esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application in Determination of Rock-Mass Properties
### Hoek-Brown Constants and Tensile Strength of Rock Mass  
For the subsurface excavations design in hard rock, [PERSON] and [PERSON] introduced their empirical failure criterion, known Hoek-Brown failure criterion [99; 118]. The criterion for rock masses is accepted widely in a huge number of excavation projects worldwide [103]. The development history of this empirical failure criterion [119] shows that \(RMR\) served this criteria before replacing it with the \(GSI\) system [12] for the calculation of the rock-mass Hoek-Brown constants \(m\) and \(s\). The original Hoek-Brown failure criterion is shown in Equation (54) for intact rock and the generalized Hoek-Brown failure criterion for rock mass is expressed in Equation (55) [103].  
\[\sigma_{1}^{\prime}=\sigma_{3}+\sqrt{m_{i}\times\sigma_{3}\times\sigma_{c}+ \sigma_{ci}^{2}} \tag{54}\]  
\[\sigma_{1}^{\prime}=\sigma_{3}+\sigma_{ci}\times\bigg{(}m_{b}\times\frac{ \sigma_{3}^{\prime}}{\sigma_{c}}+s\bigg{)}^{a}, \tag{55}\]  
where \(m_{b}\) in the above equations is a reduced value for the rock mass of the material constant \(m_{i}\) and \(s\) and \(a\) are constants for the rock mass. The rock-mass constants \(m_{b}\) and \(s\) were expressed in terms of the basic \(RMR\) before the introduction of \(GSI\) in the Hoek-Brown criterion [22; 120], as shown in Equations (56)-(59).  
Disturbed rock masses:  
\[\frac{m_{b}}{m_{i}}=\exp\bigg{(}\frac{RMR_{b}-100}{14}\bigg{)} \tag{56}\]  
\[s=\exp\bigg{(}\frac{RMR_{b}-100}{6}\bigg{)} \tag{57}\]  
Undisturbed or interlocking rock masses:  
\[\frac{m_{b}}{m_{i}}=\exp\bigg{(}\frac{RMR_{b}-100}{28}\bigg{)} \tag{58}\]  
\[s=\exp\bigg{(}\frac{RMR_{b}-100}{9}\bigg{)} \tag{59}\]  
These rock-mass constants were also expressed in \(Q^{\prime}\) (\(Q\) without \(SRF\) and \(\mathrm{J}_{w}\)) value, as expressed in Equations (60) and (61) [104].  
\[\frac{m_{b}}{m_{i}}=0.135\times\big{(}Q^{\prime}\big{)}^{\frac{1}{3}} \tag{60}\]  
\[s=0.002\times Q^{\prime} \tag{61}\]  
By substituting \(\sigma_{1}\)=0 in the original Hoek-Brown Equation (54), the resultant equation, Equation (62), determines the \(\sigma_{t}\).  
\[\sigma_{t}=\frac{\sigma_{c}}{2}\times\bigg{(}m_{b}-\sqrt{m_{b}^{2}+4\times s} \bigg{)} \tag{62}\]This equation indirectly yields the \(\sigma_{tm}\) from the \(RMR\) and \(Q\) values, as \(m_{b}\) and \(s\) are functions of these systems.  
The direct method for the calculation of the \(\sigma_{tm}\) from the \(RMR\) value is given in Equation (63) [121].  
\[\frac{\sigma_{tm}}{\sigma_{t}}=\frac{RMR}{RMR+6(100-RMR)} \tag{63}\]
## 3 Results
### Cr (III) Adsorption Isotherms
Table 4 shows the \(\Delta\)G values obtained in the adsorption process of all the treatments at 30 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**WTOP**} & \multicolumn{4}{c}{**MTOP**} & \multicolumn{4}{c}{**OPTmW**} \\ \cline{3-10}  & & **0.91** & **1.78** & **2.72** & **0.91** & **1.78** & **2.72** & **0.91** & **1.78** & **2.72** \\ \hline \multirow{4}{*}{Freundlich} & KF & 0.0340 & 0.0318 & 0.0265 & 0.1551 & 0.1874 & 0.0726 & 0.0309 & 0.0204 & 0.1328 \\  & n & 0.9835 & 0.9504 & 0.9207 & 1.4019 & 1.3572 & 1.0936 & 1.1246 & 0.9192 & 1.3047 \\  & R2 & 0.9897 & 0.9974 & 0.9990 & 0.9843 & 0.9909 & 0.9800 & 0.9973 & 0.9694 & 0.9911 \\  & \(\Delta\)q, \% & 0.4123 & 0.3465 & 0.5436 & 1.6518 & 1.7081 & 0.7291 & 0.7770 & 2.1495 & 1.5222 \\ \hline \multirow{4}{*}{SIPS} & KS & 0.0021 & 0.0026 & 0.0021 & 0.0022 & 0.0028 & 0.0024 & 0.0008 & 0.0020 & 0.0009 \\  & qm & 39.5687 & 45.9814 & 48.9957 & 26.0913 & 30.0010 & 39.0051 & 32.5885 & 26.3521 & 55.1599 \\  & n5 & 2.531 & 2.5680 & 2.5339 & 1.6525 & 2.0878 & 2.7234 & 1.2414 & 1.9425 & 1.1245 \\  & R2 & 0.9809 & 0.9703 & 0.9558 & 0.9974 & 0.9864 & 0.9984 & 0.9995 & 0.9119 & 0.9935 \\  & \(\Delta\)q, \% & 1.7376 & 0.7235 & 0.3352 & 11.7912 & 1.0686 & 0.0803 & 58.4605 & 1.7788 & 51.6805 \\ \hline \multirow{4}{*}{RP} & KR & 10.1023 & 2.2159 & 1.7722 & 0.0374 & 0.0495 & 0.0477 & 0.0175 & 46.9522 & 0.0404 \\  & aR & 264.3194 & 48.2727 & 36.6792 & 0.0008 & 0.0009 & 0.0002 & 0.0002 & 0.1265 & 0.0005 \\ \cline{1-1}  & \(\beta\) & \(6.17\times 10^{-18}\) & \(1.19\times 10^{-17}\) & \(2.35\times 10^{-18}\) & 1.0000 & 0.9581 & 1.0000 & 1.0000 & \(2.96\times 10^{-18}\) & 1.0000 \\ \cline{1-1}  & R2 & 0.9896 & 0.9965 & 0.9968 & 0.9915 & 0.9947 & 0.9820 & 0.9985 & 0.9693 & 0.9933 \\ \cline{1-1}  & \(\Delta\)q, \% & 71.5062 & 2.9880 & 10.9911 & 13.1963 & 13.8905 & 12.7091 & 113.5888 & 26.9331 & 9.7678 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of isotherm models at different pH.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 4 Discussion
### Training  
In general, the training process of a human-doctor is different from the one of an ML-based model. People are presented with a limited number of images, usually unique and distinctive, whereas the ML model is fed repeatedly with the same, vast set of unstructured images.  
A few images were captured after the 5 th, 13 th, and 38 th epoch as presented in Figure 11 to provide a better insight into the model's training process. It may be noticed that the training process is strongly non-linear, e.g., after 13 epochs, the model starts to distinguish between histiocytoma and lymphoma. We also observed that the model sometimes temporally loses skills to detect objects to regain them a few epochs later in the training process. Therefore, picking a specific number of training epochs is not a straightforward task.  
It may be noticed that the model learns to localize objects before it actually can classify them correctly. The training is non-linear, which means that some valuable results may be obtained later on, after a few tens of epochs. Closer examination of Figure 11 allows us to notice that longer training tends to eliminate duplication of bounding boxes in histiocytoma and lymphoma cases. Additionally, a differentiation between the two is visible only in the later training stages.  
However, the case of mastocytoma is quite the opposite. Its regular shape can be easily explained to an inexperienced doctor, resulting in physicians' high detection performance. On the other hand, the model learns to classify it during the first few epochs, but only in the latter part of the training process starts to increase the detected frames.
# Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions
## 2 Data Preparation
Furthermore, on highways, most accidents occur in areas without physical isolation, whereas, on urban roads, accidents tend to happen in areas with isolation. For roadside protection, most accidents on highways occur in areas without protection, while on urban roads and expressways, accidents are more frequent in areas with protection. Regarding environmental conditions, visibility and lighting conditions also differ across road types. Most highway and expressway accidents occur in low visibility (under 200 m), while urban road accidents typically occur with normal visibility (over 200 m). In terms of lighting, the number of accidents during the daytime and nighttime is relatively similar for urban roads and highways, whereas, on expressways, most accidents occur at night. Regarding violations, the main violations contributing to accidents on urban roads include distracted driving, not yielding as required, drunk driving, pedestrian or non-motor vehicle violations, and traffic signal violations. On highways, the main violations are distracted driving, not yielding as required, not following lane regulations, drunk driving, and traffic signal violations. On expressways, not maintaining a safe distance in the same lane, distracted driving, vehicle faults or failure to meet technical standards, illegal parking, and overloading are the major violations leading to accidents. It is evident that distracted driving is a major contributing factor across all three road types, accounting for 40.4%, 22.1%, and 20.4% of accidents, respectively. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multicolumn{3}{c}{**Variable Frequency**} \\ \cline{2-4}  & **Urban Roads** & **Highways** & **Expressways** \\ \hline
**Violations** & & & \\ Not Following Lane Regulations & 14 & 59 & 18 \\ Not Maintaining Safe Distance in Same Lane & 4 & 17 & 122 \\ Illegal Parking & 7 & 25 & 57 \\ Not Yielding as Required & 65 & 64 & 8 \\ Vehicle Faults of Not Meeting Technical Standards & 5 & 38 & 60 \\ Violating Traffic Signals (Signs, Markings, etc.) & 38 & 45 & 18 \\ Illegal Overtaking, Reversing, Meeting, U-turning & 3 & 13 & 15 \\ Speeding & 9 & 13 & 8 \\ Overloading (Excessive Load, Height, Width, Length) & 12 & 38 & 41 \\ Drunk Driving & 45 & 47 & 40 \\ Unqualified Driver for Heavy Truck & 14 & 31 & 5 \\ Pedestrian or Non-motor Vehicle Violation & 41 & 19 & 15 \\ Distracted Driving & 206 & 122 & 108 \\ Other (natural disasters, accidental event) & 47 & 22 & 14 \\ \hline \hline \end{tabular}  
* Under expressway conditions, intersections represent the converging and diverging areas.  
\end{table} 
Table 3: _Cont._This analysis indicates that heavy truck accident characteristics vary by road condition, warranting separate modeling and targeted discussions for each road type.
# Slipping Trend Prediction Based on Improved Informer
## 4 Experiment
### Comparing Other Models  
Since models such as RNN and LSTM cannot predict multiple time steps in the future at the same time, in the test set, the next second was predicted, and a comparative test was carried out, as illustrated in Table 2. In the experiment, Informer and LSTM achieved high accuracy, but for slipping recognition, the recall rate is more important. Due to the multi-axle simultaneous prediction and the uneven proportion of slipping states, the recall rate is generally lower than the accuracy rate. In order to better measure the model in this task, this study calculated the F1 measure as the reference index.
## 3 Identification Scheme with Privacy-Preserving
### Identification of Single Secondary User
#### 3.2.2 Generalized Likelihood Ratio Test in Real Scenarios
\(\Lambda_{GLR}(\dot{M})\) is normalized to obtain the GLR \(\bar{\Lambda}_{GLR}(\dot{M})\) under hypothesis \(H_{0}\), then we have  
\[\bar{\Lambda}_{GLR}(\dot{M})=\frac{\Lambda_{GLR}(\dot{M})-\widetilde{\mu}_{0} ^{*}}{\sqrt{v_{0}^{*}}}, \tag{19}\]  
where \(\widetilde{m}_{0}^{*}\) and \(\widetilde{v}_{0}^{*}\) denote the expectation and variance of \(\Lambda_{GLR}(\dot{M})\), respectively. \(\widetilde{m}_{0}^{*}\) and \(\widetilde{v}_{0}^{*}\) can be obtained from parameter estimation [12] based on our hybrid fingerprint model. According to \(\bar{\Lambda}_{GLR}(\dot{M})\) in (19), \(\delta_{GLR}(\dot{M})\) in (15) can be reformulated as  
\[\bar{\delta}_{GLRT}(\dot{M})= \begin{cases}H_{0},\text{if}\ \bar{\Lambda}_{GLR}(\dot{M})<\bar{ \Theta}^{*},\\ H_{1},\text{if}\ \bar{\Lambda}_{GLR}(\dot{M})\geq\bar{\Theta}^{*},\end{cases}  \tag{20}\]  
where the threshold \(\bar{\Theta}^{*}\) is set within a reasonable range based on empirical values.
# Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images
## 6 Experiments
### Empirical Distribution of Coefficients  
In this section, we display the statistic coefficients of 100 images sourced from the Pano3D dataset [34]. Our analysis adopts a block length configuration of \(L=8\), resulting in a total of 64 coefficients within each block, derived from the \(8\times 8\) DCT matrix. The image size is \(256\times 512\), so there are \(K=\frac{\text{M}}{L}=32\) latitudes. We denoted the coefficients by \(\mathbf{c}_{k,l}\in\mathbb{R}^{64}\) where \(k\) represents the latitude and \(l\) represents the \(l\)-th quantizer in the block. In the context of the DCT, the initial coefficient \(\mathbf{c}_{k,1}\) is identified as the direct current (DC) coefficient at latitude \(k\), which represents the average intensity of the block. The subsequent 63 coefficients are classified as alternating current (AC) coefficients, encapsulating higher frequency components within the image block.  
Figure 3 displays the frequency of coefficients at latitude 1 in each interval, used to demonstrate the distribution of coefficients. Our observations distinctly reveal a substantial disparity in the distribution patterns of AC and DC coefficients. Consequently, it is not optimal to operate under the assumption that all coefficients adhere to the same distribution. Instead, employing an empirical distribution presents a more efficacious approach, as it better accommodates the inherent structure of the coefficients. Figure 4 displays the coefficient frequencies at latitude 17 within each interval. As presented in Figures 3 and 4, it is evident that the distribution of coefficients varies with latitude. This variation implies that the bit allocation and the design of distinct scalar quantizers for coefficients at various latitudes could yield significant benefits in terms of quantization efficiency.  
In this aspect of our research, we primarily encountered two obstacles. The first obstacle pertains to how we model distributions. Initially, we modeled each coefficient using the parametric model. Fitting all coefficients with the Gaussian distribution overlooks the differences in distribution among coefficients. Thus, we applied a Gaussian mixture distribution to the DC coefficient and a Laplace distribution to the AC coefficients. Nevertheless, the effectiveness of fitting remains unsatisfactory. Ultimately, we resorted to empirical distributions and improved the performance of the quantizers.  
The second problem was that the number of bits allocated to DC coefficients was comparatively lower than in other methods. However, the quality of DC coefficients has a greater impact on image quality compared to AC coefficients. Therefore, we increased the amount of input data and enhanced the importance of DC coefficients in bit allocation, which led to improved results.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 6 Sensor Fusion for Detection
### Feature Map Fusion  
Feature-map fusion leverages the semantics from both radar and images. From Section 5.3, we find that high-resolution radars can provide sufficient semantic cues for classification. Therefore, feature map fusion utilises two encoders to map radar and images into the same latent space with high-level semantics. The detection frameworks are flexible, includingone-stage methods [179; 180] and two-stage methods [181; 33; 182]. The one-stage methods leverage two branches of neural networks to extract feature maps from radar and images, respectively, and then concatenate the feature maps together. The two-stage fusion methods are adapted from the classical fusion architecture AVOD [183]. They firstly fuse the ROIs proposed from the radar and image in the first stage. In the second stage, the fused ROIs are projected to the radar and visual feature maps, respectively. The feature maps inside the ROIs are cropped and resized to an equal-sized feature crop. The feature crop pairs from the radar and image are then fused by the elementwise mean and sent to a detection head. Generally speaking, the two-stage method has better performance, but it is much slower than the one-stage method. Anchor-free methods [184; 185] further avoid the complicated computation related to anchor boxes, such as calculating the IOU score during training.  
Feature map fusion allows the network to flexibly combine radar and visual semantics. However, the fusion network may face the problem of overlooking weak modalities and modality synergies [186]. Some training techniques are needed to force the network to learn from radar input. [PERSON] et al. [171] adopted a modalitywise dropout approach that randomly deactivates the image branch during training. [PERSON] et al. [179] used a weight freezing strategy to fix the weights of the pre-trained feature extractors when training the fusion branch. Experiments show that freezing only the image branch works best. However, the fusion of multiple modalities is not guaranteed to always be better than using a single modality. Sometimes, we want the network to lower the weight of the radar branch if it gives noisy inputs. To achieve adaptive fusion, [PERSON] et al. [187] adopted self-attention and global channel attention [188] in their network. The self-attention is used to enhance real target points and weaken clutter points. Then, the global attention module is applied to estimate modalitywise weights. [PERSON] et al. [43] estimated the sensor entropy as the modality weight. For each modality, the entropy was evaluated pixelwise as a weight mask. Then, these weight masks are multiplied with the corresponding feature maps at each fusion layer.
## 2 Methodology
### Clustering Method Based on Dimensionality Reduction
#### 2.1.2 Clustering Method Based on K-Shape
\(\overset{\rightarrow}{\mu_{k}}^{*}\) is calculated as shown in Equation (9):  
\[ \begin{split}\overset{\rightarrow}{\mu_{k}}^{*}=argmax& \sum\limits_{\overset{\rightarrow}{\mu_{k}}}NCC_{c}\left(\overset{ \rightarrow}{x_{i}},\overset{\rightarrow}{\mu_{k}}\right)^{2}\\ &=argmax\sum\limits_{\overset{\rightarrow}{\mu_{k}}}\sum \limits_{\overset{\rightarrow}{x_{i}}\in P_{k}}\left(\frac{max_{x_{i}}CC_{c} \left(\overset{\rightarrow}{x_{i}},\overset{\rightarrow}{\mu_{k}}\right)^{2} }{\sqrt{R_{0}\left(\overset{\rightarrow}{x_{i}},\overset{\rightarrow}{x_{i}} \right)\cdot R_{0}\left(\overset{\rightarrow}{\mu_{k}},\overset{ \rightarrow}{\mu_{k}}\right)}}\right)^{2}\end{split}  \tag{9}\]  
The final clustering method is realized through iteration, and each iteration is divided into two steps. The first step is to recalculate the centroid. The second step is to redistribute each sequence to different clusters according to the distance between each sequence and the new centroid. We keep iterating until the tag stops changing. In the daily load prediction of power grid group residents, the K-Shape method is used to deal with the load curve of group residents and cluster it. This method can balance the large difference in electricity consumption habits among residents and reduce the heavy workload of forecasting individual residents. K-Shape relies on an extensible iterative refinement process that creates homogeneous and well-separated clusters. As its distance measure, K-Shape uses a normalized version of the cross-correlation measure to consider the shape of the time series when comparing them. K-Shape is a domain-independent, highly accurate, and efficient clustering method, and it is very suitable for time series.
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Slide Behavior with Different Mobility Models
#### 5.2.3 Effect of Mobility Model Parameters
Figure 15c shows the result of changing the standard deviation of the speed from 0.1 to 1. As with the other standard deviations (angle and pitch), the number of missed alarms increases when the standard deviation increases. However, the algorithm is more sensitive to the speed change than the angle or altitude change. For instance, if the selected speed is 3, and the speed standard deviation is 0.5, the speed can reach 5 m/s. As the drone will move in the new direction for a longer distance, the errors will be accumulated much faster. Figure 14: Impact of time step on the number of missed alarms.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### GAE Enhanced Pulse Peak Limiter  
GAE enhanced PPL is a modified or improved form of GAE enhanced PPA, where a selective attenuation of the received signal is performed continuously until there exist no amplitude values greater than threshold value \(\alpha_{th}\). The repeated attenuation will not affect the subcarriers which are not affected with the impulse noise, as the attenuation is applied only to the subcarriers whose amplitude is greater than \(\alpha_{th}\). The selective attenuation process is possible in two ways. To detail both the ways, we formulated two different algorithms, included in Appendices B.1 and B.2. The execution of these two algorithms resulted in two types of pulse peak limiters, named Type 1 GAEPPL and Type 2 GAEPPL.  
Execution of Algorithm A1 in Appendix B.1 resulted in Type 1 GAE Enhanced PPL, which process the input signal \(y\) and delivers output \(\hat{x}_{k}(y)\) as stated in (12).  
\[\hat{x}_{|\text{{\scriptsize{\sc{\sc{\sc{\sc{\sc{\sc{\sc{\sc{\sc{\sc{\sc{  
occurs in between two threshold values. The lower threshold value \((\alpha_{thl})\) is the same threshold value \((\alpha_{thl})\) used for the GAE enhanced pulse peak attenuator/limiter. As the high amplitude of DME noise causes high amplitude of the received signal, the signal contained in subcarriers whose amplitude exceeds the upper threshold \((\alpha_{tlu})\) value is made to zero or blanked. The operation of the joint GAE enhanced pulse peak attenuator is mathematically depicted as in (18).  
\[\hat{x}^{*}_{|kJGA|}(y)=\begin{cases}|y|e^{jarg(y)}&\text{if }|y|\leq\alpha_{ thl}\\ \rho_{k}|y|e^{jarg(y)}&\text{if }\alpha_{thl}<|y|\leq\alpha_{tlu}\\ 0&\text{for }|y|>\alpha_{tlu}\end{cases} \tag{18}\]  
Joint GAEPPL can be seen as a cascaded combination of a pulse blanker with a threshold cut-off frequency of \(\alpha_{tlu}\) followed by GAEPPL with a threshold cut-off frequency of \(\alpha_{thl}\). Hence, with the use of Algorithms A1 and A2, it is possible to design Type 1 and Type 2 Joint GAE PPL. The functioning of Type 1 and 2 Joint GAE enhanced pulse peak limiter is expressed as in (19) or in (20),  
\[\hat{x}^{*}_{|kJGL|}(y)=\begin{cases}|y|e^{jarg(y)}&\text{if }|y|\leq\alpha_{ thl}\\ \rho_{mod}|y|e^{jarg(y)}&\text{if }\alpha_{thl}<|y|\leq\alpha_{tlu}\\ 0&\text{for }|y|>\alpha_{tlu}\end{cases} \tag{19}\]  
\[\hat{x}^{*}_{|kJGLs|}(y)=\begin{cases}|y|e^{jarg(y)}&\text{if }|y|\leq\alpha_{ thl}\\ M.\rho_{k}|y|e^{jarg(y)}&\text{if }\alpha_{thl}<|y|\leq\alpha_{tlu}\\ 0&\text{for }|y|>\alpha_{tlu}\end{cases} \tag{20}\]  
where \(\rho_{mod}\) and \(M\) are same as defined section GAE PPL.