esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 2 Weighting Model of Longitudinal Profile
### Calculation Method for Weighting Model of Longitudinal Profile  
In addition to characterizing road surface performance, road roughness reflects the dynamic excitation effect exerted by the road surface on vehicle wheels. Across the lateral direction of a roadway, various longitudinal profiles, due to their specific locations, exhibit differences in the weight of excitation imparted to the wheels. To analyze road roughness more accurately and scientifically, it is necessary to assign weights to the point cloud data of each longitudinal profile. Based on the generation mechanism of road wheel trajectory zones, this study selects the lateral distribution model of wheel trajectories \(f(x)\) as the theoretical foundation for calculating the weights of different longitudinal profiles. However, considering the amplitude of elevation, it is essential to normalize \(f(x)\).  
Specifically, the normalization of \(f(x)\) should adhere to the following principles: During vehicle operation, the wheel trajectory zone is a critical region that significantly affects the dynamic response of the vehicle. Hence, the elevation in this area needs to be effectively preserved. Conversely, the regions in the middle and on both sides of the roadway have lesser impact on vehicle travel, and the corresponding elevation should be suppressed to minimize its influence on the IRI calculation results. The lateral distribution model of wheel trajectories \(f(x)\) can achieve the suppression effect for the latter but is inadequate in preserving the former. Therefore, the overall approach for transformation involves normalizing \(f(x)\) to map its range of values to the [0, 1] interval, where 1 represents the maximum weight of the wheel trajectory zone and 0 represents the minimum weight in the middle or on both sides of the roadway. The normalization process of \(f(x)\) is presented in Equation (7).  
\[f_{norm}(x)=\frac{f(x)-f_{\min}}{f_{\max}-f_{\min}} \tag{7}\]  
In the Equation, \(f_{\max}\) and \(f_{\min}\) represent the maximum and minimum values of \(f(x)\), respectively. This normalization operation enhances the elevation representation in the wheel trajectory zone area while attenuating the elevation influence in the middle and on both sides of the roadway. Through the normalization process, the lateral distribution model of wheel trajectories \(f(x)\) is transformed into the weighting model of longitudinal profile \(f_{\rm norm}(x)\).
## 5 Experiments  
### Datasets  
To evaluate the effectiveness of our proposed method, we downloaded more than 25,000 APKs and removed the samples that cannot be analyzed by Androguard. Our final dataset consists of 20,297 APKs, with 10,361 classified as benign and 11,043 as malicious, spanning the years 2018 to 2022, as shown in Table 3. The benign samples were obtained from Google Play Store and AppChina with the assistance of Androzoo [33], while the malicious samples were acquired from VirusShare [34]. Specifically, we consider APKs from Androzoo as benign only if fewer than five antivirus engines from VirusTotal detected them as malware. The distribution of samples across each year is approximately equal, considering cases where APKs failed to be analyzed by Androguard or APKTool [35]. The dataset is partitioned into three subsets for training, validation, and testing, with a ratio of 8:1:1, respectively. It is worth noting that, due to memory constraints, experimentswere conducted on datasets of varying sizes, all of which are subsets of the larger dataset containing 21,404 APKs.  
For our proposed model and the baselines based on GNNs, we begin by extracting six node types and six edge types using Androguard to construct a heterogeneous graph for our dataset. The graph is then stored in a Neo4j database for further use. Our constructed graph comprises over 178,000 nodes and 1.36 million edges based on our dataset. As for other baselines, the dataset is decompiled into Smali code using APKTool and prepared to be fed into these models.
## 2 Composite Laminates Toughened by Polymeric Nanofibers
PVDF nanofibers were the second choice for toughening the laminates, but according to the results it was not successful enough and could decrease the absorbed energy about 13%. As the melting point of PVDF is about 170 \({}^{\circ}\)C and curing temperature is 130 \({}^{\circ}\)C, the bridging between the  
Figure 8: FESEM micrographs of fracture surfaces in 1.0 wt % O-CNFs incorporated composites showing nanofibers impregnation and bridging with epoxy [40]. composite layers is the main mechanism of toughening, similar to PA66. It is worth mentioning that recently it was proved by [PERSON] et al. [21; 50] that a curing temperature higher than the melting point can increase the fracture toughness significantly. Therefore, it was suggested to study the effect of this method of providing PVDF-modified sample on damaged area under low velocity impact test. Interleaving composite laminates using SAN nanofibers and investigating its toughening effect was presented by [PERSON] et al. [46]. These results showed that presence of the electrospun SAN nanofibers could deflect the created microcracks, leading to direct them along more tortuous paths, and consequently, increasing the resistance of resin rich area to crack propagation. Hence the microcracks broke away from the SAN nanofibers; they induced kinked fracture surfaces, which offered more strain energy to be dissipated. In this way, the absorbed energy during impact (Izod impact test) was increased by 8%. The toughening effect of PVA on composite laminates was studied by [PERSON] et al. [47]. Although this nanofibrous mat had a significant effect on compressive strength, its effect during Charpy impact test was not so good and could enhance impact strength by about 11% as compared to those for the unmodified specimens. [PERSON] et al. [48] interleaved CFRP (Unidirectional and woven) laminates by PAN nanofibers and cured the sample at 60 \({}^{\circ}\)C. Therefore, the nanofibers were available with their initial configuration between composite layers. They conducted various mechanical tests, but according to topic of this review, the Charpy impact and drop-weight tests are reported. The results showed that all impact parameters were improved by incorporating nanofibers, but the effect of nanofibers was higher in unidirectional laminates in comparison with the woven one during Charpy impact test and the absorbed energy was increased by 31% in woven laminate in drop-weight impact test. The decrease of the damaged area is one of the important parameters that can be used as a reference for finding the efficiency of a nanofiber type. Figure 9 summarizes this parameter for various nanofibers to understand which nanofibrous mat has the best effect on toughening the laminate during impact. In the figure, PCL-1 shows the laminates toughened by PCL using \"Bridging\" mechanism while PCL-2 presents the other toughening mechanism (Phase separation). As can be seen, the best choice is applying carbon nanofibers, which is followed by NY66 and PCL-1.
## 5 Evaluation
### Communication Efficiency Evaluation  
We aim to trade computational overhead for reduced transmission overhead to enhance the stability of VANETs. This approach is justified as the mRSUs leverage backbone networks, which are more robust and reliable, while vehicles are limited to mobile wireless networks.  
Figure 9 illustrates the minimum number of fake trajectories \(\psi\) required to achieve various success rates for fake beacon schemes. The MZ scheme is excluded since it does not generate fake beacons. For instance, when the success rate is 30%, both the AFB and FB schemes become resource-intensive, particularly at low vehicle density. In contrast, the CPCROK scheme achieves a 30% success rate reduction for adversaries, with only one-third of the overhead of the FB scheme and one-sixth of the AFB scheme when the arrival interval is 10 s. Therefore, CPCROK strikes a balance between privacy protection and resource conservation by generating a minimal number of fake trajectories.  
Figure 8: The success rates of vehicles traversing different numbers of mix zones under varying vehicle densities. (**a**) \(\lambda=2\), (**b**) \(\lambda=6\), (**c**) \(\lambda=10\).  
Figure 7: Success rate under different pseudonym-changing schemes as the vehicle’s arrival interval varies.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 4 Example Analysis
### Site Selection Planning for Front Warehouses  
Using the K-means algorithm to perform cluster analysis on 68 customer points, the cluster center serves as an alternative point for establishing the front warehouse and the initial plan for selecting the location of the front warehouse is obtained. The cluster analysis results are shown in Figure 4. In the figure, the horizontal axis represents longitude and the vertical axis represents latitude.  
Eight alternative points are passed into the lower level model as the initial solution, and after obtaining the allocation of customer points and vehicle paths, they are returned to  
Figure 4: Cluster analysis results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Name** & **Latitude** & **Longitude** & **Demand** & **Start of Time** & **End of Time** & **Service** \\  & & & & & **Window/min** & **Window/min** & **Time/min** \\ \hline
1 & N01 & 22.559823 & 114.227134 & 1520.04 & 533 & 593 & 18 \\
2 & N02 & 22.560746 & 114.228704 & 501.9 & 729 & 789 & 10 \\
3 & N03 & 22.563047 & 114.228713 & 669.2 & 894 & 954 & 19 \\
4 & N04 & 22.563074 & 114.229686 & 1720.8 & 991 & 1051 & 20 \\
5 & N05 & 22.559611 & 114.229892 & 573.6 & 789 & 858 & 20 \\ \(\ldots\) & & & & & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
64 & N64 & 22.601627 & 114.307327 & 348.94 & 1077 & 1137 & 10 \\
65 & N64 & 22.60637 & 114.312096 & 2652.9 & 627 & 687 & 14 \\
66 & N66 & 22.606548 & 114.31533 & 1467.46 & 704 & 764 & 10 \\
67 & N67 & 22.606693 & 114.316908 & 2055.4 & 551 & 611 & 19 \\
68 & N68 & 22.604831 & 114.321011 & 2260.94 & 930 & 990 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The basic information of customer point.  
the upper level model. According to the capacity and time window constraints of the front warehouse, we adjust the location plan of the front warehouse and determine the location of the front warehouse from eight alternative front warehouses, as shown in Table 4.
# Investigation of Different Storage Systems for Solar-Driven Organic Rankine Cycle
## 3 Results and Discussion  
### Initial Analysis of the Organic Rankine Cycle  
The first part of the results section is a parametric investigation of the basic system with sensible storage with pure oil and the ORC. Figure 3 illustrates a comparison of different working fluids in the ORC. The analysis was performed for different values of the pressure parameter, which was a dimensionless parameter associated with the pressure in the turbine inlet. The results showed that the toluene was the best candidate with MDM, cyclohexane, isohexane, n-pentane, and isopentane to follow, respectively. So, toluene was selected as the most appropriate organic fluid in the ORC and this fluid was used in the following analysis.  
Figure 4 shows the yearly system efficiency with toluene for different saturation temperature levels in the HRS. The results showed that the maximum system efficiency was found for saturation temperature at 279 \({}^{\circ}\)C where the yearly system efficiency was 14.36% and the ORC efficiency at 31.02%. These results were found for collecting area at 160 m\({}^{2}\) and sensible storage tank volume with pure thermal oil at 10 m\({}^{3}\). Practically, the increase of the saturation temperature increased the ORC efficiency, as it is also given in Figure 4, but the very high temperatures in the system increased the thermal losses in the PTC and in the tank. Therefore, after a limit, the increase of the saturation temperature was not beneficial for the system, the fact that led to system efficiency maximization at an intermediate temperature level. Figure 5 is the temperature-specific entropy depiction of the optimized system for a saturation temperature of toluene at 279 \({}^{\circ}\)C in the HRS. It is interesting to see that the temperature (T\({}_{6}\)) was significantly lower than the temperature (T\({}_{5}\)), something that indicated the high importance of using a recuperator in order to have a high ORC efficiency.
## 4 Experiments
### Image Classification on ImageNet-1K
#### 4.1.3 Results and Discussion  
Table 1 compares the Top-1, Top-5, and computational costs of various downsampling methods when performing the image classification task. When the model was trained with 60 epochs, our method outperformed other models by 1.26-19.67% of the Top-1 accuracy. SoftPool and AdaPool show similar accuracies to the baseline method MaxPool, yet they still exhibit significant performance gaps compared to our method. Additionally, Patch Merging shows lower model performance than the above three methods, which demonstrates that patch merging loses its effectiveness when applied to CNN (Convolutional Neural Network) architectures. In terms of computational cost, MaxPool, SoftPool, and AdaPool have roughly the same number of parameters and degree of computational complexity, while Patch Merging and our method are slightly higher in comparison. Compared to the baseline model, our method is only 3% higher with respect to MACs and 0.17% higher with respect to the number of parameters (Params in the following tables). It is worth noting that the levels of performance of most of the models are similar since only one Max Pooling layer is available for replacement in the ResNet-18 model, which has a relatively small impact on accuracy. However, our method can still improve Top-1 by 1.3% and Top-5 by 0.87% over the baseline. Experiments show that our method can significantly improve model performance with a similar algorithmic complexity by replacing the Max Pooling layer in the image classification task.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Channel Decoding and Source Decoding to Video Reconstruction  
The extracted classical bitstream from the superposition states is recovered via polar decoding, followed by VVC decoding to reconstruct the original video. This marks the completion of the end-to-end quantum video transmission process.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Coverage and Range Improvement  
#### 7.1.1 Coverage Problem  
BackCom deals with the reflected signals having very low signal parameters (such as amplitude and frequency). Moreover, the round trip path loss and interference also attenuate the BackCom signals [61]. Consequently, the communication range of backscattered signals is very limited. In order to address the challenge of short range and to maximize communication range, various techniques are suggested. The coverage problem was analyzed in the wireless passive sensor network, and the analysis results can be used to determine the design procedures for battery-free wireless passive sensor networks and RFID networks [161].
## 3 Conditional Diffusion Model for OFDM-Based Image Reconstruction
### Conditional Diffusion and Reverse Processes
Therefore, the proposed CDM with LV (CDM-LV) optimizes the selection of variance based on the modulation scheme and SNR conditions, while also adjusting \(\Sigma_{\theta}(x_{t},y,t)\) through learning to achieve high-quality sampling with small diffusion time steps. In the proposed CDM, the learnable \(\Sigma_{\theta}(x_{t},y,t)\) was considered in the design of the loss function \(\mathcal{L}_{\text{CDM}}\), which is given by the following equations:  
\[\Sigma_{\theta}(x_{t},y,t)=\exp(\gamma\text{log}\sigma_{t}+(1-\gamma)\text{ log}\,\bar{\sigma}_{t}). \tag{28}\]  
\[\mathcal{L}_{\text{CDM}}=\mathcal{L}_{\text{CDM\_simple}}+\lambda\mathcal{L }_{\text{CDM\_VLB}}, \tag{29}\]  
\[\mathcal{L}_{\text{CDM\_simple}}=\left[\left\|\left(\frac{v_{t}\sqrt{\bar{ \alpha}_{t}}}{\sqrt{1-\bar{\alpha}_{t}}}(y-x_{0})+\sqrt{\frac{\sigma_{t}}{1- \bar{\alpha}_{t}}}e\right)-e_{\theta}(x_{t},y,t)\right\|^{2}\right], \tag{30}\]  
\[\mathcal{L}_{\text{CDM\_VLB}}=\sum_{t=2}^{T}D_{\text{KL}}(q(x_{t-1}|x_{t},x_{ 0},y)||p_{\theta}(x_{t-1}|x_{t},y)\,, \tag{31}\]  
where \(\lambda\) denotes the weight of \(\mathcal{L}_{\text{CDM\_VLB}}\). A summary of the training and sampling processes is presented in Algorithm 1 and Algorithm 2, respectively. ```
1 for\(i=1,\;2,\;\ldots,N_{iter}\)do
2 Sample \((x_{0},\;y)\;\sim\;q_{data},\;\epsilon\;\sim\;\mathcal{N}(0,\mathbf{I})\)
3\(t\;\sim\;\text{Uniform}(\{1,\ldots,T\})\)
4\(x_{t}=\big{(}(1-v_{t})\sqrt{\bar{\alpha}_{t}}x_{0}+v_{t}\sqrt{\bar{\alpha}_{ t}}y\big{)}+\sqrt{\sigma_{t}}\epsilon\)
5 Compute \(\sum_{\theta}(x_{t},y,t)\;=\exp(\gamma\text{log}\sigma_{t}+(1-\gamma)\text{ log}\,\bar{\sigma}_{t})\)
6 Compute \(\mathcal{L}_{\text{CDM}}=\mathcal{L}_{\text{CDM\_simple}}+\lambda\mathcal{L }_{\text{CDM\_VLB}}\)
7 endfor
```  
**Algorithm 1**Training algorithm in CDM-LV
## 2 Materials and Methods  
### Raw Materials and Characterization Procedures  
The materials selected for the present study were one lignite from the open-pit mine of Kardia (KL) located in the area of Western Macedonia in North Greece and barley straw residues (BS) collected from the fields near the power plant of Kardia, all provided by the Public Power Corporation of Greece. After air drying, the materials were ground to a particle size below 250 \(\upmu\)m, in order to avoid heat and mass transfer effects during the tests. The lignite was ground in a jaw crusher and ball mill, whereas barley straw was ground in a cutting mill. Following riffling, mixtures of lignite and straw were prepared with blending ratios of 75:25, 50:50 and 25:75, respectively. Representative fuel samples were characterized on the basis of the ASTM standards (D5142, D5373, D4239, D5865) in the case of lignite and CEN/TC335 European standards in the case of biomass, in order to determine volatiles, fixed carbon and ash contents, elemental composition and calorific value.  
Structural characteristics were derived through liquid nitrogen adsorption experiments, at relative pressures of 0.03-0.3, by applying the BET method. Prior to measurement, the samples were out-gassed under vacuum overnight at 150 \({}^{\circ}\)C. The equipment used was volumetric apparatus, namely, the Quantachrome Nova 2200 model (Quantachrome, FL, USA).  
The fusibility behavior of ashes was studied through the use of a heating microscope, equipped with a high-definition video camera, in accordance with the DDCEN/TS, 15370-1:2006 European standards. Each sample was heated in air up to 1500 \({}^{\circ}\)C with a heating rate of 5 \({}^{\circ}\)C/min, and the initial deformation temperature (IDT), the softening temperature (ST), the hemispherical temperature (HT) and the fluid temperature (FT) were recorded. To support such results and obtain a deeper insight, the ashes were analyzed by a D8 Advance X-ray diffractometer (XRD) from Bruker AXS and their mineral phases were identified using the JCPDS database in conjunction with DIFFRAC plus software v.2004. Furthermore, trace elements in ashes were determined by an inductively coupled plasma mass spectrometer ICP-MS 7500 cx (Agilent Technologies, Santa Clara, CA, USA) equipped with an Autosampler Series 3000 from Agilent Technologies and assisted by an Anton Paar Multiwave 3000 oven for sample digestion (detection limits of 0.4-34 ppb, depending on element measured).
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 4 Experimental Setup and Program  
### Experimental Setup  
To validate the proposed method and obtain more satisfactory welding products, corresponding experiments are conducted. The experimental platform is mainly composed of a Digital Signal Processor (DSP) integrated double-wire MIG arc welding power source, a traveling controller, a welding test bench, a dynamic arc wavelet analyzer, a wire feeder, a welding gun and a motion control platform. During the welding process, the welding gun is fixed on the motion control platform, and its movement is driven by the control platform. Figure 4 shows the experimental platform employed in this work.  
During the experiments, after several trials, proper experimental conditions were chosen as follows: the peak of the leading wire current was 340 A, while the base was 78 A; the duration of the leading wire current in peak stage was 5.2 s; the base of the trailing wire current was 78 A; the parent metal was Q235 steel with 8 mm of thickness; welding wire was H08 Mn2 SiA (\(\Phi\) = 1.0 mm); shielding gas was pure argon (flow 15 L/min); wire stretch was 12.0 mm; and the distance between the two terminates of the wire was 8.0 mm. In addition, bead-on-plate welding was used, and the speed of the traveling equipment was 1.0 m/min.
# Impact of Different Nose Lengths on Flow-Field Structure around a High-Speed Train
## 4 Results and Analysis  
### Aerodynamic Force Coefficient  
Aerodynamic force has a great impact on the operation of a HST, in which drag force will affect the speed increase and increase energy consumption, while lift force will have an impact on the stability of train operation. In the absence of crosswind, the lateral force tends to 0. Therefore, the drag force and lift force of the head and tail cars are studied below. \(C_{x}\) and \(C_{z}\) of the head car and tail car of the train with a sample time of \(t^{*}\) between 62 and 249 are averaged, and the time-averaged \(C_{x}\) and \(C_{z}\) of the head car and tail car with different nose lengths are obtained, respectively, as shown in Figure 8. Through comparison, it is found that \(C_{x}\) of the head car and tail car decrease with increasing nose length. \(C_{z}\) of the head car increases with increasing nose length, and \(C_{z}\) of the tail car first decreases and then increases with increasing nose length. When the nose length increases from 4 to 7 m, \(C_{x}\) and \(C_{z}\) change obviously, in which \(C_{x}\) of the head car decreases by 17.6%, \(C_{x}\) of the tail car decreases by 29.3%, \(C_{z}\) of the head car decreases by 15.8%, and \(C_{z}\) of the tail car decreases by 75.7%. The nose length increased  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(C_{x}\)} & \multicolumn{2}{c}{\(C_{z}\)} \\ \cline{2-5}  & **Head Car** & **Tail Car** & **Head Car** & **Tail Car** \\ \hline
**Numerical simulation** & 0.1371 & 0.1195 & \(-\)0.0251 & 0.0587 \\
**Wind tunnel test** & 0.1446 & 0.1144 & \(-\)0.0184 & 0.0709 \\
**Error (\%)** & 5.19 & 4.46 & 36.41 & \(-\)17.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental results and numerical simulation results.  
Figure 7: Train model: (**a**) wind tunnel model and (**b**) numerical simulation model.  
from 7 to 12 m, and the difference of \(C_{x}\) and \(C_{z}\) was not obvious. It is shown that the drag coefficient and lift force coefficient can be effectively reduced by properly increasing the nose length.