esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Routing over the Hierarchical Virtual Topology
### Path Recovery after Node Failures
#### 5.3.1 Path Recovery between Two Nodes Belonging to the Same First-Tier Module  
Each node periodically broadcasts a Hello packet. When node \(n\) does not receive a Hello packet from node \(i\) for a certain time, node \(n\) considers node \(i\) to have failed. Then, node \(n\) needs to find an alternative path for any path whose forwarding node is node \(i\).  
For this, node \(n\) floods a route request packet to each destination node whose forwarding node from node \(n\) is node \(i\). After a fixed time \(t_{reply}\) since the destination node \(d\) has received the first route request packet from node \(n\), it chooses the route request packet with the shortest path and sends a route reply packet to node \(n\) along the reverse path contained in that packet. Each node on the reverse path updates its zeroth-tier routing table when it receives the route reply packet. Note that the flooding range of the route request packet can be restricted within the module during this path recovery.
# Effective Uptake of Cadmium and Chromium from Wastewater Using Carbon-Based _Capsicum annuum_
## 4 Results
### Adsorption Data
#### 4.3.3 Temperature Effect of Activated Carbon  
The temperature effect on adsorption was investigated for 60 min at 200 rpm and the results are indicated in Figure 8. The adsorption of Cd(II) and Cr(VI) ions reached a peak as the temperature increased to 45 \({}^{\circ}\)C. Beyond this point, temperature increases did not influence the adsorption process. This may be due to fewer active sites available for the adsorption of Cd(II) and Cr(VI) ions [61]. This ultimately led to equilibrium for Cr(VI) ions, while for Cd(II) there was a slight decline [61]. [PERSON] et al. (2023) [62] had similar results in the sorption of lead ions from wastewater with activated carbon.  
The thermodynamic parameters for Cr(VI) and Cd(II) ions are displayed in Table 3. The data were determined at temperatures of 298, 308, 318, 328, and 338 K. The adsorption process for Cr(VI) and Cd(II) ions shows that \(\Delta\)H\({}^{\circ}\) was negative. This suggests that the sorption process is exothermic [63]. Positive value of \(\Delta\)S\({}^{\circ}\) suggests the escape of solvated H\({}_{2}\)O from the Cr(VI) and Cd(II) ions into the solution during sorption [64]. The change in Gibbs energy \(\Delta\)G\({}^{\circ}\) values for Cr(VI) and Cd(II) ions were negative at all temperatures, indicating that the reaction was spontaneous and exothermic [65]. This also indicates the physical nature of the process [66]. The values of \(\Delta\)G\({}^{\circ}\) decreased with increasing sorption temperature, suggesting removal of pollutants, rather than their uptake, at high temperatures [67].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Kinetic Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Pollutants**} \\ \cline{3-4}  & & **Cd(II)** & **Cr(VI)** \\ \hline IPD & C (mg/g) & 6.246 & 4.234 \\  & \(K_{i}\) (g/g-min\({}^{1/2}\)) & 0.057 & 0.067 \\  & \(R^{2}\) & 0.845 & 0.811 \\ \hline Experimental & \(q_{e}\) (mg/g) & 33.14 & 27.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 8: Temperature effect of adsorption by activated carbon.
## 3 Proposed Technology Solution
### Matching and Classification  
The access system utilizes a deep learning classifier/matching algorithm to compare the extracted features with reference templates stored in a database as displayed in Figure 4 within the edge network [21]. This enables the complete validation of user data to describe their accuracy, or otherwise, of the fingerprint. Fingerprint matching and classification using minutiae points is a core method in automated fingerprint identification systems (AFIS). The process is explained:  
1. Fingerprint Acquisition: The procedure begins with capturing an image of the fingerprint using the AccesSensors, which is then transformed into a digital format for processing. 2. Minutiae Extraction: The digital fingerprint image is processed to detect and record the orientation of the unique spots of interest on the fingerprint, such as ridge ends. 3. Fingerprint Matching: Align fingerprints before matching for accurate comparison. This entails lining up the orientation and position of the two fingerprints A and B as shown in Figure 4.
4. Point Correspondence: The minutiae point from the FingerID A is then compared to the finer details of FingerID B. The purpose is to identify corresponding points between the two sets of minutiae, as highlighted in Figure 4. The Deep Learning  
Figure 3: Metaverse User Validation System. technique is then used to calculate or score the similarity of the related minutiae points. The more corresponding minutiae points are discovered and the closer they match, the greater the similarity score. Otherwise, they are deemed mismatched, and access is refused [9; 19]. \[Match(F,F\,database) \tag{2}\]  
where _Fdatabase_ represents the feature vectors of fingerprints in the database. Based on the findings of the matching procedure, it is determined whether the input fingerprint matches any of the fingerprints in the database. This judgment can be binary (match or non-match) [4]. \[Decision=Match(F,Fdatabase) \tag{3}\]  
An accurate result guarantees an access-granted outcome with a 'green' LED indicator light in Figure 5, while a non-accurate result grants a denied access outcome with a'red' LED indicator light on the sensor [22]. Figure 4: Deep Learning Matching Process. Figure 5: Metaverse User Validation. Designing algorithms to analyze user fingerprints in real time for context-aware user validation involves several steps. Here, is a high-level overview of the algorithm design process [14]. In addition, the Algorithm 1 describes the process of developing and training a Convolutional Neural Network (CNN) model for fingerprint classification and matching using deep learning techniques.
## 4 Experiments and Results Analysis
### Comparison of Preprocessing Methods
Such under-performance could stem from the tendency of nonlinear kernels to over-fit, which in turn affects the overall performance. Furthermore, the selected kernel parameters might not be the most suitable. On the other hand, the autoencoder, another nonlinear dimensionality reduction method, showcased the best results in our tests. This implies that employing this approach to process raw CSI data can effectively diminish data complexity, thereby boosting the system's operational efficiency and positioning accuracy.
## 5 Simulation Experiment and Analysis
### Scenario One
Additionally, in the credibility calculation, some invalid nodes are excluded through filtering, which greatly reduces the energy consumption of the network. Therefore, the energy consumption of the proposed algorithm is the lowest. As the number of malicious nodes increases, the MPASR algorithm uses its safety factor (node reputation) to effectively avoid malicious nodes, thereby reducing the influence of these nodes on the network and allowing the energy consumption of the nodes to remain relatively stable.
###### Abstract  
This paper proposes a novel active disturbance compensation framework for exactly positioning control of electro-hydraulic systems (EHSs) subject to parameter deviations, unknown dynamics, and uncertain external load without velocity measurement mechanism. In order to accurately estimate and then actively compensate for the effects of these uncertainties and disturbances on the system dynamics, a combination between an extended sliding mode observer (ESMO) and a linear extended state observer (LESO) is firstly established for position control of EHSs. In addition, an inherited nonlinear filter-based trajectory planner with minor modifications is utilized to overcome the barriers of inappropriate desired trajectories which do not consider the system kinematic and dynamic constraints. Furthermore, for the first time, the command filtered (CF) approach and prescribed performance control (PPC) are successfully coordinated together and dexterously integrated into the backstepping framework to not only mitigate the computational cost significantly and avoid the \"explosion of complexity\" of the traditional backstepping design but also satisfy the predetermined transient tracking performance indexes including convergence rate, overshoot, and steady-state error. The stabilities of the observers and overall closed-loop system are rigorously proven by using the Lyapunov theory. Finally, comparative numerical simulations are conducted to demonstrate the advantages of the proposed approach.  
active disturbance compensation control (ADCC); extended sliding mode observer (ESMO); linear extended state observer (LESO); command filtered approach (CFA); prescribed performance control (PPC); electro-hydraulic system (EHS); trajectory planner (TP)
## 3 Results and Discussion  
### Normal and Abnormal Operation Data Sets  
The FDA methodology relied on 17 WWTP process variables. They were the bottom effluent sludge concentrations (10 variables), secondary settler clean effluent concentrations (six variables) and temperature. The set of these variables consisted of total nitrogen (\(N_{total}\)); total Kjeldahl nitrogen; chemical oxygen demand; free and saline ammonia (\(S_{NN}\)); nitrate and nitrite nitrogen (\(S_{NO}\)); total suspended solids (\(TSS\)); slowly biodegradable substrate (\(X_{S}\)); heterotrophic biomass (\(X_{B,H}\)); autotrophic biomass (\(X_{B,A}\)); inert particulate products (\(X_{P}\)); particulate biodegradable organic nitrogen (\(X_{ND}\)); soluble, biodegradable organic nitrogen (\(S_{ND}\)); readily biodegradable substrate (\(S_{S}\)); alkalinity (\(S_{alk}\)); and temperature (\(T\)). The first six of them characterized the secondary settler clean water, while the last eleven variables described the bottom effluent.  
Eight separate scenarios were created, and simulations were performed to generate the data sets for the different fault classes, one for normal functioning and six for malfunctioning of the DO sensor. The DO and NO-controlled WWTP were simulated for 168 days of operation. The starting set of 139 days of nominal (faults lacking) functioning was considered to bring the plant to a quasi-steady state. Sensor faults were applied from day 140 in the simulation scenario. Operational data of the simulated wastewater treatment plant were collected with a sampling time of 15 min. Data generated in the time period from the 141 st to the 145 th day of DO sensor normal and faulty operation were used for training the FDA model. Each set of 480 observations is considered to be a class. They formed the training matrix of observations (3840 lines and 17 columns). The fault diagnosis performance of the trained FDA model was tested using data corresponding to the 140 th day, i.e., the first day of faulty sensor operation. The testing data set contained 96 measurements for each fault. This testing approach was designed in order to investigate the capability of the FDA diagnosis method to identify the type of fault in the very first hours following the fault appearance.  
The chosen scenarios and the emerged dimensions of the data sets used for training and validation of the fault detection methodology were considered to make a fair trade-off between diagnosis accuracy and required computational resources.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 3 Methods
### Image Partitioning and Feature Space Creation  
In this study, an object-based image analysis approach was followed, taking into account the fact that is more advantageous compared to pixel-based approaches in crop classification tasks that deal with fields of considerable size [3; 26; 117]. Specifically, each image was segmented into field entities based on the LPIS' polygons. This procedure resulted in the formation of 12,329 reference fields. Subsequently, the reflectance value of each field was calculated, for all features, as the mean value of the pixels contained in it. The code number, the number of fields, as well as the approximate value of the total area covered in hectares corresponding to each crop class are presented in Table 2.  
The feature space of this study (Table S1) is made up of 818 features extracted from the multi-temporal Sentinel-1 and Sentinel-2 images. More specifically, feature space comprises the Sentinel-2 multi-spectral bands (190 variables), and vegetation indices (76 variables), as well as a total number of 552 polarimetric variables derived from Sentinel-1 time-series.
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
## 3 Results and Discussion
### Gene Expression and Phenotype Assay under H\({}_{2}\)O\({}_{2}\) Stress
coli trx2_ resulted in a phenotype that displayed a more sensitive response to H\({}_{2}\)O\({}_{2}\), whereas deletion of \(trx1\) or double knock-out of both \(trx1\) and \(trx2\) exhibited resistance to even higher levels of H\({}_{2}\)O\({}_{2}\)[15]. As the cytoplasmic redox potential in these mutants is more oxidized, it is proposed that these highly oxidized conditions activate the oxidative stress response, resulting in the induction of catalase or AhpC, and allowing the mutants to display a higher level of resistance towards H\({}_{2}\)O\({}_{2}\). Our results, which differed from those pertaining to _E. coli_, showed that, in _D. radiodurans_, the transcription levels of both \(trx1\) and \(trx2\) increased with H\({}_{2}\)O\({}_{2}\) concentrations. In addition, deletion of either \(dtrx1\) or \(dtrx2\) resulted in a phenotype that was more sensitive to H\({}_{2}\)O\({}_{2}\) compared to the wild-type. In Grx-negative bacteria, Trx is known to perform more critical roles in the cellular disulfide balance and oxidative stress response. For instance, _Helicobacter pylori_[52] and _Mycobacterium tuberculosis_[53] lack the Grx-system, and the functional OxyR that regulates the oxidative stress response in these bacteria is different from that of _E. coli_. Since the Grx system does not exist in _D. radiodurans_, the function of Trx proteins and the regulation of \(trx\) expression appears to differ from those of _E. coli_.
## 5 Discussion
### Influence of Non-Driving Related Task (NDRT) in Input Interaction  
After participants finished the experiment, they reported their overall preference for input interaction: VI (56.3%), TI (31.3%), and G-HI (12.5%). Then, they reported their overall preference when engaged in an NDRT: VI (56.3%), TI (6.3%), and G-HI (37.5%). Participants consistently preferred voice input, whether engaged in an NDRT or not, whereas preference for touch input decreased by 25% if the user was engaged in an NDRT. Conversely, preference for G-HI jumps by a corresponding percentage when the user is engaged in an NDRT. Therefore, we believe G-HI could assist drivers in interacting with the vehicle, especially when they are engaged in an NDRT.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 2 Methods  
Assessment of POD accuracy is mainly divided into two categories. The internal validation methods only use some data during POD to check the orbit determination results, such as carrier-phase residuals and overlapping orbit validation. The external validation uses data that were not participated in the orbit determination to check the satellite POD, such as comparing with orbits determined by other techniques and SLR range validation [23]. However, in the absence of other technical orbit determination results, there is only one external method to validate the POD, and the SLR range validation can only show the distance difference and cannot evaluate the POD accuracy in x, y, and z directions.  
In this paper, three methods are proposed for the external validation of POD: carrier differential validation, inter-satellite differential validation, and SLR 3D validation. The carrier differential validation uses the carrier-phase data of one GPS satellite and inter-satellite differential validation uses the carrier-phase data of two GPS satellites out of all viewed GPS satellites. Since the selected GPS data were not participated in the orbit determination, they can be regarded as external data to achieve validations of orbits accuracy. The SLR 3D validation can evaluate errors in the x, y, and z directions of the POD, making the results more intuitive. Since none of the data used for the validation has been participating in the orbit determination, these three methods are considered external validation.  
The carrier differential validation and inter-satellite differential validation use the GPS carrier-phase data to evaluate POD. Errors caused by the ionospheric delay error, satellite clock bias, receiver clock bias, integer ambiguity, and other factors must be considered if the carrier-phase data are used to validate the accuracy of POD without any processing of difference. While the double-difference method can eliminate the effects of these errors on the carrier, it is a good idea to use the method.
# Antioxidant Properties of Selenium Nanoparticles Synthesized Using Tea and Herb Water Extracts
## 3 Results and Discussion
### HPLC Analysis ad UV-Vis Spectroscopy of Used Plant Extracts
The absorbance spectra of studied infusions were recorded in the range of 200-850 nm, which is shown in Figure 1. It has been proven that the analyzed herbal extracts contain similar compounds as it was observed that spectra curve over the same wavelength range [33]. According to the literature, mint and chamomile extracts are characterized by a high content of terpen, terpenoids, and phenolic compounds [34; 35]. This was confirmed by UV-Vis spectra, namely by the peak registered at 270 nm and as a band for 290-320 nm [36]. Characteristic for plants, chlorophyll content was also observed as an extensive peak in the range of 360 to 450 nm [37]. An intensive band recorded in the range 380-450 nm, which was the most intensive for mint and black tea infusion, can be associated with the presence of carotenoids [38]. UV-Vis spectra can also be the basis for distinguishing black tea from green tea. Catechins and methylxanthines, which show absorbance in the region from 250 to 350 nm (related to the \(\eta\)\(\rightarrow\)\(\pi\) electronic transition) can be chemical descriptors to distinguish these two tea types [39]. Other groups of compounds which can be useful in the interpretation of tea spectra are theaflavins and thearubigins, typical for black tea as a result of fermentation  
Figure 1: The UV–Vis spectra of examined infusions of mint, chamomile, black, and green teas. processes. Their presence in the infusion can be examined at 380 and 460 nm [40]. As can be seen on the recorded spectra, green tea infusion shows higher absorbance in the region from 250 to 350 nm than black tea due to the higher content of the catechins, which was found in green tea. On the other hand, the band characteristic for theaflavins and thearubigins is significantly higher for black tea, as was expected. Additionally, green tea infusion presents the lowest absorbance in the region 450-600 nm, which was also reported earlier [41]. The obtained spectra confirmed that the analyzed extracts come from two different types of tea, which should translate into their polyphenolics content. This is very important from the point of view of their application to the green synthesis of SeNPs. As they act simultaneously as selenium reducers and stabilizers of the obtained nanoparticles, their concentration can affect the physical properties of the nanoparticles, such as size and shape, but also potentially their stability.
## 3 Wave Equations and Theoretical Wave Velocities  
The pore fluid makes the propagation properties of the waves in saturated soils different from those of common soils, especially under dynamic loadings. The propagation procedures of the waves in saturated soils are closely related to the viscous coupling, so the seepage procedure of the pore fluid affects the velocities and the attenuation of the waves in saturated soils. Therefore, analysis of the propagation theory of saturated soils is necessary. In this section, for three different viscous couplings (finite, infinite, and zero permeability), the **_u-w-p_** formulation is transformed into three wave equations for the P1, P2 and S waves, and the corresponding theoretical velocities are obtained.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 1 Introduction
### C-ITS Roll-Out
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Service** & **Examples of Use Cases** & **Cooperation Mode** & **Day X** \\ \hline \hline \multirow{2}{*}{Road Worlds Warning} & Inform the driver about a closed lane and & \multirow{2}{*}{12V} & \multirow{2}{*}{Day 1} \\  & how to drive (pass left or pass right) & & \\ \hline \multirow{2}{*}{Hazardous Location Notification} & Inform the driver about a traffic jam, an & \multirow{2}{*}{12V} & \multirow{2}{*}{Day 1} \\  & obstacle on the road or a slippery road & & \\ \hline \multirow{2}{*}{In-Vehicle Signage} & Inform the driver about the current speed & \multirow{2}{*}{12V} & \multirow{2}{*}{Day 1} \\  & limit & & \\ \hline \multirow{2}{*}{Signalized Intersection} & Inform the driver about the current traffic & \multirow{2}{*}{12V} & \multirow{2}{*}{Day 1} \\  & light status or the optimal speed to pass the & & \\ \hline \multirow{2}{*}{In-Vehicle Warnings} & Inform the driver about an approaching & \multirow{2}{*}{V2V} & \multirow{2}{*}{Day 1} \\  & emergency vehicle or exchange information & & \\ \hline \multirow{2}{*}{Augmented perception} & Perception of non-connected vehicles, & \multirow{2}{*}{V2V or I2V} & \multirow{2}{*}{Day 2} \\  & perception of non-connected vulnerable road & & \\ \hline \multirow{2}{*}{Vulnerable road user protection} & Vulnerable road user presence awareness or & \multirow{2}{*}{V2V or I2V} & \multirow{2}{*}{Day 2} \\  & collision warning & & \\ \hline \multirow{2}{*}{Semi-automated vehicle functions} & Cooperative emergency brake assistant or & \multirow{2}{*}{V2V} & \multirow{2}{*}{Day 2} \\  & cooperative adaptive cruise control & & \\ \hline \multirow{2}{*}{Vehicles’ coordination} & Cooperative lane merging or cooperative & \multirow{2}{*}{V2V or I2V} & \multirow{2}{*}{Day 3} \\  & lane change & & \\ \hline Point of Interest management & Automated vallet parking in a public garage & V2V or I2V & \multirow{2}{*}{Day 3} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of C-ITS services and use cases. in Italy, where 13 intersections have been equipped with C-ITS infrastructure. In Bordeaux, France, the focus is on the protection and guidance of vulnerable road users (VRU). Finally, Table 2 provides an overview of the services and use cases rolled out first in Europe and supported by private or public transport vehicles.