esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methods Used for Interface Quality Testing of T1 DCoad Application
#### 3.3.4 Surveys and SUS  
Testing the quality of the interface is complemented by surveys that allow obtaining of users' opinions and feelings about activities with the interface. The most popular and widely used by practitioners is the SUS method.  
This method is characterized by the simplicity and reliability of the measurements performed. It takes the form of a questionnaire consisting of 10 statements. It is used to quickly measure how people perceive the usefulness of the computer systems theyuse [44]. The word \"system\" in each question/statement may be replaced by \"website\", \"mobile application\", \"software\" or \"digital product\", because SUS is largely technology neutral [45]. The SUS scale is usually used immediately after using the system is assessed, without much thought and before any summary or discussion [46].  
Each of the 10 statements in the SUS method is rated on a five-point Likert scale from \"Strongly disagree\" to \"Strongly agree\", which corresponds to a numerical range from 1 to 5 [45].  
As a result of processing SUS evaluation surveys, an assessment of the subjective level of satisfaction is determined for each study participant after working with the application. This rating is determined for each participant and for the entire group of respondents. The subjective level of satisfaction with working with the system for each user (\(j\)) is determined according to the formula [45]:  
\[SUS_{j}=\left(\sum_{i=1,3,5,7,9}\left(S_{ij}-1\right)+\sum_{i=2,4,6,8,10}\left( 5-S_{ij}\right)\right)*2.5 \tag{1}\]  
where:  
\(i\)--question number from the SUS questionnaire (Q1,..., Q10);  
\(S_{ij}\)--rating of the \(j\)-th participant of the \(i\)-th question, where the rating \"definitely not\" is mapped to the value 1 and \"definitely yes\" to the value 5 ([PERSON]'s value).  
Although SUS results (1) range from 0 to 100, they are not percentages. The overall interface rating is the arithmetical average of individual users' ratings. After testing 500 different systems, the average SUS value (so-called industrial average) was 68 [45]. Therefore, an SUS value above 68 is above the industrial average, and below 68 is below it [45]. An SUS score of 68 is classified as acceptable.
## 2 Theoretical Background
Thus, generally, the result will be \(R_{G}\leq k_{m}(x)\cdot R_{G}^{meas}(x)\). However, for interconnected electrodes, the measurement will give an underestimation of \(R_{G}\). In either case, the proposed method will give the correct reading that corresponds to the case, and it is the task of the technical staff to determine the usefulness of the measurements. This hypothesis will be verified with the help of a numerical example in which a flat square electrode with a side length of 4 m, comprising conductive rods with a radius of 9 mm, is buried at a depth of 0.5 m. The soil is considered homogeneous with unitary resistivity. We are going to simulate the measurement of the grounding resistance by placing an auxiliary current rod 200 m away from the electrode using the fall-of-potential method. For the same type of electrode, two situations will be addressed. In one case, the electrode has no issues, while in the other, it is assumed that part of the electrode is damaged and inactive in releasing current to the ground. Figure 2 shows the profiles of the electrode, and the dotted line represents the path traced to evaluate the potentials on the ground. The difference in the potential profile \(V_{A}-V_{B}(x)\) at points on the ground (\(z=0\)) along a straight line from the left side of the electrode at \(x=-2\) m to \(x=180\) m, when the y-coordinate is fixed at \(y=2\) m, will be simulated for both electrodes. The calculations were performed using a Matlab code script developed by the authors, which has been successfully used in previous work. This code employs the charge simulation method (CSM) along with the method of moments for any system of wire conductors [16]. Since the soil has a unit resistivity and the injected current is 1A, this potential difference represents the unit grounding resistance as a function of the distance x at which the potential probe is located, measured by the three-pin method, when the auxiliary electrode is, as previously mentioned, 200 m from the electrode being measured. Figure 3 shows the grounding resistance measured (numerically simulated) using the three-pin method, placing the potential electrode at distance \(x\). The figure shows this measured resistance for both the original (red line) and the damaged electrode (beige line). The figure also shows the resistance corrected by the correction factor \(k_{m}\), which is shown in the subfigure. The blue line is the result of this correction for the original electrode. As seen, the value \(R_{G}=0.1114\)\(\Omega\) corresponding to the true resistance of the original electrode is obtained. In contrast, for the damaged electrode, applying the correction factor results in an \(R_{G}\) value above the true resistance value, which is 0.1266 \(\Omega\). Note that the correction factor is derived from the original electrode parameters and is applied to all situations, whether the electrode is damaged or not. Figure 2: The **upper panel** shows the electrode being studied, while the **lower panel** represents the same electrode partially damaged.
## 4 Results and Discussion
### Experiment and Analysis
#### 4.2.2 Enhancement of Channel Attention Mechanism  
To verify the impact of the channel attention mechanism module on the original deep learning network, we added the channel attention mechanism to the original network model alone. In the same dataset for training and testing, Figure 10 shows that adding the channel attention mechanism also improves the performance of the native network model at the far end. In Figure 11, we find that the network with the channel attention mechanism (FC) alone also significantly improve in all aspects. Although the network model with FC did not segment the street light features with less point clouds data, the segmentation accuracy of lane lines and other features at the far end was improved. The OA and average mIOU were also improved, demonstrating that the channel attention mechanism can also have a positive effect on the native deep learning network.
## 3 Materials and Methods  
### VANET Simulation  
In the present study, we simulated a realistic VANET scenario based on the combination of the road traffic simulator SUMO (simulation of urban mobility) and the network simulator NS-3. SUMO is a free, open and microscopic simulator implemented in C++. It is destined to simulate unlimited network size and number of vehicles. It offers the possibility to configure vehicle types, traffic lights, vehicle speeds, multi-lane roads; it also supports the lane-changing model, as well as generating automatic time schedules for traffic lights. SUMO also supports the import formats, such as OpenStreetMap [12; 13]. We executed a set of Python command lines on SUMO in order to generate realistic vehicle trace files that are then used as an input by the network simulator, NS-3 [11]. In our study, the simulation zone was extracted from OpenStreetMap, consisting of a map of El Jadida city, Morocco (Figure 1).  
Additionally, the AODV (ad hoc on demand distance vector) is adopted as a routing protocol. It is a reactive protocol wherein routes are created only when demanded. AODV relies on the sequence numbers to determine fresher and newer paths. The most important mechanisms of this protocol are the route discovery and the route maintenance. This protocol is comprehensively detailed in Ref. [23].  
Other simulation components are exhibited in Table 1.
## 4 Software Architecture
### Core Functional Module Design
#### 4.2.3 Communication Testing Module  
This module includes communication control and data collection.  
* Communication Control The accessed nodes serve as the communication resources the system allocates to construct the scenarios. For the communication testing, the environment is constructed based on the device control data, which should be distributed to each node once the task begins.
* Data Collection This function collects all C-V2X data from actual testing for a communication performance analysis. Some of the more critical data include the source address, destination address, C-V2X data type, associated task ID, and original C-V2X data.
## 2 Materials and Methods
### Development of Mono-Species biofilms by Salmonella Serotypes  
#### 2.4.1 Polystyrene Biofilm Formation Assays  
The ability of all _Salmonella_ strains to form biofilms was tested in 96-well flat-bottomed polystyrene microtiter plates (Corning(r) 96-Well Assay Microplate, Lowell, MA, USA) using the crystal violet (CV) assay method [5]. For each strain, three wells of the microtiter plate were filled with 230 uL of 1/20 diluted TSB and 20 uL of bacterial suspension (\(\sim\)10\({}^{8}\) CFU/mL). Then, the plates were incubated at 35 \({}^{\circ}\)C for 24 h. Wells filled with broth medium (1/20 diluted TSB) were used as negative controls, and _Salmonella_ Typhimurium ATCC 14028 was used as the positive control. Next, the content of each well was aspirated and washed three times with phosphate-buffered saline (PBS; 7 mM Na\({}_{2}\)HPO\({}_{4}\), 3 mM NaH\({}_{2}\)PO\({}_{4}\), and 130 mM NaCl, pH 7.4; Sigma-Aldrich, Dorset, England) to remove planktonic bacteria. The attached bacteria were fixed with 250 uL of methanol (Sigma-Aldrich, St. Louis, MO, USA) for 15 min; then, the plates were emptied and left to dry. The plates were stained with 250 uL of 1% (_w/v_) crystal violet (CV; Hyeel, Zapopan, Mexico) solution per well for 5 min. Excess stain was rinsed off with sterile distilled water, and the microtiter plates were air-dried. After that, adherent cells were resolubilized with 250 uL of 33% (_v/v_) glacial acetic acid (Sigma-Aldrich, St. Louis, MO, USA) per well. The optical density of each well was measured at 570 nm (OD\({}_{570}\)) using a Multiskan FC (Thermo Fisher Scientific Inc., Madison, WI, USA). The cut-off OD (ODc) was defined as three standard deviations above the mean OD of the negative control. Based on the OD of bacterial films, strains were classified into the following categories: non-biofilm producers (OD \(\leq\) ODc), weak biofilm producer (ODc \(<\) ODc \(\leq\) (2 \(\times\) ODc)), moderate biofilm producer ((2 \(\times\) ODc) \(<\) OD \(\leq\) (4 \(\times\) ODc)), and strong biofilm producers ((4 \(\times\) ODc) \(<\) O.D.), as previously described [5].
# Influencing Factors of Motion Responses for Large-Diameter Tripod Bucket Foundation
## 2 Theoretical Formulation  
As shown in Figure 3, the local coordinate system \(oxyz\) was used. The origin of coordinate is located at the center of top of bucket. The positive directions of the \(ox\) axis and the \(oz\) axis are right and upward, respectively. The \(oy\) axis is determined using the corkscrew rule. The diameter of bucket foundation is \(D\). Because the bucket foundation is a thin-walled structure, the effect of thickness on the characteristics of bucket foundation can be neglected. The cross-sectional area of foundation is \(A\); the height is \(H\); the draft is \(H_{d}\); the height of freeboard is \(H_{f}\); the structural mass is \(M_{s}\). The difference of water head between the external and interior water surface is \(H_{w}\); the height of the air column in the bucket is \(H_{a}\). The atmospheric pressure outside the bucket is \(P_{a}\). The weight \(G_{s}\) and pressure \(P_{b}\) at the internal air-water interface can be expressed as follows [7; 16]:  
\[G_{s}\ =\ M_{s}\cdot g \tag{1}\]  
\[P_{b}\ =\ \rho_{w}\cdot g\cdot H_{w}+P_{a} \tag{2}\]  
\[F_{b}\ =\ \rho_{w}\cdot g\cdot H_{w}\cdot A \tag{3}\]  
where \(\rho_{w}\) is the density of water; \(g\) is the acceleration due to gravity; \(F_{b}\) is buoyancy with the coordinates of floating center at \((0,0,-H_{w}/2)\).
### Approach  
This paper proposes a more tangible approach, extended from the previous research, to control the nondeterminism in smart IoT systems with both discretely and incrementally controllable probabilities to satisfy the probabilistic SSReqs (safety and security requirements), as shown in Figure 2 with the dark block arrows. The overview of the approach is as follows:  
1. _Probabilistic equivalence_ is the basic concept used to verify probabilistic requirements as a justification for the enhancement of probability, which overcomes the limitation of the arbitrariness from the previous approach. The equivalences can be classified into two classes. * _Process equivalence_ is for the equivalence between two processes representing two IoT devices in smart systems. It implies that two processes are statically identical and are dynamically bisimulative within the range of a \(\Delta\) probability difference or increment; * _System equivalence_ is for the equivalence between two systems representing two smart IoT systems. It implies that two systems are statically identical and are dynamically bisimulative within the range of a set of \(\Delta\) probability differences or increments for their corresponding processes. 2. (2-1) Incremental enhancement/improvement is the approach to improve the performance or capability of smart IoT systems to the level where all the SSReqs are satisfied probabilistically. Basically, the system improvement is based on process enhancement. * _Incremental process enhancement_ is the enhancement of a process representing each IoT device by increasing the values of probabilities on nondeterministic choice operations, representing replacing the units of the IoT device with a higher version, in terms of the _probabilistic process equivalence_; * _Incremental system improvement_ is the improvement for a system representing each smart IoT system by replacing its processes with the incrementally enhanced processes in terms of the _probabilistic system equivalence_.  
As a result of validation, probabilistic SSReqs are classified into the following categories, as shown on the right side of Figure 2:  
Figure 2: Overview of probabilistic equivalences and incremental improvement methodology.  
1. _Satisfied_--the probability for SSReqs is in the range of validation probability and the validation is completed;
2. _Unsatisfied_--the probability for SSReqs is out of the range of validation probability; it goes through incremental enhancement and improvement, then another round of validation performs on SSReq until the final decision is made. * _Satisfiable_--the new probability for SSReqs is in the range of validation probability and the validation is completed; * _Unsatisfiable_--the new probability for SSReqs is out of the range of validation probability. If probabilistic SSReqs are unsatisfiable at the end, a new system has to be developed for the SSReqs.
## 2 System Overview
### System Architecture  
The system architecture for CAS is divided into three parts, as seen Figure 2. The laser scanner sensor provides raw data from the observed scene. Then, from the raw data, an object recognition algorithm is processed. The object recognition algorithm consists of three processes, called segmentation, classification, and tracking. Although the object recognition algorithm that is used in this phase can be sensitive to complicated road surroundings or measurement errors, which reduce the accuracy of the algorithm, this complexity cannot affect the algorithm much comparing to other road vehicles because terms cruise on fixed railways. The feedback loop between the classification and tracking processes is used to improve the performance of object recognition. In each scan of the laser scanner, positions of the objects in the scanned area are predicted and the classification parameters are updated.  
After the object recognition algorithm, risk assessment is carried out. The system determines distance and calculates the likelihood of collision based on variables such as speed. It can detect objects within a 110-degree field in the front of the tram. If the CAS detects that an object is approaching dangerously close, then it activates a visual and acoustic warning for the driver. Should the tram driver not react to the warning signals within two seconds, the automated system slows the tram to a complete stop. According to [PERSON] et al., the driver reaction time is approximately 1.195 s [38]. If the driver does not respond in two seconds, it means that the driver does not notice the possible danger.
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 4 Sources and Recovery of REEs  
### Primary Sources of REEs  
The main source of REEs comes from the mining industry. REEs are extracted mainly from the minerals bastnastic [Ln(F,OH)CO\({}_{3}\)], monazite [(Ln,Th)PO\({}_{4}\)], and xenoting [(Y,Ln)PO\({}_{4}\)] (Ln: lanthanide). Although 250 minerals ores contain REEs [3; 4; 5], only approximately 10 minerals and ores contain REEs in considerable amounts [3]. The abundance of individual REEs varies depending on the mineral source. As a typical example, it is known that monazite is rich in lighter REEs, while xenoting is rich in heavier REEs [2; 3; 4]. REEs with an even atomic number are more abundant (see Figure 1) [1; 2; 3; 4]. Additionally, REE abundance diminishes with the increasing of atomic weight [1; 2; 3; 4]. Despite their abundance, deposits of REEs of economic interest, due to deposit size or REEs' amount present in minerals, are uncommon, and most of them are in specific geographic areas [3; 4; 5; 6; 7; 8; 9]. From primary sources, REE extraction processes include the acid digestion of REEs containing ores and the use of solvent extraction to selectively separate REEs and obtain the respective REE oxides [3; 4; 5; 6; 7; 8; 9; 10; 11; 12].
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 6 Results and Discussion
### Simplification of Building Geometry
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Multiple R\({}^{2}\)** & **Adjusted R\({}^{2}\)** & **F-Statistic** & _p-Value_ \\ \hline
0.2369 & 0.2363 & 363.9 & \textless{}2.2e-16 \\ \hline
**Variable** & **Coefficient** & **Standard Error** & _p-Value_ \\ \hline (Intercept) & 0.9918288 & 0.0006793 & \textless{}2e-16 \\ \(d_{threshold}/L_{C0}\) & \(-0.0031480\) & 0.0001650 & \textless{}2e-16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regression analysis of Bhattacharyya coefficient. Figure 8: Changes in geometrical features resulting from simplification. (**a**) Bhattachayya conflict, (**b**) Number of edges and faces, (**c**) ITC. Figure 7: Simplification of building geometry with curved faces. (**a**) Space 123 of Academic Advanced Sample, (**b**) Space B112 of OTC Conference Center. According to the characteristics of the simplification method for integrating similar surfaces, the number of faces and edges in the model tended to decrease. In the original model, the average number of faces was 1243 and the number of edges was 1866; however, with the simplification under a condition of \(d_{threshold}=10.0L_{C0}\), the numbers were reduced to 224 and 335, respectively (Figure 8b). As a result, the ITC, which represents the hexahedral meshing complexity, showed an increasing trend. The average ITC of the original model increased from 0.0425 (standard error 0.0165) to 0.0929 (SE 0.0315) under a condition of \(d_{threshold}=5.0L_{C0}\), and to 0.0847 (SE 0.0212) for \(d_{threshold}=10.0L_{C0}\).
## 5 Data Correction Module
### Missing Values Imputation  
The imputation of missing values in time series can be essential to ensure its viable use in certain ML algorithms, such as the ones supported in LSTM cells, which depend on the data sequence. For this reason, and in order to support a detailed record of readings with better quality and reliability, a solution was required that would allow estimating missing values due to, for example, transmission failures.  
The use of more complex solutions based on ML algorithms to perform this task would add a whole complexity to the system. For this reason, the method chosen was the linear weighted moving average (LWMA) algorithm, formulated according to equation\[R_{weighted}=\sum_{i=1}^{n}R_{i}w_{i}, \tag{4}\]  
where \(R_{weighted}\) is the weighted reading, \(R_{i}\)\((i=1,2,\ldots,n)\) are readings from the \(n\) previous days that occurred at the same time of the reading being weighted, and each past reading is affected by a weight \(w_{i}=\frac{2(n-i+1)}{n(n+1)}(i=1,2,\ldots,n)\).  
This solution is supported by the fact that most meters have a weekly frequency (see Section 4.1) and, as can be seen in the Tables 2 and 3, there is a bigger correlation between the most recent readings in the different analyzed meters. The LWMA algorithm has been used in other works where it was necessary to impute values, in which the authors concluded that the algorithm was effective in performing this task [61, 62].  
Figure 14 shows the results obtained using the LWMA method for the introduction of the missing values and filtered readings, in the \"Electricity 1\" meter. As can be verified, the time series was reestablished and the new values are \"imperceptible\", making this approach a reasonable solution to this problem.  
The realization of tests with ML and DL algorithms needs data integrity and quality to produce relevant results. With the data corrected using the described solution, in the following section, a presentation is made of the tests, the results achieved with the algorithms, and the conclusions obtained from them.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & & & & \\
**Days before** & & & & \\ \hline
1 & 0.66 & 0.86 & 0.91 & 0.86 \\
2 & 0.65 & 0.83 & 0.88 & 0.83 \\
3 & 0.63 & 0.82 & 0.86 & 0.81 \\
4 & 0.62 & 0.79 & 0.84 & 0.80 \\
5 & 0.61 & 0.78 & 0.83 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of existing correlations between readings at 12:00 A.M. (midday) and readings that occurred at the same time on previous days.  
Figure 13: Representation of corrected time series, associated with the different hotel meters.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## Abbreviations  
3 GPP 3 rd Generation Partnership Project  
AR  Authentication rate  
CSI  Channel state information  
eMTC  enhanced Machine-type Communication  
FAR  False alarm rate  
GEO  Geosynchronous orbit  
ISTN  Integrated satellite-terrestrial network  
IoT  Internet of Things  
LEO  Low Earth Orbit  
LTE  Long-Term Evolution  
mMTC  Massive Machine-Type Communication  
MEO  Medium Earth Orbit  
MIMO  Multiple-Input and Multiple-Output  
MIS  Mutual Information Secrecy  
MDR  Missed detection rate  
NR  New Radio  
NTN  Non-terrestrial networks  
NB-IoT  Narrow-Band Internet of Things  
PLS  Physical layer security  
PLKG  Physical layer key generation  
RAN  Radio Access Network  
RAT  Radio Access Technology  
RF  Radio Frequency  
STN  Satellite-terrestrial network  
TN  Terrestrial network  
UAV  Unmanned Aerial Vehicle  
UE  User equipment  
XR  Extended Reality