esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison between Mature Methods and More Recent Non-Thermal Plasma Technology
### Materials and Methods
#### 5.1.3 Calculations
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Non-Thermal Plasma** & \multicolumn{2}{c}{**Pollutant Description**} & \multicolumn{2}{c}{**Operation Conditions**} & \multicolumn{2}{c}{**Degradation Performance**} & \multicolumn{1}{c}{**Ref.**} \\ \cline{2-6}
**Reador/Discharge Type** & **Type** & **Category/Class** & & & & \\ \hline  & & & Applied high voltage (ADC; 20 kV & & \\  & & & Frequency; 52-30,000 Hz & & & \\ DBD photocatalyst & Chloroform & Disinfection byproduct & Chloroform vapours (in air) four rate: 0.3 & & Contact time; 2 s & \\  & & & L/min & & Removal efficiency; 70\% & \\  & & & Initial concentration (C\({}_{0}\)): 85 ppm & & & \\ \hline  & & & Peak voltage: 20 kV & & \\  & & & & Current 13.8 A & & \\ Corona discharge & \({}^{1}\) Bromate & Disinfection byproduct & Peak power: 203 kW & & & \\  & & & Frequency; 30 Hz & & \\  & & & Initial concentration (C\({}_{0}\)): 30 uM & & & \\ \hline  & & & & Applied voltage: 1.01–1.66 kV & & \\ Micro discharge plasma jet & & & Frequency (Transformed): 60 Hz & & Contact time: 40 min & \\ (MDP)/DBD & _E. coli_ & Pathogen & Air/ Nitrogen gas flow rates: 24 L/min & & \\  & & & Initial concentration (N\({}_{0}\)): 2.4 \(\times\) 10\({}^{9}\) & & Removal efficiency; 99.9\%, & \\  & & & CFU/mL. & & & \\ \hline Non-thermal plasma & & & Applied voltage 10 kV & & Contact time: 12 min (_E. faecalis_) & \\ (NTP)/spark discharge & & Enterococcus faecalis (_E._ & & Frequency; 30 Hz & & and 15 min (_E. coli_) & \\ plasma & _fiscal_) and _E. coli_ & & Initial concentration (N\({}_{0}\)): 1 \(\times\) 10\({}^{8}\) CFU/mL. & & Removal efficiency; 8-log CFU & \\  & & & & & reduction (_E. faecalis_ and _E. coli_) & \\ \hline \end{tabular} \({}^{1}\) Reduction reported, assisted by the consumers of oxidative species.  
\end{table} 
Table 1: _Cont._
## 3 Challenges in China's Weather Routing Services
### Challenges in Ocean-Atmosphere Coupled Numerical Forecasting Capabilities  
Numerical forecasting products constitute the cornerstone of weather routing services. China has established a relatively comprehensive numerical weather prediction (NWP) system for meteorology and oceanography, significantly enhancing the timeliness, precision, and objectivity of weather routing services, thereby laying the groundwork for autonomous core technologies. However, notable gaps persist compared to leading international centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) of the US National Weather Service, particularly in spatiotemporal resolution and forecast accuracy [39]. For instance, inaccuracies in forecasting the intensity or track of extratropical cyclones in the North Atlantic could lead vessels into dangerously high sea states, compromising safety and potentially causing structural damage.  
Additionally, China's meteorological and oceanic numerical models have historically been developed independently, with coupled atmosphere-ocean modeling initiatives lagging behind global advancements [40; 41]. The maturity of coupled modeling techniques--such as integrated atmosphere-ocean-wave-ice systems--remains relatively low within China's operational systems, resulting in suboptimal stability and accuracy of coupled models. Although China has increased investments in marine meteorological numerical modeling through national projects in recent years, critical gaps persist in refining physical parameterizations and advancing data assimilation. For instance, challenges include limited assimilation of satellite-derived observations and sparse integration of multi-source oceanic data (e.g., buoy and submersible measurements) [42; 43].
## 6 Conclusions  
During the last decade, crowdsourcing has extensively been used as a \"means of communication and solution exploration\" by several public and private organizations in order for the problem solving process to be supported and enriched through gaining feedback from the crowd. Crowdsourcing constitutes a useful means of capturing information and acquiring knowledge in a wide range of activities. This article focuses on its potential use in spatial planning. In this context, several respective case studies have been presented, indicating the specific framework under which crowdsourcing can enrich the whole process by exploiting the potential input/knowledge generated by a distributed network of people willing to contribute to the planning exercise by providing feedback. Such kinds of applications have suggested a new approach in spatial planning by incorporating tools and techniques that enhance public participation and the exploitation of crowdsourced information.  
The main advantages of crowdsourcing as a means of knowledge acquisition in spatial planning can be briefly summarized as follows:  
* It guarantees the transparency of the problem solving process and enhances participation;
* It \"brings people together\", thus creating the prerequisites through which the \"wisdom of the crowd\"/the ideas of participants can be exploited and lead to the discovery of innovative solutions;
* It promotes the idea of pluralism in spatial planning;
* It supports the extensive diffusion of knowledge;
* It contributes to the conservation of demanding economic resources;
* It \"removes\" time and spatial barriers;
* It sometimes offers several benefits to the generators of the most innovative and effective solutions.  
On the other hand, the relative speculation emerging from the experience gained by the implementation of several crowdsourcing techniques in spatial planning concerns issues like:  
* The background of the \"crowd\" who participates in such a process;
* The reservation of the minimal number of participants (the crowd's correspondence to the respective \"call\");* The effectiveness of the whole process, usually evaluated from the quality of the results produced;
* The availability of infrastructure supporting the crowd's access to the web;
* The lack of direct/personal interaction among participants;
* The potential reward/acknowledgement of the \"laborers\" who are offered participation in such a kind of process.  
Conclusively, crowdsourcing supports the integration of distributed knowledge and forms the base for an interdisciplinary approach to be adopted during the process of problem solving. In this way, the final result emerges through a collaborative process in which the knowledge of experts, professionals and the \"crowd\" is creatively synthesized, thus leading to the final solution of the problem under study. Crowdsourcing enhances creativity and, specifically, in the case of spatial planning, sets the foundations for an in-depth interaction with the \"crowd\" and the establishment of a fruitful collaboration among planners and the public.
## 2 Theoretical Investigation of Monitoring Models
### Analysis of the Main Component Characteristics and System Performance of the Sensor Structure
#### 2.4.3 Epoxy Resin Base  
Epoxy resins have become a cornerstone material in civil engineering applications owing to their superior mechanical robustness, exceptional adhesion strength (particularly to metallic substrates), and remarkable corrosion resistance. Crucially, these polymers demonstrate non-magnetic interference characteristics, with rigorous experimental evaluations confirming their negligible impact on permanent magnet flux density. Key material properties are quantitatively summarized in Table 2.
# Processing Fresh-Cut Potatoes Using Non-Thermal Technologies and Edible Coatings
## 5 Emerging Processing Technologies: Operating Principles and Examined Parameters
### Conventional and Novel Anti-Browning Agents
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Substance** & **Processing Conditions** & **Storage Conditions and Time** & **Reference** \\ \hline \multirow{4}{*}{Acetic acid} & Immersion in 0.8\% acetic acid at & & \\  & 25 \({}^{\circ}\)C for 18 h, potato slices to & & \\  & solution ratio = 2/3 \(w\):\(w\), vacuum & & [199] \\  & packed aluminum foil pouches & & \\ \hline \multirow{4}{*}{Acidified NaOCl\({}_{2}\)} & 500 mg/L, 5 min washing, (pH & & \\  & 2.5–2.9), mixing NaOCl\({}_{2}\) and citric & & \\  & acid at 50:50 \(w/w\), potato/solution = & 5 d, 4 and 10 \({}^{\circ}\)C & [200] \\  & 1:10 \(w/v\), vacuum packed in PE bags & & \\ \hline \multirow{2}{*}{Ascorbic acid + vacuum} & 3 min immersion, 5 g/L, vacuum & & \\  & packed in PE and PA, vacuum time: & & \\  & 30 s & & \\ \hline \multirow{2}{*}{Ascorbic acid} & 5\%, 3 min dipping, MA packaging for & & \\  & 3 min (9\% CO\({}_{2}\), 3\% O\({}_{2}\), 87\% N\({}_{2}\)) & & \\ \hline \multirow{2}{*}{Ascorbic acid} & 3\% \(w/w\), 3 min dipping, potato slices & & \\  & packed in PE bags & & \\ \hline \multirow{2}{*}{Ascorbic acid + CaCl\({}_{2}\)} & 2\% \(w/w\) + 1\% \(w/w\) CaCl\({}_{2}\), 3 min & & \\  & dipping, potato slices packed in & & \\  & PE bags & & \\ \hline \multirow{4}{*}{Ascorbic acid + citric acid + modified atmosphere} & 1.25\% AA + 1.25\% CA, 3 min & & \\  & immersion at 7.5 \({}^{\circ}\)C, & & \\  & potato/solution = 1:10, MA: & 8 d, 5\({}^{\circ}\)C & [201] \\  & 3\% O\({}_{2}\) + 12\% CO\({}_{2}\) & & \\ \hline \multirow{4}{*}{Ascorbic acid + citric acid} & 5\% \(v/w\) AA + 2.5\% \(v/w\) CA, 2 min & & \\  & immersion, sliced & & \\  & potato/solution = 1:3, packed in & 10 d, 5 \({}^{\circ}\)C & [105] \\  & plastic pouches under MA (O\({}_{2}\)) of & & \\  & 55 and 100 kPa & & \\ \hline \multirow{2}{*}{Ascorbic acid} & 0.08\% \(w/w\) AA & - & [202] \\  & 0.2\% \(w/v\) AA, 5 min immersion, & & \\  & 3 slices packed in PE bags & & \\ \hline \multirow{2}{*}{Aspartic acid} & 5 min immersion, potato/solution = & & \\  & 1:4 (g/mL), chips packed in PE bags & 7 d, 2–4 \({}^{\circ}\)C & [204] \\ \hline \multirow{2}{*}{Aronia berries extract} & Solution/sample = 15:1 & - & [6] \\ \hline \multirow{4}{*}{Citric acid} & 1\% CA (pH 2.42) or 2\% CA (pH 2.24), & & \\  & 2 min immersion, packed in & 6 d, 5 \({}^{\circ}\)C & [189] \\  & plastic bags & & \\ \hline \multirow{2}{*}{Citric acid} & 2\% CA, different cut shapes: slices, & & \\  & wedges, cubes, dice & & \\ \hline \multirow{2}{*}{_Centella asiatica_ extract} & 5 min dipping, extract concentration: & & \\  & 250 mg/mL & 4 d, 4\({}^{\circ}\)C & [63] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Anti-browning agents and edible coatings applied to fresh-cut potatoes *.
## 4 Proposed Frequency-Offset Estimation Method
### Computational Complexity  
The complexity of the frequency-offset estimation methods is discussed in terms of the number of real floating point operations (flops). For a fair comparison, a complex multiplication is counted as six real flops, whereas a complex addition as two real flops [33]. We consider the situation where the channel estimation used to obtain \(\tilde{H}_{l}(k)\) has been already performed at the receiver. The total number of real flops is calculated by \(N_{f}(8N_{b}-7)-1\) for the conventional Scheme A, \(N_{f}(6N_{b}^{2}-3N_{b}+13)+9\) for the conventional Scheme B and \(8N_{f}N_{b}-2\) for the conventional Scheme C. On the other hand, the proposed LLSE scheme demands \(N_{b}(2N_{b}+5)-1\) real flops, whereas \(N_{p}(2N_{b}+12)-2\) flops are needed in the proposed WLSE scheme. If \(N_{f}=N_{p}\) and \(N_{b}>2\), it is evident that the complexity of the proposed LLSE method is always less than that of the conventional LLSE method, which is still true for the WLSE scenarios. A detailed computational complexity analysis is presented in Appendix A.
## 2 Materials and Methods
### Design and Methodology  
#### 2.2.1 Impact Measurements through Micro-Electrical Mechanical System (MEMS)  
Participants were equipped with a Micro-Electrical Mechanical System (MEMS) (WIMU, Realtrack Systems, Almeria, Spain), which was securely fastened to the upper back of the athletes using a specialized vest to minimize any potential movement of the unit and ensure its accuracy [22]. The micro-technology units contain a 10 Hz Galileo GPS positioning device, a 3D accelerometer 100G recording at 1000 Hz, and a 3D gyroscope recording at 1000 Hz. The devices were calibrated prior to their placement. A self-calibration system that included all devices in the internal configuration of the boot was used. During the self-calibration process, three factors were considered: (i) keeping the device stationary for 30 s, (ii) placing it on a flat surface, and (iii) ensuring no magnetic devices were present nearby. Previous studies have reported good accuracy and reliability for the sensors in these devices [26; 27; 28].  
The raw acceleration data were extracted from each MEMS device and subsequently processed using a vector summation method, referred to as AceT, along three axes: mediolateral (x), anteroposterior (y), and vertical (z). This calculation methodology was adopted following the approach established by [PERSON] et al. [28]. AceT exclusively represents acceleration levels measured in g-force units, as captured by the 3D accelerometers embedded within the inertial device, and it was sampled at a frequency of 1000 Hz. Importantly, no data transformation procedures were applied to alter the raw signal. Consequently, the reliability of the AceT variable ensures the overall dependability of all parameters derived from accelerometer data [28]. The GPS devices were configured to record acceleration data along three axes, with a threshold of 8G established. This threshold has been reported in the literature as acceptable for detecting high-intensity collisions to identify significant impacts [29; 30]. The MEMS data were analyzed using specialized software to determine the total number of impacts exceeding 8G that each player experienced during each match.
# Physiological and Behavioral Changes of Passive Fatigue on Drivers during On-Road Driving
### The Assessment of Driver Fatigue  
The assessment of driver fatigue can be categorized into four classes of measurements: subjective, cognitive, physiological, and behavioral. The subjective class usually involves self-rating questionnaires, for example, the Stanford Sleepiness Scale (SSS) [9]. The cognitive measurements include attention tests such as the psychomotor vigilance tasks for identifying whether drivers are undergoing a fatigue status [10].  
Regarding the physiological measurements, the eye tracking method is recommended over the others (brain activity signals, heart rate, etc.). The connections between eye movement behaviors and driver fatigue have been well established, including metrics such as the classic PERCLOS index (percentage of eyell closure over the pupil over time), pupil (e.g., pupil diameter), blinks (e.g., blink duration and blink count), and saccade (e.g., mean velocity of saccade and saccade amplitude) [11]. It is believed that although other neurophysiological methods are reliable, they are still far from daily application; in contrast, the eye tracking equipment has provided a relatively easy and convenient setup with no skin contact for measuring physiological signals [3].  
Behaviorally, one of the most widely adopted indicators is vehicle-based behavior. Prior studies have revealed that the manipulation activity of the steering wheel, driving speed compliance, together with the standard deviation of the lateral lane position (SDLP) are highly related to driver fatigue [12]. However, as summarized in Table 1, the associations between fatigue and its physiological and behavioral indicators may demonstrate different tendencies when comparing passive fatigue and active fatigue, which further implies that the assessment of these two types of fatigue should be studied separately.
# Some Generalizations of the Jensen-Type Inequalities with Applications
## 2 Main Results  
As we already mentioned in the introduction, the functions \(G_{p}\) (\(p=1,2,3,4\)) have some interesting and very useful properties, and they will also be very important for deriving our results in this paper. It is not difficult to see that for every fixed value \(s\in[\alpha,\beta]\) our functions \(G_{p}(\cdot,s)\) (\(p=1,2,3,4\)) are continuous and convex on \([\alpha,\beta]\). Furthermore, every function \(\phi:[\alpha,\beta]\to\mathbb{R}\) such that \(\phi\in C^{2}([\alpha,\beta])\) can be expressed using these functions \(G_{p}\) (\(p=1,2,3,4\)) as follows:  
\[\phi(x)=\phi(\alpha)+(x-\alpha)\phi^{\prime}(\beta)+\int_{\alpha}^{\beta}G_{1 }(x,s)\phi^{\prime\prime}(s)ds, \tag{5}\]  
\[\phi(x)=\phi(\beta)+(x-\beta)\phi^{\prime}(\alpha)+\int_{\alpha}^{\beta}G_{2 }(x,s)\phi^{\prime\prime}(s)ds, \tag{6}\]  
\[\phi(x)=\phi(\beta)-(\beta-\alpha)\phi^{\prime}(\beta)+(x-\alpha)\phi^{\prime} (\alpha)+\int_{\alpha}^{\beta}G_{3}(x,s)\phi^{\prime\prime}(s)ds, \tag{7}\]  
\[\phi(x)=\phi(\alpha)+(\beta-\alpha)\phi^{\prime}(\alpha)-(\beta-x)\phi^{\prime }(\beta)+\int_{\alpha}^{\beta}G_{4}(x,s)\phi^{\prime\prime}(s)ds. \tag{8}\]  
These representations can be easily proved after a short calculation by integrating by parts, but the interested reader can also consult [14] (Lemma 1.1). We will now give our main results, and for the sake of the clearer notation we will use  
\[\overline{s}=\frac{\int_{a}^{b}g(x)d\lambda(x)}{\int_{a}^{b}d\lambda(x)}.\]  
To begin with, we give an improvement of the Jensen inequality. Theorem 2.1: _Let \(g:[a,b]\to\mathbb{R}\) be a continuous function and let \(Im(g)\subseteq[m,M]\subseteq(\alpha,\beta)\). Suppose \(\lambda:[a,b]\to\mathbb{R}\) is a continuous function or a function of bounded variation where \(\lambda(a)\
eq\lambda(b)\) and let \(\overline{g}\in[\alpha,\beta]\). For every continuous convex function \(\phi:[\alpha,\beta]\to\mathbb{R}\) holds_  
\[\frac{\int_{a}^{b}\phi(g(x))d\lambda(x)}{\int_{a}^{b}d\lambda(x)}-\phi( \overline{g})\leq\frac{(M-\overline{g})(\overline{g}-m)}{M-m}\big{(}\phi^{ \prime}_{-}(M)-\phi^{\prime}_{+}(m)\big{)}, \tag{9}\]_if and only if for every \(s\in[\alpha,\beta]\) holds_  
\[\frac{\int_{a}^{b}G_{p}(g(x),s)d\lambda(x)}{\int_{a}^{b}d\lambda(x)}-G_{p}( \overline{g},s)\leq\frac{(M-\overline{g})(\overline{g}-m)}{M-m}\Big{(}(G_{p})^ {\prime}_{x}(M,s)-(G_{p})^{\prime}_{x}(m,s)\Big{)}, \tag{10}\]  
_where the functions \(G_{p}\)\((p=1,2,3,4)\) are defined in (1)-(4)._  
_Moreover, this equivalence holds also in the case if we reverse the inequality sign in both (9) and (10)._  
Proof. : Let for every continuous convex function \(\phi:[\alpha,\beta]\to\mathbb{R}\) inequality (9) hold. As the functions \(G_{p}(\cdot,s)\)\((p=1,2,3,4,\)\(s\in[\alpha,\beta])\) are continuous and convex on \([\alpha,\beta]\) for every \(s\in[\alpha,\beta]\), the inequality (9) holds also for them. That is: it holds (10). Let us now prove the other implication. We will first prove it in the case when \(\phi\in C^{2}([\alpha,\beta])\). Suppose that for every \(s\in[\alpha,\beta]\) inequality (10) holds.
## 3 Damage Detection Case Studies  
In order to investigate different objective functions for the model updating problem, three beams were considered. Two beams were simulated beams, and one was an experimental beam. The first simulated beam is only damaged at one location, and the second simulated beam is damaged at more than one location to check the effectiveness of objective functions at various noise levels. For the third beam, experimental data are available for damage [45]. The effectiveness of the three objective functions is then evaluated on both simulated and experimental data. Only the first three mode shapes and model frequencies are considered, as it is likely that only a few modes can be identified during actual experiments. The FE model updating procedure and damage assessment are carried out in the MATLAB environment.
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
###### Abstract  
In view of the complex asymmetric deformation characteristics of inclined coal seam roadways and the tensed shear failure of anchor cable supports, the asymmetric support scheme of an anchor cable with a C-shaped tube is proposed. In order to study its supporting effect on an inclined coal seam roadway, this paper first explores the difference in shear performance between an anchor cable with a C-shaped tube and an anchor cable through double shear tests. Then, based on the asymmetric deformation characteristics of an inclined coal seam roadway in the Pangpangta Mine, a numerical simulation is used to study the asymmetric support effect of an anchor cable with a C-shaped tube in an inclined coal seam roadway. The results of the double shear test show that the anchor cable with the C-shaped tube has stronger resistance to shear load than that of the anchor cable. Through the results of the numerical simulation, the original stress field distribution on both sides of the roadway was found to be uneven due to the influence of the coal seam dip angle, and after the excavation of the inclined coal seam roadway, the displacement and plastic zone distribution on both sides showed obvious asymmetric characteristics. Compared with the symmetric support, the asymmetric support can obviously alleviate the asymmetric deformation characteristics of the two sides and effectively control the deformation and plastic failure zone of the roadway. The anchor cable with the C-shaped tube has better resistance to shear deformation than that of the anchor cable. The anchor cable with the C-shaped tube can reduce the deformation and plastic area of the roadway more effectively.  
anchor cable with C-shaped tube; double shear test; inclined coal seam; asymmetric deformation +
Footnote †: journal: Applied Sciences
# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 3 Methodology of RBFP
### Point Cloud Processing  
As shown in Figure 2, there are three coordinate frames to describe the motion of the USV system. \(\{\mathcal{W}\}\) represents the fixed world frame, \(\{\mathcal{L}\}\) represents the LiDAR frame and \(\{\mathcal{B}\}\) represents the body frame on the center of the USV. The green riverbank points represent all LiDAR points reflected on the riverbank. The orange one is the scanning point which is selected as the bank shape reference point within the field-of-view (FOV). The red one is the USV following point, which is the waypoint for USV guidance.  
A set of scanned PCD in the LiDAR frame \(\{\mathcal{L}\}\) is denoted as:  
\[\{\mathcal{L}\}p_{i}=\{(x_{i},y_{i})\in\mathbb{R}^{2}|i=1,\dots,N_{p}\} \tag{1}\]  
where \(\{\mathcal{L}\}p_{i}\) is the position of scanned points in the LiDAR frame. \(i\) is the index of each point in a single scan. \(N_{p}\) is the number of scanned points. Then, we transform the \(\{\mathcal{L}\}p_{i}\) into the USV body frame \(\{\mathcal{B}\}\), which is written as \(\{\mathcal{B}\}p_{i}\).  
Figure 1: System framework.  
Figure 2: USV follows by scanning the riverbank.  
The scanning point is the LiDAR point that has the minimum Euclidean distance in the set \(\{^{\{\mathcal{B}\}}p^{*}\), which is denoted as:  
\[{}^{\{\mathcal{B}\}}p^{*}=\{^{\{\mathcal{B}\}}p_{j},\;\;\text{where}\;\;j=\min_ {i}\lVert{}^{\{\mathcal{B}\}}p_{i}\lVert\} \tag{2}\]  
Then we transform the \({}^{\{\mathcal{B}\}}p^{*}\) into the world frame \(\{\mathcal{W}\}\) using ROS _tf_ package [38], which is denoted as \({}^{\{\mathcal{W}\}}p^{*}\).  
The following point \({}^{\{\mathcal{W}\}}\hat{p}\) is calculated by:  
\[{}^{\{\mathcal{W}\}}\hat{p}=\frac{\lambda}{\lVert{}^{\{\mathcal{W}\}}p \lVert}{}^{\{\mathcal{W}\}}p^{*} \tag{3}\]  
where \(\lambda\) is a pre-defined scaling parameter to determine the distance between the riverbank and the USV, the allowed range for \(\lambda\) is \([0,1]\), a range established based on our experiments and simulation results to ensure the system's stability and performance. Then we transform the following point into the USV body frame \(\{\mathcal{B}\}\), denoted by \({}^{\{\mathcal{B}\}}\hat{p}\).