esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Survey Damage Mapping  
The CNVVF approaches seismic emergencies with a systematic assessment method aimed at accurately dimensioning the operational response through subsequent approximations.  
When a seismic event happens, the first step is an analysis of the first data available related to the seismic event, such as shake maps, a localized call for help to the operations rooms of the National Fire and Rescue Service with territorial competence, information about traffic, information from social media, etc. In this way, areas \"with greater criticality\" can be defined, and they can be useful for the first sizing of the operational emergency response system. On these areas, a second assessment step is carried out by helicopter overflights, outlining smaller areas with evidence of critical issues. A third in-depth study is carried out in such smaller areas, CNVVF expert technicians make a site inspection in the field in order to detect structural criticalities through an activity structural analysis called \"QuickTriage\".  
To cross-check the satellite damage map, we use the findings of the conducted QuickTriage in Norcia village by CNVVF (i.e., a building damage mapping based on field surveys). Specifically, QuickTriage results are derived from the rapid on-field detection of severe and imminent danger for the safety of people and buildings, and their aim is to estimate the countermeasures to be adopted in the days immediately after the event. This activity was performed using a building-to-building survey based on a dedicated form that was manually filled out by expert technicians. Starting from these results, only the buildings indicated to be affected by partial or total collapse were selected.  
Furthermore, the CEDIT catalog [22], the most complete database of earthquake-induced ground effects in Italy, was also examined. Particular attention was given to the EQtLs (EarthQuake triggered Landslides) that impacted transportation routes during the 2016-2017 central Italy seismic sequence.
# The Vicious Cycle of Obesity and Low Back Pain: A Comprehensive Review
## 4 Spinal Changes Related to Obesity
### Parasimal Muscles  
Excessive body weight significantly impacts PSMs, leading to fatigue, reduced strength, and an increased risk of strain injuries. Obesity is strongly associated with greater fatty infiltration in these muscles, particularly in women, compromising muscle quality and spinal stability [83]. As muscle fibres are replaced by adipose tissue, functional muscle mass and strength decline, resulting in a condition known as sarcopenia obesity [97]. Fatty degeneration of the PSMs is often classified using [PERSON]'s scoring system (in short defined as the following: grade 1, indicating normal muscle with fatty infiltration up to 10% of the muscle's cross-sectional area; grade 2, moderate degeneration with 10-50% fatty infiltration; and grade 3, severe degeneration with over 50% fatty infiltration) [98]. These changes reduce the functional capacity of PSMs, increase mechanical stress on the spine, and accelerate spinal degeneration, contributing to the chronicity of LBP in individuals with obesity [85]. Fat infiltration increases with both BMI and age, as demonstratedby [PERSON] et al. [99], who reported that the combined fat infiltration ratio of the multifidus and erector spinae muscles rises from 9.7% \(\pm\) 5.0% in individuals aged 40-49 years to 25.8% \(\pm\) 7.6% in those aged 70-79 years. Concurrently, multifidus and external oblique muscle thickness decreases with increasing BMI, particularly in LBP patients [84]. Notably, reduced muscle quality at the upper lumbar levels increases mechanical stress on the lower lumbar segments, accelerating spinal degeneration and exacerbating chronic pain [85]. This redistribution of load and reduced stability further perpetuate the cycle of obesity, inactivity, and spinal dysfunction, emphasizing the critical role of maintaining muscle integrity to mitigate LBP in obese individuals (Figure 2). Here, strengthening the PSM through targeted exercise may enhance spinal stability, reduce mechanical strain, and serve as a protective factor against the development and progression of LBP [100].
## 8 Binary Inter Nodes (BIN)-TANVEER  
As the data packet delivery is increased, the probability of the empty regions inside the network is also increased [45; 46; 57]. Therefore, the BIN-TANVEER is used to ensure the packets that are not delivered to the next nodes due to empty regions is must be delivered to the desired destination via the path change mechanism. For this, we introduced 'Binary Inter Nodes' called watchman nodes inside each layer. These watchman nodes are responsible for controlling the communication at both ends, like the left and right of the network. Figure 7 illustrates the proper mechanism of BIN-TANVEER. For example, the source node '\(S\)' wants to send the data packet to the nearest nodes, and it first obtains the information of the above two layers with all corresponding neighbor nodes. Up to two layers, those nodes that are closer to each other are '\(n_{1}\), \(n_{2}\), \(n_{3}\)', and '\(n_{4}\)'. In the first layer, the '\(n_{2}\)' node is nearest with respect to energy, and TANVEER further explores to the next node to '\(n_{2}\)_like \(n_{3}\) and \(n_{4}\)'_ for other next two layers. The angle is computed for the best three possible directions towards the destination with the value of '\(K\)', as discussed in Algorithm 2. As '\(n_{4}\)' shows the empty region, it moves toward the nearest node in a relevant direction with a reference of the calculated angle. The possible routes of movements are horizontal with the same layer or vertical, and the diagonal path is available for the next upper layer according to the availability of the nodes. If the data are delivered and '\(n_{4}\)' becomes an empty node, then BIN-TANVEER works.  
It takes the data from the empty node like '\(n_{4}\)' and delivers them to the next available node. The recovery procedure works with looking forward to the least number of layer count and tries to find out the next nearest node in order to proposed mechanism energy efficient with all aspects. In this way, instead of dropping the packets and increasing the packet loss ratio of the overall network, BIN nodes are responsible for doing this every time. Therefore, they come into contact or active only when the empty regions are detected. Meanwhile, the detection of empty regions is done by receiving the '\(P_{ACK}\)' of the nodes as in Equation (6), and '\(P_{CHG}\)' is basically the function of BIN-TANVEER, and it used for the recovery procedure. In an exceptional case, if the availability of the node is not present around the empty regions, then BIN sends a data packet greedily to sonobuoys. Otherwise, it transfers the information to the nearest node for successful data communication among the network nodes. Figure 7 shows the working of BIN-TANVEER and Figure 8 represents the transmission adjustment of watchman, while Algorithm 3 depicts the proposed mechanism. Moreover, the flowchart of proposed scheme and its sub-sections are illustrated in Figure 9.  
Figure 7: Working mechanism of binary inter nodes (BIN)-TANVEER with watchman nodes.  
Figure 8: Possible transmission adjustment of Watchman Node (WN) with Empty Node _(EN)_.  
Figure 9: Flowchart of the proposed scheme.
## 2 Materials and Methods
### Materials and Solutions  
The montmorillonite and kaolinite samples, sourced from Science Words (Los Angeles, CA, USA), underwent comprehensive characterization using cutting-edge techniques such as scanning electron microscopy (SEM) and energy-dispersive X-ray (EDS) analyzers (Hitachi SU 500, Hitachi, Japan, and Zeiss EVO MA 10, Jena, Germany), and X-ray diffraction (XRD) (Bruker Advance D8, Billerica, MA, USA). Test solutions of concentrations 0.1, 0.3, and 0.5 M NaCl were meticulously prepared using NaCl (analytical grade) and deionized water. The solution pH was adjusted by adding drops of 1 M NaOH. All chemicals were purchased from Merck (Merck, Providencia, Chile).
# An Improved Data-Driven Integral Sliding-Mode Control and Its Automation Application
## 5 Conclusions  
An improved ISMC scheme is proposed for the boiler water-level-control system. The algorithm is based on an FFDL model, and a discrete ISMC law is designed. The control scheme is designed to utilize only the input and output data without model information. After providing a stability proof analysis of the proposed method, in the special-equipment testing facility, the control problem of the DHX25-1.25-AII CFB boiler-drum water level is analyzed, along with the efficacy and viability of the enhanced-control approach. When the working conditions of the CFB system change, it can also respond quickly. The specific performance is as follows for the FFDL-ISMC method:  
Figure 11: Control methods’ system-input curve.  
1. The FFDL-ISMC method improves the tracking speed and the fast-response characteristics of the MFAC strategy.
2. Compared with the FFDL-MFAC and FFDL-SMC methods, the FFDL-ISMC method provides higher accuracy and better stability. The FFDL-ISMC method is still able to respond faster to the feedback under variable operating conditions, thus better tracking the given boiler bubble level.
3. The FFDL-ISMC method maintains the feedwater level within a reasonable operating range with little variation. When the working conditions change, it can also respond quickly.  
This paper designs an improved adaptive ISMC and verifies its effectiveness in practical systems through a transfer function model of the boiler-drum water level. Future research will consider the effects of two main factors: the impact of water supply and steam flow on the changes in the drum water level.  
Conceptualization, Z.S. and F.X.; methodology, F.X. and Z.S.; software, F.X. and Y.W.; validation, F.X. and Z.S.; formal analysis, Z.S.; resources, F.X.; writing--original draft preparation, F.X. and Z.S.; supervision, Z.S.; funding acquisition, F.X., J.X. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Quzhou City Science and Technology Plan project (2023K263, 2023K265, 2023K045), the General Research Project of the Zhejiang Provincial Department of Education (2023) (Y202353440, Y202353289), and Quzhou Vocational and Technical College university-level scientific research project (QZYZ2305-2023).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
## 2 BDS/GNSS for Earth Observations  
BDS/GNSS for Earth observation includes GNSS PPP, GNSS-RTK, GNSS-RO (radio occultation) with LEO satellites, GNSS ionosphere monitoring, GNSS meteorology, and GNSS-R (reflectometry), which are certainly the most current topics in the field of GNSS observations and scientific applications.  
In BDS/GNSS Earth observation, GNSS technology is used to obtain the required information, for which GNSS raw observations are used. The GNSS observation equation describes the linear or non-linear relationship between the pseudo-range and phase observations and the unknown parameters, which is the basis of GNSS data processing. The observation equation can be expressed as  
\[P^{s}_{r,j}=\rho^{s}_{r}+\epsilon^{s}_{r}+\mathrm{dt}_{r}-\mathrm{dt}^{s}+u_{j }I^{s}_{r}+d_{r,j}-d^{s}_{j}+\epsilon^{s}_{p,j} \tag{1}\]  
\[\mathcal{O}^{s}_{r,j}=\rho^{s}_{r}+\epsilon^{s}_{r}+\mathrm{dt}_{r}-\mathrm{dt }^{s}-u_{j}I^{s}_{r}+\lambda_{j}N^{s}_{r,j}+\delta_{r,j}-\delta^{s}_{j}+ \epsilon^{s}_{\mathcal{O}j} \tag{2}\]  
where r, s, and j represent the receiver, satellite, and frequency, respectively; \(P^{s}_{r,j}\) represents the pseudo-range; \(\mathcal{O}^{s}_{r,j}\) represents the phase observation; \(\rho^{s}_{r,j}\) represents the distance from the satellite to the receiver; \(t^{s}_{r,j}\) represents the tropospheric delay from the satellite to the receiver; \(dt_{r}\) represents the receiver clock error; \(dt_{s}\) represents the satellite clock error; \(I^{s}_{r}\) is the ionospheric delay; \(\lambda_{j}\) is the wavelength of the phase at the j-th frequency carrier; \(u_{j}\) is the conversion factor of the ionosphere at j frequency; \(d_{r,j}\) and \(d^{s}_{j}\) are receiver code deviation and satellite code deviation, respectively; \(\delta_{r,j}\) and \(\delta^{s}_{j}\) are receiver phase deviation and satellite phase deviation; \(N^{s}_{r,j}\) is the phase ambiguity; and \(\epsilon^{s}_{p,j}\) and \(\epsilon^{s}_{\mathcal{O}j}\) are random noises.
# Tracking of Moving Targets Through Asynchronous Measures
## 3 Benchmark Tracking Algorithms
### Extended Kalman Filter  
The EKF algorithm is implemented as described here below:  
1. First we estimate the non-tracked 3D position of the target as \[\mathbf{\hat{x}}=\arg\min_{\mathbf{x}}\sum_{k=1}^{N_{A}}\left(\frac{|\mathbf{ x}-\mathbf{x}_{k}|-\rho_{k}}{\sigma_{\rho_{k}}}\right)^{2}\] (11) where \(\mathbf{x}_{k}\) is the 3D position of the \(k^{th}\) anchor, \(\rho_{k}\) is the corresponding range and \(\sigma_{\rho_{k}}\) is the RMSE of the range measurement.
2. The covariance matrix of \(\mathbf{\hat{x}}\) is computed through local linearization of (11).
3. Finally, the estimates of position, velocity and acceleration are tracked through the widely used EKF [8]. The update and prediction steps are performed when a complete set of ranging measures from all the anchors is available.
# In Situ Interaction of Enterocin A/P with _Staphylococcus aureus_ SA5 in Goat Milk Lump Cheese
## 1 Introduction  
In Slovakia, goat milk is used for the production of traditional/local products, for example goat milk lump cheese. In this specific livestock sector, most goats are concentrated in the small-scale breeding of white shorthair goats [1]. Milk for the production of goat milk cheeses made at farm does not always have to be pasteurized [2]. That means that concern over its microbial safety is important. It can be achieved, for example, by focusing on prevention, following hygiene conditions and the principles of hazard analysis and critical control points (HACCP) [1]. However, following prevention, the utilization of beneficial bacteria and their metabolic substances and bacterioxins can also be recommended for this purpose. In dairy products, the most important bacterial group represent lactic acid bacteria (LAB). Those bacteria mostly belong to the phylum Firmicutes, and due to lactic acid production they have the ability to inhibit the growth of spoilage bacteria [3,4] but also due to their antimicrobial substances (bactericins) [5]. Among bacterioxins, entercions have been previously used in dairy experimental processes with beneficial effects [6, 7, 8]. Entercions are bacteriics produced mostly by the representatives of the genus _Enterococcus_, which belongs in lactic acid bacteria from the phylum Firmicutes. On the other side, staphylococci, the species involving _Staphylococcus aureus_, are the most frequent contaminants of milk and/or dairy products [9, 10, 11]. For example, coagulase-positive staphylococci were detectedin goat cheese (at day 0/1) 4.41 \(\pm\) 0.41 cfu/g log 10 [1]. The genus _Staphylococcus_ within various species was also detected as the most abundant genus in goat milk [11; 12]. Following the above information, the aim of this work was testing in situ the anti-staphylococcal effect of Enterocin A/P (characterized in our laboratory and produced by the non-autochthonous strain _Enterococcus faecium_ EK13 = CCM7419) against the contaminant strain _S. aureus_ SA5 experimentally inoculated in a goat milk vat during the processing of goat milk lump cheese to show the possible application potential of enterocin. In addition, the most important parameters for ripening (lactic acid, acidity \({}^{\circ}\)SH and pH) were checked.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology
### Variables Selection for GPP Estimation Model  
In this study, 13 variables associated with vegetation GPP were selected based on photosynthesis mechanisms, including Band1-7, DSR, EVI, Band11, Month, Band31, and Band32. Variable selection was performed prior to model construction. A Pearson correlation analysis, a widely used method, was employed to examine the correlation between variables and GPP. The Pearson correlation coefficient is defined in Equation (2). Usually, a  
Figure 2: Flowchart of the methodology.  
significance level of 5% is employed, indicating that if the \(p\)-value falls below 0.05, the null hypothesis is rejected at a 95% confidence level. Consequently, variables with a Pearson correlation coefficient \(p\)-value \(>\) 0.05 were excluded due to uncertain reliability. For significant variables, those with an absolute Pearson correlation coefficient exceeding 0.1 were considered to have a substantial relationship [45]:  
\[r=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum_{i =1}^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{2}\]  
where \(x_{i}\) and \(y_{i}\) represent the values of an individual variable and GPP, respectively, while \(\overline{x}\) and \(\overline{y}\) represent the mean values of each variable and GPP.  
A feature importance evaluation was performed. Feature importance is quantified by measuring the reduction in model performance when predictions are made on a dataset where the values of a particular feature have been randomly shuffled across rows. For instance, a score of 0.01 implies that predictive accuracy decreased by 0.01 when the feature was shuffled. Features with higher scores are deemed more critical for the model's performance. Conversely, a negative score indicates that a feature could have an adverse impact on the model and that eliminating it may enhance predictive performance.
## 3 Proposed System Model  
We consider the scenario that sensor nodes are randomly deployed in a three dimensional undersea area and sink node (base station) is placed on top of the area as shown in Figure 1. As introduced before, CS can only be used when the signal has sparsity in some domain. In the next, we would prove that the monitored oceanic data such as temperature, hardness, and salinity are indeed sparse in Fourier transform domain because of their spatial correlation. Take the ocean underwater temperature data rendered by National Aeronautic and Space Administration (NASA) ([[https://www.nasa.gov/specials/ocean-worlds](https://www.nasa.gov/specials/ocean-worlds)]([https://www.nasa.gov/specials/ocean-worlds](https://www.nasa.gov/specials/ocean-worlds))) for example, we transform the data into its fast Fourier transform (FFT) domain and the results validate the sparsity as show in Figure 2.  
Another property of oceanic data is that its sparsity varies in different depth. As a result, we divide the network into different segments in line with its depth. Note that data with higher sparsity needs more information for recovery. Therefore, segments with higher sp  
Figure 1: The segment-divided model of the 3-D network.  
cluster-heads chosen probability so that for these segments, more measurements are obtained. In our model, the data in different segments are transmitted to the sink node and reconstructed separately, hence every segment can be considered as a sub-model. The segment-divided network model is presented as Figure 1.  
In reality, the data is collected in a 3-dimensional network. We need to transform them into one-dimensional data in accordance with its horizontal position and depth first. Hence the data in the \(i\)-th segment is represented as \(\mathbf{z}_{i}=[z_{i,1},z_{i,2},...,z_{i,N}]\), where \(N\) is the total number of nodes in the \(i\)-th segment. However, for simplicity, in the sequel, we take every sub-model as an integral model and the data are regarded as \(\mathbf{z}=[z_{1},z_{2},...,z_{N}]\). Its sparse form is denoted as \(\mathbf{z}=\mathbf{\
abla}\mathbf{x}\), where \(\mathbf{x}\) is a sparse vector and \(\mathbf{\
abla}\) is the FFT transform matrix.  
In each segment, we use CS to reduce the transmission burden to the sink node. Therefore, energy is saved and the network lifetime is prolonged. Clustering is also applied in our model to further economize on energy. The noisy measurement vector collected in the sink node is \(\mathbf{y}=\mathbf{\Phi}\mathbf{z}+\mathbf{n}=\mathbf{\Phi}\mathbf{\
abla} \mathbf{x}+\mathbf{n}\). For the purpose of making network energy-consuming balanced and easier for a node to find the next relay node, we divide all nodes into different layers according to their distances to the sink node. In the following work, we divided them into four distinct layers, those who are closer to the sink node are given smaller layer number, which is depicted in Figure 3.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 1 Introduction
### The Future of Workplaces and Meetings  
Additionally, the COVID-19 pandemic has resulted in a rapid shift towards remote work for many organizations and employees. According to a report by Organization for Economic Co-operation and Development (OECD), industries that are highly digitalized, such as information and communication services, professional, scientific and technical services, and financial services, saw the greatest increase in teleworking, with over 50% of employees working remotely on average [2]. This trend has been driven by the need to reduce the spread of the virus by limiting in-person contact, as well as by the increasing availability of technology that enables remote work.  
Although the trend was largely driven by the pandemic, it is likely to shape the way we work in the future. One reason for this is that online meetings and remote work offer a number of benefits that are likely to continue to be valued even after the pandemic subsides [3]. They can also enable more flexible and agile ways of working, which can increase productivity and enable organizations to respond more quickly to changing needs and conditions [4]. In addition, online meetings, classes and remote work can reduce the need for travel, which can save time and money and reduce the environmental impact of work [5; 6].  
Another reason is that the shift towards online meetings and remote work has required organizations to invest in new technologies and infrastructure as well as cybersecurity measures that are likely to continue to be used even after the pandemic subsides and will continue to pay dividends in terms of increased productivity and efficiency [7].