esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Results
### Numerical Model Setup  
The size of the model had a notable influence on numerical modeling. According to the previous studies [30], the model size of FLAC3D should be at least three times larger than that of the field project, and the relative error of the calculated results was about 5%; while the model size was five times larger than that of the prototype, and the relative error was about 1%. The adopted model size was five times larger than the prototype size in this study. The length, width, and height of the model were 30 m \(\times\) 20 m \(\times\) 30 m, respectively. The vertical stress of 23.8 MPa was applied to the upper boundary to simulate the overburden rocks. The lateral pressure coefficient was adopted as 1.3 to simulate the horizontal tectonic stress. The model was run to equilibrium to generate the initial stress before the roadway was excavated.  
In the numerical simulation, the built-in cable unit was adopted to simulate the bolt materials, and the zone unit was adopted to simulate the concrete. The rock materials were assumed to be an elastoplastic model, which obeys the Mohr-Coulomb yield criterion. The model and the supporting structures are shown in Figure 5.
###### Abstract  
The role of social influence (SI) as a determining factor in accepting new technologies has been addressed in several studies using the initial or extended technology acceptance model (TAM). This research uses an adaptation of the extended technology acceptance model (TAM) to analyze the behavioral intention of Romanian consumers regarding the use of energy efficiency smart solutions (EESS) under the effect of social influence. Data were processed with the structural equation modeling technique (PLS-SEM). The sample consisted of 302 domestic electricity consumers in Romania. The study's findings show that the respondents' perceptions of the two social influence dimensions--network of friends, colleagues, or family (SL_FF); and public space (SL_PS)--and their effects on other conceptual model variables are significantly different. The main implications highlight that the proposed model addresses social influence on two levels (SL_FF and SL_PS), to highlight not only the differences in users' perceptions, but also the main directions in which efforts to promote these technologies should be focused more intensively, in the context of implementing European policies regarding the reduction of energy consumption at the level of household consumers. An important component of the proposed model is the analysis of the role of hedonic motivation constructs, expected performance, perceived usefulness, and perceived ease of use in mediating the relationship between social influence and behavioral intention to use.  
social influence; social norms; EESS; TAM; hedonic motivation; technology acceptance; UTAUT; UTAUT 2; social networks; smart home; smart connected objects; behavior intention +
Footnote †: journal: Applied sciences
## 1 Introduction
In this study, we applied gait tube analysis to investigate how a semi-rigid bilateral hip exoskeleton affects gait stability across different cycle phases. We utilized data from a previously published experimental protocol involving ten healthy adults walking under twelve conditions, which systematically varied actuation magnitude and timing [4]. By analyzing COM velocity trajectories and computing ellipsoid volumes for each condition, we aimed to identify which gait phases were most sensitive to exoskeleton assistance and how stability patterns varied with assistance profiles. We hypothesized that hip exoskeleton assistance would significantly alter gait stability, with the greatest effects occurring during the late stance phase (50-60% of the gait cycle) when assistive torque was maximized. Additionally, we expected that ellipsoid volume, as a proxy for multidimensional gait variability, would be significantly increased in powered conditions compared to unassisted walking, exhibiting phase-specific patterns across the gait cycle. This analysis seeks to enhance our understanding of phase-dependent gait stability under assistive conditions and inform the development of intelligent exoskeleton controllers that adapt assistance timing and magnitude to meet individual dynamic stability needs, with applications in clinical rehabilitation and performance enhancement. We specifically expected that ellipsoid volumes would increase during the late stance phase (approximately 50-60% of the gait cycle), coinciding with the peak in exoskeleton torque delivery. Based on prior studies of push-off mechanics, we anticipated that this added torque would introduce greater vertical and mediolateral variability due to alterations in limb propulsion and trunk control demands. While we did not perform an a priori power analysis due to the secondary nature of the data, our study was designed to detect medium-to-large effects in variability metrics, particularly during stance phases under powered assistance. Therefore, the aim of this study was to investigate the phase-dependent effects of hip exoskeleton assistance on the center of mass variability during walking, using gait tube analysis to quantify multidimensional stability throughout the gait cycle. This approach represents a novel application of gait tube methodology to human-exoskeleton interaction. Unlike traditional gait stability metrics that rely on discrete gait events or stride-averaged statistics, gait tube analysis provides a continuous, multidimensional visualization of center of mass variability. To our knowledge, this is the first study to apply this method to evaluate the destabilizing or stabilizing effects of wearable hip assistance throughout the gait cycle.
## 4 Etiology of NMO
AQP4-NMOSD pathogenesis involves loss of APQ4 in astrocytes, infiltration of neutrophils, macrophage, and microglial cells, eosinophil degranulation, and loss of myelin in neurons [38]. NMO selectively insults the CNS tissue more than the other AQP4-expressing tissues because the aggregation of AQP4 is greater in the spinal cord [39]. The two isoforms AQP4 M1 and M23 play a prudent role in NMOSD pathogenesis; their expression and distribution in the CNS determine the NMOSD stages in patients of different age groups [40; 41]. The M23 isoform activates the immune complement pathway and creates orthogonal arrays ofparticles (OAPs), which initiate NMOSD pathogenesis [42]. [PERSON] and his team evaluated the genetic analysis of AQP4 by evaluating AQP4 single nucleotide polymorphisms and their relation to NMO. Their results showed that two missense allelic mutations at the Arg 19 site of AQP4 produce changes in AQP4 proteins that lead to NMO [43]. AQP4 M1 proteins can be internalized completely, whereas M23 resists internalization, activating the immune complement system more competently than M1 [44]. Another important NMO pathoetology is that brain lesions occur due to macrophage-mediated inflammatory responses because of the high expression of NFkB and Blimp-1 genes [45]. Myelin oligodendrocyte glycoprotein (MOG) is located on the outer surface of oligodendrocyte and myelin sheaths. Autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG-IgG) were observed in AQP4 seronegative NMO patients [46]. The MOG-Ig-related NMO showed different features than AQP4-Ig cases in parameters such as sex distribution and disease outcomes [47]. MOG-Ig-related pathology is characterised by perivencous demyelination, gliosis in the white and grey matter, demyelinated lesions in intracortical regions, and no change in AQP4 expression, whereas AQP4-Ig pathology is characterised by a loss of AQP4 protein, astrocyte dystrophy, and the absence of cortical demyelination [48]. In AQP4-IgG NMOSD, serum antibodies affect the astrocytes and cause astrocytopathy. In MOG-IgG NMOSD, the antibodies affect the oligodendrocytes of myelin sheaths and produce demyelinating diseases [49]. Thus, the AQP4-IgG and MOG-IgG NMOSD are two different entities with distinct pathophysiological patterns.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 5 Voting Experience
### Post-Election Phase and Tracking-Number Retrieval  
Figure 6 illustrates the post-election phase processes. Once the election has ended, the front-end system WBB has the cast votes, in plaintext form, and the corresponding tracking numbers. Email or post is subsequently used by the TTP to send the \(\alpha\) (trapdoor key which opens to a tracker \(\beta\) which is already posted on the DL) term to the voter. When the voter receives the \(\alpha\) term, if they wish to verify their vote was cast as intended they can open their tracking-number commitment, access the information contained in the WBB and check their own vote in plaintext.  
The voter's unique tracker is calculated by a supported web application within the front-end system using the received \(\alpha\) term, the public \(\beta\) (a tracker commitment which is publicly assigned to each voter and paired with the encrypted and decrypted votes. \(\alpha\) opens to a \(\beta\), then the voter tracking number can be constructed) term, and the trapdoor key \(sk\). The purpose of this action is to support individual verifiability.  
Figure 5: Voter experience during the voting phase: Once authentication is complete, the voter is forwarded to the voting webpage. The voter is shown the choices and is allowed to make their selection(s).
## 2 Research Background
### Primary Issues in the Mining of Isolated Working Faces  
In the context of the 76 working faces in Wuyang Mine, and through comparisons with coal burst incidents in isolated working faces under similar mining conditions, it has been analyzed that the combination of factors such as \"goal on both sides, large mining depth, and thick coal seam\" significantly influences these isolated working faces. The primary challenges in predicting, preventing, and controlling coal bursts in isolated working faces before mining are as follows:  
1. Prior to mining, a complex \"goaf-coal pillar-goal\" overlying strata spatial structure forms between the isolated working face and the surrounding mining environment. This structure results in load transfer in the goal's overlying strata and redistribution of surrounding rock stress in the working face, influencing the type and risk level of coal bursts. This complexity primarily contributes to the challenges in preventing and controlling coal bursts in isolated working faces. It is essential to elucidate the  
Figure 4: Borehole histogram.  
relationship between the overlying strata spatial structure and the distribution of mine pressure.
2. The optimal design of the isolated working face before mining is crucial for preventing disaster sources. Fracture and movement of the overlying strata during excavation may vary significantly under different conditions, leading to diverse coal burst behaviors. Currently, there is no established prediction principle or assessment method for the \"overall\" or \"local\" coal burst types of pre-mining isolated working faces.
3. Pre-mining anti-coal burst measures for the isolated working face are not tailored to the \"overall-local\" types and characteristics for targeted implementation. The design of primary and secondary coal burst prevention and control measures, and parameter changes are not clearly defined, often resulting in suboptimal application effects, and coal burst incidents may still occur despite pressure relief measures. It is imperative to establish an \"overall-local\" coal burst type classification method for the prevention and control of isolated working faces to achieve precise and effective prevention and control goals.  
The coal burst risk of isolated working faces is significant, and the \"overall-local\" coal burst prediction and prevention and control have substantial theoretical and practical importance. Subsequently, modeling analysis will be conducted in conjunction with the geological and mining conditions of the site to quantify the main parameters for \"overall-local\" coal burst prediction and prevention and control.
## 2 Problem Definition and Scenario of Application  
The TLP is an NP-Hard combinatorial optimisation problem [37]. It is similar to the coverage NLP in which the designer looks for an optimal deployment of sensors in space for maximising the capabilities of a sensor network [41; 42]. In the TLP, the objective is the finding of the optimal Cartesian coordinates of the tables in space (i.e., the decision variables) to particularly fit a table disposition in an indoor environment both allowing the introduction of the highest number of tables and the guaranteeing of the maximum distance among the tables to preserve the social distance in the COVID-19 pandemic.  
These are inverse optimisation criteria. While the introduction of a higher number of tables reduces the maximum reachable distance among the tables, the reduction of the number of tables to be located allows a higher distance among the tables. Therefore, the designer must choose between the maximisation of the number of tables while maintaining the social distance or increasing the distance for a predetermined number of tables.  
In this section, we address the TLP through its mathematical characterisation, a state-of-art revision of the close NLP for defining the typical methodologies to solve this problem and the definition of the real scenario in which we perform the table location optimisation.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Atmospheric Turbulence Analysis
#### 2.4.2 Rain Attenuation  
The attenuation, \(\gamma_{\text{rain}}\) (dB/km), due to rain is a function of precipitation intensity, R (mm/h), and is independent of wavelength, since the raindrops are large compared with the wavelength. Thus, the geometric optics limit can be applied to get the attenuation as [21; 22; 23].  
\[\gamma_{\text{rain}}=\mathbf{a}\cdot\mathbf{R^{b}} \tag{17}\]  
The power law parameters a and b depend on the raindrop size distribution and values determined from measurements at a specific location. Some of the models that estimated rain distribution according to their location are listed in Table 2[23].
## 3 Results and Discussion
### Gelling Characteristics  
#### 3.4.1 Breaking Force, Deformation, and Gel Strength  
Effect of washing cycle with cold water on the breaking force, deformation, and gel strength of the surimi gels is illustrated in Table 2. One of the most essential factors in determining the quality and pricing of surimi is the gel strength. Two criteria were used to determine it: breaking force, and deformation. The former referred to surimi gel's hardness, while the latter referred to its springiness ([PERSON], 2017). Unwashed mince gel showed the lowest breaking force and deformation in the current study, while W2 and W3 samples had the highest breaking force (_p_ < 0.05). Nonetheless, there was no significant difference in deformation between surimi gels (11 mm) (_p_ > 0.05). Furthermore, all surimi had a higher gel strength (breaking force \(\times\) deformation) than unwashed mince.  
Unwashed mince had the highest lipid content (Table 1), and may have more sarcoplasmic protein, which could interfere with the gel network formation during the heat setting process. Furthermore, surimi gel strength increased in tandem with increases in pH, reactive SH content, and surface hydrophobicity, as well as a decrease in TCA-soluble peptide content (Table 1). According to [PERSON] et al. (2019), increasing the number of washings raises the pH of the hen surimi, and enhances its folding score. Two-step heating is commonly used to produce gel, according to [PERSON] et al. (2018). Solubilized myofibrillar protein with salt was held at roughly 40 \({}^{\circ}\)C to generate an elastic gel before being cooked at >80 \({}^{\circ}\)C to form a stronger cooked gel. Interestingly, surimi gel strength from W2 and W3 (-440 g-cm) was higher than the Thai Industrials Standard value for frozen mince fish (not less than 400 g-cm) ([PERSON], 2017), implying that surimi from Indian mackerel prepared with fewer washing cycles met the standard quality.
## 4 Numerical Results  
The main goal of this paper is to determine which of the combinations of the cooling duct cross-section geometry and position, shown in Figure 2, is able to remove heat the most. This was achieved by examining the heat flux density and heat transfer distributions for all proposed cooling duct geometries and positions. As mentioned earlier, the simulation and boundary conditions were kept the same for all configurations as listed in Table 1 except for the cross-section shape and position of the cooling duct. In this regard, Figure 4a,b show the contour plots of the static pressure and velocity magnitude, respectively, along the length of the cooling duct for all configurations. The cooling fluid (water) enters the inlet of the cooling duct located at the top-left corner and exits the cooling duct outlet at the bottom-right corner in each case. Inlet and boundary conditions for all cases were fixed as stated earlier. Figure 4b shows that the cooling pipe curvature imparts a slight fluctuation in the flow velocity after each bend due to the serpentine shape of the duct. This fact is also observed in flow simulations of all the configurations.  
Figure 4: Static pressure (**a**) and velocity (**b**) distribution inside the cooling duct.  
Figure 3: Typical solution convergence plots: (**a**) wall \(y+\), (**b**) residuals, (**c**) net heat transfer and (**d**) net mass flow rates.  
Amongst all of the configurations investigated, the semi-ellipse cross-section configuration, case 3(b) in Table 2, yields the maximum increase in the cooling fluid temperature at the duct exit. However, if the objective is to achieve the lowest possible back sheet temperature, then the triangular duct placed flush with the back sheet top, i.e., case 4(d) in Table 2, is seen to be the best candidate, and may enhance the electric output power of the mono-crystalline PV module by at least 8% since most of the solar PV modules have a power-temperature coefficient of around \(-0.4\%/^{\circ}\)C.  
It is also interesting to note that placing the duct at the mid-height location within the back sheet results in the maximum heat transfer rate leaving the system through the fluid flowing out of the duct for each of the duct cross-sections. And, amongst these four duct cross-sections, the semi-elliptical duct placed in the middle of the back sheet is found to dissipate heat the most within a given time.
## 3 Proposed Scheme to Secure Device Layer Network
### Proposed Secure Communication Protocol
#### 3.2.2 Registration Phase  
The registration phase is composed of five steps (Figure 7) detailed as follows:  
Step 1: The device computes Y, the hash of (DID \(\mid\) PsK). A lightweight variant of SHA-3 with 256 bits is adopted as it is stated by the NIST, the most likely to be used in area of constrained designs.  
Step 2: The device sends DID and Y to the base station.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Length (bits)** \\ \hline DID & Device identity & 128 \\ \hline DPbK & Device Public Key & 160 \\ \hline DPrK & Device Private Key & 160 \\ \hline BSPbK & Base Station public Key & 160 \\ \hline BSPrK & Base Station private Key & 160 \\ \hline PsK & Pre-shared key & 128 \\ \hline SK & Session key & 80 \\ \hline MAC & Message Authentication Code & 256 \\ \hline Tstmp & TimeStamp & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter notations and lengths.  
Figure 7: Registration phase.  
Step 3: The base station checks the existence of the device in the blockchain, and if it does not exist, the registration is refused. In the other case, function 3, \"GetAllTheInfo\", in Figure 8 from the smart contract is called to get the device information. The BS computes hash(DID \(|\) PsK) and compares it to Y, and if the comparison holds, it computes the MAC as follows: MAC = Hash(DID XOR PH), where PH is the previous hash of the block, which contains the identifier of the device (if the device is stored in the block I, PH would be the hash of the block i\(-\)1).  
Step 4: The base station encrypts the MAC and the PsK' using the PsK where PsK' is the device pre-shared key stored in the blockchain.  
Step 5: The device decrypts M6 and checks if PsK' equals to its PsK, the MAC is then stored for future use if the comparison holds, or else the registration fails.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### State of-the-Art Onset Detection Algorithms
#### 2.2.2 Madmom  
This Python library provides two real-time onset detection methods [24; 29]. The first approach (called \"MadmomCNN\" in this article) uses a Convolutional Neural Network model [14] trained on 26,000 annotated onsets. The model was trained to predict percussive and harmonic onsets with a frame rate of 100 per second. Next, the spectral onset processor method detects the onsets from a logarithmically scaled audio signal representation based on the spectral magnitude and phase, which is referred to using the name \"MadmomSF\" in this paper.
## 4 Brief Literature Survey on ANNs Applied to Time Series Modeling
### Forecasting the Economy with Artificial Neural Networks
Sensitivity analysis (SBP) [147] and visualization tools [154] can help achieve a better interpretation of the variables that affect the model obtained by ANNs. Moody [95] explains in detail some of the techniques cited above and illustrates their use with an empirical example, in which IIP monthly values are predicted (12-month prediction horizon) via an FFNN model with three sigmoidal units, a single linear output unit, and a large number of input nodes. Initially, 48 macroeconomic and financial time series variables are considered potential explanatory variables. Use of sensitivity-based pruning (SBP), guided by estimations of prediction errors provided by nonlinear cross-validation (NCV), finally leaves 13 explanatory variables that optimize prediction performance at the same time, with respect to the initial 48-variable FFNN model. ANN modeling hints proposed by Moody [95] include the following:  
* After selecting the number of hidden units, input removal and weight elimination can be carried out in parallel or sequentially;
* In order to avoid an exhaustive search over the exponentially large space of architectures obtained by considering all possible combinations of inputs, we can employ a directed search strategy using the sensitivity-based input pruning (SBP) algorithm;
* We can employ some of the following optimization criteria in order to select competing models: maximum a posteriori probability (MAP), minimum Bayesian information criterion (BIC), minimum description length (MDL), and estimation (from the training data) of generalization ability, also called prediction risk;
* It is easier to over-fit a model to a small training set, so care must be taken to select a model that is not too large;
* The sensitivity analysis provides a global understanding about which inputs are important for predicting quantities of interest, such as the business cycle. Further information can be gained, however, by examining the evolution of sensitivities over time;
* Given the difficulty of macroeconomic forecasting, no single technique for reducing prediction risk is sufficient to obtain optimal performance. Rather, a combination of techniques is required.