esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Utilization of GNSS  
This section describes how GNSS measurements are utilized in different development stages of the localization algorithm. Starting from GPS L1 C/A code pseudo-ranges and time-differenced carrier-phase observations only (Section 2.1), multi-frequency GPS measurements are incorporated in Section 2.2 to enhance accuracy by eliminating the ionospheric error. In the concluding Section 2.3, multi-frequency Galileo observations are added to increase satellite availability.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN  
This subsection presents a brief description of the advantages of C-RAN architecture: adapting dynamic traffic, load balancing, convenience of operation and maintenance, cost reduction, and interference minimization.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 4 Sensor Experiments and Discussions
### Sensor Performance Evaluation
#### 4.2.3 Hysteresis Measurement  
Meanwhile, the hysteresis characteristics of the FeGa thin film coated SAW devices were evaluated as shown in Figure 9a. In the measurement, the applied current increased first and then decreased with a step of 1 A, and each applied current lasted for 5 s. Usually, the hysteresis error is defined by  
\[\delta_{H}=\max(y_{ui}-y_{di})/(2y_{FS})\times 100\% \tag{9}\]  
where the \(y_{ui}\) and \(y_{di}\) are the response at the same current input when the current increases and decreases, \(y_{FS}\) is the full-scale output of the current sensor. Hence, the hysteresis errors of 0.81%, 0.92%, 2.57% were calculated from the sensing devices coated with dotted, grating, and membranous FeGa thin film, respectively. Obviously, the excellent symmetry in sensor response was observed from the dotted FeGa thin-film device. Dotted pattern releases the coercivity well and enhances the magnetostrictive strain, therefore lowering the hysteresis error significantly. This can also be demonstrated by measuring the hysteresis loops of the FeGa thin film with various patterns, as shown in Figure 9b. It indicates that lower coercivity of 61.23 Oe (Hcd) was observed from the dotted pattern over the grating (Hcg = 72.85 Oe) and the membranous pattern (Hcm = 100.17 Oe). Hence, it is reasonable to  
Figure 8: The repeatability of the proposed sensors.  
be concluded that the dotted pattern can not only improve the detection sensitivity but also suppress the hysteresis effect effectively in magnetostrictive thin film.  
Table 1 concludes the sensing performance of the proposed sensors with various FeGa film patterns. Obviously, the dot-patterned FeGa film coated sensing device features larger sensitivity and lower hysteresis error. Table 2 offers the sensing performance comparison of the proposed sensor in this work with the existing sensor prototype with similar structures. It is obvious that larger sensitivity was achieved from our work, and compared with the sensors coated with FeCo thin film [30], the sensitivity of the proposed sensors increases by ~77.1%. The main reason is that the FeGa thin film has a larger magnetostriction coefficient than FeCo thin film [22,23].
# Size-Dependent Mechanical Behaviors of Defective FGM Nanobeam Subjected to Random Loading
## 4 Uncertain Analysis of FGM Timoshenko Nanobeam with Defects
### The Uncertainty Model of the Defective Nanobeam Subjected to Random Loads  
In FGM Timoshenko nanobeam analysis, uncertain parameters with specific probability distribution can be quantified as a random vector \(\mathbf{\alpha}=[\alpha_{1},\alpha_{2},\ldots,\alpha_{s}]\), and those without sufficient statistical information are modeled as interval vectors \(\mathbf{\beta}=[\beta_{1},\beta_{2},\ldots,\beta_{t}]\). Considering the two types of uncertain parameters, a hybrid uncertain system was established, where uncertain parameters in \(\mathbf{\alpha}\) and \(\mathbf{\beta}\) are assumed to be independent. The expectation and variance vector of \(\mathbf{\alpha}\) can be written as  
\[\mathbf{\mu_{\alpha}}=(\mu_{1},\mu_{1},\ldots,\mu_{s})^{\mathrm{T}},\ \mathbf{\sigma_{\alpha}^{2}}=\left(\sigma_{1}^{2},\sigma_{2}^{2},\ldots,\sigma_{s} ^{2}\right)^{\mathrm{T}} \tag{33}\]  
where  
\[\mu_{i}=\mu(\alpha_{i}),\ \sigma_{i}^{2}=\sigma^{2}(\alpha_{i}),\ (i=1,2, \ldots,s) \tag{34}\]  
Figure 6: Dimensionless deflection along the axial direction: (**a**) [PERSON]’s modulus of aluminum; (**b**) Young’s modulus of alumina; (**c**) shear modulus of aluminum; (**d**) shear modulus of alumina.  
The interval vector is described as  
\[\beta\in\left[\beta,\,\overline{\beta}\right] \tag{35}\]  
where  
\[\beta\,=\left(\beta_{1^{\prime}},\beta_{2^{\prime}},\cdots,\beta_{t}\right)^{ \mathrm{T}},\,\overline{\beta}\,=\,\left(\overline{\beta}_{1^{\prime}}, \overline{\beta}_{2},\ldots,\overline{\beta}_{t}\right)^{\mathrm{T}} \tag{36}\]  
in which \(\beta\) and \(\overline{\beta}\) denotes the lower and upper bound vector, respectively. Additionally, the midpoint and radius of \(\beta\) is defined as  
\[\beta^{\mathrm{c}}=\frac{\left(\overline{\beta}+\beta\right)}{2},\,\,\beta^{ \mathrm{r}}=\frac{\left(\overline{\beta}-\beta\right)}{2} \tag{37}\]  
Therefore, the SFPM equation of the FGM Timoshenko nanobeam, considering the two types of uncertain parameters, is convert to uncertain system, as follows  
\[[G(\alpha,\beta)]\{C(\alpha,\beta)\}=\{P(\alpha,\beta)\} \tag{38}\]  
Thus, a hybrid uncertain framework to analyze the size-dependent mechanical behaviors of defective FGM nanobeams is subjected to random loading. The method to solve the hybrid uncertain system is introduced in next subsection.
## 4 Bionanosystems
### Bionanosystems for RA
#### 4.1.1. Bionanosystems Based on Organic Carriers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Carrier** & **Host-Guest System** & \multicolumn{2}{c}{**Main Features**} & **Refs.** \\ \hline \multirow{6}{*}{Gelatin} & \multirow{6}{*}{Gelatin-based films containing RA} & \(\bullet\) & RA is covalently bond to the carrier; & \\  & & \(\bullet\) & good water resistance property, & \\  & & & strong mechanical properties and & \\  & & & excellent ultraviolet light & [115] \\  & & & barrier ability; & \\  & & \(\bullet\) & effective antioxidant and & \\  & & & antibacterial activities. & \\ \hline \multirow{6}{*}{RA} & \multirow{6}{*}{Crosslinked poly(RA) particles} & \(\bullet\) & Effective release of RA in acidic, & \\  & & neutral and basic environments; & \\  & & antioxidant activity & & \\  & & indistinguishable from RA; & \\  & & & poly(RA) particles are less cytotoxic & \\  & & & to mammalian cells than RA & \\  & & & monomer and show inhibition of & \\  & & & \(\alpha\)-glycos & \\ \hline \multirow{2}{*}{Polyclactic-co-glycolic acid (PLGA) and poly-lactic  acid (PLA)} & \multirow{2}{*}{RA-loaded PLGA or PLA microparticles} & \(\bullet\) & The encapsulation efficiency of RA & \\  & & & increased by co-encapsulation with & \\  & & & \(\beta\)-CA and \(\alpha\)-TOC. & \\ \hline \multirow{2}{*}{Polycaprolactone (PCL)} & \multirow{2}{*}{RA-loaded PCL microspheres} & \(\bullet\) & Better long-term stability of RA in & \\  & & & cream formulations containing & \\  & & & RA-loaded PCL than with only RA & \\ \hline \multirow{6}{*}{Poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)} & \multirow{2}{*}{RA-loaded ultrathin fibers of PHBV} & \(\bullet\) & Functional fibers were obtained by & \\  & & & electrospinning; & \\  & & & continuous films produced by & \\  & & & annealing of resultant electrospun & \\  & & & mats showed antimicrobial and & \\  & & & antioxidant activity in both open & \\  & & & and closed systems. & \\ \hline \multirow{2}{*}{Liposomes, ethosomes and ethosomes} & \multirow{2}{*}{RA-loaded liposomes and ethosomes} & \(\bullet\) & Used to increase the transdermal & \\  & & & penetration of RA & [118,120,121] \\ \hline \multirow{2}{*}{Solid lipid nanopoarticles (SLNs)} & \multirow{2}{*}{RA-loaded SLNs} & \(\bullet\) & Used for oral nutraceutical & \\  & & & formulation; & \\  & & & Used for therapeutic approach for & \\  & & & neurodegenerative disorders. & \\ \hline Nanostructured lipid  carrier (NLC) & \multirow{2}{*}{RA-loaded NLC} & \(\bullet\) & Used for topical application. & [125] \\ \hline \multirow{6}{*}{Nanoemulsions} & \multirow{6}{*}{RA containing nanoemulsions} & \(\bullet\) & Used for nasally delivered & \\  & & neuroprotective therapy when & \\ \cline{1-1}  & & associated to mucoadhesive & [126,127] & \\ \cline{1-1}  & & & biopolymer; & \\ \cline{1-1}  & & & used for topical application & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Accuracy  
Intelligent Vehicle-to-Vehicle Communication Networks use AI to improve network configuration and responsiveness but face challenges in data accuracy and algorithms. Large amounts of quality data are needed for training and consolidating knowledge, and real-time communication is crucial.
## 2 Materials and Methods
### Development of the TMT-VR  
The development of the TMT-VR adhered to the standards for Ergonomics of Human-System Interaction [58], which were essential in ensuring the system met the required ergonomic and usability criteria [58]. The design and development process followed the guidelines and recommendations for cognitive assessments in immersive VR, as outlined by [35], ensuring that the VR cognitive and psychomotor tasks developed were both scientifically valid and immersive. UCD principles played a pivotal role in the TMT-VR's development. UCD emphasizes creating interactive systems that prioritize the needs and capabilities of users, incorporating human factors and ergonomic considerations to maximize effectiveness, efficiency, and safety while minimizing performance issues [59]. In designing the TMT-VR tasks A and B, which require target selection from a distance, the method of ray casting was employed. Ray casting, a technique that simulates pointing with a laser pointer, was utilized across all interaction modes, including controller, headset, and eye-tracking, ensuring consistency and accuracy in user interactions.  
Critical factors were considered to maintain the reliability of TMT-VR and its consistency with the traditional TMT. One such factor was the \"Heisenberg effect\", which posits that simultaneous confirmatory actions, such as pressing a button during pointing tasks, can impact performance accuracy [60]. To mitigate this, confirmatory button presses were eliminated in favor of automatic target selection after the participant focused on the target for 2 s. This approach aimed to reduce unintentional movements that could affect accuracy, as highlighted by [60]. Moreover, the development process addressed common VR interaction challenges, such as the Midas touch effect, where unintended targets might be selected during a search, and the occlusion effect, where targets might be obscured by other objects. These issues were mitigated by requiring a sustained focus of 2 s before selection and by designing the edges of occluded spheres to remain visible with slight head movements. These adjustments improved spatial awareness and task performance [61]. The selection of controllers over other modalities, such as joysticks or hand tracking, for ray casting in the TMT-VR was guided by UCD principles. Controllers were chosen due to their superior speed, precision, and user-perceived control, making them the most effective option for immersive VR environments [62]. To maintain the integrity of the experimental design, haptic feedback was excluded to avoid confounding variables, ensuring a consistent and reliable user experience. Additionally, the development of the TMT-VR took into consideration the accessibility needs of users with color vision deficiencies. The VR environment was designed using color-blind-friendly colors, as recommended by [63], to improve accessibility and ensure that all users, regardless of color vision status, could effectively engage with the tasks.
## 3 Results and Discussion
### Properties of Composite Portland Cements (CPCs) Manufactured by 20% Slag
#### 3.2.1 Compressive Strength
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Slag**} & \multirow{2}{*}{**Grinding Time (min)**} & \multicolumn{6}{c}{**Compressive Strength (MPa)**} \\ \cline{3-8}  & & **1 Day** & **2 Days** & **7 Days** & **28 Days** & **60 Days** & **90 Days** \\ \hline – & 75 & 17.3 & 28.3 & 41.6 & 57.9 & 63.1 & 64.4 \\
3-1a & 82 & 14.5 & 23.8 & 34.3 & 50.0 & 53.6 & 56.0 \\
3-2a & 111 & 19.5 & 25.2 & 37.1 & 52.5 & 53.8 & 56.5 \\
4-1a & 81 & 16.6 & 27.1 & 39.9 & 48.2 & 55.0 & 57.9 \\
4-2a & 106 & 16.7 & 25.7 & 39.6 & 57.0 & 60.1 & 64.2 \\
5a & 85 & 16.2 & 22.9 & 34.6 & 52.3 & 55.2 & 57.3 \\
3-1b & 89 & 14.1 & 23.0 & 35.1 & 50.4 & 59.0 & 60.3 \\
3-2b & 95 & 13.5 & 22.3 & 32.6 & 48.6 & 58.6 & 58.5 \\
4-1b & 90 & 15.9 & 25.3 & 38.2 & 51.3 & 56.2 & 58.0 \\
4-2b & 100 & 19.1 & 28.1 & 41.0 & 59.4 & 64.6 & 67.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Compressive strength and grinding time of manufactured CPCs (20% slag). Blaine 4500 cm\({}^{2}\)/g, w/c: 0.5. Figure 6: Compressive strengths of CPCs (20% slag) manufactured with: (**a**) air-cooled slags; (**b**) water-cooled slags, Blaine: 4500 cm\({}^{2}\)/g.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 3 Method and Analysis  
In PV systems, hotspot faults and bypass diode failures are just two of the many possible fault types. These flaws reduce the PV system's generated output power and impair system performance. This study will provide a native and national solution for hotspot and bypass diode problems, starting with panel architecture and ending with analysis results. Two distinct verifications will be made using both the images obtained from thermal images with image processing techniques and the numerical data obtained from the real-time monitoring system.
# Targeting Type II Toxin-Antitoxin Systems as Antibacterial Strategies
## 3 Strategies for the Artificial Activation of Toxin-Antitoxin Systems
### Engineered TA Systems in a Targeted Killing Strategy  
An innovative strategy for the targeted killing of selected pathogenic bacteria, without harming beneficial members of the microbiota inhabiting eukaryotic host organisms, was recently proposed by [PERSON] et al. [82] (Figure 4). This strategy is based on the use of the _ccdA-ccdB_ type II TA system, encoding the toxin CcdB that poisons DNA gyrase--an enzyme responsible for the negative supercoiling of bacterial DNA [83]. CcdB interferes with the activity of DNA gyrase, inducing it to form a covalent GyrA-DNA complex that cannot be resolved, thus promoting DNA breakage and cell death. This mechanism is closely related to the action of quinolone antibiotics, which also target DNA gyrase [84].  
To better control CcdB production in vivo, the gene for this toxin was divided into two parts, and each was fused with DNA encoding split inteins. The generation of a functional toxin occurs in three stages: (i) expression of the two polypeptides, (ii) their association and ligation into a single fusion protein, and (iii) self-splicing of the intein (Figure 4). An important step in this strategy was the construction of a mobilizable plasmid containing genes encoding the antitoxin and the engineered toxin-intein, whose expression could be independently regulated in bacteria harboring specific transcription factors. Although the plasmid could be readily transferred by conjugation from _E. coli_ to other bacteria, the toxic effect was observed exclusively in strains of _Vibrio cholerae_. This host specificity was achieved by cloning the engineered _ccdB_ toxin-intein genes downstream of a promoter regulated by the transcriptional activator ToxRS, a cholera toxin-associated activator characteristic for _V. cholerae_[85] (Figure 4). Bacterial strains lacking ToxRS were unaffected, including the _E. coli_ donor strain and non-pathogenic _Vibrio_ spp.  
The second component of the TA module, the antitoxin gene _ccdA_, was placed under the transcriptional control of the repressor SetR. The presence of _setR_ is considered a hallmark of SXT--an integrative and conjugative mobile element (ICE) of _V. cholerae_ that often includes various antibiotic resistance genes [86]. Therefore, expression of the antitoxin gene is repressed in pathogenic multidrug-resistant _V. cholerae_ cells containing the _setR_ gene, allowing the toxin to poison gyrase and cause a bactericidal effect (Figure 4). However, in cells that lack _setR_, the antitoxin CcdA is produced, neutralizing the effects of the toxin. Thus, the described approach allows species-specific killing of antibiotic-resistant _V. cholerae_ strains without affecting the growth of other bacteria present in the mixed populations. The effectiveness of this strategy was confirmed in vivo, in the microbiota of zebrafish and crustacean larvae, where _Vibrio_ spp. naturally occur [82].
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods
### Evaluation Metrics  
APD's output, a list of participants, can be analysed in two ways: The number of participants in it, and the quality of the ranking's order. Both aspects are important; the number of participants describes the algorithm's ability to identify as many entities as possible, while the order describes how well the technique distinguishes between real participants and named entities that are unrelated to the event. We suggest standard IR metrics to evaluate these two qualities.  
In many events, like the football matches and basketball games that we follow in the next section, the participants are easily-enumerable. Precision and recall, defined as follows, can be used to evaluate APD's retrieval performance:  
\[precision=\frac{tp}{tp+fp} \tag{2}\]  
\[recall=\frac{tp}{tp+fn}. \tag{3}\]  
The true positives and the false positives, \(tp\) and \(fp\), refer to the number of captured participants and the number of incorrect named entities in the ranking respectively. The false negatives, \(fn\), are the missed participants. Together, precision and recall assess the APD algorithm's ability to capture as many participants as possible without introducing noise. The two can be combined into the harmonic mean, or the F-measure [27].  
To evaluate the order of the ranking, we propose Mean Average Precision (MAP), or the mean of the Average Precision (AP) over all datasets. AP is the mean precision score of a list of items, considering only precision values at ranks where the item is correct [28]:  
\[AP=\frac{1}{r}\sum_{k=1}^{n}P@k\cdot rel_{k}. \tag{4}\]  
In this equation, \(r\) is the number of correct participants in the ranking, \(n\) is the number of elements in the ranking, and \(P@k\) is the precision at rank \(k\). \(rel_{k}\)'s role is to consider only those elements that are relevant; it is set to 1 if item \(k\) in the ranking is relevant and 0 otherwise. The R-precision, equivalent to \(P@r\), is another suitable metric [28]. In the next section, we evaluate our approach using precision, recall and MAP--all bound between 0 and 1.
## 4 Experimental Results  
### Parameter Fine-Tuning and Complexity of the ML Model  
The parameters of the proposed GFSDP and conventional algorithms were set before training. These parameters are essential because they can improve the algorithm's performance when configured effectively. This study applied a fivefold cross-validation and grid search to fine-tune these parameters. The core parameters for each ML algorithm were defined, and the range of possible values for each parameter was determined.  
Subsequently, a grid search on all possible combinations of the parameters was performed for each ML algorithm, which observed the best parameter sets that helped the ML algorithms obtain the highest results. Finally, a fivefold cross-validation was performed to improve the robustness of the algorithm with the optimal parameters obtained. The training data were randomly segregated into five nonoverlapping subsets of equal sizes. Five iterations were obtained: four folds were used for learning each iteration, and the remaining fold was applied to perform the evaluation. The final output was the average of the five folds. Table 5 lists the parameter ranges for each parameter of each ML algorithm under consideration and the optimized parameters after performing the grid search. The feature training and testing times were calculated using MATLAB \(\copyright\)2022 [45], as listed in Table 6. The proposed GFSDP algorithm required more computational time than the GP algorithm using the MIMIC II dataset.
## 4 User Testing Methodology
### Evaluation Process  
The evaluation process was conducted through quantitative and qualitative assessments, ensuring a comprehensive VA performance analysis. The responses were quantitatively scored based on the above criteria, resulting in a data set that provided insights into accuracy rates, hallucination frequency, and average scores across various metrics. The data was analyzed to determine trends and identify areas where the VA exhibited consistent performance or weaknesses.  
Participants qualitatively provided open-ended feedback on interactions with the VA. This feedback offered valuable insights into user perceptions, highlighting specific aspects of the VA's performance that might not be captured through quantitative scores alone. For instance, users noted areas where contextually relevant responses were particularly adeptly provided by the VA and instances where the system struggled with ambiguous or complex questions.  
The evaluation process was conducted through quantitative and qualitative assessments, ensuring a comprehensive VA performance analysis. The scoring of each response was conducted quantitatively, based on the criteria outlined in Table 1, which enabled structured scoring across different response dimensions. The scoring system was comprised of both binary and Likert scale evaluations. The assessment of response accuracy, detection of hallucinations, and identification of logical errors were conducted using binary scores. The binary ratings provided a straightforward assessment of whether the output of the VA met essential accuracy and coherence standards, with each response categorized as either correct or containing an error.  
A Likert scale ranging from 1 to 5 was employed in addition to binary scoring to rate responses across various dimensions, including relevance, quality, and overall user experience. A more nuanced evaluation was facilitated by the Likert scale, with the degree of alignment between the response and the query (relevance), the grammatical and syntactic quality (quality), and the participant's satisfaction with their interaction with the VA (overall user experience) being captured. This multi-dimensional scoring approach provided a detailed view of the VA's performance, with not only binary correctness being identified but also subtler factors influencing user satisfaction.