esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion
### Laboratory Tests  
The static and dynamic tests were conducted at the Coastal Ocean Monitoring Center, National Cheng Kung University, Tainan, Taiwan. To ensure a clear sky view, both tests were conducted on the roof of a nearby campus building that has twelve floors. The goal of the static test was to evaluate the accuracy of the monitored altitudes. Time series altitude data received by the GNSS receiver are plotted in Figure 2. An MR-1 receiver with an external PG-A1 antenna (Topcon Positioning Systems, Inc., Livermore, CA, USA) was utilized [24]. We recorded both the data for the altitude above the geoid and the quality index every second, and we chose the altitude above the geoid of the RTK fixed solution for further analysis.  
During the period of the static test (24 August, 13:00 to 25 August, 08:00, 2012), a total of 68,400 samples were obtained and 3843 data points without a fixed solution were excluded. The time series of the altitude data are plotted in Figure 2. The obtained average altitude was 67.226 m, the standard deviation was 0.012 m, the maximum was 67.291 m, the minimum was 67.042 m, and the full range was 0.249 m. According to the specifications of the GNSS receiver, the root-mean-square error (RMSE) in the vertical direction was 1.5 cm \(\pm\) 1.0 ppm (parts per million). The RMSE, which by definition equals the standard deviation (1.2 cm), under the static condition, was smaller than the specified RMSE. Furthermore, the standard deviation of the static data was also smaller than the declared accuracy in the vertical direction of the VBS-RTK (5 cm). There was a drawdown of the altitude data, which was 67.04 m in Figure 2. This might be caused by the multipath effect.  
Figure 1: Working principle of a GNSS buoy for monitoring the water surface elevation.  
Figure 2: Altitude of the GNSS receiver above the geoid in the static test.  
The goal of the dynamic test was to check the accuracies of the amplitude and period obtained by a GNSS receiver under a vertical circular motion. The dynamic test was conducted with a well-trained person holding the GNSS antenna and rotating it to simulate a vertical circular motion. During the test, the upside of the GNSS antenna was kept up; the person holding it did not crook his arm, and he kept the rotating speed steady. The solid line with dot symbols in Figure 3 indicates that the GNSS receiver discern the circular motion of the antenna. A ruler was utilized to measure the highest (2.04 m) and lowest (0.9 m) positions of the GNSS antenna. The difference corresponds to a diameter of approximately 1.14 m. According to the data shown in Figure 3, the estimated difference was approximately 0.92 m, which was close to the measured value. In Figure 3, sixteen periods are displayed, the total duration is approximately 3 min, and the average period is therefore approximately 11.2 s. The actual rotation period estimated using a stopwatch was approximately 10.0 s, which is close to the value obtained from Figure 3.
## 4 Case Study
### Architecture Configuration
#### 4.2.3 Software Environment Configuration  
As mentioned above, there are two major kinds of servers in the M\({}^{2}\)ES Apps system. The one is the Centralized server for the platform, which is configured in the centralized server room. The other is the M\({}^{2}\)ES App servers, which is configured in _Storeroom 2_ and _QC station_. The centralized server mainly deploys services such as M\({}^{2}\)ES App Store, M\({}^{2}\)ES App Authorization, and Graph DB Management Systems (DBMS). App servers are supposed to deploy separate Apps, and only two servers were used in this case due to the small numbers of Apps. It should be noted that the B/S architecture was selected in order to enable the clients to use the M\({}^{2}\)ES Apps easily.
# A Study on the Stoichiometry of Casein/Chitosan Gel Complexes as a Delivery System for Quercetin
## 3 Results and Discussion
### Formation and Characterization of Casein/Chitosan Gel Polyelectrolyte Complexes
[33], in which the authors raise the hypothesis that highly hydrophilic chitosan packs some individual casein lamellas together and tightly adheres to their surface, forming the so-called core-shell structure. Additionally, the boundary between the individual particles is not clear, and, in places, they can even connect into one piece. In their works, other authors talk about a \"hair layer,\" formed by the chitosan adsorbed on the casein lamellas' surface [34]. The image from the AFM microphotograph (Figure 3a) indicates the presence of both individual spherical complexes and aggregates. After obtaining cross-section data (Figure 3b), the sizes of the casein-chitosan structures were estimated and found to be similar to the results obtained with the DLS technique. One of the most essential characteristics of the synthesis process that determines its economic viability is its yield. Based on the data presented in Table 1, the yield of casein/chitosan formations greatly depends on the polyelectrolyte charge ratio. It is the highest for samples with an excess of casein, with 72.5% for complexes synthesized at \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & **Cas [COO\({}^{-}\)/]** & **Particle Size** & & **Yield (\%)** & **Loading Efficiency (\%)** \\  & **Rh**[**\(\ast\)**] & **[nm]** & & **(\%)** & **(\%)** \\ \hline S1 & 1:1 & 460 & 1.237 & 37.5 & 95.0 \\ S2 & 2:1 & 460 & 7.068 & 43.2 & 95.5 \\ S4 & 4:1 & 377 & 0.912 & 65.2 & 95.8 \\ S6 & 6:1 & 5720 & 0.949 & 72.5 & 99.6 \\ \hline \hline \end{tabular} \({}^{\ast}\)The total amount of the reaction mixture is always constant and equal to 20 mL.  
\end{table} 
Table 1: Size, yield, and loading efficiency of casein/chitosan gel complexes. casein/chitosan ratio 6:1, and it then drops to 37.5% with an increase in the chitosan content. The observed trend may be related to the structure of the complex--namely, an insoluble core created from casein micelles surrounded by a loose shell of chitosan molecules [35].
# Advances in Information Security and Privacy
## 5 Authentication  
In the field of the Internet of Vehicles, achieving both the privacy and traceability of nodes is a challenging task. To address this need, [PERSON] et al. [11] present an authentication scheme based on blockchain to provide vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication process. The proposed scheme has been implemented by utilizing Hyperledger Fabric as a blockchain and provides conditional privacy to users and vehicles to ensure the anonymity, traceability, and unlinkability of data sharing among vehicles. Passwords are the most commonly used mechanism for authentication, and the use of password checkers to prevent users from creating easy-to-guess passwords is considered the best practice. The study presented in [12] analyzes how Markov models can help create a more effective password checker that would be able to check the probability of a given password to be chosen by an attacker. The authors determine that one Markov model is insufficient for the creation of a more effective password checker, and multiple Markov models are required to carry out strength calculations for a wide range of passwords.
###### Abstract  
All over the world, probability-based flood protection designs are the ones most commonly used. Different return-period design floods are standard criteria for designing structural measures. Recently, risk-based flood management has received a significant appraisal, but the fixed return period is still the de facto standard for flood management designs due to the absence of a robust framework for risk-based flood management. The objective of this paper is to discuss the economics and criteria of project appraisal, as well as to recommend the most suitable approach for a risk-based project feasibility evaluation. When it comes to flood management, decision-makers, who are generally politicians, have to prioritize the allocation of resources to different civic welfare projects. This research provides a connection between engineering, economics, and management. Taking account of socioeconomic and environmental constraints, several measures can be employed in a floodplain. The Kaldor-Hicks compensation principle provides the basis for a risk-based feasibility analysis. Floods should be managed in a way that reduces the damage from minimum investments to ensure maximum output from floodplain land use. Specifically, marginal losses due to flood damage and the expense of flood management must be minimized. This point of minimum expenses is known as the \"optimum risk point\" or \"optimal state\". This optimal state can be estimated using a risk-based assessment. Internal rate of return, net present value, and benefit-cost ratio are indicators that describe the feasibility of a project. However, considering expected annual damage is strongly recommended for flood management to ensure a simultaneous envisage of the performance of land-use practices and flood measures. Flood management ratios can be used to describe the current ratio of expected annual damage to the expected annual damage at the optimal risk point. Further development of the approach may replace probability-based standards at the national level.  
cost-benefit analysis; multicriteria analysis; comparative risk assessment; expected annual damages; economic rent; internal rate of return; present value; optimal risk point; flood management ratio
## 4 Conclusions and Recommendations  
In this paper, six L-shaped short-limb shear walls constructed with high-strength rebars and high-strength concrete and six L-shaped short-limb shear walls constructed with high-strength stirrups were loaded to destruction under cyclic loading. Based on the experimental results, the following conclusions can be obtained:  
1. Short-limb shear walls usually are dominated by the flexural effect. In this test, specimens with a larger aspect ratio exhibited the expected flexural-dominant behavior with concrete crushing as well as buckling or tensile failure of the longitudinal rebars at the free web boundary elements, such as specimens with an aspect ratio of 2.15 and 2.80. Specimens with a smaller aspect ratio tended to damage in bending-shear failure, such as specimens with an aspect ratio of 1.75.
2. The free web boundary was still the weakest part of HPLW walls. Therefore, closer-spaced transverse rebars and longer confined boundary elements should be used at the free web boundary to prevent premature failure of the web in compression.
3. With a decrease in the aspect ratio, the ductility decreased, and with an increase in the axial compression ratio, the load-bearing capacity increased but ductility decreased accordingly. An obvious pinching effect was found in specimens with a smaller aspect ratio and a higher axial compression ratio.
4. The most effective method to increase the load-bearing capacity of the L-shaped short-limb shear wall is using high-strength longitudinal rebars or increasing the ratio of  
Figure 15: Horizontal reinforcement strain distributions along the wall height.  
longitudinal rebars at the free web boundary. The most effective method to improve the deformation capacity of the short-limb shear wall with L-shaped section is using high-strength transverse rebars or increasing the volumetric transverse reinforcement ratio at the free web boundary.
* All HPLW and LW walls showed excellent deformation capacity. The average ultimate drift ratios of the HPLW and LW walls were respectively 1/37.6 and 1/68.0, which greatly exceeded the allowable interstory drift ratio value (1/120) of RC shear walls according to the design provisions of the Chinese GB50011-2010 code.
* Compared with LW, HPLW walls had a higher peak point, a larger ultimate displacement, and overall starter hysteresis curves, indicating that the load-carrying capacity, deformation capacity, and energy dissipation capacity of the latter were improved.  
Conceptualization, P.Z. and J.G.; validation, J.W.; formal analysis, P.Z. and J.G. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (grant no.: 52168069, 51568028). The writers wish to express their sincere gratitude to the sponsor.  
Data available on request from the authors.  
The authors declare no conflict of interest.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 4 Experiments
### Generalization Study  
It is computationally tremendous and practically intractable to train models from scratch to tackle various complicated routing problems. Hence, we attempt to evaluate the generalization ability of our proposed approach on three types of scenarios: (1) larger-scale instances with the same uniform distribution; (2) same-scale but out-of-distribution problems as the training set; and (3) real-world benchmarks TSPLIB and CVRPLIB with varying problem sizes and node distributions.
## 4 Experimental Validation
### Effective Range and Precision Analyses  
Effective range is defined as the distance between the sensor and human, which allows effective human skeleton extraction. Effective human skeleton extraction in the effective range generates valid human joint positions. For the RGB-D based method, each extracted joint position comes with a  
Figure 6: Experimental results. (**a**) A comparison of tracking results; (**b**) Effective ranges of RGB-D-based results and SSSE-based results.  
confidence index. Valid joints are joints with confidence above 0.7. For the SSSE method, valid joints are extracted joints not affected by sheltering. In the following experiments, frames with all valid simulated human joints are defined as effective frames. In order to obtain the effectiveness-distance relationship of both methods, the shares of effective frames at different distance levels are measured. In addition, 1000 to 1200 frames containing 3D human skeletons sampled at each photographic distance from 1 to 20 m are evaluated for each method. For the RGB-D-based skeletonization procedure, effective frames are automatically labeled based on the corresponding joint confidence. For an SSSE-based procedure, effective frames are chosen based on the number of valid joints in each skeleton. The effectiveness of both methods at the same distance can be represented by the shares of effective frames among all frames. For each method, effective range covers photographic distances whose effectiveness exceed a specified threshold.  
The official parameter of Kinect [1; 15] indicates the effective range of state-of-art Kinect result is from 0.8 m to 3.5 m. Thus, the range of distance where effectiveness is above 0.8 is regarded as the effective range. As shown in Figure 6b, the effective range of SSSE is 7-10 m. Note that the effectiveness of SSSE decreases when photographic distance exceeds 10 m because of the limitation of camera resolution. The experimental result in Figure 6a shows that SSSE can provide reliable 3D human skeletonization at an effective range of 7-10 m, while Kinect is unable to extract human skeleton information when the photographic distance exceeds 5 m.
## 2 Cable with Passive Transverse Damper
### Cable with Flexural Rigidity and Fixed Supported Ends  
Modeshape measurements on a single steel strand with seven wires and fixed support ends were performed using a laser-based displacement sensor (Figure 2a). The length of the steel wire strand was approximately 15.5 m and the mass per unit length including the additional masses was approximately 1.17 kg/m and the pre-stressing force was approximately 33 kN. The measured modeshape, which can be described by sinus-hyperbolics and cosine-hyperbolics functions, clearly deviates from the sinusoidal modeshape function being valid for a taut string with simply supported ends and without flexural rigidity (Figure 2b). The measurements show that the transverse cable motion especially deviates from the sinusoidal modeshape between the anchor and approximately 5% of the cable length which is the range where transverse dampers are commonly positioned. If the cable model includes the fixed end conditions and the flexural rigidity, the cable can be modelled by the beam modelling approach  
\[E\ I\ \frac{\partial^{4}v(x,t)}{\partial x^{4}}+T\ \frac{\partial^{2}v(x,t)}{ \partial x^{2}}-m\ \frac{\partial^{2}v(x,t)}{\partial t^{2}}=f\ \delta\ (x-a) \tag{4}\]  
where \(E\) is the elastic modulus of the cable and \(I\) is the second moment of area. The boundary conditions for the case of the fixed supported ends are as follows:  
\[v(x=0,t)=0,\ \frac{\partial v(x=0,t)}{\partial x}=0,\ v(x=L,t)=0,\frac{ \partial v(x=L,t)}{\partial x}=0 \tag{5}\]  
Figure 1: Schematic of cable with (**a**) passive transverse viscous damper and (**b**) with controllable transverse viscous damper.  
These boundary conditions mean that the bending moments at both cable ends are not zero which leads to modeshape functions based on a linear combination of sine-hyperbolic, cosine-hyperbolic, sine and cosine functions  
\[V_{n}(x)=-\frac{\alpha_{n}}{\beta_{n}}\ sinh(\beta_{n}\ x)-\frac{1}{\theta_{n}} \ cosh(\beta_{n}\ x)+sin(\beta_{n}\ x)+\frac{1}{\theta_{n}}\ cos(\beta_{n}\ x) \tag{6}\]  
\[\theta_{n}=\frac{\beta_{n}\ sinh(\beta_{n}\ L)+\alpha_{n}\ sin(\alpha_{n}\ L)}{- \alpha_{n}\ cosh(\beta_{n}\ L)+\alpha_{n}\ cos(\alpha_{n}\ L)} \tag{7}\]  
where \(\alpha_{1}L=\)3.181 and \(\beta_{1}L=\)1.624 etc., see [23]. This cable model is validated by adjusting \(E\ I\) to obtain the measured modeshape.
## 2 Quasi-Static Finite Cylindrical Cavity Expansion Model
### Analytical Solution for Cavity Pressure
#### 2.2.3 Semi-Infinite Target  
For a semi-infinite target (\(r_{t}\rightarrow\infty\)), \(\sigma_{r}\) can be obtained from Equations (9) and (11):  
\[\sigma_{r}(r)=\frac{(1+b)Y_{t}}{(2+b)}\Bigg{\{}1+\ln\frac{r_{c}^{2}}{r^{2}} \Bigg{[}\frac{(2+b)^{2}E^{2}}{2(1+b)(2+b)(1+v)EY_{t}-\left((1+b)(1+v)Y_{t} \right)^{2}}\Bigg{]}\Bigg{\}}. \tag{21}\]  
In summary, the cavity stress at each stage for the finite target and the semi-infinite target is  
\[\sigma_{rc}=\left\{\begin{array}{c}\frac{(1+b)Y_{t}}{(2+b)}\left(1-\frac{r_ {c}^{2}}{r_{t}^{2}}+\ln\frac{r_{c}^{2}}{r_{c}^{2}}\right),r_{c}<r_{c1}\\ \frac{(1+b)Y_{t}}{(2+b)}\ln\left(1+\frac{r_{c}^{2}}{r_{c}^{2}}\right),r_{c1} \leq r_{c}\leq r_{c2}\\ \frac{(1+b)Y_{t}}{(2+b)}\left\{1+\ln\frac{r_{c}^{2}}{r^{2}}\Bigg{[}\frac{(2+b )^{2}E^{2}}{2(1+b)(2+b)(1+v)EY_{t}-\left((1+b)(1+v)Y_{t}\right)^{2}}\Bigg{]} \right\},r_{t}\rightarrow\infty\end{array}\right. \tag{22}\]  
where \(\sigma_{rc}\) is the radial stress at the cavity.
# Mountain Muon Tomography Using a Liquid Scintillator Detector
## 2 Materials and Methods
### Cosmic-Ray Muon and Its Interaction with Matter
#### 2.1.2 Flux Attenuation in Matter  
The energy deposit caused by the interaction between the muon and the mountain matter, such as rock, depends on the opacity [24]:  
\[\mathcal{O}\equiv\int_{L}\rho(\xi)\mathrm{d}\xi, \tag{3}\]  
where \(\rho\) is the matter density, and \(\xi\) is the coordinate along the trajectory \(L\) that the muon travels through the mountain. Muons with energies lower than the energy deposit are blocked by the mountain from reaching the detector, causing a flux attenuation. For a particular mountain, the opacity varies with the direction viewed from the detector such that the flux attenuation varies with the direction. We obtain the mountain opacity distribution by measuring the flux and direction, i.e., we conduct a mountain MT.  
Figure 2 shows the distribution of the opacity \(\mathcal{O}\) of the matter with which sea-level muons interact until they either stop or decay. It can be used to estimate the event rate for a given rock overburden above the detector. We find that the curve in the figure can be approximated to a straight line when \(\mathcal{O}\geq 2.8\) g \(\cdot\) km/cm\({}^{3}\), which indicates that the flux attenuates exponentially as the opacity increases. The slope of the curve is larger when \(\mathcal{O}\leq 2.8\) g \(\cdot\) km/cm\({}^{3}\), which indicates that the attenuation is faster than the exponential.  
Figure 1: Distribution of sea-level muon kinetic energy.  
It is worth noting that the lower limit of the studied energy range is 100 GeV, which is far greater than a muon's mass of about 0.1 GeV, making the muon an extremely relativistic particle, and the energy deposit in a unit opacity is independent of the energy.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods
### Support Vector Regression  
Support vector machines (SVMs) are a supervised learning approach used to address both classification and regression challenges. They are particularly effective in dealing with complex but relatively small to medium-sized datasets. SVMs function by drawing a line, or hyperplane, that separates the data points on a plane. This line is designed to maximise the distance between points of different classes. The points that lie closest to this separating line are referred to as support vectors [38].  
SVR consists of hyperplanes, support vectors, and margins. Hyperplanes are points in space; the support vector is the samples obtained by passing through these points; and the margin is the distance that helps to determine the correct hyperplane to maximise the distances between the data points belonging to the nearest group and the hyperplane. SVR focusses on capturing samples by drawing another hyperplane with the largest possible margin between the hyperplane and the support vector (samples). It maximises this margin to obtain the best decision limit [39].  
SVR is suitable for solving linear and non-linear problems. Linear SVR classifies the data with a hyperplane, easily separating the data groups with lines. In non-linear SVR, data groups may not always be separated by lines (curves), and, if the data cannot be classified by a hyperplane, the kernel function method is used [40]. Kernelisation (kernel trick--kernel method) is the process of dimensioning low-dimensional, unanalysable data and processing them with additional operations. There are many kernels for this process. The polynomial kernel method is an analysis model in which the dimensioning process is performed with a transition to a higher subspace in the space under study. Upon applying the radial basis function (RBF) kernel method, a non-linear interval is delineated [41].