esa-sceva/satcom-chunk-collection huggingface dataset:
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 2 Related Work
### How Fine-Tuning Changes BERT--Clustering.  
The prior research paper by [16; 17] explains that fine-tuning the BERT model in supervised learning is a process, in which data with the same class clustering are together. Data with the same label create multiple clusters, and these clusters come together to form a larger, single cluster for the same label. Figure 2 shows the anisotropy phenomenon of BERT and the characteristics of clustering clearly. As training progresses, data with the same label become extremely concentrated in a small vector space.  
Figure 1: Cold-Start Active Learning: A small portion of the dataset is separated as labeled data, while the remaining data is treated as unlabeled by detaching their labels. When the selected data points are used for training, their labels are reattached.  
How Fine-Tuning Changes BERT--Decision Boundary.  
As shown in Figure 2, in the early stages of fine-tuning, clustering is not yet fully developed, allowing a variety of data points to have the opportunity to enter the clusters. As fine-tuning progresses, there are fewer data points near the decision boundary. Although this is a 2D PCA visualization and may not fully represent the original features, it should be noted that when performing Cold-Start Active Learning tasks, the density of data points near the decision boundary differs significantly between the initial iteration of training with a small amount of data and the later iteration with a larger amount of data.  
**Representation without Classifier.**  
In prior research, it was found that the performance of a classifier is influenced not only by the hidden state representations but also by the structure and training process of the classifier [17; 21]. In that study, by excluding the classifier and analyzing the representations alone, it was shown that performance comparable to using a classifier can be achieved, and it allowed for more varied interpretations. In active learning for sentence classification, it is also necessary to focus on interpreting the representations themselves rather than relying on the entropy of the classifier's logits.
## 5 Performance Evaluation
### Comparative Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Process and \# of AUVs** & & **Demerits** \\ \hline \multirow{3}{*}{[43]} & \multirow{3}{*}{\begin{tabular}{c} AEC mechanism \\ 1. AUV \\ \end{tabular} } & \(\bullet\) & CH is selected in each virtual sector by using energy; cases which consider only this metric have the possibility to select a node as head with a smaller degree and located at farthest distance. This degrades the data gathering efficiency; \\  & & \(\bullet\) & Inappropriate criteria (delay and transmission time) for scheduling. \\ \hline \multirow{3}{*}{[44]} & \multirow{3}{*}{\begin{tabular}{c} ALP \\ 1. AUV \\ \end{tabular} } & \(\bullet\) & Use of pre-defined spiral path increases AUV’s tour length, which eventually increases data collection delay; \\  & & \(\bullet\) & Single AUV increases waiting time at sensors, which leads to drop in sensed information due to its capacity limitation. \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{\
\begin{tabular}{c} Bipartite K-means \\ 1. AUV \\ \end{tabular} } & \(\bullet\) & The cluster construction time is large since, initially, the clustering is performed with random centers and then, again, the cluster centers are reselected based on the distance metric; \\  & & \(\bullet\) & The path prediction of an AUV is based on the determination of the shortest data collection time; however, use of a single AUV increases traveling distance, which eventually extends delay. In case of an event message, it fails to deliver the message promptly. \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{
\begin{tabular}{c} ELMPP \\ 2. AUVs \\ \end{tabular} } & \(\bullet\) & More energy consumption in the relay node due to frequent selection of the same sensor as relay. This happens because the fixed path in the AUV is based on line segments; \\  & & \(\bullet\) & RSSI is measured to select relay, which is not efficient since RSSI gives only the strength of the communication link but not the strength of the node. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison on AUV-based data gathering. The efficiency of the proposed DEDG 3D-UWSN was evaluated by measuring the network lifetime in terms of energy consumption and number of alive sensors. Then, the data gathering efficiency was depicted by the estimation of packet delivery ratio, end-to-end delay, collection delay and tour length.
## 6 Conclusions  
The monitoring of tidal flats has great importance for morphological evolution, wetland protection, and coastal management. Tiaozini, the center tidal flats of the Jiangsu coast, China, changes quickly because of the swing of its tidal creeks. Thus, it is difficult to obtain relevant in situ topographic data. To solve this problem, based on remote sensing images, we constructed an ANN model with a multi-layer feed-forward back propagation algorithm to simulate the elevation of tidal flats. The structure of the ANN model includes double-hidden layers with 15 nodes in each layer. The input layer of this model has seven nodes: reflectance (B1, B2, B3, B4), geographic coordinates (X, Y), and distance (D) from the waterlines. The output layer selects the elevation data obtained from the waterline method in case of insufficient measured data. Based on the three HJ-1 images (21 March 2014; 11 February 2012; 11 July 2013), after the necessary preprocessing, three fresh DEMs were simulated by the ANN model. Through section verification, the vertical accuracy was found to be between 0.3 and 0.4 m, and the horizontal accuracy was able to reach 30 m, which is equal to the spatial resolution of the HJ-1 image. An artificial neural network, with its strong learning and adaptive ability, can be a powerful aid for the waterline method. In this way, especially in tidal flat areas of rapid change, long-term and continuous monitoring can be achieved.  
Y.K. designed and executed the entire study. [PERSON] is responsible for image processing and document correction. [PERSON] implemented accuracy assessment of the ANN model. [PERSON] constructed five tide stations and provide tidal level data for images. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFD0900906); Natural Science Foundation of Jiangsu Province (BK20170875); National Natural Science Funds of China (41806118); Major International (Regional) Joint Research Project (51620105005); Jiangsu Transportation Technology Project (2017 ZX01); and the Jiangsu Marine Science and Technology Innovation Project (HY2017-6).  
The authors are grateful to the China Center for Resource Satellite Data and Applications (CRESDA) for supplying all of the HJ-1 CCD images. We thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] who gave advices to the model. We thank [PERSON], [PERSON], [PERSON] and [PERSON] who participated in the field work for the establishment of five tide-gauge stations.  
The authors declare no conflict of interest.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 6 Real-Life Deployment
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node Types** & **Quantity** & **Function** \\ \hline Sensor node & 30 & Data sampling and transmission \\ Cluster node & 4 & Data relay \\ Sink node & 1 & Data collection \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantity and function of nodes used in the full scale deployment. Figure 7: Schematic layout of the wireless monitoring system in the RCB. impacts and send them to cluster heads every 30 min. Cluster nodes help sensor nodes relay data to the sink node. The sink node collects, stores and displays all the data in real-time. The monitoring stage lasts seven days until the pressure test stops. The sink node can acquire data from all of the 30 sensor nodes, and the average data receiving rate is similar to the transmission test that we have done before, which verifies the efficiency of the wireless monitoring system. In addition, only about 10% of the energy is consumed on average, which is consistent with our previous theoretical calculations.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 2 System Overview and Problem Statement
### Step 2: The SPs Determine Whether to Response the Task Request of the SR  
After receiving the request from the SR \(u_{i}\), the SPs will evaluate the trustworthiness of the SR \(u_{i}\) based on the direct interaction records and some recommendation opinions from several intermediate objects. The set of SPs is denoted by \(V=\{v_{1},v_{2},\ldots,v_{n}\}\). The set of intermediate objects, which have some interactions with the SR, is denoted by \(IN_{SR}=\{r_{1},r_{2},\ldots\}\). The trustworthiness of the SR \(u_{i}\) from the viewpoint of the SP \(v_{j}\) is formulated as  
\[\overrightarrow{T}_{u_{i}\gets v_{j}}(\varphi)=f_{ct}(\overrightarrow{T}_ {u_{i}\gets v_{j}}^{d}(\varphi),\{\overrightarrow{T}_{u_{i}\gets r_{ k}}^{rec}(\varphi)\}_{k=1,2,\ldots}), \tag{1}\]  
where \(\overrightarrow{T}_{u_{i}\gets v_{j}}^{d}(\varphi)\) denotes the direct trust vector of the SR \(u_{i}\) from the viewpoint of the SP \(v_{j}\). Additionally, \(\overrightarrow{T}_{u_{i}\gets r_{k}}^{rec}(\varphi)\) denotes the recommendation trust of the SR \(u_{i}\) from the viewpoint of the intermediate object \(r_{k}\). The function \(f_{ct}\) is the convergence function of the trust opinions from the different sources. Based on the evaluation result of the SR trust, the SP will determine whether to respond to the task request by solving the following formulation:  
\[Response_{u_{i}\gets v_{j}}(\varphi)\left\{\begin{array}{ll}\psi_{v_{j} }(\varphi),&g(\overrightarrow{T}_{u_{i}\gets v_{j}}(\varphi))\geq th_{v}( \varphi)\\ null,&g(\overrightarrow{T}_{u_{i}\gets v_{j}}(\varphi))<th_{v}(\varphi). \end{array}\right. \tag{2}\]  
where \(th_{v}(\varphi)\) is a response threshold set by \(v_{j}\) for the task \(\varphi\), and \(\psi_{v_{j}}(\varphi)\) denotes the price that SR \(u_{i}\) needs to pay to the SP \(v_{j}\) if \(u_{i}\) delegates \(v_{j}\) to perform task \(\varphi\). \(g(\cdot)\) denotes the function of the trustworthiness calculation.
# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 5 Summary and Conclusions  
This study focused on the recognition of rolling marks on railroad rails at high process speeds. Rolling marks are 3D lettering rolled into the rail web, comprising a distinctive sequence of characters. They convey crucial information about the rail manufacturer, production year, steel grade, and rail profile.  
Images were captured using a color camera with dark field illumination. The YOLO neural network was employed to detect the characters present in the rolling marks. Leveraging the inherent structure of rolling marks, it was possible to infer the provided information. A weighted Levenshtein distance was computed between the detected characters and predefined reference rolling marks. This approach enables corrective measures to be applied to inaccurately detected characters. Since this method allows for the interpretation of all rail information, diverse analyses can be conducted.  
For instance, it is feasible to automatically search for specific manufacturers or rolling marks from particular manufacturing periods. Additionally, transitions between different steel grades or rail profiles can be visualized. As shown in Section 4.3, potentially safety-critical areas can thus be found. Frequent repair of a track indicates a track with low quality. This provides rail infrastructure managers with a valuable tool for maintenance and the planning of maintenance intervals for their infrastructure.  
Clear practical limitations of the proposed approach are heavy surface abrasion and overlap by vegetation or engineered rail fasteners (see Figure 9), i.e., situations in which the rolling mark structure is simply no longer present in the image. In general, however, the proposed approach is surprisingly robust, due to:  
* A special camera illumination technique (dark field illumination);
* The use of optical bandpass filters for improved and thus more reliable image acquisition;
* A specially trained object detection algorithm (YOLO) for finding single rolling characters, which is not based on classical OCR (\"Optical Character Recognition\") techniques;
* A subsequent text correction, which improves reliable recognition in the case of partially incorrect detections.  
Figure 9: Correct result despite an incorrect detection due to a mounted grounding cable partially obscuring the rolling mark.
## 8 Conclusions  
This research enhances the understanding of how environmental factors influence the operational limits and performance of overhead transmission lines. By comparing SLR and DLR methodologies, the study highlights the potential of DLR to optimize power transmission capacity under changing environmental conditions. Additionally, the research provides insight into how different methodologies recommended by CIGRE and IEEE can affect the calculation of ampacity and conductor temperature in overhead transmission lines.  
The adoption of HTLS conductors, particularly ACCC, marks a significant step forward in improving power transmission. These conductors not only increase current-carrying capacity and reduce line losses but also offer economic advantages over their lifecycle despite higher initial costs.  
The findings strongly support transitioning towards DLR to maximize the potential of existing overhead power lines, as it adapts more effectively to dynamic conditions compared to the conservative approach of SLR.  
The application of ACCC conductors is a strategic means to enhance transmission line capacity without extensive infrastructure changes. Utilizing DLR methodologies, this approach provides critical insights into optimizing conductor performance under varying environmental conditions. These advancements are pivotal for improving grid reliability and operational efficiency in energy transmission. Moreover, upgrading to advanced conductor technologies promises substantial economic benefits, ensuring streamlined operations across power grids. These upgrades are not only beneficial for transmission systems but also for distribution systems.  
Conceptualization, F.M., L.B., W.M. and P.P.; methodology, F.M., L.B., and P.P.; software, F.M., L.B. and W.M.; validation, F.M. and L.B.; formal analysis, W.M. and P.P.; investigation, W.M. and P.P.; resources, F.M., L.B. and W.M.; data curation, F.M. and P.P.; writing--original draft preparation, F.M. and L.B.; writing--review and editing, F.M. and P.P.; visualization, F.M. and W.M.; supervision, F.M. and L.B.; project administration, L.B.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak Research and Development Agency under contracts No. APVV-19-0576 and APVV-21-0312.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 7 Processing Architecture
### CHP2 Physical Layer
#### 7.1.2 Receive Engine  
The receive engine (Figure 16) captures received signals and transfers the data to the PS section as in Figure 14. During the acquisition stage, a frame detector continuously correlates the received data with the waveform pre-amble. If this correlation exceeds a pre-defined threshold, the detector declares a detection and dumps the raw IQ data into the DMA blocks, which then move to the PS section. The frame detector saves the timestamp associated with this event, which corresponds to the matched filter ToA estimate \(\hat{k}\), as described in (9).  
Figure 14: CHP2 processing architecture on the ZynqMP platform. In the PL section, the critical timing components and massive mathematical tasks are implemented as HDL IP cores. In the PS section, the light tasks are implemented as processing programs running over the ARM Cortex-A53 cores. Except for the AD9361 control core and DMAC blocks that come from the ADI SDR architecture, the remaining blocks are fully customized.  
Figure 16: Receive engine block diagram.  
Figure 15: Transmit engine block diagram.
## 2 Methodology
### Information Decoding Module  
The information decoding module consists of two parts of input data: high-order features output by the feature aggregation module and multi-scale features output by the feature coding module. It decodes high-order features output by the feature aggregation module layer by layer and outputs feature maps with a consistent size of the original image. This module is mainly composed of convolutional upsampling blocks and skip connection blocks. The convolutional upsampling block consists of convolution and upsampling operations, which are mainly used to increase the size of the feature map. The skip connection block is implemented by the concatenation operation. It is mainly used to connect the multi-scale features output by the feature coding module and improve the detailed information of the feature map by introducing low-level features. The specific calculation process is as follows:  
\[y_{1}=Concat\Big{(}x_{l}^{3},Conv\_upp\Big{(}x_{gl}\Big{)}\Big{)}, \tag{4}\]  
\[y_{2}=Concat\Big{(}x_{l}^{2},Conv\_upp(y_{1})\Big{)}, \tag{5}\]  
\[y_{patch}=Concat\Big{(}x_{l}^{1},Conv\_upp(y_{2})\Big{)}, \tag{6}\]  
where \(x_{gl}\) represents the new features after feature aggregation, \(x_{l}^{1},x_{l}^{2},x_{l}^{3}\) represents the multi-scale shallow features output by the feature coding module, which is used to gradually restore the feature map to its original size, \(Conv\_upp\) represents convolution and upsampling operations, \(Concat\) stands for feature concatenation operation, \(y_{patch}\) represents the new feature after information decoding, and the size is the same as the image patch.
# Seismic Reflection Analysis of AETA Electromagnetic Signals
## 4 Results
### Seismic Case Analysis
#### 4.1.2 Analysis of Yibin M. 6.0 Earthquake  
When the Yibin M. 6.0 earthquake occurred, there were more than 30 AETA monitoring stations within 200 km of the epicenter, 27 of which were operating normally and had robust data. Table 3 shows the location and installation date of stations within 200 km of the earthquakes. Figure 10 shows the abnormal values after LAE fitting, which can more intuitively show the abnormality before and after the earthquake.  
Figure 10 shows the statistical abnormal points at the PSFR, GAX and MBXB stations. Among them, IQR \(-\) Diff refers to \(\Delta y\) in Formula (6), and IQR \(-\) Prop is the abnormal point obtained after we normalized the outlier and recalculated the IQR anomaly. We can see that some stations exhibited anomalies before the earthquake in the first and last pictures in Figure 10, and the middle section shows that some stations have abnormal data after the earthquake. The number or amplitude of the outliers obtained by Diff and Prop are similar, which further demonstrates the robustness of the method of extracting anomalies from the results of LAE processing. The IQR-Prop calculation method is defined as follows:  
\[y_{min}=min(x_{n}) \tag{7}\]  
\[y_{max}=max(x_{n}) \tag{8}\]  
\[\overline{y}=\frac{\Delta y-y_{min}}{y_{max}-y_{min}} \tag{9}\]  
Figure 8: Electromagnetic disturbance, fitting vector and reconstruction deviation in JZG and SP stations.  
\[\Delta y_{Prop}=\left\{\begin{array}{ll}0,&if\ \overline{y}\leq\overline{y}_{upper}\\ \overline{y},&if\ \overline{y}>\overline{y}_{upper}\end{array}\right. \tag{10}\]  
where \(x_{n}\) is the deviation series of LAE input and output, \(\overline{y}\) is the normalized result of \(x_{n}\), \(\overline{y}_{upper}\) is the IQR anomaly threshold calculated after the sequence is standardized, and the anomaly value is \(\Delta y_{Prop}\). The number of abnormal points at each station is shown in Table 4.
## 3 Data Enhancement Methods
### Gaussian Noise Data Enhancement  
Gaussian noise is a noise with a normally distributed probability density function, and is often used to enhance the number of image datasets. When an image dataset is too small to train a deep learning model, Gaussian noise can be added to each pixel of the original images to generate more data and reduce the overfitting phenomenon during training, thereby improving the effect of model training. The experiments in this study adopted the same concept by adding Gaussian noise to each feature of the NSL-KDD dataset to increase the amount of data for rare data categories. In addition, some data features of the original dataset were not continuous attributes like the graphical data, but were classified as discrete attributes. However, this study dealt with these data by directly assuming they were continuous, without any additional data transformation processing.
## 5 Energy Consumption Optimization Strategy
### Detection Using Time Scheduling  
This project is focused on monitoring high-speed trains which usually follow strict timetables. That is, each train will pass over the top of each slab track at the same time every day. This feature can be exploited to use a clock to wake up the nodes at the scheduled time with a margin of \(\pm\)10 min. During that time, the antennas will wait for \"beacon signals\" from the train. Upon detecting a \"beacon signal\", nodes will begin to record the data collected by the accelerometer.  
Disadvantages of this solution:  
1. The nodes would be active longer than the previous option.
2. If the train schedule changes, nodes have to be reprogrammed (it can be done over-the-air by the operator).
3. An antenna continuously emitting \"beacon signals\" needs to be placed on the train.