esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.1.3 Meteorological Data  
The meteorological data were obtained from China Meteorological Data Service Centre with a temporal resolution of 1 h, and the two main parameters utilized were visibility (VIS) and relative humidity (RH). To reduce the experimental error, the data were pre-processed as follows. (1) If the ratio of the visibility data of the current day to the data of the previous day and the next day was less than 1/3 in three consecutive days, the visibility data of that day were deleted [42]. (2) Relative humidity values greater than 98% were not involved in the statistical study. (3) To avoid affecting the aerosol hygroscopic growth fitting results, when matching environmental monitoring station data and meteorological station data, if the distance between two stations was greater than 10 km, they were not involved in the statistical study.
## 2 Materials and Methods
### SRF Calculation Through Back Analysis  
A back-analysis procedure for the \(Q\)-value calculation of rock mass, as shown in Figure 3, was suggested using the installed rock bolts and fiber-reinforced, sprayed shotcrete [46]. The back-calculation is explained through Example 1 for the calculation of the \(Q\) value. The latest support chart of the \(Q\) system [19] is used for the back-calculation for all of the sections of the four projects. The support chart indicated that the shotcrete thickness is a function of D\({}_{\rm e}\), in addition to the rock quality (\(Q\) or \(Qc\)); however, rock bolt spacing (S\({}_{\rm b}\)) is a function of rock quality only. This spacing of rock bolts was used for the back-calculation of the tunneling quality index (Q\({}_{1}\)). The thickness of the fiber-reinforced shotcrete is a function of the tunnel span and the tunneling quality index. The thickness of the fiber-reinforced shotcrete, along with the tunnel span, was also used for the back-calculation of the tunneling quality index (Q\({}_{2}\)). The D\({}_{\rm e}\) is the ratio of tunnel width/span and the excavation support ratio (ESR). The ESR value depends on the country safety standards [20] as well as on the quality of the rock mass [19]. The relation of rock bolt length (L\({}_{\rm b}\)) to D\({}_{\rm e}\) for hydropower tunnels, i.e., Project 2 and Project 4, reveals that a value of one for ESR is an appropriate rating for headrace tunnels, and was also used in this study.  
**Example 1**: Tunnel span or D\({}_{\rm e}\) (m) = 11.17  
Rock bolt spacing (m) = 1.6  
Shotcrete thickness (cm) = 9  
\(Q_{1}\) = 0.7  
\(Q_{2}\) = 1.7  
\(Q\) or \(Q_{c}\) = 1.03  
Although the modified \(Q\) value (\(Q_{c}\)) was due to the additional normalized factor in Equation (1), the support chart remained unchanged. The resultant tunneling quality index from back analysis was defined as \(Q\) or \(Q_{c}\). Equations (1) and (2) were rearranged to Equations (8) and (9), respectively, and two different \(SRF\)s (\(SRF_{Q}\) or \(SRF_{QC}\)) were calculated, taking the back-calculated rock quality value of either \(Q\) or \(Q_{c}\), respectively.  
\[SRF_{Q}=\left(\frac{RQD}{J_{n}}\right)\times\left(\frac{J_{r}}{J_{a}}\right) \times\left(\frac{J_{w}}{Q}\right) \tag{8}\]  
\[SRF_{QC}=\left(\frac{RQD}{J_{n}}\right)\times\left(\frac{J_{r}}{J_{a}}\right) \times\left(\frac{J_{w}}{Q_{c}}\right)\times\left(\frac{\sigma_{c}}{100}\right) \tag{9}\]  
Figure 3: Procedure for the back-calculation of the tunnel quality index (\(Q\) or \(Qc\)) from actual tunnel spans and installed supports [46].
## 2 Advances in the Characterization of Materials with Optical Methods
An important question in materials characterization with OMs is the quality of recorded images, which may depend on the camera optical system. Stray light (i.e., non-imaging rays distributed on the imaging surface and rays reaching the imaging surface via abnormal optical paths) reduces optical transfer function and the signal-to-noise ratio. For refraction-type optical systems, the usual method to eliminate stray light is to add a baffle and a light-blocking ring. The optomechanical structure's ability to suppress stray light increases with the length of the designed baffle. The final design of the baffle should block non-imaging light from reaching the detector and make the effective imaging light smoothly enter the imaging surface. In view of this, [PERSON] et al. [4] optimized the optical system, the baffle, and the vanes of an airborne, aspheric surface camera. The designed baffle and shading ring effectively suppress the stray light of the optical system, which can provide a reference for the structure design of other lenses. Photocatalysis is one of the most important technologies in water pollution analysis. Titanium dioxide (TiO\({}_{2}\)) nanoparticles are very promising photocatalysts in terms of reaction efficiency for many environmental and energy applications. However, activation of TiO\({}_{2}\) occurs mainly by UV irradiation, which is only 5% of solar light spectrum, thus providing relatively low photocatalytic efficiency. For this reason, [PERSON] et al. [5] developed Cu- and/or Zr-modified TiO\({}_{2}\) nanoparticles, characterizing them with photothermal beam deflection spectrometry (BDS), UV-Vis spectro-photometry, X-ray diffraction (XRD), and energy-dispersive X-rays (EDXs) to determine their thermo-optical and transport parameters. Remarkably, modification of TiO\({}_{2}\) with Cu and Zr induced spectral shifts into the visible spectral region, thus enhancing the absorption capacity of a photon under solar light. Furthermore, the material's surface area was increased, thus enabling higher absorption of light at the photocatalyst surface, as well as better pollutant adsorption and photodegradation. The last article of this Special Issue investigated light reflection and absorption properties of acrylic paints and related materials, considering how they can be used in fine artsand how to simulate them in printing [6]. For this purpose, two groups of acrylic dyes (carrying colors) were analyzed using spectroscopy to determine their light absorption properties in the visible (VIS) and near infrared (NIR) regions. Digital recordings were compared with video surveillance cameras that operate and record during the day and night. The same color had at least two formulations of dyes for print reproduction to simulate what the naked eye sees and to simulate light absorption in the NIR spectrum. Infrared design can be applied in fine arts to explore dimensions beyond the visible range of human sight. A procedure for creating a dual artwork was proposed and developed in [6] to digitally archive the entire process as a specimen.
# SAES: An Introduction to Self-Adapting Exploratory Structures
## 5 Integration of Different SAES into a Single Interface
Using a static or a dynamic clustering scheme, such as Scatter-Gather, as a SAES facet would seem to provide little benefits, because of the discussion above. However, this is not true. Embedding predefined (flat or hierarchical) clusters, or a mechanism such as Scatter-Gather as a facet of a dynamic taxonomy, completely eliminates the main objection to using these devices in isolation: namely that they produce, by construction, a monodimensional classification that has an insufficient discrimination for large databases. While a monodimensional classification is still produced, this concerns only a single facet of the dynamic taxonomy: items classified under this facet will be classified under other facets also, so that the entire structure is multidimensional and allows a much better resolution. It becomes questionable whether the additional reclustering offered by Scatter-Gather is really worthwhile in this context, since a better conceptual manipulation is offered by the hosting dynamic taxonomy. It seems that a precomputed hierarchical clustering would be adequate and this would also solve the problem of labeling clusters, since their number would be sufficiently small for manual labeling. Although the discussion focused on clustering for text documents, clustering is really an abstract strategy that groups abstract items on the basis of the similarity of their features. As a consequence, clustering can be, and was, applied to other domains, for example image retrieval. The reader is referred to [19], where the \"Rosso Tiziano\" image exploration system is described. This system was the first one to combine primitive image features, clustering, and metadata into a single exploratory system by embedding them in a dynamic taxonomy.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Related Works** & **LEO-SN** & **RL** & **Parallelization** & **Onboard** \\ \hline
[10] & \(\circ\) & X (Dijkstra) & X & X \\ \hline
[11] & \(\circ\) & \(\circ\) (GNN + DQN) & X & X \\ \hline
[12; 13; 14] & \(\circ\) & \(\circ\) (RL) & X & X \\ \hline
[15; 16] & \(\circ\) & \(\circ\) (Dueling DQN) & \(\circ\) (FPGA) & \(\circ\) \\ \hline
[17] & X & \(\circ\) (RL) & \(\circ\) (GPU) & \(\circ\) \\ \hline Ours & \(\circ\) & \(\circ\) (Dueling DQN) & \(\circ\) (GPU) & \(\circ\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of related works. this paper applies Dueling DQN to provide efficient routing in dynamic network environments and proposes a GPU-parallel processing-based architecture to meet the real-time inference requirements of OBC. The GPU-parallelized Dueling DQN-based routing algorithm, which has not been addressed in previous studies, was experimentally validated in an onboard environment. This study establishes a significant foundation for system implementation and further research. This approach not only enhances computational efficiency but also guarantees real-time routing and stability in resource-constrained environments.
## 4 System Model
### Irregular Terrain Model (ITM)  
The ITM, better known as the Longley-Rice model [24], considers terrain (forest, city, mountain) and atmospheric data. It is a point-to-point statistical model that imposes antenna heights of at least 50 cm. It is suitable for ranges beyond the kilometer. The heights of the antennas of the Hi-Rail pickup and the locomotive are over 50 cm, and the distances separating the Hi-Rail pickup from the locomotive will always be over 1 km. Therefore, the model is very suitable for railway telecommunication system study.  
The Radio Mobile telecommunication software uses this model to carry out coverage studies and link budgets. The images below show connection reports at various locations on the railway track between Emeril and Schefferville where the final system will be tested. A locomotive station has been created: it has an antenna height of 4.572 m, an omnidirectional antenna with a gain of 6 dB, a transmission power of 1 watt corresponding to the power of the XTend modules of the telecommunication system, and a 1 dB attenuation due to the connection losses and the length of the cable connecting the transmitter to the antenna. At the same time, a Hi-Rail pickup station has been implemented: it has an antenna height of 2.5 m, an omnidirectional antenna with a 6 dB gain, and a sensitivity of \(-\)100 dBm corresponding to that of the XTend modules used. As for the previous station, the losses generated by the connections and the cables (linking the antenna to the receiver) have been defined at 1 dB. The two stations have been teamed up to create a network.  
The simulation results in Figure 13 correspond to a network deployment on the railway north of Emeril. The image on the left shows the network with the locomotive as the 915 MHz transmitter and the Hi-Rail pickup as the receiver. The image on the right shows the locomotive and the Hi-Rail pickup with the topology of the area. It also shows the budget link: the distance between the two network elements is 3.88 km, and compared to the sensitivity of \(-\)100 dBm of the receiver, there is a margin of 22.1 dB. Therefore, communication is going perfectly.  
Figure 14 shows another budget link for the locomotive-Hi-Rail pickup network near the place called \"Menahec\" on the railway track. The locomotive and the Hi-Rail pickup are 15.39 km apart, and communication is right, since the power margin at the receiver (Hi-Rail pickup) compared to its sensitivity is 6.2 dB.  
Finally, Figure 15 shows a negative result: the network was placed halfway between Emeril and Schefferville. It appears from the picture on the right that a hill of about 514 m prevents the signal propagation transmitted by the locomotive: the power balance is negative since the receiver (Hi-Rail pickup) receives a power lower by 7 dB than its sensitivity.  
Figure 14: Budget link at Menihec on the railway.  
Figure 13: Budget link of the network located north of Emeril.
# Intelligent Detection of Rebar Size and Position Using Improved DeeplabV3+
## 2 Methodology
### Improved DeeplabV3+
#### 2.2.3 Adjusting Atrous Convolution  
The ASPP (Atrous Spatial Pyramid Pooling, Figure 6) module is a critical component of the Deeplabv3+ model used to capture multi-scale contextual information on top of the feature maps extracted from the backbone network. Atrous convolution forms the core of the ASPP module. Atrous convolution with a dilation rate of \(r\) expands the receptive field of the original \(n\times n\) convolution kernel to \(N=n+(n-1)(r-1)\) by adding \(r-1\) zeros between adjacent filter values in each spatial dimension, which ensures the output feature map size. Figure 7 shows the convolution schematic. The ASPP module uses parallel atrous convolutions with four different atrous rates to segment objects at different scales in combination with image-level features. The global contextual information is fused by applying average pooling on top of the last feature map of the backbone network. The results of each operation along the channel are concatenated, and a \(1\times 1\) convolution is performed to obtain the output.  
Figure 4: Efficient channel attention module.  
Figure 5: Structure of the improved Resnet50.  
The dilation rates for the parallel atrous convolutions of the ASPP module in the original DeeplabV3+ were 6, 12, and 18. When the backbone network proceeded with feature extraction, the feature map resolution gradually decreased, and the dilation rates of 6, 12, and 18 could not effectively extract features from multi-scale images about rebar if a smaller void rate was not set. This led to a lack of ability to segment small targets. Considering the existence of rebar with multiple diameters, to extract multi-scale image features more effectively and to improve the segmentation capability of targets with different size rebars, the dilation rates of the atrous convolution were adjusted to be 3, 6, and 9.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 6 Conclusions  
In this paper, a user-serving scheme was proposed to serve all users while satisfying realistic fronthaul requirements and ensuring a connection for existing users when a large number of users exist in a user-centric cell-free massive MIMO system. To reduce the complexity and the burden on the fronthaul, this paper adopted conjugate beamforming (CB) for the precoding algorithm and limited the number of antennas (equal to the number of serving users). It was confirmed through several simulations in Section 5 that when the proposed scheme was applied, the network system could serve a very large number of users in a feasible way. In addition, it is noteworthy that the sum rate performance of the proposed scheme in several situations was improved compared to the existing method. Through the proposed scheme, the spectral efficiency and per-user throughput performance were improved while maintaining the connection with the existing users. In particular, it is shown that all users can receive service as in the full cell-free massive MIMO model, ensuring service for the bottom 10% of users. It was shown that the proposed scheme can efficiently serve the unserved users in the user-centric cell-free massive MIMO system even when the number of users in the area is very large.  
Conceptualization, J.-H.O. and M.-A.K.; methodology, J.-H.O. and B.-S.; software, J.-H.O. and B.-S.S.; validation, J.-H.O. and H.-K.S.; investigation, J.-H.O.; resources, J.-H.O.; data curation, J.-H.O.; writing--original draft preparation, J.-H.O.; writing--review and editing, [PERSON]-[PERSON] and [PERSON]; visualization, J.-H.O.; supervision, Y.-H.Y., D.-D.H., and [PERSON]-[PERSON]; project administration, Y.-H.Y., D.-D.H. and [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540). This work was supported by the ICT R&D program of MSIT/IITP. [IITP-201-0-01816, A Research on Core Technology of Autonomous Twins for Metaverse]. This work was supported by the Technology Innovation Program (RS-2022-00154678, Development of Intelligent Sensor Platform Technology for Connected Sensor) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 3 Experimental Section
### The Experimental Method and Schematic Diagram of the Experimental Platform  
Firstly, the distribution morphology of graphene in different mass fractions of graphene composite couplers were observed by a microscope. In order to better describe the distribution of graphene in the microscopic image, Pro Imaging version 1.1 (OPTON, Beijing, China) was used to identify the size of the graphene particles and obtain particle diameter information. Then, an experimental platform was established; the experimental schematic diagram is shown in Figure 2. The prepared graphene composite coupling agent was uniformly applied on the upper and lower surface of ASTM 1020 steel. The sensors were fixed at the place where the coupling agent was applied for testing. Four 0.2 mm plastic sheets were added between the sensor and the specimen to keep the thickness of the coupling agent constant. The dead weight of the sensor was used as pressure to keep the pressure of the sensor constant. The RITEC-RAM-5000 emitted a sinusoidal signal of 10 cycles used as an excitation signal and an oscilloscope was used to observe the waveform received by the receiving sensor at the excitation frequency of 5 MHz; 10 groups of experimental results were recorded. By changing the excitation frequencies, corresponding experiments were conducted on the graphene composite coupling agent with different graphene contents. At last, Origin Pro 2018 (OriginLab, Northampton, MA, USA) software was used to calculate the peak-to-peak value, attenuation coefficient, and energy value of each data. The calculation formula of the final result of each index can be expressed as:  
\[X=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{8}\]  
where \(x_{i}\) is the calculated value of each test, \(n\) is the number of tests, and \(n=10\).
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
## 1 Introduction
[22] considered the non-isothermal distribution of rotor temperature, and 3D temperature-stress coupling analysis was performed to obtain the optimal sleeve thickness. [PERSON] et al. [23] proposed a multi-physical field optimization process for high-speed permanent magnet machines to achieve low loss and a low temperature. [PERSON] et al. [24] focused on the thermal effect on mechanical strength and stiffness, and a reasonable machine design was proposed to achieve a more accurate mechanical distribution. The main purpose of these studies is to illustrate the effect of temperature on the structure and to reduce the temperature through design optimization. However, there are few studies which focus on structural failure due to temperature. In this paper, the focus of temperature research is mainly to analyze the failure temperature of interference fit structure based on a variable interference value, not only limited to one condition, and uniform sampling in the range of permitted overfill for the analysis of failure.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Mathematical Foundations  
#### 3.1.1 The Finite Field \(\mathbb{F}_{p}\)  
Let \(\mathbb{F}_{p}\) be a finite field, with \(p\) being a prime number. The elements of this field are represented by the integers in the set \(\{0,1,2,\ldots,p-1\}\). These elements are operated with the two common operations, addition and multiplication modulo \(p\).
# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 3 Model Selection and Data Processing
Among the classification methods used in comparative studies, the RF-based classification gave relatively higher accuracy in a complex environment (90% at 3.2 m) without setting the activation function, learning rate, hidden layer neurons, and thresholding process. Additionally, the processing time for higher samples is lower compared to the other methods. Hence, the time needed for prediction during position hopping is less which is a critical factor for localization of robot in real-time.
## 5 Selection of Attributes, Modeling, and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Linear Model** & **Gaussian Process** & **SMO Regression** & **M5P Tree** & **Random Forest** \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, without Day of Year and without Satellites} \\ \hline Correlation coefficient & 0.2195 & 0.2289 & 0.2377 & 0.1921 & 0.2243 \\ Relative absolute error & 0.960381 & 0.958122 & 0.838752 & 0.963788 & 0.996284 \\ Root relative sq. error & 0.975826 & 0.971225 & 1.013767 & 0.983825 & 1.007389 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU and Day of Year, without Satellites} \\ \hline Correlation coefficient & 0.3044 & 0.3228 & 0.3221 & 0.3655 & 0.3810 \\ Relative absolute error & 0.953682 & 0.931039 & 0.81304 & 0.873916 & 0.867675 \\ Root relative sq. error & 0.952256 & 0.944247 & 0.990007 & 0.933188 & 0.960127 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (FSRF-TEST 20 attr), without Day of Year} \\ \hline Correlation coefficient & 0.3962 & 0.3962 & 0.3936 & 0.4011 & 0.5015 \\ Relative absolute error & 0.929776 & 0.91984 & 0.776143 & 0.913888 & 0.834525 \\ Root relative sq. error & 0.918874 & 0.916169 & 0.971942 & 0.928474 & 0.863463 \\ \hline \multicolumn{6}{c}{Model using attributes SHMU, Satellites (Relief 20 attr), without Day of Year} \\ \hline Correlation coefficient & 0.2934 & 0.3322 & 0.2697 & 0.3291 & 0.3574 \\ Relative absolute error & 0.998795 & 0.958923 & 0.827651 & 0.952494 & 0.930419 \\ Root relative sq. error & 0.969389 & 0.945013 & 1.009543 & 0.972802 & 0.936803 \\ \hline \multicolumn{6}{c}{Models using attributes SHMU, Satellites (Forward selection 20 attr), without Day of Year} \\ \hline Correlation coefficient & 0.3754 & 0.3691 & 0.2771 & 0.2891 & 0.4214 \\ Relative absolute error & 0.977038 & 0.942754 & 0.808266 & 1.005264 & 0.851436 \\ Root relative sq. error & 0.934184 & 0.928854 & 0.999298 & 1.010215 & 0.905755 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of trained regression models and their accuracy metrics for the field-measured values of target variable S (total deposit). Figure 4: Flowchart view of our data preparation, modeling, and evaluation activities.