esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The dense layer takes in the input embeddings, which could be the hidden states of the previous layer or the input features, and applies a set of learnable weights to compute a score for each embedding. These scores represent the relevance of each embedding to the current context. The output of the dense layer is then passed through a SoftMax function to obtain a probability distribution over the embeddings [5]. This distribution represents the attention weights, which indicate how much each embedding should be attended to in the next layer. The attention weights are then used to compute a weighted sum of the input embeddings, which forms the context vector. The context vector represents the attended information from the input and is used as the input to the next layer in the model [3]. On the other hand, LIME (local interpretable model-agnostic explanations) is a post-hoc explainability algorithm that works by approximating the behavior of the model in a local region around a specific input instance [6]. This generates an interpretable model that approximates the predictions of the original model, allowing the user to understand which features were important in making the prediction. LIME is highly relevant due to its capacity to make intricate machine learning models understandable, thus addressing the need for transparency and accountability in AI systems. Its significance extends to building trust, ensuring adherence to regulations, detecting and mitigating errors and biases, enabling collaboration between humans and AI, guiding model enhancements, and broadening the application of AI across diverse sectors, all of which collectively contribute to the responsible and trustworthy deployment of AI technologies. Attention mechanisms and LIME (local interpretable model-agnostic explanations) can be used together to improve the accuracy of deep neural networks. By using LIME to explain the behavior of a model in a local region, it can be easier to understand how the attention mechanism is used to focus on specific parts of the input [7]. After applying the LIME method to the dataset, we were able to observe the relevant features for the model inference. We retained only the relevant characteristics and applied the attention mechanism only to them. Thus, we managed to increase the accuracy of the prediction by a relevant value of 20%, compared to the moment when we did not apply the method of paying attention to the relevant characteristics. A prototype of the application can be accessed under the following link: [[https://cfavia-dizertatie-main-nhrwqh.streamlit.app/](https://cfavia-dizertatie-main-nhrwqh.streamlit.app/)]([https://cfavia-dizertatie-main-nhrwqh.streamlit.app/](https://cfavia-dizertatie-main-nhrwqh.streamlit.app/)) (accessed on 1 November 2023). The Pythoncode is available under a Github link: [[https://github.com/cflavia/dizertatie](https://github.com/cflavia/dizertatie)]([https://github.com/cflavia/dizertatie](https://github.com/cflavia/dizertatie)) (accessed on 1 November 2023).
## 2 Data and Methods
### Operational Retrieval of Significant Wave Height  
The lookup tables \(\mathbf{H}^{\mathbf{m}}[\sigma_{s},t]\) have been generated for 51 beam SAR echo and a LRM echo using the parameters in Table 2 in alignment with [23; 29]. The table files were read into a python function object, allowing for linear interpolation between the table entries. The object also includes all fitting functions (Equation (6)). In addition to the lookup tables a weighting \(w_{i}\) for each mode is used, defined as the reciprocal of the estimated variance in the echo power for each range bin number.  
We systematically opened the level 1-B files selecting all ocean records over 60\({}^{\circ}\)N. The data filtering and downsampling was performed as described in Section 2.1. The resulting open ocean normalised 1 Hz echoes \(\widehat{P}(t_{i})\) were then accessed over bin number [28; 88] for SAR mode and [18; 78] for LRM mode. The echoes were then matched to the idealised echo in Equation (5) using coincident satellite orientation values \((\mu,\chi,h)\) also taken from the level 1-B file, using the optimisation relation  
\[S(A,\sigma_{s},t_{0})=\frac{\sum_{i}w_{i}\Big{(}AP(\sigma_{s},t_{i}-t_{0},\mu, \chi,h)-\widehat{P}(t_{i})\Big{)}^{2}}{\sum_{i}w_{i}} \tag{6}\]where \(A\) is the echo power scale factor (analogous to \(\sigma_{0}\)) and \(t_{0}\) is the normalised wave range adjustment (equivalent to sea surface elevation). Minimising \(S(A,\sigma_{s},t_{0})\) retrieves optimal values for \(A,\sigma_{s},t_{0}\) with all values and the resulting best fit value of \(S\) recorded. We took all cases of \(S<(2\times 10^{-4},2\times 10^{-3})\) for SAR and LRM modes to be cases of acceptable wave fit with these results included within the analysis in Section 3. These parameters were chosen to balance the data quality and coverage for both modes separately [29]. The filtering removes 25% of SAR mode data and 5% of LRM mode data. The lower error fit limit and increased number of removed data for SAR mode is due to no sea ice mask being used at this stage in the processing chain. The majority of discarded SAR mode data are from ice covered regions.  
For the analysis of the Arctic wide SWH data fields were gridded onto a 100 km polar stereographic grid. The grid was chosen to allow for the maximum useful data coverage for a meaningful daily data field. Due to the orbit characteristics of CS2, greater data coverage is possible for a longer time period, though for SWH, daily variability in wind speeds restricts the limit on meaningful time resolution. We used the 15% contour from the NSIDC daily ice concentration plots to remove values retrieved from within the sea ice pack. All SWH records in each day (00:00 to 23:59) that pass the fit tests were collected into grid bins with the mean taken. Furthermore, recorded are the data record count for both modes in all grid cells.
## 6 Conclusions  
Selecting sandstone as the raw rock material, based on the principle of similarity, the optimal ratio is obtained through experiments, and a mortar rock-like material model with hole and cracks is produced. We conduct biaxial compression experiments on samples under different lateral pressures and hole depths to simulate the strength and deformation failure characteristics of sandstone surrounding rock tunnels during engineering excavation. The results achieved are as follows  
(1) One type of rock mortar test material is prepared, and its main physical and mechanical parameters are all within the range of sandstone, which can effectively simulate the stress deformation characteristics of sandstone.  
(2) When the depth of the hole in cracked rock-like material samples exceeds 50% of the length, the strength and deformation of the samples undergo a sudden change. Under the stable lateral pressure \(\sigma_{2}=4\) MPa, the peak strength decreases with the increase in hole depth, but the amplitude of the change is relatively gentle. The peak stress remains between 50 MPa-70 MPa, and for every 20 mm increase in hole depth, the peak stress decreases by about 3%. However, when the depth of the holes in the cracked specimen increases from 40 mm to 60 mm, the peak stress decreases significantly, with a decrease of about 10%. Moreover, the maximum values of strain value at peak strength and lateral strain both occur at a hole depth of 60 mm.  
(3) When the cracked specimen contains through-holes, under lateral pressure \(\sigma_{2}=2\) MPa and 4 MPa, the propagation direction of secondary cracks does not turn towards the axial  
Figure 31: Types of crack propagation in hole-crack specimens with different hole depths under lateral stress \(\sigma_{2}=4\) MPa (M—compound crack; S—shear crack; T—wing crack).  
stress direction, and the crack propagation mode is mainly composite cracks. When the lateral pressure \(\sigma_{2}\) = 6 MPa, there are not only composite cracks, but also coplanar shear cracks that crack along the direction of the prefabricated crack.  
(4) At the stable lateral pressure \(\sigma_{2}\) = 4 MPa, different hole depths exhibit different cracking patterns. When the hole depth is \(H\) = 0 mm and 20 mm, the fracture mode of the sample is tension composite fracture. When the depth of the hole \(H\) = 40 mm, the fracture mode is composite fracture. When the hole depth \(H\) = 60 mm, the sample exhibits shear composite fracture. When the depth of the hole \(H\) = 80 mm, the fracture mode is composite fracture.  
Methodology, Y.T.; Writing--original draft, X.L.; Supervision, B.H., K.S. and J.L. All authors have read and agreed to the published version of the manuscript. This study was supported by the Zhejiang Province Water Resource Department Program (RC2105) of China, and the Zhejiang Province Education Department Program (Z20230040) of China.  
Demand data used to support the findings of this study are available from the first author upon request. The authors declare that there are no conflict of interest regarding the publication of this paper.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
###### Abstract  
Thermal failure propagation is one of the most severe challenges for battery modules and it usually aggravates the thermal hazards, further resulting in serious accidents. This work conducted two groups of experiments to investigate the influence of discharging treatment and module shape on the thermal failure propagation of battery modules, where the triangle module, rectangle module, parallelogram module, line module, hexagon module, and square module were researched. Based on the results, it can be found that an evident domino effect existed on the thermal failure propagation of battery modules. Namely, the failure propagation process consisted of several phases and the number of phases depended on the shape of the module. Besides, it is indicated that discharging treatment on a battery module when it was in a high-temperature environment would aggravate its thermal failure propagation by bringing an earlier thermal failure, a quicker failure propagation, and a larger mass loss. Combining the results of safety and space utilization, it is revealed that the triangular module may be the best choice of battery module due to its smaller failure propagation speed and higher space utilization.  
b  
battery module; thermal failure propagation; discharging treatment; module shape; surface temperature; mass loss Article
## 3 Problem Description
### Solution Analysis  
To validate the results, the same problem with identical input parameters was addressed using various other widely represented MCDM methods found in the literature. These methods included TOPSIS, VIKOR, Simple Additive Weighting (SAW), COPRAS, AHP, SWARA, Preference Ranking Organization METHOD for Enrichment of Evaluations (PROMETHEE), and Elimination and Choice Translating Reality (ELECTRE). The ranks assigned to the alternatives are presented in Table 6. To gauge the similarity of the ranks obtained through these methods with those derived from the ADAM method, [PERSON]'s correlation coefficient (SCC) was calculated. A mean SCC value of 1.00 signifies 100% agreement between the results obtained via the ADAM method and those obtained using the other methods. Furthermore, all methods consistently identified the same alternative as the top-ranked choice. Consequently, it can be inferred that the obtained solution is valid, affirming that the chosen alternative is the most favorable concerning the considered criteria.  
To investigate the changes in the alternatives ranking using other methods, the results were normalized on a scale ranging from 0 to 1. The obtained values are shown in Table 7. In Table 7, it can be seen that using other methods results in the same ranking of alternatives. Even with the application of different methods, A1 consistently holds the top position compared to others. The difference between the values of A2 and A3 varies with the use of different methods; in some cases, this difference is slightly less compared to the application of other methods. A4 is consistently ranked the lowest. The obtained results indicate that the ADAM method provides a stable solution.  
Figure 3 shows the normalized values of alternatives using other methods (Table 6). The figure shows the deviations in the values for the alternatives obtained using different methods. It was observed that by applying the PROMETHEE and SWARA methods, the alternative values were lower compared to the other methods, but the ranking remained the same.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & **A1** & **A2** & **A3** & **A4** \\ \hline \multirow{3}{*}{ADAM} & Value & 0.4728 & 0.231 & 0.2071 & 0.0892 \\  & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.4798 & 0.2831 & 0.2208 & 0.0164 \\ \multirow{3}{*}{TOPSIS} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.3695 & 0.2669 & 0.2325 & 0.1311 \\ \multirow{3}{*}{VIKOR} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.3555 & 0.2583 & 0.241 & 0.1453 \\ \multirow{3}{*}{SAW} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.3549 & 0.2604 & 0.2404 & 0.1443 \\ \multirow{3}{*}{COPRAS} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.5416 & 0.2148 & 0.1714 & 0.0721 \\ \multirow{3}{*}{AHP} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.2986 & 0.2646 & 0.2447 & 0.192 \\ \multirow{3}{*}{SWARA} & Rank & 1 & 3 & 2 & 4 \\  & Value & 0.2811 & 0.2429 & 0.2468 & 0.2291 \\ \multirow{3}{*}{PROMETHEE} & Rank & 1 & 3 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Validation of the solution—normalized values and obtained ranking.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
###### Abstract  
Radio-frequency identification (RFID) is a tracking technology that enables immediate automatic object identification and rapid data sharing for a wide variety of modern applications using radio waves for data transmission from a tag to a reader. RFID is already well established in technical areas, and many companies have developed corresponding standards and measurement techniques. In the construction industry, effective monitoring of materials and equipment is an important task, and RFID helps to improve monitoring and controlling capabilities, in addition to enabling automation for construction projects. However, on construction sites, there are many tagged objects and multiple RFID tags that may interfere with each other's communications. This reduces the reliability and efficiency of the RFID system. In this paper, we propose an anti-collision algorithm for communication between multiple tags and a reader. In order to suppress interference signals from multiple neighboring tags, the proposed algorithm employs the time-division (TD) technique, where tags in the interrogation zone are assigned a specific time slot so that at every instance in time, a reader communicates with tags using the specific time slot. We present representative computer simulation examples to illustrate the performance of the proposed anti-collision technique for multiple RFID tags.  
RFID; time division; interference suppression; tag-to-tag collision; signal-to-noise ratio +
Footnote †: journal: Sensors
## 3 Results  
The ATP values (in RLUs) on the surfaces of the operator's face shield and dental chair light were determined before and after deep ultrasonic scaling (Table 1) for the control and D-DART groups. Before treatment, the ATP values for both surfaces were similar (less than 2000 RLU, \(p\) > 0.05) for both groups. However, after treatment, the ATP values median (IQR) on the operator's face shield in the control group [15,574 (43,630)] and the D-DART group [1964 (1049)] were significantly different (\(p\) < 0.05), whereas no statistical significant difference (\(p\) > 0.05) was observed on the dental chair light between the control [2645 (1107)] and D-DART groups [2115 (729)]. Notably, the ATP values after the treatment increased by 88% (1888 to 15,574) in the control group as compared to a 1% increase (1945 to 1964) in the D-DART group on the operator's face shield.  
Figure 2 shows the maximum contamination on the operator's face shield with a 31.9 \(\pm\) 0.60-fold increase in viability in the Control, in contrast to a 1.5 \(\pm\) 0.04-fold increase when the D-DART was used. There was a 1.5 \(\pm\) 0.05-fold increase in the dental chair light surface after the treatment for both Control and the D-DART groups.  
A qualitative analysis of the patients' experiences is shown in Figure 3. Statistical analyses for the feedback survey showed no statistically significant differences in treatment comfort level, noise level, and ease of communication with the dentist among patients  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Control**} & \multicolumn{2}{c}{**Dental Dart**} \\ \hline  & **Median** & **(IQR)** & **Median** & **(IQR)** & **\(p\)-Value** \\ \hline Operator’s face shield & & & & & \\ Before Treatment (RLU) & 1888 & (653) & 1945 & (610) & 0.735 \\ After Treatment (RLU) & 15,574 & (43,630) & 1964 & (1049) & 0.002 * \\ Difference (RLU) & 14,097 & (42,538) & 211 & (568) & 0.001 * \\ \hline Dental chair light & & & & & \\ Before Treatment (RLU) & 1714 & (555) & 2000 & (682) & 0.122 \\ After Treatment (RLU) & 2645 & (1107) & 2115 & (729) & 0.346 \\ Difference (RLU) & 692 & (966) & 292 & (659) & 0.065 \\ \hline \end{tabular}
\end{table}
Table 1: Changes in the ATP value during dental treatment (* means \(p\) < 0.05).  
Figure 2: Contamination on the operator’s face shield and dental chair light before and after treatment without (orange, Control) or with the D-DART (blue). The D-DART was able to prevent the surface contamination of the face shield by aerosolized particles.  
treated with or without the D-DART. Notably, all the patients in the D-DART group agreed that droplets and aerosols can spread disease, and 76% agreed that the treatment with the D-DART was comfortable.
## 3 Results and Discussion
### The Failure Pattern under Compression and Brazilian Testing
#### 3.1.2 The Ultrasonic Plus Velocity Characteristics of FSRM  
The stress-strain-UPV curves are shown in Figure 8. We observed that the UPV increased with increasing stress at the pre-peak stage and then presented a decreasing trend with the decrease of stress. In general, the UPV is associated with material properties and density. The UPV increases with an increasing density. In Figure 8, in the pre-peak stage, the increasing axial UPV indicates that the density increased. In the post-peak stage, with the growth of cracks in the sample, the UPV decreased. Nevertheless, the maximum of UPV appeared in the stress post-peak stage (as with initial surface crack), which indicates that the crack started to grow at the post-peak stage. Moreover, the UPV showed a trend of fluctuating downward. We further observed the macroscopic failure morphology by the naked eye during the loading process, where the short surface cracks initiated after the maximal UPV was recorded and the bulging deformation then occurred. It was inferred that the obvious motion of rock blocks occurred in the post-peak stage. The obvious fluctuation of the UPV could be attributed to the pre-existing cracks being closed and new cracks growing because of the movement of the rock block, especially inside frozen soil with an increasing temperature. In addition, Figure 9 shows the stress-strain curve, sample deformation in different stages, the UPV, and volumetric strain of the FRSMs at a freezing temperature of \(-20\)\({}^{\circ}\)C. The stress-strain curve can be divided into five sections. Section AB is a microcrack closures stage. Section BC is an elastic deformation stage in which the ice crystals and particles resisted external loading. Section CD is the ice crystal particles creeping and melting stage, in which the ice crystal experienced pressure melting and the soil particles were further compressed. The UPV and axial stress had the maximum values at point D. In section DE, the frozen soil particles started to creep, and the microcracks can be seen by the naked eye at point E. Moreover, the volumetric strain reached a peak point at point E. In section EF, the obvious surface cracks and lateral expansion can be seen. The rotation and slippage of rock blocks occurred in this section. Figure 9 presents a rapid descent of volumetric strain corresponding to a significant fluctuation of UPV in the rectangle. This indicates that the internal rock blocks were apparently motivated and some pre-existing cracks were closed; however, at the same time, new cracks appeared. The results for the UPV analysis are consistent with the AE analysis in the section above.  
Figure 7: AE characteristics of FSRMs: (**a**) uniaxial compression test; (**b**) Brazilian test.
## 4 Discussion
A balanced approach is required to ensure robust and meaningful model evaluation, which is widely adopted for its effectiveness in improving results. The evaluation metrics summarized in Table 5 demonstrate the diversity of approaches used to assess model performance. The DICE score and accuracy are indeed popular metrics because of their straightforward application and ability to provide immediate insights into model performance. Among these, the accuracy score, cited in 25 papers, measures the ratio of correctly identified instances (true positives and true negatives) relative to the total number of instances. The DICE score, used in 23 papers, evaluates the overlap between predicted and actual sets, making it a crucial metric for image segmentation and binary classification tasks. Sensitivity, sometimes referred to as recall, measures the proportion of actual positives that are correctly identified by the model. Specificity measures the proportion of actual negatives that are accurately identified as such, thus avoiding false positives. F1-score is the harmonic mean of precision (the number of true positive results divided by the number of all positive results, including those not relevant) and recall. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
1 & [PERSON] et al., 2021 [17] & X & X & & & & X & & \\ \hline
2 & [PERSON] et al., 2023 [18] & X & X & X & X & & & & \\ \hline
3 & [PERSON] et al., 2021 [19] & & & & & & & & X \\ \hline
4 & [PERSON] et al., 2023 [20] & X & X & X & & & & X & X \\ \hline
5 & [PERSON] \& [PERSON], 2022 [5] & X & X & X & & & X & \\ \hline
6 & [PERSON] et al., 2020 [21] & X & & & & & & X & \\ \hline
7 & [PERSON] et al., 2018 [22] & X & & & X & X & X & X \\ \hline
8 & [PERSON] et al., 2024 [23] & X & X & X & & & & X \\ \hline
9 & [PERSON] et al., 2016 [24] & & X & & & & X & X & \\ \hline
10 & [PERSON] \& [PERSON], & X & X & X & X & X & X & X & \\  & 2023 [25] & & X & X & & & & X & \\ \hline
11 & [PERSON] et al., 2021 [26] & & X & X & & & & X & \\ \hline
12 & [PERSON] et al., 2023 [27] & & X & X & & & & X & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Distribution of metrics in the articles. Additional metrics such as true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), negative predictive value (NPV), boundary displacement error (BDE), mean intersection over union (mIoU), false classification rate, pixel accuracy, mismatch, volumetric overlap, Matthews correlation coefficient (MCC), and