esa-sceva/satcom-chunk-collection huggingface dataset:
# Extracellular Vesicles for Regenerative Medicine Applications
## 2 Extracellular Vesicles (EVs)
### Biomineralization Vesicles  
Biomineralization vesicles (also known as matrix vesicles) are EVs produced by hypertrophic chondrocytes and mature osteoblasts [59; 60; 61; 62]. These biomineralization vesicles range in size from 100-300 nm in diameter and assist in bone formation in both endochondral and membranous ossification [59; 60; 61; 62]. While the specific mechanisms for biogenesis are not fully understood, current evidence supports a similar biogenesis mechanism to that of microvesicles which occurs through the outward budding of the plasma membrane [59; 63]. These vesicles are thought to bind ECM molecules including collagen and serve as nucleating foci to initiate hydroxyapatite crystallization necessary for bone formation [59; 60; 61]. Biomineralization vesicles initiate apatite formation through a combination of luminal and surface proteins. Within these vesicles, orphan phosphatase (PHOSPHO1) and sphingomyelin phosphodiesterase 3 (SMPD3) enzymes are located and function to increase the luminal concentration of inorganic pyrophosphate (Pi) [64]. On the surfaceof these vesicles, tissue-nonspecific alkaline phosphatase (TNAP) and ectonucleotide pyrophosphate/phosphodiesterase 1 (ENPP1) function to increase luminal Pi concentrations [65; 66; 67]. Extracellular calcium (Ca2+) is thought to be transported into the vesicles through annexins A5 and A6 [68]. The combination of elevated Pi and Ca2+ within the vesicles causes intraluminal apatite to crystalize, and these apatite crystals are necessary for bone mineralization and the formation of hydroxyapatite in bone [59].
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### Environment Monitoring
#### 3.1.3 Robustness  
The concerning network should be able to diagnose this problem, including the poor connectivity of radio transmission in the case of hardware failure or a bad weather situation like snow falling. It has been observed that humidity can lead to short-circuits, which may lead to restarting of the stations. Sometimes it is better to incorporate and remove the stations based on need. The new station could be added at any point of interest. It is not advisable to solely depend on earlier knowledge while redesigning the network.  
Figure 3: Environment monitoring system.  
[PERSON] et al., 2011 investigated and employed the tracking system in Seattle, New York, for the recognition and improvisation of the waste management system. [PERSON] et al., deciphered the techniques for mobility of the waste from one point to another point via the Trash Track Project, which also provided insight regarding the tag performance and some attained traces. This technology also produced a concept and idea regarding the transformation and perception of the cities.  
[PERSON] et al., 2012 studied the real time surveillance for CO\({}_{2}\) monitoring in a specific urban area network. They conducted their study via simulations resembling practical deployment in Wuxi, PR China.  
[PERSON] et al., 2014 studied the largest spatially resolved Ultrafine Particles (UFPs) data set in Zurich, Switzerland, which pertaining to air pollution measurements. The target pollutants are those such as nitrogen dioxide (NO\({}_{2}\)), carbon monoxide (CO), ozone (O\({}_{3}\)), and Ultrafine Particles (UFPs), which are damaging to human health. These pollutants cause severe diseases such as respiratory problems, cardiovascular problems, etc. They collected the data for more than two years. The authors compared the accuracy models on a different time scale and found that there are significant changes obtained during their time frame. Furthermore, they developed a technique via modeling in which the previous data were incorporated with the metadata, and the result was shown to be quite significant. Additionally, after deployment of the model, they obtained a significant reduction in the root mean square values which inferred that the data is statistically reproducible.  
[PERSON] et al., 2016 studied the deployment of sensors for air quality monitoring in Amsterdam, the Netherlands. The target compound was NO\({}_{2}\).The authors delineated the mechanism based on a bottom-up approach for the citizen of the urban area. They compared an electrochemical sensor and a semiconductor sensor. Furthermore, they concluded that these highly sensitive electrochemical sensors exhibit an enhanced performance compared to a semiconductor sensor.
## 2 Specimens and Experiment Setup
### Amplitude-Fluctuation Electronic Speckle Pattern Interferometry (AF-ESPI)  
The full-field optical measurement technique referred to as amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) is highly effective in determining the resonant frequencies and the corresponding vibration mode shapes of piezoelectric materials. We considered only out-of-plane displacement, since the displacement associated with in-plane motion is relatively small. The out-of-plane layout of the upper plate in Figure 4a,b shows that the laser beam was split into two parts using a variable beam splitter. One of the laser beams served as an objective light directed toward the specimen to be reflected to the CCD camera. The other laser beam was used to illuminate the surface of a plate to create a speckled reference light, which was also reflected into a CCD camera. The object and reference beams were then joined in CCD sensor array. Details of the AF-ESPI experiment procedure for out-of-plane vibrations can be found in [15; 16]. In this study, we measured the mode shapes and resonant frequencies of the upper and lower bimorphs. This required a special layout that included a reflector to capture images of the lower bimorph, as shown in Figure 4c,d. Unlike previous studies, this approach has the lower bimorph located in a position that is difficult to access (below the upper bimorph); hence, a reflection mirror is used for obtaining the image of the lower bimorph.  
Figure 3: Photographic images of pumping element: (**a**) outer cover, (**b**) middle chamber, and (**c**) assembled unit.  
Figure 2: Engineering draft of the (**a**) pump element cover, (**b**) pump element chamber, (**c**) dimensions of inlet and outlet pipes, and (**d**) assembly of proposed pumping element.
## 3 Application to Experimental Data
### Evaluation
Indications are found by local maximum detection as described in Section 3.3. If not stated otherwise, fusion is carried out with a fixed kernel size per sensor according to Equation (4), using \(\hat{u}=0.2\) mm. For example, the ET sensor is assigned a kernel size of \(\left(h_{x},\;h_{y}\right)=\left(0.2\;\text{mm},\;1.3793\;\text{mm}\right)\). While this automatic formula ensures that the kernel size exceeds the localization uncertainty in both spatial dimensions and retains the ratio of sampling distances, it might lead to situations in which the kernel is extremely large in the coarsely sampled direction, as is seen here: The kernel size in the y direction is even larger than the 1 mm long grooves themselves, which results from the disproportionate sampling distances in our measurements (see Table 2). To avoid introducing unrealistically large kernels, we restrict the kernel size ratio to at most 3. Consequently, for ET and MFL data, the kernel sizes \(\left(h_{x},\;h_{y}\right)=\left(\hat{u},\;3\hat{u}\right)=\left(0.2\;\text{mm },\;0.6\;\text{mm}\right)\) are applied. Using this evaluation framework, we investigate the performance of our fusion approach in the following.
## 3 Results and Discussion
### Alterations of Sensory Qualities of Green and Black Tea after the Decaffeination  
The above studies have shown that decaffeinated processing significantly affects flavor compounds in BT and GT. Therefore, it is necessary to identify changes in the sensory quality of BT and GT through sensory evaluation and to compare the effect of decaffeination processing on the sensory quality of BT and GT. The overall acceptance was evaluated according to the taste and aroma of the tea infusion. Overall, the unami, bitterness, astringency, and sourness were decreased in the DeCAF-BT (Figure 5A). Although some bitter substances were increased (arginine and methionine) and some were decreased (caffeine, ECG, phenylalanine, and lysine) after decaffeination, the bitterness of BT infusion was reduced. These results may be explained by more bitter substances lost and the lower threshold of caffeine (97 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and ECG (200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), compared with that of arginine (13,065 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and methionine (750 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) [20]. The reduction of the astringent substances ECG and guanine was consistent with the decline of astringency of the BT infusion after the decaffeination. Additionally, the reduction of umami in the DeCAF-BT infusion was consistent with the decrease in asparagine and guanine. However, the overall acceptance of the DeCAF-BT was significantly higher than the BT, indicating that the decaffeinated process helps to increase the sensory quality of the BT.  
For the GT, the umami and sweetness were increased, but the bitterness and astringency were decreased after the decaffeination (Figure 5B). The reductions in bitter compounds (caffeine, EGCG, ECG, EGC, and GCG) and astringent compounds (EGCG, ECG, and theanine) in the DeCAF-GT are consistent with the changes in the bitterness and astringency of the DeCAF-GT infusion. Although the contributor of umami (asparagine and theanine) and sweetness (serine and theanine) decreased after decaffeination, the umami and sweetness of the DeCAF-GT were increased. These results may be explained by the lower bitterness and astringency reducing their impact on the perception of sweetness and umami in the tea infusion. [PERSON], et al. [37] also reported that the strong taste from bitter and astringent components may lower the overall acceptance of GT.  
The decaffeination exhibited similar impacts on the color parameters of the GT and BT infusion (Figure 5C). The values of \(L^{\star}\) decreased after the decaffeination, while the values of \(a^{\star}\) and \(b^{\star}\) were higher in the decaffeinated tea infusion. A negative \(a^{\star}\) value represents the green color, whereas a positive \(a^{\star}\) value represents the red color. The results are similar to the reports from [PERSON], et al. [38], who observed that the greenness of GT infusion became slight with lower levels of inner compounds.
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Feature Extraction Stage
```  
**Algorithm 5** Proposed method to evaluate symmetry \((a)\), showing lines 1-14, corresponding to the images in Figure 3c. Figure 5: Symmetry and shape identification test. (**c**,**d**) comparison using the largest axis, (**a**,**b**) comparison using the smallest axis. Figure 6: Example of symmetry estimation. Ellipse and rectangle inscribed in the outline of the possible case of melanoma, identification of midpoints, and semiplanes. * Edge (\(b\))  
Finally, the border is checked, the contour is identified again, the area is quantified, and the polygon that best fits the shape is entered. A comparison is made between the area of the polygon and the area of the contour, and the result of the quotient is scaled according to the information obtained from the training. Algorithm 6 shows the corresponding pseudocode for contour estimation. Once the four descriptors are estimated, they serve as inputs for the classifier. It has three outputs, corresponding to malignant melanoma, benign melanoma, and images that are only skin. In the case of malignant melanoma, the degree of evolution is also identified, and the contour is colored according to the computed level. There are four levels to determine severity, where the highest level corresponds to the descriptor with maximum score. ```
1:\(I_{a}\), input image. 2:\(b\), estimated value for edge. 3:procedureget_edge(\(I_{a}\))
4:\(I_{b}=convert\_RGBtoGRAY(I_{a})\)\(\triangleright\) Convert RGB image to GRAY. 5:\(I_{c},area_{1}=get\_Contours(I_{b})\)\(\triangleright\) Identify the contour and calculate the area of the melanoma case. 6:\(I_{d}=Similar\_Polygon(I_{c})\)\(\triangleright\) Identify the polygon that best fits the contour identified in line 3. 7:\(area_{2}=get\_Area(I_{d})\)\(\triangleright\) Calculate the area of the polygon. 8:\(b=\frac{area_{1}}{area_{2}}\)\(\triangleright\) Calculate the coefficient of relationship between the two areas. 9: return \(b\)\(\triangleright\) The recovered value. ```  
**Algorithm 6** Proposed method to evaluate the edge, showing lines 1-7, Figure 3d.
# Effects of New Tunnelling on a Buried Pipeline with Joints and Its Detachment
## 2 Jointed Pipeline Subjected to Tunneling
### Calculation Method and Procedure
\begin{array}{c}\theta(0)=w^{\prime}(0)=w^{\prime}_{0}=-\frac{-w_{i- 1}+w_{1}}{2^{l}}=0\\ \theta(L)=w^{\prime}(L)=w^{\prime}_{n}=\frac{-w_{n-1}+w_{n+1}}{2^{l}}=0\\ Q(0)=EIw^{\prime\prime}(0)=EIw^{\prime\prime}_{0}=\frac{EI(-w_{n-2}+2w_{n-1}-2 w_{1}+w_{2}}{l^{2}}=0\\ Q(L)=EIw^{\prime\prime}(L)=EIw^{\prime\prime}_{n}=\frac{EI(-w_{n-2}+2w_{n-1}-2 w_{n+1}+w_{n+2}}{l^{2}}=0\\ S^{\prime}(0)=S^{\prime}_{0}=\frac{-S_{n+1}+S_{1}}{2^{l}}=0\\ S^{\prime}(L)=S^{\prime}_{n}=\frac{-S_{n-1}+S_{n+1}}{2^{l}}=0\end{array} \right. \tag{25}\]  
Fictitious nodes are then eliminated  
\[\left\{ \begin{array}{c}w_{-2}=w_{2}\\ w_{-1}=w_{1}\\ w_{n+1}=w_{n-1}\\ w_{n+2}=w_{n-2}\\ S_{-1}=S_{1}\\ S_{n+1}=S_{n-1}\end{array} \right. \tag{26}\]  
In that case, Equations (24a)-(24d) can be simplified into matrix form:  
\[[K_{EI}]\{w\}+[K_{k}][H]\{w\}-[K_{G}][H]\{w\}-[K_{j}]\{w  
\[[K_{\text{G}}]=G \begin{bmatrix}-2&2&&&0\\ 1&-2&1&&\\ &\ddots&\ddots&\ddots&\\ &&&1&-2&1\\ 0&&&2&-2\end{bmatrix} _{(n+1)\times(n+1)} \tag{28c}\] \[[K_{j}]=\frac{(1-\beta)EI}{l^{4}} \begin{bmatrix}0&&&&0\\ &0&&&\\ &\ddots&&&\\ &&[C]_{j}&&\\ &&&\ddots&\\ 0&&&0\end{bmatrix} _{(n+1)\times(n+1)}\] (28d) \[[C]_{j}= \begin{bmatrix}1&-2&1\\ -2&4&-2\\ 1&-2&1\end{bmatrix} _{3\times 3}\] (28e) \[[F]=\gamma gl \begin{bmatrix}\frac{1}{2}&1&\cdots&1&\frac{1}{2}\end{bmatrix} _{1 \times(n+1)}\] (28f) \[[H]= \begin{bmatrix}H_{0}&&&&0\\ &H_{1}&&&&\\ &&\ddots&&&\\ &&&H_{i}&&\\ &&&\ddots&\\ 0&&&&H_{n-1}&\\ 0&&&&H_{n}\end{bmatrix} _{(n+1)\times(n+1)}\] (28g) \[H_{i}=\left\{ \begin{array}{l}1,\!w\geq S\\ 0,\!w<S\end{array} \right.\] (28h) \[\{w\}=[w_{0}\ w_{1}\ \cdots\ w_{n}]_{(n+1)\times 1}^{T}\] (28i) \[\{S\}=[S_{0}\ S_{1}\ \cdots\ S_{n}]_{(n+1)\times 1}^{T} \tag{28j}\]  
We have  
\[\big{(}[K_{EI}]+[K_{k}][H]-[K_{\text{G}}][H]-[K_{j}]\big{)}\{w\}=[F]+([K_{k}][H] -[K_{\text{G}}][H])\{S\} \tag{29}\]  
Hence  
\[\{w\}=\big{(}[K_{EI}]+[K_{k}][H]-[K_{\text{G}}][H]-[K_{j}]\big{)}^{-1}([F]+([K_ {k}][H]-[K_{\text{G}}][H])\{S\}) \tag{30}\]  
Since this paper does not involve large deformations and nonlinear constitutive relationships, the usual iteration strategies, such as the Newton-Raphson method, could be applied to address the simple contact problem. In order to reach the initial solution, we need to initialize the problem first, and reduce the unilateral contact case to a bilateral contact one. An iterative process is repeated until a convergence criterion is achieved. The calculation procedure described previously is summarized in Figure 8.  
First, input the calculation parameters and initialize the matrix \([H]\) as the identity matrix. Second, obtain the matrices and vectors in Equation (30). Third, compute the displacement vector \(\{w\}\) and compare the vector \(\{w\}\) with the vector \(\{S\}\) to obtain \([H]\) as in Equation (28h). Fourth, update \([H]\) and recalculate \(\{w\}\) until the convergence criterion is achieved.
## 3 Results and Discussion  
Influence of Electromagnetic Fields on the Physicochemical Properties of Salicylic Acid FTIR Spectroscopy  
FTIR spectra of control and field-exposed salicylic acid samples are presented in Figure 1, while individual spectra for each condition are provided in the Supplementary Material (Figure S1a-p). The characteristic absorption bands observed in the FTIR spectra confirm the presence of salicylic acid across all samples, irrespective of electromagnetic field exposure.  
Notably, the absorption bands at 1652 cm\({}^{-1}\) and 1441 cm\({}^{-1}\) correspond to the asymmetric and symmetric stretching vibrations of the carboxylic C=O group, respectively. Additional prominent peaks were observed at 1291 cm\({}^{-1}\), 1208 cm\({}^{-1}\), 1152 cm\({}^{-1}\), 887 cm\({}^{-1}\), 756 cm\({}^{-1}\), 690 cm\({}^{-1}\), 657 cm\({}^{-1}\), and 530 cm\({}^{-1}\), all of which are characteristic of salicylic acid functional groups.  
The comparative analysis between the control and treated samples indicated no significant spectral shifts or band loss, suggesting that electromagnetic field exposure--whether oscillating, pulsed, static (positive or negative polarity), or rotating--did not induce any structural changes to the salicylic acid molecule. Minor differences in absorption intensities were noted, but are attributed to slight concentration differences during ATR-FTIR crystal deposition rather than EMF-induced effects.  
Figure 1: FTIR spectra of salicylic acid samples: control (C\({}_{\_}\)SA) and those exposed to oscillating magnetic field (OMF), pulsed magnetic field (PMF), static magnetic field with positive polarity (SMF+), static magnetic field with negative polarity (SMF\(-\)), and rotating magnetic field (RMF).
# Network Performance Optimization for Low-Voltage Power Line Communications
## 3 LVPLC Network Scheme  
### Network Problem Description and Modeling  
#### 3.1.1 Network Problem Description  
Inspired by spider webs in the natural environment, Reference [23] analyzed logical topology processes for a single-layer cobweb topology. This process cuts useful communication links to improve stability. This section describes this improved artificial cobweb logical topology. The stations may not form a ring network in the improved cobweb topology compared with the original cobweb (Figure 2). The critical issues encountered in networking the improved artificial cobweb are as follows: (1) the stations are peer to peer, and a method must be developed to auto-select the CCo and proxy in the network; (2) an increased probability of frame collisions, which causes network instability, is observed because of the effects of hidden stations; and (3) the stations are far from the CCo because of the one-hop or multi-hops, and the overall network performance is poor. To address these problems, the improved artificial cobweb consists of a backbone cluster tree network and a neighbor network. The backbone cluster tree network (objective function) is composed of several shortest dynamic random links from the CCo and proxy to terminals.  
Figure 1: Typical topology model of the low-voltage distribution network.  
Figure 2: Improved artificial cobweb.
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### A Deep Learning Model for Gunshot Detection
#### 3.1.3 Convolutional Neural Network Architecture
The job of the optimizer is to find the global minima by varying the network parameters. Global minima can be achieved when the loss function reaches its minimum value. In the proposed model, the binary_crossentropy loss is computed, and the RMSprop [45] optimizer is utilized. 3.1.4. Training the Deep Learning Model  
The dataset of 670,000 sound samples was converted to MFCC images, and it was then divided into three distinct subsets: 70% of images (i.e., 469,000) were allocated for training, 15% of images (i.e., 100,500) for validation, and 15% of images (i.e., 100,500) were reserved for testing. The latter set was withheld until after the model had undergone training and validation, allowing for a final assessment of model accuracy using previously unseen test samples. The mean and the standard deviation of the training dataset images are calculated and saved in the norm.npy file. Then, from all three data subsets, the mean was subtracted and then divided by the standard deviation to normalize the dataset. The deep learning architecture, as illustrated in Figure 3, was implemented using the Python programming language with the Keras library. Keras, a high-level neural networks application programming interface (API) built upon TensorFlow [46], was employed for its versatility and user-friendly interface. Model training was carried out on a desktop computer featuring a 12 th Gen Intel Core i7 processor (6 Cores) clocked at 2.10 GHz, 32 GB of RAM, and an NVIDIA GeForce RTX3070 graphics processing unit (GPU). After training the CNN model, an H5 model is generated that can be used for inferencing. To reduce the inference time of the H5 model on the Jetson Nano, NVIDIA-TensorRT [47; 48] is used to convert the model to a TRT engine. TensorRT encompasses a deep learning inference optimizer and runtime, optimizing deep learning inference applications for minimal latency and enhanced throughput. It offers INT8 and FP16 optimizations, wherein reduced precision substantially diminishes inference latency.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 5 Performance Evaluation
### Experimental Setup  
In the experiments, we randomly generate 1000 named data chunks (NDC) between 100 KB and 10 MB, and each chunk has a globally unique ID. These chunks are randomly placed among replica nodes within the ENRS domain. The replica node registers the mapping between the cached chunk ID and its NA in the NRS. There are several users under the edge node to continuously obtain named data chunks. Each time the user acquires a chunk, a chunk will be acquired again after the transmission of the previous chunk is completed. After several tests, \(a\) is set to 2 and \(b\) is set to 1 in expression (1). The value of \(c\) in function \(X(t)\) is set to 0.005. The user's request follows the Zipf distribution [37; 39]. The probability that the NDC \(i\) is requested is \(q_{i}=e*i^{-\alpha},\ e=1/(\sum_{i\in O}i^{-\alpha})\), and the value of \(\alpha\) indicates the degree of concentration of the request. In the experiment, the value of \(\alpha\) is 1. We set up two different experimental scenarios to evaluate the performance of the TCTE algorithm. The replica nodes and edge nodes in the experiments are both in the same ENRS domain.  
Figure 4 is the topology of Experimental Scenario 1. In this scenario, different replica nodes do not share bottleneck links (the link between the user and the edge node is not the bottleneck). It contains fifteen replica nodes and one edge node. Between the edge node and each replica node, between zero and five routing nodes are randomly generated. The link bandwidth between every two nodes on the path from the edge node to the replica node is a random number between 20 Mbps and 100 Mbps, and the link delay is set to 2 ms. A total of 60 users are set under edge nodes, and users continue to send requests within the experimental time. The simulation experiment time is 100 s.  
Figure 5 is the topology of Experimental Scenario 2. In this scenario, some replica nodes share the bottleneck link, and some replica nodes do not share the bottleneck link. It is a four-layer network topology containing fourteen replica nodes and one edge node. The bandwidth of each link is a random value, as shown in Table 2. The link between the user and the edge node is not the bottleneck. The latency of each link is 2 ms. A total of 60 users  
Figure 4: Experimental Scenario 1 topology (Black dots represent replica nodes RN3 to RN14).  
are set under edge nodes, and users continue to send requests within the experimental time. The simulation experiment time is 100 s.