esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Infrared Thermography for the Estimation of Melting Enthalpy
## 1 Introduction  
Infrared thermography (IRT) has been used in an increasing number of applications in materials science [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Recently, an IRT-based method was employed for the screening of phase change materials (PCM) by allowing the simultaneous detection of phase transitions in multiple samples submitted to a heating ramp, hence allowing the determination of phase diagrams in a record time [11; 12; 13; 14; 15; 16; 17]. This method succeeded in drastically reducing the time spent on the prospection of suitable materials and systems of materials to be implemented as PCM in Thermal Energy Storage (TES) systems. An impressive feat of the method is its ability to rapidly and accurately identify eutectic compositions and their associated melting temperature. Although identifying those properties is already an important factor in establishing the applicability of a PCM in a given context, there are numerous other thermal, physical and chemical properties that play a major role in the selection. Among them, the latent heat of fusion is likely the next most important as it determines the heat a PCM is able to store/release and therefore conditions the design of any given TES system.  
The determination of this parameter is commonly made using either Differential Scanning Calorimetry (DSC) or Differential Thermal Analysis (DTA) [18; 19; 20]. Both methods are considered standard methods for the establishment of phase diagrams as they can both provide transition temperatures as well as the melting enthalpy. Their time-consuming aspect motivated the development of the aforementioned IRT-based phasediagram establishment method, and yet their principles are very similar. The DTA associates phase transitions to temperature deviations between a reference and a sample submitted to a set heating/cooling ramp while the IRT-based method associates phase transitions to variations in temperature-dependent infrared signals. The method, initially developed for the estimation of a phase diagram, could then be extended to the approach of melting enthalpies, hence providing a rapid and thorough estimation method (compositions, transition temperatures, melting enthalpies) with an unmitigated interest in material science.  
In this work, we are looking into the applicability of IRT for the estimation of melting enthalpy on the same basis as DTA. It complements the study performed in [13] aiming at identifying eutectic blends of fatty acids (FA) and their respective melting temperature for their application as PCM in TES systems. The melting enthalpy of six eutectic mixtures of FA is estimated using the IRT method and compared to enthalpies measured with a DSC. The accuracy of the IRT measurements is assessed and possible improvements are discussed.
## 5 Confidence Intervals of \(R\)
### Asymptotic Confidence Interval of \(R\)
: _As \(n\to\infty\) and \(m\to\infty\) and \(\frac{n}{m}\to p\), then_  
\[\sqrt{n}(\hat{R}-R)\to N(0,B),\]  
_where_  
\[B=\frac{1}{u_{A}(\theta_{1}+\theta_{2})^{4}}\left[\theta_{1}^{2} (a_{11}a_{22}-a_{12}a_{21})+\theta_{2}^{2}(a_{11}a_{33}-a_{13}a_{31})-2\theta_ {1}\theta_{2}a_{12}a_{13}\right], \tag{30}\]  
_and \(u_{A}=a_{11}a_{22}a_{33}-a_{13}a_{22}a_{31}-a_{12}a_{21}a_{33}\)._Proof. : By using Theorem 1 and the delta method, we immediately derive the asymptotic distribution of \(\hat{R}\) as follows:  
\[\sqrt{n}(\hat{R}-R)\to N(0,B),\]  
where  
\[B=c_{A}^{t}A^{-1}c_{A},\]  
with  
\[c_{A}=\left( \begin{array}{c}\frac{\partial R}{\partial R}\\ \frac{\partial R}{\partial\theta_{1}}\\ \frac{\partial R}{\partial\theta_{2}}\end{array} \right)=\frac{1}{(\theta_{1}+ \theta_{2})^{2}}\left( \begin{array}{c}0\\ -\theta_{2}\\ \theta_{1}\end{array} \right),\]  
\[A^{-1}=\frac{1}{u_{A}}\left( \begin{array}{ccc}a_{22}a_{33}&-a_{12}a_{33}&-a_ {22}a_{13}\\ -a_{21}a_{33}&a_{11}a_{33}-a_{13}a_{31}&a_{21}a_{13}\\ -a_{22}a_{31}&a_{12}a_{31}&a_{11}a_{22}-a_{12}a_{21}\end{array} \right),\]  
and  
\[u_{A}=a_{11}a_{22}a_{33}-a_{13}a_{22}a_{31}-a_{12}a_{21}a_{33}.\]  
Therefore,  
\[B=c_{A}^{t}A^{-1}c_{A}=\frac{1}{u_{A}(\theta_{1}+\theta_{2})^{4}}\left[(a_{11 }a_{33}-a_{13}a_{31})\theta_{2}^{2}+(a_{11}a_{22}-a_{12}a_{21})\theta_{1}^{2} -2\theta_{1}\theta_{2}a_{12}a_{13}\right].\]  
We can derive the \(100(1-\gamma)\%\) confidence interval for \(R\) using Theorem 2 as  
\[\left(\hat{R}-z_{1-\frac{\gamma}{2}}\frac{\hat{B}}{\sqrt{n}},\hat{R}+z_{1- \frac{\gamma}{2}}\frac{\hat{B}}{\sqrt{n}}\right),\]  
where \(z_{r}\) is \(100\gamma\)th percentile of \(N\) (0, 1). The confidence interval of \(R\) can be derived by using the estimate of \(B\) in (30). To estimate \(B\), we use the MLEs of \(\alpha\), \(\theta_{1}\) and \(\theta_{2}\) and the following:  
\[\hat{a}_{11}=\frac{1}{\hat{\alpha}^{2}}\left[(1+p)(1+\gamma^{ \prime\prime}(2))+p(\ln\hat{\theta}_{1})^{2}+(\ln\hat{\theta}_{2})^{2}+2 \gamma^{\prime}(2)(p\ln\hat{\theta}_{1}+\ln\hat{\theta}_{2})\right],\] \[\hat{a}_{12}=\hat{a}_{21}=-\frac{\sqrt{p}}{\hat{\theta}_{1}\hat{ \alpha}}\left[\ln\hat{\theta}_{1}+\gamma^{\prime}(2)\right],\] \[\hat{a}_{13}=\hat{a}_{31}=-\frac{1}{\hat{\theta}_{2}\hat{\alpha} }\left[\ln\hat{\theta}_{2}+\gamma^{\prime}(2)\right],\] \[\hat{a}_{22}=\frac{1}{\hat{\theta}_{1}^{2}},\] \[\hat{a}_{33}=\frac{1}{\hat{\theta}_{2}^{2}}.\]
# A Comprehensive Review of Multimodal Analysis in Education
## 5 Discussion  
### Summary of Key Findings  
The comprehensive review of 177 studies revealed two primary themes: the diverse methodologies used to capture and process multimodal data, and the wide range of educational applications.  
* Methodologies and tools: MMLA research employs a variety of data collection modalities, including visual (e.g., facial expressions, gestures), auditory (e.g., speech, tone), physiological (e.g., heart rate, electrodermal activity), interaction logs, and contextual data. These data streams are processed using feature extraction methods like computer vision, speech analysis, and sensor fusion, supported by machine learning algorithms and multimodal frameworks such as OpenPose, CoTrack, and iMotions. The studies reviewed highlight the importance of accurate synchronisation and alignment of heterogeneous data sources to model complex learning behaviours effectively.
* Applications in education: MMLA has been applied across diverse educational contexts, demonstrating its potential to enhance student engagement, support collaborative learning, personalise assessments, monitor emotional and cognitive states, and address special needs. For example, multimodal systems have been used to track student sensemaking in classrooms, support reflection in healthcare training, and monitor cognitive load in collaborative learning. Other studies have explored immersive learning through VR and AR, personalised assessments based on physiological and behavioural signals, and ethical considerations in data-driven education.  
Together, these findings highlight the potential of MMLA to capture the complexity of learning processes, providing richer insights into learner behaviour and cognitive states. However, successful implementation often depends on the quality of the collected data, the robustness of feature extraction methods, and the interpretability of the resulting models.
###### Abstract  
Synthetic wastewater was treated in lab-scale constructed wetlands (CWs) in sequencing batch mode to evaluate roles of aquatic plants for removing an azo dye: Reactive Yellow 86 (RY86). Under hydraulic retention time (HRT) of 5 days, removal by unplanted CWs was less than 20% for RY86 of 10-50 mg/L. The CWs planted with cattail and papyrus demonstrated RY86 removal of 50-68% and 73-84%, respectively. For wastewater containing 50 mg/L RY86, removal in the unplanted CW was <12%, even under a 15 day HRT, although it was 22-71% in CWs planted with cattail and 34-81% in CWs planted with cattail, with increasing values under HRTs of 1 day to 15 days. Both cattail and papyrus grew well, extending their roots in the CWs for 90 days. RY86-decolorizing microorganisms were detected in CW effluent. Overall, RY86 removal was positively correlated with evapotranspiration in the CWs, indicating the plant uptake as the main removal mechanism. Papyrus and cattail, especially the former, are suitable plants for CWs intended to treat RY86-containing wastewater.  
cattail; constructed wetland; evapotranspiration; papyrus; plant uptake; reactive azo dye
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
###### Abstract  
_Christia__usvertilionis_ (L.f.) Bakh. f. is an ornamental plant with unique butterfly-shaped leaves, hence its vernacular name \"butterfly wing\" or \"rerama\" in Malay. In Malaysia, the green-leaf variety of this plant has gained popularity in recent years due to testimomial reports by local consumers of its medicinal uses, which include treatment for cancer. Despite these popular uses, there is very limited information on the phytochemistry of the leaf of this plant, presenting a significant gap in the cheminformatics of the plant species. Herein, we report a substantially detailed phytochemical profile of the leaf metabolome of the green-leafed variety of _C.__vespertilionis_, obtained by deploying an untargeted tandem mass spectrometry-based molecular networking approach. The detailed inspection of the molecular network map generated for the leaf metabolome enabled the putative identification of 60 metabolites, comprising 13 phenolic acids, 20 flavonoids, 2 benzyltetrahydrodroissoquinoline-type alkaloids, 4 hydroxyjasmonic acid derivatives, 2 phenethyl derivatives, 3 monoacylglycerolcools, 4 fatty acid amides, 2 chlorophyll derivatives, 4 carotenoids, 2 organic acids, 1 nucleoside, and 3 amino acids. Flavonoids are the major class of metabolites that characterize the plant leaves. Employing a mass-targeted isolation approach, two new derivatives of apigenin-6-C-\(\beta\)-glucosidase, the major constituents of the plant leaf, were successfully purified and spectroscopically characterized as apigenin-6-C-\(\beta\)-glucoside 4\({}^{\prime}\)-O-a-apiofuranoside (**28**) and apigenin-6-C-\(\beta\)-[(4\({}^{\prime\prime}\),6\({}^{\prime\prime}\)-O-dimalonyl)-glucoside] 4\({}^{\prime}\)-O-a-apiofuranoside (**47**). This work provides further information on the chemical space of the plant leaf, which is a prerequisite to further research towards its valorization as a potential phytopharmaceutical product.  
_Christia__usvertilionis_; global metabolite profiling; LC-MS/MS; molecular network; flavonoids; apigenin-6-C-\(\beta\)-glucosides
## 2 Related Work
On the other hand, due to the existence of motion abilities in the sensors, this approach requires more energy consumption. An application-aware routing algorithm has been presented in [13] which decreases camera sensor overlap and optimizes the solution of the coverage problem in video-based sensor networks. In this method, it is assumed that all camera nodes are located on a plane surface like the ceiling of the monitored room and capture the images from a parallel plane. Since the topology of presented approach in [13] is single-tier, many sensor nodes get involved in data transmission and consequently the lifetime of the network decreases, although a compatible routing algorithm for WMSNs is presented. In addition, in spite of selecting suitable subset of nodes by routing algorithm, all raw data transit through the network because camera sensor nodes have low processing capabilities and cannot filter undesired data before sending them. Recently in [14] an image registration method for low-resolution visual sensor networks has been presented which is based on registering two images. However, the topology of sensor networks and communication criteria from image capturing node to Sink is not addressed in this work. But, as far as we know, optimization of energy consumption and achieving best coverage and resolution depend highly on network topology, communication methods between sensor nodes, and camera sensor selection algorithms and techniques. In addition, the affine transform used in image registration is not suitable for sensor networks because camera nodes directions are slightly different. Finally, in order to create a large view of the area under observation in many applications, more than two images are needed to be registered.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 2 BitTorrent-Like Systems and Network Coding Background
### Detailed Network Coding Tutorial for P2P File Sharing Systems
Assuming we first draw \(c_{1}=x\), \(c_{2}=1\), then the first block of the first encoded piece \((P_{c1})\) is \(B_{c10}=x*(x^{2})+1*(x^{2}+x)=x+1\) (011), and the second block is \(B_{c11}=x*(x+1)+1*(x^{2}+x)=0\) (000). Now, \(P_{c1}(x+1,0)\) is completely encoded and ready to be shared. Next, to get the second encoded piece, we randomly draw two additional coefficients, say \(c_{1}=x+1\), \(c_{2}=x\) and generate \(B_{c20}=x+1*(x^{2})+x*(x^{2}+x)=0(000)\), and \(B_{c21}=x+1*(x+1)+x*(x^{2}+x)=x^{2}\) (100). Thus, \(P_{c2}(0,x^{2})\) is completely encoded and ready to be shared. * 5 th Step: The sender shares the encoded pieces along with their coefficients \((P_{c1},x,1)\), and \((P_{c2},x+1,x)\). This is algebraically represented as: \[ \begin{array}{ccc}A&*&B&=&C\\ \left(\begin{array}{cc}x&1\\ x+1&x\end{array}\right)\left[\begin{array}{c}b_{10}\\ b_{11}\end{array}\right]&=&\left[\begin{array}{c}x+1\\ 0\end{array}\right]\\ \left(\begin{array}{cc}x&1\\ x+1&x\end{array}\right)\left[\begin{array}{c}b_{20}\\ b_{21}\end{array}\right]&=&\left[\begin{array}{c}0\\ x^{2}\end{array}\right]\end{array} \] (5) where \(A\) is the coefficients matrix, \(B\) is the original blocks vector, and \(C\) is the encoded blocks vector. * 6 th Step: Upon receiving the encoded pieces with their coefficients, the receiver should solve the previous matrices system to recover the original blocks and thus the original pieces. This is achieved by using the formula: \(B=A^{-1}*C\). First, find the determinant of the coefficients matrix as shown in (8): \[det(A)=\left|\left( \begin{array}{cc}x&1\\ x+1&x\end{array} \right)\right|=x^{2}+x+1.\] (6) Since \(det(A)\
eq 0\), the system is solvable: \[ \begin{array}{c}A^{-1}=\frac{1}{det(A)}\left(\begin{array}{cc}x&1\\ x+1&x\end{array}\right)\\ =\left(\begin{array}{cc}x^{2}+x+1&x^{2}+1\\ x&x^{2}+x+1\end{array}\right),\end{array} \] \[ \begin{array}{c}A^{-1}=\frac{1}{det(A)}\left(\begin{array}{cc}x&1 \\ x+1&x\end{array}\right)\\ =\left(\begin{array}{cc}x^{2}+x+1&x^{2}+1\\ x&x^{2}+x+1\end{array}\right),\end{array} \] \[\begin{array}{c}A  
\[b_{1}=A^{-1}*\left[ \begin{array}{c}x+1\,0\\ \end{array} \right]=\left[ \begin{array}{cc}x^{2}&x^{2}+x\\ \end{array} \right],\]  
\[b_{2}=A^{-1}*\left[ \begin{array}{cc}0&x^{2}\\ \end{array} \right]=\left[ \begin{array}{cc}x+1&x^{2}+x\\ \end{array} \right].\]  
This leads to reconstructing the blocks and thus the pieces: \(P_{1}(x^{2},x+1)\) and \(P_{2}(x^{2}+x,x^{2}+x)\). * 7 th Step: Simply interpret the pieces' blocks as binary presentation and link the pieces together to reconstruct the original file. The previous steps can be generalized for any number of pieces and for any finite field \(GF(2^{m})\). However, as the number of pieces increases, solving such linear system becomes far expensive. Another network coding implementation guidance can be found in [35] and a ready Java and C implementations can be found in [36].
## 2 Network Model and Problem Description
### Awareness Combat Network Topology  
Based on the closed-loop control model of the awareness combat network and its analysis, in this paper, the topological structure of the awareness combat network is designed as a planar peer-to-peer self-organizing structure, as shown in Figure 2.  
Each node in the awareness combat network can communicate with other neighboring nodes and is not limited by its subordinate relationship and command level. The composition and topology of the whole network are shown in Figure 2, in which the purple dotted line represents a multi-hop forwarding wireless link, and the orange or green solid broken line represents a wireless link within a \"perceived cell\". The geographical range of the perceived cell is controlled automatically by the transmission power based on the strength of the wireless signal. In the perceived cell, the forwarding source node of the service data packet is referred to as a \"perceiving base source\", for example, the node \"Z9\", while the receiving node of the service data packet is called the \"perceiving mobile terminal\" of the \"perceiving base source\". Considering the fact that the perceived cells may have three or more overlapping distributions in the same region, the nodes may move between different perceived cells. When the signal strength is weakened or interfered with to a certain extent, hard handover is used to  
Figure 2: Awareness combat network with planar peer-to-peer structure.  
switch the nodes to a perceived cell with the best signal for communication. In order to increase the connection speed for frequent handovers, a dedicated call channel is reserved for the purpose of the communication control of the perceiving base source over its perceiving mobile terminals [2]. When a node switches from one perceived cell to another, its communication frequency must be accordingly changed immediately. Such a cross-cell handover sometimes occurs intensively in time distribution; therefore, the node radio is required to be designed as a software radio mode, and it needs to have a flexible frequency detection function at the same time. Each node adopts distributed control. A certain node only retains the information of its neighbor nodes and valid links [6].
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 3 Atmospheric Light Estimation
### Polarization Analysis of Natural Scenes  
In general, we consider sunlight to be unpolarized light. The polarization information detected by the sensor mainly comes from two sources. One is the polarization phenomenon caused by the scattering of sunlight in the atmosphere, and the other is the polarization phenomenon caused by the reflection of natural objects from the atmospheric light. After conducting polarization analysis and statistical analysis on a large number of polarized images (more than 500), we reached the following conclusions: In general, the polarization degree of atmospheric light is lower than that of natural objects, and this phenomenon is observed in both haze-free and hazy images.  
In our analysis and experiments, the polarized images used were obtained from two sources. One part of the data came from the publicly available polarized image dataset, PolarLITIS ([[https://zenodo.org/record/5547760](https://zenodo.org/record/5547760)]([https://zenodo.org/record/5547760](https://zenodo.org/record/5547760))), accessed on 4 October 2021. The other part of the data was captured using our own polarization detector, which utilizes the Sony IMX250 sensor chip.  
In Figure 3, three images with sky regions are displayed. Among them, Figure 3a,b are grayscale images captured by our detector, and Figure 3c is a color image from the PolarLITIS dataset.  
It can be observed that the polarimetric degree of the sky region differs significantly from the scene's polarimetric degree. The polarimetric degree of the sky region is close to 0, while the objects exhibit more pronounced polarization characteristics. In Figure 3c, the part in the green rectangle region is brighter than the part in the red rectangle region but has a lower degree of polarization than what we can see in Figure 3f. In fact, we conducted the above method's analysis on a large number of natural scene images, and over 85% of the  
channel dehazing algorithm. This enables us to determine a more accurate atmospheric light and enhances the method's adaptability compared to the original approach.  
Figure 3: (**a–c**) The intensity image obtained from the synthesis of four polarized images. (**d–f**) The degree of polarization (DoP) image of (**a–c**). (**g–i**) DoP map. (**j–l**) DoP map of the columns marked with red lines in (**d–f**).
## 5 Experimental Results and Discussion and Analysis
### Discussion on Dataset Biases and Limitations  
The SVRDD dataset used in this study is heavily based on urban road conditions, particularly those in specific regions such as Beijing districts. As a result, the model trained on these dataset may exhibit certain biases related to the types of road damage and environmental conditions prevalent in urban areas, such as road cracks, potholes, and surface wear that are common in highly trafficked city streets. This could limit the model's ability to generalize effectively to rural roads or less-developed regions, where road conditions and types of damage might differ significantly. For example, rural roads often have different surface types, wear patterns, and environmental factors (e.g., less  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**CPDA** & **MCD** & **P** & **R** & **mAP50** & **mAP 50-95** & **Params** & **FLOPs** & **Model Size** \\ \hline  & & 70.7 & 59.3 & 64.5 & 37.8 & 3.0 & 8.1 & 6.3 \\ ✓ & & 68.5 & 62.1 & 66.1 & 39.2 & 2.3 & 4.6 & 5.1 \\  & ✓ & 68.9 & 60.8 & 65.2 & 38.3 & 2.8 & 7.8 & 5.8 \\ ✓ & ✓ & **71.6** & **66.6** & **70.1** & **42.8** & **1.6** & **1.7**-frequent maintenance, varying weather conditions) that may not be adequately represented in the SVRDD dataset. Additionally, the dataset may not fully cover seasonal variations in road damage, such as cracks caused by freezing and thawing or damage due to extreme weather conditions like heavy rains or flooding.