esa-sceva/satcom-chunk-collection huggingface dataset:
# Interface Design for Responsible Remote Driving: A Study on Technological Mediation
## 4 Interface Design Analysis
### Sight: Video Feedback  
The crucial importance of sight as a condition to safe and responsible driving is evident. While good eyesight is required to be declared fit to drive regular vehicles, it is also necessary to provide drivers with an adequate and unobstructed Field of Vision (FOV) through the windshield and side windows, which e.g., regulation in Europe sets as spanning at least 180 degrees horizontally (cf., e.g., [40]). Front and lateral visibility must also be completed by rear visibility through rear-view mirrors.  
While good eyesight is bound to remain crucial to be declared fit to drive remotely, FOV requirements are to be entirely translated into HMI requirements. Indeed, the possibility of seeing the road environment directly is transformed to the point that every piece of visual  
Figure 2: Example of a common HMI setup with visual, auditory, and haptic interfaces.  
information must now be mediated through interfaces-usually, monitors reproducing video feeds incoming from vehicle cameras. Video feedback evidently holds great importance in RD, providing ROs with essential information to navigate urban environments safely and responsibly.  
Unsurprisingly, this kind of feedback constitutes a necessary condition to RD. The varying ways in which visual information is conveyed to ROs, however, diversely influence their ability to drive responsibly. The main challenge in the technological mediation of visual information, then, is to design HMIs displaying video feed in ways that are conducive to responsible RD. The following subsections take a closer look at how visual HMIs such as monitors (Section 4.1.1), augmented reality solutions (Section 4.1.2), and head-mounted displays (Section 4.1.3) co-shape responsible RD by discussing the associated amplifications and reductions.
## 2 Literature Review
### Integrative Model of Offline-Online Channels
#### 2.2.2 Effects of Offline Satisfaction on Online Behavioral Processes  
[PERSON]'s four-stage loyalty framework [31], which includes cognitive, affective, intentional, and active loyalty, can be applied to understand the offline-to-online behavioral transition processes across digital and physical channels. For example, a customer's in-store experience (offline behavior) can influence their online interactions, while digital engagement (online behavior) can shape their expectations and actions in the physical store. This interplay between channels highlights the complex nature of customer engagement in the modern retail environment. Integrating these two behavior streams in a research setting is essential to understand these processes comprehensively.  
Satisfaction is generally understood as an emotional response to a product or service that can shape a consumer's overall attitude toward it [11]. This conceptualization suggests that user satisfaction and user attitude are closely related constructs. Consumer satisfaction with one channel does not always guarantee satisfaction with the other, as online and offline features may interact with or hinder each other, as highlighted by [PERSON] and [PERSON] [32]. [PERSON] and [PERSON] [33] have also cautioned that failures of online services, such as higher prices, could potentially drive customers away from offline channels. Furthermore, [PERSON] et al. [25] have argued that certain offline habits of bank customers may negatively impact their intention to transfer online. Recently, researchers have re-examined the impact of offline habits on users' online behaviors [2; 34; 2]. Existing research on these questions lacks consistent conclusions. This gap motivates the need for the exploration in this study to determine whether consumers' frequent use of self-service kiosks in convenience stores has a positive or negative impact on their behavior of utilizing online kiosks.  
In the field of information technology (IT), [PERSON] [11] introduced the Expectation-Confirmation Model of IT Continuance (ECM-IT). This model proposes that users' intention to continue using an IT system is primarily determined by their satisfaction with its use. Going beyond initial acceptance theories, ECM-IT emphasizes the importance of post-adoption satisfaction in predicting long-term usage behavior. This approach highlights the critical role of user experience and subsequent satisfaction in driving sustained engagement with technology. Organizations should focus on driving initial adoption and ensuring positive user experiences to drive long-term usage and loyalty. In retailing context, [PERSON] [14] verified that consumers' satisfaction with SST enhances their continuance use intention toward SST. The effect of satisfaction on continuance use intention has also been demonstrated in the context of mobile applications [34]. When customer satisfaction is enhanced, repeated consumption behavior becomes more frequent [11].
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 6 Performance Analysis and Computational Complexity
### Performance Analysis
#### 6.1.1 Special Channel State Series  
In this section, a linearly varying channel state series is presented in Figure 8, and the corresponding transmission performance will be analyzed. It is assumed that only the buffer threshold limits the data queue length, so the buffer length can grow without other restrictions. Moreover, the transmitter does not discard any data packets. Based on this situation, the transmission action of energy cost minimization will be derived. And the impacts of buffer threshold and data arrival rate on communication performance will be analyzed, in terms of energy consumption, average buffer length, and transmission delay. In Section 5.1, the buffer threshold is used to set the amount of data that needs to be sent once every \(N_{sch}\) epochs, while, in this section, the buffer threshold always limits the buffer length.  
Because the channel fluctuation in the first half cycle (from \(t_{0}\) to \(t_{5}\)) is similar to that of the second half cycle (from \(t_{5}\) to \(t_{6}\)), only the transmission actions from \(t_{0}\) to \(t_{5}\) need to be analyzed. Let \(k_{h}\) be the change rate of the channel state during \(t_{0}\) and \(t_{5}\),  
\[k_{h}=(h_{m}-h_{0})/(t_{5}-t_{0}), \tag{30}\]  
where \(h_{m}=h_{t_{5}}\) is the best channel state, and \(h_{0}=h_{t_{0}}\) is the worst channel state. At time \(t\), the transmission rate is  
\[R_{t}=\rho log_{2}(1+P_{t}c_{t}), \tag{31}\]  
where \(P_{t}\) is the transmission power, and \(c_{t}\) is the channel gain at time \(t\). And \(\rho\) is the ratio of real transmission rate and upper bound of the achievable transmission rate. The logarithmic channel gain, i.e., the channel state at time \(t\) is  
\[h_{t}=10 log_{10}c_{t}. \tag{32}\]  
And this equation is equivalent to  
\[log_{2}c_{t}=D_{2}h_{t},D_{2}=\frac{log_{2}10}{10}. \tag{33}\]
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 1 Introduction  
### Motivation  
Distribution networks are important because of their interesting properties and conditions, such as span, dispersion, unbalanced loads, and non-homogeneity. They are also important because they are the last point of power delivery to consumers. Distribution networks are vulnerable to different faults which affect the system's reliability, security, and quality. Therefore, maintaining system stability, minimizing interrupted consumers and damaged networks as quickly as possible are very important. Therefore, fault location techniques play a vital role in repairing and fixing system faults in the least amount of time possible [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14].  
All of the fault location methods which have been presented so far have been developed for single circuit distribution networks, and double circuit networks have unfortunately not been addressed. Past studies on fault location have usually used a short line model and neglected the effect of capacitors.  
However, recent papers have used more complete models, such as the medium line model (\(\pi\)) or the widespread line model for fault location. Needless to say, they only address single circuit lines and not double circuit lines. In double circuit lines, the mutual effects of the lines on each other and power outages are greater than single circuit lines. Because of the high amount of consumers, if power outage occurs on the lines there will be a vast amount of undistributed power (energy). Most of the double circuit lines are overhead cables with no insulator.  
If a power outage occurs somewhere along the power lines, it must be considered that the fault could be in either of the adjacent lines. Thus, locating faults in double circuit lines is more complicated than in single lines. Because of the complexity and specific conditional and operational features, fault location in double circuit lines remains a challenge to this day.
## 3 Problem Description and Model Formulation
### Markov Decision Process (MDP)  
This inventory management problem is a sequential decision-making problem with uncertainty. We can formulate the problem as an MDP. The state \(s_{t}\) at any time \(t\) is defined as shown in Equation (6).  
\[s_{t}=(i_{1,t}^{trans},\ldots,i_{l-1,t}^{trans},i_{1,t},\ldots,i_{m-1,t}) \tag{6}\]which includes the inventory levels for each shelf life \(I_{t}=(i_{1,t},\ldots,i_{m-1,t})\) and the inventory in transit \(l^{trans}=(i_{1,t}^{trans},\ldots,i_{l-1,t}^{trans})\). In other words, the state is represented as an \((m+l-1)\)-dimensional vector. Furthermore, the state at the next time step \(t+1\) is represented in Equation (7).  
\[s_{t+1}=(i_{1,t+1}^{trans},\ldots,i_{l-1,t+1}^{trans},i_{1,t+1},\ldots,i_{m-1,t+ 1}) \tag{7}\]  
The action \(a_{t}\) at any time \(t\) is defined as shown in Equation (8).  
\[a_{t}=(q_{t},p_{1,t},\ldots,p_{m-1,t}) \tag{8}\]  
which includes the order quantity \(q_{t}\) and the prices \(p_{t}=(p_{1,t},\ldots,p_{m-1,t})\) for each shelf life. This study defines a pricing strategy as a discount choice, an \(m\)-dimensional discrete vector. The ordering incurs a unit ordering cost \(c_{o}\), the inventory carried over to the next time step incurs a unit holding cost \(c_{h}\), unmet demand incurs a unit lost sales penalty \(c_{t}\), and inventory that is discarded incurs a unit disposal cost \(c_{p}\). The total cost \(c_{t}^{total}\) is expressed as the sum of these, as shown in Equation (9).  
\[c_{t}^{total}=c_{o}q_{t}+c_{h}\biggl{(}\sum_{k=1}^{m-1}i_{k,t+1}-\epsilon_{t} \biggr{)}+c_{t}d_{t}^{\prime}+c_{p}\epsilon_{t} \tag{9}\]  
The reward \(r_{t}\) is defined as the value obtained by subtracting the total cost \(c_{t}^{total}\) in Equation (9) from the gross profit \(g_{t}\), as shown in Equation (10).  
\[r_{t}=g_{t}-c_{t}^{total} \tag{10}\]
# Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires
## 5 Experimental Results
### Visible Erosion and Degradation  
In this work, the degradation behavior of XL-ETFE insulated wires under wet arc tracking conditions was investigated. During the experiment, the samples were subjected to consecutive aging cycles. The electrical stress applied to the wire samples resulted in a progressive degradation in the insulation material. This effect is clearly seen in the images taken after each degradation cycle shown in Figure 5. In some cases, the degradation manifested as material erosion, which was then followed by the emergence of a tree path. In other samples, the degradation manifested as radial tracks that penetrated directly through the insulation.
## Materials and Methods
### Data Analysis  
Data analysis was conducted in collaboration with a biostatistician using the STATA 13 statistical software. All the collected questionnaires were sequentially numbered before data collection, making it easier to go back to each questionnaire during the data cleaning process. Different items and responses in the questionnaire were assigned codes for easy capturing and analysis. An Excel spreadsheet was used to capture the data and converted to STATA 13. Both descriptive and inferential statistics were applied during the data analysis. A Z-score test was applied to determine the level of respondents' HIV PEP knowledge and compared to a critical z-score of 1.96, and a Chi-square test was conducted to determine the association between the biographical characteristics and knowledge of HIV PEP of the participants. A \(p\)-value of 0.05 was used as the level of significance of the study. The results were summarized in frequencies and percentages. A scoring system was generated manually, and the respondents were given a score on each correct answer provided. The overall knowledge score was determined based on the sum of correct answers to the eight knowledge-based questions. A correct response to each question received one point. Respondents who scored greater or equal to seven correct answers out of eight were considered to have \"good knowledge\". Those who scored less than seven but above five were considered to have \"average knowledge\", while those who scored less than five were considered to have \"poor knowledge\".
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 3 State of the Art, Regulation, Opportunities, and Challenges
### 5G Interference and Regulatory Requirements of mmWave Bands  
The operating frequency band from 4.2 to 4.4 GHz is universally designated for radar altimeters in commercial aviation. Recently, due to the proliferation of 5G communication, the co-existence of radar altimeters has emerged as a critical issue [42]. Beyond the interference concern, the current 200 MHz band provides restricted room for potential advancements aimed at achieving very high-ranging accuracy in the future. In response to this limitation, a viable option is transitioning to alternative frequency bands. Given the substantial growth in the commercial drone sector and the supplementary use of radar altimeters on board, it is prudent to explore unlicensed frequency bands for such migration. Amidst the advent of 5G networks, mmWave bands stand out as the optimal choice, boasting substantial unlicensed bandwidth. Accordingly, the 24 to 29 GHz band initially offered a 5 GHz bandwidth (BW) with a lot of promise for high-accuracy applications. However, over time, regulatory procedures have reduced the available BW to a mere 250 MHz in many regions, with only a few exceptions [43]. Consequently, the mmWave band spanning 77-81 GHz has emerged as a popular alternative for automotive applications, offering a substantial 4 GHz of usable BW [44].
## 5 Conclusions  
We demonstrated refractive index sensing of monolayer molecules outside their fingerprint region using both LSPRs and PSPs in a mid-infrared metagrating. Within a broad band of 3 to 9 \(\upmu\)m, PSPs with mode numbers \(L=1,2\), and LSPRs with mode numbers \(m=-1,-2\) were excited and their sensing properties were examined. In response to monolayer ODT molecules, the PSPs exhibited wavelength redshifts of 2-5 nm, while the LSPRs showed wavelength redshifts in the range of 5-16 nm. The larger redshifts of LSPRs result from their larger surface field enhancement, as compared with the PSPs. In addition, the LSPR modes worked at essentially fixed wavelengths, while the PSPs modes operated in a broad band with a wavelength tuning range of more than 0.782 \(\upmu\)m, as originated from their different angular dispersions. Our proposed metagrating sensor relies on refractive index change caused by analyte molecules in a spectral band without fingerprint absorptions. This approach is less sensitive to frequency variations of plasmon resonance, and thus is advantageous in terms of fabrication tolerance.  
Conceptualization, D.H. and Z.L.; methodology, D.H. and S.G.; formal analysis, D.H., T.Z. and L.L.; writing--original draft preparation, D.H.; writing--review and editing, Z.L.; supervision, Z.L.; funding acquisition, Z.L. This research was funded by National Natural Science Foundation of China (NSFC) under grant numbers 61875030, 61575036, and 61421002.  
The authors acknowledge the fabrication facility of State Key Laboratory of Electronic Thin Films and Integrated Devices at University of Electronic Science and Technology of China.  
The authors declare no conflict of interest.
## 3 Result
### Visual Scale  
As shown in Figure 5, the core landscape area has the largest number of photographs of close-up view, followed by the mid-range view and the least number of photos from long-range view. In particular, the proportion of long-range view photos of the Old Dormitory and Yifu Building is less than 10%, indicating that observers are inclined to take close-up photographs at these attractions. With regard to the Neo-confucianism Building and Songqing Gymnasium, the difference value between view 1 and view 2, as well as view 2 and view 3, is approximately 15%, implying that visitors can relatively easily observe different levels of landscape and have a broad view when taking photos in these two scenic spots.  
The standard deviation of photographs at each type of view suggests the extent of dispersion for the photograph data at each attraction, while the extreme deviation reflects the difference between the maximum and minimum values. Table 2 illustrates that both the standard deviation and extreme deviation of the Neo-confucianism Building and Songqing Gymnasium are two minimum values, showing that these two attractions have various depths of view. The difference between the maximum value (at the close-up view) and the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Complexity**} & \multicolumn{3}{c}{**Visual Scale**} & \multicolumn{3}{c}{**Color**} \\ \cline{2-10}
**Name of the Spot** & & & \multicolumn{2}{c}{**\%Value of**} & \multicolumn{2}{c}{**The Standard**} & \multicolumn{2}{c}{**Extreme**} & \\  & **PR\({}_{i}\)** & **Ai (m\({}^{\star}\))** & **FRD\({}_{i}\)** & **View1:View2:View3** & **Deviation of V\({}_{i}\)** & **Deviation of V\({}_{i}\)** & **CC\({}_{i}\)** & **AMS\({}_{i}\)** & **SDH\({}_{i}\)** \\ \hline Wuhan University Library & 7 & 1589 & 44.05 & 58:26:16 & 17.91 & 42 & 65.80 & 0.28 & 0.22 \\ The Old Dormitory & 9 & 1722 & 52.26 & 63.289 & 22.37 & 54 & 67.61 & 0.31 & 0.28 \\ Neo-confucianism Building & 6 & 3115 & 19.26 & 45:18:37 & 11.32 & 27 & 69.43 & 0.29 & 0.18 \\ Yifu Building & 5 & 3505 & 14.27 & 52:38:10 & 17.46 & 42 & 83.16 & 0.21 & 0.17 \\ College of Engineering & 6 & 2035 & 29.48 & 52:02:7 & 14.20 & 33 & 64.36 & 0.31 & 0.23 \\ Songqing Gymnasium & 6 & 1549 & 38.73 & 47:35:18 & 11.90 & 29 & 76.07 & 0.25 & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of landscape visual indicators depicted in photographs in each study scenic spot.  
Figure 5: Analysis of landscape visual scale.  
minimum value (at the long-range view) of the Old Dormitory, Wuhan University Library and Yifu Building are above 40%, indicating that the distribution of each type of photo is unbalanced. It is difficult for observers to appreciate different levels of landscape in such scenic spots, probably because they face the campus's main road with undulating terrain.
# Nosemosis in Honeybees: A Review Guide on Biology and Diagnostic Methods
## 5 Diagnostic Methods
### Chromatography  
In studies from 2012, researchers used gas chromatography-electron impact mass-spectrometry for metabolite profiling of hemolymph of bees infected by _N. ceranae_[65]. Hemolymph was sampled with extreme care (from a small cut in the thorax, collected by pipette) to avoid contamination of the samples, especially with the hemolymph from the intestine. A matrix of data, which were collected during GC/MS for healthy and infected bees, was developed. It was proven that the metabolic profile of the hemolymph differs in healthy and infected honey bees. The most significant differences were in the levels of the carbohydrates and amino acids, i.e., in infected bees the level of glucose was higher [65]. The presence of _N. ceranae_ spores in the examined bees was checked by qPCR.  
Other studies have shown that, in honey bees infected with _N. ceranae_ (Spanish strain), the level of juvenile hormone increases [66]. However, the concentration of JH in the hemolymph of healthy honey bees was slightly lower in comparison to bees infected with both species of _Nosema_. Chromatography in these methods was a reliable and simple method to use; however, it can be better used for metabolic testing during infection rather than for diagnosis (Table 2).
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 4 Results Analysis
### Performance of the Proposed GGS-MAF-1 Algorithm with Different \(B\)  
The effects of parameter \(B\) on the estimated system performance for the GGS-MAF-1 are discussed with different inputs (WGN, CN and speech signal (SS)). Herein, four different \(B\) of 4, 8, 16 and 64 are selected for evaluating the GGS-MAF-1 algorithm. The ZA factors are set to be \(\rho_{\mathrm{ZA-LMS}}{=}\,1.5\times 10^{-6}\), \(\rho_{\mathrm{RZA-LMS}}{=}\,3\times 10^{-6}\), \(\rho_{\mathrm{ZA-PNLMS}}{=}\,1.5\times 10^{-7}\) for WGN input, \(\rho_{\mathrm{ZA-LMS}}{=}\,5\times 10^{-7}\), \(\rho_{\mathrm{RZA-LMS}}{=}\,1.2\times 10^{-6}\) for CN input, and \(\rho_{\mathrm{ZA-LMS}}{=}\,1.8\times 10^{-8}\), \(\rho_{\mathrm{RZA-LMS}}{=}\,6\times 10^{-7}\) for SS input, where \(\rho_{\mathrm{ZA-LMS}}\), \(\rho_{\mathrm{RZA-LMS}}\) and \(\rho_{\mathrm{ZA-PNLMS}}\) corresponds to the ZA-LMS, RZA-LMS and ZA-PNLMS algorithms. Parameter \(\beta\) is set to be 20 under the condition that the input signal is WGN or CN, and \(\beta=5\), \(\mu_{\mathrm{MEC}}=1.25\times 10^{-4}\), \(\mu_{\mathrm{LMF}}=1.5\times 10^{-5}\) are selected for SS input. Simulation results with different inputs are presented in Figure 3. From the results in Figure 3a, it is obvious to see that the GGS-MAF-1 algorithm converges quickly and arrives at the lower steady state error for the sake of comparison with the other algorithms when the input is WGN signal. Figure 3b,c show that the convergence speed of the GGS-MAF-1 algorithm degrades for CN and SS inputs, but the performance of the GGS-MAF-1 algorithm is still better than the other algorithms. Note that the GGS-MAF-1 algorithm with \(B=4\) exhibits the best performance while GGS-MAF-1 algorithm with \(B=64\) behaves the worst. This is because that the size for each group is 16 in the two different systems. If the group size \(B\) is greater than 16, each group will cover much more inactive taps, and hence, the performance will be degraded. In addition, we can see that there is a sudden jump in Figures 2 and 3 because the system is changed from one group system to two group system. This is to say that the proposed algorithm can track the time-varying systems, which is similar to [9; 14; 19]. Since the active system coefficients in the two group system are doubled in comparison with the one group system, the two group system is less sparse, and hence, the original normalized misalignment is higher.  
Figure 2: Performance comparisons of the four GGS-MAF algorithms.