esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the \"Overall-Local\" Coal Burst Type of Isolated Working Face
### Overall Stability of Isolated Working Face
#### 4.1.3 Overall Stability Coefficient of Isolated Working Face  
Based on the total support stress \(\sigma\) and total bearing strength R of the isolated working face, and considering the actual engineering conditions where the working face is also subjected to dynamic loading, the stability coefficient of the isolated working face needs to be multiplied by the dynamic load coefficient \(\eta\).  
The dynamic load coefficient (DLC) is a parameter used to assess the additional load a structure experiences due to dynamic effects, such as vibrations or sudden impacts. The equation for the dynamic load coefficient is typically expressed as [31]:  
\[\eta=\frac{\text{P}_{\text{d}}}{\text{P}_{\text{s}}} \tag{10}\]  
In this equation, \(\text{P}_{\text{d}}\) represents the dynamic load (the load experienced by a structure under dynamic conditions), and \(\text{P}_{\text{s}}\) represents the static load (the load experienced under static conditions).  
In the context of mining and coal burst prediction, the dynamic load coefficient is commonly used to assess the increased load on structures (such as supports or coal pillars) due to dynamic events, such as coal bursts. The specific value of the coefficient depends on several factors, including the energy release rate, material properties, and the specific dynamic conditions of the mine.  
Thus, the overall stability coefficient I of the isolated working face is obtained as follows:  
\[\text{I}=\eta\frac{\sigma}{\text{R}}=\eta\frac{(3\gamma\text{H}+\Delta\sigma) \text{Hcot}\beta+\left(\gamma\text{H}+4\Delta\sigma-\frac{4\text{Ltan}\beta}{ \text{H}}\right)(\text{L}-4\text{Hcot}\beta)}{\left[\text{k}\left(\text{L}-3 \text{x}_{\text{p}}-\text{x}_{\text{e}}\right)+2\text{x}_{\text{p}}+\text{x}_{ \text{e}}\right]\sigma_{\text{c}}} \tag{11}\]If I \(\geq\) 1, the working face as a whole is more likely to experience a coal burst; the greater the value of I, the higher the possibility of a coal burst. If I \(<\) 1, the possibility of a coal burst on the overall working face is small, and the smaller the I, the lower the possibility of a coal burst.  
The classification of the \"overall stability grade of isolated working face\" is a relatively new approach, specifically developed to assess the likelihood of coal bursts under complex geological and operational conditions. Although not yet widely implemented in underground workings, this method has been adopted in specific engineering studies, including the present work, to evaluate coal burst risks systematically. This classification allows mining operations to quantitatively assess and monitor the stability of isolated working faces, enhancing safety measures and risk management.  
Based on the actual project conditions, the specific engineering discrimination grades are divided, as shown in Table 1.
## 4 Separation Methodology
### The Case of Identifying Abnormal Conditions for Daily Data
The three types of cluster distributions are also marked in three different colors, as shown in Figure 10.  
The identification results with different methods are calculated for analyzing the advantages and disadvantages of the two proposed methods, as shown in Figure 10(a),b. The accuracy a of identification results for the OATPM method is 83.9%, and the accuracy for the KNN method is 93.1%. This shows that the accuracy of the OATPM method is slightly lower than that of the KNN method, but it still reaches a high level. At the same time, the OATPM method only needs to calculate the second derivative without a complex algorithm, so the calculation speed is high. A more detailed comparison is made in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Identification Accuracy** & **Applicable Occasion** & **Degree of Identification** & **Can It Be Used to Predict Energy Consumption?** \\ \hline Temperature model & 83.9\% & For daily data & Closed/open & Yes \\ Clustering & 93.1\% & For daily data & Closed/slightly open/fully & No \\ algorithm & 96.2\% & For hourly data & open & No \\ \hline \hline \end{tabular} Note: The temperature models can be used to predict energy consumption.  
\end{table} 
Table 4: Comparison of the OATPM and KNN methods.
# Evaluation of Biodentine Tricalcium Silicate-Based Cement after Chlorhexidine Irrigation
###### Abstract  
The effectiveness of biocements applied in specialistic endodontic procedures can be influenced by multiple factors, including the postplacement chemical action of the irrigating solution. This in vitro study aimed to assess the impact of 2% chlorhexidine digluconate on the surface structure and chemical composition of Biodentine as a perforation repair cement. A total of 54 Biodentine specimens were prepared with strict adherence to the manufacturer's instructions and irrigated with 2% chlorhexidine with or without ultrasonic activation. The material specimens were divided into three setting-time-based groups: group A--rinsed after 12 min of setting, group B--after 45 min, and group C--after 24 h. The control group was not subjected to any irrigation protocol. The evaluation of the microappearance of biocement surface was performed with the aid of a scanning electron microscope (SEM). The chemical composition of Biodentine was analyzed with the energy dispersive spectroscopy (EDS) method. The SEM images of the specimens in group B and C revealed a heterogeneous and layered surface morphology. The EDS results are comparable between pairs of cement specimens in both groups: after 5 min and 20 min CHX irrigation as well as after 5 min and 20 min ultrasonically activated CHX irrigation. To conclude, the 12 min Biodentine setting time is not recommended when used in perforation closure. Irrigation protocol involving 2% chlorhexidine visibly affected the tested material surface. The EDS results did not confirm any significant changes in Biodentine chemical composition. Further research is required to analyze the influence of the observed changes on the outcome of the endodontic treatment.  
Biodentine; tricalcium silicate cement; chlorhexidine; endodontic irrigation; perforation; ultrasonic activation +
Footnote †: journal: Applied Sciences
## 3 Optimization Methods and Results
### Network Model Design and Parameter Reduction
designed a CNN model with three convolution layers and two FC layers [7]. Under the same experimental setup, we tested the design on our dataset, and the average classification accuracy is 99.47%, which is slightly higher when compared to our model; however, the number of the CNN model's parameters is 731,214, far more than that of our model, which means it is difficult to realize on FPGA. The results illustrate that we substantially reduced the scale of the model at the cost of a small reduction of accuracy. To further check the evaluation result's correctness, we employ principal component analysis (PCA) to realize FC1's output tensor's visualization, which is shown in Figure 8f. It shows that most of the data can be recognized and classified correctly and hence, the model is suitable for the classification of the pulse waveform. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Layer 1** & **Layer 2** & **Layer 3** & **Layer 4** & **Layer 5** & **Layer 6–9** \\ \hline
1 & (1–16) 1 & (16–32) & (32–32) & (32–32) & (32–32) & (32–32) & \\
2 & (1–8) & (8–32) & (32–32) & (32–32) & (32–32) & \\
3 & (1–8) & (8–16) & (16–32) & (32–32) & (32–32) & \\
4 & (1–4) & (4–16) & (16–32) & (32–32) & (32–32) & \\
5 & (1–4) & (4–8) & (8–16) & (16–32) & (32–32) & \\
6 & (1–2) & (2–4) & (4–8) & (8–16) & (16–32) & \\ \hline \hline \end{tabular}  
* (a–b): a, b are the number of input channels and output channels. 2 (a–b): a, b are the number of input features and output features.  
\end{table} 
Table 1: Different structures of 6 CNN models.
## 2 Material and Methods
### Application of Hydrologic Modeling  
Daily data from 2006 to 2013 were used for the inflow, which has a great influence on the performance evaluation. In this period, sufficient time has elapsed since the inflow pattern changed due to the construction of Imnam Dam located upstream of Hwacheon Dam in 2003. Also, this period was before 2014-2015, when the severe drought occurred. To consider a general situation, not a disaster, a period with a relatively constant inflow pattern was set as a time interval.  
The operating standards for hydropower reservoirs in Korea were investigated and reviewed to establish an operation scenario. The power generation method of the hydropower reservoir is a regular power generation type, but the outflow discharge according to the water level is not determined. However, since various dams are built in a small area, it is necessary to establish an operation plan every month in accordance with the River Act and the operation standards for dams and weirs. In the process of establishing this plan, after predicting the monthly inflow based on historical data, the monthly target water level and discharge plan are set. The basic principle is to prevent drought and flood damage from occurring, and the discharge plan is finally decided by referring to the electricity supply and demand plan and the power generation stop plan. The establishment of this plan determines the outflow discharge by predicting the inflow and setting the target water level. The target water level is generally selected based on June and September, before and after the flood season, and this value is also calculated as the average value of historical data. Therefore, in this study, a scenario was established in which the target water level and target discharge were adjusted and operated based on the monthly average water level and outflow discharge of each dam. Figure 6 is a diagram showing how to set the target water level and target discharge in Hwacheon Dam, and all are set to be adjusted at a certain rate based on the average value. Actually, the current dam operation is performed with the values indicated by the solid lines in Figure 6a,b. As shown in the dotted lines in Figure 6a, the scenario was set to increase or decrease the target water level at a certain rate. In addition, the outflow discharge in Figure 6b was also increased and decreased at a certain rate based on the average value to operate. Scenarios that correspond to both the scenario of changing the target water level and the scenario of changing the target discharge were set up as scenarios. As a result, both the target water level and discharge were divided into 15 stages between the lowest and highest values to construct a scenario. The total number of scenarios is 225.
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 2 Related Works
### Autoencoders  
Autoencoders are neural models with two components (encoder and decoder) whose task is to learn the identity function, replicating the input data in the output [21]. A specific hidden layer, called a latent vector, will learn to generate a representation of the input data.  
Undercomplete Autoencoders use hidden layers with smaller dimensions than the input and are useful to reduce the dimensionality of the input data. Only those characteristics that differentiate one entry from the rest are reflected in the encoding of the latent vector, thus achieving an extraction of relevant characteristics from the entry.  
Stacked Autoencoders use more than one layer to facilitate both the encoding and the decoding processes, in the sense that they allow a progressive reduction in dimensionality. In this way, they manage to improve the representation of the data in the latent vector.  
To improve the learning process and, specifically, the generalization capacity of the model, it is possible to apply different techniques based on regularizers. A particular case is the addition of noise at the input. This modifies the training instances and allows the dataset to be expanded. As a result, the trained network will improve its ability to generalize and its tolerance to noisy inputs, generating a more robust network [22]. In a general sense, training a network with noise promotes more neurons in the network to contribute effectively to its operation [23].  
Denoising Autoencoders apply these techniques to remove noise from input data or to obtain more noise-tolerant models. One of the consequences of learning based on this noise is an improvement in the representation of the data in the latent vector and, thus, a better extraction of features [24] with the help of Stacked Denoising Autoencoders [25]. What is sought with this type of network is an improvement in the representations of the input data.  
Combining these architectures with convolutional models results in Convolutional Autoencoders that are adequate for image processing [26].  
One application of Autoencoders is the segmentation of images. In this case, the training datasets associate each input image with the corresponding segmented image, making it possible to obtain Autoencoders capable of performing segmentation [27]. Similarly, they can be applied to determine the area of interest of an input image. By training with output images in which only the elements of interest are present, it is possible to obtain a model that learns to isolate these elements in an input image.
## 4 Conclusions  
Our research into the impact of noise in annotation labels suggests that for certain semantic segmentation tasks, near-perfect hand-drawn contours are not a strict necessity. Through our experiments, we show that the proposed NR-UNet, coupled with the ADL strategy, performs competitively under varying levels of label noise.  
While the results from the proposed NR-UNet and ADL strategy are promising, there is room for further improvement. As part of our future work, we plan to broaden the applicability of the NR-UNet and ADL strategy to other medical imaging modalities, such as MRI and ultrasound. We will also assess how generalizable they are across a wider range of clinical applications. These methods can also be used in conjunction with weakly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proportion (\(\beta\))** & **Network** & **ADL** & **Dice** & **IoU** \\ \hline \multirow{2}{*}{75\%} & \multirow{2}{*}{Residual UNet} & ✗ & 0.7962 & 0.6614 \\  & & ✓ & 0.8210 & 0.6964 \\ \hline \multirow{2}{*}{75\%} & \multirow{2}{*}{UNet} & ✗ & 0.8004 & 0.6672 \\  & & ✓ & 0.8337 & 0.7148 \\ \hline \multirow{2}{*}{75\%} & \multirow{2}{*}{NR-UNet} & ✗ & 0.8196 & 0.6943 \\  & & ✓ & **0.8466** & **0.7340** \\ \hline \hline \multirow{2}{*}{50\%} & \multirow{2}{*}{Residual UNet} & ✗ & 0.8179 & 0.6919 \\  & & ✓ & 0.8453 & 0.7321 \\ \hline \multirow{2}{*}{50\%} & \multirow{2}{*}{UNet} & ✗ & 0.8188 & 0.6932 \\  & & ✓ & 0.8564 & 0.7489 \\ \hline \multirow{2}{*}{50\%} & \multirow{2}{*}{NR-UNet} & ✗ & 0.8362 & 0.7185 \\  & & ✓ & **0.8832** & **0.7908** \\ \hline \hline \multirow{2}{*}{25\%} & \multirow{2}{*}{Residual UNet} & ✗ & 0.9002 & 0.8185 \\  & & ✓ & 0.9213 & 0.8541 \\ \hline \multirow{2}{*}{25\%} & \multirow{2}{*}{UNet} & ✗� & 0.9084 & 0.8322 \\  & & ✓ & 0.9303 & 0.8697 \\ \hline \multirow{2}{*}{25\%} & \multirow{2}{*}{Dense UNet} & ✗ & 0.9096 & 0.8342 \\  & & ✓ & 0.9284 & 0.8664 \\ \hline \multirow{2}{*}{25\%} & \multirow{2}{*}{NR-UNet} & ✗ & 0.9101 & 0.8350 \\  & & ✓ & **0.9532** & **0.9106** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation studies of adaptive denoising learning via various segmentation networks under different proportions of noisy labels.  
Figure 7: The example images of input CT spine images; prediction of NR-UNet against ground truth under various data situations.  
supervised learning tasks, improving the reliability of safety-critical scenarios like medical screening or diagnosis on the strength of minimal hand-annotation requirements.  
Conceptualization, Z.W. and I.V.; Validation, Z.W.; Investigation, Z.W.; Resources, I.V.; Writing - original draft, Z.W.; Writing--review & editing, Z.W. and I.V.; Supervision, I.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset used in this study is public available at [[http://spineweb.digitalimaginggroup.ca/](http://spineweb.digitalimaginggroup.ca/)]([http://spineweb.digitalimaginggroup.ca/](http://spineweb.digitalimaginggroup.ca/))  
The authors declare no conflict of interest.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Energy Consumption  
Dissipation of energy in the radio transmitter includes energy to run radio electronics, a power amplifier, and in a receiver. A threshold, \(d_{\textit{crossover}}\) is set to determine whether Friis free space, \(\varepsilon_{\textit{fs}}\) represents (\(d^{2}\) power loss) or multipath fading \(\varepsilon_{\textit{mp}}\) model (\(d^{4}\) power loss) for channel model as in Reference [23]. Therefore, the energy used to transmit the \(l\)-bit message at distance \(d\) is:  
\[E_{T_{X}}(l,d)=E_{T_{X}-\textit{{elec}}}(l)+\ E_{T_{X}-\textit{{amp}}}(l,d) \tag{11}\]  
\[E_{T_{X}}(l,d)=lE_{\textit{{elec}}}+l\varepsilon_{\textit{{fs}}}d^{2},\ d<\ d_{\textit{crossover}} \tag{12}\]  
\[E_{T_{X}}(l,d)=lE_{\textit{{elec}}}+l\varepsilon_{\textit{{mp}}}d^{4},\ d\geq\ d_{\textit{crossover}} \tag{13}\]  
\[d=\ d_{\textit{crossover}},\ d_{\textit{crossover}}=\ \sqrt{\frac{\varepsilon_{\textit{fs}}}{ \varepsilon_{\textit{mp}}}}=87.7m \tag{14}\]To receive the message, the energy value is:  
\[E_{Rx}(l)=E_{Rx-elec}(l)=IE_{dec} \tag{15}\]  
Electronics energy, \(E_{elec}\), is a combination of several factors such as sensing and actuation activity, signal coding, modulation, filtering, transmission and reception of the signal. Amplifier energy, \(\varepsilon_{fs}\) or \(\varepsilon_{mp}\), depends on distance to the receiver and acceptable bit-error rate. The value for \(\varepsilon_{fs}\), \(\varepsilon_{mp}\) and \(E_{elec}\) are fixed as in References [23; 24]. These formulas have been used in References [25; 26; 27; 28; 29] to calculate WSN nodes energy in a cluster formation environment.  
For the size of the l-bit message in (12), (13) and (15), the calculation of message size as in Pongle [22] is implemented. Messages exchange between the blind nodes with other nodes only occurrs when they are related. To exchange the messages, each node must have its own neighbor's information and send the information to the other nodes that are related to them. The total number of message exchanges can be represented as the overall communication cost (Section 5.3).  
One communication unit (message exchange) is represented by one packet data. This packet data is transferred or received by each node. Therefore, each number of messages exchanged between nodes in Section 5.3 is represented in bit for the energy consumption calculation. The structure of the packet data for the neighbor information that was used to calculate the size of the message sent and received is shown in Figure 5. However, the equation used bit unit for the packet size, therefore, 1 Byte = 8 bits.  
In the simulation, the energy consumption of data transfer is calculated only from the blind node. As previously explained, the total energy consumption of each node is \(E_{Tx}+E_{Rx}\) ((12), (13) and (15)) which depends on the total size of messages transmitted and received by each node per time slot.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 5 Prototyping and Performances Exploration
### Packet Loss Ratio  
In the quest to assess the reliability of the proposed protocol CERP, we measure the packet loss ratio, defined as the number of lost packets to the total number of sent packets. As depicted in Figure 12, the proposed protocol outperforms the direct transmission and the RPL transmission [47] regarding reliability. Furthermore, we notice an improved level of reliability as the number of relays increases (i.e., as the number of hops increases). The selection of more reliable relays will lead to the building of reliable links where the predefined level of reliability is maintained.
## 7 Discussion
### Research Questions  
Addressing the research questions, this research provides an in-depth examination of AR's influence on multiple sectors like education, healthcare, industry, commerce, and urban design. In each of these areas, AR offers distinct advantages. For instance, the educational field benefits from more engaging learning tools, healthcare sees improved surgical precision, industrial sectors experience optimized real-time guidance, and commerce leverages virtual try-ons to enhance customer experience. However, it's important to recognize the challenges these sectors face in adopting AR. Data reliability is crucial in the medical field, and the computational constraints can hamper the broader implementation in industrial and retail environments. From a technology perspective, there's notable progress, especially with machine learning techniques like CNNs enhancing object identification in medical applications, and the rise of powerful graphic processors and cloud solutions boosting AR efficiency. The fusion of AR with other evolving technologies like AI, IoT, and the 5G infrastructure is paving the way for broader applications. Despite these advances, issues such as privacy considerations, social stigma attached to wearable AR technology,and user interface complexities remain. These obstacles highlight the need for focused research and development to realize the full capabilities of AR. As AR continues to evolve, multi-sectoral collaboration is essential for navigating its challenges and ethical questions to fully tap into its potential advantages.
# Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator
## 3 Proposed Method
### Rectified Embedding Vector for Zero-Shot Classification  
In the testing stage of zero-shot classification, the generated caption is transferred to text feature extraction and combined with the image feature on the joint-embedding space to obtain the rectified vector. As displayed in Figure 2, the rectified vector created through this combination is expected to obtain a better representation for matching with the prototype defined using textural descriptions. For a given \(j\)-th test image \(I_{j}^{ts}\), the rectified embedding vector \(R^{ts}\) can be obtained through a weighted combination of the image-embedding feature \(I_{j}^{ts}\) and the embedding feature of the generated text \(\varphi_{T}\big{(}G\big{(}I_{j}^{ts}\big{)}\big{)}\), which can be written as follows:  
\[R_{j}^{ts}=(1-\alpha)\varphi_{I}\big{(}I_{j}^{ts}\big{)}+\alpha\varphi_{T} \big{(}G\big{(}I_{j}^{ts}\big{)}\big{)}, \tag{5}\]  
where \(\alpha\) is a weight value in \([0,1]\) and is empirically adjusted. The rectified vector \(R\) is applied to the distance-based classifier with class prototypes. Finally, the predicted class is obtained as follows:  
\[y\big{(}I_{j}^{ts}\big{)}=\underset{k\in\mathbb{T}_{bs}}{argmin}\ Dist\big{(}R_{j}^{ts}, \mathbb{P}_{k}\big{)}. \tag{6}\]We verified the performance of our proposed method using the rectified vector against the benchmark dataset based on Equation (6), and the results show considerable improvement in performance compared with individual vectors, such as the single image and single text vectors.