esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Obtaining the Solution of Centroid Coordinates  
### Transformation between the Body Coordinate System and Global Coordinate System  
The coordinate system \(X_{1}OY_{1}\) is rotated relative to the coordinate system \(X_{0}OY_{0}\), i.e., it is rotated \(\delta\), \(\theta\), and \(\eta\) degrees around the \(X\)-, \(Y\)-, and \(Z\)-axes, respectively. Then the RPY equivalent rotation matrix \({}^{0}_{1}R_{zyx}(\eta,\theta,\delta)\) can be obtained. Because the rotation in each direction is around the corresponding axis of the reference coordinate system, we can obtain [28]:  
\[{}^{0}_{1}R_{zyx}(\eta,\theta,\delta)=R_{z}(\eta)R_{y}(\theta)R_{x}(\delta) \tag{1}\]  
where  
\[R_{x}(\delta)=\begin{bmatrix}1&0&0\\ 0&c\delta&-s\delta\\ 0&s\delta&c\delta\end{bmatrix},R_{y}(\theta)=\begin{bmatrix}c\theta&0&s\theta \\ 0&1&0\\ -s\theta&0&c\theta\end{bmatrix},R_{z}(\eta)=\begin{bmatrix}c\eta&-s\eta&0\\ s\eta&c\eta&0\\ 0&0&1\end{bmatrix}\]  
In the above equation, \(\sin\) and \(\cos\) are represented by \(s\) and \(c\), respectively. Substituting into Equation (1), one can obtain:  
\[{}^{0}_{1}R_{zyx}(\eta,\theta,\delta)=\begin{bmatrix}c\eta c\theta&c\eta s \theta s\delta-s\eta c\delta&c\eta s\delta c\theta+s\eta s\delta\\ s\eta c\theta&s\eta s\theta s\delta+c\eta c\delta&s\eta s\theta c\delta-c\eta s \delta\\ -s\theta&c\theta s\delta&c\theta c\delta\end{bmatrix} \tag{2}\]  
The transformation matrix of the body coordinate system \(X_{1}OY_{1}\) relative to the fixed coordinate system \(X_{1}OY_{1}\) can then be obtained:  
\[{}^{0}_{1}T={}^{0}_{1}R_{zyx}(\eta,\theta,\delta)T_{rams}\left(P_{x},P_{y},P _{z}\right) \tag{3}\] \[=\left[\begin{array}{cccc}c\eta c\eta c\delta&c\eta s\delta \delta-s\eta c\delta&c\eta s\theta c\delta+s\eta s\delta&P_{x}\\ s\eta s\delta&s\eta s\delta\delta+c\eta c\delta&s\eta s\theta c\delta-c\eta s \delta&P_{y}\\ -s\theta&c\theta s\delta&c\theta c\delta&P_{z}\\ 0&0&0&1\end{array}\right]\]  
where \(T_{trans}(Px,Py,Pz)\) is the translational transformation matrix, and \(Px\), \(Py\), and \(Pz\) are the translation vectors of each coordinate axis, respectively.  
When the robot actually climbs the steps, the robot only rotates around the \(Z\)-axis. Therefore, \(\theta=0\), \(\delta=0\), and \(Pz=0\). Substituting these into the above equation, one can obtain:  
\[{}^{0}_{1}T=\begin{bmatrix}c\eta&-s\eta&0&I_{2}\\ s\eta&c\eta&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{4}\]
# A BIM-Based Smart System for Fire Evacuation
## 2 Materials and Methods
### Smart Evacuation System Framework
The fire reaction should be created, and smoke devices should be placed at critical points. Over the FDS, the fire extension and the environmental data such as temperature, smoke, and air intoxication are estimated. According to the tenability boundaries, the ASET is computed for proposed scenarios, including visibility, temperature, smoke, and air intoxication [38]. ABS should be conducted for fire scenarios to determine the appropriate evacuation paths. FDS should be integrated with ABS to investigate the impact of fire on occupants' movement and evacuation paths. Steering mode is used to deviate occupants from risky paths that could include obstacles, high smoke density, low visibility, and high Carbon Monoxide (CO) concentration. Accordingly, the model selects the evacuation path depending on (1) FDS parameters; (2) queue time to evacuation exits; (3) estimated time from each door to exit; and (4) the total travel distance to stay safe [39]. * Evacuation services: AI is used to determine the optimal solution by analyzing historical and real-time data. AI proved to be effective in resilient fire hazard management [40]. The system is based on an ML algorithm implemented in Dynamo. When BIM detects a fire source through sensors and identifies its cause and location, the system automatically generates the optimal evacuation solution by analyzing the historical data. The advantage of AI lies in self-learning, reasoning, and adaptation of the best fire evacuation scenario from the previous scenarios generated and incorporated in the database. Smart Services  
The system offers valuable services via BIM, such as: (1) detecting the fire's location, cause, and type; (2) tracking the occupants and defining their locations; (3) displaying the environmental data; (4) sounding the alert; (5) identifying the optimal evacuation paths; (6) taking decisions to limit the fire's spread such as shutting off equipment.
## 4 Proposed Method  
We will provide a detailed introduction to the proposed ESO-GJO protocol in this section. Each iteration of the ESO-GJO protocol encapsulates both the CH selection phase and the subsequent data transmission stage, amalgamating these crucial processes within every round. In the cluster head selection phase, we use the SO optimization algorithm for cluster head selection and in the data transmission phase, we use the GJO optimization algorithm for optimal path selection. In addition, the fitness function plays a crucial role in the SO algorithm and GJO, which is an indicator of the degree of superiority of a solution, and provides a search direction for the algorithm. Through the rational design of the fitness function, we can help the algorithm to find the optimal solution of the problem faster. The choice of fitness function should be determined according to the needs of the specific problem, and it can be defined based on the constraints of the problem and the objective function. Figure 2 shows the flow block diagram of the ESO-GJO algorithm.
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 7 Conclusions  
Experimental validation of numerical contact models, that are developed to predict the LPT blade response levels, is vital to accurately define the non-linear dynamic behavior of friction damping components. In case of shrouded blades, the need to measure the contact forces at the shroud contacts must be addressed during the experimental campaign. In this study, a tri-directional contact force measurement system consisting of three uniaxial force sensors arranged in a tripod configuration was developed. The following conclusions can be drawn:  
* The proposed solution is cost effective and adaptable without any complex assembly and with a straightforward calibration procedure.
* Although a complete mechanically decoupled measurement system could not be fully achieved, good results were obtained by using the decoupling matrix of the calibrated system to compute the decoupled applied forces for the static as well as for dynamic forces.
* The tri-directional contact force measurement system investigated in this study can be effectively used to measure the actual three directional contact forces at blade shrouds, representing an improvement with respect to the existing contact force measurement systems currently limited to the uni- and bi-directional force measurements.  
As a result, a better and detailed representation of the shroud contact parameters can be achieved, and a more reliable experimental validation of the contact models can be accomplished in future.  
Conceptualization and methodology, R.A., C.M.F., and S.Z.; investigation, R.A.; validation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, C.M.F. and S.Z.; supervision, C.M.F. and S.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Aer-Mec Laboratory Research Group, DIMEAS, Politecnico di Torino.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Evaluation in Anechoic Chamber  
To evaluate the effect of the car body on the measurement of the E-field strength, we placed the car in a large anechoic chamber prior to an actual measurement on the road (Figure 1). The car was placed on a turntable, and the relative angle from the transmitting antenna was changed by 90 degrees (front, rear, left, and right). A log periodic antenna (UHALP 9108, Schwarzbeck) and a double-ridged guide antenna (Model 3117, ETS-Lindgren) were used as transmitting antennas for the low- and high-frequency probes, respectively. The antennas were inclined at 45 degrees. The height of the transmitting antenna was adjusted to the height of the probe. Continuous waves (CWs) were input to the transmitting antennas while the frequency was swept in 100 MHz steps. As a result, variations in the horizontal plane were 2.2 dB and 2.1 dB, on average, for each of the probes for the low and high frequencies, respectively. The measured results were compared with the E-field strength calculated from the gain of the transmitting antennas (Figure 2). The measured results were obtained by averaging the data of the angles in four directions at each frequency. It was confirmed that they were within the range of \(\pm\)2.5 dB from the calculated value, although they differed depending on the frequency.  
Figure 1: E-field measuring equipment in a radome mounted on a car in an anechoic chamber.  
Figure 2: Measured and calculated E-fields.
## 5 Validation
### Practical Validation  
The point position errors calculated previously offer only a qualitative significance for the given tail cone due to the simplifying assumptions regarding the surface being made, which do not correlate well to the particularly curved surface of the actual part, thus motivating a real-world validation. For the validation, the reference points are overlaid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equation** & **Angle** & \(\mu\) & \(\sigma\) & \(\mu_{steps}\) & \(\sigma_{steps}\) \\ \hline (16): \(f_{1}\) & \(\Delta pan\) & \(-0.01492^{\circ}\) & \(0.1557^{\circ}\) & \(1.80\) & \(18.82\) \\ \hline (19): \(f_{II}\) & \(\Deltatilt\) & \(0.01748^{\circ}\) & \(0.2026^{\circ}\) & \(6.36\) & \(73.75\) \\ \hline (23): \(f_{III}\) & \(pan\) & \(0.05552^{\circ}\) & \(0.1056^{\circ}\) & \(6.71\) & \(12.77\) \\ \hline (24): \(f_{IV}\) & \(tilt\) & \(-1.0343^{\circ}\) & \(0.1406^{\circ}\) & \(-376.52\) & \(51.18\) \\ \hline (26): e & \(Combined\) & \(0.02640\) m & \(0.00482\) m & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results from the algorithmic accuracy analysis of the position and transformation estimation and the combined point position error.  
Figure 12: Relation of the equivalent position.  
with a target similar to Figure 11, and the outlines of the light spots of the manually jogged position and the calculated position are manually traced.  
This manual process is necessary due to the aforementioned distortion of the light spot, and on the curved surface of the workpiece especially, the center point is difficult to reconstruct. As the spot size of the two ellipses is identical, there will always be two intersection points and two points with maximum distances, as shown in Figure 13. Due to the angled surface, one spot will be elongated more than the other one, resulting in two distances, named \(e_{l}\) for the lower and \(e_{u}\) for the upper distance (comparable to the \(e\) value from the algorithmic accuracy evaluation). The measurements are listed in Table 12, along with the mean values and standard deviations.  
The combined mean value is 4.64 mm, and the combined standard deviation is 0.83 mm. These values are significantly lower than the predicted theoretical values, implying that the simplifications made in Section 5.3 may not be applicable to all cases. Particularly, the angle between the light beam and the surface in the real world experiment is much shallower than the perpendicular angle assumed in the simplification. Depending on the general orientation between moving head and workpiece, the real-world errors may become more or less favorable compared to the theoretical values.
## 5 Discussion
### NLP for Governance and Policy  
Government departments face thousands of documents to be processed every day [70,71], and how to efficiently extract accurate information from such documents is a vexing problem. As a type of information retrieval model, supervised classification models were suggested to automatically create large-scale annotated datasets for the categorisation of government documents [6]. As an example, the most important terms in the content of twelve national planning documents on AI were extracted and summarised using NLP approaches [72]. Such approaches enable applications to classify information  
\begin{table}
\begin{tabular}{c c c} \hline \hline \# & **Category** & **Description** \\ \hline \multirow{4}{*}{1} & Governance and Policy & \
\begin{tabular}{c} Efficiently extract and analyse \\ documents and policies; \\ obtain insights for better \\ governances and services \\ Evaluating citizens’ sentient \\ and public opinions; \\ understanding attitudes \\ regarding policies \\ Measuring the reception of \\ health policies; clarifying the \\ situations and the efficiency of \\ public health measures during \\ the pandemic \\ \end{tabular} \\ \multirow{4}{*}{2} & Understanding Citizen and \\  & Public Opinion &
\begin{tabular}{c} Efficiently extract and analyse \\ documents and policies; \\ obtain insights for better \\ governances and services \\ Evaluating citizens’ sentient \\ and public opinions; \\ understanding attitudes \\ regarding policies \\ Measuring the reception of \\ health policies; clarifying the \\ situations and the efficiency of \\ public health measures during \\ the pandemic \\ Retrieving the reactions of \\ markets; identifying risks and \\ threats in economics \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of NLP applications in governments.  
and extract topics from policy documents [70], as well as to discover policy issues [73]. In addition, government services for citizens are frequently intricate and tied to a variety of requirements [2], which is why e-government is still acknowledged as a method for enhancing public services and the efficacy of public policies and partnerships [74]. To facilitate e-government, automatic question-document classification strategies can map pertinent documents and questions to different e-government services [75]. [PERSON] and co-workers provide a retrieval-based question-answering (QA) system for the e-government sector [2]. In general, a majority of the studies discussing the \"governance and policy\" topic apply NLP techniques to tasks such as text classification, information retrieval, question answering, and topic modelling.
# Bot Datasets on Twitter: Analysis and Challenges
## 3 Database Analysis
### Cresci et al. Database (2017)  
Cresci et al.'s [35] 2017 public database is one the most popular collections in the literature of bot detection on Twitter (with its article reporting a Field-weighted Citation Impact of 51.88 on Scopus), having nine datasets that were obtained from various sources: the genuine accounts dataset (verified profiles managed by humans from around the world obtained randomly via hybrid crowdsensing [57]), social spambots #1 (automated accounts identified as retweeters of an Italian political candidate), social spambots #2 (fake profiles that spam paid mobile apps on the platform), social spambots #3 (fake users discovered to be spamming products on sale on an e-commerce site), traditional spambots #1 (regular spammers used in [58] by [PERSON] et al.), traditional spambots #2 (fake accounts that spam scam URLs), traditional spambots #3 (automated users dedicated to spam job offers), traditional spambots #4 (another group of false profiles spamming job offers), and fake followers dataset (simple faux accounts used as statistics enhancers).  
In [35], [PERSON] et al. used a training set composed of genuine accounts & social spambots #1; we follow their methodology by selecting these two datasets for our data revision and representation. The first dataset, genuine accounts (GA), has 3474 verified human accounts from Twitter with 248,533 tweets in total. The second dataset, social spambots #1 (SS1), has 991 social spambot accounts extracted from the platform with 1,610,176 tweets in total. We also identified two classes from these datasets: the humans from the genuine accounts dataset and the bots from the social spambots #1 dataset.
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 5 Simulation Results
### Applicability to NOMA  
Figure 10 shows the effect of the number of time slots \(K\) on the harvesting probability and the conditional BER. Due to high computational complexity for large \(K\), we use \(M=2\) for this scenario. As the number of time slots \(K\) increases, the duty cycle decreases as \(D=1/K\) for TD-AmBC and \(D=2/K\) for SC-AmBC. Consequently, backscatter power is reduced to \(\beta=\frac{\alpha}{K}\) for TD-AmBC and \(\beta=\frac{2\alpha}{K}\) for SC-AmBC, providing sufficient power to rectifier circuits in RF tags. Remarkably, increasing \(K\) reduces the performance gap between the TD-AmBC and the SC-AmBC. In this manner, increasing \(K\) improves the harvesting probability by reducing the duty cycle \(D\) in Figure 10a.  
Figure 9: Effect of the number of multipath \(L^{+}\) (\(M=4\), \(K=4\), \(\lambda=1.5\) for SC-AmBC).  
On the other hand, increasing \(K\) degrades the conditional BER as shown in Figure 10b. Since the backscatter power \(\beta\) is reduced in both the TD-AmBC and the SC-AmBC in Equation (7), the BER performance is degraded accordingly. It is noteworthy that the TD-AmBC detectors have smaller backscatter power (i.e., \(\beta=\frac{\alpha}{K}\)) than those of the SC-AmBC (i.e., \(\beta=\frac{2\pi}{K}\)), exhibiting the worse BER in Figure 10b. More importantly, the sparsity caused by a small duty cycle is not utilized in those TD-AmBC detectors. Those detectors can only support OMA with a limited number of RF tags (i.e., \(\lambda=1\)) and thus are not suitable for massive connectivity. The proposed SC-AmBC can efficiently utilize the sparsity for reliable AmBC detection as well as for overloading multiple RF tags (i.e., \(\lambda>1\)), supporting NOMA. Although MAI may degrade the BER performance, low-complexity MPA achieves near-optimal detection performance by the virtue of the sparsity [32], As a result, the proposed SC-AmBC can enable massive and reliable connectivity over the conventional TD-AmBC.
###### Abstract
Initially developed for general health monitoring, these devices primarily focused on basic metrics such as step count and heart rate [16]. However, technological advancements have expanded their capabilities to include more complex physiological measurements, although the scientific validity of some of these measurements remains to be fully established [17]. The integration of triaxial accelerometers has enhanced the functionality of these devices, enabling the detection of movement patterns [10], sedentary behavior [18], and sleep characteristics [19; 20]. Recent research has highlighted the importance of these parameters not only for general health, but also for athletic performance [20]. The COVID-19 pandemic has further accelerated the adoption and integration of these technologies [21]. In the realm of sports science and training, wearable technologies have become increasingly prevalent [22]. These devices offer unprecedented access to a wealth of data across various training phases, including active sessions, recovery periods, and off-season intervals. Their non-invasive nature and ease of use allow for continuous data collection without requiring active participation from athletes or medical staff [23]. Despite their widespread adoption, concerns persist regarding the validity, accuracy, and reliability of many wearable devices, particularly with regard to measuring complex physiological parameters such as cardiac function [17; 24; 25; 26]. This underscores the need for rigorous scientific evaluation of these technologies. Projections suggest that wearable technology will experience significant growth over the next quarter-century, potentially yielding substantial healthcare cost savings and reducing the need for direct doctor-patient interactions [27; 28]. The global adoption of wearable devices has seen rapid growth, with usage increasing from approximately 600 million devices in 2020 to over 1.1 billion in 2022 [28; 29]. The distribution of wearable technology usage varies globally, with North America leading at 39.90%, followed by Asia at 28.27%, and Western Europe at 17.45% [30]. This review aims to explore the utility of wearable technologies in athletic performance monitoring, with a particular focus on their potential for identifying marginal gains. We will address several key questions:  
* What is the reliability of data collected from wearable devices? * How do wearable technologies impact marginal gains in sports performance? * What are the primary categories of wearable technologies used in sports? * How are these technologies applied in order to enhance sports performance? * What limitations exist in the current state of wearable technologies in sports?By addressing these questions, this review seeks to provide a comprehensive overview of the current state and future potential of wearable technologies in sports science and performance optimization.
# Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
## 1 Introduction
This research aims to provide automakers with a guideline for the fine-tuning of the current ADB system by adjusting the non-glare zone width. The research is conducted on a virtual night drive simulator called LucidDrive and presents a proof of concept for using fuzzy logic control to mimic a human test driver in finding the best non-glare zone width.