esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction
### Methodologies Used to Analyze Pedestrian Behavior
#### 1.3.1 Observational Studies  
In a study conducted in Birmingham, Alabama, researchers conducted an observational study to compare pedestrian distractions in four urban settings: college campuses, downtown business districts, school areas, and entertainment zones. They observed 112 intersections--46 downtown, 30 near schools, 25 in entertainment areas, and 11 around college campuses. They collected data on distraction levels, crossing safety, pedestrian demographics, and traffic volume. Statistical analyses, including chi-square tests and log-binomial regressions, were used to examine the relationships between pedestrian characteristics and distracted behavior. The results demonstrated the highest distraction rates (52.9%) at campus locations and the lowest distraction rates in entertainment districts (16.2%). In entertainment settings, pedestrians walking alone were more likely to be distracted, and higher traffic volumes were correlated with lower distraction risk downtown, but higher risk in entertainment areas [22].  
Another observational study focused on pedestrian behavior at signalized intersections by examining distractions, violations, and walking speeds. Researchers observed 3038 pedestrians using video cameras at one intersection in New York City and three intersections in Flagstaff, Arizona. Each pedestrian was categorized according to the distraction type (no distraction, talking on the phone, texting, listening to headphones, or others). The results showed that 14% of the pedestrians were distracted by their phones while crossing. Pedestrians crossing alone or outside crosswalks were more likely to be on their phones. Young adults (ages 16-29) and those walking alone were the most frequently seen texting. Headphone use is common among young males, people walking alone, and those waiting for a pedestrian signal [19].
## 1 Introduction
These approaches leverage the powerful representation and learning capabilities of neural networks and achieve excellent performance by enhancing facial feature representation or utilizing attention mechanisms to guide the network in recovering facial structures. By circumventing the reliance on explicit prior information, these methods offer a more robust and effective solution for FSR. Although deep learning-based methods have significantly improved FSR tasks, they often overlap the intrinsic features of face images. Firstly, the face possesses fixed key parts and shapes, such as symmetrical features on both sides and consistent texture structures in the hair. However, these inherent features are underutilized, leading to a limited representation capability of the network. As a result, facial deformations occur in the reconstructed faces, making it challenging to recover accurate details. Additionally, LR face images exhibit non-local self-similarity, where similar feature patterns occur at different parts and scales. Unfortunately, existing CNN-based methods tend to neglect the exploitation of this self-similarity, hindering the recovery of fine facial details. In light of the aforementioned issues, inspired by SPARNet [16], this paper presents an improved face super-resolution network that fuses multi-scale features while preserving structural information, without additional facial prior to supervising the reconstruction process. The primary contributions are as follows:  
* We propose a novel multi-scale residual structure that effectively extracts features and integrates feature information from two branches: key face components and intrinsic image structure. This approach aims to restore facial images with improved structural clarity. * To address feature loss resulting from network depth and maximize the utilization of information at different scales, we incorporate pyramid attention and feature enhancement module into the network architecture. These components effectively explore the correlations among features at various scales, compensating for the loss of information and aiding in the reconstruction of finer details. * The proposed method is evaluated on five publicly available datasets and compared with other state-of-the-art methods, and the results show that the proposed method outperforms other methods in both qualitative and quantitative results.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 5 Conclusions  
The present study confirmed the biosynthesis of zinc nanoparticles using extracts of three different seaweed species (_Uliva lactuca_, _Uliva intestinalis_, and _Sargassum muticum_). FTIR, XRD, TEM, and zeta potential studies were used to describe the ZnO-NPs synthesized using seaweed and validated the synthesis of zinc nanoparticles. The _Uliva lactuca_ ZnO-NPs demonstrated the greatest antimicrobial effects among the three tested seaweeds against two bacterial and two fungal species. The antioxidant potentiality of the _Uliva lactuca_ ZnO-NPs was confirmed by the three antioxidant assays. Moreover, the _Uliva lactuca_ ZnO-NPs were found to have very weak cytotoxic effects on WI 38, indicating its safety for use on normal cells. The _Uliva lactuca_ ZnO-NPs had moderate cytotoxic effects on the HepG2 and MCF7 cell lines.  
Based on the results of this study, we recommend using seaweed-derived ZnO-NPs for pharmacological and biological purposes. Future research is still required to identify the mechanism(s) through which ZnO-NPs exercise their biological antimicrobial and anticancer potentiality as well as to determine their therapeutic safety limitations, particularly when used near people.  
Conceptualization, S.F.G., A.M.S., E.H.E.-S. and S.I.A.; methodology, S.I.A., S.F.G., N.G.A., E.H.E.-S. and A.M.S.; software, S.F.G.; validation, S.F.G.; formal analysis, S.I.A., S.F.G. and A.M.S.; N.G.A., E.H.E.-S. and L.P.; investigation, S.F.G., S.I.A. and S.F.G.; resources, S.F.G.; writing--original draft preparation, S.F.G., S.I.A., N.G.A., A.M.S. and E.H.E.-S.; writing--review and editing, S.F.G., S.I.A., J.C. and L.P.; visualization, L.P.; supervision, S.F.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by FCT-Fundacao para a Ciencia e Tecnologia, I.P., in the framework of the Project UIDB/04004/2020 and DOI identifier 10.54499/UIDB/04004/2020 ([[https://doi.org/10.54499/UIDB/04004/2020](https://doi.org/10.54499/UIDB/04004/2020)]([https://doi.org/10.54499/UIDB/04004/2020](https://doi.org/10.54499/UIDB/04004/2020)) accessed on 14 March 2024).  
Swiss female albino adult mice, weighing 25 to 30 g on average, were purchased from Theodore Bilharz Institute in Giza, Egypt, and housed at the Faculty of Science animal facility at Ain Shams University. The research ethics committee of Faculty of Science at Ain Shams University in Cairo, Egypt approved all animal experiments conducted for this study in accordance with the principles of laboratory animal care. Code: ASU-SCI/MICR/2023/5/4.  
Not applicable.  
The data supporting the findings of this article are available within the article.  
The authors declare no conflicts of interest.
## 6 Conclusions and Future Works  
In this study, a method for automatically constructing connectivity and adjacency relationships in bubble diagrams is proposed. First, in response to the heterogeneous  
Figure 19: Generated failure cases.  
Figure 18: Auxiliary standard floorplan design based on floorplan retrieval system.  
nature of bubble diagrams, a DBGNN with two branches is designed. Then, the DNS and CNS strategies are introduced to enhance the learning of local links. Finally, VGAE is incorporated to address the diversity issue of the generated results. Based on the research results, the conclusions are as follows: The DBGNN can learn more accurate connectivity and adjacency features of bubble diagrams. Compared to the baseline models, this model significantly improves prediction accuracy, achieving 92.39% ACC-Door and 78.84% ACC-Wall. To enhance the feature learning of both central and non-central subgraphs, the DNS and CNS strategies are proposed to improve the model. After incorporating subgraph sampling, prediction accuracy increased by 2.04% ACC-Door and 1.33% ACC-Wall, achieving optimal link prediction performance. Additionally, after introducing VGAE, DBGNN can generate 50 distinct bubble diagrams for various input node combinations simultaneously, demonstrating its exceptional diversity and stability. The floor plan generation application based on HouseDiffusion confirmed the effectiveness of the generated bubble diagrams.  
Future work will further develop a hybrid attention mechanism that considers the number of node links to enhance both local and global link prediction capabilities. Additionally, to maximize the proposed method's potential, future research will extend its application to other building types, such as public buildings, to support the development of more universal intelligent design systems. Moreover, we plan to integrate the method with layout generation models and architectural design software like AutoCAD 2024, enabling designers to manually define nodes and edit links, thereby improving design efficiency.  
Conceptualization, G.L. and X.Z.; Methodology, G.L. and Y.L.; Software, G.L. and Y.L.; Resources, X.Z. and J.L.; Writing--original draft, G.L.; Writing--review & editing, X.Z., Y.D. and Y.X.; Supervision, Y.D. and H.Q.; Project administration, H.Q.; Funding acquisition, J.L. and H.Q. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Key Research and Development Program of China (2022 YFC3801700), the National Natural Science Foundation of China (No. 52308142), and the Chongqing Key Research and Development Program (No. CSTB2022 TIAD-KPX0140) to which the authors are very grateful.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 4 Simulations  
In this section, EDETA protocol will be evaluated simulating its enhanced version. First of all, in Section 4.1 different tests are conducted in order to study the suitability of the protocol for the underwater medium.  
Since the underwater medium is a harsh environment, in Section 4.2 different retransmission techniques for the EDETA-e protocol are evaluated along with different scheduling mechanisms. The reason behind doing so is to identify which technique is more efficient and convenient for each situation.  
Finally, in Section 4.3 a comparison of the EDETA-e protocol is made with the DBR protocol in terms of packet delay, energy consumption and packet delivery ratio.  
Unless stated differently, all nodes except CHs and sink start with \(150\) Joules of energy. The transmission power was set to \(0.203\) wats, the reception and idle power is set to \(0.024\) wats and the sleep power to \(3\times 10^{-6}\) watts. The transmission range of the nodes is limited to 100 m adjusting the model transmission power. These values were extracted by [PERSON] _et al_. from their low-cost, low-power underwater acoustic modem [31].
## 2 Results and Discussion
### X-ray Photoelectron Spectroscopy (XPS)  
XPS is used to determine the binding energy of elements detected in unwashed and washed immobilized TP2 samples in order to examine the effect of washing. This effect had successfully produced an oxidized PEG. The O1s and C1s spectra of TP2 unwashed are shown in Figure 6a,b, while Figure 6c,d represents the O1s and C1s spectra for washed TP2 sample.  
All samples demonstrated the chemical composition information where three elements existed: Ti, C and O. Figure 6a represents the O1s characteristic peaks of Ti-O and C-O at 529.9 and 531.6 eV, respectively, while C-O and C-C bonds are given by the values of 288.2 and 284.8 eV, respectively,  
Figure 6: The O1s and C1s X-ray photoelectron spectroscopy (XPS) spectrums of immobilized for (**a**,**b**) unwashed TP2 and (**c**,**d**) washed TP2. CPS, cycles per second.  
in Figure 6b. The O1s spectra in Figure 6c of washed TP2 revealed peaks at 529.4, 531.0 and 532.9 eV attributed to Ti-O, C-O and C=O bonds, respectively. Figure 6d shows the spectrum of washed TP2 in the C1s spectra deconvulted into three distinct curves, which are represented by three forms of carbon as C-C, C-O and C=O at 284.8, 286.7 and 288.7 eV, respectively. As such, the formation of C=O bond can only be found in washed TP2. Yet, no peak for C=O bond was presented in unwashed TP2. This occurrence of C=O bond could be due to the oxidation process of hydroxyl radicals with PEG, introduced by the washed sample of TP2 in Figure 6c,d.
# MLMD--A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm
## 4 Evaluation
### Comparison of MLMD and VirusTotal
#### 4.2.1 Test Samples  
The test suite consisted of 105 samples. Its composition in terms of maliciousness is shown in Table 16.  
The malicious samples consisted of executables downloaded from the VirusShare repository of malicious samples, namely the _VirusShare_00164.zip_ package. The maliciousness was assessed by the VirusShare repository. This was also used to obtain our training data. As the aforementioned package contains a total of 65,536 malicious samples, and we only used the first 3000 samples for training, we selected samples that were not used in training our machine learning models. The benign samples also consisted of executables randomly selected from the free PortableApps site.  
We tested each test sample with both our program and the VirusTotal tool. After testing a particular sample by our program, we recorded one of the following results:  
* Malicious--both classification models classified the sample as malicious;
* Suspicious--one classification model classified the sample as malicious, the other as benign;
* Benign--both classification models classified the sample as benign.  
In case of the VirusTotal tool, the score, recorded for the respective sample, consisted of the following:  
* The number of systems that detected the sample as malicious;
* The number of systems that detected the sample as benign.
# Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks
## 4 Results and Discussion
By inspection of Figure 8, it can be seen that, for the Dir-Dir case, the probability of having 1 ray/cluster ranges from 0.4 to 0.6. In contrast, the probability does not exceed 0.25 for the Omni-Omni case, which explains why the Dir-Dir case has more clusters than others. Figure 5: CDF plot of RMS-DS for NLOS scenarios for Dir–Dir (dotted dashed lines) and Omni–Omni (solid lines) antenna radiations. Figure 6: CDF plot of RMS-DS for LOS scenarios for Dir–Dir (dotted dashed lines) and Omni–Omni (solid lines) antenna radiations. Figure 7: Number of ray clusters for NLOS propagations scenarios: (**a**) Omni–Omni, (**b**) Dir–Dir, and (**c**) Dir–Omni. Figures 9 and 10 present the number of clusters and the number of rays/cluster for LOS scenarios, respectively, for different antenna configurations. The number of clusters follows a decaying exponential distribution, where most rays fall within two clusters, especially in directional and omnidirectional cases. In the directional scenarios, the probability for rays to arrive in a single cluster is around 0.8 for all frequencies, while, in the omnidirectional scenarios, it ranges from 0.7 to 0.9. It is worth mentioning that, in the Dir-Omni case, the majority of arrays are coming in four clusters or less; this is due to the combined effect of using a directional antenna and omnidirectional antenna; this can be seen in the number of rays per cluster as depicted in Figure 10.  
Figure 11 shows the arrival times of multipath at a receiver point. There are three clusters, each with a different number of intra-cluster paths. Considering the first ray of each cluster, it was found that the best fit for the amplitude of these rays follows a negative exponential distribution. In contrast, the inter-arrival times of each cluster follow a modified Poisson distribution and their corresponding amplitudes follow a negative exponential distribution [42]. Figure 8: Number of rays/cluster for NLOS: (**a**) Omni–Omni, (**b**) Dir–Dir, and (**c**) Dir–Omni. Figure 10: _Cont._  
Figure 9: Number of ray clusters in LOS propagation scenarios: (**a**) Omni–Omni, (**b**) Dir–Dir, and (**c**) Dir–Omni. Additionally, diffraction becomes less prominent as frequency increases; for example, diffraction loss around a concrete wall increases by 6 dB as frequency increases from 28 GHz to 39 GHz, while it increases by 25 dB as frequency increases from 60 GHz to 73 GHz. The effect of wall penetration losses and diffraction losses gives an indication for network designers when deploying access points in the environment, for example, in an environment like offices where most of the partitions and walls are made of drywalls, the number of the required hot spots that operate at 60 GHz and 73 GHz will be less compared to an environment that has concrete walls.
## 2 Introduction to Related Theories
### Domain Adaptation Technology  
Researchers have applied domain adaptation (DA) during RUL prediction to address the domain shift. DA usually uses specific knowledge learned from existing (source) domains to build deep learning models for the task (target) domain by exploiting similarities among data, tasks, or models. Typical DA approaches are mainly categorized as metric-based methods and adversarial-based methods.  
Metric-based distribution discrepancy methods employ different metrics to alleviate domain discrepancies, such as maximum mean discrepancy (MMD) [23]. MMD, as the most popular and used distribution differences metric for transfer learning, minimizes distribution differences between the source and target domains to predict RUL. MMD focuses on measuring the difference between two distributions of source and target domains by comparing their mean values in the feature space. Let \(\mathcal{H}\) be a Hilbert space. Given \(m\) data samples \(X:\{x_{k}\}_{k=1}^{m}\) in source domain \(\psi_{s}\) and \(n\) data samples \(Y:\{y_{k}\}_{k=1}^{n}\) in target domain \(\psi_{t}\), the mathematical expression of the MMD is shown below:  
\[\begin{array}{ll}MMD(\mathcal{H},\psi_{s},\psi_{t})&=\dfrac{1}{m^{2}} \sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\sigma\big{(}x_{i},x_{j}\big{)}+ \dfrac{1}{n^{2}}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\sigma\big{(}y_{i}, y_{j}\big{)}\\ &-\dfrac{2}{mn}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\sigma\big{(}x_{i}, y_{j}\big{)}\end{array} \tag{3}\]  
where \(\sigma\) is the Gaussian kernel function. By minimizing this difference, knowledge transfer and sharing are realized to improve the generalization ability and performance of the model under cross-working condition scenarios.  
Adversarial mechanism-based methods draw inspiration from generative adversarial networks (GANs) [24]. GANs comprise a generator \(G\) and a discriminator \(D\). Taking \(x^{s}\) as input, the generated samples obtained from generator \(G\) need to confuse the discriminator\(D\) to make a wrong judgment. Therefore, the loss function \(L_{G}\) of the generator \(G\) is given as follows:  
\[L_{G}=E_{x^{g}Px^{s}}[\log(1-D(G(x^{s})))] \tag{4}\]  
where \(n\) is the sample number. In addition, the discriminator \(D\) needs to judge the real samples \(x^{r}\) from the generated samples \(x^{g}\), so a fully connected layer is added to output the probability that the generated samples \(x^{g}\) are derived from the actual space. The loss function \(L_{D}\) of the discriminator \(D\) is given as  
\[L_{D}=-(E_{x^{r}Px^{s}}[\log(D(x^{r}))])+E_{x^{g}Px^{s}}[\log(1-D(G(x^{s})))] \tag{5}\]
# Design and Research of a Flexible Foot for a Multi-Foot Bionic Robot
## 4 Flexible Foot Mechanism Calibration
### Different Flexible Materials Foot Force Calibration  
This section describes a comparative analysis of three materials to determine the effects of flexible materials on the perceived force performance of the foot mechanism. The selected three types of silicone rubbers, HC9000, HC9008, and HC9015, were injected into separate foot molds to cure, and the other parameters were the same, except for the materials. For each material, the foot tip was fixed, the direction of the force was fixed, only the quality of the weight (As shown in Figure 5) was changed, and the change of the voltage value of the foot tip was recorded. Three groups of each material were subjected to the force test, and the average experimental results are shown in Figure 6. The solid line is the forward stroke of the weight increase, and the dashed line is the reverse stroke of the weight reduction.  
Figure 4: The platform of the foot-end calibration experiment.  
Figure 5: Calibration schematic diagram of foot mechanism.  
It can be seen from the figure that under the same external force, the material with the smallest hardness, HC9000, had the largest voltage variation, and the voltage variation was basically linear with the pressure received. The reverse stroke was more obvious, which was better than the linear relationship of HC9008; for HC9015, when the mass of the weight was greater than 400 g, the flexible foot shape variable of HC9015 was saturated due to the shape of the foot end. As the mass of the weight increased, the voltage variation decreased.  
The voltage variation \(\Delta\)U of the FSS pressure sensor was proportional to the pressure F\({}_{\rm ss}\) received, namely,  
\[\Delta\rm U=k\times F_{\rm ss} \tag{23}\]  
where \(\Delta\)U is the output voltage, k is the force-voltage conversion coefficient, and k = 6 mV/N was measured at no load. In the force range of the robot foot end 0-10 N, the force acting on the flexible material is F\({}_{\rm N}\), and the force of the pressure sensor FSS is F\({}_{\rm ss}\). The following observations can be made from Figure 6:  
* When pressure was applied to the HC9000 material, \[\rm F_{ss}\approx\frac{1}{3}F_{\rm N}.\] (24)
* When pressure was applied to the HC9008 material, \[\rm F_{ss}\approx\frac{1}{4}F_{\rm N}\] (25)
* When the pressure was applied to the HC9015 material, the relationship was not obvious.  
Therefore, when the hardness of the flexible material was within a certain range, it could effectively reduce the falling impact between the foot tip and the ground. Combined with the analysis of flexible materials in the previous section, it can be seen that compared with HC9008, the mass of the robot was known, and within the range of 0-10 N, the HC9000 could reach its own yield limit, and the foot end force was 1/3 of the contact force.