esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Tightly Coupled Pose Compensation
The timestamp of the first fan sector in the current frame serves as the calibration timestamp, establishing the body coordinate system for the entire laser panoramic frame. The posture transformation between the first fan angle and the \(i\)th fan angle is calculated using the timestamp linear interpolation method:  
\[\left[\mathbf{R}_{fan_{1}}^{n},\mathbf{t}_{fan_{1}}^{n}\right]=\left[\mathbf{ R}_{n}^{\text{init}},\mathbf{t}_{n}^{\text{init}}\right], \tag{6}\]  
\[\left[\mathbf{R}_{fan_{i}}^{n}\mathbf{t}_{fan_{1}}^{n}\right]=\left(i-1\right) \Delta t_{fan_{i}}\left[\mathbf{R}_{fan_{1}}^{n},\mathbf{t}_{fan_{1}}^{n} \right]. \tag{7}\]  
Solve the pose-transformation relationship between the two coordinate systems of the \(i\)th fan-shaped sector and the first fan-shaped sector:  
\[\mathbf{R}_{fan_{i}}^{n}=\mathbf{R}_{fan_{1}}^{n}\left(\mathbf{R}_{fan_{1}}^{ n}\right)^{-1}, \tag{8}\]  
\[\mathbf{t}_{fan_{i}}^{n}=\mathbf{t}_{fan_{1}}^{n}-\mathbf{R}_{fan_{1}}^{n} \mathbf{t}_{fan_{i}}^{n}. \tag{9}\]  
Then, according to the coordinate mapping, the position and posture of the \(i\)th fan-shaped sector laser data are mapped to the coordinate system of the first fan-shaped sector:  
\[\mathbf{\dot{p}}_{fan_{i}}^{n}=\mathbf{R}_{fan_{1}}^{n}\mathbf{\dot{p}}_{fan_ {1}}^{n}+\mathbf{t}_{fan_{1}}^{n}. \tag{10}\]  
Based on the above formula, the data of each fan-shaped sector can be reassembled into a complete unbiased panoramic surround laser frame to complete the posture compensation of the current laser frame \(\mathbf{p}_{n}\).
# Risk Assessment of Lift-Jacking Accidents Using FFTA-FMEA
## 3 Methodology
### Fault Tree Analysis  
Fault Tree Analysis (FTA) is a tool used to investigate failure behavior in risk assessment and safety engineering [29]. A fault tree is a logic diagram that denotes the relationships between an event (typically a system failure) and the causes of the event (typically component failures) [30]. It selects the event in the system that needs to be analyzed as the TE, finds the intermediate events through cause-and-effect analysis layer by layer, and continues to decompose to the smallest cause of failure that cannot be decomposed  
Figure 1: The framework of the Risk Assessment.  
as the BE. It uses logic gates and events to model how the component states relate to the whole state of the system in the process. FTA commonly utilises three types of logic gates, which are the OR-gate, AND-gate, and conditional gate. In addition, FTA also employs four different event types, which include the top or intermediate event, basic event, diamond (undeveloped) event, and conditional event [31]. In safety systems engineering, the FTA method has the advantage of being intuitive, causal, and logical.  
FTA allows for qualitative analysis to find out all the combinations of causes, and to determine the magnitude of the influence of each cause on the fault event in terms of the fault tree structure. This provides a basis for managers to prioritise the preventive and corrective measures that would be taken.  
FTA also allows for quantitative analysis and system evaluation. It identifies key causal factors and provides data support for the development of targeted management measures. The results of this analysis help managers identify problems timely and carry out targeted prevention and rectification, thereby improving the safety and reliability of complex lift systems.  
(1) The qualitative analysis of the fault tree includes the minimum cut set and the structural importance. The relevant Equation (1) is as follows:  
\[I_{\varnothing(i)}=\frac{1}{k}\sum_{j=1}^{n}\frac{1}{n_{j}}\big{(}j\in k_{j} \big{)} \tag{1}\]  
where \(I_{\varnothing(i)}\) is the structural importance, \(k\) is the total number of minimum cut sets, \(k_{j}\) is the \(j\)th minimum cut set, and \(n_{j}\) is the number of BEs in the \(j\)th minimum cut set.  
(2) The quantitative analysis of the fault tree includes the probability importance coefficient and the relative probability importance coefficient. The relevant Equations (2) and (3) are as follows:  
\[I_{p(X_{i})}=\frac{\partial P(T)}{\partial P(X_{i})} \tag{2}\]  
\[I_{c(X_{i})}=\frac{P(X_{i})}{P(T)}\,I_{p(X_{i})} \tag{3}\]  
where \(I_{p(X_{i})}\) is the probability importance coefficient, \(I_{c(X_{i})}\) is the relative probability importance coefficient, \(P(T)\) is the probability of the TE, and \(P(X_{i})\) is the probability of the occurrence of the BE \(X_{i}\).
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 4 Methodology Based on Cooperative Game Model for Platoon
### Model Validation  
We are trying to validate the performance of the proposed method. It will take seconds to recover the stable state from the disturbance, and maintain a new stable state. However, the described progress is determined by the control parameters of spacing policy and motion controller. With the numerical simulation, the relationships between final stable states and parameters is shown in Figures 5-7.  
From these images, we can see that the final following error of space and velocity is directly influenced by the control parameters. The color denotes the value of the error which is basically divided into 2 part according to \(\omega_{c}\). The error is near zero while \(\omega_{c}\) >1, and hardly affected by other parameters. It is consistent with the analysis in previous section. However, Sharpley value leads to two other unstable regions, as shown in Figure 6. The stable region and unstable regions are separated by red rectangle which is not parallel to the planes of the coordinate system. It reveals that the unstable regions have a strong correlation with the control parameters: As \(\eta\) increases, the following effect passes from the unstable region to the transition zone and gradually reaches the stable region; as \(\omega_{c}\) decreases, the final error also gradually decreases. Through numerical simulation, the results show that the theoretical analysis on stability is effective, and it has the significance of the parameter selection in the simulation.  
Figure 5: The relationships of the stable states and control parameters in flock’s model: (**a**) Spacing following error; and, (**b**) The velocity following error.  
Figure 6: The relationships of the stable states and control parameters in Shapely value and \(\tau\) value policies: (**a**) Spacing following error; and, (**b**) The velocity following error.
## 3 Results and Discussion  
### Morphology Analysis  
Figure 2 shows SEM images of the cryo-fractured surfaces of the 5 wt.% \(\mathrm{MWNT}/\mathrm{NR}\) composite, and the excellent adhesion interfaces of the bilayer composite. Because CNTs exist as bundles by van der Waals forces, a well-dispersed state must be obtained when used as a conductive filler in composites. Because obtaining well-dispersed high contents of MWNTs in the composite is more challenging than it is with low contents, measuring  
Figure 1: Scheme of the fabrication method of the \(\mathrm{MWNT}/\mathrm{NR}\) single composite and the \(\mathrm{MWNT}/\mathrm{NR}\) hybrid structure.  
the dispersion state of high contents is essential. Therefore, we measured the dispersion state image of the 5 wt.% MWNT/NR composite at low and high magnifications, as shown in Figure 2a,b, respectively. As shown in Figure 2a,b, we obtained uniformly dispersed MWNTs in the composite by ultrasonication. Ultrasonication is a useful method for dispersing nanofillers in a matrix in a solution process [30; 31]. Therefore, our sonication operating conditions (200 W, 2-step process) were excellent for dispersing MWNTs in the NR matrix.  
We then measured the adhesion between the NR layer and the 5 wt.% MWNT/NR composite using SEM images. Because the absence of cracks on the interface after stretching indicates that the NR and MWNT/NR layers are compatible, a tensile broken sample was prepared. As shown in Figure 2c, there were no cracks at the interfaces, indicating that adhesion between the homogenous polymer systems was successful. For precise analysis, a high-magnification image of the interface was measured, as shown in Figure 2d.  
Furthermore, the thicknesses of the MWNT/NR composite and pure NR regions were calculated to be -500 \(\upmu\)m.
## 3 Results and Discussion
### Expansion Ratio  
Expansion is a main characteristic of extruded products, like those which are attractive to consumers as well puffed [23]. During extrusion, the effect of high expanded products with low density is looked for. The response surfaces (Figure 2) showed that the feed moisture content and working screw speed negatively influenced negatively the \(ER\), and the feed moisture had the biggest effect among the three factors investigated. The amount of chokeberry pomace had an insignificant effect on the \(ER\) in this range of the change in amount (Figure 1). [PERSON] et al. [24] established a sectional extrusion ratio between 0.85 and 1.95 for pure chokeberry pomace with co-rotating twin screw extruder. Our experimental values are higher (between 1.51 and 2.74), which could be explained by the presence of rice in the raw material used for extrusion. The decrease in the feed moisture provokes a decrease in the level of starch gelatinization, and thus the expansion decreases [22]. [PERSON] et al. [25] found that low feed moisture led to increased shearing rate and increased extrusion duration, thus leading to increased starch gelatinization and expansion. The increase in the working screw speed led to a reduction of the sectional expansion of pure chokeberry pomace [24]. Our experimental data are in accordance with these results, which could be explained by the reduced duration of the product in the extruder [26]. [PERSON] et al. [27] also established that the sectional expansion index decreased with increasing screw speed of the co-rotating screw extruder, and an increase in chokeberry pomace in the mixture based on corn starch resulted in a decrease in the sectional expansion index. The decrease in the sectional expansion index with the increase in the amount of pomace was also established for other fruit pomaces [28; 29; 30; 31; 32]. [PERSON] et al. [33] also found a decrease in the sectional expansion index when the barley-tomato pomace level increased. The high feed moisture content of the raw material limited the evaporation, which resulted in a product with a lower expansion and higher density [34]. Figure 2 also shows that, at a low level of feed moisture content, the effect of the working screw speed was insignificant, but at a high level of the same factor a clearly manifested decrease in the expansion was observed.
## 3 Materials and Methods  
### Hardware and Data Acquisition  
The video data for this investigation were captured by BeePi monitors, multi-sensor EBM systems we designed and built in 2014 [8], and have been iteratively modifying since then [7; 22]. Each BeePi monitor (see Figure 1) consists of a raspberry pi 3 model B v1.2 computer, a pi T-Cobbler, a breadboard, a waterproof DS18B20 temperature sensor, a pi v2 8-megapixel camera board, a v2.1 ChronoDot clock, and a Neewer 3.5 mm mini-lapel microphone placed above the landing pad. All hardware components fit in a single Langstroth super. BeePi units are powered either from the grid or rechargeable batteries.  
BeePi monitors thus far have had six field deployments. The first deployment was in Logan, UT (September 2014) when a single BeePi monitor was placed into an empty hive and ran on solar power for two weeks. The second deployment was in Garland, UT (December 2014-January 2015), when a BeePi monitor was placed in a hive with overwintering honeybees and successfully operated for nine out of the fourteen days of deployment on solar power to capture \(\approx\)200 MB of data. The third deployment was in North Logan, UT (April-November 2016) where four BeePi monitors were placed into four beehives at two small apiaries and captured \(\approx\)20 GB of data. The fourth deployment was in Logan and North Logan, UT (April-September 2017), when four BeePi units were placed into four beehives at two small apiaries to collect \(\approx\)220 GB of audio, video, and temperature data. The fifth deployment started in April 2018, when four BeePi monitors were placed into four beehives at an apiary in Logan, UT. In September 2018, we decided to keep the monitors deployed through the winter to stress test the equipment in the harsh weather conditions of northern Utah. By May 2019, we had collected over 400 GB of video, audio, and temperature data. The sixth field deployment started in May 2019 with four freshly installed bee packages and is still ongoing as of January 2021 with \(\approx\)250 GB of data collected so far. In early June 2020, we deployed a BeePi monitor on a swarm that made home in one of our empty hives and have been collecting data on it since then.  
We should note that, unlike many apiarists, we do not intervene in the life cycle of the monitored hives in order to preserve the objectivity of our data and observations. For example, we do not apply any chemical treatments to or re-queen failing or struggling colonies.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion  
### Implementation Details and Experimental Setup  
The dataset employed for this research work comprises 46,026 Roman Urdu-English social media code-mixed comments annotated as \"Offensive\" and \"Not Offensive\". The dataset was hand-annotated by three bilingual annotators with a Cohen's Kappa coefficient of 0.86, thus ensuring annotation consistency. The data were split 80/20 train/test and five-fold cross-validated at the time of model training.  
The preprocessing chain involved translation of Roman Urdu to English via Google Translate API, then lowercasing the text, then removing all the punctuation, then removing all the stopwords, then lemmatization, and finally spell correction. Three embeddings (TF-IDF, Word2 Vec, and FastText) were applied according to the classifier. Traditional ML models (SVM, LR, RF, NB, KNN, GBM) were trained in Scikit-learn with grid search-based hyperparameter optimization. The network architectures (CNN, RNN, LSTM, GRU) were programmed with Keras and included early stopping and dropout regularization. We fine-tuned LLaMA 3, LLaMA 2, and Mistral using QLoRA with PEFT and bitsandbytes libraries for LLMs. Decoder-only models were fine-tuned from prompt templates and trained with a binary classification head over the last token. We computed the evaluation metrics (Precision, Recall, F-score).
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 1 Introduction
This study aims to highlight the effects of particle hydrophobicity on the water retention curves of different materials, including coal. This fundamental level research may allow for deeper understanding of the retention properties of coal and coal tailings, providing more information for their management. To clearly outline how hydrophobicity affects water retention, this study compares the measured WRCs of two soil materials with hydrophilic particles (silica sand and crushed rock) and two counterparts with hydrophobic particles (hydrophobic sand and crushed coal). The goal is to compare the WRCs of natural sand and hydrophobized sand (where the only difference is the degree of hydrophobicity) and compare the WRCs for the crushed materials (where the differences are the nature of the particles and the hydrophobicity). The WRCs of several sand mixtures with varying proportions of hydrophobized particles are also considered, allowing insight into how the proportion of hydrophobic particles in the soil affects the WRC. Furthermore, the crushed coal and rock materials were separated into different particle size fractions with the WRCs measured for each to observe how hydrophobicity affects the WRCs for different-sized particles. To ensure that any differences seen in the WRCs are due to hydrophobicity, comparisons are only made between materials that have a similar particle size distribution (PSD), particle shape and void ratio. A simple model to predict the WRC is applied to the sand mixtures with the objective of investigating how a WRC model may be modified to capture the WRC of hydrophobic materials. The wetting behavior of hydrophilic and hydrophobic materials was also observed qualitatively at a particle scale using an environmental scanning electron microscope, which allows the condensation of water droplets on particle surfaces to be observed.
## 6 Results and Discussion
### Application of Using Proposed System in Alexandria City and Srinagar City  
As Alexandria city has clearer weather than Srinagar city, it can achieve longer transmission distance in FSO. Figures 10 and 11 show the maximum propagation range that all channels achieved in Alexandria city and Srinagar. It is visible in Figure 10 that the system can propagate up to 1400 m in Alexandria city, which is 400 m longer than Srinagar city, and that is clear in Figure 11.  
Table 8 summarizes the _log_(BER) values at maximum computed FSO link for our proposed model in Alexandria city at 1400 m and in Srinagar city at 1000 m.  
Variation of BER versus received optical power (ROP) under different foggy, dusty, and Alexandria and Srinagar cities' weather conditions are depicted in Figure 12. One can observe that as ROP increases, the BER improves.  
Figure 11: _Log_(BER) versus FSO link for Srinagar, India.  
Figure 10: _Log_(BER) versus FSO link for Alexandria, Egypt.  
Table 9 summarizes the comparison between different multiplexing techniques used in previous works and our present work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Schemels**} & \multirow{2}{*}{**OAM Beams**} & \multicolumn{2}{c}{_log(BER)_} \\ \cline{3-4}  & & **Alexandria City (1400 m)** & **Srinagar City (1000 m)** \\ \hline Channel \#1 & & \(-\)25.18 & \(-\)25.61 \\ \hline Channel \#2 & \(LG_{0,0}\) & \(-\)5.29 & \(-\)5.46 \\ \hline Channel \#3 & & \(-\)9.72 & \(-\)9.41 \\ \hline Channel \#4 & & \(-\)24.96 & \(-\)25.25 \\ \hline Channel \#5 & \(LG_{0,10}\) & \(-\)5.31 & \(-\)5.22 \\ \hline Channel \#6 & & \(-\)9.78 & \(-\)9.21 \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Log_(BER) values at maximum computed FSO link for our proposed model in Alexandria and Srinagar cities.  
Figure 12: _Log_(BER) versus ROP under foggy, dusty, and Alexandria and Srinagar cities’ weather conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**References**} & [27] & [52] & [53] & **Present Work** \\ \hline
**Technique** & NRZ-OAM-FSO & FSO & MDM-Ro-FSO & OAM-OCDMA/FRS-FSO \\ \hline
**Laser input power** & 10 dBm & 22 dBm & Not specified & 15 dBm \\ \hline
**Number of channels** & 4 & 1 & 3 & 6 \\ \hline
**Overall capacity** & 40 Gbps & 2.5 Gbps & 12.5 Gbps & 60 Gbps \\ \hline  & LF: 0.48 km & & & LF: 0.6 km \\  & MF: 0.375 km & & & MF: 0.425 km \\
**Similar weather conditions** & HF: 0.32 km & LD: 1 km & LF: 0.6 km & \begin{tabular}{c} LF: 0.6 km \\ MF: 0.34 km \\ LD: 0.31 km \\ MD: 0.13 km \\ HD: 0.064 km \\ \end{tabular} & \
\begin{tabular}{c} LF: 0.6 km \\ MD: 0.2 km \\ HD: 0.02 km \\ \end{tabular} &
\begin{tabular}{c} LF: 0.6 km \\ MF: 0.42 km \\ HF: 0.34 km \\ HD: 0.315 km \\ HD: 0.105 km \\ HD: 0.4 km \\ \end{tabular} \\ \hline
**Cities** & - & - & - &
\begin{tabular}{c} Alexandria, Egypt. \\ Srinagar, India. \\ \end{tabular} \\ \hline
**Authenticated** & No & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison between different multiplexing techniques used in previous work and present work.
## 2 Materials and Methods
### Instruments  
_E4 wristband:_ The E4 wristband is an empathic band that offers real-time physiological data through four sensors. (1) an electrode for electrodermal activity (EDA), (2) a 3-axis accelerometer, (3) a temperature sensor, and (4) a photoplethysmograph (PPG) to measure blood volume pulse (BVP) from which it derives HR and the inter-beat interval (IBI) [20].  
_Rated Perceived Exertion (RPE) Scale:_ The perceived exertion scale is a scale that allows measurement of the level of intensity of physical activity. In this study, we used the OMNI rating of perceived exertion [21]. Previously, before participating in the VR game, all participants were individually instructed on the specifics of the OMNI scale, as well as when they were asked individually. The researcher interviewed each participant right after each VR game using the OMNI picture system, which clearly elucidates the different levels of effort and the different possible response options (with 0 indicating a minimum response and 10 indicating a maximum response)  
_System Usability Scale (SUS):_ The usability of the VR games was assessed using the European Portuguese Validation of the System Usability Scale (SUS) [22]. This self-administered instrument allows the evaluation of the usability of the system as well as the user interface. The criteria help to assess the effectiveness, efficiency, and satisfaction with the system. The scale contains 10 questions rated from 1 to 5, with 1 being strongly disagree and 5 strongly agree.  
_Intrinsic Motivation (IMI):_ The Portuguese version of the Intrinsic Motivation Inventory was applied to assess the intensity of intrinsic motivation concerning the performance of VR games [23]. Intrinsic motivation refers to behavior driven by internal rewards. In other words, the motivation to engage in a behavior arises from within the individual because it is naturally satisfying to the behavior [24; 25]. The score was obtained by averaging the quotation of the items for each dimension. A \"Global Intrinsic Motivation\" may be considered, with higher scores indicating a greater intrinsic motivation to perform the target activity.  
_Immersive Tendencies:_ The level of immersion in the VR games system was assessed using the Immersive Tendencies Questionnaire [26]. This instrument comprises 18 items and utilizes a scale of 1 (never) to 7 (often), organized into three subdimensions, namely, involvement (seven items), attentional focus (seven items), and tendency to play video games (two items). Two items (item 9 and item 13) are not included in the subdimensions.  
_Presence:_ The Witmer-Singer Presence Questionnaire was used to characterize the participants' experience in the VR games environment [26; 27]. The Presence Questionnaire is a questionnaire that measures the extent to which a user feels present in the virtual experience, consisting of 24 questions divided into four components: involvement, sensory fidelity, adaptation/immersion, and interface quality. This instrument uses a scale of 1 to 7, with 1 being not convincing and 7 very convincing.