esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theory and Methodology
### The Proposed Tsallis-Entropy-Based Method  
Our previous work [58] incorporates Bayesian posterior probability estimation into the traditional Tsallis entropy framework and dramatically improves the estimation of the optimal threshold. Unfortunately, the intensity distributions of the pixels from objects and the background are assumed to be Gaussian, which is not quite accurate and is susceptible to noise in most cases. To address this problem, we introduce the Cauchy distribution as a replacement; this distribution has a higher tolerance for outliers. In addition, we further extend the segmentation method to multiple classes to achieve effective multilevel thresholding.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slice** & **U Statistic** & \(p\)**-Value** \\ \hline
45 & 28,878.5 & 5.852 \(\times\)\(10^{-49}\) \\
60 & 16,829.5 & 9.648\(\times\)\(10^{-67}\) \\
75 & 46,223.0 & 3.902\(\times\)\(10^{-14}\) \\
90 & 10,089.5 & 7.101\(\times\)\(10^{-77}\) \\
105 & 6505.5 & 3.320\(\times\)\(10^{-63}\) \\
120 & 9205.5 & 4.634\(\times\)\(10^{-71}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mann–Whitney test results.
# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 2 Materials and Methods
### Differences with Commercial Naifold Analysis Tools  
Unlike commercial nailfold capillaroscopy systems that rely on static image analysis, our neural network-based software performs frame-by-frame processing of entire videocapillaroscopy sequences. This comprehensive approach enables the following:  
1. Systematic evaluation of all captured frames rather than selected snapshots.
2. Dynamic tracking of capillary behavior across the temporal dimension.
3. Robust averaging of measurements to account for natural vascular variability.  
Specifically designed for oral mucosa assessment, the algorithm demonstrates particular effectiveness in managing the buccal and labial regions' unique challenges--including tissue mobility, variable transparency, and complex capillary architectures. By processing complete video recordings at 120 FPS, our solution captures transient morphological features that would be missed by conventional single-frame analysis, while simultaneously reducing selection bias inherent in manual methods. This represents the first implementation of full-video computational analysis in oral mucosa capillaroscopy, addressing both clinical needs and research requirements for comprehensive microvascular assessment.  
Figure 3: Sketch of the manual location (only for validation) together with the main steps of automatic methodology.
## 4 Conclusions  
This research aimed to define suitable bitumen extenders by the use of rubber from ELTs and REOB maximizing the quantity of both recycled materials used. Furthermore, the feasibility of using these materials as bitumen extenders, replacing 25% by weight of bitumen itself, has been studied.  
Thanks to the NMR and SEM measurements, the optimum proportion of the two recycled materials was determined; both spectroscopic and imaging investigations confirmed the ratios R:Ox = 1:1 and R:Ox = 1:2 as the optimal to maximize the quantities of both recycled materials. Hence, the NMR and SEM measurements were found to be useful to study the R-REOB interactions.  
The rheological results of extended bitumens by means of DSR are promising. The partial substitution of standard bitumen by the use of R-REOB blends can improve the performance of binders at low temperature; most probably the addition of REOB, which is a softening agent, leads to an enhancement of their thermal cracking resistance. The extended bitumens have acquired a greater elasticity at all tested conditions, which can be related to the introduction of rubber. In addition, the waste polymers may improve the binder responses at high temperature; however, one extended bitumen did not show greater resistance to permanent deformation.  
Due to the variability of the components used and the complexity of interactions that takes place between constituent materials, further studies are necessary to fully understand the effect of each added product and ensure good performances of the resulting extended bitumen. Furthermore, other materials can be used as constituents of the bituminous binders, which should be investigated.  
Conceptualization, C.S. and G.T.; Software, M.P.; Validation, M.P., P.C., and G.T.; Formal analysis, M.P.; Investigation, G.T. and M.P.; Resources, G.T. and M.P.; Data curation, G.T. and M.P.; Writing--original draft preparation, G.T.; Writing--review and editing, M.P., P.C., and C.S.; Visualization, G.T.; Supervision, C.S.; Project administration, C.S.; Funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ECOPNEUS Scpa, Italy.  
The authors would like to acknowledge ITELYUM Regeneration SrI for providing the REOBs.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 4: G* and \(\delta\) master curves of the neat and extended bitumens.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 6 Conclusions  
In this work, we have presented MIMO experimental results on LoS data over the whole 1-40 GHz band in an 80 m\({}^{3}\) office. Based on a VNA setup, measurements of the relative received power, PL, RMS DS and K-factor were extracted and compared with ray-tracing simulations to have an idea of how propagation behaves in all possible 5G frequency bands; the results obtained were compared to relevant results found in the literature. The decay factor of the PL behaves nearly constant with frequency whereas the slope of the RMS DS is quite sensible to frequency. The contribution of the propagation mechanisms is investigated using the simulated radio channels across the contiguous and 5G bands. Finally, the new WRC'15/19 5G bands are considered, and models are proposed for this scenario. In future works we will assess similar complete band analysis in other scenarios as outdoor and indoor to outdoor ones; the linear relation between the RMS DS and the transmitter-receiver distance in logarithmic units will be also thoroughly analyzed. Finally, a MIMO Rician model will be used to fit the measured data to achieve a deeper insight in the channel behavior.  
Figure 16: Percentage of power of different propagation mechanisms for all positions when the frequency is 38.50 GHz, central frequency of the band 37–40 GHz.  
Figure 15: Percentage of power of different propagation mechanisms for all positions when the frequency is 25.875 GHz, central frequency of the band 24.25–27.5 GHz.  
**Supplementary Materials:** The measurements data is available online at [[https://osf.io/afqv/?view_only=1952671327](https://osf.io/afqv/?view_only=1952671327) da4f978991 be8e511405d6]([https://osf.io/afqv/?view_only=1952671327](https://osf.io/afqv/?view_only=1952671327) da4f978991 be8e511405d6).  
**Author Contributions:** Conceptualization, J.P.-G., D.P.G. and J.-M.M.-G.-P.; funding acquisition, J.P.-G., M.-T.M.-I., [PERSON] and J.-M.M.-G.-P.; investigation, [PERSON]-T.M.-I. and J.-M.M.-G.-P.; methodology, [PERSON], [PERSON] and [PERSON]-G.-P.; software, [PERSON], [PERSON] and [PERSON]-G.-P.; writing--original draft, [PERSON], [PERSON] and [PERSON]-G.-P; writing--review and editing, [PERSON], [PERSON], [PERSON] and [PERSON]-G.-P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Education and Science, Spain (PID2019-107885 GB-C33/AEI/10.13039/501100011033 and TEC2016-78028-C3-2-P) and by the European FEDER funds.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Call Redistribution for a Call Center Based on Speech Emotion Recognition
## 1 Introduction
By way of illustration, if a call is the fifth call in the queue in a given moment, a caller which is terrified, angry, or upset would be left to wait for a certain period of time before his/her call is considered. This period of time is equivalent to the time in which all the preceding calls are answered. This classical approach in call centers does not take into consideration the urgency of a call and calls are processed in the order in which they are received. [PERSON] utilized the emotion recognition as a part of a decision support system for prioritizing telephone voice messages in a call center and assigning a proper agent to respond the message [6]. His goal was to recognize two possible states: \"agitation\" which includes anger, happiness and fear, and \"calm\" which includes normal state and sadness. The average recognition accuracy was in the range of 73-77%. Figure 1: The circumplex model of affect in the valence–arousal space. Adapted from [10]. In this research, the first presumption was that there are some calls which are more urgent and which should be processed faster. The second presumption was that the urgency of the call correlates with a caller emotional state reflected through speech. The motivation behind this research was to improve the effectiveness of call center service through giving the first level priority to the callers who are experiencing a negative valence emotional state (fear and anger), the second level priority to a sad or neutral emotional state, and the third level priority to a joyful emotional state. The proposed approach consists of recognition of caller's emotional speech and redistribution of the calls according to the proposed emotion ranking. Thus, faster processing and the decrease in waiting time for callers estimated as more urgent, is achieved. The paper is organized as follows: Section 2 covers related works including acoustic modeling of emotional speech and the underlying emotional speech corpus, as well as methods for emotion classification. The proposed algorithm for redistribution of calls is described in detail in Section 3. The simulation and experimental results are reported and discussed in Section 4. Finally, conclusion remarks and future research directions are summarized in Section 5.
## 2 Shell Dynamics
### The Motion Equation of the Shell
\begin{array}{l}\mathbf{A}=\left\{a_{1},a_{2},\cdots,a_{M}\right\}^{T}\ \ \ \ \mathbf{\varphi}_{u}=\left\{\varphi_{1}^{u},\varphi_{2}^{u},\cdots,\varphi_{M}^{ u}\right\}^{T}\\ \mathbf{B}=\left\{b_{1},b_{2},\cdots,b_{M}\right\}^{T}\ \ \ \ \ \mathbf{\varphi}_{v}=\left\{\varphi_{1}^{v},\varphi_{2}^{v}, \cdots,\varphi_{M}^{v}\right\}^{T}\\ \mathbf{C}=\left\{c_{1},c_{2},\cdots,c_{M}\right\}^{T}\ \ \ \ \ \mathbf{\varphi}_{w}=\left\{ \varphi_{1}^{w},\varphi_{2}^{w},\cdots,\varphi_{M}^{w}\right\}^{T}\\ \mathbf{D}=\left\{d_{1},d_{2},\cdots,d_{M}\right\}^{T}\ \ \ \ \mathbf{\varphi}_{\varphi_{x}}=\left\{ \varphi_{1}^{v},\varphi_{2}^{v},\cdots,\varphi_{M}^{w}\right\}^{T}\\ \mathbf{E}=\left\{e_{1},e_{2},\cdots,e_{M}\right\}^{T}\ \ \ \ \ \mathbf{\varphi}_{\varphi_{\theta}}=\left\{ \varphi_{1}^{\varphi_{\theta}},\varphi_{2}^{v},\cdots,\varphi_{M}^{v_{\theta }}\right\}^{T}\end{array}  \tag{33}\]  
When substituting Equation (32) into Equation (17), the following expressions can be obtained:  
\[\delta\mathcal{S}^{T}\cdot\left(\mathbf{K}_{ss}+\mathbf{K}_{ss}+\mathbf{K}_{sst}+\mathbf{K}_{ sgt}+\mathbf{K}_{sgr}\right)\cdot\mathbf{S}+\delta\mathcal{S}^{T}\cdot\mathbf{M}_{ss} \cdot\mathbf{S}=0 \tag{34}\]  
where \(\mathcal{S}=\{\mathbf{A}^{T},\mathbf{B}^{T},\mathbf{C}^{T},\mathbf{D}^{T},\mathbf{E}^{T}\}^{T}\) represents the coefficient vector, and \(\delta\mathcal{S}=\{\delta\mathbf{A}^{T},\delta\mathbf{B}^{T},\hat{\varrho}\mathbf{C}^{T}, \hat{\varrho}\mathbf{D}^{T},\hat{\varrho}\mathbf{E}^{T}\}^{T}\) represents the first-order variant of the coefficient vector \(\mathcal{S}\). In addition, for the sake of concise presentation, the matrices \(\mathbf{K}_{ss}\), \(\mathbf{K}_{gs}\), \(\mathbf{K}_{gs^{\prime}}\), \(\mathbf{K}_{gs^{\prime}}\), \(\mathbf{K}_{gs^{\prime}}\), \(\mathbf{K}_{gpr}\), and \(\mathbf{M}_{ss}\) are listed in Appendix A. Based on Equation (34), the motion equation of the MRCS can be derived as follows:  
\[\Big{(}i\mathbf{K}^{\text{Im}}+\mathbf{K}^{\text{Re}}-\omega^{2}\mathbf{M}\Big{)}\mathbf{S}=0 \tag{35}\]  
where \(\mathbf{K}^{\text{Im}}\) and \(\mathbf{K}^{\text{Re}}\) are the real and imaginary parts of the stiffness matrix, respectively. By assigning nontrivial solutions to Equation (35), the following results can be obtained:  
\[\text{Det}\Big{(}i\mathbf{K}^{\text{Im}}+\mathbf{K}^{\text{Re}}-\omega^{2}\mathbf{M}\Big{)}=0 \tag{36}\]  
The solution of Equation (36) can be obtained as follows:  
\[\omega^{2}=(2\pi f)^{2}(1+i\eta) \tag{37}\]where \(f\) is the natural frequency, \(\eta\) is the modal loss factor, and its expression can be expressed as follows:  
\[f=\frac{\sqrt{\text{Re}(\omega^{2})}}{2\pi},\eta=\frac{\text{Im}(\omega^{2})}{ \text{Re}(\omega^{2})} \tag{38}\]
## 3 Methodology
### Model Training by Machine Learning Classifiers
#### 3.6.5 Support Vector Machine (SVM)  
SVM is a supervised learning model used for regression and classification. A support vector machine is a statistical learning algorithm [31]. Equation (7) explains the ArgMax for SVM. It is common for multi-class classification models to predict a vector of probabilities (or probability-like values), with one probability for each class label. The probabilities represent the likelihood that a sample belongs to each class label.  
\[\mathrm{ArgMax(w^{*},b^{*})}\frac{2}{||w||}\mathrm{SuchThat(Y_{i}(\overrightarrow {w}.\overrightarrow{X})+b)}\geq 1 \tag{7}\]where w represents the weight matrix, b is bias, and X and Y are dependent and independent variables.  
As for SVM:  
* SVM is memory efficient.
* It is a highly-dimensional space if the number of dimensions is greater than the number of samples.
* A custom kernel could be specified and support common kernel functions as well.
* If the number of features is much greater than the sample, over-fitting in the selecting kernel functions shall be evaded.
* Five-fold cross-validation is expensive to use as SVM does not estimate probabilities.
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 2 Data and Methods
### Methods
#### 2.3.3 DEM Co-Registration  
Due to the limitations of imaging methods and sensors, there are differences between DEM terrains, so it is necessary to co-register DEMs. The paper proposes to correct DEMs by comparing and analyzing ridge lines. The specific method of generating ridge lines is by combining plane curvature and slope shape. The main workflow is as follows:  
1. Use equation \(\mathrm{PNT}=\mathrm{DEM}-\mathrm{MEAN}\) to obtain the distribution areas of PNT (Positive and Negative Terrain), where \"MEAN\" is a 12 \(\times\) 12 rectangular average value data layer obtained from DEM data through neighborhood statistics analysis;
2. Extract the ridgeline by using \(\mathrm{Redgeline}=(\mathrm{PNT}>0)\mathrm{\updelta}(\mathrm{SOA}>80)\), where SOA (Slope of Aspect) is the planar curvature, calculated by 1 to obtain the DEM data layer \"IDEM\" (Inverse DEM) that is inverse to the original DEM, using the equation \(\mathrm{IDEM}=\mathrm{DEM}(\mathrm{max})-\mathrm{DEM}\);
3. Use the ArcGIS slope tool to extract slope direction data \"Aspect\" from \"IDEM\" data, and use slope tool to extract slope \"SOA2\" from \"Aspect\" data;
4. Perform the same operation as in 3 on the original DEM data to obtain \"SOA1\";  
Figure 3: DEMs information. (**a**) NASA DEM information in the Tailan River basin; (**b**) ALOS DEM information in the Tailan River basin; (**c**) InSAR DEM information in the Tailan River basin.  
Use equation \(\text{SOA}=((\text{SOA}1+\text{SOA}2)-\text{abs}(\text{SOA}1-\text{SOA}2))/2\) to obtain the planar curvature SOA.  
After processing DEMs of different types, the ridge lines of three different periods (2000, 2009, and 2022) were obtained in the study area (Figure 4). Ridgeline characteristics differed across periods based on the DEM's resolution. It is hypothesized that the elevation value along the ridge line was less affected by snow and vegetation; therefore, it is presumed that fixed points along the ridge line remain constant. Ridgeline characteristics were extracted by fitting the fixed ridge lines of the three periods. Pairwise registration was then performed to calculate the offset values of the DEM data in the x, y, and z directions based on the fitted ridge line characteristics. These values were used to shift the DEM data for repeated co-registrations, which were stopped once the offset values or standard deviation no longer decreased. After the registration is completed, the GSE change can be obtained by DEM difference calculation.
###### Abstract  
With the advent of Industry 4.0, the manufacturing industry is facing unprecedented data challenges. Sensors, PLCs, and various types of automation equipment in smart factories continue to generate massive amounts of heterogeneous data, but existing systems generally have bottlenecks in data collection standardization, real-time processing capabilities, and system scalability, which make it difficult to meet the needs of efficient collaboration and dynamic decision making. This study proposes a multi-level industrial data processing framework based on edge computing that aims to improve the response speed and processing ability of manufacturing sites to data and to realize real-time decision making and lean management of intelligent manufacturing. At the edge layer, the OPC UA (OPC Unified Architecture) protocol is used to realize the standardized collection of heterogeneous equipment data, and a lightweight edge-computing algorithm is designed to complete the analysis and processing of data so as to realize a visualization of the manufacturing process and the inventory in a production workshop. In the storage layer, Apache Kafka is used to implement efficient data stream processing and improve the throughput and scalability of the system. The test results show that compared with the traditional workshop, the framework has excellent performance in improving the system throughput capacity and real-time response speed, can effectively support production process judgment and status analysis on the edge side, and can realize the real-time monitoring and management of the entire manufacturing workshop. This research provides a practical solution for the industrial data management system, not only helping enterprises improve the transparency level of manufacturing sites and the efficiency of resource scheduling but also providing a practical basis for further research on industrial data processing under the \"edge-cloud collaboration\" architecture in the academic community.  
Keywords:intelligent manufacturing; OPC UA; edge computing; high concurrency; real time
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 3 Results and Discussion  
### Scanning Electron Microscopy (SEM)  
SEM was used to analyze the material's morphology, particle size, and shape. The epoxy resin was made of biphenyl. The most common type of biphenyl is a combination of acetone and phenol. Figure 3 illustrates that the surface was severely roughed [27]. The SEM analysis of C shows that the connection between the natural sand and cement paste is  
Figure 2: Sample shapes of epoxy PVC waste (EP), epoxy sand (ES), epoxy marble dust (EM), and epoxy steel slag (ESS) for tensile strength.  
not tight, resulting in internal defects and weakening the strength of the sample [28]. In addition, the SEM analysis of ESS shows that steel slag is wholly bonded with the polymer binder. From these observations, it can be noted that the polymer binder perfectly infuses (induces) the surface of the aggregate particles in co-matrix formation [29]. The use of steel slag as a filler in the preparation of mortar is further evidence of a compact structure. This is because steel slag particles tend to have angular shapes and irregular surface textures, which can interlock with one another and fill voids within the mortar matrix, resulting in a denser and more cohesive material. This increased packing density can contribute to improved mechanical properties and the overall performance of the mortar [30]. Further, the SEM analysis of ES indicates that the use of sand as a filler provides good strength due to the strong bonding between the sand particles and the epoxy resin. This strong bonding can result in a more cohesive and less porous material [31]. EM has a denser structure because marble dust is excellent. Therefore, the pores in the epoxy-containing marble dust are reduced. For that reason, the marble dust causes an increase in mechanical strength due to calcium silicate hydrate and calcium hydroxide [31; 32]. The SEM micrograph of EP indicates that the surface of the sample contains pores. These pores are likely a result of the polymer nature of PVC and epoxy, which can create voids or empty spaces within the material. The porous nature of the sample results in reduced mechanical strength and weight of the materials. Moreover, it is observed that the particles have a weak disjoint nature, with no significant interlocking observed among them. This lack of interlocking may further contribute to the porosity and reduced mechanical strength of the material [33]. Overall, it has been noted that porosity is inversely proportional to mechanical strength; for that reason, the mechanical strength of ESS is higher than that of all the mentioned specimens taken in this work.
## 3 Results
### Sea-ice Concentration Evaluation
#### 3.2.2 Comparison to MODIS and Merged Sea-Ice Concentration  
The MODIS and merged SIC time-series corresponding to the 66 Sentinel-2 scenes with a time lag below two hours are shown in Figure 8. The AMSR2 SIC are not shown as their mean is identical to that of the merged SIC by definition. There is no significant bias between the datasets; the mean merged and MODIS SIC are 93.1% and 92.8%, respectively. The Sentinel-2 thin- and thick-ice SIC means are 94.4% and 87.3%, respectively. The root mean square deviation (RMSD) between the MODIS and merged SIC is 5.0%, which shows that the two datasets indeed differ. In the case with the largest discrepancy, the merged SIC is 94.4% while the MODIS SIC is only 83.3%. Only points where MODIS data are available are considered in the means, so that different coverage can not be the reason. We will later investigate this scene in detail to understand the reasons for the deviation. Generally, the absolute differences to the thin-ice Sentinel-2 SIC are smaller (mostly within \(\pm\)10%). The absolute differences to the thick-ice SIC range from 10% to \(-\)30%, where a positive difference means that the Sentinel-2 SIC are higher. Next, we investigate two scenes in detail: one where the MODIS SIC are of poor quality and one where they are of good quality.  
Figure 5: MODIS SIC calculated with the ice tie-point from Figure 4a plus/minus the standard deviation from Figure 4d. The blue dots show the SIC. The orange dots show the difference between the MODIS SIC resulting from varying the ice tie-point by plus/minus one standard deviation. The x-axis shows the date; the vertical lines mark the days with Sentinel-2 scenes.  
Figure 6: Sentinel-2 SIC in the Kara Sea on 12 March 2019. (**a**) RGB image showing open water, thin ice and thicker ice. (**b**) Frequency distribution of the 665 nm reflectance. Mind the logarithmic scaling of the y axis. Vertical dashed lines mark the threshold for the thin and thick-ice class. (**c**) Sentinel-2 SIC if thin ice is treated as ice. (**d**) Sentinel-2 SIC if thin ice is treated as water.  
Figure 7: Time-series of the mean thin-ice and thick-ice SIC of all Sentinel-2 scenes. The x-axis shows the date, and the vertical lines mark the days with Sentinel-2 scenes. Scenes which are not used for intercomparison with the other SIC datasets because of the time lag exceeding 120 min are indicated by faint markers.