esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress-Strain Model for Freezing Silty Clay under Frost Heave Based on Modified [PERSON]'s Equation
## 5 Mechanical Stress-Strain Relationships in the Freezing Soil Layers
### Decomposition of Potential Frost Heaving Stress  
Next, \(\sigma_{\text{e,i}}\) can be decomposed into two components and is expressed as:  
\[\sigma_{e,i}=\sigma_{v}+\sigma_{f,i} \tag{15}\]  
Figure 15: Potential frost heaving stress vs. temperature gradient and temperature at the top of the \(i\)th freezing soil layer.  
where \(\sigma_{\text{f},i}\) is the effective frost heaving stress that reflects the actual amount of frost heave.  
Accordingly, \(\varepsilon_{i}\) can be divided into two parts, as follows:  
\[\varepsilon_{i}=\varepsilon_{r,i}+\varepsilon_{f,i} \tag{16}\]  
where \(\varepsilon_{r,i}\) is defined as the resilient strain, which is generated due to \(\sigma_{\text{v}}\) in equilibrium with a portion of \(\sigma_{e,i}\). Here, \(\varepsilon_{f,i}\) is termed as the real \(\varepsilon\) induced only by \(\sigma_{f,i}\).  
Based on this concept, the resilience modulus (\(E_{r,i}\)) is expressed as:  
\[E_{r,i}=\frac{\sigma_{\text{v}}}{\varepsilon_{r,i}} \tag{17}\]  
Similarly, the compression modulus (\(E_{\text{c,i}}\)), which only relates to \(\sigma_{f,i}\), can be defined as follows:  
\[E_{\varepsilon,i}=\frac{\sigma_{f,i}}{\varepsilon_{f,i}} \tag{18}\]  
Thus, the following formula can easily be obtained by combining Equations (16)-(18):  
\[\varepsilon_{i}=\frac{\sigma_{\text{v}}}{E_{r,i}}+\frac{\sigma_{f,i}}{E_{ \varepsilon,i}} \tag{19}\]  
Equation (15) is then substituted into Equation (19):  
\[\varepsilon_{i}=\frac{\sigma_{\text{v}}}{E_{r,i}}+\frac{\sigma_{e,i}-\sigma_{ \text{v}}}{E_{c,i}}=\frac{\sigma_{e,i}}{E_{c,i}}-\left(\frac{1}{E_{c,i}}- \frac{1}{E_{r,i}}\right)\sigma_{\text{v}} \tag{20}\]  
By defining:  
\[\frac{1}{E_{\text{eq},i}}=\frac{1}{E_{c,i}}-\frac{1}{E_{r,i}} \tag{21}\]  
Equation (20) can be rewritten as:  
\[\varepsilon_{i}=\frac{\sigma_{e,i}}{E_{c,i}}-\frac{\sigma_{\text{v}}}{E_{ \text{eq},i}} \tag{22}\]
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 3 Basic System Design
### Insertion  
Within a sector, data are further distributed among nodes according to their distance from the SH. In order to do so, a sector is divided into segments. Figures 6 and 7 and Table 2 illustrate the idea of sector segmentation. Given a _kth_ sector containing \(M_{k}\) member nodes, the \(SH_{k}\) first sorts all member nodes based on RSSI in ascending order. The member nodes are then divided into \(r\) segments. Each segment forms a circle, denoted by \(B_{(X,Y)}\left(r_{i}\right)\), where the center of the circle is (X, Y) with radius \(r_{i}\). (X, Y) is the geographic co-ordinates for \(SH_{k}\). The number of segments depends on the WSN application, sector size and the number of member nodes in each sector. Thus the set of sensors that are within a Euclidean distance \(r_{i}\) from (X, Y) form the segment defined by:  
\[B_{(X,Y)}(r_{i})=\left\{SensorsCoordinate\left(x,y\right):\left|\left(X,Y \right),\left(x,y\right)\right|\leqslant r_{i}\right\} \tag{12}\]  
\[\beta_{k}=\left(\mathrm{P}_{k+1}-\mathrm{P}_{k}\right)/r,\ \ \ \ \ 1\leqslant k\leqslant S \tag{13}\]  
\[\left\{\mathrm{P}_{M_{K}(i)}\right\}_{k=1}^{S}=\left\{\begin{array}{ll} \mathrm{P}_{k},&i=0\\ \mathrm{P}_{k}+\beta\times i,&0<i<r\\ \mathrm{P}_{k+1},&i=r\end{array}\right. \tag{14}\]  
By Equations (13) and (14), the pivot points of \(r\) segments within the _kth_ sector are calculated. An event with hash value, denoted by \(h\), is stored in a member sensor node of _ith_ segment where \(\mathrm{P}_{M_{K}(i)}\leqslant h\leqslant\mathrm{P}_{M_{K}(i+1)}\). In order to balance the load, data are distributed among the nodes inside a segment in a round robin fashion (see Algorithm 2).  
```
Input:segment[i] (a data structure containing member node ID and tally to count the number of packets stored in this member node) Output: return the target Member Node ID.
```  
```
1:sort segment[i] in ascending order based on segment[i].tally
2:segment[i].tally = segment[i].tally +1
3:memberNodeId = segment[i].ID
4:return memberNodeId
```  
**Algorithm 2**Search_Target_Node (_segment[i]_), implemented at each \(SH\) node.  
Figure 6: Formation of Segments inside a Sector.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 4 Experimental Results and Analysis
### Analysis of Realization Results
#### 4.3.3 Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Improvement Strategies**} & \multirow{2}{*}{**Parameters/**} & \multirow{2}{*}{**FFS/**(**Frame/s)**} & \multirow{2}{*}{**mAP/\%**} & \multicolumn{6}{c}{**Ap/\%**} \\ \cline{2-13}  & **A** & & & & & **B** & **C** & **D** & **10\({}^{6}\)** & **(Frame/s)** & **Cr** & **In** & **Pa** & **Ps** & **RS** & **Sc** \\ \hline YOLOv8+ & & & & & & **3.01** & 157.9 & 76.6 & 40.7 & 79.8 & 92.3 & 80.5 & 70.0 & 96.0 \\ YOLOv8+ & \(\surd\) & & & & 4.87 & 181.8 & 77.4 & 43.4 & 80.6 & 91.1 & **85.1** & 69.3 & 94.9 \\ YOLOv8+ & & \(\surd\) & & & 3.35 & **225.0** & 77.8 & 44.2 & **85.0** & 92.1 & 83.9 & 65.3 & 96.1 \\ YOLOv8+ & \(\surd\) & \(\surd\) & & & 5.21 & 206.9 & 78.5 & 43.7 & 84.6 & 93.2 & 83.8 & 68.4 & **97.2** \\ YOLOv8+ & \(\surd\) & \(\surd\) & & & 5.28 & 178.2 & 79.5 & 43.3 & 82.7 & **95.3** & 83.2 & **78.2** & 94.2 \\
**YOLOv8+ (ours)** & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 5.20 & 219.5 & **79.8** & **50.1** & 82.6 & 93.4 & 84.8 & 72.3 & 95.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation experiment. Figure 12: P-R curve of YOLOv8n algorithm. The comparison between Figures 15 and 16 shows that after adding the large detection head, the model can extract features from a broader field of view. Except for patches, the missed detection rate of other defects is reduced, effectively demonstrating that the large detection head can lower the missed detection rate. Figure 14: Comparison chart. Figure 13: Part of the visualization results. (**a**) Original Figure, (**b**) YOLOv8, (**c**) YOLOv8-TLC.
## 4 Numerical Examples
### Crack Propagation under Dynamic Loading
#### 4.2.2 Dynamic Shear Loading of Kalthoff Experiment  
The treated example for a dynamic fracture is related to a doubly notched specimen subjected to an impact load, commonly referred to as the Kalthoff-Winkler experiment [45], which has already been investigated by many other researchers [46; 47; 48; 49; 50; 51; 52; 53; 54]. The specimen geometry is shown in Figure 12, and the impact loading is generated by a projectile. The symmetry conditions are adopted to reduce the computational cost.  
The following velocity \(\
u=\)\(\left\{\begin{array}{ll}\frac{\
u}{\mathrm{t}}\
u_{0}&\mathrm{t}\leq\mathrm{t}_{0} \\ \
u_{0}&\mathrm{t}>\mathrm{t}_{0}\end{array}\right.\) was applied with \(\mathrm{t}_{0}=1\) us and \(\
u_{0}=16.5\) m/s.  
The material parameters were as stated in [42]: E = 190 GPa, \(\rho=8\times 10^{-6}\) kg/mm\({}^{3}\), \(\
u=0.3\) and G\({}_{\mathrm{c}}=22.2\times 10^{-3}\) kN/mm. The length scale parameter \(\eta\) was fixed as 0.4 mm and the time step \(\Delta\)t was defined as 0.04 us. The fracture began to propagate at 24 us. The angle between the horizontal axis was 68\({}^{\circ}\), which corresponded well with the experimental results of [PERSON] [48] and the numerical results of [PERSON] et al. [54].  
Then, the effect of G\({}_{\mathrm{c}}\), on the crack path, was tested. The phase field variable distributions at 90 us for G\({}_{\mathrm{c}}=5\times 10^{-3}\), G\({}_{\mathrm{c}}=10\times 10^{-3}\), G\({}_{\mathrm{c}}=22.2\times 10^{-3}\) and G\({}_{\mathrm{c}}=30\times 10^{-3}\) kN/mm are displayed in Figure 13. For smaller G\({}_{\mathrm{c}}\), more complicated crack schemes were found. When G\({}_{\mathrm{c}}=5\times 10^{-3}\) and \(10\times 10^{-3}\) kN/mm, crack branching occurs. The reflected wave caused secondary cracks in the lower right-hand corner of the specimen, as shown by [PERSON]  
Figure 11: Numerical crack paths for dynamic tensile tests for: (**a**) h = 5 mm; (**b**) h = 6 mm; (c) h = 9 mm.  
Figure 12: Geometry of the specimen and adopted boundary conditions: [PERSON] experiment.  
et al. [54]. However, only one crack was observed for a greater G\({}_{\rm c}\). The principal crack pattern, which began from the point of the pre-existing crack, was unaffected by changes in G\({}_{\rm c}\).  
Eventually, higher impact velocities of v\({}_{0}\) = 25, 40 and 60 m/s were examined. The crack paths are given in Figure 14. As the impact velocity increased, earlier and faster crack formation and greater crack branching were observed.  
As a result, the developed model can easily capture multiple dynamic crack branching with appropriately refined meshes. When several small fractures exist along a major domi  
Figure 14: Crack paths at 90 \(\upmu\)s for different velocity values.  
Figure 13: Crack paths at 90 \(\upmu\)s for different G\({}_{\rm c}\) values.  
nant one, this result is intriguing for the study of dynamic crack instability problems [55]. It is worth mentioning that the tension-compression slit model [56] affects the development of the crack patterns.
## 3 Results
### Risk of Bias Assessment (ROBINS-E)  
The risk of bias across non-randomized studies was assessed using the _Risk of Bias In Non-randomized Studies of Exposures_ (ROBINS-E) tool. The results revealed considerable methodological limitations, particularly in controlling for confounding factors and in participant selection. A small subset of studies received an overall rating of _\"Some concerns\"_, indicating a relatively low risk of bias and appropriate control across most domains [26; 29; 37; 45; 68; 74; 75; 83; 99]. These studies generally demonstrated adequate classification of exposures, low levels of missing data, and well-defined outcome assessments.  
However, the majority of studies were judged to have a high overall risk of bias. The domains most commonly affected were confounding and participant selection. Several studies were classified as having a very high or high risk of bias due to insufficient adjustment for key confounding variables [25; 29; 36; 53; 61; 75; 82; 89]. Likewise, issues with participant selection were observed in studies that lacked adequate reporting or had flawed selection mechanisms, leading to a high risk of bias [31; 38; 46; 62; 69; 77; 84; 90].  
Regarding the classification of exposures, many studies performed adequately, receiving low risk ratings [26; 37; 54; 68; 88; 96], though some raised concerns or were rated as high risk [58; 65; 80]. The outcome measurement domain also showed variability. Some studies were rated as low risk, with clearly defined and validated outcome criteria [29; 44; 60; 99], while others were rated as high risk due to subjective measures or lack of assessor blinding [43; 66; 85; 88; 92]. A detailed domain-specific and overall risk of bias summary is provided in Table S4: Risk Of Bias In Non-randomized Studies of Exposures Assessment (_ROBINS-E_).
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 1 Introduction
### Objective and Contribution  
In this research, our objective was to present a novel sentiment analysis approach that strengthens not only accuracy but also ethical decency. We are particularly interested in the possible risks of unfair or inaccurate forecasts in contexts where these could lead to very significant harm, for example, sexuality, religious belief, and disability.  
The key contributions of our work are as follows.  
1. Development of Bias-BERT and bias-aware loss function: A novel sentiment analysis model, named Bias-BERT, is tailored to handle biased sentiment prediction; there is a special loss function for this model, which includes a bias-aware term based on KL divergence. These target class distributions are specially created during the model training for it to learn and reduce biases towards those classes, so that predictions can be fairer.
2. Real-time user feedback integration: Our unique approach integrates the real-time user feedback loop. This enables users to help provide feedback on the accuracy of the model as well as any biases that may be present in the predictions. It relays this feedback to the other models, and they all learn together while maintaining a higher quality model that accounts for any bias.
3. Comprehensive bias evaluation and ethical considerations: We also achieved an unbiased evaluation of biased predictions using the same sets of test examples by validating across multiple demographic groups including but not limited to race, gender, and sexuality; comparative experiments demonstrate that debiasing does indeed reduce bias significantly from the baseline. We also look at the ethical considerations when utilizing bias-aware sentiment models and provide best practices for responsible and equitable use.  
4. Contribution to fair AI research: Our research contributes towards the broader movement for fair AI by offering a blueprint for the development of bias-aware models. Although we are centered around sentiment analysis, this framework is versatile and works for a wide range of NLP tasks, contributing to fairness in AI applications across numerous industries.  
The real significance of this research lies in its ability to advance sentiment analysis by tackling the key challenge of bias. By creating a model that prioritizes both accuracy and fairness, our study hopes to contribute to the development of more equitable and trustworthy sentiment analysis tools. These tools can be used in a wide range of fields, from helping businesses better understand their customers to helping researchers in the social sciences.  
The rest of this paper is structured as follows. In Section 2, we review existing work in sentiment analysis and bias mitigation. Section 3 explains our proposed methodology. Section 4 presents the experimental setup and results, followed by a discussion of ethical considerations in Section 5. Finally, Section 6 concludes the paper and highlights potential directions for future research.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 4 Modeling Channel Occupation Using SCHC
### LoRa Frame Time Calculation  
A frame is the protocol data unit (PDU) of layer two in the OSI model: a LoRa frame. Remember that the protocol layer structure is IPv6 over SCHC, SCHC over LoRaWAN, and LoRaWAN over LoRa. The frame time is the time it takes for the transmitter to send a frame over the wireless medium. The frame time calculation is based on the sum of the Time on Air (ToA) defined by Semtech [13] and the additional processing time \(t_{proc}\) used by the end device (LoPy4, WisDuino, etc.) to send a LoRa message to the network. Equation (4) shows how the SCHC Fragment and SCHC ACK request times are calculated; both depend on the time on air and the processing time.  
\[\begin{split} T_{F}=\mathit{To}A_{SCHC\_fragment}+T_{frag\_proc}\\ T_{AR}=\mathit{To}A_{SCHC\_ackreq}+T_{ackreq\_proc}\end{split} \tag{4}\]  
When a user executes the send(msg) method, the program not only transmits the message, but also performs other tasks before returning. The code that describes these other tasks is established within the send method and cannot be externally changed. \(t_{proc}\) time is calculated from the time used to execute the send method minus the time it takes to send in the wireless medium (ToA). \(t_{proc}\) time is mainly an execution of other parts of the code necessary to send a frame in the wireless medium (e.g., build the frame to be sent).  
The time on air bases its calculation, among other things, on the size of the payload carried in the message LoRaWAN(tm). Therefore, the frame time will be different if a Regular SCHC Fragment, an All-1 SCHC Fragment, or an SCHC ACK request is sent.
## 4 Experimental Verification and Analysis
### Analysis of Experimental Results
This is due to the camera installation error, unevenness, experimental operation and other errors, resulting in the inconsistency of density. Further analysis shows that the correlation coefficients of the fitting results in the length and width directions are also inconsistent. This is because the fixed focus lens is used in this paper. When the workpiece changes, the impact of \"defocus blur\" will inevitably appear in the imaging delay of the camera. However, the error caused by this reason is within the tolerance range of the system since the workpiece is always captured in a certain height range in practical application. Moreover, the lenses used now have a certain depth of field (DOF), so there is no need to make up for the error caused by defocus blur in practical application. From Figure 13, the correlation coefficient of the fitting result in the width direction is large, and the pixel density in the width direction is greater than that in the length direction. In order to better reflect the relationship between pixel density and calibration height, this paper takes the fitting result in the width direction as the pixel density \(Pr\) of the workpiece at height \(H\). Namely  
\[P_{r}=0.0004\times H+1.4317\]  
It can be seen from Equation (11) that there is still a highly linear relationship between the parameters of the rigid transformation matrix from the image coordinate system to the robot coordinate system and the calibration height under multiple errors. In the eye-to-hand hand-eye system, the parameters of \(\frac{R}{C}M\) matrix in the calibration system at any height can be calculated quickly according to the fitting results. The measured result is the display data of the teaching pendant at a certain height of a point on the calibration board measured according to the four-point calibration method shown in Figure 9. The result is to calculate the \(\frac{R}{C}M\) matrix under the calibration according to Equation (11) and substitute it into Equation (10) to calculate the coordinate value. The verification results are shown in Table 7.  
As can be seen from Table 7, firstly, the rigid transformation matrix is calculated according to the linear relationship between the transformation matrix parameters and the calibration height, and then the transformation matrix is used to calculate the position of the robot's target point under the visual guidance. The error is less than 0.08%, so this linear relationship is reasonable and effective in the calibration system. Figure 13: Relationship between length–width pixel ratio and calibration plate height.
# The Effects of Methane Storage Capacity Using Upgraded Activated Carbon by KOH
## 2 Materials and Methods  
### Preparation of Activated Carbons  
Two types of commercial ACs (wood: JIG-SC-2040, Ja Yeon Science Ind. Co., Chulwon, Korea; coconut: LGL-100, Daelim Carbon Ind. Co., Seoul, Korea) were utilized as precursors for this study. To upgrade the low-grade AC, 1 kg of AC was treated with potassium hydroxide (KOH, Samchun chemical, Seoul, Korea) and the mass ratio of AC:KOH was 1:2 under an inert environment. After the chemical activation, the average particle size of the upgraded ACs was around 500 \(\upmu\)m,The mixture was put into a tubular furnace under N\({}_{2}\) flow and heated to 1123 K at a rate of 10 K/min, and then held for 3 h, as described in a previous study [31]. After completion of the activation processes, the prepared AC was washed with deionized water until the pH was neutral, and was subsequently dried at 378 K. The commercial ACs based from wood and coconut were denoted as AC-WR and AC-CR, respectively. The chemically activated ACs using the commercial ACs were also denoted as AC-WA and AC-CA, respectively (AC: activated carbon, W: wood based, C: coconut based, R: raw/commercial materials, and A: activation).
# Survey on Modelling and Techniques for Friction Estimation in Automotive Brakes
## 1 Introduction
### Motivations  
It is worth noting that in recent decades phenomenological and empirical models, along with experimental techniques, have evolved compared to the first studies of [PERSON] more than 200 years ago. Friction modelling has undergone consistent development, mainly in applications involving small displacements and velocities where the static friction and pre-sliding displacement play crucial roles [26]. As an example, friction modelling has been a matter of study for control engineering due to the necessity of developing estimators to compensate for the friction forces of the electric machines for high-precision applications (e.g., haptic applications) [26; 27]. Indeed, the modelling and compensation of friction dynamics is a topic of interest for the machining and assembly industry, where high-accuracy positioning systems are much in demand.  
The main task of the BLCF model is to correlate the friction coefficient to system inputs such as the brake pressure, and to system states such as the brake temperature and disc speed. Friction is a very complex force to render; it depends on a large variety of parameters such as sliding speed [28], sliding acceleration [29], temperature, normal load [30], environment conditions [18; 31], surface finishing [32] and chemical composition [12]. With the aim of identifying a model capable of predicting the real BLCF, it is necessary to understand the phenomena affecting the brake operation. The performance of this task would represent an important advancement in the field of driver assistance systems for the enhancement of innovative functions such as conventional and regenerative brake blending in electric vehicles [33] and the improvement of well-known features such as wear and NVH (noise-vibration-harshness) [34]. A model capable of predicting the value of the BLCF could be useful to improve driving safety during emergency braking, and to enhance brake actuation during service braking.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 2 Materials and Methods
### Simulation Scenarios and Network Model  
As mentioned above, the process used three typical WSN scenarios to optimize the weights of the rules, which were also used by other clustering methods for comparison. The dimensions of the node deployment area were the same for the three variants: a square area of 100 \(\times\) 100 m\({}^{2}\), where nodes were randomly placed. The number of nodes deployed was fixed for all scenarios (100 nodes). The main difference between the three scenarios was the location of the BS, which can be observed in Figure 5. The three scenarios can be identified as follows:In addition, the network model used in this approach specifies that each node transmits and receives through a symmetric communication channel without interference. The BS, CH, and contributing nodes used time division media access (TDMA) to send messages in a single-hop transmission directly to the destination. All messages containing information from normal nodes to CHs and from CHs to BS used the minimum power required to reach the destination based on the information the BS knew about the networks. The power consumed by the BS was not considered, because it was assumed that the BS had an unlimited power source. In addition, all nodes had the same initial energy. Finally, the values of the parameters used in the optimization process are shown in Table 2. As can be seen, these parameters belong to the first-order radio model described in Section 2.3.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Initial energy of nodes & 0.5 J \\ Length of control message & 200 bits \\ Length of data message & 2000 bits \\ \(E_{elc}\) & 50 nJ/bit \\ \(E_{DA}\) & 5 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the setup parameters for the energy model.  
Figure 5: Simulation scenarios.