esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 3 Results and Discussion
### FTIR Test Results
#### 3.2.2 Analysis Impact of Additives Based on Orthogonal Test
The reason may be that the rubber oil added to the modified asphalt promotes the cross-linking of SBS polymer without changing the characteristic functional groups in SBS. However, after the addition of sulfur, the double bond of polybutadiene block in the SBS polymer reacts with the hetero atom in asphalt to form SBS-asphalt grafts, which changes the characteristic functional groups in SBS, thereby affecting the magnitude of \(A\) value. Therefore, the method of rapidly determining the SBS content in modified asphalt by infrared spectroscopy needs to be further improved. Figure 7: Marginal mean of all three factors.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 5 Results
### Average Number of Handoffs  
Figure 9 depicts the connection between the average number of handoffs and the WDs. The ratio of the total number of handoffs carried out by all WDs to the total number of WDs is known as the average number of handoffs. The average number of handoffs for all four approaches is almost the same for smaller numbers of WDs. As load and the number of WDs increase, the performance disparity between all four schemes widens. The MPD approach has a lower handoff among 100 WDs because additional probe-request packets aid in more precise estimation of each AP's load. The ASLB that is being suggested has the fewest handoffs. This is because the ASLB can predict the order of an AP's switch based on the activity of its WDs. The change will begin with the WDs with the weakest RSSI connections. As a result, there are fewer handoffs because the load on the APs remains balanced.  
The WDs autonomously pick the handoffs in the MPD algorithm. The number of handoffs rises as a result of active wireless users contributing less to the handoffs. The universal controller, which chooses the order of the handoffs, monitors the handoffs in the proposed ASLB. In order to prevent handoffs from being repeated, the WDs are switched to the least-used AP when a particular AP becomes overused. In contrast to MPD system, DASA scheme chooses the best destination AP based on the APs SINR values. Higher handoffs times are the result of longer SINR calculation times. Accuracy provided by the SINR measurement results in fewer handoffs but more expensive probing overheads. The DASA scheme relies solely on the probe-request and probe-response between the APs and the WD's for SINR measurement. The additional probing frames necessary for a precise estimate of the SINR are not taken into account. The CMAS technique uses the bandwidth that is available as the metric for identifying the handoffs. The number of handoffs for a high density Wi-Fi network cannot be entirely determined by this measure, which is only useful in calculating the contention in the Wi-Fi network. When compared to the CMAS, DASA and MPD schemes, ASLB reduces the packet loss rate by 26%, 29%, 35% and 44%, respectively.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Model Architecture
#### 2.4.2 Lstm  
LSTM is the evolution of RNN and was first proposed by [PERSON] and [PERSON] (1997) [31]. It is often used in speech recognition [32], stock price prediction [33] and pedestrian trajectory [34]. LSTM improves the problem of RNN long-term memory and adds the concept of cell state. As shown in Figure 11, LSTM adds four structures, including forgetting gate, cell state, input gate and output gate, to keep the important features.  
The main core of LSTM is the cell state. By designing a structure called gate, LSTM has the ability to delete or add information to the cell. The forgetting gate \(f_{t}\) can determine the information that needs to be discarded in the cell state, which is defined as [31]  
\[f_{t}=\ \sigma_{g}\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)}, \tag{11}\]  
where the lowercase variable is a vector, the matrix \(W_{f}\) is the input weight, the matrix \(U_{f}\) is the weight of the cyclic connection, \(b_{f}\) is the partial weight, and the subscript \(f\) is for Forget Gate. Similarly, the subscript of information of Input Gate, Output Gate and Cell State can be expressed by \(i\), \(o\), and \(c\), respectively. \(t\) is the time, \(h\) is the output vector, and \(\sigma_{g}\) is the Sigmoid function. Observe Forget Gate's activation vector \(f_{t}\) is used to determine whether to retain this information.  
Knowing what new information will enter the cell are the input gate \(i_{t}\), and the activation vector for the new candidate value \(\widetilde{c}_{t}\), which are defined as [31]  
\[i_{t}=\ \sigma_{g}(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{12}\]  
Figure 11: Long short-term memory (LSTM) [31]. (**A**) Forget Gate; (**B**) Input Gate; (**C**) Cell State; (**D**) Output Gate.  
and  
\[\widetilde{c}_{t}=\sigma_{h}(W_{c}x_{t}+U_{c}h_{t-1}+b_{C}), \tag{13}\]  
respectively, where \(\sigma_{h}\) is the tanh function.  
Cell renewal is calculated through \(c_{t}\), _which_ is the cell state vector, \(o_{t}\), which is the activation vector of the output gate, and \(h_{t}\), the current output vector. They are defined in [32] as  
\[c_{t}=\ f_{t}\odot c_{t-1}+i_{t}\odot\widetilde{c}_{t} \tag{14}\]  
\[o_{t}=\ \sigma_{g}(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{15}\]  
and  
\[h_{t}=\ o_{t}\sigma_{h}(c_{t}), \tag{16}\]  
respectively, where \(\odot\) represents the Hadamard product.
## 3 Design Challenges of MAC Protocol for IBSN
### Types of Access Mechanisms Recommended for IBSN  
The MAC protocols applicable to IBSN are broadly divided into the following categories:  
* Time Division Multiple Access Mechanisms (TDMA)
* Carrier Sense Multiple Access Mechanisms (CSMA)
* Hybrid Access Mechanisms
* Random Access Mechanisms  
Each access mechanism has its own advantages and disadvantages. The basic principle of each mechanism is further explained below.
# Detection of Pediatric Femur Configuration on X-ray Images
## 3 Results
### Femur Configuration Estimation (Test Stage)  
In this section, we present the combined performance of both the LA and PS estimator, to evaluate the femur configuration on each X-ray image frame. Both estimators were designed and tuned using images from train and development sets, according to the description in Table 1. We assume that no further changes will be made in the architecture as well as parameter values of both estimators, once the training phase is finished.  
In the test stage, we will evaluate the performance of the estimators on new data, not used during training, i.e., included in the test set.  
Remember that, the reference configuration of the femur \(\mathbf{g}_{m}\) is calculated from positions of manually marked keypoints. The same set of transformations (5) is applied to both manually denoted and estimated keypoints, to calculate the configuration. The overall performance of the algorithm is defined as a difference between \(\mathbf{g}_{m}\) and \(\mathbf{g}_{e}\). The results for each configuration element separately are presented in Figure 10.  
Position error is defined in pixels, whereas orientation is given in degrees. Note that the orientation error (\(\theta_{m}-\theta_{e}\)) is purely dependent on the performance of the gradient-based estimator and the results correspond to the values presented in Figure 9. Therefore, the estimator detects LA keypoints on new image data with similar accuracy to the one observed in the training stage.  
Position error combines the inaccuracies of both estimators, nevertheless proposed redundancy of keypoint selection causes slight robustness to those errors. Estimation errors of both position components of femur configuration is limited. The overall performance is satisfactory, given the size of the input image.  
Interestingly, the femur coordinate center was swiped to the left (\(x_{e}<x_{m}\)) on most X-ray image data, in comparison to manually denoted configuration. It could be interpreted as a systematic error of the estimator and could be canceled out in the forthcoming validations. However, the sources of error may be connected to the reference configuration, which is calculated for manually placed keypoints. This assumption could lead to the remark that CNN actually performed better than the human operator.  
Figure 10: Femur configuration estimation results.  
The results achieved by the proposed algorithm of femur configuration detection cannot be compared with any alternative solutions. The femur coordinate system proposed in this study was not incorporated in any outgoing or previous studies. Other authors proposed different representations [35; 36], but those do not apply for this specific image data. As far as the author's knowledge is concerned, there are no alternative configuration detectors of the pediatric femur bone in the lateral view.
## 3 Results and Discussion  
### Optimization of Culture Conditions for Beta-Cypermethrin Degradation by Strain BCC01  
The degrading abilities of _B. cereus_ BCC01 under different culture conditions are presented in Table 1. The data were analyzed using a response surface regression procedure as the following predicted quadratic polynomial equation:  
\[\begin{array}{l}Y=98.2914+5.78545X_{1}-0.012575X_{2}-0.98936X_{3}-7.582225X_ {1}{}^{2}-0.36935X_{1}X_{2}\\ +1.855X_{1}X_{3}+1.489975X_{2}{}^{2}-0.20375X_{2}X_{3}-2.107075X_{3}{}^{2}\,\end{array} \tag{4}\]  
where \(Y\) represents the predicted degradation percentage (%), and \(X_{1}\), \(X_{2}\) and \(X_{3}\) are the values for pH, temperature and inoculum volume, respectively. Further, the ANOVA results of the fitted model are shown in Table S2, where the data suggested that pH (\(X_{1}\)) and inoculum volume (\(X_{3}\)) had a significant influence on beta-cypermethrin degradation activity, while the effect of temperature (\(X_{2}\)) was smaller. To establish a reliable model for the CCRD experiment, only statistically significant factors (\(p<0.05\)) were included in the model, that is, a first order effect (\(X_{2}\), \(X_{3}\)), and three interaction effects (\(X_{1}X_{2}\), \(X_{2}X_{2}\), \(X_{2}X_{3}\)) were not significant (\(p>0.05\)) and were removed from the model, so that the effects of inoculum volume and pH on biodegradation were found in a three-dimensional response surface with fixed temperature (the non-significant variable) at the coded \"0\" level (30 \({}^{\circ}\)C) (Figure 1). The model predicted maximum biodegradation (99.4%) at the stationary point, where the optimum codes for \(X_{1}\) and \(X_{3}\) were 0.37143 and 0.07346. After being translated, the uncoded values were pH at 8 and inoculum volume at 9.6%. Therefore, the optimal conditions for beta-cypermethrin degradation by strain BCC01 were to be produced by inoculating 9.6% inocula at pH 8 at 30 \({}^{\circ}\)C. Applying the deduced parameters to the medium with beta-cypermethrin at an initial 100 mg/L, the biodegradation by strain BCC01 reached 99.6% after five days of incubation, which was consistent with the predicted degradation (99.4%), demonstrating a reliable model. Such a method to optimize the culture condition has been generally accepted and widely used in many studies [27; 34; 35].
# A Self-Supervised Detail-Sensitive ViT-Based Model for COVID-19 X-ray Image Diagnosis: SDViT
## 3 Methods
### ViT  
ViT is obtained by applying the Transformer structure to vision tasks.  
ViT is composed of the following main components:  
Image chunking  
Transformer encoder  
Classifier  
In this section, we explain ViT in terms of these three components.  
Image Chunking (Making Patches)  
This step can be considered the pre-processing of an image. In a CNN, the images are convolved in two dimensions, height and width, and no further pre-processing is required. However, the Transformer structure requires the images to be chunked. Assuming that an input image of size \(x\in H\times W\times C\) is divided into patches of size \(P\times P\times C\), the total number of patches is \(N=HW/P^{2}\). Each patch can be described as \(N\times P\times P\times C\) with an additional dimension. Here, \(N\) stands for the sequence's length from the input to the Transformer, \(C\) stands for the image's channel count, and \(P\) stands for the image patches' size.  
Image chunking is only a pre-processing step. A block embedding operation on an image is required to convert the vector dimension of \(N\times(P^{2}\times C)\) into a two-dimensional input of size \(N\times D\). This works in a similar way to how word2 vec transforms a high-dimensional vector into a low-dimensional one in NLP. A unique code, similar to 'CLS' in BERT, is added to the embedding as the outcome of the category prediction.  
In the image block embedding, a vector for position encoding must be included; spatial location information is then maintained between input image patches. ViT's position encoding utilizes a direct 1D learnable position embedding variable rather than the updated 2D position embedding method, as the experimental results of using 2D and 1D embedding methods are comparable.  
Transformer  
To obtain the desired classification outcomes, the Transformer module is used for feature extraction, which is then input to the final classifier. The Transformer architecture significantly enhances the fusion of higher-order information and combined features. However, various experiments have demonstrated that it disrupts many tasks due to its structure.  
Classifier  
A shallow network such as MLP can achieve extremely high performance when sufficient features have been extracted. It requires only a simple network structure to produce accurate classification results. The images can be converted into sequence information, which is more conducive to extracting their relationships. By chunking the images and separating the features, the classification effect can be further improved.
## 2 Experiment 1
### Method  
#### 2.1.1 Participants  
A statistical power analysis was performed to estimate the necessary sample size (G*Power 3.1.9.7, Dusseldorf, Germany). With a power of 0.80 and an alpha of 0.05, the projected sample size needed for medium effects of \(w=0.40\) was at least \(N=50\). However, due to an anticipated high drop-out rate due to the level of awareness of the used video, we decided to request approximately twice as many participants as calculated with G*Power. Therefore, a total of 93 male subjects, aged 18 to 38 years (\(M_{\rm age}=24.8\) years, \(SD=4.5\) years) participated in Experiment 1. Players were regarded as amateurs (\(n=50\), \(M_{\rm age}=25.3\) years, \(SD=4.8\) years) who played in the 9 th and 10 th division of the German football league at the time of data collection. The experts (\(n=43\), \(M_{\rm age}=24.3\) years, \(SD=4.0\) years) participating in Experiment 1, played in the 5 th and 6 th division of the German football league. Overall, 26 of the subjects reported playing the offense, usually as a striker; all other subjects usually played as goalkeepers (\(n=7\)), defenders (\(n=29\)), or midfielders (\(n=31\)).  
Participants were excluded from analysis (24 amateur and 18 expert players) in two conditions: if they indicated in the follow-up questionnaire that they had anticipated the unexpected object because of previous knowledge of intentional blindness or related research; or if they failed to notice the unexpected object in the control condition where they did not have to perform the primary task (full-attention trial). Consequently, only the data of 61 participants (35 experts, \(M_{\rm age}=24.6\) years, \(SD=4.0\) years, 26 amateurs, \(M_{\rm age}=24.9\) years, \(SD=4.5\) years) was included in the analysis.  
All participants reported normal or corrected-to-normal vision (with either glasses or contact lenses) and were not involved in any sensorimotor research within the preceding six months. The study was approved by the local University ethics board (number 094/21). Written consent was obtained from all participants prior to testing according to the Declaration of Helsinki, 1975.
## 5 Experiments and Data Analysis
### Analysis of Computational Load and Processing Time  
This paper uses Matlab for signal processing, and the computer CPU used is an Intel(R) Core(TM) i5-9300H CPU. According to the processing flowchart shown in Figure 12, Table 8 shows the computational complexity of each step.  
The computational load and processing time are mainly determined by the amount of data used in data processing. The data processing in this paper generally takes several seconds. Taking the data in Section 5.1.1 as an example, \(N_{1}=400\), \(M_{1}=1000\), \(I=1024\), and \(J=989\), the data processing takes 5.891 s.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 5 Geographic Information Interpretation and Analysis
### Stability Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Projects** & \multicolumn{3}{c}{**Classification of Indicators and Quantitative Scores Ci**} \\ \cline{2-5}  & **1** & **0.618** & **0.382** \\ \hline C1: Rock and Soil Type & loose stacked layer (geology) & semi-hard rock & solid bedrock \\ \hline C2: Structural Plane Density & low density & Medium density & low density \\ \hline C3: Weathering Degree & Strong weathering: weathering: weathering index \textgreater{}0.6 & Medium weathering: 0.2 \textless{} weathering index \(\leq\) 0.6 & Unwearthemed: weathering index \(\leq\) 0.2 \\ \hline C4: Slope Gradient & Steep slopes: slope \textgreater{}30\({}^{\circ}\) & Medium gradient: 15-30\({}^{\circ}\) gradient & Gentle slopes: slopes \textless{}15\({}^{\circ}\) \\ \hline C5: Slope Aspect & downhill & transverse slope & reverse slope \\ \hline C6: Curvature & convex slope & straight slope & concave slope \\ \hline C7: Vegetation Coverage & Low coverage: \textless{}30\% coverage & Medium coverage: 30–70\% coverage & High coverage: \textgreater{}70\% coverage \\ \hline C8: Annual Average Rainfall & High rainfall areas: annual rainfall \textgreater{}800 mm & Medium rainfall zone: annual rainfall \textless{} 400 mm \\ \hline C9: Groundwater Influence & Long-term saturation: water table depth \textless{}3 m & Seasonal impact\textgreater{}3 m \(\leq\) water level depth \(\leq\) 5 m & No groundwater: depth to water table \textgreater{}5 m \\ \hline C10: Seismic Intensity & High intensity (\textgreater{}VII) & Medium intensity (degrees VI–VII) & Low intensity (\textless{}VI) \\ \hline C11: Human-Induced Disturbance & Large-scale excavation \textgreater{}30\% of excavated area & Partial excavation: \(10\%\leq\) excavated area \(\leq\) 30\% & Undisturbed: \textless{}10\% of excavated area \\ \hline C12: Vegetation Destruction Condition & Severe damage: recovery rate \(<\) 30\% & Moderate damage: 30–60\% recovery rate & Minor damage: recovery rate \(>\) 60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification and scoring of secondary evaluation indicators. is \(\rm R=0.596\). The slope is classified into four stability grades: stable (I) (\(\rm 0\leq R<0.25\)), basically stable (II) (\(\rm 0.25\leq R<0.5\)), more unstable (III) (\(\rm 0.5\leq R<0.75\)), and unstable (IV) (\(\rm 0.75\leq R<1\)). Based on this classification, it can be concluded that the slope is in a more unstable state (III), indicating it is a high-risk slope. As such, corresponding professional monitoring measures and prevention programs must be considered to prevent slope hazards that could negatively impact nearby residents and traffic.
# Prevalence of Root Canal Treatments among Diabetic Patients: Systematic Review and Meta-Analysis
## 1 Introduction  
When bacteria, their toxins or their antigens reach the dental pulp, the pulpal inflammatory reaction, pulpitis, ends up inducing pulp necrosis [1]. If an adequate treatment isnot instituted, toxins and antigens will pass through the apical foramen into the periapical tissues, inducing the immune and inflammatory reaction characteristic of apical periodontitis (AP) [2]. To control AP, it is necessary to interrupt the passage of antigens from the root canal to the periapex, which is achieved via a root canal treatment (RCT) [3]. RCTs are focused on eliminating bacteria that cause an infection in the root canal system using chemical and mechanical methods [4]. Both AP and RCT are highly prevalent, with at least one tooth affected by AP in 52% of people [5], and 8% of the teeth of the world population being root-filled teeth (RFT) [6].  
On the other hand, several epidemiological studies on endodontic medicine have reported the high prevalence of AP among patients affected by some systemic diseases [7; 8; 9; 10]. In the specific case of diabetes, there are many cross-sectional and case-control epidemiological studies that have found a higher prevalence of AP among diabetic patients [11; 12; 13; 14; 15; 16]. Diabetes mellitus is characterized by an inadequate carbohydrate, lipidic and protein metabolism; its primary aspect is hyperglycemia [17; 18]. Hyperglycemia acts as the main cause of the incidence and progression of microvascular complications associated with the disease (retinopathy, nephropathy and neuropathy). Two main types of diabetes have been established: type 1 diabetes (insulin-dependent diabetes) is characterized by deficient production of insulin by the pancreas and requires external administration of this hormone; type 2 diabetes (non-insulin dependent diabetes) is characterized by the ineffective use of insulin by the cells of the body, representing 95% of all diabetics [19; 20].  
Given the high prevalence of AP among diabetic patients [11; 13; 15; 16; 21; 22], it can be expected that the prevalence of RFT is also high among those patients. However, several systematic reviews have concluded that diabetes is a major preoperative prognostic factor in root canal therapy [23; 24; 25], negatively influencing the treatment outcome and RFT survival rate [26; 27]. Therefore, diabetics could have a lower prevalence of RFT than the general population does. On the other hand, diabetic patients have a high prevalence of periodontal disease [28; 29], which also leads them to lose a greater number of teeth than the healthy non-diabetic population does [30].  
The aim of this study was to conduct a systematic review and meta-analysis investigating the prevalence of RFT among diabetic patients.