esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, a novel automated proximity visualization technique that visually expresses the approaching of hazardous objects to workers was developed using the CCTV video of a construction site to monitor construction workers' safety. The visualization technique for workers' safety is composed of identifying the location in space, imposing the accurate volume of equipment, and proximity sensing considering workers' perception according to their posture.  
First, object detection is conducted on the workers and hazardous objects (the trucks) to generate the proximity visualization. Second, object tracking is performed, and for trucks, the orientation is simultaneously calculated based on the tracking path. In the case of workers, after pose estimation is applied to extract the orientation, a safety ellipse is created based on the developed posture estimation algorithm. Third, the worker and truck are overlapped on the plane map with the actual dimensions through camera calibration. In the plane map, the worker and truck, for which the safety ellipse and occupied space are calculated, respectively, are monitored.  
As a showcase of the method proposed in the study, a CCTV video file of a construction site was processed, and its results are presented with a detailed explanation in the paper; a YOLO v3 pretrained model was utilized for object detection, and OpenPose was used for pose estimation. In the 12 min long video file, all image frames were successfully converted for proximity visualization. Up to two workers and two trucks were detected and marked with safety ellipses and areas, respectively, in the plane. The proposed method shows advanced collision risk monitoring by considering the truck's volume and the worker's perceived field of view. In the future, the proposed system could be applied to CCTV cameras considering various places and angles.  
Conceptualization Y.-S.S. and J.K.; Formal analysis Y.-S.S.; Methodology Y.-S.S. and J.K.; Data curation Y.-S.S.; Funding Acquisition J.K.; Writing-original draft Y.-S.S. and J.K.; Project administration J.K.; Writing-review & editing Y.-S.S. and J.K. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Representative sequential results of worker proximity visualization generation: tracking results in the video (**left**) and generated plane map (**right**).  
**Funding:** This work is supported by the Basic Research Programs (NRF-2020 RIF1A1074371) of the National Research Foundation (NRF) of Korea funded by Ministry of Education, Science, and Technology (MEST).  
**Data Availability Statement:** The data used to support the results in this study are included within the article. Furthermore, some of the data in this research are supported by the references mentioned in the article. If you have any queries regarding the data, the data of this research would be available from the correspondence upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 5 Security Analysis
### Statistical Attack and Perfect Secrecy  
According to [PERSON]'s theory, a system works with perfect secrecy if the size of the key space is equal to the size of the message space and the size of the scrambled space, and if all the available keys are equally likely. In our case, the size of the possible message with \(m\) samples is \(L!\), that is the same size of the possible scrambled messages and ordering sequences (keys); and, on the other hand, any key can be used. Then, even if the attacker finds some keys that provide him possible messages, there in no guarantee as to which of them is the right one. Therefore, our system overcomes the statistical attack.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### CuS\({}_{x}\)-Based Nanomaterials
formation of other products was suppressed to a faradaic efficiency of 4%. Based on Raman spectroscopy experimental results, the authors determined that the HCOO\({}^{\star}\) radical was the major intermediate. The authors also concluded that the selectivity of the catalyst towards formate was due to the S doping in the Cu matrix which caused the formation of the HCOO\({}^{\star}\) and not the COOH\({}^{\star}\) intermediate species. [PERSON] et al. investigated the electrocatalytic reduction of CO\({}_{2}\) to CH\({}_{4}\) on CuS nanosheet arrays [175]. The authors synthesized CuS nanosheets supported on Ni foam. The study showed excellent faradaic efficiency of 73% for the formation of CH\({}_{4}\), with stability for up to 60 h of reaction time. The formate was formed during the reaction; however, it was a minor product, as was the formation of CO, while H\({}_{2}\) was formed at higher over-potentials between \(-1.3\) and 1.25 V. At over-potentials of \(-1.2\) to \(-0.8\) V, CH\({}_{4}\) was the preferred product of the reaction and above \(-0.8\) V, H\({}_{2}\) became the major product [175]. [PERSON] et al. conducted experiments to investigate the sulfur modification of Cu catalysts for the electrocatalytic reduction of CO\({}_{2}\) to form formate [176]. The CuS was synthesized through modification of Cu electrocatalysts with sulfur forming CuS nanoparticles. The study showed a positive correlation between particle size and selectivity towards the formation of formate, as the nanoparticle size increased so did the amount of formate that was formed. The results in that study showed a maximum faradaic efficiency of 80% at an over-potential of \(-0.8\) V [176]. Other products in the reaction mixture were also observed, including CO, CH\({}_{4}\), C\({}_{2}\)H\({}_{4}\), CO, and H\({}_{2}\), but formate was the major product [176]. The results reported on the use of CuS as an electrocatalyst showed great diversity in the products resulting from the CO\({}_{2}\)RR. Within the CuS/Cu\({}_{x}\)S nanomaterials, a higher diversity of products is observed compared to the MoS\({}_{2}\), WS\({}_{2}\), TiS\({}_{2}\), and the Bi\({}_{2}\)S\({}_{3}\) materials. The higher diversity of products indicates that the CuS/Cu\({}_{x}\)S materials provide more pathways for the CO\({}_{2}\)RR.
# Imaginary Speech Recognition Using a Convolutional Network with Long-Short Memory
## 2 State of the Art
The **patient-specific** developed system offered an accuracy of 84.5% for all nine words classification and 87.9% for **binary classification** using the deep learning architecture of ResNet18, in combination with two layers of gated recurrent units (GRUs). Finally, the researchers claimed that there are strong differences between the signals from different subjects, and it is more plausible to develop a patient-specific system with a high accuracy than a generalized one. Deep learning architectures showed a better performance of imaginary speech signals and gained popularity over the years. Recently, EEG studies started to concentrate on the LSTM neural network, due to the advantages it has for a continuous series. The LSTM neural network offered significant results on applications such as epilepsy prediction [13] and imaginary speech recognition [14]. The results obtained by the researchers in [14]registered a maximum accuracy of 73.56% for a subject-independent system and a **four-class** problem: \"sos\", \"stop\", \"medicine\", and \"washroom\". In this paper, we focused on developing a subject's shared system for imaginary speech recognition. By the subject's shared system, we mean that one system was developed for all registered users in the database. However, this system was different from a patient-specific system in such a matter that, when introducing a new subject in the database, it will only need a fine-tuning of the neural network and not an entire subject training. During our research, we used Kara One database, which was preprocessed for further usage. Cross-covariance in frequency domain was computed in the feature extraction stage, and all the signals were further introduced into a CNNLSTM neural network. We showed that CNNLSTM performed better than the CNN neural network by increasing the accuracy from 37% to 43%. In the paper, the system behavior after reducing the number of channels and selecting the channels from the main brain areas and the areas corresponding to the anatomical structures involved in imaginary speech production was also tested. We showed that the channels corresponded to the anatomical structures involved in speech production concentrates 93% of information, obtaining a maximum accuracy of approx. 40%. Even if the accuracy dropped by 3%, a lot more was gained in terms of time of execution, comfort, portability, and costs. The system was developed using python as programming language, and the CNNLSTM neural network was developed using TensorFlow, an end-to-end open-source platform for machine learning [15].
## 3 Results and Discussion
### Characterization of LDH Powder
#### 3.1.2 Structure  
XRD characterization was performed to present the crystalline structure of the synthesized samples. The result is shown in Figure 6. It can be seen that both the two samples present the typical structure of LDH, including the (003), (006), (012), (015), (018), (110), (113), and (1310) planes. It is worth noting that the position of the planes of the two samples differs obviously. The (003) and (006) planes of C-high appeared at 9.97\({}^{\circ}\) and 19.93\({}^{\circ}\), respectively. In addition, the peak related to the (011) plane of MgO also appeared at 29.48\({}^{\circ}\) in the XRD spectrum of this sample. The (003) and (006) planes of C-low appeared at 11.10\({}^{\circ}\) and 22.34\({}^{\circ}\), respectively. The interlayer distance of C-high and C-low can be calculated to be 8.87 A and 7.96 A according to the position of the (003) plane and Bragg's Law, corresponding to the intercalated NO\({}_{3}\)\({}^{-}\) and CO\({}_{3}\)\({}^{2-}\)/OH\({}^{-}\), respectively [14, 15, 16]. The underlying reason for this difference may be that a lower concentration of Mg\({}^{2+}\) and Al\({}^{3+}\) reacted with less hydroxyls in the case of C-low coprecipitation; as a result, the concentration of remained hydroxyls in the solution would be higher. The pH after the coprecipitation in the case of C-high and C-low was measured to be 8.0 and 12.7, respectively, which supported the above explanation powerfully. A higher pH usually involves more carbonate contamination in the preparation, and instead of nitrates, more CO\({}_{3}\)\({}^{2-}\)/OH\({}^{-}\) would be intercalated in the LDH gallery, leading to the position shift of the planes towards higher 2\(\theta\) angles [17, 18]. In addition, a higher pH may have obvious influence on the formation of Al(OH)\({}_{3}\) and Mg(OH)\({}_{2}\), thus the structure of the finally obtained MgAl-LDH would probably be different.  
Figure 2: The SEM images of sample C-high (**a1**–**d1**) and C-low (**a2**–**d2**) (Please note that (**b1,c1,d1**) corresponded to the magnified images of zones A, B, and C in (**a1**); (**b2,c2,d2**) corresponded to the magnified images of zones D, E, and F in (**a2**)).  
Figure 4: Schematic illustration of hexagonal LDH platelet.  
Figure 5: The TEM images of sample C-high (**a**–**c**) and C-low (**d**–**f**) (Please note that the inset images in (**c**,**f**) were the Fast Fourier Transform (FFT) image).  
Figure 3: Size distribution of LDH.
## 3 Proposed Protocol  
We propose a hole-bypassing routing protocol which addresses at the same time the three designing factors described in Section 1, i.e., minimizing the routing path length, maximizing the load balance and minimizing the control overhead.  
In the following, we first briefly describe the metrics related to the three design factors. The routing path length can be evaluated through the so-called routing path stretch. The routing path stretch of a packet is defined by the ratio of its hop count to that of the theoretical shortest path. The average routing path stretch of a routing protocol is the average value of routing path stretches of all the routing paths determined by the protocol. Another metric that also reflects the routing path length is the delay of packets which are the duration from when the packets are sent until they successfully arrive at the destination.  
The load balance is evaluated by the maximum packet forwarding ratio which is the maximum ratio of the number of packets forwarded by a node to the total number of packets sent. In general, the smaller maximum packet forwarding ratio reflects a better load balance. Another metric related to the load balance is the network lifetime which is the time from when the network starts till the first node dies.  
The control overhead is measured by the total amount of control packets, where control packets are defined as packets that are not data packets.  
We aim at designing a distributed geographic routing protocol that generates dynamic routing paths with the stretch upper bounded by a constant. This constant can be controlled to be as small as \(1+\epsilon\) (\(\epsilon\) is a predefined positive number, which we call the stretch factor). The variation of the routing paths ensures the load balance over the network.
# Influence of Climate Conditions on Deficiencies of Building Roofs
## 3 Results
### Analysis of Climate-Geographical Conditions
#### 3.4.1 Localized Infiltration (TCD1)  
It was found that this construction deficiency is more frequent in flat roofs (FR = 70.99%) than in pitched roofs (PR = 29.01%). It occurs more often in the Mediterranean climate (43.10%) than in the Continental (28.45%), Oceanic (25.35%) or Sub-tropical (3.10%) climates. It was also found that it is more frequent in the North latitude (60.00%) than in the Central (26.76%) or South (13.24%) latitudes. It is more often found Inland (31.83%) than in Coastal (68.17%) zones, and in zones with Medium (60.56%) precipitation than in those with High (23.95%) or Low (15.49%) precipitation.  
The high percentage of infiltrations in flat roofs, when compared to pitched roofs, is due to the fact that, in the former, the slope is lower, which results in slower drainage and higher probability of water retention. Furthermore, the construction system is more complex and includes additional layers of material, leading to a higher probability of errors and implying a greater professional preparation on the part of the workers executing them.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Situation-Latitude-Climate-Precipitation** & \%RD & \begin{tabular}{l} **Total** \\ **\%RD** \\ \end{tabular} & \begin{tabular}{l} **Relative Freq.** \\ **(\(\times 10^{-5}\))** \\ \end{tabular} & \begin{tabular}{l} **Normalized** \\ **Relative** \\ **Frequency** \\ \end{tabular} & \
\begin{tabular}{l} **Climate-** \\ **Geog.** \\ **Area** \\ \end{tabular} &
\begin{tabular}{l} **Rank** \\ **Concent.** \\ **Anomalies** \\ \end{tabular} \\ \hline Coastal-North-Oceanic-High Precipitation & 22.60\% & & & & & \\ Coastal-North-Mediterranean-Medium Precipitation & 18.10\% & 56\% & 2.60 & 1.00 & R & \%RD \textgreater{} 15 \\ Coastal-Central-Mediterranean-Medium Precipitation & 15.48\% & & & & & \\ \hline Inland-Central-Continental-Medium Precipitation\({}^{*}\) & 11.24\% & & & & & \\ Inland-North-Continental-Medium Precipitation\({}^{*}\) & 9.61\% & 28\% & 1.42 & 0.55 & Y & 6 \textless{} \%RD \textgreater{} 12 \\ Coastal-South-Mediterranean-Low Precipitation\({}^{*}\) & 7.12\% & & & & & \\ \hline Inland-North-Continental-Low Precipitation\({}^{*}\) & 5.37\% & & & & & \\ Coastal-South-Subtropical-Low Precipitation & 2.59\% & & & & & \\ Inland-Central-Continental-Low Precipitation\({}^{*}\) & 2.37\% & & & & & \\ Coastal-South-Mediterranean-Medium Precipitation\({}^{*}\) & 2.37\% & 16\% & 0.94 & 0.36 & W & \%RD \textgreater{} 6 \\ Inland-South-Mediterranean-Medium Precipitation & 1.50\% & & & & & \\ Inland-South-Continental-Medium Precipitation\({}^{*}\) & 0.62\% & & & & & \\ Inland-North-Oceanic-High Precipitation\({}^{*}\) & 0.50\% & & & & & \\ \hline \hline \end{tabular}  
* New division of climate-geographical area due to the incorporation of precipitation as a fourth condition.  
\end{table}
Table 5: Classification by ranks of the climate-geographical areas incorporating precipitation as a fourth condition.
## 3 Simulation of Thermal Processes in the Food Industry Using CFD  
### Conduction, Convection, and Thermal Radiation in CFD Context  
In CFD simulations for food processing applications, the accurate modeling of heat transfer mechanisms--namely conduction, convection, and thermal radiation--is essential to predict thermal behavior and ensure the quality, safety, and efficiency of food processing. Each mechanism requires specific mathematical formulations to capture its unique contribution to overall heat transfer.  
Conduction refers to the transfer of thermal energy through solid materials, governed by molecular interactions where kinetic energy is exchanged among particles. In CFD, conduction is typically modeled using Fourier's Law (Equation (3)) [66; 67]:  
\[q=-k\
abla T \tag{3}\]  
where \(q\) is the heat flux, \(k\) represents the thermal conductivity of the material, and \(\
abla T\) denotes the temperature gradient. The accurate CFD modeling of conduction requires precise values for \(k\), which can vary anisotropically within food products due to heterogeneous compositions, including variations in fat, water, and fibre. The thermal properties of food matrices often exhibit temperature-dependent behaviors, adding complexity to the model and requiring empirical data or experimental validation.  
Convection involves heat transfer between a solid surface and an adjacent fluid in motion and is categorized as natural convection (buoyancy-driven) or forced convection (induced by external sources like pumps or fans). In CFD simulations, convective heat transfer is governed by solving the Navier-Stokes equations coupled with the energy equation, which accounts for both fluid flow and temperature distribution (Equation (1)) Convection modeling in food systems may be complicated by the non-Newtonian nature of some food fluids, necessitating custom viscosity models [67].  
Thermal radiation becomes particularly relevant at high temperatures or in low-pressure (vacuum) environments, where it can be a dominant mode of heat transfer. Radiation is governed by the Stefan-Boltzmann Law (Equation (4)):  
\[q=\epsilon\sigma T^{4} \tag{4}\]  
where \(\epsilon\) is the emissivity of the surface, \(\sigma\) is the Stefan-Boltzmann constant, and \(T\) is the absolute temperature [68]. In CFD, modeling thermal radiation is complex, especially in media where participation effects (absorption, emission, scattering) occur. In these cases, the RTE is employed, which requires iterative solution methods or approximations, like the discrete ordinates method (DOM) or the P1 approximation, to simplify calculations while accounting for radiation interactions in semi-transparent food layers [69; 70; 71].  
By integrating these three mechanisms, CFD allows for high-resolution simulations of temperature distribution and thermal gradients within food products (Figure 1). This level of modeling supports the optimization of thermal processes by predicting potential hot and cold spots within food matrices, ensuring uniform heating and improved energy efficiency.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
## 5 QoE Optimization Algorithm
Subsequently, to calculate the SNR associated with each level of each RLC parameter, the SNR value obtained in each experiment in which that level was used is averaged (lines 9-19 of Algorithm 1). As a result, the combination of values of the RLC configuration parameters that provide the maximum result is obtained, \(optimumValues=\{values_{1},...,values_{N}\}\). Next, the stop criterion, \(SC\), is checked. The optimization process is finished if SC is satisfied. Otherwise, the search for the optimal values of the RLC configuration parameters continues. To do so, the following step is to narrow down the value ranges of each input parameter. For this, the optimal value of each parameter obtained in the previous iteration is taken as the central value, and the \(stepSize\) is reduced by multiplying it by the reduction factor \(RF\in(0,1)\subset\mathbb{R}\). From here, a new iteration would be performed starting with the mapping of the levels of the different RLC configuration parameter using their new ranges of values.
## 3 CFD Simulation Results
### NO Emission  
Figure 11 presents the NO emissions formed inside the engine cylinder in the three cases of using various piston bowls. The results pointed out that the NO emissions generated in the \(\omega\)-type and re-entrant piston bowl cases were remarkably reduced compared to those in the original U-type bowl. The NO emissions generated in the \(\omega\)-type and re-entrant piston cases were reduced by up to 25.61% and 46.09%, respectively, in comparison to the U-type piston bowl.  
It is universally known that thermal NO formation is significantly and mainly influenced by the in-cylinder local maximum temperature and the oxygen (O\({}_{2}\)) concentration in the cylinder of ICEs. NO emissions are mainly formed in regions with temperatures  
Figure 11: NO emissions diagrams for the three various piston types.  
Figure 10: Cylinder powers and ISFOCs for the three various piston types.  
above 1800 K, and the rate of formation increases strongly according to the increase in temperature [14; 40; 49].  
As can be observed in the temperature distribution shown in Figure 7, the in-cylinder peak temperatures, in the cases of using the \(\omega\)-type and re-entrant piston bowls, were significantly reduced in comparison with the U-type bowl, and the re-entrant piston bowl produced the lowest peak temperature. This resulted in the lower NO emission formations in the \(\omega\)-type and re-entrant piston bowls compared to the U-type bowl, especially in the re-entrant piston bowl. A better TKE and the simultaneous reduction in NOx emissions when using a re-entrant piston bowl had also been reported by [PERSON] et al. [27; 28].