esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Group Window Attention  
In this research, our main focus is on Window Grouping Attention-based 3D object reconstruction. Recently, some researchers have proposed the token grouping technique [23] to aggregate similar tokens [17] from different views and improve the accuracy of 3D object reconstruction. However, the introduction of ViT [16] represents a breakthrough in how computer vision handles image processing. ViT groups image information into tokens and apply self-attention to understand global dependencies. Demonstrating the application of transformers in multi-view analysis to capture spatial and temporal relationships has been explored by employing STTN [24] for video retrieval systems. STTN utilizes full-range attention in both spatial and temporal dimensions [24], and it is while effective for video, it faces challenges in efficiently handling large datasets. To solve this problem, DSTT [25] employs short-range grouping attention and increases local connectivity within each view, enhancing feature extraction for temporal coherence tasks to increase computing efficiency. However, it struggles to build long-range dependencies between views and cannot effectively handle interactions across multiple views, which is a significant problem in multi-view 3D reconstruction tasks. LRGT [11] uses long-range attention, which merges tokens from fixed positions in the images, attempting to capture long-range dependencies between views. It improves inter-view correlations but faces difficulties in grouping semantically similar tokens between multiple views and lacks a mechanism for handling intra-view relationships, which are essential for detailed 3D reconstruction, so attention-guided fusion [26] is introduced to enhance multi-view feature integration. Recently, the swin transformer [20; 27] has introduced window-based attention, such as R3D-Swin [19], where an image is divided into non-overlapping windows, and attention is computed within individual windows. To capture long-range dependencies between windows, the swin transformer employs a window-shifting technique [28]. Compared to traditional transformers, which compute attention for the entire image, this approach reduces computational complexity. In addition, the fixed window structure still limits the model's ability to effectively capture complex relationships between views in multi-view 3D reconstruction tasks.  
As a result, we propose the Window Grouping Attention (WGA) mechanism, which segments tokens within each window based on semantic importance, allowing separate attention operations within each group. This approach enables efficient attention processing while preserving critical intra- and inter-view relationships. By focusing on the most relevant features and flexible attention manipulation, WGA addresses the difficulties of working with large and complex datasets and enhances the performance without compromising the global feature representation necessary for high-quality 3D reconstruction.
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 5 Discussion  
The experimental results demonstrate in an obvious manner that modifications to the objective function of the classical Fuzzy C-Means (FCM) algorithm, by applying proper regularization or penalization terms, contribute to the improvement of the quality of the generated clusters. Generally, all five modifications (L2 regularization, entropy term, sparsity-inducing term, penalty for large clusters, and spatial regularization) in all datasets had an overall higher Fuzzy ARI and Fuzzy NMI score compared with the classical FCM, and this is valid for all three optimization techniques (gradient descent, root mean propagation, and adaptive moment estimate). From the summarizing table and the charts can be noted that the spatial regularization (SPAT-REG) and large-cluster penalty (PEN-LSC) modifications typically achieved the highest performance scores, especially when optimized using the Adam method, highlighting their robustness in dealing with noisy or imbalanced data distributions.  
The comparison of optimization methods points out that adaptive techniques such as RMSprop and Adam generally perform better compared with the classical gradient descent, offering more stable convergence and less susceptiblity to oscillations for sophisticated objective functions. Especially the adaptive moment estimation (Adam) technique demonstrated very effective results across almost all modifications of the objective function and optimization techniques. Moreover, the numerical methods provide flexibility in designing and optimizing complex objective functions without requiring sophisticated derivatives or the Lagrange multipliers approach. A significant disadvantage of the proposed approaches, compared with the classical methods, is the higher computational complexity, which is considered a natural trade-off for the algorithm simplicity that it offers.  
This work also holds possibilities for future directions of research, such as devising hybrid approaches combining multiple regularization techniques to further improve the quality of generated clusters or utilization of adaptive optimization techniques in the  
Figure 2: Fuzzy NMI scores across objective function modifications and optimization methods.  
hyper-parameter tuning area. Finally, applications of these modified algorithms on new datasets, not well known to the machine learning community, would confirm their validity in real-world problems.
# Fatigue Performance and Model of Polyacrylonitrile Fiber Reinforced Asphalt Mixture
## 6 Conclusions  
By comparing the fatigue test results of the mixture with and without PAN fibers, it was shown that the sensitivity of the fatigue life of the mixture to the strain was decreased significantly after adding fiber. Therefore, adding a proper content PAN fiber in asphalt mixture can significantly improve the anti-fatigue properties. At the same fiber content, the fatigue performance of the mixture was been improved and the fatigue sensitivity declined with an increasing asphalt content. The effect of the fiber content on the fatigue life at a low strain level was more obvious than that in the high strain level.  
From a comparison of the asphalt mixture fatigue life with different fiber contents, it was obvious that reinforced asphalt mixture has an optimum fiber content. When the fiber content was less than the optimum fiber content, the fatigue life increased with the fiber content increasing. Once the fiber content exceeded its optimum level, the fatigue life deceased with the fiber content increasing.  
By introducing a new variable that can reflect the effects of fiber on the fatigue life, a new fatigue model of fiber reinforced asphalt mixture was generated. Its accuracy has been improved significantly compared to other earlier models. Meanwhile, the model can preferably reveal the effect of the fiber content, strain level, initial stiffness, and VFA on the fatigue life. Fatigue life decreased as the strain level and the stiffness increased, while it increased as VFA increased. For the fiber content, when the content is less than the optimum fiber content, increasing the fiber content will improve the fatigue life; and while over the optimum content, increasing the fiber content will compromise the fatigue life.  
[PERSON] designed the experiment and wrote manuscript; [PERSON] and [PERSON] conducted experiments and wrote manuscript; [PERSON] and [PERSON] helped analyzing experimental data.  
This research was funded by \"the National Natural Science Foundation of China, grant number 51478052\", and \"Human Transportation Department, grant number 201302\", and \"Guangdong Transportation Department, grant number 2013-01-002\".  
The authors declare no conflict of interest.
## 2 Literature Review
### Perceived vs. Actual Waiting Times  
The discrepancy between perceived and actual waiting times is a recurring topic in the literature. [PERSON] et al. [24] found that passengers in Maribor, Slovenia, perceived waiting times to be about 35% longer than they actually were, especially on low-frequency routes where they experienced higher levels of frustration. The implementation of real-time passenger information (RTPI) systems has proven effective in reducing this negative perception.  
In their study on the Shenzhen Metro in China, [PERSON] and [PERSON] [25] observed that complaints about waiting times were frequent, particularly during peak hours. These negative perceptions were influenced not only by the actual duration of the wait but also by the lack of accurate information regarding train arrivals.  
[PERSON] and [PERSON] [14] argue that comfort conditions in transfer terminals significantly affect waiting time perception, emphasizing that an improved physical environment can partially mitigate the discomfort associated with prolonged waits.  
[PERSON] et al. [15] provided neuroscientific evidence by demonstrating that waiting times are quantified in terms of \"cognitive distance\", a factor that explains why users often perceive waiting times as longer than they really are. This finding underscores the importance of addressing not only actual waiting times but also subjective perceptions to enhance the overall user experience.
## 3 Results and Discussion
### Photo-Fenton
#### 3.1.1 Determination of the Optimised Operational Conditions
A larger increase of H\({}_{2}\)O\({}_{2}\) dosing was found necessary to generate a notable increase in the model compound removal. These results are in concordance with those obtained by other authors [41]. The removal of alprazolum increased as iron increased, until a concentration of 7.7 mg L\({}^{-1}\), and the same trend was observed with the H\({}_{2}\)O\({}_{2}\) concentration until a concentration of 47.5 mg L\({}^{-1}\)[35; 41]. From the results, 50 mg L\({}^{-1}\) of H\({}_{2}\)O\({}_{2}\) appears to be the maximum value at which a positive effect can still be appreciated under the tested conditions with 500 W MP lamps. Regarding the performance of the 40 W LP lamp (Figure 2b), it was observed that the removal rates increased when a higher H\({}_{2}\)O\({}_{2}\) concentration was applied. The same trend was observed with the iron concentration. The ANOVA model confirmed that the 40 W LP lamp follows a linear model. The optimised dose with the aim of increasing the target compound removal and decreasing the reagent's dosage was 6.5 mg L\({}^{-1}\) of iron and 30 mg L\({}^{-1}\) for the 40 W LP lamp. Once the behaviour of each lamp was known, the optimal conditions were defined as the ones achieving the highest pollutant elimination (minimum 80%) while using as little iron and H\({}_{2}\)O\({}_{2}\) dosage as possible. According to that premise, the most favourable irradiation was achieved with the 500 W MP combined with an optimal dosage of iron and H\({}_{2}\)O\({}_{2}\), 6.3 and 24 mg L\({}^{-1}\), respectively. These results were promising since a reduction of reactants was achieved regarding previous works [42]. [PERSON] et al. [43] obtained similar behaviour despite their optimal concentrations being significantly higher (iron was 15 mg L\({}^{-1}\) and H\({}_{2}\)O\({}_{2}\) 90 mg L\({}^{-1}\)) than the ones obtained in this study. They also worked at circumneutral pH. The obtained optimal iron dose and its subsequent discharge can be considered too high for a real application of the technology, since few German regions recommend  
Figure 2: Response-surface plot and experimental values (circles) show the influence of iron and H\({}_{2}\)O\({}_{2}\) dose to remove the pollutant. (**a**) 500 W MP Lamp, (**b**) 40 W LP lamp. maintaining between 0.7 and 1.8 mg L\({}^{-1}\)[44] the iron concentration in the receiving water bodies. Previous works reported a satisfactory \({}^{\bullet}\)OH yield for the removal of CECs with 5.5 mg L\({}^{-1}\) of iron [45]. For that reason, the iron was limited to 5.5 mg L\({}^{-1}\), obtaining an optimal adapted dosage of 5 mg L\({}^{-1}\) of iron and 40 mg L\({}^{-1}\) of H\({}_{2}\)O\({}_{2}\). An additional experiment was carried out to validate the optimisation. The removal obtained was close to the model extrapolation, with a difference lower than 5%. In this assay, both COD and TN were monitored, resulting in the COD content slightly decrease and TN remaining constant.
## 1 Introduction
The spatial resolution of the satellite stereo imagery has also improved steadily in recent years, with the recently launched WorldView-3 and WorldView-4 satellites able to capture stereo imagery with up to 0.31 m resolution. With the availability of these data, research on space-borne photogrammetry is gaining more attention, including for forest monitoring [4]. Elevation data can also be provided through SAR techniques. Using radargrammetry, height information is obtained from two or more multi-view SAR images, and Interferometric SAR (InSAR) derives height information through phase differences. Recent advances in very high resolution SAR imagery and the launch of TerraSAR-X and TanDEM-X [26] have enabled the use of this technique for forest biomass monitoring [27; 28]. Tree height, which is closely related to tree age (young stands) and volume (especially old stands), is an important parameter in forest management. At regional to enterprise level, remote sensing data are capable of quickly locating and estimating damages after a disturbance event, especially in the case of a sudden storm. Forest monitoring at national/global level or after a sudden event usually employs data from various sensors. Therefore an evaluation of the data quality for the different sensors, their derived DSMs and behavior in change detection is of high importance. The primary objectives of this paper are to introduce the most important sensors which can provide DSMs, including optical stereo data, InSAR systems, and airborne LiDAR, and assess the quality of the extracted height information through investigating their ability for forest change monitoring. As a case study, we chose a highly-structured forest area near Traunstein in Bavaria, Germany, since sufficient data from various sensors including reference data are available for this region. The remainder of this paper is organized as follows. In Section 2, the test region and the characteristics of the imagery presented and DSM data are introduced. The change detection approaches, including forest damage detection and forest annual growth detection are presented in Section 3. As the accuracy of the generated DSMs directly influence the height-oriented change detection, a quality evaluation of these DSMs is necessary before performing the change detection experiments. The DSM quality is investigated at pixel- and inventory plot-level. The evaluation approaches and experimental results are described in Section 4 as Experiment I. The forest change detection results are grouped as Experiment II and presented in Section 5. In Section 6, a discussion on the datasets and experimental results is reported. Finally, the conclusions are presented in Section 7.
## 4 Experiments
### Evaluation Indicators  
Five objective indicators were used to assess the enhanced speech quality, with higher scores indicating better performance. The indicators included:  
* PESQ [32]: The perceptual evaluation of speech quality--ranges from \(-0.5\) to \(4.5\);
* CSIG [33]: The MOS prediction of the signal distortion--ranging from 1 to 5;* CBAK [33]: The MOS prediction of the background noise intrusiveness--ranging from 1 to 5;
* COVID [33]: The MOS prediction of the overall effect--ranging from 1 to 5;
* STOI [34]: The short-time objective intelligibility--ranging from 0 to 1.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology
### Fault Feature Extraction Stage  
Due to the changing frictional forces, resistance forces, and loads between components of the rotating machinery, the vibration signals present nonlinear and noise disturbance characteristics. Therefore, it is necessary to diminish the disturbing of the noise under nonlinear condition and fetch the helpful information. In fault feature extraction stage, CEMDAN is applied to decompose the vibration signals of the rotating machinery into a number of IMFs and a residual.  
In general, only parts of the IMFs are sensitive to the failure. To diminish the disturbing of the noise effectively, MIC is used to select eligible IMFs to reconstruct signals with helpful information.  
To describe the degradation process accurately, ApEn-weighted energy value of the reconstructed signals is used to track the degradation process of the rotating machinery. In other words, CEEMDAN-ApEn is the feature proposed for RUL prediction in this paper.
## 4 Application  
Next, we illustrate the previous result with the following example given in [7]. We choose the max-norm.  
Let \(F:\mathbb{R}^{3}\rightarrow\mathbb{R}^{3}\) be defined as \(F(x,y,z)=(x,y^{2}+y,e^{z}-1)\). It is obvious that \(x^{*}=(0,0,0)\) is a solution of the system.  
From \(F\), we have  
\[F^{\prime}(x,y,z)=\left(\begin{array}{ccc}1&0&0\\ 0&2y+1&0\\ 0&0&e^{z}\end{array}\right)\]  
and \(F^{\prime}(x^{*})\) is the identity matrix \(3\times 3\). So, \(\|F^{\prime}(x^{*})^{-1}\|=1\) and \(\beta=1\). On the other hand, there exists \(r=1\), such that \(B(0,r)=\{w\in\mathbb{R}^{3}:\,\|w\|<1\}\subset\mathbb{R}^{3}\), and it is easy to prove that  
\[\|F^{\prime}(x,y,z)-F^{\prime}(u,v,w)\|=\max\{2|y-v|,|e^{z}-e^{w}|\}\leq 3\|(x,y,z)-(u,v,w)\|\]  
in \(B(0,r)\). Therefore, \(R=\frac{1}{2\beta K}=\frac{1}{6}\) and [PERSON]'s method is convergent from any starting point belonging to \(B(x^{*},0.166667\ldots)\). However, from Theorem 1 and once fixed \(p=1\) in family Equation (3), this iterative process is convergent from any starting point belonging to \(B(x^{*},0.184262\ldots)\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\boldsymbol{p}\) & \(\boldsymbol{\zeta_{p}}\) & \(\boldsymbol{R}\) \\ \hline \(0.1\) & \(0.00555393\ldots\) & \(0.005523\ldots/\beta K\) \\ \hline \(0.2\) & \(0.02493\ldots\) & \(0.024323\ldots/\beta K\) \\ \hline \(0.3\) & \(0.0635481\ldots\) & \(0.059751\ldots/\beta K\) \\ \hline \(0.4\) & \(0.128869\ldots\) & \(0.114158\ldots/\beta K\) \\ \hline \(0.5\) & \(0.229815\ldots\) & \(0.18687\ldots/\beta K\) \\ \hline \(0.6\) & \(0.373733\ldots\) & \(0.272057\ldots/\beta K\) \\ \hline \(0.7\) & \(0.560476\ldots\) & \(0.35917\ldots/\beta K\) \\ \hline \(0.8\) & \(0.778457\ldots\) & \(0.437715\ldots/\beta K\) \\ \hline \(\boldsymbol{0.896037\ldots}\) & \(\boldsymbol{1}\) & \(\boldsymbol{0.5}/\boldsymbol{\beta}\mathbf{K}\) \\ \hline \(\boldsymbol{0.9}\) & \(\boldsymbol{1.00916\ldots}\) & \(\boldsymbol{0.502280\ldots/\beta\mathbf{K}}\) \\ \hline \(\boldsymbol{1}\) & \(\boldsymbol{1.23607\ldots}\) & \(\boldsymbol{0.552786\ldots/\beta\mathbf{K}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positive real roots of the equation \(g(t)-1=0\) and the values of \(R\) for different values of \(p\) in Equation (3).
# Geotechnical and Structural Damages Caused by the 2023 Kahramanmaras Earthqu in Golbasi (Adyaman)
## 2 East Anatolian Fault Zone (EAFZ)  
The East Anatolian Fault (EAFZ) creates a NE-SW left-lateral strike-slip transform boundary between the northward-moving Arabian Plate and the westward-moving Anatolian Block, with an average width of 30 km and a length of 580 km. It merges with the Oldidenz Fault around Turkoglu and with the NAFZ around Karlnova. The zone between Karlova and Antakya, consisting of many left-lateral strike-slip faults with different characteristics and complementing each other, is called the East Anatolian Fault Zone (EAFZ) [34]. Different researchers have evaluated EAFZ as consisting of different numbers of segments. [PERSON] et al. [35] stated that it was divided into six segments based on the fault traces on the surface. [PERSON] et al. [36] divided the fault into five segments in terms of changes in geometry and orientation. [PERSON] and [PERSON] [37] divided it into 14 segments according to geometric discontinuities, surface fractures, and seismicity features. [PERSON] and [PERSON] [38] divided the EAFZ into five segments for similar reasons. These faults are expressed as Karlova-Bingol, Palu-Hazar, Hazar Lake-Sincik, Celikhan-Erkenek, Golbasi-Turkoglu and Turkoglu-Antakya segments. Syria-Latakia earthquakes further south are also included in this zone. EAF, which is one of the most active fault systems in Turkiye and forms the border between the Anatolian and Arabian Plates, meets the westward movement of the Anatolian block together with the North Anatolian Fault Zone (NAFZ). Current GPS data give today's slip rate in the 11 \(\pm\) 2 mm/year range. The closest settlements to this fault zone are Hatay, Osmaniye, Kahramanmaras, Adyaman, Malatya, Elazig, Bingol and their affiliated centers. A representation of these settlements on the map is shown in Figure 2.  
Important historical and instrumental period earthquakes that occurred on the EAFZ are shown in Table 1. Here, earthquakes are discussed in two groups, historical and instrumental earthquakes. It is seen that both historical and instrumental period earthquakes are generally more severe in the central and northeastern parts of the EAFZ. On the other  
Figure 2: Settlements affected by Kahramanmaras earthquakes (Modified from [39]).  
hand, it is understood that no destructive earthquake has occurred in the last 500 years, especially in the Golbasi-Turkoglu segment [39; 40].
## 3 Results and Discussion  
### Variation Rules of Enzyme Activity during the Tugu Fermentation of Hunan Light-Flavor Baijiu  
In the Baijiu brewing process, enzymes, as key substances in Baijiu fermentation, have various functions, such as degrading fermented raw material particles, promoting microbial reproduction and catalyzing biochemical reactions to produce aroma substances. The variations in enzyme activities during the Tugu fermentation of Hunan light-flavor Baijiu are shown in Table 1.  
As shown in Table 1, the \(\alpha\)-amylase and glycolytic enzyme activities decreased during the fermentation process, and the \(\alpha\)-amylase enzyme activity decreased from 28.1 U/g to 3.8 U/g. The acidity and alcoholic content increase during the fermentation process, which inhibits \(\alpha\)-amylase activity [14]. The glucoamylase activity decreased from 1027.1 U/g to 766.7 U/g. _Aspergillus_ can produce glucoamylase at the beginning of fermentation, which makes raw starch material decompose rapidly. As fermentation progresses, fungi such as _Aspergillus_ decline gradually, and glucoamylase activity also decreases gradually [15]. Acid protease activity was highest on the second day of fermentation. Fungi such as _Aspergillus_, _Penicillium_, and _Rhizopus_ can produce acid proteases. In the early stages of fermentation, there was sufficient oxygen and nutrients in the fermented grains, which promoted the proliferation of microorganisms; therefore, the activity of acid protease was increased. Cellulose activity and lipase activity showed an overall increasing trend, reaching peaks of 668.7 U/g and 36.4 U/g on day 10, respectively. Cellulose can act on fibrous raw materials, while lipase can act on ester bonds, making full use of the raw materials.