esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Review Results
### Theory of Planned Behavior  
The Theory of Planned Behavior (TPB) is a widely used theoretical framework to explain and predict human behavior across various domains, including technology adoption. Developed by [PERSON] [51], the TPB posits that an individual's intention to engage in a behavior is the most immediate predictor of actual behavior. The TPB identifies three key constructs that influence behavioral intention (BI):  
* Attitudes (ATs): These refer to the positive or negative evaluations of performing the behavior. In the context of ChatGPT adoption, attitudes represent the degree to which students believe that using ChatGPT will benefit their academic work.
* Subjective Norms (SNs): This construct represents the perceived social pressure to perform or not perform the behavior. It captures how peers, instructors, or significant others influence the individual's adoption of ChatGPT. If students believe that others expect them to use ChatGPT, they are more likely to have stronger behavioral intentions to adopt it.
* Perceived Behavioral Control (PBC): PBC reflects the individual's perception of the behavior's ease or difficulty. It is shaped by access to resources, skills, and external support. In the case of ChatGPT, students who feel confident in their ability to use the technology or believe they have the necessary resources are more likely to adopt it.  
The TPB framework suggests that these constructs--attitudes, subjective norms, and perceived behavioral control--jointly predict the intention to adopt a technology like ChatGPT, which in turn influences the actual usage behavior. The following sections will explore the findings from three studies on ChatGPT adoption, analyzing the influence of these constructs, along with the confirmed and unconfirmed factors, in the educational settings studied.
## 3 Modeling Method for \(\mathbf{S_{h}}|\mathbf{X}^{F},\mathbf{X}^{S}\)  
### Introduction of Partial Pooling to Address Heterogeneity in Risk Clustering: RQ1  
The partial pooling method, proposed by [PERSON] and [PERSON] (2013) [21], tackles data heterogeneity among risk clusters by finding a middle ground between two perspectives: 'complete pooling', where a single global parameter overlooks cluster differences, and 'no-pooling', where separate parameters are created for each cluster. Partial pooling techniques are naturally integrated within the hierarchical GLM framework, which permits each parameter to be modeled independently. Specifically, within the hierarchical GLM framework, the cluster-specific intercept, or 'varying intercept', models both the overall data and cluster-level data, flexibly moving between complete and no-pooling settings. For example, in a log-normal model of aggregate claims given by:  
\[ \begin{split} lnY_{i}\sim\mathbf{N}\Big{(}E[lnY_{i}|\mathbf{X}],\ \sigma_{lnY}^{2}\Big{)},\ \text{for}\ I=1,\ldots,N\\ E[lnY_{i}|\mathbf{X}]=\ E[\mathbf{\alpha}_{[j]}]+\mathbf{X}^{T}\mathbf{\beta},\ \text{for}\ j=1,\ldots,J\\ \alpha_{[j]}\sim\mathbf{N}(\mu_{\alpha},\ \sigma_{\alpha}^{2}), \end{split}  \tag{2}\]  
where \(\mathbf{\beta}\) represents the regression parameters, and \(\sigma_{lnY}^{2}\) is the log-scale variance of the outcome, the varying intercept \(\alpha_{[j]}\) has its own normal density with parameters \(\mu_{\alpha}\), \(\sigma_{\alpha}^{2}\), representing the cluster's mean and variance. In no-pooling, \(\alpha_{[j]}\) varies across clusters, while in complete pooling, \(\alpha_{[j]}\) takes a single value for all data. The partial pooling approximates the value of the varying intercept by weighting the no-pooling estimate \(\overline{lnY_{j}}-\overline{\mathbf{X}}_{j}^{T}\mathbf{\beta}_{j}\) and the complete pooling estimate \(\mu_{\alpha}\) as given below [22]:  
\[E[\alpha_{[j]}]\approx\frac{n_{j}/\sigma_{lnY_{j}}^{2}}{n_{j}/\sigma_{lnY_{j}}^{ 2}+1/\sigma_{\alpha}^{2}}\cdot(\overline{lnY_{j}}-\overline{\mathbf{X}}_{j}^{T }\mathbf{\beta}_{j})\ +\ \frac{1/\sigma_{\alpha}^{2}}{n_{j}/\sigma_{lnY_{j}}^{2}+1/\sigma_{\alpha}^{2}} \cdot\mu_{\alpha} \tag{3}\]  
in which \(n_{j}\) is the sample size in the cluster \(j\), \(\sigma_{lnY_{j}}^{2}\) is the variance of the cluster \(j\) (within-cluster variance), and \(\sigma_{\alpha}^{2}\) is the total cluster variance. In interpreting Equation (3), when the cluster \(j\) has small samples (i.e., \(n_{j}\to 0\)) or the total cluster variance \(\sigma_{\alpha}^{2}\) is small and reliable (i.e., \(\sigma_{\alpha}^{2}\to 0\)), the estimate \(E[\alpha_{[j]}]\) shifts towards the global intercept \(\mu_{\alpha}\). Otherwise, it leans towards the local cluster intercept \(\overline{lnY_{j}}-\overline{\mathbf{X}}_{j}^{T}\mathbf{\beta}_{j}\). Figure 1 gives a further illustration of how the partial pooling technique is integrated into the hierarchical GLM.
## 4 Discussion  
Generally, to control and eradicate microbial colonization, synthetic biocides are applied, but several factors, such as environmental pollution and toxicity to humans, need to be evaluated [23; 24; 25; 26]. In recent times, the choice of sustainable methods to counteract microbial colonization on cultural assets is focused on plant bioactive compounds, representing valid alternatives to synthetic preservatives [56; 57; 58; 59; 60; 61]. Specifically, essential oils can certainly be considered antimicrobial agents, containing several chemical compounds (phenols, quinines, tannins, etc.) acting on microbial systems through different mechanisms and their potential synergic effect [31]. Undoubtedly, the main limit of the use of plant extracts is the method of application on cultural assets, as avoiding direct contact may induce unpredictable modifications on surface layers (vamishing, pigments, or other substances). In this study, _Thymus vulgaris_ essential oil and hydro-alcoholic solutions are proposed as a novel protocol to counteract microbial-colonized Sogo Bo wooden sculptures.  
Gas-chromatography analysis showed that carvacrol and thymol are the main compounds in both EO and HA solutions; the antimicrobial activity of both is reported in the literature [62; 63].  
In this combined treatment, the wooden sculpture's surface was preliminarily treated with a _T. vulgaris_ HA solution, followed by exposure to EO volatile compounds in a dedicated \"clean chamber\", under environmental conditions. On the bigger section of the wooden sculpture, three areas were selected to monitor the action of thymus products on microbial colonization. As summarized in Figure 8a, before treatment, a high microbial load was revealed, slightly decreasing after exposure to EO volatile compounds in the ad hoc assembled \"clean chamber\". Total microbial load was further reduced when combining the exposure to EO volatile compounds with an application of 70% Et-OH, becoming zero when the _T. vulgaris_ HA solution was applied.  
Monitoring was continued for a further ten weeks after the artifacts were re-exhibited in the museum hall, which highlighted that the microbial load was still very low.
## 1 Introduction
However, the role of lateralized ERP components, such as P\({}_{\text{D}}\) (distractor positivity) and N2 pc (N2-posterior-contralateral), as measures of distinct aspects of attentional selection and control under low and high perceptual load conditions has not been fully investigated in previous studies. The N2 pc is observed as a negative deflection that is larger when contralateral to the attended stimulus and reflects the neural process of orienting and focusing covert attention on peripheral object features among competing distractors, and it is indicative of attentional capture by salient stimuli [13; 14; 15; 16]. Research utilizing magnetoencephalographic (MEG) recordings has identified two distinct neural sources contributing to the N2 pc: the Early Parietal Source between 180 and 200 ms after stimulus onset, associated with the initiation of attentional shifts within the visual field, and the Later Occipito-Temporal Source between 220 and 240 ms after stimulus onset, thought to reflect the focusing of attention, implemented by extrastriate areas of the occipital and inferior temporal cortex [17]. Furthermore, numerous studies have identified P\({}_{\text{D}}\) as a lateralized posterior ERP component elicited by salient distractors. While P\({}_{\text{D}}\) shares a similar topography with the N2 pc, it exhibits the opposite polarity, presenting as a contralateral positivity rather than contralateral negativity. \(\mathrm{P_{D}}\) typically occurs within 100 to 500 ms after stimulus onset and reflects neural mechanisms involved in suppressing irrelevant or competing stimuli during tasks that demand focused attention [18; 19; 20; 21]. In prior studies, when contralateral positivity appeared relatively early (e.g., 100-275 ms) before the first shift in attention (i.e., N2 pc), it has been classified as early \(\mathrm{P_{D}}\), providing supporting evidence for the development of the signal suppression hypothesis (SSH) of controlled attention capture [18; 22; 23; 24; 25; 26]. According to the SSH, the salience of distractors triggers an automatic signal that, under certain conditions, can be proactively suppressed through top-down attentional control mechanisms to prevent attentional capture, particularly when distractors conflict with task goals (for reviews, see [20; 27]). Previous ERP studies promoting SSH have considered attentional control as a process where either a distractor captures attention or is suppressed, without directly examining for the possible modulatory effect of VWM load on this process. However, an exception is represented in the study of [PERSON] and [PERSON] (2019), who examined the processes of enhancement and suppression during the encoding of information into VWM. Using a change detection task, participants were required to memorize certain items while ignoring others. The results showed that to-be-ignored items elicited a \(\mathrm{P_{D}}\) component, which increased its amplitude with respect to the salient distractor load, suggesting that the \(\mathrm{P_{D}}\) serves as an index of suppression efficiency [28].
# Acupressure for Managing Osteoarthritis: A Systematic Review and Meta-Analysis
## 2 Methods
### Study Selection and Data Extraction  
Two review authors (LA and ES) searched the databases independently, removed duplicates, and assessed the eligibility of the studies. Based on the predefined inclusion criteria, the potentially eligible studies were screened with the full texts. Any discrepancies in the selection of a study for inclusion in this review were discussed with a third review author (MSL) until a consensus was reached.  
For data extraction, the two review authors (LA and ES) independently extracted the data using a standard data extraction form. The following information was extracted: first author's last name, publication year, country, study design, sample size (total number and male/female numbers), patient age, type of OA, disease course (years and severity), details of the interventions and controls (regimens), outcome measures, study results, and adverse events. All disagreements between the two authors' judgments were resolved with the third review author (MSL) through discussion. The authors of the included studies were contacted for unreported data or missing data.
## 6 Conclusions  
In conclusion, a lightweight graphene-assembled film with high flexibility is presented. For the first time, we designed a 5G MIMO antenna array based on GAF and employed GAF FSS and isolation strips to improve the isolation between MIMO antenna elements. GAF-based microstrip antenna element working at 3.5 GHz has good radiation ability and high realized gain of 2.87 dBi. GAF FSS and isolation strips can effectively suppress the coupling between MIMO antenna elements and increase the isolation by more than 10 dB. In addition, GAF FSS and isolation strips correct the working frequency and radiation pattern of the MIMO antenna array due to coupling. All results prove that GAF and its electronics are in line with the development requirements of 5G wireless communication and can be a strong candidate as a metal alternative material in next generation wireless communication systems.  
Conceptualization, R.S., X.C. and B.M.; methodology, Z.H., T.L. and D.H.; validation, B.M. and D.H.; writing--original draft preparation, R.S., S.J. and B.M.; review and editing, R.S., D.G.C. and B.M.; funding acquisition, R.S. and D.H.; R.S. and X.C. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51/01146) and the Fundamental Research Funds for the Central Universities (WUT: 2020-YB-032 and 2020 IB005).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: The measured radiation patterns of MIMO antenna array. (**a**,**b**) The E-plane and H-plane radiation patterns with/without FSS and IS of Antenna 1, respectively, (**c**,**d**) The E-plane and H-plane radiation patterns with/without FSS and IS of Antenna 2, respectively.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 1 Introduction
### Our Contribution  
In this work, we investigate the problem of efficiently determining meaningful alternative routes, succinctly represented by the AG subgraph, on the more realistic setting of a _time-dependent_ road network. The meaningfulness is quantified by certain criteria (minimum overlap, small stretch, small size, and low complexity), which are formally defined in Section 2.  
We propose a new heuristic algorithm, called _TDAG_, which computes an alternative graph containing routes of guaranteed quality, for a time-dependent road network. In particular, based on precomputed minimum-travel-time metadata between a small set of (landmark) nodes and all other nodes in the graph, TDAG selects, first, an initial alternative graph (AG), which is induced by a carefully selected candidate set of _od_ routes. Consequently, TDAG improves the constructed AG during an iterative pruning phase, which discards uninteresting or low-quality routes until the resulting AG meets all the quality criteria.  
Our experimental evaluation of TDAG on real-world benchmark time-dependent road networks shows that the entire AG can be computed fast, even for continental-size networks, outperforming typical baseline approaches by one to two orders of magnitude. In particular, the entire AG can be computed in less than 0.384 s for the road network of Germany, and in less than 1.24 s for that of Europe. TDAG also provides \"quick-and-dirty\" initial results of decent quality, in about 1/300 of the above mentioned query times.  
To our knowledge, this is the first work achieving efficient computation of alternative routes in the more realistic setting of time-dependent road networks.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 4 Experimental Results and Analysis
### Comparison Experiment  
#### 4.5.1 Comparative Analysis of Different Modules  
In order to verify the lightweight effect of the proposed modules, the C3, ELAN, C2f, and RG-C2f modules are selected to evaluate the inference speed and resource consumption individually in this paper. The average inference time, frame rate, floating-point operations, and parameters of each module were recorded by performing 2000 inference tests on GPUs. The results are shown in Table 2.  
The experimental results show that the C3 module has medium computational and parameter counts and relatively fast inference speed, which makes it suitable for use in scenarios that balance performance and computational complexity. The RG-C2f module has the highest inference speed (481.65 FPS) and the lowest resource consumption (3.565G of FLOPs and 217,920 of parameters), while the ELAN module has the largest parameter (492,288) and has the slowest inference (328.00 FPS). This indicates that the RG-C2f module has a significant advantage in scenarios that require high inference efficiency.
## 2 Satellite Systems and Errors in Position Determination
### Errors in Position Determination
Due to the fact that satellite systems work on a huge scale, i.e., the satellites circulate in orbits and the distances between them and the receivers range from several to tens of thousands of kilometers, such a distance causes huge problems in the accuracy of position determination. Moreover, receivers and satellites must synchronize in real time, so any delay in the satellites' basic atomic clocks can lead to significant errors. Since this research focuses on positioning and navigation in varying weather conditions, apart from the technical aspects of error generation, natural phenomenon from Earth's atmosphere layers must also be taken into account. It seems that pressure, temperature, air humidity, or solar flares have a huge impact on the positioning accuracy. How and to what extent is analyzed in Section 3, but once the electromagnetic wave is delayed, the receiver cannot calculate the exact position of the satellite.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 2 Problem Formulation  
### Frequency Hopping Signals of Frequency Hopping Spread Spectrum Network  
In this study, we consider an FHSS network in which \(K\) FH signals are observed in a single receiver. To consider the ability of attackers to imitate FH signals similar to those of an authenticated user, we assume that the \(h\)th hopping times of the \(k\)th FH signals \(t_{h}^{k}\) have the same value, that is, the FH signals hop simultaneously. An example of an FHSS network with the two different FH signals is presented in Figure 2.  
A single FH signal is defined as follows  
\[x^{k}(t)=a^{k}e^{2\pi(f^{k}(t)t+\varphi^{k}(t))} \tag{1}\]  
where \(x^{k}(t)\) is the FH signal emitted by the th emitter, \(a^{k}\) is the amplitude and is the hopping frequency of the \(k\)th FH signal \(x^{k}(t)\), and \(\varphi^{k}(t)\) is the phase difference modulated by the \(k\)th message signal \(m^{k}(t)\). When the message signal is modulated with frequency modulation (FM), the phase difference is defined as follows  
\[\varphi^{k}(t)=\int\limits_{-\infty}^{t}m^{k}(\alpha)d\alpha \tag{2}\]  
From Equation (1), all the \(K\) FH signals simultaneously observed by a single receiver can be defined as follows  
\[y(t)=\sum\limits_{k=1}^{K}x^{k}(t)+n(t) \tag{3}\]  
where \(y(t)\) is the observed RF signal and \(n(t)\) is the additive white Gaussian noise (AWGN) present in the channel environment.  
The FH signal is observed during the observation time \(T\). During this time, a total of \(H\) hops are observed. Within a single hop duration of the \(h\)th hop signal, \(t_{h}\leq t<t_{h+1}\), the hopping frequency \(f^{k}(t)\) is held constant at \(f^{k}_{h}\), denoting the \(h\)th hopping frequency of the \(k\)th FH signal. Thus, Equations (1) and (3) can, respectively, be reformulated as follows  
\[x^{k}_{h}(t)=Ae^{2\pi(f^{k}_{h}+\varphi^{k}_{h}(t))},\text{ for }t_{h}\leq t<t_{h+1} \tag{4}\]  
Figure 2: FH signals in two FHSS networks.  
\[y_{h}(t)=\sum_{k=1}^{K}x_{h}^{k}(t)+n(t),\text{ for }t_{h}\leq t<t_{h+1} \tag{5}\]  
where \(x_{h}^{k}(t)\) is the \(h\)th hop signal of the \(k\)th FH signal and \(y_{h}(t)\) is the observed RF signal during the \(h\)th hop duration, \(t_{h}\leq t<t_{h+1}\), where a total of \(K\) hop signals exist.
## 3 Phenomenological Model: The MINA Model  
### The Importance of Solidification Laws during the Welding Process  
Several grain structure prediction models have been developed that differ depending on the various welding processes available (electron beam, tungsten inert gas, coated electrode, laser, plasma, etc.). These models also differ depending on the material used such as steel, aluminium or titanium. There has been steady progress in welding modelling since the 1960s, when mainly the temperature gradient was taken into account. Substantial progress was made in the 1990s with studies on solidification mechanisms [46]. The goal was to define a correlation between the final microstructure and the solidification parameters [47]. In the case of austenitic steels, modelling is mainly performed using a two-dimensional approach. Models differ in the rules governing the initiation of crystal growth, in the progression of the grain boundary and in the change in the crystallographic orientation. What is difficult is the modelling of the links between the growth of the primary arms and the growth of the secondary arms of the dendrites [11; 48]. The solidification of an austenitic stainless steel varies according to the Fe-Ni-Cr ternary alloy composition. The KGT model [49; 50] assumes that dendrite formation is controlled by nickel diffusion, and assuming a constant weld pool, the model derives relationships between heat source displacement rates, solidification front velocity and dendrite tip growth rate. This model predicts the preferred solidification directions. Another important model is the CAFE model [51; 52], which is a two-dimensional cellular automaton algorithm. It simulates dendritic grain formation. The model includes heterogeneous nucleation, growth kinetics and preferential growth directions. A finite element model based on an enthalpic method makes it possible to predict the growth of grains in the cells at each time step. The growth competition between grains is then simulated. In the literature, similar approaches have been developed for different welding processes [53; 54; 55]. It should be emphasised that the properties of the parent metal modify the texture of the welded joint, in particular the average grain size. Depending on the differences in composition and crystal structure between the base metal and the filler metal, epitaxial growth or heterogeneous nucleation can occur [56]. For thin sheets, the total melting of the welded thickness could be modelled. A flat solidification is observed, as well as the absence of secondary dendrite arms. The direction of grain growth is calculated by using the direction of the gradient and the directions of the two grains that meet during the solidification to decide on the resulting orientation. There are not yet models that deal with multipass welding, and work is being started in this area. Analytical models for predicting heat flux in this type of process are beginning to be proposed [57; 58; 59]. All these models are mainly concerned with automatic or semi-automatic processes and therefore do not take into account the particularities of the manual welding method.