esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Deviation Analysis  
The deviation analysis is performed for the attributes between sample update requests \((spl^{\pi})\), \(\left(N_{q}\right)\), and \(\xi^{\Delta}_{h^{\prime}}\) by using the SDQL technique. The traditional Q-Learning is introduced due to its high learning capability. The proposed work establishes the Standard Deviation Empirical Rule (SDER) based on Q-learning. This is discussed as follows.  
Initially, the \(s^{\prime}=1\)_to_\(S^{\prime}\)_number of attributes between \((spl^{\pi})\) and \(\left(N_{q}\right)\) is defined as \(\left(\varphi^{att}_{s^{\prime}}\right)\). Here, the deviation score \((std^{\theta})\) between \(\varphi^{att}_{s^{\prime}}\) and \(\xi^{\Delta}_{h^{\prime}}\) is measured as,  
\[std^{\theta}\rightarrow\left[\frac{\left|\varphi^{att}_{s^{\prime}},\xi^{ \Delta}_{h^{\prime}}\right|-mn^{\partial}}{sn^{\lambda}}\right] \tag{37}\]  
where \(mn^{\partial}\) and \(sn^{\lambda}\) indicate the mean and standard deviation of \(\varphi^{att}_{s^{\prime}}\) and \(\xi^{\Delta}_{h^{\prime}}\). Then, the SDER \((rules^{\prime})\) is applied as,  
\[rule^{s}=\begin{cases}if\big{(}std^{\theta}\approx 68\%\big{)},&low\left(sn^{ \lambda}\leftrightarrow mn^{\partial}\right)\\ if\big{(}std^{\theta}\approx 95\%\big{)},&medium\left(sn^{\lambda} \leftrightarrow mn^{\partial}\right)\\ if\big{(}std^{\theta}\approx 99.7\%\big{)},&high\left(sn^{\lambda} \leftrightarrow mn^{\partial}\right)\end{cases} \tag{38}\]  
\[Ql(ste_{s^{\prime}},act_{s^{\prime}})\rightarrowrule^{s}*\left(1-\ell_{r} \right)\cdot Ql(ste_{s^{\prime}},act_{s^{\prime}}) \tag{39}\]  
where \(Ql\) represents the Q-value; \(ste_{s^{\prime}}\) and \(act_{s^{\prime}}\) indicate the current state and action, respectively; \(\ell_{r}\) denotes the learning coefficient; \(rew_{s^{\prime}+1}\) specifies the reward; \(dis^{f}\) represents the discount factor; and \(ste_{s^{\prime}+1}\) and \(act_{s^{\prime}+1}\) denote the next state and action, respectively.  
If the \(std^{\theta}\) is around 68%, then it is assumed as \(rew_{s^{\prime}+1}\). Likewise, if the \(std^{\theta}\) is around 95% to 99.7%, then it is assumed as \(act_{s^{\prime}}\). Here, \(rew_{s^{\prime}+1}\) is referred to as an update and \(act_{s^{\prime}}\) is referred to as an error of the GT.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 3 Core of the PVPBC Voting System
### Capability Token Structure  
The \(T_{m}\) is responsible for maintaining a profile record for eligible voters on the DLT. In the profile record, all registered voters are associated with a virtual identity (VID) which is globally unique (we use two forms of ID. The voter ID is the normal identification required at the registration phase. It is typically a name or an email address. VID is another form of voter ID on the DLT. VID is a key which identifies the eligible voter on the authentication blockchain.), which is used as the prime key for identifying voters'off-chain profile. It is maintained by the TTP. At least one main account belongs to each voter. It is indexed by its VID address in the permissioned DLT. Consequently, the DLT is used to capture the VID for profiling registered voters. Generally, each eligible voter should be assigned a capability access token, which is stored in a smart contract. The capability token specifies the access rights associated with the voting ballot. It also contains awareness information. The capability token uses the following parameters.  
* \(F\): a cryptographic hash function which operates in one direction only;
* \(VID_{V}\): the virtual ID of an eligible voter which solicits access, with the purpose of taking part in the election;
* \(EXP_{R}\): the token expiration time;
* \(AR\): the set of access rights pertaining to a predetermined set of actions;
* \(T\): a timestamp;
* \(n_{i}\): the tracking number for registered voters. This tracking number is a sparse selection of integers.  
The capability token is then defined as a function of these parameters,  
\[Token_{R}=F(VID_{V},EXP_{R},AR,T,n_{j}). \tag{1}\]  
Figure 2: Voter-access control is carried out using two phases: the registration phase and the authentication phase. Effective access-control processes for voting services and information in e-voting systems is achieved by PVPBC by offering an anonymous authentication mechanism where the voter’s vote can be revoked by the election service provider with the cooperation of a trusted organization.  
In the adopted authentication mechanism, the capability token corresponding to a particular voter is identified by the VID of the voter. Example actions in the set of access rights, AR, are the \(vote\) and the \(NULL\) actions. If the access-rights set is null, \(AR=NULL\), the voting operation is not allowed.
## 2 Background
### Main Drivers and Barriers
Thus, policy incentives for the CE must include renewable gas, and public support has to be provided for the plant construction to foster investment in PtG in a CE framework. Other incentives could also address the upgrade of facilities to reduce the cost of investment using public funds or other institutional support to local biogas or biofuel [36; 52] because PtG contributes to close material loops. From a perspective similar to that of RES technologies, PtG should be supported by subsidies to maintain the leadership of some European countries as technology suppliers and to foster the deployment of these systems [53]. Moreover, it can be supposed that the reliability of PtG technologies is a necessary condition for future investment and the policy framework must materialize political support for PtG, taking into account the social acceptance in a region. A challenge for the acceptance of PtG in a given region is therefore the installation of wind farms to generate renewable electricity [53]. Examination of previous technological innovation processes reveals that first attempts of large-scale implementations are perceived more poorly than pilot projects [19]. Confidence in actors, and perceived benefits and risks of the technology, imply that citizens are willing to support transformation processes if they are convinced of their purpose [19] and specific dissemination actions are developed in the territory during the initial stage [40]. Additional barriers are challenges that concern public knowledge and understanding. In particular, the safety and use of hydrogen must be addressed, in addition to the promotion of the environmental benefits of synthetic natural gas as a means to decarbonize the energy system. Based on this analysis of the research background and the literature, we considered it necessary to present the following research question to the interviewed experts:  
**RQ 1:**: _What are the main incentives and barriers to the deployment of PtG technologies?_
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Organic Materials
#### 4.2.2 Paper  
The manufacturing process of paper varies across different historical periods. This means that the structure and composition of paper, and hence its quality and susceptibility to deterioration, differ. Two main events signify a sharp change in the manufacturing process of paper, namely:  
1. 1799, the Fourdrinier machine;
2. 1844, groundwood pulp.  
Moreover, the end of the eighteenth century saw the beginning of the use of bleaching agents to whiten paper. What primarily determines the strength and quality of paper are the fibers. With the utilization of groundwood pulp, fiber sources shifted from cotton or linen rags to wood. This change results in different percentages of cellulose and lignin, leading to a different fiber structure in the final paper. Table 6 reports the percentages of cellulose in the various raw materials utilized in the evolution of paper production.  
Paper of different ages undergoes distinct deterioration processes and responds differently to irradiation; old paper exhibits better quality in terms of mechanical resistance and chemical stability, making it less prone to biodegradation than modern paper [14; 18; 21; 104]. Because it is highly hydrophilic, paper, in general, is less resistant than wood to biodegradation. Due to its hydrophilicity, paper is particularly vulnerable to microscopic biodeteriogens. Some fungi can grow even at humidity levels as low as 8%. Speaking of paper, insect attacks are a secondary effect related to the presence of other materials needed, e.g., for the creation of books (glue, leather); these materials are the real targets of insects. The results of insect presence on paper are similar to those observed on wood, such as holes and tunnels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fiber Source** & **Cellulose \%** \\ \hline Cotton & 95 \\ Linen & 80 \\ Wood & 45 \\ Grasses & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Percentage of cellulose for different fiber sources [94].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Structure** & **Behavior** \\ \hline Cellulose & Highly organized & hydrophilic \\ Lignin & amorphous & hydrophobic \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cellulose and lignin characteristics.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 1 Introduction
[21] investigated the clay content sensitivity associated with various different spectral configurations, for the Sentinel-2, ASTER, Landsat-8 OLI and Landsat-7 ETM+ satellites, and used PLSR models with this multi-sensor data. Radar remote sensing, which is generally used to estimate soil moisture and soil roughness, has also been evaluated for its ability to perform texture measurements. [PERSON] et al. [28] proposed the estimation of soil moisture from TerraSAR-X radar data following a rain event, and were able to relate the drying speed of the surface moisture to the clay content, which was determined with an accuracy of 12%. [PERSON] et al. [29] used a linear regression relationship between soil moisture products (derived from TerraSAR-X radar data), and clay content, which was estimated with a Root Mean Square Error (RMSE) of 108 g/kg. In this context, the aim of the present study is to make use of multi-sensor data derived from the Sentinel-1 and Sentinel-2 satellites, to evaluate different optical and radar indicators for soil clay content estimations. This analysis is based on field and satellite measurements acquired over the Kairouan Plain, in central Tunisia. We analyze the sensitivity of several indicators to soil texture, and make use of the RF and SVM algorithms to determine clay content classifications. This paper has four main sections. Section 2 describes the study site, the database, and the methodologies used for clay content mapping, based on two classification algorithms (support vector machine and random forest). The results and discussion are presented in Sections 3 and 4, and the conclusions in Section 5.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 4 Experiment  
### General Settings  
#### 4.1.1 MOT20 Dataset  
A large amount of experimental work was based on the MOT20 [8] dataset from the MOT Challenge [65]. MOT20 is a dataset concerning multiobject pedestrian tracking in dense crowds. The number of targets in MOT20 is overwhelming, and thus the targets in the dataset have abnormally serious occlusions, and the frequency of occlusions is relatively higher than other typical datasets. In MOT20, there are four video sequences lasting 357 s, a total of 8931 frames and 1,336,920 targets in the training set (average 149.7 targets per frame). There are four video sequences lasting 178 s in the training set, with 4479 frames and 765,465 targets in total, and 170.9 targets per frame on average [8]. Those raw videos were shot in many places during the day or night with dense pedestrians, including squares, stations, and streets. With indoor and outdoor, and day and night sequences, the rich scene elements can fully demonstrate the performance of the tracker.  
The role of cross-validation is to reduce the negative impact of overfitting, and obtain as much effective information as possible from limited training data. Because the training set of MOT20 consists of four video sequences, we used fourfold cross-validation when  
Figure 5: Definition and comparison of IoC and IoU.  
training. In each fold, three videos were used for training and one video was used for testing, as shown in Figure 6.
## 3 Mathematical Model
### Constraint Conditions
#### 3.4.5 Energy Balance Constraints  
Energy balance constraints can generally be divided into two types. Based on the first law of thermodynamics, the first type refers to the amount of unchanged energy during the conversion process, which mainly corresponds to the energy balance among the gas, steam, and electricity during the mutual conversion processes. The other type requires a constant total energy amount during the replacement of energy sources to guarantee a constant production capacity, which mainly corresponds to the replacement of the energy source in the heating furnace.  
(1) Gas-electricity conversion energy balance  
\[v_{BFG}^{j}\cdot Q_{BFG}+v_{LDG}^{j}\cdot Q_{LDG}+v_{COG}^{j}\cdot Q_{COG}= \frac{r_{ele}^{j}\cdot h_{ele}}{\eta_{j}}. \tag{8}\](2) Gas-steam conversion energy balance  
\[v_{BFG}^{j}\cdot Q_{BFG}+v_{LDG}^{j}\cdot Q_{LDG}+v_{COG}^{j}\cdot Q_{COG}=\frac{h_ {s1}\cdot r_{s1}^{j}+h_{s2}\cdot r_{s2}^{j}+h_{s3}\cdot r_{s3}^{j}}{\eta_{j}}. \tag{9}\]  
(3) Steam-electricity conversion energy balance  
\[h_{s1}\cdot v_{s1}^{j}+h_{s2}\cdot v_{s2}^{j}+h_{s3}\cdot v_{s3}^{j}=\frac{r_{ dek}^{j}\cdot h_{ele}}{\eta_{j}}. \tag{10}\]  
(4) Energy balance caused by the mutual replacement of energy source in the heating furnace.  
\[v_{BFG,\text{initial}}^{j}\cdot Q_{BFG}+v_{LDG,\text{initial}}^{j}\cdot Q_{LDG}+v _{COG,\text{initial}}^{j}\cdot Q_{COG}=v_{BFG}^{j}\cdot Q_{BFG}+v_{LDG}^{j}\cdot Q _{LDG}+v_{COG}^{j}\cdot Q_{COG} \tag{11}\]  
The heating furnace contains regenerative heating technology. It is possible to use BOG instead of COG to establish the equilibrium constraint stated in Equation (11), where \(\eta_{j}\) represents the power generation efficiency or the thermal efficiency of the steam production device; subscripts s1, s2, and s3 indicate high-pressure steam, medium-pressure steam, and low-pressure steam, respectively; \(h_{de}\) represents the calorific value of electricity; \(h_{s1}\), \(h_{s2}\), and \(h_{s3}\) refer to the enthalpy values of the three types of steams; and \(v_{BFG,\text{initial}}^{j}\)\(v_{LDG,\text{initial}}^{j}\), and \(v_{COG,\text{initial}}^{j}\) indicate the initial energy consumption levels of BOG, LDG, and COG in working procedure \(j\), respectively, when the production rate of the iron and steel manufacturing process is constant.
## 2 Instrumentation and Methods  
### Site Description  
The measurements of CO\({}_{2}\) fluxes, solar radiation, and meteorological parameters were carried out from a 45 m tower installed by the Chinese Academy of Sciences (CAS) at the Qianyanzhou subtropical coniferous plantation, Taihe county, Jiangxi province (26\({}^{\circ}\)44\({}^{\prime}\)8\({}^{\prime\prime}\) N, 115\({}^{\circ}\)04\({}^{\prime}\)13\({}^{\prime\prime}\) E, 110.8 m) since 2003 [46]. BVOC emission fluxes, solar radiation, and meteorological variables were also measured at this tower from 22 May 2013 to 4 January 2016 [47]. Main trees are _pinus massonian, Pinus elliotifi, Cunningham lanceolata_, and broadleaf trees, with a coverage of 42.0%, 49.6%, 5.8%, and 2.6%, respectively. The main shrubs are _Loropetalum Chinese, Adinandra millettii_, and _Lyonia Compta_. The forest surrounding the tower covers about 90% in 1 km\({}^{2}\) and 70% in 100 km\({}^{2}\), and the average canopy height is 18 m. The mean slope of this study region is 2.8-13.5\({}^{\circ}\). Annual precipitation is 1485.1 mm, and annual temperature is 17.9 \({}^{\circ}\)C [48]. The annual sum of global solar radiation was 4579 and 4496 MJ m\({}^{-2}\) in 2013 and 2014, respectively, and the annual sum of PAR was 7998 and 7656 mol m\({}^{-2}\) in 2013 and 2014, respectively [47].
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 5 Results  
The evaluation of the MVT was performed within a Docker-based cloud simulation using the MaLeX dataset, comprising binary files encoded as images. This section presents the results obtained through extensive training and validation processes. The primary objective was to assess the model's ability to detect zero-day threats by leveraging deep learning techniques that transform binary files into visual representations. The performance metrics considered include accuracy, recall, F1-Score, and confusion matrix, evaluated over multiple experimental runs to ensure consistency and reliability.  
The results are presented with a focus on comparing the MVT model with traditional machine learning approaches, highlighting the advantages and potential limitations identified during the evaluation. In addition, a detailed analysis of the model's performance in detecting both benign and malicious files is provided to address the challenge of class imbalance inherent to the dataset.
## 4 Experimental Results
### Dataset and Exploratory Analysis
#### 4.1.4 Topics Analysis  
In our continued exploration of textual data for insights into mental health discourse, we turned our analysis to topic modeling using the sklearn library's Non-negative Matrix Factorization (NMF) algorithm. As illustrated in Figure 7, the topic detection model revealed several themes that shed light on the underlying sentiments and concerns expressed by social media users. Upon closer inspection of the model's output, we discerned a pattern suggestive of distress and existential dread. The first topic, with phrases like \"don't know\", \"feel like\", and \"like I'm\", potentially captures the ambivalence and introspection characteristic of individuals grappling with deep-seated uncertainties. The second topic, encompassing terms such as \"I'm bored\", \"anyone wanna\", and \"wanna talk\", may reflect a yearning for interaction or a plea for companionship, which is often lacking in individuals contemplating suicide. Moreover, the third topic, containing \"among us\", \"guys girls welcome\", and \"commitment guys girls\", hints at a search for community or belonging, possibly indicating the social dimension of the users' experiences. Finally, the fourth topic starkly presents expressions such as \"don't want\", \"want die\", and \"want live\", directly pointing to a conflict between the will to live and the inclination toward self-harm.  
These topics, in aggregate, suggest not only a prevalence of suicidal ideation and depressive thoughts but also illuminate the multifaceted nature of the conversation around mental health. The presence of terms like \"want die\" alongside \"want live\" within the same topic underscores the complexity of suicidal ideation, which often includes ambivalent feelings about life and death. This nuanced understanding of the topics prevalent in social media discussions provides valuable context for mental health professionals. It may inform  
Figure 7: Topics analysis.  
the development of targeted interventions and support systems, potentially enabling the early detection and prevention of suicide among individuals with mental health challenges.
## 4 Development and Validation of the Platform
### Platform Performance Results
#### 4.2.1 Evaluation of the 5G Core
The fact that control-plane latency remains stable under such demanding conditions is highly encouraging, signaling robustness in the network's core. In a more ideal scenario, where a single UE connects once to the core network, the control-plane latency is anticipated to be significantly lower. These results demonstrate the 5G core's resilience and suitability for supporting complex, latency-sensitive applications across IoT, Edge, and Cloud environments.
## 1 Introduction
In particular, it is said about the thickness of the material that it should not exceed more than 10 mm. There are no clear references to specific sizes in open sources. However, it is clear that the setup was designed to study small samples. It is easier to create and produce the research material itself. In this case, an industrial batch quality control approach probably requires dedicated time to process the material or create a separate probe for each batch. SPEAG also has DAK series rigs, which consist of three separate probes (DAK 12, DAK 3.5, and DAK 1.2E) that cover the frequency range from 4 to 67 GHz. This series of installations is distinguished by the fact that, using coaxial probes, it allows you to quickly and easily measure liquids. But the measurement of solid materials takes place by applying probes close to each other. In addition, a plane and smooth surface is also important for this installation. The Keysight Technologies measurement setup allows measurements in the range from 18 to 110 GHz. At the same time, it uses two horn antennas to measure the dielectric properties of materials, which means that a vector analyzer is required for use. This significantly increases the cost of this technology. Each of the horn antennas of the installation is equipped with lenses made of a dielectric material with frequency dispersion. This indicates a less accurate measurement of samples. Additionally, this setup only measures plane and smooth samples. As the analysis of existing methods and implemented installations shows, each has its own disadvantages: The precision of installations, the duration of measurements, complex mechanics, and expensive positioning systems with various drives for measurement accuracy, sensitivity to lateral radiation and the influence of external radiation, the requirement for measurements in an anechoic chamber, or the use of absorbers. Thus, the problem of creating a method for measuring the electrophysical parameters of materials that is easier to operate, less demanding on various factors, and ensures a sufficient measurement accuracy remains relevant. In this paper, it is proposed to relate the measured reflected signals at different frequencies with the electrophysical parameters of the measured materials on the basis of the well-known mathematical apparatus developed for the reflection of plane waves [25]. It is proposed to use a collimated beam obtained by focusing the probe radiation on the surface of the material under study to form a plane wave in the measurement area. The parabolic metal mirror was used for focusing, which significantly reduces the frequency dispersion. This method is new and has not been previously noticed in the literature. The proposed approach was tested on a laboratory setup developed by the authors and presented in the article.
## 3 Applications
### Phase-change Memory (PCM)
#### 3.1.1 Device Architectures
[PERSON] et al. [78] demonstrated an electrically driven nano-scale guided wave plasmonic optical latch integrated with GST. The optimum dimensions are detailed in Figure 15. When the waveguide width is fixed to 30 nm, the thickness effect on the insertion loss is shown in Figure 15a. Figure 15b indicates a larger waveguide width can give a higher extinction ratio between ON and OFF state but suffers from a higher energy cost. Figure 15c,d denote the optimum waveguide width when both extinction ratio and energy cost are considered. Other shapes of the bottom electrode such as ring [79] or edge contact [80] have been also reported. One of the major concerns for the highly confined structures discussed above is the fabrication and GST deposition process since it is not possible to use low-cost conventional sputtering processes due to the inherent poor step coverage. Therefore, we are going to discuss the fabrication methods that can provide good conformality, such as Metal-Organic Chemical Vapor Deposition (MOCVD) and atomic layer deposition (ALD) in Section 5.