esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Combined Solutions  
Based on the individual tests presented in the modeling section, the study aimed to analyze the potential of combined solutions for improving the thermal environment across different scenarios. To achieve this, various solutions were applied to houses with flat roofs, inclined roofs with concrete slabs, and gypsum ceilings to assess their impact on air temperature and annual energy demand.  
Throughout the analysis, solutions were incrementally integrated into each scenario, resulting in a final configuration that illustrates the outcomes of employing the selected  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location** & **Min [\({}^{\circ}\)C]** & **Max [\({}^{\circ}\)C]** & **Unmet Cooling Hours** & **Unmet Heating Hours** \\ \hline Teresina & 28.2 & 41.4 & 8760 & 0 \\ Evora & 5.3 & 49.1 & 3705 & 3072 \\ Sanluurfa & 9 & 40.7 & 3586 & 3097 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Maximum (Max) and minimum (Min) house air temperatures and unmet hours for each location.  
Figure 6: Results of the base simulations for each location. **(a)** Thermal load and annual energy demand. **(b)** Annual indoor air temperature distribution with no AS.  
solutions together. The study focused on evaluating the practical impact of these solutions on existing houses, prioritizing ease of implementation. As a result, no modifications were made to house orientation or the window-to-wall ratio.  
Natural ventilation was selected as the first solution to be applied. This choice was based on its effectiveness in cooling while acknowledging that it increases energy needs when calculated using mechanical systems. Additionally, other solutions, such as insulation, are better evaluated when combined with ventilation. Therefore, the solution with ventilation serves as the new baseline for evaluating passive solutions.  
The air changes per hour (ACH) due to natural ventilation were determined using the Air Change Rate Calculator from WindowMaster (Vedbeek, Denmark). Despite variations in average wind speeds across different locations, a standardized ACH value of 10 was applied uniformly to all houses studied. Additionally, all simulations utilized glazing with a lower SHGC.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
###### Abstract  
Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is divided into multi-scale objects by graph-based superpixel segmentation, and the optimal scale is determined by the overall goodness F-measure (OGF). Then, except for the extraction of the spectral features, the multi-channel texture features based on the color co-occurrence matrix (CCM) are extracted to consider the correlation among multiple bands. To accurately find the representative features to overcome the impact of feature redundancy, a cumulative backward search strategy (CBSS) is further designed. Finally, the change detection is completed by inputting the difference image of dual time points to the trained random forest model. Taking Shenzhen and Dapeng as the study areas, with Sentinel-2 and Skysat images under different spatial resolutions, and the forest-bueland change type as an example, the effectiveness of the proposed algorithm is verified by qualitative and quantitative analyses. They show that the proposed algorithm can obtain higher detection accuracy than the texture features without band correlation.  
color co-occurrence matrix; cumulative backward search strategy (CBSS); random forest; object-oriented; remote sensing image change detection +
Footnote †: journal: Applied  
**Critical:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Object-Oriented Remote Sensing Image Change Detection Based on Image Change Detection Based on Color Co-Occurrence Matrix. _Aprl. Sci._**2023**, _13_, 6748. [[https://doi.org/10.3390/app1316748](https://doi.org/10.3390/app1316748)]([https://doi.org/10.3390/app1316748](https://doi.org/10.3390/app1316748))
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 2 The Wavelet Transform
### Discrete Wavelet Transform
#### 2.2.2 Wavelet Packets Transform  
Due to the incapability of MRA to decompose high frequency bands, in 1992, [PERSON], [PERSON] and [PERSON] developed the Wavelet Packets Transform (WPT). Using WPT, all the approximation and detail information can be decomposed until the desired level. This process can be observed in Figure 4.  
Figure 3: MRA decomposition procedure until decomposition level 3 of decomposition. Each decomposition is based on DWT (see Figure 2).  
The term \(W(k,j)\) represents the coefficients obtained for a packet, where \(k\) is the decomposition level (here, the number of packets obtained is \(2^{k}\)) and \(j\) is the position of the packet within the decomposition level. Then, each correlation vector \(W(k,j)\) has the structure of Equation (2):  
\[W(k,j)=\{w_{1}(k,j),...,w_{N}(k,j)\}=\{w_{i}(k,j)\} \tag{2}\]  
WPT has been used in the last years for fault detection in rotating machinery and condition monitoring [52; 53]. WPT coefficients can be directly applied for fault detection as they content valuable information about them, according to [54]. Features related to the statistical parameters of WPT coefficients have been used combined with SVM in [55]. It was highlighted in [25] that wavelet energy-based features are often not suitable to detect incipient defects, since slight changes in a signal will be masked. Later, normalized wavelet packet quantifiers were proposed for condition monitoring, such as relative energy [56]. WPT energy has been successfully used to detect incipient induced cracks in shafts at steady state, using vibration signals coming from a rig [33], solving the problem of how to select the best feature. WPT has also been used to detect the appearance of cracks in railway axles during fatigue testing at steady state, using thresholding methods [57]. Signals coming from Jeffcott rotor models during start-up, under different crack conditions simulated using crack breathing functions, have also been successfully analyzed via WPT energy [58].  
Studies have also been carried out to examine the application of WPT to detect faults in rotating machinery using signals obtained experimentally from a custom-built rotor kit [59]. The experimental set-up simulated the main operating conditions of rotating machinery, at laboratory scale, which are used in a wide range of industrial applications involving several different pieces of equipment. It has been affirmed that the WPT is a powerful tool for detailed feature extraction. For impulsive faults like rubbing, it is proposed a combined method of WPT and CWT for fault detection.  
WPT has also been applied to AE signals [60], showing advantages in the diagnosis at early period crack faults. The diagnosis results obtained were clear, reliable and accurate.
## 5 Results  
### Location of the Initial Entry to the Privacy Notice (Figure 3)  
Question 1. Is the initial entry to the privacy notice located on the first page of the hospital website?  
In Figure 3, ninety-five (95%) of the websites had the initial entry point to the privacy policy or statement document on the first page of the website. Across the hospital types, the average was 96% had the initial entry on the first page with a standard deviation of 4% and a coefficient of variance of 5.39%. Of the five that did not have an initial entry on the first page, three were major teaching hospitals, and two were small community hospitals. As described prior, it is important for legal agreements like a privacy policy to be accessible. If they are not, then the legality of the terms may not be enforceable. By being available on the first page for consistency, a person going to the hospital website can assume there is a link to the privacy policy on the first landing page.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 6 Experiments  
### Experimental Setup  
Experiments were conducted following the settings outlined in [5]. We generated ten ARP benchmark instances utilizing the instance generator and the specific seeds provided by the authors, which enabled us to leverage the results made available for the competitor and baseline algorithms. In particular, we considered two instances for each size \(n\) in the set \(\{10,15,20,25,30\}\) using the seeds 42 and 73 (these seeds were arbitrarily chosen by the authors of the ARP instance generator and we adopt them in order to make our analysis comparable with their work). Each instance is denoted using the naming convention \(n\_seed\).  
Two distinct experimental settings were employed as follows:  
* _Uninformed setting._ In this scenario, the competing algorithms--FAT-RLS, UMM, and CEGO--are initialized randomly, while RS serves as the baseline method.
* _Informed setting._ Here, the GNN heuristic is utilized both as the baseline method and to generate reasonably effective initial solutions for FAT-RLS, UMM, and CEGO.  
In both settings, all algorithms were executed 30 times for each instance, with a budget of 100 objective function evaluations allocated to each execution. Note that the low budget of 100 evaluations is motivated by the fact that the objective function is expensive to evaluate, a typical scenario in real-world black-box optimization. Indeed, we recall that the goal of this work is to investigate the performance of FAT-RLS in a costly black-box permutation problem. Moreover, the same budget was also used by [PERSON] et al. in [5].  
Furthermore, note that in [5] two variants of UMM and CEGO were assessed: one that evaluates the evolved permutation directly and another that inverts the permutation before evaluation. However, as discussed in Section 2, only one of these variants is valid. Therefore, in this work, we focused on a single version of each competitor algorithm: the UMM-rank and the CEGO-order variants from [5], which also demonstrated superior performance compared to their counterparts UMM-order and CEGO-rank.  
Lastly, the hyperparameters for the competing algorithms were configured according to the specifications in their original publications: [21] for FAT-RLS and [5] for CEGO and UMM. More specifically, the FAT-RLS hyperparameters were set to \(d_{\text{ini}}=0.5n\), \(\beta=1.2\), and \(k=n\); UMM used weights 0.9 and 0.1, as explained in Section 5.4; while CEGO adopted the Kendall's-\(\tau\) distance to build its surrogate model.
## 4 Signal Pretreatment
### Digital Filter Denoising  
Considering that low-frequency components dominate in the frequency domain of the optical Morse signal (Figure 7), we adopt a FIR low-pass filter with a cutoff frequency of 40 Hz using the Hamming window to mitigate the noise effect, and for the ease of implementation in the MCU as well.
_Applied_  
_sciences_  
_Article_  
Correlation of Concurrent Extreme Metocean Hazards  
Considering Seasonality  
**[PERSON] 1,2, [PERSON] 1,*() and [PERSON] 2,3**  
1 Department of Civil Engineering, Universidad de Guanajuato, Juarez 77, Zona Centro, Guanajuato P.C. 36000, Mexico; [EMAIL_ADDRESS]  
2 Laboratori d'Enginyeria Maritama, Universitat Politecnica de Catalunya, [PERSON] 1-3, Modul D1, Campus Nord, 08034 Barcelona, Spain; [EMAIL_ADDRESS]  
3 Centre Internacional d'Investigacio dels Recursos Costaners, [PERSON] 1-3, Modul D1, Campus Nord, 08034 Barcelona, Spain  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +52-473-1020100-2235  
Received: 17 June 2020; Accepted: 9 July 2020; Published: 13 July 2020  
**Abstract:** Simultaneous occurrence of metocean variables can present a multihazard to maritime systems. However, simplified design approaches to assess simultaneous significant wave heights and wind velocities are lacking, especially if seasonality is considered. This is addressed in this study by using extreme significant wave heights and companion wind velocities recorded in the Gulf of Mexico. Time-dependent, generalized extreme value (GEV) models and classical regression are the basis to propose a simplified approach to estimate correlated extreme significant wave heights and wind velocities associated with given return periods, accounting for seasonality and including measures of uncertainty. It is found that the proposed approach is a new but simple method to adequately characterize the concurrent extreme metocean variables and their uncertainty. It is concluded that the method is an effective probabilistic design tool to determine simultaneous extreme significant wave heights and companion wind velocities for desired return periods and seasonality.  
**Keywords:** metocean variables; seasonality effects; multihazard; time-dependent GEV model
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 7 Research Questions
### General Questions  
During the writing of this review, particularly in Section 5, most of the general research questions were addressed. We analyzed trends (GQ1), publication venues (GQ3), and research groups (GQ4), while also conducting an additional analysis of the distribution of publications by country. However, among the initial general research questions, we lacked a complete answer to GQ2 regarding application domains. We provided a partial answer to this question through the taxonomy in Section 6. During the evaluation of each class within the taxonomy, we analyzed the target application area of each paper. To provide a comprehensive response to this research question, Table 5 presents all identified domains.  
The analysis of application domains clearly demonstrates that research on explainable drift predominantly occurs in contexts where regulatory compliance, human safety, or significant financial risk necessitates clear interpretability. Three main sectors stand out distinctly: healthcare (12 papers), finance (6 papers), and energy (6 papers). Together, these three domains account for approximately 30% of the reviewed studies, with healthcare alone constituting about 14% of the total. This reflects strong regulatory and operational requirements for transparency and accountability in these sectors. Furthermore, aggregating domains with fewer studies under the category \"Other\" helps to clarify the distribution of research interests. Notably, fields grouped under \"Other\" collectively account for a significant portion of studies, highlighting broad but diffuse interest across various specialized or niche applications. This diverse collection underscores that while certain sectors clearly dominate the research landscape due to immediate safety or regulatory implications, explainability in drift detection and interpretation has wider, even though less concentrated, relevance. The evidence from different application areas shows a clear pattern: external responsibility and human safety concerns, not just the presence of drift, are the strongest reasons why a field will invest in explainable drift methods. For applications where reputation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application Domain** & **N. of Paper** \\ \hline Healthcare & 12 \\ Cybersecurity & 8 \\ Financial & 6 \\ Energy & 6 \\ Process Mining & 5 \\ Manufacturing & 5 \\ Generic Data Stream & 5 \\ Data Mining & 5 \\ Retail & 2 \\ Online Community & 2 \\ Video Surveillance & 2 \\ Other & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Domains that adopted explainability and interpretability of drift.  
tion or legal responsibility is high, researchers are already testing transparent or at least questionable solutions. When these pressures are low, interest is rare.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment  
### Experimental Setup  
We first evaluate the proposed method on the NYU Depth V2 benchmark dataset [37], which contains 464 indoor scenes captured by a Kinect camera. We follow the official train/test split, using 249 scenes for training (\(\sim\)50 K images) and 215 scenes (654 images) for testing. Each image is downsampled to \(320\times 240\) and subsequently center cropped to \(304\times 228\). For performance evaluation, we only show the most primary evaluation metrics,root mean square error (RMSE), to showcase the results for clear comparison. Based on the predicted depth map \(Z\) and the ground truth depth map \(Z^{*}\), the calculation formula for RMSE is as follows, where \(N\) represents the number of valid pixels in \(Z^{*}\).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(Z-Z^{*})^{2}} \tag{15}\]  
The proposed method is implemented using the PyTorch framework [76] and trained on a single NVIDIA GTX 2080 Ti GPU. During training, we employ the AdamW optimizer [77] with \(\beta 1=0.9\) and \(\beta 2=0.999\). We train the model for a total of 30 epochs, about nearly 100,000 iterations, with a batch size of 16. The learning rate is scheduled using polynomial decay, starting from a base learning rate of \(10^{-3}\) with a power of \(p=0.9\), as seen in Figure 3(a). The parameter \(w\) in the overall loss function is set as follows, assuming the total number of iterations is T, and the current iteration is \(t\). When \(t<0.25\) T, \(w=1\); when \(t<0.5\) T, \(w=0.5\); when \(t<0.5\) T, \(w=0.25\); and when \(t<0.75\) T, \(w=0.1\). Figure 3(b) shows the overall loss curve of the model. From the figure, it can be seen that there is a significant drop in the loss curve at the corresponding iterations. Additionally, the loss function steadily decreases from the beginning of the training. Although the rate of decrease changes in the mid to late stages of training, it still shows a slow convergence to stability.  
In addition, we adopt a mixture strategy that involves training with data of different sparsity levels. Specifically, we randomly sample 5, 50, 100, 200, or 500 sparse depth points from the ground truth maps. Approximately 50% of the depth maps contain 500 depth points, while the remaining 50% consist of varying sparsity levels. This strategy allows the model to learn from a diverse range of sparse depth inputs. Furthermore, we augment the training data by applying random horizontal flipping to both the images and the paired sparse depth maps. We also introduce random contrast, brightness, and color adjustments within a range of \([0.9,1.1]\) to the color images and utilized the CutDepth method [78] to augment depth maps.
## 3 Speech Recognition Results
### Size of Transformer Model  
We compared the performance difference between the small and large Transformer architectures. Here, the small Transformer consists of four attention heads with 256 dimensions, and the large Transformer consists of eight attention heads with 512 dimensions. The detailed configuration of both models is described in Appendix A. In addition, they used the 2306 syllable units that produced the best performance in the previous experiments as the sub-word unit. Table 5 displays the performance of two Transformer models with different sizes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Eval Sets**} & \multirow{2}{*}{**Metric (\%)**} & \multicolumn{5}{c}{**No. Sub-Words**} \\ \cline{3-6}  & & **2306** & **5000** & **8000** & **10,000** & **12,000** \\ \hline \multirow{4}{*}{Dev} & CER & **6.5** & 6.9 & 6.8 & 6.8 & 7.2 \\ \cline{2-6}  & WER & **17.1** & 17.7 & 17.6 & 17.4 & 18.4 \\ \cline{2-6}  & sWER & **12.7** & 13.4 & 13.2 & 13.2 & 14.0 \\ \hline \multirow{4}{*}{Eval-clean} & CER & **8.0** & 8.1 & 8.2 & 8.3 & 8.5 \\ \cline{2-6}  & WER & **21.9** & 22.0 & 22.1 & 22.3 & 22.7 \\ \cline{2-6}  & sWER & **14.2** & 14.4 & 14.6 & 14.6 & 15.1 \\ \hline \multirow{4}{*}{Eval-other} & CER & **9.0** & 9.1 & 9.3 & 9.0 & 9.7 \\ \cline{2-6}  & WER & 26.6 & 26.5 & 27.1 & **26.5** & 27.8 \\ \cline{1-1} \cline{2-6}  & sWER & **16.5** & 16.7 & 17.2 & 16.6 & 18.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance according to the number of sub-word units.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Metric (\%)** & **Dev** & **Eval-Clean** & **Eval-Other** \\ \hline \multirow{4}{*}{Small Transformer} & CER & 6.5 & 8.0 & 9.0 \\  & WER & 17.1 & 21.9 & 26.6 \\  & sWER & 12.7 & 14.2 & 16.5 \\ \hline \multirow{4}{*}{Large Transformer} & CER & **6.1** & **7.6** & **8.5** \\  & WER & **16.4** & **21.1** & **25.5** \\ \cline{1-1}  & sWER & **11.9** & **13.4** & **15.4** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the large and small Transformer models.  
In the experimental results, we observed that the large Transformer architecture performed better than the small Transformer architecture. The large Transformer model showed a reduction in relative sWER of 6.3%, 5.6%, and 6.7% over the small Transformer model for Dev, Eval-clean, and Eval-other, respectively. The actual sizes of the two models were 116 and 297 MB, respectively, with the small Transformer model being 2.6 times larger than the large Transformer model. We present the performance of the Large Transformer model in Table 5 as the baseline performance of the KsponSpeech corpus.
# Two-Phase Deep Learning-Based EDoS Detection System
## 6 Result Analysis  
A successful EDoS attack mitigation mechanism requires correctly identifying attacks and a quick response time (Quality of Service), while minimizing the resource consumption [1]. In this section, we will evaluate our proposed model based on two main criteria: quality of service and resource consumption. In addition, we evaluate the efficiency of the proposed model at the cloud service provider end by comparing the CPU usage of the victim server when being protected by our proposed model to that when not being protected.