esa-sceva/satcom-chunk-collection huggingface dataset:
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Implementation  
**Hardware setup.** RF-Access uses an enterprise-level commercial reader, Impinj Speedway R420, which has four antenna ports and a high tag reading rate with hundreds of tags per second. The antenna is Laird S9028 PCL [20], which is a circularly polarized directional panel antenna and can receive and transmit signals within the 902-928 MHz frequency band. The antenna gain is 9 dBiC, and the beam width corresponding to 3 dB is 70 degrees. A Laird S9028 PCL antenna can concentrate energy in the access control passageway for radiation with the far radiation distance.  
**System deployment.** As shown in Figure 12, RF-Access deploys an RFID reader with three antennas and an RFID tag array at a two-way single passageway access control with  
Figure 11: Top view of RF-Access deployment  
a width of 80 cm. One reader works at 32.5 dBm power and 924.375 MHz frequency. Two antennas (antenna #1 and antenna #2) are deployed on one side of the passageway with an interval of 40 cm and an incline of 20 degrees to both ends of the passageway, respectively. The third antenna #3 is deployed between the two antennas, facing the other side of the passageway for intrusion detection. A 6\(\times\)10 tag array is deployed on the other side of the passageway. RF-Access uses Impini) H47 RFID tags with a size of 44 mm \(\times\) 44 mm. The distance between two adjacent tags is 10 cm. The height of the tags is from 0.4 m to 1.15 m. Antenna #3 faces the center of the tag array, which is 80 cm away from the ground. The antenna continuously records the signal changes from all tags in the array. The passageway is about 1.5 m long.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 1 Introduction
[11] classified effective signals using ensemble empirical mode decomposition and deep convolution neural networks. Firstly, the empirical ensemble mode decomposed the seismic signals, and the modal function components obtained the three-dimensional Hilbert spectrum using the Hilbert transform. Then, the three-dimensional Hilbert spectrum was used as a training set for the deep convolutional neural network. The Hilbert spectra of natural earthquake and explosion signals were predicted after training, obtaining excellent results. [PERSON] et al. [21] classified seismic images into saline and non-saline datasets on a patch-by-patch method. They used the trained deep convolution neural network to automatically capture subtle salt features from 3D seismic images. [PERSON] et al. [22] proposed a lightweight unsupervised network that combines a convolutional neural network and an unsupervised classification method. The method improves computational efficiency and achieves excellent classification results by extracting signal features to classify seismic signals. Seismic data denoising methods based on deep learning have achieved significant advancements and found widespread use compared with traditional seismic data denoising methods. However, it is difficult to gather sample seismic data because the deep learning denoising method requires a large amount of labeled seismic data to train the network. The trained model has weak generalization ability, which significantly limits the use of deep learning networks in denoising seismic data [19]. In this paper, two-dimensional seismic data were denoised using SVD combined with supervised deep learning techniques. The singular vectors after SVD also exhibited characteristics that discriminate between noise and effective signals, as demonstrated by [PERSON] et al. [12]. We used their study as a foundation, to develop a supervised deep learning technique that automatically determines the right singular vector (RSV) relating to noise and effective signal. Only the RSV corresponding to effective signals were used to reconstruct the seismic data.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 2 Methods
### NASA's Harmonized Landsat-8 Sentinel-2 Product (HLS)  
The United States National Aeronautics and Space Administration (NASA) have released a product which combines Landsat-8 and Sentinel-2 imagery in a consistent form. For this purpose, they have fitted linear equations to adjust the surface reflectance of each Sentinel-2 band to match the corresponding Landsat-8 band. However, their approach was to simulate the two instruments from hyperspectral data collected by the Hyperion instrument (16), from 500 individual sites around the world. This has the advantage of being generated from exactly the same data, so there is no possibility of differences due to change on the ground, or differences in observation conditions. [PERSON] et al. (16) show that this kind of technique is capable of simulating the data observed from orbit by Landsat-7, and, in principle, this should be true more widely.  
The User's Guide to version 1.2 of the HLS product [6] gives the coefficients fitted to this simulated dataset, which use the same equation as used here (Equation (1)) to adjust Sentinel-2 surface reflectance to match Landsat-8. In principle, this should be equivalent to the adjustment procedure outlined here. To test the consistency of these two models, the HLS adjustment was tested here on the same dataset, to see whether it also reduces the difference between the two instruments. The coefficients used for adjustment in the HLS product are reproduced here in Table 2.  
The HLS project chose to use the Sentinel-2 band 8A for their NIR band, as opposed to band 8 used in the current work (see Section 2.1). For this reason, comparing the adjustments for the NIR band is meaningless, and these are not included in the comparisons presented.
## 3 Experiment Setup and Evaluation
### Algorithm Configuration  
Machine learning algorithms require parameter settings for training. Various features, such as Haar-like, local binary patterns (LBPs), and HOG, can be used in the Viola-Jones detector. In this study, the detector is trained using these features. The ROI size was automatically calculated as 32 \(\times\) 32 px. The methodology followed in this study to train and test the detector is shown in Figure 9.  
Figure 9: Methodology for the training and testing of the Viola-Jones detector [42].  
To create negative images, scenes that did not contain an ROI were selected. The ROIs were then extracted from the positive images and used to generate 360,000 negative images through 360\({}^{\circ}\) rotations. The NumCascadeStages and FalseAlarmRate parameters were crucial to the process, and were set to 10 and 0.01, respectively. Although decreasing the FalseAlarmRate or increasing the NumCascadeStages can improve the robustness of the detector, it significantly lengthens the training time. For this study, it took approximately 60 h to train the Haar-like feature, whereas LBP and HOG features took 10 h and 20 h. All other parameters were set to their default values.  
Several experiments were performed on an ACF detector with 10 stages each. The modified parameters were the number of weak classifiers and the ROI size. In the first experiment, these parameters were not modified, that is, the ROI was 23 \(\times\) 23 px, and the number of weak classifiers was 2048 in all stages. In the other experiments, variations were made in these parameters to analyze their influence on the final detector. The ROI used in the combinations was 52 \(\times\) 52 px, and the weak classifiers were [256 256 512 512 1024 1024 2048 2048 4096 4096]. This ROI was obtained from the maximum anchor box size of the deep learning algorithms. The experimental details are presented in Table 1.  
In this case, the negative images were automatically generated by the algorithm. The remaining parameters were set to default values. The total training time ranged from 0.15 to 0.20 h.  
In the case of detectors based on deep learning, images were resized to 224 \(\times\) 224 px. The anchor boxes were then estimated. According to this database, the obtained anchor boxes were [52 26; 42 28; 47 21; 34 25; 38 19; 26 24; 29 16; 22 15; 34 8]. A CSPDarkNet53 backbone was used in the YOLO v4 detector. ResNet-50 and MobileNetV2 were used as backbones for the Faster R-CNN and SSD detectors. The selection of these backbones was justified by the good results presented in [43; 44; 45; 46]. The training times for the detectors were 2.25 h and 0.25 h.
## 3 Running Logic and Data Organization of the M\({}^{2}\)ES Apps System
### Identification of Key Enabling Technologies for M\({}^{2}\)ES Apps System
#### 3.2.1 Creating Data and Knowledge Graph  
As mentioned in Section 3.1.1, the Semantic Web seems to be the basis of M\({}^{2}\)ES Apps System. So, a schema for entity-centered resources will be created in order to describe the resources and manage them. Semantic technologies, such as especially OWL 2 ontology, are ideal models for the heterogeneous resources to enable the data exchange across different entities [43]. OWL 2 provides a rich and flexible modeling language for creating a domain ontology, which uses shared vocabularies to describe classes, properties, individuals, and data values. An ontology model of an entity can be denoted as  
\[O=\{URI,C,P\} \tag{1}\]  
where \(C\) denotes the ontology classes and class hierarchy. \(P\) stands for the property collection of the entity.  
In the meantime, it's needed to create a data Table in the Basic DB for each entity resource \(O\) in the operating level, which can save the properties and status data of the registered resource in the form of \"_propertyName: propertyValue_\".
## 4 Experiments
### Usability Evaluation  
In this section, we assess the usability of the generated results through qualitative evaluation. Specifically, we compare the generated bubble diagrams with real reference diagrams to analyze the modifications needed to refine the generated outputs into practical and usable designs. This assessment approach allows us to observe the ease with which users can adjust the generated diagrams to meet practical needs, highlighting the efficiency and adaptability of the model's output in real-world scenarios. The GED metric is employed to assess the difference between the generated bubble diagrams and the actual bubble diagrams. A smaller GED indicates that fewer edits are required to align the generated diagram with real data. The GED range is divided into four intervals, each representing different evaluation levels, as shown in Table 3. We first trained four models using the bubble diagram dataset and saved their parameters. Subsequently, the models were tested with 500 samples for clear comparison. The GED distributions of the four models are presented in Figure 16.  
The proposed method demonstrates high competitiveness in practical applications. DBGNN achieved a remarkable 93.8% of generated bubble diagrams requiring fewer than five graph edits, while other models produced fewer than 52%. Additionally, DBGNN generated 299 bubble diagrams with a GED of 0, significantly surpassing other models. This indicates that DBGNN's results have exceptional utility, allowing users to quickly obtain satisfactory bubble diagrams. These experimental results emphasize the advanced  
Figure 15: (a) Total GED variation curve of 100 experiments. (b) Minimum GED statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**GED Range** & **Level** & **Description** \\ \hline
0 & Outstanding & No additional modifications needed; fully compatible with real data and entirely usable. Modifications can be completed in a short time, demonstrating high efficiency and usability. Edits can be made within an acceptable timeframe, maintaining usability. Excessive editing required cannot be completed within a tolerable timeframe, resulting in low practical value.  
\end{table}
Table 3: Evaluation levels of bubble diagram generation results.  
performance of our model in creating bubble diagrams with precise local links and coherent global structure, highlighting its usability and efficiency.
# An Exploit Traffic Detection Method Based on Reverse Shell
## 3 Methodology
### Traffic Feature Analysis
#### 3.1.2 Fusion Information Features  
The original session features extracted in Section 3.1.1, such as packet delay sequence, packet direction sequence, and packet length sequence, are relatively simple; however, it may be impossible to extract the information thoroughly. Therefore, in this section, based on the concept of delay packet, information fusion is carried out on the above features from the two dimensions of time and space. The fused feature vector is taken as the model input.  
The delay packet in a TCP session is defined as follows.  
**Definition 2**.: _Delay packet (delayed packet) is a packet the delay ratio of a packet in a session to the duration of the whole session (two decimal places reserved) is greater than 0. Otherwise, it is a non-delay packet (non-delayed packet)._  
Based on the original session features extracted in Section 3.1.1, the fusion information feature vector is available. It contains five flag bits, and each sub-features are as follows:  
1. The number distribution sequence of delay packets within 3 min before reverse flow. Three flag bits represent the number distribution of delay packets in the first three minutes. The number \"0\" indicates that no reverse delay packet exists in the time range, and \"1\" represents that the reverse delay packet exists in the time range;
2. Whether the packet length sequences of the delayed packet and non-delayed packet obey the same distribution. First, we extract the packet length sequences of the session. The directions are symbolized by positive and negative values. Then, we decide whether the two packet length sequences obeyed the same distribution (whether the \(p\)-value was less than 0.05, which indicated that the distribution was inconsistent) by the Kolmogorov-Smirnov test (K-S test). \"0\" means to obey the same distribution, and \"1\" means contrarily;
3. The difference between the packet direction sequence of the delayed packet and the packet direction change sequence. Firstly, the packet direction sequences of all delayed packets and non-delayed packets were extracted, respectively, with \"0\" representing forward and \"1\" representing reverse. Then, the sequence of packet direction change is calculated, respectively. \"0\" means the direction of adjacent packets remains unchanged, and \"1\" means the change. Next, we calculate the Hamming distance between the delay packet direction sequence and the packet direction change sequence, and its ratio to its sequence length. The feature is represented by a bit of a flag bit, and the threshold is set as 0.58. If the ratio is greater than 0.58, the flag bit is \"1\"; otherwise, it is \"0\". For details about the meaning of the threshold setting, see the following.
## 5 Conclusions  
To improve energy efficiency and environment friendliness of space heating ASHP, a new mixture refrigerant RHR-1 was proposed in this study. A theoretical model for refrigerants performance  
Figure 13: The regressed relation between \(COP_{h}\) of ASHP using RHR-1 with temperature differences of supply water and outdoor air.  
analysis was developed based on refrigeration cycle equations integrating REFPROP. The performance of RHR-1 including heating coefficient of performance, compression ratio and discharging temperature were simulated and compared with R134a, R410A, R407C and R22. The following conclusions were obtained from this study.  
1. RHR-1 has no ozone depletion potential and relatively low global warming potential compared to commonly used refrigerants including R134a, R410A, R407C and R22.
2. The \(COP_{h}\) of RHR-1 is in the range of 2.43-4.93, which is higher than other candidates in most design cases. The CR and DT of RHR-1 are in the middle levels among the compared refrigerants.
3. RHR-1 might be a reasonable refrigerant in ASHP for space heating due to its high \(COP_{h}\), appropriate compression ratio and discharging temperature, no ODP and low GWP.
4. According to the regression analysis, the temperature difference between outdoor air and supply water of ASHP using RHR-1 is suggested to be controlled within 47.5 \({}^{\circ}\)C to get a reasonable \(COP_{h}\).
5. Constant water flow control strategy was suggested to improve the energy efficiency of air source heat pump units under partial heating load cases.  
At present, clean heating reforms are underway in North China. This study proposed a new mixture refrigerant RHR-1 for ASHP used in North China which is a typical region facing energy and environment problems. The analysis was conducted using North China as a representative area. The conclusions of this study can also be used in countries and regions having similar meteorological parameters.  
This work was funded by Beijing University of Civil Engineering and Architecture in the doctoral research startup project: Experimental research on a solid desiccant air-conditioning system which integrates the function of indoor air cleaning (No. 00331616013) and supported by National Natural Science Foundation of China: Study on air dehumidification and purification mechanism of metal organic framework materials and its application in solid desiccant air conditioning system (No. 51708013).  
[PERSON] conceived the refrigerant study and developed the theoretical model, the mixture refrigerant; [PERSON] suggested the mixture refrigerant components; [PERSON] analyzed the simulation results and wrote the paper; [PERSON] improved the paper organization and English of the manuscript.  
The authors declare no conflict of interest.
## 5 Quantitative Analysis of Pore-Throat Network
### Quantitative Analysis of Throat
#### 5.2.3 Throat Shape Factor  
The factor of throat shape followed the same definition as the factor of pore shape in Section 5.1.3. The larger the value of the factor of throat shape, the more regular the shape of the cross section of the throat. Figure 19 shows the distribution of factors for the throat shape in the carbonate and sandstone samples. Normal distribution was observed for the shape factor in all the samples. The value of the shape factor was primarily in the range of 0.015-0.045, indicating irregular triangular cross sections for the majority of throats. By comparing Figures 15 and 19, it can be seen that the distributions of the factors of pore and throat shape were similar. The type of rock and the porosity seem to have little effect on the shape of the throats.  
Figure 19: Distribution of throat shape factors of REV2s of test samples.  
Figure 18: Distribution of throat volume in the REV2s of test samples.
## 2 Comparative Electromagnetic Analysis
### MPF and UMF  
The electromagnetic force or UMF generated by the motor can be derived using the Maxwell stress tensor equation. The electromagnetic force or UMF of the object placed in an electromagnetic field can be calculated by integrating the electromagnetic stress with the closed surface of the object. Electromagnetic stress is defined in Equation (4). These equations are used to obtain the radial and circumferential (tangential) components of MPF.  
\[f_{r}=\frac{1}{\mu_{0}}\Big{(}B_{r}^{2}-B_{\alpha}^{2}\Big{)},\ f_{0}=\frac{1} {2\mu_{0}}B_{r}B_{\alpha} \tag{4}\]  
Here, \(f_{r}\) is the radial force density, \(f_{a}\) is the radial force density, \(\mu_{0}\) is the permeability, \(B_{r}\) is radial flux density, and \(B_{a}\) is tangential flux density.  
Figure 3a,b show the MPF analysis results, indicating that the 9-slot model has an unbalanced distribution. In general, the symmetry of the MPF distribution can be used to determine its influence on vibration.  
UMF is derived by converting the radial and circumferential force density distributions into the \(x\)- and \(y\)-components of the force density distribution and integrating them into the air-gap, as follows:  
\[\begin{split} F_{x}&=[r_{la}/2\mu_{0}]\int_{0}^{2 \pi}\left(B_{r}^{2}-B_{\alpha}^{2}\right)\cos(\alpha)-2B_{r}B_{\alpha}\sin( \alpha)d\alpha\\ F_{y}&=[r_{la}/2\mu_{0}]\int_{0}^{2\pi}\left(B_{r}^ {2}-B_{a}^{2}\right)\sin(\alpha)+2B_{r}B_{\alpha}\cos(\alpha)d\alpha\end{split} \tag{5}\]  
Here, \(Fx\) is the force along the \(x\)-axis, \(Fy\) is the force along the \(y\)-axis, r is the radius of the rotor, la is the axial-length of the machine, \(\mu_{0}\) is the permeability, \(B_{r}\) is the radial flux density, \(B_{a}\) is the tangential flux density, and \(\alpha\) is the rotor position. In addition, UMF is generated by the specific harmonic orders of magnetic flux density, which is \(np\pm 1\). This can be confirmed by the results of the fast fourier transform (FFT) analysis.  
Figure 4a,b show the results of the FFT analysis, indicating that the 9-slot model has specific harmonic orders. Figure 4c,d show the UMF results, indicating that the 9-slot model is larger than the 12-slot model. According to the comparative analysis results, the harmonics of the spatial flux density distribution increase the amplitude of the UMF; the UMF values of the 9- and 12-slot models are 72.42 and 0.08 N, respectively, and it can be confirmed that the MPF and UMF characteristics of the 12-slot model are better than that of the 9-slot model. It was confirmed that the 9-slot model had good torque characteristics and the 12-slot model had good force characteristics; thus, the electromagnetic-mechanical analysis can confirm which factors have a significant influence.
## 3 Results
### Web Application
#### 3.1.1 Creating PCEs Based on the MRCM  
For the first task, the user starts with choosing an appropriate Focus concept for their intended postcoordinated expression. Based on this input, WASP deduces the Focus concept's domain by subsumption testing and determines the corresponding MRCM Domain Template, which states all Attributes and Attribute value ranges applicable in this particular domain. Furthermore, the Attribute relationships present in the Focus concept's definition are incorporated into the given MRCM Domain Template as well. This is necessary because a PCE can only refine the existing relationships of its Focus concepts, i.e., the valid Attribute range consists of the subhierarchy of the former Attribute value concept. Apart from this, the PCE can qualify the Focus concept by adding additional Attribute relationships.  
According to these constraints--MRCM Domain Template and Focus concept definition--the user interface is generated, as shown in Figure 5. It is rendered as a form already containing the Attributes and Attribute values of the Focus concept, as well as the At  
Figure 4: WASP offers three main functions, which are stated on the left. The user interaction process is accordingly depicted on the right, with internal queries to the terminology server added.  
tributes mandatory in the MRCM. For qualifying, the user can then add further Attribute relationships by selecting an Attribute from the list of permitted Attribute concepts and by choosing the corresponding Attribute value. For refining, only the Attribute value of an existing relationship is changed. As described in Section 2, Role groups can lend an additional structure to PCE refinements. To differentiate the different groups of Attribute relationships as well as those that remain ungrouped, multiple tabs are used within the form.  
When the user has finished editing their custom elements, clicking the button \"Create PCE\" will initiate WASP to serialize the form's content into a string representation complying with the Compositional Grammar. A summary of the created expression and a schematic diagram are shown. \"Save PCE\" will finally add the PCE to a CodeSystem supplement available on the terminology server.
## 4 Adaptive Modulation for the Single User
In the water-filling power allocation algorithm, the power in the \(k\)-th channel is allocated by \(P(k)=C-\frac{P_{n}}{|h_{k}|^{2}}\) for \(C>\frac{P_{n}}{|h_{k}|^{2}}\); \(P(k)=0\) for otherwise. \(P_{n}\) denotes the noise power, and \(h_{k}\) is the channel power gain of the \(k\)-th channel.