH, and DFT analyses are summarized in Table 2. The XDA-1G resin exhibits a high specific surface area of 1439 m\({}^{2}\) g\({}^{-1}\) based on the BET method [33]. Micropores contribute significantly, accounting for 611.0 m\({}^{2}\) g\({}^{-1}\) of the total surface area, as determined using the t-plot method [34]. BJH analysis shows mesoporous surface areas of 177.6 m\({}^{2}\) g\({}^{-1}\) (adsorption branch) and 106.5 m\({}^{2}\) g\({}^{-1}\) (desorption branch). Additionally, DFT modeling yields a total pore volume of 0.404 cm\({}^{3}\) g\({}^{-1}\) and a surface area of 1093 m\({}^{2}\) g\({}^{-1}\)[35].  
These results confirm that the XDA-1G resin possesses a hierarchical porous architecture, combining micropores and mesopores. Such a structure provides abundant accessible adsorption sites and facilitates efficient mass transport, making it highly suitable for adsorptive removal of organic pollutants like m-cresol.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 3 Determination of Friction Angle in A Nonlimiting State
### Calculation of Nonlimit State FRICTION Angle  
#### 3.2.1 Establishing the Friction Angle as A Function of Horizontal Displacement  
A qualitative analysis of the development of the earth pressure with wall rotation in the RB model in the previous section leads to the following conclusion: the magnitude of the earth pressure is primarily dependent on the horizontal displacement of the soil. Therefore, the earth pressure can be calculated by establishing an equation for earth pressure as a function of displacement. [PERSON]'s experiments [19] revealed that when the soil was in the nonlimit state, the shear strength was not fully developed, and the soil's and interface friction angles could not be considered simply as \(\varphi\) and \(\delta\); however, the effective values were \(\varphi_{m}\) and \(\delta_{m}\). Both [PERSON] [25] and [PERSON] [26] established the functional relationship between friction angle and displacement. This paper adopted [PERSON]'s theory because it was derived from experimental data, which was more convincing.  
\[\tan\varphi_{m}=\tan\varphi_{0}+K_{d}(\tan\varphi-\tan\varphi_{0}) \tag{1}\]  
\[\tan\delta_{m}=\tan\delta_{0}+K_{d}(\tan\delta-\tan\delta_{0}) \tag{2}\]  
In Equations (1) and (2), \(\varphi_{0}\) is the soil friction angle at the bottom of the wall; \(\varphi_{m}\) is the developed value of soil friction angle related to depth; and \(\varphi\) is soil friction angle when shear strength is fully exerted. In addition, \(\delta_{0}\) represents the interface friction angle at the bottom of the wall; \(\delta_{m}\) represents the developed value of interface friction angle related to depth; \(\delta\) represents wall-soil friction angle when shear strength is fully exerted. Moreover, \(K_{d}\) is the coefficient of soil horizontal displacement, which can be expressed as [26]:  
\[K_{d}=\left\{\begin{array}{ll}\frac{4}{\pi}\arctan\frac{S_{z}
# Soft Gripper Design and Fabrication for Underwater Grasping
## 5 Experimental Results  
We demonstrate the applicability of the proposed low-cost soft gripper in underwater environments performing operations in a commercial ROV. We place the developed soft gripper and the devices needed for the hydraulic circuit in the commercial Sibiu Pro underwater vehicle from the Nido Robotics company. Such an underwater platform is based on previous developments of Blue Robotics company used for inspection tasks [38] and improves the design, robustness, and capabilities to operate in more aggressive environments. The Sibiu Pro standard platform is a fully operational underwater vehicle operated with an umbilical cable. Its design is conceived for the inspection and maintenance of submerged systems. The propulsion system uses a TVC (Thrust Vector Control) with eight propellers that allow the vehicle to move/rotate in any direction by combining them [39]. Figure 8(b) shows the soft gripper installed on the underwater platform.  
The experiment consists of grasping and transporting a complex object on the pool bottom. We emulate the conditions of the depths of the open sea by running the purification pool, which generates currents that make it difficult to operate the underwater vehicle. The object that the ROV should grasp can move due to the water flow generated by the purification pool. We anchor the load with a weight to constrain the movements in the pool bottom. In these conditions, the ROV should be able to grasp the object and transport it to another place in the pool.  
Figure 9: (**a**) Hydraulic system for controlling and acting the soft gripper, (**b**) hydraulic scheme, and (**c**) pump consumption under different operating pressures.  
Figure 10a shows the underwater vehicle approaching the object, a plastic lobster tied with a rope to a weight of 250 g. We have to remark that we do not anchor the plastic lobster at the bottom of the pool due to the water flow. The ROV aligns the object and pushes it to the pool bottom, as shown in Figure 10b,c. The soft actuators adapt to the shape of the plastic lobster and the bottom pool. This fact facilitates the maneuver because such an adaptation would be a collision using rigid end-effectors for the grasping. Therefore, we can affirm that using these soft devices increases the safety of complex maneuvers under uncertain conditions. The underwater vehicle generates a pressure of about 400 kPa to the soft gripper bending the soft actuators and grasping the plastic lobster. Figure 10d,e show the soft gripper grasping the plastic lobster tied with a rope to the weight of 250 g and the underwater vehicle transporting the load for several seconds. We can watch similar experiments using objects of different weights and sizes in the video attached to the paper (the video can be watched in the following [[https://youtu.be/UmutuhY7](https://youtu.be/UmutuhY7) Das]([https://youtu.be/UmutuhY7](https://youtu.be/UmutuhY7) Das), accessed on 10 October 2022).
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 4 System and Security Model
### Formal Definition
It outputs either a credential presentation \(pst\), a ticket \(tkt\), the ticket's double-spending identity \(dsid\), and validity period \(VP\) if the execution of the algorithm succeeds, or if the execution fails, \(\bot\). Note that \(\{m_{j}\}_{j\in\mathcal{D}}\) should satisfy an attribute disclosure policy generated by I to pass I's verification, and CTX is a random value to prevent replay attacks. * \(\mathsf{PriSign.Trace}(msk,\mathcal{L},pqst)\to uid/\bot\): This algorithm is operated by CA, which takes \(msk\), \(\mathcal{L}\), and \(pst\) as inputs, and outputs either a user's identity \(uid\) if the execution of the algorithm succeeds or \(\bot\) if the execution fails. * \(\mathsf{PriSign.SignOn}(tkt,dsid,VP,\vec{u},\vec{v},mpk)\to tok/\bot\): This algorithm is operated by U to create an access token \(tok\) to a designated verifier with the policy \(\vec{v}\). It takes \(tkt\), \(dsid\), \(VP\), an attribute vector \(\vec{u}\) that matches the policy \(\vec{v}\) (i.e., \(\vec{u}\in\Sigma,\vec{v}\in\Delta,\mathcal{F}(\vec{u},\vec{v})=0\)), and \(mpk\) as inputs, then outputs an access token \(tok\). * PriSign.Verify\((vid,tok,\)\(tk_{vid,\bar{u},\bar{u}},mpk)\to 0/1\): This algorithm is operated by a designated verifier V that takes _vid_, _tok_, _tk\(_{vid,\bar{u}}\)_, and _mpk_ as inputs. It outputs 1 if the token is validated and not double-spending, and 0 otherwise. **Correctness**. A PriSign scheme is correct if it satisfies the following conditions: (1) access tokens generated by honest users can be verified by designated verifiers; (2) the identities of users who have successfully obtained tickets can be traced by the CA. The formal correctness definition is shown in Supplemental Material E.1.
## 3 Results and Analyses
The relationship between the increase in filtration resistance and the increase in filtration velocity was also shown. For the dust from the furniture factory, the author obtained the values of the filtration resistance (for the same filtration velocity) for the clean non-woven fabric of 21.4 Pa, while in the tests described in the article, this value was 42.6 Pa. These differences should be explained by the different basis weight and properties of the nonwovens used. In 2017, [PERSON], [PERSON], [PERSON], and [PERSON] also dealt with the aging of filter nonwovens and their filtration properties [35]. PPS nonwoven tests were carried out by GB/T 5453-1997 using a III air permeability tester (Model FX3300) manufactured by TEXTEST AG (Zurich, Switzerland) with a circular test area of 20 cm\({}^{2}\). The particle size was analyzed according to ISO 13320-2009 with a laser particle analyzer (Model HELOS/BR-OM/RODOS) from Sympact GmbH, Germany, with a dispersion pressure of 4.00 bar. The sample's morphology was examined using a HITACHI S-3000N (Japan) scanning electron microscope with a voltage of 15 kV and a working distance of 22 mm. Researchers focused on the separation of PM 2.5 particles. For this purpose, the filter material was aged using a four-phase process:  
* Phase 1: Conditioning 30 filtration cycles with bag cleaning with 1000 Pa pulses. * Phase 2: Aging of the nonwoven fabric for 2500 cycles using a regeneration pulse with an interval of 20 s each. * Phase 3: Stabilization of 10 filtration cycles with differential pressure-controlled pulse cleaning. * Phase 4: Measurement of 30 dust loads using a regenerative pulse with a filtration resistance of 1000 Pa. In addition to showing an increase in filtration resistance with increasing airflow velocity over increasingly shorter periods between regenerative pulses, scientists also demonstrated the ability to filter particles at 99.971% and PM 2.5 particles at an efficiency level of 99.854%.
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 3 Materials and Methods
### Sigmoidal Models
#### 3.3.3 Von Bertalanffy Model  
It is one of the most widely used mathematical tools for modeling the growth curve where the rate of change does not follow a constant pattern [24] and is useful in construction applications. In rigid pavement projects, project time and cost do not always increase linearly or exponentially; instead, they may experience phases of accelerated growth followed by periods of stabilization or deceleration, depending on multiple factors such as resource availability, technical complexity, and economic conditions.  
In this model, time is considered the independent variable that drives project progress, while cost is the dependent variable that reflects the cumulative impact of decisions and events over time [25]. In contrast to conventional models that presuppose a direct and constant relationship between time and cost, the model captures the dynamic and nonlinear nature of this relationship [26]. It is imperative to anticipate and manage unanticipated cost escalations or schedule delays that may emerge during the various phases of a project. The model is expressed by Equation (16): The following text is intended to provide a comprehensive overview of the subject matter.  
\[f(x)=\frac{ce^{a(x-b)}}{1+e^{a(x-b)}} \tag{16}\]  
The lower limit of this function is set at 0, while the upper limit, denoted by c, is a parameter of interest. The function's asymmetry suggests a decelerated initiation relative to the logistic function, a phenomenon that can be attributed to the presence of initial bottlenecks. The inflection point, which is defined as the point at which the rate of change of a function is equal to zero, occurs below 50% of c. This property is particularly useful in cases where the initial stages of a project require a significant amount of time.  
Demonstrations of the limits of extremes of intervals 0 and 1.  
\[f(x)=\frac{ce^{a(x-b)}}{1+e^{a(x-b)}} \tag{17}\]  
Figure 5: Logistics model.  
Limit when \(x\rightarrow\infty\) :  
\[\lim_{x\rightarrow\infty}f(x)=c\] (18) Derivative: \[f^{\prime}(x)=\frac{ace^{a(x-b)}}{\left(1+e^{a(x-b)}\right)^{2}} \tag{19}\]  
\(f(x)\) is the valued cost of the project ($), \(a\) is the parameter that reflects the rate at which costs increase over time, \(b\) represents the moment in time when costs increase at the highest rate, \(c\) indicates the expected total cost of the project in the long term, and \(x\) is the project execution time (Month) (See Figure 6).
## 3 Performance Analysis
### Propagation Models
#### 3.3.2 SUI Model Formulation  
The mathematical expressions linked with the SUI model facilitate the computation of propagation loss in wireless systems, taking into account variables such as distance, frequency, and terrain characteristics. The formulations are delineated as follows:  
\[PL=A+10\lambda\log(\frac{d}{d_{0}})+X_{f}+X_{h}+s, \tag{1}\]  
\[A=20\log[\frac{\pi d_{0}}{\delta}], \tag{2}\]  
\[\lambda=a-bh_{b}+\frac{c}{h_{b}}, \tag{3}\]  
\[X_{f}=6\log[\frac{f}{2000}], \tag{4}\]  
\[X_{h}=-20\log[\frac{h_{r}}{2}], \tag{5}\]  
where \(A\) is the decibel path loss, \(d\) is the distance between Tx and Rx in meters, \(d_{0}\) is equivalent to 100 m, \(\lambda\) represents the wavelength amplitude in meters, \(\delta\) represents the losses due to scattering, \(f\) is the frequency in MHz, \(h_{b}\) and \(h_{r}\) are transmitter height and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Frequency & \(\leq\)3500 & MHz \\ Base station height & 1–80 & m \\ Mobile antenna height & 1–10 & m \\ Distance & \(1\times 10^{2}\)–\(1\times 10^{4}\) & m \\ \hline \hline \end{tabular} Data extracted from the SUI model [23,24].  
\end{table}
Table 2: SUI model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Category A** & **Category B** & **Category C** \\ \hline a & 4.6 & 4 & 3.6 \\ b & 0.0075 & 0.0065 & 0.005 \\ c & 12.6 & 17.1 & 20 \\ s & 10.6 & 9.6 & 8.2 \\ H & 10.8 & 10.8 & 20 \\ \hline \hline \end{tabular} Data extracted from the SUI model [23,24].  
\end{table}
Table 3: Category parameters for the SUI model.  
average receiver height in meters, respectively, \(s\) is the fading coefficient, \(X_{f}\) is the frequency correction factor, and \(X_{h}\) is receiver height correction factor.  
The constants \(a\), \(b\), and \(c\) of the SUI model are detailed in Table 3. The application of the SUI propagation model provides information on power levels as the receiver moves away from the transmitter.  
Considering that the park in the Checa parish is flat terrain with low vegetation density, Category C is used for the application of the model using the following parameters:  
* Height of the base station: 5 to 12 m.
* Height of the mobile receiver: 1.7 m.
* Transmission frequency: 2.4 GHz.
* Terrain: Flat, without obstructions.
* Distance from the base station: 0 to 100 m.  
Considering that the station's operating parameters fall within the ranges established in Table 2, and that Category C determines the constants \(a\), \(b\), and \(c\) for the application of the SUI model as in Table 3, Equation (6) presents the expression that allows calculation of signal losses along the path, thereby generating the curve illustrated in Figure 4.  
\[PL=-70-52.0667\times\log(d/100) \tag{6}\]
## 2 Related Work
In both cases, the objective is to establish a relationship between input and output variables [19]. One-hot encoding (OHE) is used to convert categorical variables such as \"Application_Type\" into binary vectors, where each category is represented as a distinct binary feature. Unlike integer encoding, which can introduce misleading assumptions about the relationships between categories, OHE ensures that no ordinal relationships are implied between categories. This encoding is crucial, particularly for the regression models used in this study, which treat encoded categories as independent features and learn separate weights for each category [20; 21]. Establishing a reliable communication channel is a common challenge with a moving device or object. This study aims to leverage machine learning to achieve improved results. A combination of 5G specifications and multiple machine learning models will be utilized. However, one of the main difficulties is intelligently and dynamically allocating bandwidth to accommodate different types of traffic, including data, voice, and video, particularly when UAV movement is unpredictable. In these situations, fixed allocation or rule-based approaches frequently fail to maintain QoS. The purpose of applying the ML models in optimization-based techniques is to maximize the network's efficiency or effectiveness. Despite growing interest in UAV-based communication systems, a technical gap remains in the application of machine learning (ML) techniques for real-time bandwidth estimation and QoS prediction. Our UAV communication dataset's features are well suited to LSGB's efficiency in small-to-medium datasets and robustness in handling non-linear data. However, the BRFFNN's regularization ability, which avoids overfitting, is particularly important in highly variable wireless environments, and makes it especially well suited for noisy, real-world data. These models were chosen in order to assess the relative strengths of a regularized neural network and a robust ensemble tree-based approach in bandwidth prediction. These techniques are commonly employed in QoS simulation approaches, especially in specific applications, to derive values and assess performance. Our main contribution is the comparison of these ML models' dynamic adaptation to traffic types and movement patterns, which shows how well ML models perform in producing better QoS results. Below are the objectives of this paper:  
1. Investigate network simulation for UAV-GS applications using 5G specifications, employing the BRFFNN and LSGB machine learning models for bandwidth allocation;Develop two different UAV movement patterns and test the effectiveness of using machine learning models for bandwidth allocation;
3. Evaluate the QoS for UAV-GS communication within 5G networks. The QoS parameters used are the PDR, throughput, and delay.