esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Analysis
#### 4.1.7 Recall  
Recall, often referred to as the true positive '_Tp_' rate, represents the percentage of correctly detected attack flows out of the total number of flows. In essence, it measures the algorithm's ability to capture and identify all instances of actual attacks. The mathematical representation is shown in Equation (13).  
\[Recall=\frac{T_{P}}{T_{P}+F_{N}}*100 \tag{13}\]  
Figure 8 shows that the proposed BSHNN outperforms existing methods such as LSTM, RNN, and IDS in network classification. Its higher recall rate indicates its effectiveness in identifying more attack flows, which is shown in Figure 8a. This is due to its innovative approach, which streamlines the training process and reduces training time, making it an efficient choice for packet flow and content verification compared to traditional methods.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 2 Proposed Model Based on Cascade R-CNN
### Block Recognition Algorithm  
The statistics of fabric defect datasets state that the difference in defect sizes is usually large, and numerous small defects exist. The detection of small targets in high-resolution images is a difficult task in deep learning. If the high-resolution image is resized directly for training, the pixels with small- and medium-size defects in the image will be seriously lost, resulting in the inability of the model to learn its features. Thus, we proposed a defect recognition method called the block recognition algorithm.  
In the training process, large images were divided into smaller images, which were then used to train the model. In the inference process, large picture inputs were divided into small images and fed into the network, as shown in Figure 2. Then, the detection results of small images were merged to obtain the final detection results of the large picture input. This novel block recognition method can effectively improve the detection precision of small and medium size defects in high-resolution images.  
Figure 1: Overall structure of Cascade R-CNN.  
Figure 2: Big images cropped into smaller images.  
The algorithm for merging the detection results can be divided into three parts. First, after determining the detection results for each small image, each defect position in the large image was obtained according to the relative position of the large picture and small pictures. We used the Non-Maximum Suppression (NMS) [23] algorithm to remove repeated detection results in the intersection region of small images. Finally, if the IoU [24] of two detection boxes was found to be higher than a threshold, they were merged into a big box. Meanwhile, the largest enclosing rectangle of two small detection boxes was set as the new detection box and the maximum of two scores was set as the new score.  
As shown in Figure 3, the two images on the left had overlapping areas, which led to repeated detection. To determine the number of defects and evaluate the quality of the cloth, we located the positions of detected defects in the large image on the right and used the NMS algorithm on the results.  
As shown in Figure 4, the two images on the left had overlapping areas, and the defects detected on them were incomplete; however, to accurately evaluate the fabric quality, complete defects must be obtained. The yellow rectangle on the right side of Figure 4 shows the detection result after merging.
## 2 Materials and Methods
### Photocatalytic Efficiency Evaluation  
The photocatalytic efficiency was evaluated by the photodegradation of RhB (rhodamine B) (10\({}^{-5}\) Mol/L) solution under UV-C (254 nm) light irradiation. The samples were immersed in the beaker with 50 mL of RhB solution and placed on the magnetic stirrer under the UV-C light irradiation. Additionally, oxygen gas was introduced into the solution during the measurements, with a 3.6 L/h flow rate, to slightly enhance the photodegradation of RhB. The measurements were conducted in the following order. First, the setup was left in the dark for 30 min to reach the equilibrium between the photocatalyst and RhB solution. Then, the specimen of \(\sim\)3 mL of RhB was taken, and the light absorbance was measured (which corresponds to the \(C_{0}\)). The subsequent measurements were taken under UV-C irradiation every 20 min. The obtained absorbance values corresponded to \(C_{1}\), \(C_{2}\), \(C_{3}\), etc. The taken RhB solution was returned to the beaker after each measurement. The light absorbance was measured by a UV-Vis spectrophotometer USB4000 (Ocean Optics Inc., Rochester, NY, USA) in the 500 nm-600 nm range, and the spectra were analyzed using the \"OceanArt\" software. The degradation rate constant (\(k\)) was calculated using this formula [32]:  
\[k=-\frac{ln\left(\frac{C_{i}}{C_{0}}\right)}{t}, \tag{1}\]  
where \(C_{0}\) is initial concentration of RhB, and \(C_{i}\) is concentration of RhB at time \(t\).  
While the degradation efficiency \(D_{gfi}\) was calculated using the formula [33]:  
\[D_{efi}=\frac{C_{0}+C_{i}}{C_{0}}\cdot 100\%, \tag{2}\]
# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 3 Proposed Algorithm  
### Overview  
Figure 1 shows an example of the operations of the proposed SBR algorithm. For the sake of simplicity, only samples produced from a 3-axis accelerometer were considered for gesture recognition, as shown in the top graph of Figure 1. Furthermore, the system is capable of the detection/classification of two gesture classes. The detection of a single gesture involves the detection of PKI and SKI, which are based on the scores produced by the proposed neural network model. We can observe from the top graph of Figure 1 that the samples in the detection sliding window are served as the inputs to the neural network. For each class, there are separate scores for the detection of PKI and SKI at the output of the neural network.  
The second and third graphs of Figure 1 reveal the resulting PKI and SKI scores for the sensory data. The scores are subsequently compared with prespecified thresholds for the detection of PKI and SKI. When a detection of a PKI is followed by a detection of an SKI, and both the PKI and SKI belong to the same gesture class, the detection is matched. Otherwise, the detection is unmatched, and is ignored by the subsequent classification. After the occurrence of a matched detection, the subsequent gesture classification is straightforward. As shown in the second and third graphs of Figure 1, because the key intervals and their associated gesture should belong to the same class, we select the class that both PKI and SKI belong to as the result of the gesture classification.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests  
Conventional interpretation techniques analyse the output signal of bender element tests to extract the travel time of the shear wave from the transmitter to the receiver. The travel time enables the calculation of the shear modulus, typically using the continuum elasticity expression,  
\[G_{0}=\rho\times\left(v_{S}\right)^{2}=\rho\times\left(\frac{h}{t}\right)^{2} \tag{1}\]  
where \(G_{0}\) is the shear modulus, \(v_{S}\) is the shear wave velocity, \(\rho\) is the density of the material, \(h\) is the effective travel distance of the wave, typically (but not always) measured between the tips of the emitter and receiver bender elements, and \(t\) is the travel time that the wave required to cover this distance.  
Measuring the travel time of the shear wave is the most complex issue, as its arrival at the receiver is often concealed by polluting compression waves. Various methods for measuring the travel time have been proposed to overcome this problem, but considerable disagreement still exists regarding the interpretation technique that would give the most accurate results [8; 9; 15; 21; 22; 23; 24; 25; 26]. Five time domain methods and one frequency domain method are used to validate the GeoHyTE measurements in Section 4.2. These methods are succinctly described below, for reader's convenience.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 5 Conclusions  
A novel approach to a side-by-side investigation of 2-D and 3-D PRS techniques for successful vineyard yield estimation has been presented. This study assessed RGB imagery captured by a digital camera and RGB-D imagery captured by a Kinect V1 sensor across three experiments, with in-situ measurements obtained under two canopy treatments. Our results show that the Kinect RGB-D sensor produced the highest yield estimation agreement under laboratory conditions (bunch-level). At bunch-level, RGB and RGB-D PRS techniques performed equally under both canopy treatments for in-situ yield estimation. At plant-level, the best in-situ results were obtained using the RGB imagery, which significantly outperformed the RGB-D results. Both sensors support the use of PRS technology and techniques for vineyard yield estimation, with improved accuracies presented. The results of this study confirm the operational potential of 2-D RGB imagery for accurate yield estimation, with the recommendation that future work should investigate a more automated RGB methodology suitable for operational environments. Regarding the presented RGB-D methodology, the Kinect demonstrates the potential for vineyard yield estimation using 3-D RGB-D imagery. Future work should investigate the use of the Kinect V2 sensor coupled with suitable lighting conditions for in-situ yield estimation.  
C.H.--data analysis, writing and editing of the manuscript; N.P.--content supervision, and editing of the manuscript; N.M.--data collection and analysis; C.P.-E.--conceptualisation, experimental study design, content supervision and editing of the manuscript.  
This research was funded by Winetech, project DVO 07: \"Near-real-time characterization of vines for more efficient vineyard management\".  
The authors would like to thank [PERSON], [PERSON], [PERSON] and [PERSON] for their technical and research support.  
The authors declare no conflicts of interest.
## 2 ELM-AE and Shrinkage-Thresholding Algorithms
### The Shrinkage-Thresholding Class of Optimization Algorithms
#### 2.2.1 Fast Iterative Shrinkage Thresholding Algorithm  
FISTA [38] is characterized by its convergence speed and high level of efficiency. When minimizing the convex problem in (4), the algorithm has a convergence rate on the order of \(\mathcal{O}(1/k^{2})\). The sequence of steps of the FISTA algorithm is as follows:  
(1) Update: \(\mathbf{\tau}_{k}=\text{prox}_{\rho\mathbf{\phi}}(\mathbf{z}_{k}-\rho\
abla f(\mathbf{z}_{k}))\).  
Execute the intermediate step: \(t_{k+1}=\frac{1+(1+4t_{f}^{2})^{1/2}}{2}\).
3. Update: \(\mathbf{z}_{k+1}=\mathbf{\tau}_{k}+\gamma_{k}(\mathbf{\tau}_{k}-\mathbf{\tau}_{k-1})\).  
The term \(\rho\in(0,\frac{1}{L_{f}}]\) represents the step size, and \(\gamma_{k}=\frac{t_{k}-1}{t_{k+1}}\) represents the impulse factor. FISTA's convergence rate depends mainly on its proximal operator and the use of a weighted step \(\mathbf{z}_{k}\), which is expressed as a combination of both \(\mathbf{\tau}_{k-1}\) and \(\mathbf{\tau}_{k}\). In addition, the configuration of the Lipschitz constant \(L_{f}\) of the gradient \(f\) plays an essential part in the convergence of the algorithm.
## 2 Materials and Methods
### Experimental Apparatus  
A DTF was used to identify NOx exhaust concentrations during the combustion state of the chosen coals. A schematic of the DTF device is provided in Figure 1. The DTF is divided into three systems--fuel injecting, fuel reaction, and collecting. The fuel injection system is located in the upper part of the device. Solid fuels, such as coal and biomass, are injected into the reactor through a carrier gas. The fuel supply is created by continuously injecting a fixed amount of coal with a nitrogen carrier gas using a syringe pump. A highly pure alumina tube with strong thermal resistance, an inner diameter of 700 mm, and 1200 mm long was used as the fuel-reaction system. Kanthal Super, which can be heated to 1850 \({}^{\circ}\)C, was used as the heating element. A cyclone and gas analyzer were used as collectors. The cyclone was located at the lower part of the device and it collected the ash, including unburned carbon emitted after combustion. The gas emitted from the reactor was analyzed/recorded in real time using the gas analyzer (Green Line MK2, Eurotron Instruments, Chemsford, UK). To reduce the experimental margin of error, three tests were conducted for each combustion condition for each sample and NOx generated after combustion was marked according to the 6% oxygen standard. The upper and lower parts of DTF consisted of assemblies of piper and water jacket and were designed to prevent the reaction from occurring near the input of the coolant. A mass flow controller (EX-250S, Kofloc, Kyoto, Japan) was used to control the flows of the carrier gas (N\({}_{2}\), 1 L/min) and reaction gas (N\({}_{2}\), O\({}_{2}\)). To create the test environment near the second boiler in the Hadong Thermal Power Site, the temperature of the DTF main furnace, particularly the vertical alumina tube, was fixed at 1400 \({}^{\circ}\)C to conform to the combustion environment of the actual coal boiler. In addition, samples with a particle size of 75 to 90 \(\upmu\)m were provided with a fixed heat of 5,600 kcal/kg. Therefore, the input heat was calculated to be 1.68 kcal/min considering that the standard fuel supply was 0.3 g/min. By applying a stoichiometric ratio of 1.16, the total gas flow rate was 5 L/min [17].  
Figure 1: Schematic of the drop tube furnace.
# Coordination and Private Information Revelation
## Appendix B Uniqueness of the Non-Revelation PBEM Over a Larger Class of Disclosure Strategies
: Consider any \(\hat{\theta}=\theta^{\prime}-\epsilon\), where \(\epsilon\) is vanishingly small. Whereas \(\theta^{\prime}\) is indifferent between revealing and not revealing (it is the infimum of \(R^{h}\)), \(\hat{\theta}\) reveals its type as it is in the contiguous revelation range. However, for all \(\theta<\hat{\theta}=\theta^{\prime}\), \(q_{nr}>q_{R}\) ensuring that by deviating from revelation, \(\hat{\theta}\) can get a higher payoff (switching without revealing will give a payoff \(q_{nr}c>q_{R}c\)). Thus, \(\hat{\theta}\) will not reveal. This proves that there cannot be any contiguous range of revelation with \(R^{h}\subset R^{c}\). As we can show deviations from revelation for any \(\hat{\theta}\) contiguous with the non-revelation set which contains \(\theta_{h}\), the non-revelation set \(R^{c}\) is a continuous interval and not a finite union of disjoint intervals. **Proposition A1**. : _Non-revelation is unique in the class of disclosure strategies where A reveals its type over \(R\) (either a continuous interval or a finite union of disjoint intervals)._  
Proof. : Lemma A1 proves that as long as \(\theta_{h}\) is an element of the non-revelation interval, no type below it will reveal in equilibrium. Lemma 2 proves that \(\theta_{h}\) will never reveal and will always belong to \(R^{c}\). Hence, the only equilibrium over the disclosure set R involves no information revelation.
## 3 Materials and Methods  
The research methodology, as illustrated in Figure 3, was designed to test the localization capability of the proposed signal segmentation technique. The purpose of the proposed technique is to localize a misalignment fault on the linear guide rail based on the vibration signature the linear axis stroke generates in both directions.  
First, a right horizontal misalignment fault was introduced on the linear guide rail component at the halfway mark of the stroke. Then, vibration data were collected and processed through the segmentation technique. The segmented data were analyzed to localize the area of the misalignment, assess its severity, and provide a repair decision to the user. The details of the linear axis testbed and data acquisition equipment used in the presented article are outlined in Section 3.1. Finally, a detailed description and flowchart of the signal segmentation technique are outlined in Section 3.2.
# A Multi-Segment Expanded Anchor for Landslide Emergency Management
###### Abstract  
Conventional mortar cemented anchors cannot meet the needs for landslide emergency management, as they can withstand loads only after mortar curing for 14-28 days. This study developed a multi-segment expanded anchor (MSEA) that expands using a sliding mechanism and rapidly bears loads using multiple load-bearing bodies by the frictional drag along the perforated wall, and the reaction force of the underreams' platforms. Field tensile tests without grouping and the secondary tensile tests of six MSEAs, each with a length of 23 m, were carried out. The field tests show that the installed load-bearing bodies expanded steadily when the MSEAs were tensioned without grouping. In hard sandstones, the initial tensile force of each load-bearing body exceeded its design value of 150 kN, and the initial bearing capacity of each MSEA was greater than 300 kN, which exceeded the bearing capacity of the existing anchors regardless of grouting. The secondary tensile tests of the MSEAs were conducted after they were grouted for three days, and their lock-off load was greater than their design value of 600 kN. Based on the elastic mechanics and the stress process of the MSEAs, this study derived the theoretical equations of the ultimate bearing capacity of the MSEAs without grouting. The results calculated using the theoretical equations were roughly consistent with the field test results of the ultimate bearing capacity.  
multi-segment expanded anchor; landslide; emergency management; rapid construction; tension without grouting; bearing capacity
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 1 Introduction
### Contributions  
In this paper, we propose a new algorithm for 5G NR beam pair selection at the IBE stage, referred as BSAE. The main contributions of this work include:* The design of a new 3 GPP-standard-compliant algorithm that aims to improve the beam pair selection stage by using multiple synchronization signal blocks (SSBs) to maximize the RSRP value at the receiver, considering the equivalent channel propagation matrix estimation based on SSSs to subsequently enhance the overall performance of the system.
* The optimization is performed using the SSSs present in each SSB to perform channel estimation in the digital domain, which contains the analog processing effects. Subsequently, the set of those estimations are used to estimate the channel without the analog equalizer effect, providing information about the spatial correlation of the channel. Finally, through the estimated channel propagation matrix, the angle that maximizes the RSRP is determined to compute the most suitable beam.
* Simulation and performance analysis comparison of different beam pair selection algorithms employed in an NR system that makes use of BM procedures at both gNB and UE ends for the IBE process.  
The simulation results show that the proposed BSAE algorithm achieves better performance than a conventional beam pair selection algorithm based on RSRP maximization in terms of the physical broadcast channel (PBCH), physical downlink control channel (PDCCH), and physical downlink shared channel (PDSCH) throughput.  
The remainder of this paper is organized as follows: Section 2 describes the system model considered in this work, and Section 3 presents the design of the proposed algorithm. Finally, the main performance results are shown in Section 4, and the conclusions in Section 5.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 5 Numerical Experiments
### Grayscale Video Completion  
We used three grayscale video datasets ([[http://trace.eas.asu.edu/yuv/](http://trace.eas.asu.edu/yuv/)]([http://trace.eas.asu.edu/yuv/](http://trace.eas.asu.edu/yuv/)), accessed on 16 August 2023), namely \"hall\", \"carphone\", and \"container\", each with dimensions of \(144\times 176\times 150\). We compared our model against TNN, UTNN, FTNN, and NTTNN. To highlight the effectiveness of our model, we conducted quantitative comparisons at extremely low sampling rates (SR) of 1%, 5%, 10%, and 20%. Table 1 enumerates the numerical results of different image restoration methods, with the best results displayed in bold. Except for partial sampling of some images, our model's SSIM scores are slightly lower than those of the FTNN method. In all other cases, our model achieves the best numerical results. It can be observed that our model demonstrates a significant advantage, particularly at ultra-low sampling rates of 1% and 5%, recovering a substantial amount of information from the grayscale videos while preserving image structure and details better than other methods. In Figure 3, it can be seen that our model is able to complete more detailed information. For example, in the \"container\" image, our model more accurately restores the details of the woman's hair and lips, while other models only recover rough outlines. This indicates that our nonconvex nonlinear model yields excellent results in recovering grayscale video images, particularly in the restoration of finer details.