esa-sceva/satcom-chunk-collection huggingface dataset:
# Percolation and Transport Properties in
## 1 Introduction  
The macroscopic properties of the randomly organized media has attracted research interest for many decades. Nowadays, the the critical study points are the determination of the properties of composites filled with the carbonaceous fillers, like carbon nanotubes (CNT) [1; 2; 3; 4], or graphene nanoplatelets (GNP) [5; 6; 7; 8]. All these objects have high aspect ratios, and there is an interest to develop anisotropic composites using the partially oriented fillers [9; 10; 11]. There are several ways to reach the anisotropy in case of a CNT-filled composite, for instance: mechanical deformation, curing the CNT-filled polymer in external fields, or template-based techniques (see [12] and Refs therein).  
Currently, there are different ways to model the macroscopic properties of carbon composites. Among them are: general effective media model [13], excluded volume theory [14], Monte Carlo models [15; 16], finite and boundary element methods [17; 18]. For the conductive properties modelling, the Monte Carlo based techniques are one of the most interesting due to their simplicity, extremely high tunability and good agreement with the experimental results (See [19] and Refs therein).  
However, Monte Carlo models for the aligned CNT composites lack in several aspects. Firstly, many studies introduce the cut-off zenith angle as an anisotropy parameter (AP) and investigate the dependencies of the macroscopic properties on the cut-off values [20; 21; 22]. In case of the real systems,one can measure the deformation degree or the externally applied field, but not the cut-off angle. Secondly, there is some contradiction in conductivity data for the partially oriented composites. Many report on the descent of conductivity with the increase of the AP [22; 23; 24], and several demonstrate a maximum of the conductivity with the small values of the AP [20; 21; 25]. To figure out the conductivity dependence, the critical concentration dependence on the anisotropy parameters should be investigated. Finally, the properties of the aligned composite should be studied along different directions, while most of the studies usually report on the behaviour along one direction [21; 22; 23].  
In this paper, the CNT-filled composite after mechanical deformation, which in our model is introduced as non uniformity in angular coordinate distribution, is investigated. The percolation threshold and conductivity are computed as a function of the mechanical deformation. Two main directions (along and perpendicular to the deformation axis) are studied.
###### Abstract  
Visible light positioning (VLP) is a promising indoor localization system in which light emitting diode (LED) luminaires are used as positioning beacons. Data communication is an essential aspect of any VLP system, as each luminaire must transmit information about its own location to the receiver. The quadrature angular diversity aperture (QADA) is a new receiver designed specifically for VLP systems using angle-of-arrival estimation. Previous QADA research has focused only on positioning and assumed error-free communication. In this paper, we investigate, via simulations and experiment, the actual communication characteristics of a VLP system that uses a QADA receiver. We calculate the signal-to-noise ratio and bit-error-rates for a range of scenarios and demonstrate the impact of the dimensions of the receiver. We show that reliable communication is assured in typical operating scenarios, proving that communication will not be a limiting factor when using QADA in VLP systems.  
ACO-OFDM; aperture; optical communication; photoreceiver; visible light positioning (VLP) +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Stress of the Anchorage Zone at the Bottom Slab  
#### 4.2.1 Stress Distribution of Steel Bars  
The steel stresses at Sections A-A through E-E were measured. For the eight loading cases, the top layer and bottom layer steel bars at Sections B-B were under the tension and compression conditions, respectively, due to the eccentricity of the prestressing tendons. All steel bars at Section D-D were under compression condition. The steel stress level at Section B-B was lower than that at Section D-D when the sections were under the same loading case. The linear relationship between the stress and load at Sections B-B and D-D can be observed in Figure 12. Moreover, compared with the yield stress of the steel bars, the maximum is much less than the yield stress. Thus, the steel bars showed good mechanical performance and elastic condition under each loading case.  
Because Test Points 203, 205, 204, and 206 at Section B-B were arranged at the mid-width of this section, their compressive stress levels are higher than those of other test points at the same loading case, as shown in Figure 12. This result agrees with principle of Saint Venant, as shown in Figure 2d. It can be seen in Figure 12 that the stress distribution at Section D-D is more uniform than that at Section B-B at these eight loading cases.  
The maximum stresses of the bottom layer steel at Sections D-D and E-E was 73.08 and 58.59 MPa, respectively when the load was equal to the prestressing force of 100%. It can be concluded that the compressive stress level and effect of local bearing pressure at each section were gradually decreased when the distance from the anchorage zone increased along the longitudinal direction of the slabs. No stress exceeded the yield strength of reinforcement. The steel bars showed good mechanical performance.
## 2 Materials and Methods
### Accuracy Assessment of LiDAR-Based Height Measurement  
The accuracy of LiDAR-based height was validated by comparing height metrics obtained from inventory and LiDAR data in the plot area. We used maximum and average tree height as inventory height metrics. LiDAR height measurements including percentiles and an average of elevation was obtained after subtracting ground elevation from LiDAR z values. Points greater than 2 m above ground was selectively used to obtain average LiDAR height. Additionally, the maximum CHM was obtained as a LiDAR height metric (Figure 7).  
We validated height accuracy using a pair of inventory and LiDAR metrics with the highest correlation coefficient. The correlation between maximum CHM and LiDAR height was shown to investigate whether the raster-based maximum height can be used instead of maximum LiDAR height. We used multiple plot radii to clip spatial data (3.7, 7.3, 11.0, and 14.6 m) to investigate whether the size of the clipping area affects the correlation between the inventory height and LiDAR height metrics.  
Height measurement accuracy was evaluated by examining the difference between LiDAR and inventory height. The height difference was assessed according to LiDAR point density, inventory height, LiDAR data acquisition year, inventory year, and year difference between LiDAR and inventory data collection to identify potential sources of error.  
A supplementary height validation was conducted on forest plots with a lower positional difference between GPS measurements. The purpose of this validation was to evaluate measurement accuracy on forest plots when co-registration between LiDAR data and inventory information was accurate. Inventory plots in Yellowwood State Forest (YSF), Indiana, USA, were chosen for ease of access to densely located forest plots. We remeasured geolocations of metal stakes at plot centers in YSF area using Trimble Juno (Sunnyvale, CA, USA) and calculated 2D distance between previous and remeasured GPS coordinates. Inventory plots were excluded in the validation if the 2D distance error was larger than 0.1 m [8].
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.4 The LCA of a Methanol-Powered Ship  
Onboard ships, methanol is mainly used as a second fuel in dual-fuel engines. For this assessment, it is assumed that the purse seiner operates only in a dual-fuel mode with 95% methanol and 5% pilot fuel (diesel). Specific fuel consumption is obtained from [97], while the fuel consumption of a methanol-powered ship is calculated with Equations (11) and (12). The processes included in the LCA of a methanol-powered ship are presented in Figure 9.  
The environmental impact of the engine is the same as that of the diesel engine in Section 3.2.1, while TTW emissions are calculated based on Equation (13), with emission factors for methanol and diesel presented in Table 3.
## 6 Simulation Results
### Sensitivity Tests  
We conducted sensitivity tests to analyze the impact of the proportion of PIRs (PP) on platform adoption and on the effect of promotional strategies that platform managers may use. The input parameters a campaign manager can influence are service quality (Q\({}_{0}\)), the reach of mass media (MM), the seed number (SN), interaction times (IT) and the proportion of opinion leaders (\(P_{OL}\)) [7,27,28] (see Table 3). We varied each of these parameters once at a time (while keeping all other parameters constant and in line with the default value) and simulated the process of social platform adoption. Output indexes included (i) market penetration in each step; and (ii) final market penetration within the promotion period.
###### Abstract  
The regular inspection of underground facilities such as pipelines is absolutely essential. Pipeline leakage caused by corrosion and deformation must be detected in time, otherwise, it may cause fatal disasters for human beings. In our previous research, a robot chain system (RCS) based on visible light relay communication (VLRC) for pipe inspection has been developed. This system can basically realize the light-based transmission of control command signals and illuminance-based coordinated movement, whereas the collection and transmission approach of the pipe leakage image have not been studied. Compared with former in-pipe wireless communication techniques, VLRC can not only overcome the instability and inefficiency of in-pipe data transmission but also extend the communication range with high transmission rates. The most important feature is that it can provide a stable illumination and high-quality communication for pipe inspection robot and finally improve the energy efficiency. Hence, the aim of this article is to analyze the performance of VLRC-based image transmission in the pipe and in the future provide a high-quality, long-range, and high-efficiency image transmission for complex infrastructure inspection with RCS. The transmission systems based on two signal transmission modes analog image signal relay transmission (AISRT) and digital image frame relay transmission (DIFRT) have been proposed. Multiple experiments including the waveform test, the test of transmission features with different bit error rate (BER), and in the different mediums were conducted between these two systems. The experiment revealed that DIFRT was superior to the AISRT in terms of the relatively high-quality image transmission and reconstruction quality. It could better overcome the attenuation brought by the absorption and scattering effects and finally increased the transmission range than former communication methods. The DIFRT system could also operate at 50 kbps with relatively low BER whether in the air or water. The technique in this research could potentially provide a new strategy for implementations in the stable, effective, high-speed, and long-range image transmission of the robots in some other special environments such as tunnel, mine, and underwater, etc.  
visible light relay communication (VLRC); image quality; pipe inspection +
Footnote †: journal: Sensors
## 1 Introduction
### Prior Work  
Prior work falls into three related areas: alcohol detection devices, alcohol calculation apps, and other gait-based analysis studies. Several alcohol detection devices exist including the SCRAM ankle monitor [40], which measures BAC via perspiration every 30 min, and the Kisai Introicated LCD Watch [41], which includes a built-in breathalyzer. However, these require additional devices or complex operation. In contrast, our gait-based detection works passively using only a smartphone, minimizing user burden. Many alcohol calculation apps such as IntelliDrink [42] and AlcoDroid [43], estimate BAC based on user-inputted drink counts, which can be unreliable, as users may wrongly estimate or forget to log their drinks. While some sensor-based apps proposed by [PERSON] et al. classify alcohol consumption, they only provide a \"Yes/No\" result. In contrast, our gait-based approach runs passively on a smartphone, requiring no user input and detecting BAC levels more accurately. While gait analysis has been used for the detection of diseases [44], such as Parkinson's disease [33], few studies focus on alcohol detection from gait. Compared to prior work by [PERSON] et al. [39], this work extracts a more comprehensive set of 27 signal processing features (vs. 11) from accelerometer gait signals for improved alcohol detection.  
Deep learning methods [45, 46, 47] have recently become popular, performing well on diverse tasks and data types. However, these methods are not explainable, require large amounts of data to perform well, and tend to overfit on smaller datasets (overfitting typically occurs on datasets with fewer than 50 subjects and fewer than 10,000 gait samples). Additionally, it is challenging and expensive to collect intoxicated gait data samples due to issues with obtaining institutional review board (IRB) approvals, recruiting study participants, and risks associated with administering alcohol. These factors result in small datasets that are not suitable for training deep learning models. Hence, our work focuses on alcohol consumption detection on small datasets.  
The rest of this paper is organized as follows: Signal processing features, our alcohol gait intoxication dataset, the preprocessing pipeline, and the machine learning architecture are introduced in Section 2. Our evaluation and experimental results are presented in Sections 2.5 and 3, respectively. Our results are discussed in Section 4. Finally, Section 5 presents our conclusions and future work.
# Trends and Challenges in Network Covert Channels Countermeasures
## 2 Background
Concerning network covert channels, embedding secrets directly in the unused bits of the TCP could lead to a channel with a capacity of up to 4 bits/segment. Clearly, the more bits are used, the higher will be the impact on the statistical distribution of bits composing the header, hence making the channel more detectable. Yet, the channel can be easily disrupted as it is sufficient to overwrite unused fields with random values or revert them to a standard configuration. This can be mitigated by using a more sophisticated embedding method, but paying something in terms of complexity and channel capacity. By contrast, a channel modulating the secret information in the inter packet time exploits a less obvious hiding scheme, but it is also slower and weaker since natural delays of the networks (e.g., due to queuing and competing with other flows) can completely destroy the information. An additional metric, is the _steganographic cost_, which evaluates the degradation experienced by the carrier due to the application of a steganographic method. For instance, for a network covert channel living within a VoIP flow, the steganographic cost can represent the impact on the quality of the conversation experienced by users (e.g., additional delays, reduced SNR performances of the codec or audible artifacts) [13]. Figure 1: Reference scenario for the usage of network covert channels and their detection.
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 6 Applications  
The system was first applied to develop a high repetition rate target system for laser-initiated neutron generation experiments [26] to be conducted at the ELI ALPS laser facility. The neutrons are generated in a two-step process. First, ultrashort laser pulses accelerate deuterons from a primary target (pitcher). Next, the accelerated deuterium ions hit a second target (\"catcher\") rich in deuterium. Neutrons are then generated through the DD fusion reaction. All such experiments are conducted in vacuum.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Initially, 52 patients aged between 15-57 years who presented with chronic apical periodontitis of single-rooted anterior teeth, characterized radiographically as radiolucency greater than 4 mm around the root apex with loss of lamina dura and periodontal ligament space due to incompletely attempted or previously failed root canal treatment or periapical lesion around an open root apex in the tooth, where root formation was halted prematurely due to trauma, were enrolled in this study. Medically compromised patients with any uncontrolled systemic disease (ASA III), patients with pre-existing periodontal disease or patients in whom the lesion was smaller than 4 mm, multirooted teeth, teeth with cracks and root fractures, pregnant or lactating women and patients who did not turn up for follow-up visits were excluded from this study.
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
###### Abstract  
The configuration is essential to diagnose the status of the grounding grid, but the orientation of the unknown grounding grid is ultimately required to diagnose its configuration explicitly. This paper presents a transient electromagnetic method (TEM) to determine grounding grid orientation without excavation. Unlike the existing pathological solutions, TEM does not enhance the surrounding electromagnetic environment. A secondary magnetic field as a consequence of induced eddy currents is subjected to inversion calculation. The orientation of the grounding grid is diagnosed from the equivalent resistivity distribution against the circle perimeter. High equivalent resistivity at a point on the circle implies the grounding grid conductor and vice versa. Furthermore, various mesh configurations including the presence of a diagonal branch and unequal mesh spacing are taken into account. Simulations are performed using COMSOL Multiphysics and MATLAB to verify the usefulness of the proposed method.  
grounding grid; magnetic field; orientation; transient electromagnetic method (TEM) +
Footnote †: journal: Applied
## 2 Materials and Methods
### In Vitro Simulated Physiological Condition Degradation
#### 2.2.3 Molecular Weight Changes  
Gel permeation chromatography (GPC) experiments were performed to determine variations in the molecular weight during scaffold degradation. This was conducted using an Agilent 1260 Infinity II GPC with two PLgel 5 \(\upmu\)m MIXED-C columns (PS/DVB), a PLgel 5 \(\upmu\)m guard column running at 35 \({}^{\circ}\)C, and a flow rate of 1.0 mL/min. The eluent used was tetrahydrofuran (THF) containing 2.0% \(v/v\) triethylamine and 0.05% \(w/v\) BHT inhibitor. The sampled amount was 100 \(\upmu\)L, and the sample concentration was 3 mg/mL prepared by stirring overnight, followed by filtering through a PTFE syringe filter with a pore size of 0.45 \(\upmu\)m. The refractive index detector (RID) detector used was operated at 35 \({}^{\circ}\)C. The GPC was calibrated using twelve EasiVial PS-H (2 mL) standards, with molecular weights equal to 162, 580, 1210, 4880, 10,330, 22,790, 75,050, 194,500, 479,200, 885,000, 3,152,000, and 6,570,000 g/mol. The average molecular weight (Mn) was calculated using the Agilent GPC/SEC software 1.3.
# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 2 Theoretical Background and Previous Studies
### Machine Learning Methodology
#### 2.3.4 Logistic Regression (LR)  
Given a training set \(\Big{\{}\bigg{(}\overset{\rightarrow}{x}_{1},y_{1}\bigg{)},\cdots,\bigg{(} \overset{\rightarrow}{x}_{m},y_{m}\bigg{)}\Big{\}}\), \(\overset{\rightarrow}{x}_{1}\in R^{n}\) and \(y_{i}\in\{0,1\}\), \(1\leq i\leq m\), logistic regression (LR) defines the class of \(\overset{\rightarrow}{x}_{i}\) by Equation (6):  
\[y^{\prime}_{i}=\left\{\begin{array}{ll}0,\ p_{i}<0.5\\ 1,\ p_{i}\geq 0.5\end{array}\right. \tag{6}\]  
where \(p_{i}\) is calculated using Equation (7) and \(\sigma\) is the sigmoid function defined by Equation (8):  
\[p_{i}=\sigma\Big{(}\overset{\rightarrow}{w}\overset{\rightarrow}{x}_{i} \Big{)}, \tag{7}\]  
\[\sigma(a)=\frac{1}{1+e^{-a}}. \tag{8}\]  
The goal of the LR algorithm is to learn weights \(\bigg{(}\overset{\rightarrow}{w}\bigg{)}\) that minimize a cost function, such as a cross-entropy function with ridge regression, which reduces the complexity of the model and prevents overfitting. The cross-entropy function with ridge regression is given by Equation (9).  
\[J\Big{(}\overset{\rightarrow}{w}\Big{)}=-\underset{i=1}{\overset{m}{\sum}} y_{i}\log(p_{i})+(1-y_{i})\log(1-p_{i})+\frac{\lambda}{2}\underset{j=1}{ \overset{m}{\sum}}w_{j}^{2}, \tag{9}\]  
where \(\lambda\) is a regularization parameter. The gradient descent algorithm starts with some initial \(\overset{\rightarrow}{w}\) and repeats the update in Equation (10) until the termination criteria are satisfied.  
\[\overset{\rightarrow}{w}=\left[\begin{array}{c}w_{1}\\ w_{2}\\ \vdots\\ w_{n}\end{array}\right]_{n\times 1}+\alpha\left[\begin{array}{c}-\overset{ \rightarrow}{x}_{1}^{\rightarrow}-\\ \overset{\rightarrow}{x}_{2}^{\rightarrow}-\\ \vdots\\ -\overset{\rightarrow}{x}_{m}^{\rightarrow}-\end{array}\right]_{m\times n} \times\left(\left[\begin{array}{c}y_{1}\\ y_{2}\\ \vdots\\ y_{m}\end{array}\right]-\left[\begin{array}{c}p_{1}\\ \vdots\\ p_{m}\end{array}\right]\right)_{m\times 1}-\lambda\left[\begin{array}{c}w_{1}\\ w_{2}\\ \vdots\\ w_{n}\end{array}\right]_{n\times 1} \tag{10}\]  
where \(\alpha\) is the learning rate, which affects how expeditiously the learning model can converge to local minima. If \(\alpha\) is too small, slow convergence may occur; if \(\alpha\) is too large, gradient descent can overshoot a local minimum. \(\alpha\) and \(\lambda\) are important hyper-parameters of LR models. [PERSON] et al. applied the evolutionary strategy and simulated annealing to optimize the coefficients of LR [41].