esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 4 Rockburst Mechanisms
### Factors Influencing Rockburst Instability  
Identifying the primary elements that influence the incidence and severity of rockbursts is a key step in understanding this complex process. However, unifying the rock-burst processes that occur under different geological contexts and excavation disturbances remains a challenge. Researchers use technical examples and physical model testing  
Figure 7: Stress–energy mechanism of rockburst preparation and development (modified from ref. [122]).  
to gain insight into the formation circumstances and development processes of rockbursts [7; 8; 34; 126; 127]. The damage caused by rockbursts is influenced by a combination of seismic events, geology, geotechnical factors, and mining activities, according to [PERSON] and [PERSON] [2], as shown in Figure 8. Seismic events and geology determine the severity of dynamic loads at the damage areas, while geotechnical and mining factors dictate the site's reaction to seismic impulses. By focusing on mining activities, such as the mining sequence, excavation span, and rock support system, it is possible to control the potential for rockburst harm. Modifying the mining process, changing the mining sequence, and changing drift sites are some ways to achieve this goal. A solid subterranean building technique will result in a safer work environment, but it is not enough on its own. A comprehensive rockburst support plan must also be in place. Numerous case studies have demonstrated the importance of appropriate rock support systems in preventing explosive ground.  
[PERSON] et al. conducted physical model testing to study the elements that affect slide rockburst caused by a structural plane. The results showed that the average stress level had a significant impact on the failure mode of joints, which aligned with the conclusions drawn from in-situ microseismic monitoring analyses [128; 129]. [PERSON] [130] conducted research on rockburst incidents in subsurface constructions and created an influence diagram, depicted in Figure 9, that showcased the parameters affecting the occurrence of rockbursts.  
Despite extensive research on the rockburst process, a unified hypothesis has yet to be established. This is due to the complexity of the numerous and diverse factors that influence the rockburst mechanisms and the nonlinear relationships between these factors.  
Figure 8: The main factors influencing rockburst damage and its severity (modified after ref. [2]).  
Figure 9: Influence diagram for rockbursts (modified from ref. [130]).  
As a result, it is difficult to fully explain the mechanics of rockbursts in different situations and creating a general mechanical theory for rockbursts remains a challenge.
## 3 Results
### Surface Roughness Evaluation  
The surface roughness results (measured by the R\({}_{\text{a}}\) amplitude value) are recorded in Table 1. One-way ANOVA analysis showed that there were statistical differences between the three groups (\(p<0.05\); Figure 3a). Compared with the NT group (R\({}_{\text{a}}\) = 0.98 \(\pm\) 0.18 \(\upmu\)m) and the APA group (R\({}_{\text{a}}\) = 1.12 \(\pm\) 0.28 \(\upmu\)m), it was found that the FSL group achieved significantly superior roughness (R\({}_{\text{a}}\) = 1.42 \(\pm\) 0.16 \(\upmu\)m; Table 1). Therefore, the highest roughness of the zirconia surface was due to the FSL treatment (Figure 3a).  
Figure 2: SEM images of zirconia surfaces from different groups at 500\(\times\) and 10,000\(\times\) magnifications: (**a,b**) NT; (**c,d**) APA; and (**e,f**) FSL. NT: no treatment group; APA: airborne particle abrasion group; FSL: femtosecond laser group.
## Nomenclature  
\begin{tabular}{l l l l} Adam & The optimizer used for training & SA & Spatial attention \\ Avgpool & Average pooling layers & Skew & Skewness value \\ CA & Channel attention & SMAPE & Symmetric mean absolute \\ CNN & Convolutional neural network & SNR & Spinal-noise ratio \\ Conv2d & The 2D convolution operation & Std & Standard deviation \\ CV & Coefficient of variation & \(\sigma\) & Activation function \\ DL & Deep learning & \(k\) & The forecast horizon \\ DNN & Deep neural network & \(X\) & Time series \\ ELFNet & Electricity load forecasting networks & \(N\) & Constant normalization factor \\ GAF & Gramian angular field & \(R\) & Correlation coefficient \\ GASF & Gram summation angular field & \(\theta_{i}\) & Polar angles in polar \\  & & coordinate system \\ Kurt & Kurtosis value & \(\widetilde{x}_{i}\) & The normalized time series \\ LSTM & Long short-term memory neural network & \(P_{n}\) & The effective power of the noise \\ MAE & Mean absolute error & \(P_{s}\) & The effective power of the \\ MAPE & Mean absolute percentage error & \(r_{i}\) & Polar radius in polar \\ Max & Maximum & \(t_{i}\) & The time stamp of the time series \\ MaxPool & Max pooling layers & \(T\) & The total sample of the time serie \\ Med & Median & \(Y_{1}^{(t,t)}\) & Time series after GAF processing \\ Min & Minimum & \(Y_{2}^{(\frac{t}{2},\frac{t}{2})}\) & Feature map data obtained \\ MLP & The multiple linear perceptron & \(Y_{3}^{(\frac{t}{2},\frac{t}{2})}\) & Feature map data obtained after \\ MSELoss & Error function used for training & \(Y_{pre}\) & Forecast results \\ NN & Neural network & & \\ ReLU & Rectified linear unit & & \\ ResNet & & & \\ RMSE & Root mean square error & & \\ RNN & Recurrent neural network & & \\ \end{tabular}
# Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
## 2 Temporal Database Architecture
### Temporal Database Architecture--Granularity Levels
The chain characteristics are covered by the basic type Varchar2 (max). Thus, for example, separate tables do not have to be defined for the real data types Varchar2 (20) and Varchar2  
Figure 5: Attribute oriented temporal model. (30), the values of outdated states can be combined. In addition, the attributes expressing the date and time can be implemented together as a timestamp with the appropriate precision. The temporal layer on the second level consists of the following attributes:  
* Id_change--primary key, the value of which is set using a sequence and a trigger. * Id_previous_change--the value is obtained as the maximum value of the ID_change attribute for the object identified by ID_orig in the relevant table (ID_tab). If it is a new object, the attribute will be NULL. * Statement_type determines the type of command executed: \(\circ\)  I = Insert. \(\circ\)  U = Update. \(\circ\)  D = Delete (physical deletion of the historical record from the system). \(\circ\)  P = Purge (removal of historical states in terms of their transfer to external archive repositories, extended by an optional transformation module (anonymization, aggregation, etc.)). \(\circ\)  X = Invalidate (creation of a complete undefined state of an object preceded by moving the object as such under the management of undefined objects). \(\circ\)  V = Validate (also known as Restore)--restoring an invalid object and replacing its state with an applicable one that meets all integrity restrictions. * ID_tab--identifier of the table in which the change occurred. * ID_orig--attribute that carries information about the identifier of the changed row. * Data_type--an attribute that defines the basic data type of the changed temporal attribute (the real data type can be obtained from the image of the data model valid at the defined time instant): \(\circ\)  C = Char/Varchar2, \(\circ\)  N = numerical values (Real, Integer,... ), \(\circ\)  D = date type Date, \(\circ\)  T = Timestamp, \(\circ\)  L = Lob, \(\circ\)  X = XML, etc. * ID_column--refers to a temporal column whose values change. * ID_row--associates the table with historical data (if the Update command was executed, otherwise it is NULL). * BD--time value expressing the beginning of the validity of the new state.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### How the Data Used to Train the Deep Learning Models Was Gathered/Generated?
#### 3.4.1 Telecom Italia Big Challenge Dataset  
The Telecom Italia dataset [93] was used by majority of works [6; 10; 41; 45; 46]. It was provided as apart of a _Big Data Challenge_ and is composed of various open multi-source aggregations of telecommunications, weather, news, social networks and electricity data. In 2014, the data was collected from two Italian areas: the city of Milan and the Province of Trentino.  
With regard to the Call Detail Records (CDRs) present in the dataset, Telecom Italia recorded the following activities: (i) data about SMS, (ii) data about incoming and outgoing calls, and (iii) data about the Internet traffic. A CDR is generated every time a user starts or terminates an Internet session, if a connection takes more than 15 min, or more than five MB is transferred during a user session.  
Further, the Telecom Italia dataset also includes the Social Pulse dataset (The Social Pulse dataset is composed of geo-located tweets that were posted by users from Trentino and Milan between 1 November 2013 and 31 December 2013), and other data such as weather, electricity (only for the Tentrino region), and news. For more information about this dataset, please see [93].  
It comes at no surprise that the Telecom Italia dataset was used in several papers found in this systematic review. In [45], the dataset was used to train a model for predicting the minimum, maximum, and average traffic (multitask learning) of the next hour based on the traffic of the current hour. In [6], the models were proposed to predict traffic in a city environment taking into account spatial and temporal aspects. The data was sliced using a sliding window scheme generating several samples according with the closeness and the periodicity. In [10; 41], the dataset was used to train the model to detect anomalies and data for short messages (SMS), calls, and Internet usage were considered. The authors divided the dataset into samples of three-hour ranges (morning, from 6 to 9 a.m.; afternoon, from 11 to 2 p.m.; and evening, from 5 to 8 p.m.). Another work that used the dataset for traffic prediction was presented in [46]. Here the authors compiled the traffic volume from the covered areas of cells of the dataset, and then normalized to the [0, 1] range for the convenience of carrying the analysis.
## 5 Discussion
### Limitations  
Evaluating image registration is a challenging task, mainly since ground truth data are usually not available [6]. We applied a combination of different approaches for evaluation. If segmentations of organs or structures are available in the fixed and moving image, segmentation-based metrics such as the Dice coefficient can be used to quantify the overlap between the segmentations in the two images before and after registration. Structural differences can be determined visually by color overlay of the fixed and moved images and segmentations (if available). In addition, the deformation field was examined. By calculating the Jacobian determinant of each point in the deformation field, the image folding can be determined. And optical evaluation of the deformation field visualizes the regions with smooth or large deformations.  
Training the multistage network with three subnetworks requires a lot of GPU memory. Training the multistage network was only possible with a maximum image size of 256 \(\times\) 256 \(\times\) 64 (resolution 2 \(\times\) 2 \(\times\) 4 mm\({}^{3}\)) on an NVIDIA RTX A6000 GPU with 48 GB of GPU memory. This could limit the registration result, especially in the deformable network, as the higher the resolution of the images, the smaller the deformations that can be corrected. In future work, a multistage network could be developed that combines rigid, affine and deformable transformation within a single network without multiple subnetworks (for example, a U-Net-like network), which could be trained with a larger image size (e.g., 512 \(\times\) 512 \(\times\) 128, resolution 1 \(\times\) 1 \(\times\) 2 mm\({}^{3}\)) due to the smaller network size.
## 3 Results and Discussion
### Result of the Test Conducted Using the Test Rig  
The excitation input condition of the 1/5 scale analysis model was set to be identical to the test condition to compare it with the test result. Figure 7 shows the excitation displacement input condition of \(\pm\)150 mm, \(v\) = 0.1 m/s (0.106 Hz). Figure 8 shows the basic characteristics of the analysis model, and confirms that the displacement of the crown block is attenuated from the excitation pressure (heave) due to control by the DSC and AHC cylinders. The displacement of the accumulator (piston), resulting from the movement of the DSC cylinders moving in the direction the displacement of the crown block, can be reduced to attenuate the excitation input.  
Figure 6: Results of oil pressure analysis, (**a**) \(\pm\)0.3 m (1/9) Hz; (**b**) \(\pm\)0.3 m (1/6) Hz.  
Figure 7: Excitation displacement input condition.  
Figure 9 shows a comparison of the test and analysis results, which showed good agreement. The time data in (a) and the frequency characteristics in (b) show a good correspondence. Figure 9 confirms that the analysis model is reliable.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 4 Discussion  
### Sum of Absolute Coordinates Differences  
Figure 14 illustrates the summed coordinate differences, which were calculated using the formula:  
\[sum_{XYH}=|d_{X}|+|d_{Y}|+|d_{H}| \tag{5}\]  
where:  
\(d_{X},\ d_{Y},\ d_{H}\)--differences from the reference coordinates.  
The Leica GS18T obtained the smallest sum (the highest measurement accuracy) at the Pt 4 station (horizon covered from the north) and it was 1.7 cm. On the other hand, the highest sum (the worst measurement accuracy) at the Pt 5 station (horizon covered from the west and directly from above) was 7.3 cm.  
Figure 13: Cumulated diagrams with results of measurements at the Pt 7 station (urban canyon conditions; differences in relation to the reference points with standard deviations [m]).  
The u-blox ZED-F9P with the Leica AS10 professional surveying antenna had the smallest sum at the Pt 2 station (horizon obscured from the south) and it was 1.2 cm, while the largest sum at the Pt 7 station, with a value of 13.6 cm.  
The u-blox ZED-F9P set with the standard ANN-MB-00 antenna attached by the manufacturer to the C099-F9P application board obtained the smallest sum at the Pt 4 position and it was 1.9 cm, while the highest sum at the Pt 7 position, with the value of 32.6 cm.  
An increasing trend of results can be seen in the graph. Surprisingly, better results were obtained at station Pt 6 than at the station Pt 1 with a fully open horizon. It can be assumed that despite many obstacles on the horizon from different sides, there were also free gaps that made it possible to create a good-quality angular resection of signals coming from the satellites of all constellations.  
Considering the conditions at all measurement stations, it can be concluded that both receivers performed well. The u-blox ZED-F9P, equipped with the Leica AS10 antenna, had a slight advantage in the clear areas, while its rival excelled in the very demanding \"urban canyon\" type conditions at the last station.
###### Abstract  
The task of image captioning in low-resource languages like Tamil is fraught with challenges due to limited linguistic resources and complex semantic structures. This paper addresses the problem of generating contextually and linguistically coherent captions in Tamil. We introduce the Dynamic Context-Aware Transformer (DCAT), a novel approach that synergizes the Vision Transformer (ViT) with the Generative Pre-trained Transformer (GPT-3), reinforced by a unique Context Embedding Layer. The DCAT model, tailored for Tamil, innovatively employs dynamic attention mechanisms during its Initialization, Training, and Inference phases to focus on pertinent visual and textual elements. Our method distinctively leverages the nuances of Tamil syntax and semantics, a novelty in the realm of low-resource language image captioning. Comparative evaluations against established models on datasets like Flickr8k, Flickr30k, and MSCOCO reveal DCAT's superiority, with a notable 12% increase in BLEU score (0.7425) and a 15% enhancement in METEOR score (0.4391) over leading models. Despite its computational demands, DCAT sets a new benchmark for image captioning in Tamil, demonstrating potential applicability to other similar languages.  
Keywords:image captioning; context-aware transformer; low-resource language processing; vision transformer; natural language processing +
Footnote †: journal: Applied sciences
## 3 Results
### Comparative Analysis  
Based on the fusion results of NTL data with LandScan and heatmap data, respectively, the NTL-fused heatmap data demonstrate higher accuracy and consistency in identifying  
Figure 8: Spatial Range of the PRD Urban Agglomeration Identified by NTL_HM Data.  
the spatial range of the PRD urban agglomeration (Table 2). Specifically, the identification accuracy of NTL_HM reaches 89.17%, compared to 80.37% for NTL_LS. Additionally, the Kappa coefficient for NTL_HM is 0.7342, significantly higher than the 0.5225 for NTL_LS. The Kappa coefficient measures the consistency of classification results, with higher values indicating more reliable classifications. Therefore, in terms of accuracy and consistency, NTL_HM clearly outperforms NTL_LS in identifying the range of the PRD urban agglomeration.  
The advantage of NTL_HM in identification lies primarily in its capacity to capture dynamic population mobility and its high adaptability to economic activity levels. The dynamic population mobility information provided by heatmap data compensates for the limitation of LandScan data, which only represent resident population density. This enables NTL_HM to more accurately identify areas of frequent economic activity and population mobility within the PRD urban agglomeration, particularly in transition zones and expansion areas between cities. Specifically, compared to NTL_LS data, NTL_HM data capture a wider spatial range due to their high sensitivity to dynamic population mobility. For example, in the Guangzhou-Shenzhen corridor and cities along both sides of the Pearl River Estuary, the NTL_HM method identifies a broader range, as these areas experience frequent population mobility and active economic activities. In contrast, the NTL_LS method, which relies on resident population distribution, focuses more on core urban areas and overlooks the mobility characteristics of secondary cities and transitional zones. This difference highlights the unique advantage of dynamic data in identifying spatial ranges. Therefore, with its sensitivity to economically active zones and population mobility characteristics, NTL_HM is better suited for identifying fast-growing and highly dynamic urban agglomerations like the PRD urban agglomeration. In contrast, due to its reliance on resident population distribution, NTL_LS data respond less effectively to dynamic economic and population characteristics, resulting in relatively limited spatial range identification for peripheral areas and secondary cities.  
Overall, NTL_HM data demonstrate higher accuracy and consistency in identifying the spatial range of the PRD urban agglomeration, confirming the importance and effectiveness of dynamic population mobility information in urban agglomeration spatial identification.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 3 Performance Evaluation and Field Measurement for Validation
### Synchronization Stability Test  
The described time synchronization method is experimented in the field for validation. We set up a GPS module as a signal source to generate PPS signals. Ten sensor nodes are distributed in different random locations, and they collect the PPS signals continuously. The schematic of the time synchronization stability test is shown in Figure 12. Each sensor node has its own GPS and active antenna, which time-stamps the PPS data. In addition, each node uses the proposed T3I method to test the synchronization performance of the system at different locations. The test time was from 8:00 a.m. on 2 November to 8:00 a.m. on 25 November 2019. Figure 13a shows the time synchronization stability of the different nodes in different locations on the 15 November 20:00 p.m., and Figure 13b shows the long-time stability for the first node at 20:00 p.m., with the step of 3 days from the test time. The PPS signal output by the GPS module is a step signal. Actually, the signal collected by these nodes is the minimum phase response of this step signal. The parameters of the instruments are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Indicators/Type** & **Value** \\ \hline Sampling Rate (\(\upmu\)s) & 1000 \\ Gain (dB) & 0 \\ SYNC Mode & GPS + TCXO \\ Digital Filter & Sinc + FIR + IIR \\ High-pass Cut-off Frequency (Hz) & 200 \\ Low-pass Cut-off Frequency (Hz) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the measurement.  
Figure 13a shows that the PPS signals are on one curve from the ten nodes, which indicates that the time synchronization method has good stability for the distribution of multiple nodes at different locations. Figure 13b shows that the time synchronization changes with time in the performance of the sensor node are negligible.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment  
### Basic Assumptions  
The following assumptions and parameter settings need to be made for the set-up algorithm before establishing the mathematical model of the evacuation path optimization problem:  
Figure 4: Collision prediction.  
Figure 5: (**a**) Collision type III; (**b**) collision prevention approach III.  
Figure 3: (**a**) Collision type II; (**b**) collision prevention approach II.  
1. The initial node is the accident site, the endpoint is the safe area, and all the nodes between them are evacuation points; 2. Each evacuation point can be passed only once; 3. When starting the operation, the route must be executed to completion without interruption; 4. During the execution of the task, the speed change is uniform; 5. Human factors and obstacle changes are ignored when the accident occurs.