esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Monitoring Program and Result Analysis
### Analysis of Monitoring Results of Transverse Deformation of the Pier and Track  
Figure 10a displays the transverse deformation of the metro viaduct pier end at the site. The final transverse deformation value of each pier of the existing subway viaduct is seen to be within the specified control value of 3 mm. The pier test point with the pile foundation underpinning experiences a maximum transverse deformation of \(-0.5\) mm. This distortion occurs in the north of Pier Test Point 7 and is sloped towards the inside of the excavation. The final transverse deformation of the pier at the road foundation pit and other measuring places has a maximum value of \(-0.1\) mm. The deformation is sloped toward the inside of the excavation. The mean transverse deformation of the pier with the pile foundation underpinning is \(-0.37\) mm, while the mean transverse deformation of the pier near the road foundation pit and other pier is \(-0.05\) mm. It can be concluded that the influence of pile foundation underpinning on the transverse deformation of the pier measurement points of the existing subway viaduct bridge is as follows: the pier measurement points at the position of pile foundation underpinning > the pier measurement points at the adjacent road foundation pit and other pier measurement points.  
The monitoring data for the ultimate lateral deformation of the track structure during the pile foundation underpinning stage are chosen. The resulting lateral deformation of the underground track structure is illustrated in Figure 10b. Based on the figure, the transverse deformation values of all measurement places in the track structure are below the control value of 3 mm. The ultimate transverse deformation at the measuring site of the track structure reaches a maximum value of \(-0.5\) mm. This maximum deformation occurs at Pier 7# and Pier 11# and is in line with the maximum transverse deformation of the pier. The transverse deformation trend of the pier is indicative of the transverse deformation trend of the track. The influence of pile foundation underpinning on the transverse deformation of the existing subway track structure is as follows: the corresponding track structure above the pier with the pile foundation underpinning > the corresponding track structure above the pier near the road foundation pit and other pier  
Figure 9: Time history diagram of vertical deformation of typical bridge pier measuring points. (**a**) Time course diagram of vertical deformation of bridge abutments. (**b**) Vertical deformation time course diagram of the track.
## 3 Results
### Mono- and Oligosaccharides of Modified LP  
Megazyme's methodology for TDF determination does not allow for the evaluation of oligosaccharides present in the tested material which, according to the Codex Alimentarius Commission's definition, can be classified as dietary fiber and are important due to their prebiotic properties [44]. Therefore, the influence of enzymatic hydrolysis on the changes in mono- and oligosaccharides in LP was analyzed (Figure 4). Samples after SFE-CO\({}_{2}\) extraction were selected for mono- and oligosaccharides evaluation because of the higher SDF content.  
The enzymatic modification of LP-CO\({}_{2}\) using Viscozyme(r) L and Pectinex(r) Ultra Tropical increased the amount of galacturonic acid (by 47 and 46%, respectively), which is the main component of pectin. Celluclast(r) 1.5 L increased monosaccharides (glucose and fructose) and disaccharides (sucrose). In LP-CO\({}_{2}\) samples hydrolyzed with Celluclast(r) 1.5 L, the glucose, fructose, and sucrose contents increased by 4, 12, and 43%, respectively, compared with the LP-CO\({}_{2}\) sample, which was not treated with enzyme preparations. Viscozyme(r) L and Pectinex(r) Ultra Tropical did not significantly affect mono- and disaccharide changes.  
Enzyme preparations with pectinolytic activity, Viscozyme(r) L and Pectinex(r) Ultra Tropical, degraded the SDF (Table 2), which led to a significant increase in oligosaccharides (Figure 3). After evaluating the profile of oligosaccharides, it was observed that the mentioned enzyme preparations led to a significant increase in oligosaccharides consisting of 5-6 monosaccharide units (DP5-6). The content of these oligosaccharides increased from 0 g/mg DM to 3.2 g/mg DM and 2.8 mg/g DM in the LP-CO\({}_{2}\) samples hydrolyzed with Viscozyme(r) L and Pectinex(r) Ultra Tropical, respectively, while Celluclast(r) 1.5 L only resulted in an increase to 0.6 mg/g DM. This is also in accordance with the dietary fiber evaluation results, which show that Celluclast(r) 1.5 L, in contrast to the pectinolytic enzyme preparations, increased the amount of SDF (Table 2).  
The number of oligosaccharidesarcharides, including kestoses and nystoses, did not change significantly. The results of this study suggest that LP may be a source of a new class of prebiotics called pectin oligosaccharidesarcharides (POSs). POSs are indigestible oligosaccharidesarcharides that selectively stimulate the growth and activity of colonic bacteria (_Bifidobacteria_ and _Lactobacillus_) and positively affect human health [45]. Therefore, the enzymatic hydrolysis of pectins with Viscozyme(r) L and Pectinex(r) Ultra Tropical enzymatic preparations can increase the amount of POS in pomace, thus making it a potentially attractive ingredient for developing new functional products.
###### Abstract  
The theory of vertical pile vibration is the essential basis for pile integrity determination and dynamic analysis. The pile vibration characteristics are inevitably affected when the surrounding soil is frozen due to the low environmental temperature. Therefore, the investigation of pile vibration considering the surrounding soil as a saturated frozen porous medium is of great importance. In this paper, an analytical model for vertical pile vibration was established by employing the theory of composite saturated porous media, that is, by simplifying the upper frozen soil layer as a homogeneous isotropic saturated frozen porous medium and the foundation soil beneath the pile as an elastic half-space subjected to the motion of a rigid disk. By employing the integral transform and variable separation method, analytical solutions for the proposed model were derived under the three-dimensional axisymmetric condition. The analytical model and its solutions were verified by comparing them with the existing solutions for an end-bearing pile embedded in a homogeneously frozen soil layer as well as for a saturated half-space. A parametric study was conducted by utilizing the proposed solutions, and the results indicated that the pile bottom stiffness, the freezing temperature, the shear modulus of the unfrozen soil, etc., had a significant influence on the dynamic responses of the vertical pile vibration in both the frequency and the time domains.  
frozen soil; porous medium; elastic half-pace; vertical pile vibration; analytical solution; integral transformation 2022
## 3 Results
### Blind Docking Results  
Table 5 presents the amino acid sequences of the active sites of the enzymes investigated in this study. Table 6 presents the results of the blind docking analysis of the interactions between the selected terpenes and the enzymes involved in arachidonic acid metabolism. For comparative evaluation, this table lists the amino acid residues involved in ligand-enzyme interactions as predicted by molecular docking, along with a comparison to known catalytic residues (\"Match\"). \"Match\" refers to overlap between contact residues and literature-defined catalytic residues for each enzyme, suggesting potential for functional inhibition. Figure 4: The comparative 3D visualization of the binding interactions between the selected terpenes, arachidonic acid (AA), and the PLA2 enzyme. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Enzyme** & **Active Site** & **Bibliography** \\ \hline \multirow{2}{*}{COX-1} & LEU117, ARG120, PHE205, PHE209, VAL344, ILE345, TYR348, VAL349, LEU352, & \multirow{2}{*}{[50]} \\  & SER353, TYR355, PHE381, TYR385, TRP387, ILE523, HIS207, HIS388, GLN203 & \\ \hline \multirow{2}{*}{COX-2} & TYR385, SER530, LEU531, VAL116, ALA527, VAL349, TYR355, LEU352, VAL523, & \multirow{2}{*}{[51]} \\  & PHE518, PHE205, PHE209, TYR348, VAL349, LEU352, PHE381, TYR385, TRP387, & \\ \hline \multirow{2}{*}{5-LOX} & HIS367, HIS372, HIS550, PHE177, TYR181, LEU368, LEU414, ILE406, ALA603, & \multirow{2}{*}{[52]} \\  & ALA606, TRP599, ASN407, HIS432 & \\ \hline \multirow{2}{*}{PLA2} & PHE5, HIS6, ILE9, ALA18, TYR21, GLY22, GLY29, GLY32, CYS44, HIS47, TYR51 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Active site amino acid residues in COX-1, COX-2, 5-LOX, and PLA2 enzymes. For each ligand-enzyme complex, the determined parameters are as follows:  
* Active site sequence: a list of amino acid residues forming the catalytic site of the corresponding enzyme. * Binding residues: specific residues through which the terpene establishes contact with the enzyme during docking. * Matches: residues that appear most frequently across simulations, indicating potentially stable interactions and playing a key role in stabilizing the complex. The data indicate that the terpenes interact selectively with the enzymes involved in arachidonic acid metabolism, establishing specific contacts within their active sites.
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 2 Materials and Methods  
### THz-CCS System  
The THz-CCS system used in this paper was developed at the Department of Electrical and Photonics Engineering at the Technical University of Denmark. The THz-CCS system was proven useful in several settings including measuring variations in paper thicknesses, with thicknesses on the same order of magnitude as ship paint [27].  
Standard THz-TDS systems usually use a single pulse from a femtosecond laser to generate a signal. To reduce the size of the device and enable the use of fiber optic cables throughout the whole system for increased robustness, the THz-CCS system replaces the femtosecond laser with a broadband CW output from a C-band amplified spontaneous emission (ASE) light source. The delay stage of the THz-CCS system does not rely on free-space propagation, as in most THz spectroscopy systems. In the THz-CCS system, light is restricted in fiber optic cables wrapped around piezoelectric crystals than can stretch the cables, resulting in a delay of the light as in [28]. By utilizing a fiber-coupled light source, the system is more resilient to vibrations, which inevitably will affect the system outside of a laboratory. The full THz-CCS system, other than the antennae, is enclosed in a transportable 25 cm \(\times\) 25 cm \(\times\) 10 cm box with an IP66 rating [29], and the antennae, seen in Figure 2A, are connected by fiber optic cables. This makes it possible to keep the box above water and only submerge the antennas; hence, only the antennas have to be waterproof. This concept can be seen in Figure 1.  
Figure 1: Outline of a potential solution, showing the area of interest on the hull, the underwater part of the prototype containing the THz antennae with a silicone contact patch, and the box containing other necessary components of the THz-CCS system above water.
# Improving Convolutional Neural Networks' Accuracy in Noisy Environments Using k-Nearest Neighbors
###### Abstract  
We present a hybrid approach to improve the accuracy of Convolutional Neural Networks (CNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (kNN) algorithm for inference. Although this is a common technique in transfer learning, we apply it to the same domain for which the network was trained. Previous works show that neural codes (neuron activations of the last hidden layers) can benefit from the inclusion of classifiers such as support vector machines or random forests. In this work, our proposed hybrid CNN + kNN architecture is evaluated using several image datasets, network topologies and label noise levels. The results show significant accuracy improvements in the inference stage with respect to the standard CNN with noisy labels, especially with relatively large datasets such as CIFAR100. We also verify that applying the \(\ell_{2}\) norm on neural codes is statistically beneficial for this approach.  
convolutional neural networks; k-nearest neighbor; hybrid approach; label noise Article
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Evaluation  
#### 4.2.1 Performance Measurement  
A square sensor field of \((500\times 500)\) m\({}^{2}\) consists of three scenario with 400, 700, and 1000 nodes. Furthermore, with regard to false report attacks, two cases of FTRs of 50%, 70%, and 90% are considered for each of the above scenarios. The BS is located at bottom center of the sensor field. The sensor range, cluster height, and width are 50 m. Initial node energy of each sensor node is 1 joule with  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Sensor field size & \((500\times 500)\) m\({}^{2}\) \\ Cluster size & \((50\times 50)\) m\({}^{2}\) \\ BS location & \((250,\ 0)\) m \\ Sensors nodes & 400, 700, 1000 \\ \(R_{i}\) & 50 m (\(\pm 10\%\)) \\ \(E_{\mathit{elac}}\) for Tx and Rx & 50 nJ/bit \\ \(E_{\mathit{amp}}\) & 100 pJ/bit/m\({}^{2}\) \\ Node energy & 1 Joules (\(\pm 5\%\)) \\ MAC verification & 20 mJ \\ Data packet & 32 bytes \\ Round & 128 bytes \\ \(FTR\) & 50\%, 70\%, 90\% \\ Path loss constant (\(\lambda\)) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: Square sensor field with the BS at the bottom center.  
5% perturbation and 10% fluctuation in the sensor range to reflect physical conditions. One round of communication consists of 128 bytes of data received at the BS. For each simulation experiment, 100 iterations have been carried out.  
Network lifetime is measured using first cut-off (FCO), which is the number of rounds required to consume all energy of a sensor node, i.e., first sensor node is depleted. Moreover, 10 percent of nodes depleted (10 PCO) and last cut-off (LCO) are also used to measure the network lifetime. 10 PCO is 10% of the total number of nodes depleted. The LCO performance metric is defined as the total number of nodes depleted when there is no more communication possible between the BS and sensor nodes. Energy efficiency is measured for the average energy consumed per round. Detection capacity is defined as the number of attacks actually detected (AD) divided by total attacks (TA), as shown in Equation (11).  
\[DP=\frac{AD}{TA} \tag{11}\]  
In the proposed method, the forwarding node from candidates nodes is selected based on the number of keys corresponding to the current FTR. A total of nine cases are considered for network lifetime using three FTR and over three performance metrics. Three cases for each energy-efficiency and detection capacity are illustrated.
###### Abstract  
Inter-satellite links (ISLs) can improve the performance of the Global Navigation Satellite System (GNSS) in terms of precise orbit determination, communication, and data-exchange capabilities. This research aimed to evaluate a simulation-based processing strategy involving the exploitation of ISLs in orbit determination of Galileo satellites, which are not equipped with operational ISLs. The performance of the estimation process is first tested based on relative weighting coefficients obtained with methods of variance component estimation (VCE) varying in the complexity of the calculations. Inclusion of biases in the ISL measurements allows evaluation of the processing strategy and assessment of the impact of three different sets of ground stations: 44 and 16 stations distributed globally and 16 located in Europe. The results indicate that using different VCE approaches might lower orbit errors by up to 20% with a negligible impact on clock estimation. Depending on the applied ISL connectivity scheme, ISL range bias can be estimated with RMS between 10% to 30% of initial bias values. The accuracy of bias estimation may be associated with weighting approach and the number of ground stations. The results of this study show how introducing VCE with various simulation parameters into the processing chain might increase the accuracy of the orbit estimation.  
inter-satellite links; Galileo; precise orbit determination; ISL range biases; simulation study 2022146387. [[https://doi.org/10.3390/rs14246387](https://doi.org/10.3390/rs14246387)]([https://doi.org/10.3390/rs14246387](https://doi.org/10.3390/rs14246387))  
15 November 2022  
Accepted: 12 December 2022  
Published: 17 December 2022
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 4 Security and Performance Analysis
### Performance Analysis  
In a sensor network environment such as smart dust, the light weighted certification hierarchy shall be used on a continual basis. However, the existing studies use algorithms such as hash and AES, which are hard to use in the sensor, having the limited calculation capacity due to the performance of hardware. Accordingly, in this section, the energy efficiency would be measured when the protocol is used, which is suggested for use in the limited sensors such as smart dust. So, here, the energy efficiency would be compared between the leach routing protocol, which is used for maximization of the energy efficiency, and the proposed protocol, without applying the security in the sensor network environment.  
For the performance simulation, this study used a hundred-sensor node. The sensor nodes were randomly set. The virtual environment's size is 100 m \(\times\) 100 m. The base station was designated in a specific location in consideration of the placement area. The distances between the base station and all sensor nodes are within the range from 50 m to 125 m depending on the placement. Each round was set at 20 s. In overall, the above settings were configured so that they would be equivalent to the existing routing protocol environment.  
Figure 7 is the simulation result that analyzed the energy consumption of each routing protocol by increasing the critical distance. As can be seen in the deducted graph, it was confirmed that there was an insignificant difference between the energy consumption of the proposed security scheme and the energy consumption of the basic LEACH routing protocol [11] or LEACHC routing protocol [17].  
Figure 7: Performance analysis result.
## 2 System Model
### Short Block Length Codes  
The design of channel coding schemes for URLLC presents a challenging trade-off between latency and reliability, due to the requirement of short-block-length codes for message transmission. Short packets, used to reduce latency, cause a significant loss in coding gain of the error-correction scheme employed. The normal approximation is used in practice to identify the limits of information for a code of block length N, and is considered the performance benchmark for comparison in a finite block-length regime [21]. For an AWGN channel, the maximal coding rate for finite-codeword-length transmission is given as  
\[R=C-\sqrt{\frac{V}{N}}Q^{-1}(\epsilon)+\frac{1}{2N}\log_{2}(N) \tag{1}\]  
where \(R\) is the channel coding rate, \(C\) is the channel capacity, \(V\) is the channel dispersion coefficient, and \(\epsilon\) is the average error probability [22]. The approximation (1) implies that to achieve a given error probability \(\epsilon\) for codes with finite block lengths, the rate loss is proportional to \(1/\sqrt{N}\). For instance, achieving \(\epsilon<10^{-9}\) is extremely challenging for block lengths \(N<50\) as the value of \(Q^{-1}(10^{-9})\) is \(\approx 5.998\), which significantly increases the rate loss.
# Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning
## 4 Results and Discussion
### Ensemble Learning Model Evaluation

Table 2: Results of boosted ensemble learning model. In Figure 7, a comparison between the observed and the predicted values is presented for all the evaluated ensemble learning methods. In Figure 7A, major differences occurred when there were higher values of schedule generation, resulting in higher errors, making the stacked ensemble learning model unsuitable for this evaluation. The boosted model (Figure 7B) was better than the previously evaluated staked model; however, there were still major differences between the predicted and the observed values. The bagged model, presented in Figure 7C, had promising results with predictions close to the desired output. The prediction results presented here consider one step ahead (1 h), which is adequate for a schedule generation problem. In the following, a statistical assessment of these ensemble learning models considering their best setup parameters is presented. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Solver** & **Kernel** & **MSE** & **MAE** & **MAPE (\%)** & **R\({}^{2}\)** & **Time (s)** \\ \hline \multirow{3}{*}{ISDA} & LIN & 6.39 \(\times 10^{3}\) & 4.20 & \(1.02\times 10^{-1}\) & 0.9751 & 9.71 \\  & RBF & 4.21 \(\times 10^{5}\) & \(3.42\times 10^{2}\) & \(1.48\times 10^{1}\) & 0.6403 & 1.91 \\  & POLY & 1.00 \(\times 10^{6}\) & \(2.65\times 10^{2}\) & \(1.00\times 10^{1}\) & - & 430.01 \\ \hline \multirow{3}{*}{L1 QP} & LIN & 6.39 \(\times 10^{3}\) & 4.23 & \(9.71\times 10^{-2}\) & 0.9751 & 36.75 \\  & RBF & 4.48 \(\times 10^{5}\) & \(3.55\times 10^{2}\) & \(1.54\times 10^{1}\) & 0.7482 & 41.035 \\  & POLY & 4.03 \(\times 10^{5}\) & \(6.01\times 10^{1}\) & 1.91 & 0.5721 & 37.813 \\ \hline \multirow{3}{*}{SMO} & LIN & **6.36 \(\times\) 10\({}^{3}\)** & **3.77** & **7.34 \(\times\) 10\({}^{-2}\)** & **0.9752** & 16.97 \\  & RBF & 4.49 \(\times 10^{5}\) & \(3.56\times 10^{2}\) & \(1.54\times 10^{1}\) & 0.7494 & **1.52** \\ \cline{1-1}  & POLY & 4.43 \(\times\) 10\({}^{5}\) & \(6.99\times 10^{1}\) & 2.24 & - & 865.87 \\ \hline \hline \end{tabular} Best results are in bold.  
\end{table} 
Table 3: Results of bagged ensemble learning model. Figure 7: Predicted versus observed values: (**A**) Stacking; (**B**) Boosting; (**C**) Bagging ensemble models.