esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Evaluation of Dynamic Task Allocation for Human-Robot Collaboration in Assembly
## 3 Results
### Effectiveness  
All subjects were able to successfully complete all assembly runs of the study (process completed and components correctly assembled), i.e., TCR = 100%.  
During the assembly process, however, some of the subjects made mistakes (hereafter referred to as errors) that were either corrected immediately (e.g., picking up the wrong part) or were not relevant to the successful completion of the assembly (e.g., joining and screwing were performed in immediate succession, without confirmation in between). We categorized the errors into three types--picking errors (e.g., wrong part gripped), assembly errors (e.g., wrong task performed), and operator interaction errors (e.g., forgotten confirmation).  
During the total of 120 runs, a total of 36 errors occurred, which were composed as follows: \(n_{\text{errpicking}}=10\); \(n_{\text{errassembly}}=14\); and \(n_{\text{errconfirmation}}=12\). The errors made by the individual subjects mainly occurred in the first two runs of the study, which can be explained by learning effects. Other errors are evenly distributed across the runs and subject groups, implying that neither the task allocation mode nor the duration of delays have a significant effect on the number of errors. Most errors resulted from inattentive observance of the assembly instructions. As all errors were either uncritical for a successful component assembly or were noticed and corrected by the subject itself, they had no effect on the TCR. However, they do have relevance against the background of quality assurance.
# Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration
## 2 Materials and Methods
### Frequency Entropy  
We, further, calculated the frequency entropy of EEGs during sleep-wake states (Figure 4). The entropy elevated when the EEG contained various instantaneous frequencies (Figure 4A-G). During NREM sleep (Figure 4B,C), we noticed a rising frequency entropy as a function of time, no matter whether in the control or footshock groups. This finding suggests that the composition of frequencies in NREM sleep gradually became more complex as the resting time (light period) moved toward the active time (dark period). Regarding REM sleep (Figure 4D,E) and wakefulness (Figure 4F,G), the frequency entropies significantly increased after the footshock stimuli (Figure 4E, REM sleep: control vs. footshock: 3.623 \(\pm\) 0.003 vs. 3.665 \(\pm\) 0.003, F(1,1199) = 109.71, \(p\) < 0.01; Figure 4G, wakefulness: control vs. footshock: 3.667 \(\pm\) 0.002 vs. 3.688 \(\pm\) 0.002, F(1,1513) = 66.91, \(p\) < 0.01). These results implied that the footshock stimuli increased the complexity of instantaneous frequencies during REM sleep and wakefulness.  
Figure 3: Comparison of the amplitude entropy after the footshock stimuli. (**A**) Uniform amplitudes of EEGs result in a low entropy, whereas the variety of amplitudes causes uncertainty of occurrence rate and results in a high entropy. (**B**) Distribution of amplitude entropy for NREM sleep. The solid line and light gray shadow are the values of means \(\pm\) SEMs for every 15 min during the light period, obtained from the control group. The dashed line and dark gray shadow are results acquired from the footshock group. (**C**) Statistical significant test for the amplitude entropy. The white bar represents the value of mean \(\pm\) SEM for 15 min bins across the total 12 h light period, obtained from the control group, and the gray bar depicts the amplitude entropy acquired from the footshock group. (**D,E**) Represented the data of REM sleep. (**F,G**) Demonstrated the results for the wakefulness. ** Indicates the difference reaches statistical significance of \(p\) < 0.01.**
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Atmospheric Turbulence Analysis  
This section discusses specific attenuation caused by different weather conditions as follows.
# Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink
## 4 Performance Analysis  
In this section, we perform the security analysis of the NA-based uplink CV-QKD protocol for the asymptotic case and the finite-size regime. Since the ZPC operation belongs to a kind of Gaussian operation (see Equation (9)), we can derive the secret key rate via the conventional method of the Gaussian protocol. The simulation parameters are shown in Table 1. We consider a collimated Gaussian beam with an initial radius \(W_{0}\) transmitted  
Figure 4: The attenuation coefficient vs. transmittance and the corresponding success probability.  
onto a telescope with radius \(a\), and illustrate the results of clear night-time. Note that CV protocol has the potential to reach an all-day communication [36; 37], and the performance of the day-time situation can be derived by updating the atmospheric parameters, i.e., refraction index structure parameter \(C_{n}^{2}\), and the mean number of scatterer particles \(n_{0}\).
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 2 Related Works
In [33], [PERSON] et al. show that, on average, machine-learning models exhibit 10% more accuracy than scoring-based ones. Specifically, in this study, Support Vector Machines (SVM), Random Forests (RF) as well as bagging and boosting techniques were tested for predicting bankruptcy events and were compared with results from the discriminant analysis, Logistic Regression, and Neural Networks. The authors found that bagging, boosting, and RF outperformed all other models. However, since the dataset has not been released and the models' hyper-parameters are not reported, it is difficult to replicate such results and understand if the performance improves because of the quality of the models or because the authors take into account some other financial variables as features. Considering that the comparisons of the models are still inconclusive, new studies exploring different models, contexts, and datasets are relevant. A firm's failure is likely to be caused by difficulties experienced over time and not just the previous year. In light of this, the dynamic behavior of firms should be considered in a theoretical framework for bankruptcy prediction such as growth measures and changes in some variables [33]. Several research works investigated this aspect, but the results are again inconclusive and often not reproducible. In [28], the authors show that firms exhibit failure tendencies as much as five years prior to the actual event. On the other hand, [PERSON] et al. [34] pointed out that the models are only capable of predicting bankruptcy two years prior to the event, which improves to three years if used for multiperiod corporate default prediction [35]. In most studies, ratios are analyzed backward in time starting with the bankruptcy event and going back until the model becomes unreliable or inaccurate. Moreover, most of the bankruptcy prediction models in the literature do not take advantage of the sequential nature of the financial data. This lack of multi-period models is also emphasized in a literature review by [PERSON] et al. [36].
# Proving Properties of [PERSON]'s Algorithm for Mutual Exclusion of N Processes
## 2 Modeling a Mutual Exclusion Algorithm  
The basic steps of a process involved in a mutual exclusion competition concerning the access to a common shared data resource \(R\), is depicted in Algorithm 1. The process, with unique identifier \(i\) in \([1..N]\), obeys a protocol that regulates the enter/exit of \(i\) to/from its local critical section. This is the code of process \(i\) that accesses/modifies \(R\). The entry/exit parts of the protocol depend on some (hopefully few) _shared communication variables_. In an important case, a shared variable can be an _exterior_[23] or _output_[10] variable. Its write operation is the responsibility of only one process, but its value can be read, also concurrently, by all the remaining processes. In the more general case, a shared communication variable can be written/read by any process. In addition, multiple read/write accesses can occur simultaneously.  
```
shared communication variables Process(i) local variables repeat  NCS protocol-entry-part CS Protocol-exit-part forever
```  
**Algorithm 1** Abstract structure of a process involved in mutual exclusion.  
In Algorithm 1, NCS denotes the non-critical section. A process in its NCS is not using the resource \(R\); it is also not interested in entering its critical section. In NCS, a process can remain an arbitrary time or an infinite time to model process termination. CS indicates the critical section of a process. The protocol is played in the entry/exit sections, where the shared communication variables are operated. In the entry part, a process is competing with peers for entering its critical section. If the process cannot proceed, it is assumed that it enters a _busy waiting_ state, where it actively awaits for the shared communication variables to change, thus favoring the process. Depending on the mutual exclusion algorithm, some busy wait states could also be required in the exit part of the protocol.
## 2 Literature Review
### Integrative Model of Offline-Online Channels  
#### 2.2.1 From Online to Offline Services  
With the rapid development of the Internet, most traditional brick-and-mortar companies encouraged customers to use their online services to expand their business service channels [21]. Research by [PERSON] and [PERSON] [22] suggests that offline services can attract online customers and that customers' positive perception of a company's offline brand image can increase their trust in its online platforms [23]. This approach can help companies to expand their services from brick-and-mortar stores to online platforms. For example, digital transaction activities in banking industries can be conducted timely and efficiently. Mobile payment can thus enhance the consumer experience [24]. Researchers reported that the advantages of mobile banking services (e.g., accounts inquiry and management, money transfer, and bill payment) have a positive impact on users' willingness to switch to online services [25].  
Conversely, when e-retail channels are extended offline, consumers can enjoy the shopping experience in a physical environment. For example, Amazon and Alibaba have launched trials of physical stores, enabling consumers to purchase using their mobile phones in the center of a showroom [26]. One of the benefits of online-to-offline retail channels is the spillover effect in terms of increased purchases [27]. For example, consumers who engage with online ads, reviews, and exclusive in-app discounts develop purchase intent. This shortens in-store decision time, increases conversion rates, and strengthens the spillover effect. [PERSON] et al. [28] suggested that online and offline channel integration promotes the channel's competitive advantage (e.g., willingness to pay) by improving perceived service quality and reducing perceived risk in online channels. Researchers have demonstrated that in-store technologies (e.g., QR codes and RFID-supported smart cards) can increase consumers' purchase intention [29].  
Accordingly, in-store SST services can play a role in encouraging consumers to engage with online kiosks. In the retail industry, when workforce service capability reaches saturation, SST provides an opportunity to serve customers. Research in the fashion industry by [PERSON] et al. [30] suggests that online services, such as web showrooms, have a positive impact on consumers' purchase intentions due to their timesaving benefits. However, there is limited academic literature on the spillover effects of offline-to-online services in the retail industry, particularly in the context of convenience stores. Additionally, examining whether the synergy between online and offline services can enhance customer satisfaction and encourage continued usage or encourage customers to seek alternative channels, such as an online kiosk app, is essential.
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
## 3 Experimental Process
### Hole-Drilling for Measurement of Residual Stress  
The evolution mechanism of welding residual stress and the factors affecting its formation are complicated. Theoretical analysis alone cannot solve the problem. Therefore, a destructive test using the hole-drilling method was applied to validate the correctness of the ultrasonic laser method. A ZDL-type drilling device and a TJ-120-1.5 type strain rosette were applied in the experiment. The local radial strain induced by boreholes was measured and the longitudinal and transverse stresses were calculated based on the following relationships:  
\[\sigma_{x,y}=\frac{\varepsilon_{1}+\varepsilon_{2}}{4A}\pm\frac{1}{4B}\sqrt{ \left(\varepsilon_{1}-\varepsilon_{2}\right)^{2}+\left[2\varepsilon_{3}- \left(\varepsilon_{1}+\varepsilon_{2}\right)\right]^{2}} \tag{7}\]  
\[\tan 2\theta=\frac{2\varepsilon_{3}-\varepsilon_{1}-\varepsilon_{2}}{ \varepsilon_{2}-\varepsilon_{1}} \tag{8}\]  
where \(\varepsilon_{1},\varepsilon_{2}\) and \(\varepsilon_{3}\) are the radial strains measured by the strain rosette. The strain rosette is composed of three different angle sensitive grids arranged in the radial direction. As shown in Figure 7, \(\theta\) is the  
Figure 6: The residual stress distribution in TC4 laser welding at a welding power of 2800 W, welding speed of 6.0 m/min, and heat input of 28 kJ/m. The longitudinal residual stresses measured by the hole-drilling method and ultrasonic laser method were consistent.  
angle between the principal stress \(\sigma_{x}\) and the strain gauge \(R_{1}\). The release coefficients \(A\) and \(B\) can be obtained by the following formulas [36]:  
\[A=-\frac{1+\mu}{2E}\cdot\frac{a^{2}}{r^{2}}\cdot B=\frac{1}{2E}\bigg{[}3(1+\mu) \frac{a^{4}}{r^{4}}-4\frac{a^{2}}{r^{2}}\bigg{]} \tag{9}\]  
where \(a\) is the radius of the hole, \(r\) is the distance between the center of the hole and the middle point of the sensitive grids, and \(E\) and \(\mu\) are the elastic modulus and Poisson ratio, respectively. As shown in Figure 6, the longitudinal residual stresses measured with the hole-drilling method and the ultrasonic laser method were consistent. In the high stress region, the hole-drilling method produced a larger result than the ultrasonic laser due to the stress concentration caused by the hole.
## 4 Discussion
A study similar to the current one would be required to estimate exposures for different usage scenarios. The exposure from a user's own handset is heavily dependent on the position of usage. In this study, this is partially accounted for by taking the averaged psaSAR value determined over all test positions. The use of mobile devices away from the body (e.g., by placing the device on a desk in front of the user) can reduce the exposure from a user's own device by a factor much larger than 10. It is also not possible to draw conclusions about the actual exposure of sensitive organs like the human brain or even specific cells, as the exposure from a user's own device is very local and thus highly dependent on the usage pattern. However, the use of higher frequencies is likely to reduce the exposure of deeper brain regions for the head-mounted use of mobile devices. The study is also not designed to analyze any kind of direct biological effects due to exposure to electromagnetic fields. It may, however, be valuable for the modeling of doses in epidemiological studies.
## 3 The Proposed \((k,n)\)-XVC Scheme with Adjustable Pixel Expansion  
In this part, we propose a \((k,n)\)-XVC scheme with adjustable pixel expansion being \(m^{\prime}\), which is built on any \((k,n)\)-OVC scheme with pixel expansion \(m\) denoted as \((k,n)_{m}\)-OVC scheme. We denote the proposed scheme as a \((k,n)_{m^{\prime}}\)-XVC scheme. It greatly improves the contrast and allows pixel expansion to be changed among a lightly large interval \([m-2^{k-1}m\alpha+1,m]\). It can ensure each pixel being exactly recovered with the same average contrast no matter it takes any pixel expansion. The least pixel expansion is much smaller than previous schemes.
###### Abstract  
This study aimed to evaluate the retrievability and potential damage to implant-abtment connections caused by fractured abtment screw removal using conventional and drilling techniques. A total of forty abtment screws were randomly inserted into forty dental implants, and then they were fractured and extracted using different removal methods: Group A employed a conventional approach utilizing an exploration probe and an ultrasonic device without irrigation (\(n=10\)) (conventional); Group B used the Phibo drilling removal system without irrigation (\(n=10\)) (Phibo); Group C utilized the Rhein83\({}^{\text{\textregistered}}\) drilling removal system without irrigation (\(n=10\)) (Rhein83); and Group D implemented the Sanhigia\({}^{\text{\textregistered}}\) drilling removal system without irrigation (\(n=10\)) (Sanhigia). Pre- and postoperative micro-computed tomography (micro-CT) scans were performed on the dental implants, and Standard Tessellation Language (STL) digital files were generated for morphometric analysis to measure the wear volume. ANOVA was used to assess the volumetric differences (mm\({}^{3}\)) and percentage ratios of the internal thread volumes of the implant-abtment connections before and after the procedures. Results: This study found no statistically significant differences in the volumetric and percentage ratios of internal threads among the implant groups (Phibo, Rhein83, Sanhigia, and conventional). However, the success rate for retrieving fractured abtment screws was higher (90%) with the drilling systems compared to the conventional technique (50%). These results suggest that drilling systems are more effective for the retrieval of damaged screws. Although drilling techniques without irrigation demonstrated higher removal efficiency compared to the conventional method, both approaches resulted in similar wear volumes at the implant-abtment connections when used to extract fractured screws.  
abtment screw; dental implants; dental prostheses; fractured; implant-supported prostheses; screw retain +
Footnote †: journal: Applied Sciences  
1 Faculty of Dentistry, Alfonso X El Sabio University, 28691 Madrid, Spain; [EMAIL_ADDRESS] (S.d.l.C.-J.); [EMAIL_ADDRESS] (A.Z.-M.)  
Department of Surgery, Faculty of Medicine and Dentistry, University of Salamanca, 37008 Salamanca, Spain 2
## 4 Simulation Results and Analysis
### Throughput  
As illustrated in Figure 7, the throughput of CUs and D2D pairs in different uplink states for proposed WE-I-A algorithm is shown. For CUs, we can see that the throughput decreases inversely with the ascending number of D2D pairs, and the situation is much more evident in the case of \(\alpha=3\), since the CUs are easily influenced by D2D communication when the uplink state is good. Approaching \(\alpha=5\), the throughput of CUs is much lower than that of \(\alpha=3\) and \(\alpha=4\), whereas the uptrend of throughput is much gentler.  
It is noted that, the poor uplink state has been the significant effect factor of cellular communication rather than the interference caused by D2D pairs at that moment, so the throughput of D2D pairs in different uplink states increases with the ascending number of D2D pairs. Therefore, the performance  
Figure 5: SINR of active D2D pairs for proposed WE-I-A algorithm: (**a**) \(N=8\), \(M=10\) and (**b**) \(N=16\), \(M=20\).  
Figure 6: Number of active D2D pairs for proposed WE-I-A algorithm: \(N=8\), \(M=10\).  
of system throughput is advocated by proposed WE-I-A algorithm. Additionally, the throughput curve of D2D pairs with \(\alpha=3\) is slightly larger when the abscissa is smaller, \(\alpha=4\) becomes larger as increases in the abscissa. With the cellular system more sensitive in the case of \(\alpha=3\), multiple D2D pairs can interfere the cellular communication. Conclusively, D2D communication is enhanced by the ascending number of D2D pairs.  
From Figure 7, two essential points are observed. First, the uptrend (throughput) of D2D pairs is evident with the downtrend of CUs for each \(\alpha\), so D2D communication disturbs the performance of CUs within a certain degree but has a promotion to the whole system. Moreover, D2D communication would have few effects on CUs when the uplink state is weak, whereas leading to a considerable promotion for a communication system. Overall, the proposed WE-I-A algorithm can perform well even if the uplink state is found to be reduced.  
The system throughput for different allocation algorithms in the case of \(\alpha=5\) is shown in Figure 8, the exhaustive search guarantees an upper bound of the system throughput, whereas the proposed WE-I-A algorithm is found to be a suboptimum method with much lower complexity than that of exhaustive search. Even though the interference-aware allocation is a classical algorithm as well as the random allocation [32; 33], the proposed algorithm has superior performance over both algorithms, even in the case of the poor uplink state.
###### Abstract  
Rain is a typical meteorological event that affects the visual appeal of outdoor pictures. The presence of rain streaks severely blurs image details, negatively impacting subsequent computer visual tasks. Due to the challenge of acquiring authentic photographs of rainfall, most deraining methods have been developed using generated samples. However, the inherent differences between generated and real data lead to poor generalization performance in practical applications. This study proposes a semi-supervised single-image rain removal approach using Transformer and wavelet transform. It fully utilizes the feature information of rainy images, addressing the issue that current methods focus too much on network structure innovation while neglecting rain streak features. The algorithm leverages the directional properties of wavelet transform to decompose rainy images into multi-scale components, with networks of varying sizes generating rain streak maps across different directions and scales. By combining supervised and unsupervised training in a semi-supervised system, the model improves deraining performance and generalization capability. Additionally, a residual detail recovery network restores fine-grained image details, further enhancing the deraining effect in real-world scenarios. Comprehensive tests on multiple standard datasets show that the proposed approach outperforms current methods, confirming its effectiveness in practical applications. Experimental results on common datasets demonstrate that it performs better than advanced rain removal algorithms. The method's superiority is further validated by the PSNR and SSIM values of 34.86 dB and 0.961 on the Rain1200 synthetic dataset, and the NIQE and PIQE values of 11.52 and 9.13 on the RealRain dataset.  
Keywords:rain streak removal; semi-supervised learning; wavelet transform; multi-scale networks; computer vision