esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Application
### Effect Analysis by Monitoring Measurement  
To evaluate the treatment effects, the tunnel stability is an important factor, which includes arch settlement, clearance convergence, floor vertical displacement, surrounding rock pressure, initial support structure stress, and secondary lining stress. Because the tunnel body is not in contact with the surrounding rock, its stability is mainly reflected in the settlement of the tunnel body caused by pile settlement. Therefore, only settlement monitoring is needed for the tunnel. Three monitoring points (MS-1 to MS-3) were arranged at _D1K760+955_, _D1K760+940_, and _D1K760+925_, as shown in Figure 25.  
The monitoring data are shown in Figure 26. The settlement of the vault is accelerated in three stages and finally reaches a stable settlement state. According to the change rule of the accumulated settlement value of the vault, the settlement trend can be divided into stages I-IV. Stage I is the settlement in the later stage of lining. In this stage, monitoring activities are initiated after completing the lining construction (after the stripping). The collected data do not include the settlement during the lining construction and before the stripping. Stage II is the settlement of the structural protection layer, which is due to the increased load caused by the concrete structure protection layer on the main body of the tunnel, resulting in the acceleration of the settlement. The third stage is the construction settlement of the buffer layer. In this stage, the settlement acceleration changes owing to the increased termination caused by filling the upper part of the protection layer of the concrete structure with coarse sand. Stage IV is a stable stage. After the completion of all processes, the cumulative settlement value remains within a stable range.  
Figure 24: Treatment of the karst in section Z4.  
Figure 25: Arrangement of the subsidence monitoring points in the tunnel.
## 3 Results and Discussion  
### Comparison of Glyceride Distributions  
The glyceride distributions of each type of camellia oil are illustrated in Figure 1, indicating a high composition (96.04-96.88%) of TAG and a small amount of DAG (2.63-3.43%) in C.O, C.S, C.G, and C-C.O. This corresponds to a previous study demonstrating that DAG is a natural component of glycerides in various fats and oils, typically present at levels less than 10% [44]. Additionally, it was anticipated that no monoacylglycerolglycerids (MAGs) and free fatty acids (FFAs) would be detected in C-C.O, as commercial oil usually undergoes refining and other processes to remove phospholipids, FFA, and pigments. Regarding DAG-enriched camellia oils, the DAG composition in C-C.O-DAG (80%) was found to be up to 84.83%, with a small amount of MAG and FFA. C-C.O-DAG (40%), obtained from the mixture of C-C.O and C-C.O-DAG (80%), exhibited 44.89% of DAG (Figure 1a).  
Furthermore, by utilizing the composition of TAG, DAG, MAG, and FFA as variables for principal component analysis (PCA) (Figure 1b), it can be clearly distinguished that the three varieties of camellia oil (C.O, C.S, and C.G) differ from commercial refined camellia  
Figure 1: Comparison of glyceride distributions. (**a**) Glyceride distributions of different types of camellia oils, and (**b**) principal component analysis (PCA). The composition of TAG, DAG, MAG, and FFA of each type of camellia oils were used as variables. PC1 and PC2 factors (the first and second principal components, respectively) refer to the ordering scores obtained from the samples. PC1 accounts for 62.5% and PC2 for a further 35.3%. C.O, _C. oleifera_; C.S, _C. semiserata_, C.G, _C. gauknownesis_, C-C.O, commercial _C. oleifera_; C-C.O-DAG (40%), commercial _C. oleifera_; diacylglycerol glycerol oil (40%), and C-C.O-DAG (80%), commercial _C. oleifera_ diacylglycerol glycerol oil (80%).  
oil (C-C.O) and DAG-enriched camellia oils [C-C.O-DAG (40% and 80%)]. This suggested that the glyceride distributions can serve as useful markers for distinguishing between different types of camellia oils.
# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 5 Discussion
When the number of records is low, the reliability of the statistical analysis performed by the algorithm may be compromised, potentially leading to inaccuracies in the identification of transit traffic. The impact of this limitation will be further researched to determine how it influences the overall performance of the algorithm and to explore potential solutions, such as adaptive interval sizing or the integration of additional data sources to increase record density.
## 8 Membrane and Cell-Wall Modifications in Response to PPCs
### Membrane Modifications  
Proteomic and DNA microarray experiments revealed that PPCs markedly regulate the expression of genes and proteins involved in membrane lipid biosynthesis. Most _L. plantarum_ genes from the fatty-acid (FA) biosynthesis (_fab_) locus were downregulated in the presence of _p_-CA [25] or OLE [28]. Decreased expression of the _fab_ locus is indicative of membrane modifications in its FA composition, which is a common strategy in _Lactobacillus_ spp. to counter different types of stress conditions [71; 72; 73; 74]. _L. casei_ BL23 or _L. brevis_ upregulated one protein of the FAB pathway in response to _p_-CA [24] or ferulic acid [23], respectively.  
Proteomic and transcriptomic studies have shown that _L. plantarum_ WCFS1 downregulates a cyclopropane-fatty-acyl-phospholipid synthase (Cfa2) in the presence of TA [37] or OLE [28]. Cfa2 was also downregulated in _E. coli_ in response to cranberry polyphenols [75] or naringerin [76]. This adaptation alters the cyclic to saturated membrane FA ratio (CFA/SFA) which influences the membrane fluidity. Combined downregulation of the _fab_ locus and _cfa2_ suggests changes in membrane FA composition to counteract the damage inflicted by some PPCs in the _Lactobacillus_ cell membrane.  
Membrane phospholipid remodeling can also be involved in the adaptation of _Lactobacillus_ to PPCs. The OLE-responsive transcriptome showed a set of genes coordinately expressed by _L. plantarum_ in ways to increase the synthesis of _sn_-glycerol-3-P, the obligated glycerophospholipid precursor [28]. This modification may be important in the adaptation of _Lactobacillus_ spp. to PPCs since membrane phospholipid alterations are crucial for bacterial adaptability to environmental stress [77].  
Since a high number of genes encoding membrane proteins were overexpressed during the responses of _L. brevis_ to ferulic acid [23] and _L. plantarum_ to _p_-CA [25], membrane crowding with proteins has been proposed to stabilize the _Lactobacillus_ membranes against hydroxycinnamic acids.  
Genes encoding transporters of compatible solutes including glycine-betaine/carnitine/choline, glycerol, trehalose, or GABA were downregulated upon exposure of _L. plantarum_ to _p_-CA [25] or HXT [29]. This can contribute to stabilize the membrane, as previously reported for trehalose in _L. acidophilus_[78]. Membrane damage can cause proton motive force (PMF) dissipation [79; 80]. In _Lactobacillus_ spp., this disturbance can be in part compensated for by an increased abundance of F\({}_{1}\)/F\({}_{0}\) ATPase that pumps protons to the outside environment [81; 82]. Upregulation of the _L. plantarum_ F\({}_{1}\)/F\({}_{0}\) ATPase components in the presence of HXT [29] suggests that this PPC causes PMF dissipation.
## 2 Methodology and System Architecture
### The PLC Module  
In this application, Siemens S7-1200 was adopted and communication with the S7-1200 was first approached using the TCP functional blocks in _STEP 7_ development environment from Siemens. In the ladder program, these functional blocks are used to initiate TCP/IP communication with other ethernet capable devices.  
Within this framework, the S7 protocol is the one which Siemens use to exchange data between PLCs, in addition to accessing the PLCs from SCADA systems. It implements the ISO-on-TCP, which is a block-oriented protocol that fulfills the TCP/IP standard. The S7 protocol is function/command-oriented, which means each communication is a command or a reply to a command. The command of interest is Data Read/Write. This interfacing option does not require TCP socket programming in the ladder program. Wrappers for the Snap7 are available in C, C++, and Python.  
Fortunately, the S7 protocol has a LabVIEW VI library allowing reading and writing to data blocks. This was adopted but with some modifications to facilitate the objective of this process.
## 4 Examples of the Procedure
Now, \(|\overline{x}-\overline{y}|=|13.9  
\(1.96\sqrt{40^{-1}[\widehat{\omega}\widehat{u}(X)+\widehat{\omega}\widehat{u}(Y)]}=1.96 \sqrt{40^{-1}[67.482+31.899]}=3.089\), and the null hypothesis is correctly rejected. Taking real-life data, the interest is now focused on whether there was a significant across-time difference in the amount of July rainfall in all of the Czech Republic, the first data sample spanning the time period 1979-1999 and the second the years 2000-2020. Both data samples are of size \(n=21\). The officially published data are available at [[https://www.chmi.cz/historica-data/pocasi/uzemni-srazky](https://www.chmi.cz/historica-data/pocasi/uzemni-srazky)]([https://www.chmi.cz/historica-data/pocasi/uzemni-srazky](https://www.chmi.cz/historica-data/pocasi/uzemni-srazky)) (accessed on 16 August 2023). The data published by the Czech Hydrometeorological Institute are (in millimeters and chronologically) 66, 154, 163, 72, 29, 72, 79, 74, 86, 88, 71, 34, 76, 69, 96, 56, 61, 89, 204, 93, 86, for 1979-1999; 121, 119, 87, 81, 64, 131, 38, 84, 86, 111, 118, 145, 113, 34, 102, 36, 115, 90, 42, 58, 61 for 2000-2020. Employing the gamma_test function in R, the procedure returned the \(p\)-values 0.2956 and 0.316, respectively, for the two samples, allowing us to model them with gamma distributions. Since \(\overline{x}_{1}=86.57\), \(s_{1}^{2}=1685.46\), the parameter estimates for the first-sample gamma distribution are \(\hat{k}_{1}=86.57^{2}/1685.46=4.44\), \(\hat{\theta}_{1}=1685.46/86.57=19.47\). For the second, the estimates are \(\overline{x}_{2}=87.43\), \(s_{1}^{2}=1131.76\), \(\hat{k}_{2}=87.43^{2}/1131.76=6.75\), \(\hat{\theta}_{2}=1131.76/87.43=12.94\). The sample variances were calculated with the term \((n-1)^{-1}\) for the moment. This brings the analyst to scenario 1, for which the multiples to calculate the critical percentiles are equal to \(-1.96\) and \(1.96\). The test criterion in absolute value is \(|\overline{x}_{1}-\overline{x}_{2}|=0.86\), and by (13)-(15), \(1.96\sqrt{21^{-1}\left[(n-1)/(n+1)\left(s_{1}^{2}+s_{2}^{2}\right)\right]}=21.65\) can be used as the critical value of the test. Since the test criterion in absolute value is much lower than the critical value, or equivalently, the test criterion lies in the interval \(\left(-1.96\widehat{\omega}\widehat{u}^{1/2},\;1.96\widehat{\omega}\widehat{u }^{1/2}\right)\), the null hypothesis of no shift in the average amount of July rainfall across time is accepted.
###### Abstract  
The mechanism of the combined process of ion beam sputtering (IBS) and HF acid etching on the chemical structure defects of fused silica and its laser damage resistance performance were investigated in this paper. During the removal process of surface material, the sputtering effect causes lattice atoms to flee their native space locations, and a large amount of unsaturated chemical structures are produced on the silica surface, which improves the chemical activity of Si and O atoms, accelerates the chemical reaction process between surface atoms and water molecules, increases the content of hydroxyl groups (OH-) in the shallow layer, and enhances the photothermal weak absorption intensity. However, the increase in hydroxyl content weakens the binding strength of silicon-oxygen bonds, destroys the spatial network structure of silica bulk, and reduces its mechanical strength, resulting in a decrease in its laser damage resistance performance. The paper reveals for the first time the mechanism by which IBS changes the structure characteristics of silica material, accelerates the surface hydroxylation process, and thereby reduces the laser damage resistance performance. This work provides technical guidance for effectively suppressing chemical structure defects on silica surfaces and improving the laser damage resistance performance of optical components under high-flux laser irradiation.  
ion beam sputtering; laser damage; hydroxyl groups; surface hydroxylation; chemical structure defect +
Footnote †: journal: Applied Sciences
## 4 Evaluation Metrics and Validation Scheme  
### Evaluation Metrics  
In order to evaluate the effectiveness of the proposed model, different evaluation indicators were used, including the accuracy, confusion matrix, and feature visualization. For a fault diagnosis problem of multiclass classification, the accuracy score [39; 40] was used, which is a common, comprehensive metric for measuring the performance of classification methods, and it is defined as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
The specific meaning of TP, FP, TN, and FN is given in the following:  
* TP indicates that a true positive is correctly classified as a positive sample.
* FP indicates that a false positive is misclassified as a positive sample.
* TN indicates that a true negative is correctly classified as a negative sample.
* FN indicates that a false negative is misclassified as a negative sample.  
Accuracy is one of the important indexes that reflect model performance. The evaluation metrics regarding TP, FP, TN, and FN are components for creating the confusion matrix, as shown in Table 1. The feature extraction performance of the proposed model was evaluated by the visualization method of the t-distributed stochastic neighbor embedding (t-SNE).
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis
### Error Sources
#### 3.3.4 Tropospheric Modeling Errors  
In the troposphere, or more generally the neutral atmosphere, the index of refraction departs from unity much less than in ionosphere at GNSS frequencies, causing a delay of \(\sim\)2.4 m at zenith. The index of refraction in the troposphere is non-dispersive, and thus cannot be estimated using multiple-frequency signals. The tropospheric delay is obtained from models of the climatological parameters (temperature, pressure, and water vapor pressure) along the propagation path.  
State-of-the-art tropospheric models [16] fit a small number of location- and day-of-year-dependent coefficients to climatological data from numerical weather models (NWMs) to model the zenith tropospheric delay. The zenith delay is mapped to any elevation angle using mapping functions [47]. Similar to the ionospheric models, the tropospheric mapping functions may introduce a differential azimuth- and elevation-dependent error. For empirically-derived mapping functions such as VMF1 [47] and GMF [48], the mean error at lowest elevation of \(5^{\circ}\) has been shown to be under 50 mm (this value is typically reported as 10 mm station height error, which is approximately one-fifth of the delay error at lowest elevation [47]). As a result, this paper assumes that time-averaged tropospheric model errors would introduce sub-5-cm errors in the position domain, and would thus not impede asymptotically accurate collaborative mapping in both horizontal and vertical components at the several-decimeters level.
## 2 Materials and Methods
### Clustering  
A clustering analysis was performed to assess possible similarities in the time series of the chl-a and turbidity values of the 16 coastal areas. The analysis was based on the K-means clustering of the time series. The time series consisted of continuous daily satellite-derived chl-a and turbidity data from April to September of the year 2022. In order to obtain a continuous time series, a 7-day average was used.
# Longitudinal Jitter Analysis of a Linear Accelerator Electron Gun
## 4 Conclusions  
We report the effects to the longitudinal and transverse beam parameters caused by trigger timing jitter to the electron gun in the pre-injector of BEPCII linear accelerator. In the pre-injector of linear accelerators, generally, there are many parameters, which should be properly adjusted. The trigger timing to an electron gun is one of the most important parameters, which should be stabilized within some allowable tolerance. Because any timing jitter larger than the tolerance may affect the longitudinal and transverse beam parameters in the pre-injector, it may not only deteriorate the beam qualities but also cause serious beam loss.  
In order to address these issues, we investigated quantitatively the effects to the longitudinal and transverse beam parameters caused by the trigger timing jitter based on experimental measurements along with numerical simulations by using PARMELA code, and calculated the degrees of the deterioration in terms of the beam parameters, that is, the beam profile, average energy, energy spread, phase jitter between the beam and SHBs, and the transverse emittances.  
To apply this method, it is very important to evaluate the beam parameters under experimental conditions with some timing jitter in the pre-injector that maintains the beam quality and stability in order to keep the injection rate to the storage rings (or to maintain the peak luminosity) as high as possible, especially in the collider experiments. This paper gives an important step towards further stable operation in linear accelerators.  
We are highly indebted to [PERSON] due to his useful suggestions and discussions. The authors acknowledge the financial support from Institute of High Energy Physics, Beijing, China and University of the Punjab, Lahore-Pakistan. All authors contributed equally. [PERSON] proposed the idea, designed the experiment and collected the data, [PERSON] and [PERSON] wrote the manuscript and discussed the results. The authors declare no conflict of interest.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 2 Piezoresistive Mechanisms
### Interface Contact Effect  
At the beginning stage of strain, the interface contact effect dominates amongst all principles. Given that the material surfaces are rough and uneven on the micro- or nanoscale, the interface contact effect occurs at the contact points between the conductive materials. The current is transmitted through these contact points [31] (Figure 1a). The number and size of contact points change with the deformation process, leading to the change in the total contact area; thus, the resistance varies. Through adjusting the number and size of contact points, high sensitivity can be obtained if the relative change rate of the contact area is large [32]. Flexible piezoresistive sensors based on the interface contact mechanism are divided into two categories. One category is adjusting the interface contact resistance between the electrode and the active layer. For example, [PERSON] et al. used silver nanowire-coated cotton paper as conductive fiber and nanocellulose paper as the bottom substrate of a printed interdigital electrode [33]. The compression deformation of the cotton paper will cause more conductive fibers to contact the interdigital electrode to form more conductive paths; hence, the contact resistance of the sensor is reduced (Figure 1b). The other category is regulating the contact resistance between the conductive particles inside the active layer. For example, [PERSON] et al. fabricated a flexible sensor by electrodepositionpolyethyleneimine-functionalized carbon nanotubes (CNTs) on nonconductive fibers as coatings [34]. The compression deformation of the nanocomposite leads to the formation of fiber electrical contact in the active layer, which changes the resistance (Figure 1c).
## 1 Introduction
This paper discusses many aspects of the M2M ecosystem in the healthcare domain, however, the main novel contribution is threefold:  
* a systematic review of existing physical (PHY) layer technologies for WBANs, which, to our knowledge is not available in the literature,
* an up-to-date survey of the key Medium Access Control (MAC) layer design approaches to tackle the specific challenges of mHealth applications in WBANs, and
* an end-to-end perspective of M2M systems for mHealth, focusing on the integration and convergence of different communication technologies employed in WBANs and long-range communication networks and the presentation of testbed implementations. The remainder of this paper is organized as follows. First, the high-level ETSI architecture for M2M systems is described in Section 2, whereas the main design challenges for end-to-end mHealth applications are discussed in Section 3. Section 4 is dedicated to the most recent developments in wireless technologies for WBANs. After a brief description of the IEEE 802.15.6 standard for WBANs, the focus is laid on channel modeling, PHY technologies and MAC strategies. Then, Section 5 is devoted to end-to-end connectivity, focusing on integration challenges, existing testbeds and current research projects. Finally, some concluding remarks are given in Section 6.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 4 Results and Discussion
### Directional Site-Effects  
The rotational HVSR curves have been analyzed and plotted as polar plots for all stations in Figure 10. These plots illustrate the amplitude variation in the HVSR in the frequency domain, with azimuthal directions ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\) (Figure 10). 0\({}^{\circ}\) represents the north for all stations since all stations are north-oriented.  
In general, ambient noise is mainly caused by marine waves and man-made activity, such as cars and factory machines. This is probably what could influence the change in directionality in our study area. Anyway, the results show that in most of the analyzed sites, the directionality is approximately perpendicular to the coastline (see Figure 11), which may be related to the sea waves that hit the cliffs bordering the city of Oran from the north.  
Figure 9: (a) Regression line (\(V_{s30}\) vs. \(V_{R47}\)) and the derived equation. R\({}^{2}\) is the correlation coefficient. (b) The respective residual values.  
Figure 10: HVSR polar diagrams. The color bar indicates the HVSR amplitude variations, and the dashed line represents the estimated directionality.