esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Catalytic Activity Study  
Catalytic activity measurements were carried out at atmospheric pressure in a tubular quartz reactor (6 mm i.d.), which was placed inside a programmable furnace and the temperature was measured by a type K thermocouple located in a pocket in the center of the catalyst bed. Methanol was fed to a purpose-vaporizer by means of a Master Flex C/L variable speed tubing pump (1 to 6 rpm) EW-95990-18 [23]. The gas flow was adjusted by a Cole-Parmer compact mass flowmeter. All chemical pipes were heated to avoid condensation in the system. The feed and the produced gas compositions were determined by on-line gas chromatography (GC), using a Bruker 450 GC equipped with two channels: channel one is for analyzing oxygenates and alkenes (propylene, ethylene, methanol, formaldehyde, and formic acid); the separation was accomplished by Varian select formaldehyde and HayeSep Q. The second channel is for analyzing the nonflammable gases (viz., \(\rm O_{2}\), \(\rm N_{2}\), \(\rm CO\), \(\rm CO_{2}\)) using a thermal conductivity detector (TCD) and separation was accomplished by MolSieve 13X and HayeSep Q columns connected in a series. Prior to loading the clay sample in the reactor, the powder was sieved and the fraction containing particles of 0.12-0.25 mm was used. Therefore, a considerable pressure decrease over the catalyst bed could be avoided and reproducible gas flow and mass transport conditions were ensured. The clay-catalyst (0.1 g) was mixed with \(\rm SiO_{2}\) to 10 wt.% in all catalytic tests to prevent hot-spot formation in the catalyst bed, [23]. All the catalytic experiments were carried out under the following conditions: methanol liquid injection feed flow rate = 2.4 mL h\({}^{-1}\) and reaction temperatures at 350 \({}^{\circ}\)C. On the other hand, acid-activated clay minerals have proved to be effective catalysts for several reactions [24; 25]. Therefore, in this work, Al-Hasa clays in the study were treated with hydrochloric acid solutions at 0.1 mol L\({}^{-1}\) concentration (1.0 g clay/25 mL). The dispersions were maintained at 100 \({}^{\circ}\)C with stirring for 60 min. The samples were cooled to room temperature and filtrated and then washed with deionized water until free of excess acid. The samples were dried at 110 \({}^{\circ}\)C for 12 h. The produced acid-activated clays were tested in methanol conversion at the same reaction condition as mentioned above. The calculated conversion percentage is that of the reacted methanol, selectivity percentage is calculated as the division of product yield and conversion of methanol.
## 3 Data Analysis
### Image Processing and Visualization of Chemical Composition Mapping  
In this study, we evaluated the efficiency performance of the PLS-DA model for the classification of adulterated almonds. According to the above illustration, the visualization mapping of hyperspectral images was conducted by multiplying the beta coefficient values obtained by the PLS-DA model by each pixel of the corrected processed image. Firstly, a threshold of 0.5 was used to remove the background before visualizing the chemical map. Next, the PLS-DA image with the eliminated background was converted into a binary image (as previously modeled with PLS-DA, with the value of '1' for apricot kernels and '0' for almonds). In the binary images, an intensity over 0.5 was considered for every single seed. Accordingly, the model classified the seeds as almond or apricot seeds, depending on the intensity. The detection rate counting considered pixels' intensity reflection, and a pixel intensity greater than 50% from the total number of pixels was considered to indicate almonds, while the seeds with less than a 50% pixel intensity were counted as apricot seeds.  
One major advantage of hyperspectral imaging is its ability to create a chemical image of the component distribution from the simultaneous measurement of spectral and spatial data. The novel benefit of the chemical image is the inclusion of a measurement of different parameters of the chemical component sample to sample, even with the same sample at each pixel location [28]. The PLS-DA model was used to visualize and map the quality attributes of kernels in every pixel of the hyperspectral image based on the results of the calibration model. The hyperspectral image was advanced into a 2D matrix and multiplied by PLS-DA classification. The obtained vector folded back into the 2D image, and this 2D image showed the visualization of different chemical parameters in the nut samples. The final chemical images were obtained by applying Equation (5):  
\[\textit{Chemical Image}=\sum_{i=1}^{n}I_{i}\ R_{i}+C, \tag{5}\]  
where \(I_{i}\) is the image of a non-reflectance spectral image and \(R_{i}\) is the beta coefficient derived from the PLS regression model.
# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview  
Figure 1 introduces the proposed system framework. The development phase is composed of oriented object detector training and validation, and roadway safety rules validation. In the oriented object detector training and validation phase, the collected dataset is annotated with oriented bounding boxes of over 17 different object classes and ten safety features, the selection of these objects and features is determined based on the road safety comparative review summarized in Section 2.1. The dataset (detailed in Section 4.2) is split into training, validation, and testing subsets. The training data is passed through a data augmentation phase and the ground-truth labels are encoded to train a variant of YOLOv3 [5] object detector adopted to this study. The evaluation results of the validation data are used to fine-tune the learning process hyperparameters, and along with the evaluation results of the training data, the deep neural network object detector iterative learning process stopping criterion is examined. The automatic roadway safety features assessment can be used to pervasively enable or disable autonomous driving (AD) or advanced driver-assistance systems (ADAS). The system can work reactively in case of no connectivity by relying on the self-vehicle camera and GPS. In case of connectivity, connected vehicles would send and receive estimated safety features targeting ubiquitous transposition network coverage and achieve higher accuracy by confirming and reinforcing detected features from multiple vehicles. Fusing safety features estimations for a roadway sector based on data coming from multiple vehicles, like averaging the scores, can make them more accurate, and allow for sending alerts to drivers about black spots or relatively less safe segments or roads. With modern prosperity in connected car technologies [51], sensor data coming from vehicles roaming the geographical area under deployment and the estimated safety features are aggregated in a remote server or in one host vehicle from the vehicles in the area for fusion and distribution.
## 5 Developing the Models for Predicting UCS
### Empirical Model  
Multivariate regression analysis (MRA) is a useful tool to create an empirical model for predicting UCS, as summarized in Table 1. MRA is made of various parameters to achieve the best-fit equation through performing the least squares fit, which can be described as a multivariate equation of first order by [PERSON] et al. [97]. Therefore, an empirical formula, Equation (19), was proposed to predict UCS based on the four input variables considered in this study.  
\[UCS=-128.37-3.34\times\text{PLS}-0.776\times\text{P}_{\text{n}}+0.0212\times \text{V}_{\text{p}}+2.789\times\text{SHR} \tag{19}\]
# Check for special characters and punctuation
## 4 Experimental Results and Analysis
### Results Analysis  
Table 1 presents the core performance metrics of DeepStego across different message sizes and embedding modes. The standard mode achieves embedding capacities ranging from 8.22% to 8.83%, while the enhanced mode significantly improves capacity to 16.64-19.48%. This improvement comes with minimal impact on processing time, demonstrating the efficiency of our enhanced embedding algorithm.  
Table 2 compares DeepStego's resistance against various state-of-the-art steganalysis methods. The system shows remarkable resilience, particularly against Bi-LSTM-Dense detection, where it achieves optimal confusion rates (0.5000 accuracy), indicating complete undetectability. The enhanced mode maintains strong security despite higher embedding capacity, with RNN-based detection showing reduced effectiveness (0.5538 accuracy) compared to standard methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Message** & **Embedding** & **Capacity** & **BPW** & **Words** & **Processing Time (s)** \\
**Size (Bits)** & **Mode** & **(\%)** & **BPW** & **Required** & **Time (s)** \\ \hline
128 & Standard & 8.22 \(\pm\) 0.12 & 4.34 & 29.55 \(\pm\) 1.32 & 19.35 \(\pm\) 3.69 \\
256 & Standard & 8.54 \(\pm\) 0.14 & 4.26 & 60.05 \(\pm\) 2.21 & 22.87 \(\pm\) 2.62 \\
512 & Standard & 8.83 \(\pm\) 0.12 & 4.09 & 124.95 \(\pm\) 2.84 & 24.89 \(\pm\) 1.90 \\
128 & Enhanced & 16.64 \(\pm\) 0.21 & 9.84 & 13.00 \(\pm\) 0.00 & 19.35 \(\pm\) 3.69 \\
256 & Enhanced & 18.37 \(\pm\) 0.26 & 9.84 & 52.00 \(\pm\) 0.00 & 22.87 \(\pm\) 2.62 \\
512 & Enhanced & 19.48 \(\pm\) 0.18 & 9.84 & 29.55 \(\pm\) 0.00 & 24.89 \(\pm\) 1.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Embedding capacity and efficiency metrics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Method** & \
\begin{tabular}{c} **Standard Mode** \\ **(1 Bit/Word)** \\ \end{tabular} &
\begin{tabular}{c} **Enhanced Mode** \\ **(4 Bits/Word)** \\ \end{tabular} \\ \hline BERT-LSTM-Att [14] & 0.7644/0.5543 (Acc/Rec) & 0.9432/0.8933 (Acc/Rec) \\ CNN-based [19] & 0.6351/0.4503 (Acc/Rec) & 0.6552/0.5415 (Acc/Rec) \\ RNN-based [8] & 0.6212/0.4942 (Acc/Rec) & 0.5538/0.2134 (Acc/Rec) \\ Bi-LSTM-Dense [18] & 0.5000/0.0000 (Acc/Rec) & 0.5000/0.0000 (Acc/Rec) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Steganalysis detection resistance results.  
Table 3 evaluates the linguistic quality of generated texts. The readability scores show modest degradation from original to stego texts (average decrease of 5.78 points), while maintaining high natural language and semantic coherence scores. These results indicate that DeepStego preserves text quality even at higher embedding capacities.
## 5 SDN-based VANETs Security Challenges
### Sybil and Masquerade Attacks  
The sybil attacks are considered as one of the most perilous attacks in the SDN-based VANETs, where the malevolent vehicle acts that it is in excess of a hundred vehicles by causing confusion and countless pseudonyms (Figure 20). The objective of this conduct is to mislead dissimilar vehicles and ask them to change their routes. In actuality, as the name shows, in disguise attacks, a noxious vehicle changes its personality [55] to be another vehicle, attempting to deliver various messages, modify, and replay with data to bambooz different vehicles [97].  
For instance, a malevolent vehicle can change its personality to be a rescue vehicle and power different vehicles to back off or change their courses. Previous studies [98; 99] proposed a trust model dependent on SDN-based VANETs for self-sufficient vehicular applications so as to counteract bugged data and vehicle following. The authors structured a progressive naming plan made out of four levels: independent vehicle, producers, vehicles, and information. They utilized an intermediary pseudonym based plan so as to make it hard for aggressors to follow vehicles. SDN-based VANETs tie content names utilizing cryptography calculations; for example, open private keys that may verify the official and exceed these issues. In addition, circulated arrangements, such as square chain, can be applied to implement the substance name authoritative and save substance and client security.  
Figure 19: Replay attack in SDN-based VANETs.
## 2 Principle of the Crack Detection and Lifetime Prediction Model
### Fusion Lifetime Prediction Model
For each prediction method, calculate the prediction relative error (\(e_{mt}\)) at all N monitoring points, obtain the sequence composed of relative error, and normalize it, to obtain \(p_{mt}\):  
\[p_{mt}=\frac{e_{mt}}{\sum_{t=1}^{N}e_{mt}},t=1,2,\cdots,N \tag{5}\]  
Then, the information entropy of the relative error sequence of the physical model prediction method, \(h_{m}\) is calculated as follows:  
\[h_{m}=-k_{h}\sum_{t=1}^{N}p_{mt}lnp_{mt} \tag{6}\]  
where \(k_{h}\) is a constant greater than 0 and \(h_{m}\geq 0\). Then, the coefficient of variation of the prediction method \(d_{m}\) is calculated. Since \(0\leq d_{m}\leq 1\), according to the principle that the information entropy of the prediction relative error sequence is opposite to the variability, the variability coefficient \(d_{m}\) can be denoted as:  
\[d_{m}=1-h_{m} \tag{7}\]Finally, the weight of the physical model prediction method \(w_{m}\) can be calculated by the following equation:  
\[w_{m}=\left(1-\frac{d_{m}}{d_{m}+d_{r}}\right) \tag{8}\]  
Similarly, the parameters of the lifetime prediction method of RVM (\(e_{rt}\), \(p_{rt}\), \(h_{r}\), \(d_{r}\) and \(w_{r}\)) can also be calculated. The final lifetime prediction result can be obtained by weighted sum of the predicted results [32]:  
\[RUL=w_{m}rul_{m}+w_{r}rul_{r} \tag{9}\]
# Planning of Optical Connections in 5G Packet-Optical xHaul Access Network
## 3 Main Assumptions  
In this section, we present the details of the optical transmission system and transmission-reach model considered in this study. Moreover, we discuss the assumptions that we take concerning the latencies of transported radio data and their impact on the transmission path length.
## 3 Results and Discussion
### Damage Tolerance Tests  
For the damage tolerance assessment, one of the fins was cut to create a crack from which damage could propagate. The purpose of this experiment was to identify whether crack propagation in such a thin fin can be captured. For that aim, a load range between 100 and 1000 N with a fatigue ratio R equal to \(-1\) was applied. After several cycles (>20,000 cycles), the load was increased to 120-1200 N due to the absence of any propagation of the crack, and not long after (10,000 cycles), acoustic signals emerged prior to rupture. As illustrated in the image (Figure 19), high acoustic signals were observed at the end of the tests while few or insignificant events (in terms of acoustic energy) were observed before that point. It must be mentioned that rupture did not occur from the cracked fin but from multiple initiation points, which was proven in the fracture surface. The cracked fin broke at the final stage of the test together with the rest, and therefore, the signal footprint ascribed to the crack propagation may be masked by the abrupt acoustic signals of multiple cracks.  
Figure 19: Acoustic signals increase at the final stage of the test.  
Figure 18: Different locations of strain over the whole test; non-accelerated test.  
With respect to signals classification, the resulting number of events was too low to apply the same machine learning algorithm as in the two previous cases. However, the types of signals had similar features to the ones corresponding to potential cracks: high-peak-frequency events, similar to those appearing in phase 3 of the previous tests. Based on that, this again indicates that the failure mechanism is based on multiple fins cracking and debonding.  
Finally, the strain evolution showed similar behavior to the two aforementioned cases. That is, strain 4's level was kept at a lower level while the rest of the values increased considerably, indicating a rupture on the left side of the structure as it deforms much more under the same loading conditions (Figure 20). The image of the final rupture is illustrated in Figure 21.
# QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things
## 4 Proposed CBF for IoT  
Dividing the whole network into different routing clusters is the core idea of CBF. Intralayer clustering (IALC) is responsible for proactive maintenance, with the help of route query exchange and update query packets. The MDP-level MAC initiates route updates, which further updates IALC in the case of broken or established links among the directly-connected neighbor mote, and neighbor-mote is defined as the one which is directly connected and shares a communication link and, thus, is one mote away. The MDP-level (MAC) media access control protocol is responsible for the mote's neighbor's identification, while interlayer clustering (IELC) is responsible to reactively transfer route query packets to motes that reside outside of the mote's cluster via query-reply packets. IELC uses a broadcast delivery service to send the routing queries to its border or peripheral motes. IELC maintains updated route clustering information of peripheral motes using IALC tables; this information is  
Figure 3: Sink mote, destination mote, and redundant mote system model.  
further used to determine that either the query for destination more refers to their cluster based on QueryLimitThreshold (QLT). QLT assists in regulating the maximum sending ability of motes to send the maximum number of query packets, and also empowers the occurrence of attack detection in the network.  
In the following subsection the CBF network, formal definition, algorithm of the MDP, IALC, IELC, QCM, and network assumptions are described.
# Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry
## 1 Introduction
The flow stagnates on the non-seam side (NSS) of the ball, before bifurcating and traversing the seam side (SS), and NSS. The flow remains laminar on the smooth and polished NSS, and subsequently separates from the surface at \(\theta_{\rm L}\)\(\sim\)80\({}^{\circ}\). This angle, and those below are quantified from experimental measurements on balls used in first-class match conditions by [PERSON] et al. [5] at \(\mathrm{Re}=1.5\times 10^{5}\) (equivalent to 67 mph). On the SS, the seam trips the flow into turbulence, ensuring that it remains attached until a separation angle of \(\theta_{\rm T}\)\(\sim\)120\({}^{\circ}\), by virtue of the increased momentum in the boundary layer near to the surface. The unequal separation angles create an asymmetric distribution of pressure, and hence a net lateral aerodynamic force on the ball, causing it to swing. CS is lost if the fielding side can no longer maintain a smooth surface on the NSS, as the ball naturally roughens due to wear as the match progresses. However, RS may occur under conditions with an appropriate roughness on the NSS; here the turbulent separation angle has increased to \(\theta_{\rm T}\)\(\sim\)135\({}^{\circ}\), and the separation angle of the SS remains similar to the CS case at \(\theta_{\rm T}\)\(\sim\)110\({}^{\circ}\) (Figure 1c). This results in a reversal of the pressure asymmetry, and so RS is in the opposite direction to CS. RS is not as well understood as CS and different theories have been proposed to explain the fluid dynamics. Experiments performed by [PERSON] et al. [5], using a novel heated flow visualization method, show that a laminar separation bubble (LSB) forms on the aged and roughened NSS. The laminar boundary layer separates at \(\theta_{\rm L}\)\(\sim\)95\({}^{\circ}\), reattaches as a turbulent layer at \(\theta_{\rm R}\)\(\sim\)110\({}^{\circ}\), and eventually detaches further downstream as a turbulent layer at \(\theta_{\rm T}\)\(\sim\)135\({}^{\circ}\)--see Figure 1c. Although the SS will also roughen, and consequently the angle of turbulent separation advances, it is the condition of the NSS that principally governs the onset of RS. This paper presents an investigation into the flow field surrounding a cricket ball using particle image velocimetry (PIV) in a low speed wind tunnel. The experiments used a scaled cricket ball at Re and _k/d_ values typical of match conditions, exploring the role of the boundary layer and flow separation during reverse swing. Experimental evidence is presented in this paper, for the first time on a ball with backspin, to demonstrate the formation and stability of a laminar separation bubble. PIV measurements were also collected to demonstrate how the Magnus effect is influenced by the rate of backspin. The results are discussed in the practical context of a cricket match.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 5 Simulation Results  
In this section, we provide simulation results of LEO NOMA and cooperative OMA methods. Then, the transmission reliability is further discussed. Throughout the simulation, system and Shadowed Rician fading channel parameters are provided in Tables 1 and 2[30]. Let \(R_{1}=\log(1+T_{1})\) and \(R_{2}=\log(1+T_{2})\) denote the achievable rate of \(U_{1}\) and \(U_{2}\) at thresholds \(T_{1}\) and \(T_{2}\) in the LEO NOMA method, respectively. For comparable reasons, we set \(R_{0}=R_{1}+R_{2}=\log(1+T_{0})\) as the achievable rate for both \(U_{1}\) and \(U_{2}\) in a cooperative OMA scheme.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Point Cloud Partition  
Given the planar primitives, a point cloud partition algorithm was employed to slice the vertical primitives into several layers. Since these layers always share repetitive structures on the upward direction, we utilized the histogram of boundary points to eliminate the redundant layers. Finally, we selected the representative layers to facilitate the contour extraction.  
Figure 5: An illustration of labeling points and their fitting planes. The white dots and red dots represent the points with different labels assigned to plane A and plane B, respectively. The distances between points and planes are perpendicular distances. The points in the red circle illustrate the neighboring points of \(p_{2}\) and the smoothness cost to the different points.  
Figure 6: The top-down view and side view results of planar primitive detection (rendered in different colors).  
Figure 7a illustrates the vertical planar primitives extracted by normals. We sliced the point clouds to several consecutive blocks along the upward direction. Several 2D layers could be obtained through 2D projections as shown in Figure 7b. Subsequently, a least square linear fitting could be applied to extract linear primitives based on labels and orientations on each 2D layer. However, these 2D layers possessed considerable redundancy which decreasedd the efficiency of post-processing. For instance, the layers with elevations from 40 cm to 55 cm in Figure 8 shared almost the same structures which were supposed to be merged to one representative layer.  
Figure 8: The instance of repetitive layers sharing the same geometry on the vertical direction (truncated elevation from 40 cm to 55 cm).  
Figure 7: Point cloud partition and linear fitting: (**a**) The planar primitives from vertical structures, (**b**) the 2D projections of consecutive blocks (each color represents ten layers), (**c**) the result of the linear fitting (Red lines: Vertical primitives; Green lines: Horizontal primitives).