esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Digital Image Processing  
All images were processed in ImageJ (Fiji) using a method developed to measure checking by means of digital photography (Figure 3) [59]. Images were scaled using board's width (\(h\)), which was obtained at the time when the photo was taken, and cropped to 300 mm by 140-145 mm were PosX was the midpoint of this area (Figure 3, step 2). The resolution after cropping was between 2000 \(\times\) 1000 and 3000 \(\times\) 1500 pixels. No corrections for perspective and/or lens distortion were made. To correct for different ambient light conditions between photos, images were converted into 8-bit grey scale and brightness and contrast were adjusted to absolute levels of black and white on the object (Figure 3, step 3). Pixels were grouped into discrete regions by means of segmentation to distinguish checks from other features, such as colour differences (caused by e.g., earlywood and latewood) and/or shade (caused by rough weathered surfaces and sunlight) on the board surface. First, images were converted into the Fourier frequency domain by using a bandpass filter to remove noise and emphasize edges. Large structures were filtered down to 29 pixels and small structures up to 2 pixels (Figure 3, step 4). The image was converted into a 'binary' image to separate objects of interest from background by using a threshold range (Image), threshold 0-25). All pixels above the threshold were set to white and all pixels below to black (Figure 3, step 5). The noise left after processing was removed in the followingsteps by including only larger and slender objects (i.e., 'larger' sized checks), and manual operations. First, checks were identified by automatic particle counting removing objects with areas smaller than 5 mm\({}^{2}\) and a circularity, i.e., defined as 4 pi-(area/perimeter2), larger than 0.3 (Figure 3, step 6). Area in mm\({}^{2}\) and the position of centre of mass of each object were exported, and outlines of objects plotted over original images (Image), Region of Interest [ROI] manager) and used for post-processing. Then, objects were classified manually as check, check in knot, resin pockets or measurement error (Figure 3, step 7). The relative checked area, i.e., the total checked area divided by total area of the evaluated region in percentage, was calculated for the whole region and the centre and sides of this region for each board in Matlab(r) (version R2018a) (Figure 3, step 8). Checks in knots, resin pockets and measurement errors were excluded when calculating the total checked area. Low quality images were removed from the data set.  
Checks may grow in size upon seasoning, modification or in service life. The separation is permanent and it is, therefore, assumed that the checked area only increases by time. This minimises fluctuations in board's degree of checking between intervals due to differences in MC or image quality (e.g., images taken inside were of lesser quality than the ones taken in the field), and allows for better analysis of development of checks by exposure time.
# DOSS Protocol for Data Caching and Sharing Security in a 5G Network
## 3 Data Caching and Data Sharing Security in 5G
### Problem Definition  
With 5G promising to offload data traffic from the backhaul to the fronthaul, it will facilitate the pushing of content to the edge closer to the user. The content dissemination will facilitated by CDN technologies such CCN. This is due to the end-users' increasing demand for quick access and downloads of multimedia content, to enable this service, users with their UEs will be allowed to cache and share content. This raises security concerns at the edge and while the UE is accessing, caching, and sharing the content. Related work has suggested new ways to access service in 5G [4] but there is still a lack of security mechanism that can address the security concern while the UE is caching and sharing data.  
Our research proposes a multi-tiers security framework as shown in Figure 1 below that addresses security at three levels namely:  
* Network: Deals with securing connections at the network level
* Service: addressees security challenges between devices and service providers to access and cache data.
* D2D: support secure sharing of data aiming devices with or without network operator involvement.  
This paper presents a potential protocol to address the security challenges at the service level because it enables mobile devices-having already been authenticated at the network level-to be authorized by service provider to cache and share data, while the work  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Description** \\ \hline Token ID & security token identifier \\ Issuer & ID of the token issuer \\ Issue date & timestamp when token was created \\ Issue Sign & Token’s digital signature \\ Subject & UE ID granted the rights of the token \\ Service & SP associated with token \\ Audience & the target entity for token \\ Nonce & authentication nonce (optional) \\ Expiry date & time when token becomes invalid \\ Access rights & set of attribute and capabilities (Label) \\ Security Info & nonce key ID \\ Scope & set of conditions (type of grant, token and offline access setting) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generic security token structure.  
Figure 1: Security Framework.  
in [45] presents protocols at the D2D level to achieve mutual authentication among devices prior to data sharing.
## 1 Introduction
(3) Because similar VIIRS instruments will fly on several JPSS missions, our algorithms can be easily adopted by these missions to generate a global dust detection data record that could potentially last for several decades.
## 3 Analysis and Results
### Analysis of Unstable and Low-Speed Traffic Flow Segments  
Using the previously outlined methodology, this study analyzed eight expressway routes (1, 15, 35, 37, 50, 52, 60, and 153) within Gyeonggi Province, South Korea. Individual vehicle data were sourced from truck DTG records available on these expressways, with analysis focused on peak traffic periods when variations in speed homogeneity were most evident. Tuesday was identified as the day with the highest average travel time (339.5 min) and travel distance (165.1 km), marking it as the optimal day for data collection. Consequently, data from peak hours, 9 AM to 11 AM on Tuesdays, were selected for this analysis.  
The raw DTG data fields were preprocessed to ensure data quality, where encrypted trip keys and vehicle identification numbers were used to eliminate erroneous data. Key data fields included vehicle speed, vehicle location, and GIS azimuth, with the 'TIME' field used for temporal filtering. Across the eight expressway routes, the space was subdivided between 5 and 12 times, creating a total of 1207 segments. Of these, 943 segments were identified as unstable flow, 264 as stable flow, and 57 as low-speed segments. The distribution of segments per route is detailed in Table 1.  
Subsequent overlay analysis was conducted using data from the eight expressway links. Figure 7 provides a visualization of the segmented links: Figure 7a shows the spatial classification based on individual vehicle data from the eight expressway routes, while Figure 7b isolates the segments of each expressway route within the classified spaces. The speed data for these segments are represented using a spectral color scheme, providing a visual differentiation of traffic flow characteristics within each partitioned space.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Expressway Number**} & **1** & **52** & **60** & **15** & **50** & **37** & **35** & **153** \\ \hline \multicolumn{2}{c}{Total} & 428 & 94 & 27 & 82 & 394 & 61 & 108 & 13 \\ \hline \multirow{2}{*}{SF} & SF-N \({}^{\star}\) & 9 & 8 & 14 & 38 & 43 & 37 & 49 & 9 \\ \cline{2-10}  & SF-L \({}^{\star}\) & 26 & 2 & 2 & 13 & 9 & 0 & 3 & 2 \\ \hline \multicolumn{2}{c}{UF} & 393 & 84 & 11 & 31 & 342 & 24 & 56 & 2 \\ \hline \hline \end{tabular}  
* SF-N: stable flow with normal speed; SF-L: stable flow with low speed.  
\end{table}
Table 1: Results of expressway route analysis.  
Figure 7: Unstable flow and low-speed segments.
# YAG Laser Rod 3D Corrective Process Optimization through Tool Influent Function Shape Inspection
## 4 Conclusions  
Based on the performed experiments and the detected behavior of the various tools, the VEH R10 tool was selected, and the modified ring form presented in Figure 8 was chosen for the 3D corrective polishing of YAG laser rod faces.  
The polyurethane (PU) polishing pad was reduced to a ring, shaped by CNC machining for better control of the spot size and of the tool lateral resolution due to a reduction in the tool contact area at the YAG corrected surface and the tool mutual compression. The entire optimized process was then verified by a 3D correction of the \(8\times 8\) mm YAG plate in the Zeeko IRP 100, where the correction task was programmed in the Zeeko Precessions and Zeeko TPG software. The process parameters are shown in Table 2 and the correction results in Figure 9. In one 16-min correction step, the input shape error of 46 nm root mean square (RMS) was reduced to the value of 9 nm RMS. This satisfies the intended application both for shape accuracy and time consumption.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tool** & **VEH (PU Pad)** \\ \hline Polishing slurry & Cerox \\ \hline Tool tilt (\({}^{\circ}\)) & \(15^{\circ}\) (sequential in the +X, +Y, -X, and -Y axes) \\ \hline Spot diameter (mm) & 2 \\ \hline Kinematics & Raster (\(0.1\times 0.1\) mm) \\ \hline Tool speed (rpm) & 800 \\ \hline Correction time (min) & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 3D correction experiment settings.  
Figure 8: VEH R10 tool optimized for laser rod 3D correction.  
Figure 7: Tool influence function (TIF) fitting results comparison—FEM R10 tool (a); VEH R10 tool in the dynamic mode (b).  
**Author Contributions:** Conceptualization and theoretical background, F.P. and K.N.; methodology and simulations, O.M.; experiment realization, J.B. and D.T.; result analysis and article writing, F.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was carried out within the sub-project \"Laser rod faces characterization and 3D corrective microplishing processes development (RMPT)\" of the TACRNCK1 project \"Center of electron and photonic optics\" (number TN01000008), and co-funded by the European Structural and Investment Funds within the framework of the \"Partnership for Excellence in Superprecise Optics\" project (Reg. No. CZ.02.1.01/0.0/0.0/16_026/0008390).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
## 4 Research Methodology
### Experimental Setup  
In this research, a quantitative experimental approach was utilized to evaluate emotion features for rumor detection in the Arabic language. The experiments used the Python programming PyTorch library on Google Colab (Python development environment). This section outlines the experiments that were carried out in classifying false news. Hyper-parameter tuning is a crucial aspect of the experimental process, and several factors are considered, including the optimizer, learning rate, number of neurons, and epoch numbers throughout the tuning process on the development dataset. The parameters we utilized are listed below:* Optimizer: we tested various optimizers: SGD, ADAM, and ADAMW optimizers
* Learning rate: we explored a variety of values between \(1\times 10^{-2}\) and \(1\times 10^{-6}\) before deciding that \(1\times 10^{-6}\) was the best value for fine-tuning and \(1\times 10^{-4}\) was the best value for feature-based learning.
* Neuron numbers: several numbers were evaluated for the dense layer, including 256, 512, 1024, and 2048 neurons. After multiple tests, we settled on 1024 neurons since it provided the highest accuracy.
* Epoch numbers: we investigated with a range of epoch numbers, from 1 to 50. We employed an early stopping technique. This involves halting the training phase before the validation error arises, and the model's performance cannot be enhanced to prevent overfitting and underfitting problems.  
To prevent overfitting and ensure the generalization of the model, it was crucial to monitor the accuracy and loss values on both the training and validation sets during the training phase. To achieve this, we examined the accuracy and loss values at each training step to identify when the model was starting to overfit.
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 3 Results and Discussion  
### Heavy Metals Content in Soil  
The results of statistical analysis of heavy-metal soil contents in the study area are displayed in Table 1. The mean concentration of heavy metals followed the order of Mn (588.25 mg/kg), Zn (158.32 mg/kg), Cr (94.4 mg/kg), Ni (47.47 mg/kg), Cu (43.16 mg/kg), Pb (33.27 mg/kg) and Cd (0.92 mg/kg) (from greatest to least). Compared with the background values, the average concentrations of studied metals besides Mn were higher than the background limit of soil in Hebei Province, which indicated that the topsoil's heavy metal content was mainly affected by external and human factors [37]. It is worth mentioning that the average content of Cd was approximately 10 times that of the soil background, indicating that Cd was enriched in the topsoil. According to Table 1, the coefficient of variation (\(CV\)) of Cu, Cr, Ni, Cd and Pb was great, which indicated that the distribution of these metals was uneven. In contrast, the \(CV\) of Mn and Zn was relatively low, showing a relatively uniform spatial distribution. The K-S test indicated that the concentrations of Zn, Cd and Pb obeyed normal distribution (\(p>0.05\)), while the concentrations of Cr, Mn, Ni and Cu obeyed lognormal distribution.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.7 Impact of Monitoring Interval  
Figure 11 explores the impact of the monitoring interval \(\psi\) during which traffic volume counts are collected for each sensor node in Layer 1. Increasing \(\psi\) enables more accurate determination of the relative amount of traffic on each of the sensor nodes in Layer 1, thus reducing delay. However, the end-to-end delay is invariant under the uniform distribution, as one would expect.
## 1 Introduction
By utilizing this approach, there is a move towards more data-driven, adaptable, and efficient noise management practices, ultimately creating healthier and more livable cities for everyone [21]. A combination of noise sensors scattered around a city and mobility data captured by mobile operators represents a comprehensive urban data solution that can provide valuable insights into the dynamics of urban life. Noise sensors are strategically placed throughout the city, typically in areas prone to noise pollution, near transportation hubs, commercial districts, residential neighborhoods, and public spaces. These sensors are equipped with microphones and data collection capabilities. In the context of the data collection process, these noise sensors continuously monitor and record environmental noise levels in real time, capturing information such as decibel levels, frequency spectra, and the time of day when noise events occur. For data transmission, these noise sensors are usually connected to a central data collection device via wired or wireless connections [22]. The collected noise data are then transmitted to a central server through the Internet for further processing and analysis. The combination of noise sensors and mobile operator mobility data offers a holistic view of urban life, allowing city planners and authorities to make informed decisions, improve services, and enhance the overall well-being of residents while addressing environmental concerns. Data privacy and security are paramount, and are achieved in the context of this work because information was provided not for a specific place, but for the street [23]. This paper's main contribution is describing and demonstrating a novel approach for analyzing nocturnal urban noise patterns and identifying distinct noise zones using mobile phone data and stationary noise sensors. By leveraging machine learning (ML) and clustering techniques, it is possible to process the collected audio recordings and location data to create detailed noise maps and categorize zones based on their noise signatures. The resulting insights can inform urban planning decisions, guide noise mitigation strategies, and improve city residents' quality of life. Based on our work, municipalities may understand nightlife patterns better, and decisions can be made based on these data. The rest of this paper is organized as follows. Section 2 presents a literature review highlighting the work related to the subject of this paper. Section 3 describes the datasets used in this work and their pre-processing, while Section 4 describes the process of identifying the nightlife areas and the corresponding results. Finally, Section 5 presents the conclusions and suggestions for future work.
## 3 Results  
Figure 4 shows the initial and second phantom. Both phantoms appeared on visual inspection to be transparent, bubble free, and suitable for PIV analysis (Figure 4). However, the 0.5 mm wall thickness of the initial phantom led to the failure of the brachiocephalic artery.  
Figure 3: **(a)** schematic diagram of the fluid circuit and particle image velocimetry (PIV) setup; A: data acquisition system, B: piston pump, C: flow straightener, D: electromagnetic flowmeter, E: pressure box, G: header tank with weir; (**b**) physiological flowrate waveform.  
Figure 5 shows the refractive index testing of the phantoms. While neither phantom seems to distort the background image, the shape of the initial phantom can be easily discerned. Both phantoms are the same material, submerged in the same working fluid, and the images were taken using identical optical measurement settings. Hence, the visible outline of the initial phantom is likely to be due to the surface finish. Figure 6 shows the raw PIV images of initial and second phantoms. The initial phantom has lower transparency compared to the second one and the seeding particles are barely visible. In addition, the surface of the initial model can be distinguished from the surrounding fluid.  
Figure 4: Extracted phantoms. The initial phantom (**a**) was cast using machined Ebalta board female mould and simple end restraints. The second phantom (**b**) was moulded with 3D printed male and female mould with more rigid end supports.  
Figure 5: Silicone phantom in the refractive index matched fluid; (**a**) the initial phantom, (**b**) the second phantom. Note that neither phantom distorts the checker plate background.  
Figure 7 shows the wall thickness variation distribution of both phantoms. Figure 8 shows a cumulative distribution of the distortions in wall thickness. The median deviation of the initial phantom was 0.013 mm (quartiles (\(-0.117\), 0.210), range = (\(-0.301\), 0.514)) compared to the median deviation of the second phantom: 0.045 mm (quartiles = (0.013, 0.082), range = (\(-0.111\), 0.206)). Figure 9 shows the results of PIV analysis from both phantoms during the deceleration. Both models indicate flow rates that reduce to zero at the wall as expected. Note that the failure of the initial phantom in the brachioccephalic region led to the truncation of the arch to only the superior region. While this did not allow much information about the aortic flow field, it allowed for determination of the effects of surface finish on subsequent PIV analysis.  
Figure 6: Raw PIV images; (**a**) initial model, (**b**) second model.  
Figure 7: Distribution of wall thickness variation for the initial (**a**) and second (**b**) phantom. The intended wall thickness was 1 mm.
## 5 Example Analysis
To verify the validity of the single-season aggregation model proposed in this paper, the aggregation results of multi-scenario in other seasons are given in Table 5. And \(\text{DER}_{\text{n}}\) represents the resource type in the \(n\)-th region. The number of aggregated resources of multi-scenario in summer is 33. Due to the increase of solar irradiance and the decrease of wind speed from spring to summer, the aggregation results increase photovoltaic resources in two regions and energy storage resources in one region. Due to the decrease of solar irradiance and the increase of wind speed from summer to autumn, the aggregation results decrease wind power resources in two regions, increase energy storage resources in one region and decrease demand resources in one region. Due to the decrease of solar irradiance and the increase of wind speed from autumn to winter, the aggregation results increase wind power resources in one region, decrease energy storage resources in two regions and increase demand resources in one region. Compared with the multi-scenario in spring, with the increase of resource utilization, the average daily cost, average daily output, volatility and aggregation degree all increase. Affected by the changes in solar irradiance and wind speed, the aggregation results in autumn and winter are also different from those in spring. That is, the aggregation results in different seasons are different, which verifies the validity of the single-season aggregation model. To verify the validity of the multi-scenario aggregation model proposed in this paper, the single-scenario aggregation results in spring and in a year are given in Table 6. In the single-scenario aggregation model in spring, this paper only considers the existence of scenario 1, that is, \(p_{1}=1\), \(p_{2}=p_{3}=p_{4}=0\). Compared with the multi-scenario in spring, the number of aggregated resources in single scenario 1 is 29. Although the volatility is slightly smaller, it is inferior to multi-scenario in terms of daily average cost, daily average output and aggregation degree. In the single-scenario aggregation model in a year, the annual average output curves are used to replace the output of DG. Compared with multi-scenario in spring, although the daily average output is larger, it is inferior to multi-scenarios in other aspects. Therefore, it is better to consider the aggregation results of multiple-scenarios. Figure 8: Output Curve of RA.