esa-sceva/satcom-chunk-collection huggingface dataset:
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 2 Extraction and Selection of Multidimensional Features  
Feature extraction and selection are crucial for accurate SOH prediction in lithium-ion batteries. In order to mine the key features that can effectively characterize the degree of battery aging, we extracted multidimensional data including time domain and frequency domain features of voltage, energy, temperature curves, and time dimension features from the battery's charge and discharge cycle data. These features comprehensively represent the battery's operating status and degradation process. However, the number of features needs to find a balance between accuracy and model complexity: too few features may lead to insufficient model prediction performance, while too many features will increase the training burden of the model. Therefore, selecting more relevant and high-information-value feature inputs can significantly improve the accuracy of SOH prediction while avoiding unnecessary complexity caused by redundant features.
## 5 Discussion
### About the Analysis of the Natural Frequencies and Vibration Modes  
#### 5.4.1 Study of the Available Background Information: Computational Models and Load Test  
As a prior stage to carrying out the analysis of the vibration frequencies, a study of the available background information must be undertaken, particularly of the structure computational model and the load test.  
The computational model of the structure provides very relevant information: the natural frequencies and vibration modes of the structure. Therefore, an attempt should be made to find the correspondence between each of the recorded events with the theoretical vibration frequencies and, if feasible, also with those obtained during the load test. Thus, for example, when the analysis of the E-1 structure was carried out, a very acceptable correspondence was observed.  
However, on other occasions, this analysis is not so immediate, and a further step should be taken. This can happen in the case of concrete structures, where the moving load may not be enough to excite the structure as a whole or do so to a lesser extent. Therefore, the possibility of the spans being excited independently should not be ignored, even with the same frequency of passage of the moving load.  
An example of this is indicated in Figure 5, corresponding to a dynamic event recorded in the E-2 structure (event recorded on 13 August 2008 at 2:46 p.m., selected channel AV011, vertical accelerometer installed in the centre of the first span of the viaduct). The spectrum shows its highest peak around 3.4 Hz. However, in this structure, the first vibration modes are between 1.36 and 1.77 Hz (from vibration mode number 1 to number 8, respectively), an area where several peaks of the spectrum would occur but very far from the highest peak. Indeed, a possible explanation would have to be sought in moving loads. The train that caused this event was, according to the information available, a type S-102 train traveling at 141.85 km/h. Taking into account the characteristics of the composition of this train, the dynamic load would have a frequency spectrum with a peak in the vicinity of 3.1 Hz. Likewise, based on the computational model, this first span of the viaduct vibrating independently would have a natural frequency between 2.8 Hz (supposed simple support at both ends, which obviously does not happen) and 4.2 Hz (supposed simple support at one end and perfect embedment at the other, which is the theoretical behaviour). However, with the information available, it is not feasible to specify at what point the span would  
Figure 4: Influence of the circulation speed on the maximum vertical accelerations obtained with the computational model of the E-1 structure.  
behave as perfectly supported-embedded and how much mass would be really mobilized in the dynamic event so it can explain the displacement of the natural frequency from 4.2 Hz to the vicinity of 3.1 Hz.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 4 Experiments and Results
### Experimental Results
#### 4.2.3 The Analysis of the Performance of the Ensemble Classifier  
Figure 10 shows the confusion matrix of the ensemble classifier based on the base learners: ResNet50 with the SE attention block, ResNet50 with the CA attention block, and ResNet50 with the NL block. This matrix shows that the proposed method correctly classifies almost all classes; there are just a few misclassifications. All checkered and floral patterns are correctly classified. Only 1% of striped patterns are misclassified as checkered, floral, solid, and zigzag each. Further, 1% and 2% of zigzag patterns are misclassified as striped and floral, respectively. About 2% of dotted patterns are misclassified as solid and striped each. Only 1% of solid patterns are incorrectly classified as zigzag.
## 3 Results
The equivalent CO\({}_{2}\)e emissions for each kWh of electricity produced in mainland Portugal in 2023 is 166 gCO\({}_{2}\)e/kWh, and emission values to produce the battery, according to the global  
Figure 8: GHG emissions (in tCO\({}_{2}\)e) throughout the life cycle of medium-sized ICEVs and BEVS, showing the influence of the national electricity grid in the use phase. Figure 9: Evolution of CO\({}_{2}\)e emissions until carbon parity is reached over the life of medium-sized BEVs and medium-sized ICEVs. average, is around 97 kg CO\({}_{\text{2e}}\) for each kWh of battery. Figure 10 shows the Portuguese CO\({}_{\text{2}}\) equivalent emission results. Figure 10 shows that BEVs are approximately 70% less polluting when compared to ICE vehicles with the same dimensions, and even when comparing a large BEV with a small ICEV, the large BEV is approximately 60% less polluting. Considering only exhaust pipe emissions for ICEVs and total emissions for BEVs, it appears that BEVs are 2.3 and 2.1 times less polluting than petrol and diesel ICEVs, respectively. This calculation assumed the best-case scenario for battery manufacturing (see Table 3). In Figure 11, carrying out the same calculations but for BEVs in Portugal in the best- and worst-case scenarios and considering the segment and location of battery production. The analysis considered the production of BEV batteries. In the best-case scenario, the batteries produced in Sweden had a carbon intensity of 45 kg CO\({}_{\text{2e}}\)/kWh, while in the worst-case scenario, they were manufactured in China and had a density of 108 kg CO\({}_{\text{2e}}\)/kWh. Table 10 shows the mileage required for BEVs to reach environmental parity with diesel and petrol ICEVs in Portugal in the various scenarios in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **ICEVs-Diesel (km)** & **ICEVs-Petrol (km)** \\ \hline Best scenario (small BEVs) & 5535 & 5334 \\ Worst scenario (small BEVs) & 20,841 & 20,086 \\ Best scenario (medium BEVs) & 7131 & 6864 \\ Worst scenario (medium BEVs) & 27,865 & 26,840 \\ Best scenario (large BEVs) & 9894 & 9525 \\ Worst scenario (large BEVs) & 36,231 & 34,878 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Mileage of a BEV up to parity in Portugal, considering the best- and worst-case scenarios for each segment, compared to diesel and petrol ICEVs. Figure 10: CO\({}_{\text{2}}\) equivalent emissions over the life of a vehicle driven in Portugal by vehicle segment. From another point of view, the greater carbon intensity of battery production leads to a considerable increase in the initial emissions associated with BEVs, which may require wider use to offset the environmental impact.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 3 The TDAG Algorithm  
In this section, we present our new algorithm, TDAG, which, given a time-dependent road network \(G=(V,E)\) with a small set \(L\subset V\) of landmark-nodes accompanied with preprocessed landmark-to-node travel-time metadata and an arbitrary query \((o,d,t_{o})\) of an origin-node \(o\in V\), a destination-node \(d\in V\), and a departure time \(t_{o}\in[0,T)\) from \(o\), computes a collection of meaningful (short and significantly non-overlapping) alternative \(od\)-routes. The solution set is succinctly represented by an alternative graph \(H\), i.e., the subgraph of \(G\) induced by the arcs of the chosen \(od\)-routes. Of course, within \(H\), there may exist even better combinations of \(od\)-routes for the query \((o,d,t_{o})\), which are also considered as part of the solution.  
TDAG is based on the rationale of the CFLAT oracle [21]. In a nutshell, it first grows two small-size TDD-balls, an origin-ball emanating from the origin and a destination-ball leading to the destination. It then connects the two balls by reconstructing (not necessarily the shortest, but short enough) paths ending in nodes of the destination-ball and starting from nodes within the origin-ball. This path construction is landmark-directed: the preprocessed information for the \(N\) landmarks of the origin-ball is exploited to direct these paths. A pruning step then takes over the responsibility of \"cleaning\" the resulting subgraph of uninteresting arcs. Eventually, the penalty method is applied to the final alternative graph, in order to conclude with a small set of _meaningful_ alternative \(od\)-routes. TDAG also takes into account the following input parameters:  
1. the number \(N\in\mathcal{O}(1)\) of nearby landmarks that will be settled by TDD in the origin's neighborhood;
2. the upper bounds \(maxAverageDistance\) for the _averageDistance_ criterion, \(maxStretch\) for the maximum stretch of each \(od\)-path that is accepted as an alternative route of \(H\), compared to the minimum-travel-time \(D_{H}[od](t_{o})\) in the alternative graph \(H\) (Since TDAG essentially mimics the preprocessing of the CFLAT oracle [21], one can easily deduce that \(D_{H}[o,d](t_{o})\) is a very good approximation of \(D_{G}[o,d](t_{o})\), for all possible departure times from the origin.), and \(maxDecisionEdges\) for the _decisionEdges_ criterion.  
All these input parameters directly affect the size, the quality, and the computation time for constructing \(H\).  
TDAG consists of two building blocks, the preprocessing phase and the query algorithm, which will be presented in the rest of this section.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 2 Initial Registration  
Figure 1 shows the process of our two-step registration method. By optimizing several key steps shown in yellow, the accuracy and speed for registering field unstructured terrain point clouds are much improved. Specifically, there is an obvious translation and rotation dislocation between the point clouds of consecutive frames when the scanning frequency of LIDAR is low and therefore we firstly use the dead reckoning data to perform initial registration on the point clouds to improve the accuracy and speed of the subsequent accurate registration. In order to implement this method, IMU (Inertial Measurement Unit) is used to obtain attitude data, encoders are used to obtain displacement data, and 3D LIDAR is used to obtain terrain data. These data are matched by using time stamp.  
We simplify the calculation by registering the point cloud of the current frame to the navigation coordinate system of the point cloud of the previous frame. The calculation process is as follows:  
\[\left[\begin{array}{c}x_{0}\\ y_{0}\\ z_{0}\end{array}\right]=R_{s}\cdot\left(\left[\begin{array}{ccc}\cos\alpha&0& \sin\alpha\\ 0&-1&0\\ -\sin\alpha&0&\cos\alpha\end{array}\right]\cdot\left[\begin{array}{c}x_{l}\\ y_{l}\\ z_{l}\end{array}\right]+\left[\begin{array}{c}d_{lx}\\ 0\\ d_{lz}\end{array}\right]\right)+R_{s}\cdot\left[\begin{array}{c}\Delta\beta \cdot r\\ 0\\ 0\end{array}\right] \tag{1}\]  
where (\(x_{0},y_{0},z_{0}\)) is the coordinate in the navigation coordinate system of the previous frame; (\(x_{l},y_{l},z_{l}\)) is the coordinate in the LIDAR coordinate system; \(\alpha\) is the angle between the plane of LIDAR and the vertical direction; (\(d_{lx},d_{lx}\)) is the origin of the LIDAR coordinate system relative to the vehicle body coordinate system (IMU coordinate system); \(\Delta\beta\) is the encoder increment; \(r\) is the tire radius. \(R_{s}\) is as follows:  
\[\left[\begin{array}{ccc}\cos\theta\cos\psi&\sin\varphi\sin\theta\cos\psi-\cos \varphi\sin\psi&\cos\varphi\sin\theta\cos\psi+\sin\varphi\sin\psi\\ \cos\theta\sin\psi&\sin\varphi\sin\theta\cos\psi+\cos\varphi\cos\psi&\cos\varphi \sin\theta\sin\psi-\sin\varphi\cos\psi\\ -\sin\theta&\sin\varphi\cos\theta&\cos\varphi\cos\theta\end{array}\right] \tag{2}\]  
where (\(\varphi\), \(\theta\), \(\psi\)) is the vehicle body attitude (Euler angle).  
Through formula (2), the point cloud of the current frame is firstly transformed into the vehicle body coordinate system, and then transformed into the current navigation coordinate system, and finally transformed into the previous navigation coordinate system. The point cloud of the previous frame is transformed into its own navigation coordinate system. For subsequent accurate registration, the transformed point cloud of the current frame is the target point cloud and the transformed point cloud of the previous frame is the source point cloud.
# SDMw: Secure Dynamic Middleware for Defeating Port and OS Scanning
## 5 SDMw: The Secure Dynamic Middleware Architecture
### The SDMw Architecture
#### 5.1.5 Connection-Oriented TCP Service from Inside Hosts  
Even though TCP and UDP uses the same network layer (IP), TCP provides a totally different service to the application layer than UDP does. TCP provides a connection-oriented, reliable, byte stream service. Like UDP services, TCP connections are also identified by the 4-tuple, source IP address, source port, destination IP address and the destination ports. The term connection-oriented means the two applications using TCP (normally considered a client and a server) must establish a TCP connection with each other before they can exchange data. The logical connection is established and maintained by means of 32-bit sequence and acknowledgement numbers. This process starts with an Initial Sequence Number (ISN). TCP is very sensitive on establishing, maintaining and tearing a connection. An on-going connection will be terminated if there is a mismatch in the sequence number.  
As before, from a socket, no host OS information is exposed to the attacker. However, compared to IP and UDP, TCP has several design ambiguities that are traditionally exploited to fingerprint a host. Our proposed SDMw architecture preserves the original socket information. However, we change other TCP parameters, including the sequence numbers that can mask the OS detection. Since we are changing the logical connection information, we need to maintain a translation table in order for the reply from the destination to be forwarded to the end-host properly without tearing an on-going connection. While deciding on the congestion window parameters, SDMw chooses the minimum of the one it generates based on the UHGA and the host's advertised congestion window parameters. Based on the particular OS chosen by the UHGA algorithm, the TCP parameters are determined on a per-socket basis. Entries in the translation table are maintained for \(2\times MSL\) (maximum segment lifetime) wait period before they are removed.
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
## 3 Results
### Essential Oil Yield and Composition  
The effect of drying temperature on essential oil yield and composition of _M. officialis_ plants is shown in Table 4 and Figure 3, along with the retention indices of each compound. Essential oil analysis identified twenty-three individual compounds, each contributing more than 0.05% to the total oil profile. The majority of these compounds (93.07-94.04%) were oxygenated monoterpenes, with the dominant constituents being geranial (36.30-45.23%), neral (23.07-29.66%), and citronellal (17.86-33.32%), followed by \(\beta\)-caryophyllene (1.00-1.95%). The rest of the compounds were present in amounts below 1% of the total oil profile. Drying at both low (20 and 35 \({}^{\circ}\)C) and high (49 \({}^{\circ}\)C) temperatures significantly reduced the content of the two major compounds of the EO, geranial and neral, which reached their highest concentrations (45.23% and 29.66%, respectively) when plants were dried at 42 \({}^{\circ}\)C. At the same time, plants dried at 42 \({}^{\circ}\)C appear to have the lowest citronellal content (17.86%), significantly lower than those dried at the other tested temperatures. While 20 \({}^{\circ}\)C maximized EO yield, 42 \({}^{\circ}\)C optimized the composition of key compounds like geranial and neral. The formation of these compounds is derived from geraniol, which follows two different metabolic pathways, producing either geranial and neral or citronellal. The remaining identified compounds were also influenced by drying temperatures; however, since they were present in low amounts (<1%), the effect was likely less pronounced. As for the EO yield, it was relatively low compared to other MAP species, ranging from 0.03 to 0.10%. The highest EO yield was observed in _Melissa_ plants dried at 20 \({}^{\circ}\)C (Figure 3).  
Figure 3: Effect of the drying temperature on the yield (%) of essential oils of _M. officinalis_ plants. Values are expressed as means \(\pm\) standard (n = 3). Different Latin letters indicate significant differences (\(p<\) 0.05).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature** & **Chl a** & **Chl b** & **Tot Chl** \\  & **(mg/g Dw)** & **(mg/g Dw)** & **(mg/g Dw)** \\ \hline
20 \({}^{\circ}\)C & 0.44 \(\pm\) 0.06 & 2.34 \(\pm\) 0.23 b & 2.77 \(\pm\) 0.17 bc \\
35 \({}^{\circ}\)C & 0.57 \(\pm\) 0.00 & 3.48 \(\pm\) 0.16 a & 4.05 \(\pm\) 0.16 ab \\
42 \({}^{\circ}\)C & 0.51 \(\pm\) 0.08 & 3.66 \(\pm\) 0.37 a & 4.16 \(\pm\) 0.46 a \\
49 \({}^{\circ}\)C & 0.36 \(\pm\) 0.08 & 1.54 \(\pm\) 0.34 b & 1.90 \(\pm\) 0.43 c \\ \hline \end{tabular} Values represent the mean \(\pm\) standard error (n = 5). Different Latin letters in each column indicate significant differences (\(p<\) 0.05).  
\end{table}
Table 3: Effects of drying temperature on _M. officinalis_ chlorophyll content (chlorophyll a-Chl a, chlorophyll b-Chl b, and total chlorophyll-Total Chl).
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## Full Field Detection Photoacoustic Tomography
### Mathematical Model  
In the case of variable sound speed, acoustic wave propagation in PAT is commonly described by the initial value problem [34; 38; 39; 41]  
\[p_{tt}(\mathbf{x},t)-c^{2}(\mathbf{x})\Delta p(\mathbf{x},t) =0, (\mathbf{x},t)\in\mathbb{R}^{3}\times\mathbb{R}_{>0} \tag{1}\] \[p(\mathbf{x},0) =f(\mathbf{x}), \mathbf{x}\in\mathbb{R}^{3}\] (2) \[p_{t}(\mathbf{x},0) =0, \mathbf{x}\in\mathbb{R}^{3}. \tag{3}\]  
here \(c(\mathbf{x})>0\) is the sound speed at location \(\mathbf{x}\in\mathbb{R}^{3}\) and \(f\in C_{0}^{\infty}(\mathbb{R}^{3})\) is the initial pressure distribution that encodes the inner structure of the object. It is assumed that the object is contained inside the ball \(B(0,R)=\left\{\mathbf{x}\in\mathbb{R}^{3}\mid\|\mathbf{x}\|<R\right\}\), of radius \(R\) centered at the origin, and that the sound speed is smooth, positive and has the constant value \(c_{0}\) outside \(B(0,R)\). We denote by \(C_{0}^{\infty}(B(0,R))\) the set of all smooth functions \(f\colon\mathbb{R}^{3}\to\mathbb{R}\) that vanish outside \(B(0,R)\).  
In FFD-PAT, linear projections (integrals along straight lines) of the 3D pressure field \(p(\,\cdot\,,T)\) for a fixed time \(T>0\) are recorded. As illustrated in Figure 1, this can be implemented using a special phase contrast method and a CCD-camera that records full field projections of the pressure field [30; 31]. The linear projections are collected for rotation angles \(\theta\in[0,\pi]\) around the \(e_{3}=(0,0,1)\) axis and are given by  
\[g_{R}(\theta,s,z)=\int_{\mathbb{R}}p(s\cos(\theta)-t\sin(\theta),s\sin(\theta )+t\cos(\theta),z,T)\,dt\,, \tag{4}\]  
for \((\theta,s,z)\in M_{R}\coloneqq\left\{
## 1 Introduction
Hence, there is a clear gap in the literature regarding the examination of the role and use of learning analytics and educational data mining within extended reality environments and the representation of the existing state of the art. Given the potential benefits that their combination can bring and to address this literature gap, this study aims to explore the role and use of educational data mining and learning analytics within augmented reality and virtual reality learning environments and the metaverse. Specifically, the goal of this study is to showcase the state of the art, to highlight the impact that the combination of these technologies and methods has on teaching and learning activities, and to suggest future research directions to help shape this novel field of study based on the existing literature. Hence, the main research questions of this study are how educational data mining and learning analytics can be used within virtual reality and augmented reality learning environments as well as the metaverse and how they impact the processes of teaching and learning. To meet the objective of this study and address the research question set, a systematic literature review following the \"Preferred Reporting Items for Systematic Reviews and Meta-Analyses\" (PRISMA) [54] framework and an in-depth content analysis was conducted. Additionally, topic modeling using Latent Dirichlet Allocation (LDA) [55] was also used and Bibliometrix (v.4.3.0) [56] and VOSviewer (v.1.6.20) [57] tools were adopted to further examine the document collection. This study contributes to the existing literature by providing an in-depth analysis of this emerging field of study. Specifically, it presents findings from theoretical, proposal, and experimental studies and synthesizes their outcomes. Additionally, it provides a content analysis of the experimental and case studies to identify key aspects in the realization of learning analytics and educational data mining within augmented reality and virtual reality environments. Furthermore, this study also contributes through its bibliometric analysis and scientific mapping of the existing literature as well as the identification of emerging topics and limitations within the literature. Finally, this study also highlights future research directions and areas. The remainder of this study explores related works (Section 2) and presents the methodology used in detail so as to provide clarity, transparency, and reproducibility (Section 3). Moreover, it presents the data analysis and the related results (Section 4) and focuses on analyzing the document collection as a whole (Section 4.1) as well as each different document type separately following a mixed-method approach (Sections 4.2-4.4). The findings are further examined, synthesized, and analyzed and  
Figure 1: The relationship among extended reality technologies, educational data mining, and learning analytics. implications are provided (Section 5). In Section 6, the conclusions arisen are presented, the study limitations are discussed, and future research directions are provided.