esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
Results are presented in Figures 1-3, for various MIMO orientations. Note that in all figure legends, the notation (\(M_{t}\),\(M_{r}\),\(L\),\(PG\)) has been considered. In Figure 1, three MIMO cases are depicted (three group of curves from left to right): (4,4,6,32), (4,4,2,32) and (8,8,6,32). In this particular set of simulations, pdf curves of \(\gamma_{k,d}\) from MC simulations considering the first transmission mode are compared with the ones derived using curve fitting with normal distribution. As is evident from Figure 1, for large MIMO orientations, normal distribution can accurately model the desired user signal.  
In Figures 2 and 3, BER results from MC simulations are compared with the theoretical ones, as derived by Equation (21). For a maximum transmission rate, processing gain (\(PG\)) is set to 32 [7]. Hence, the data rate per user is \(120\times M_{t}\) Kbps. Mean BER simulation values for the BPSK modulation were calculated for two MIMO cases (\(4\times 4\), \(8\times 8\)) (denoted as \(S\)), and compared to the theoretical ones (denoted as \(Th\)). Users may vary from 5 to 31 with a step of 2. Hence, total throughput varies from 2.4 Mbps to 14.88 Mbps in the \(4\times 4\) orientation and from 4.8 Mbps to 29.76 Mbps in the \(8\times 8\) orientation. In Figure 2, results are depicted considering reduced diversity order MIMO cases (i.e., \(L\times M_{r}\)). In particular, in each group of curves, two resolvable multi-path components are taken into account. Hence, the diversity order can be either 8 (\(4\times 4\) system) or 16 (\(8\times 8\) system). As can be observed, significant variations may occur among the calculated BER values with the proposed approach and the ones derived from simulations. However, in both groups of curves, convergence is improved for an increased number of interfering users, since, in this case, CLT is more accurate for the MAI estimation.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Step 1**: \(n\gets 0\).  
In Figure 3, the number of resolvable multipath components in an \(8\times 8\) MIMO orientation can be either 4 or 6, which improves diversity order (i.e., 32 or 48). As can be observed, there is good agreement per group of curves for a reduced number of users compared to the previous case. In particular, for 25 users, simulation and theoretical BER values are \(6.9\times 10^{-3}/7.1\times 10^{-3}\) for four multipath components, respectively. Hence, the calculation error is 3%. For six multi-paths and 23 users, corresponding values are \(5.3\times 10^{-3}/5.4\times 10^{-3}\). Therefore, the calculation error is further reduced to 2%.  
Figure 1: CLT curve fitting for various MIMO orientations.  
Figure 2: BER values for reduced diversity order MIMO orientations.
## 2 Compressive Sensing and Measurement Matrix
### Measurement Matrix  
The key in signal observation by CS is the construction of the measurement matrix. Using the same sparse matrix and reconstruction algorithm but different measurement matrices, better signal reconstruction is achieved by higher-performance measurement matrices. [PERSON] et al. [2; 3] formulated the famous restricted isometry principle, which is a required condition on the measurement matrix for it to recover the original signal without distortion.  
In the RIP, for signal \(x\) with length \(N\) and sparsity \(K\), let \(\Phi\) be the measurement matrix of dimension \(M\times N\), and \(I\) be the set of subscripts corresponding to the \(K\) columns selected from \(\Phi\) and having a number of elements less than or equal to the sparsity \(K\). If a constant exists that makes the following inequality valid:  
\[(1-\delta_{K})\lVert x\rVert_{2}^{2}\leq\lVert\Phi_{I}x\rVert_{2}^{2}\leq(1+ \delta_{K})\lVert x\rVert_{2}^{2},\]  
then the matrix \(\Phi\) is said to satisfy the \(K\)-restricted isometry property, which is written in short as \(RIP-(K,\delta_{K})\).  
Despite its perfect form and rich connotation, RIP cannot be used to guide the construction of a particular measurement matrix, as it is only a necessary but not a sufficient condition. According to the study by [PERSON] [29], the measurement matrix must satisfy the following three conditions: (1) the column vectors of a measurement matrix must possess certain linear independence; (2) the column vectors are random and independent; and (3) the solution that satisfies the sparsity is the vector that makes the norm minimum. These three characteristics are decisive in the construction of the measurement matrix, while other considerations include: (1) the amount of data should be as small as possible; (2) hardware implementation and possibility of algorithm optimization; and (3) broad applicability.  
Two basis vectors that are orthogonal are mutually independent, i.e., they have no correlation with each other. Therefore, the measurement matrix could be orthogonalized to achieve pairwise independence between the vectors, and made practical for CS. It is known from matrix properties that the singular values of a matrix are closely related to its linear correlation property. A larger least singular value indicates greater independence among the column vectors of the matrix. When the least singular value approaches zero, the matrix tends to singularity. It is thus possible to construct a measurement matrix based on this idea to achieve the desired effect.
## 3 Results and Discussion
### Probability Characteristics of Extreme Pressure Coefficients
#### 3.1.3 Comparison of Extreme Pressure Coefficients with Current Specifications  
For the claddings of high-rise buildings, Chinese code GB50009-2012 recommends using \(g_{\text{gz}}\mu_{\text{sl}}\) to represent the extreme pressure coefficient in the wind tunnel test, where \(\mu_{\text{sl}}\) is the local mean pressure coefficient, and \(\beta_{\text{gz}}\) is the gust factor at the height \(z\).  
\[\beta_{\text{gz}}=1+2 gI_{10}(Z/10)^{-\alpha} \tag{7}\]  
where \(g\) is the peak factor that is equal to the 2.5 recommended in the GB50009-2012; \(I_{10}\) refers to the nominal turbulence intensity at a height of 10 m, taken as 0.39; and \(\alpha\) is the index of the power law, taken as 0.30. Taking the 0\({}^{\circ}\) wind direction and 90\({}^{\circ}\) wind direction as examples, the contour of maximum or minimum extreme pressure coefficients for the windward wall or leeward wall is shown in Figures 9 and 10. The extreme pressure coefficients are compared between the envelope coefficients (EC) from the point pressure coefficients among all wind directions. The area-averaged coefficients (AAC) account for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extreme Probability** & **Extreme Value Type** & **Extreme Value Type** & **Extreme Value Type** \\
**Distribution** & **I Distribution** & **II Distribution** & **III Distribution** \\ \hline Minimum & 0 & 0 & 100\% \\ Maximum & 7\% & 55\% & 38\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proportion of each probability distribution.  
Figure 8: Ratio of D\({}_{\text{K}}\) the Gumbel distribution to the D\({}_{\text{K}}\) of GEV distribution. (**a**) Minimum pressure coefficient; (**b**) Maximum pressure coefficient.  
the area reduction effects of pressure coefficients among all wind directions, as well as the value from GB50009-2012 (GB). The envelope value (EC), the area-averaged value (AAC), and the value from the code (GB) are also shown in Figures 9 and 10.  
Figures 9 and 10 show that the area-averaged coefficients (AAC) are smaller than the envelope coefficients. It is indicated that the envelope coefficients are conservative, and the Chinese code may be non-conservative from the comparison between the area-averaged value (AAC) and the value from the code (GB). Furthermore, the Chinese code zoning for the CAARC building facades parallel with the wind direction does not follow the extreme suction distributions measured. There is a need for updating the Chinese code values and zoning, especially for buildings with heights of about more than five times the crosswind dimension similar to the CAARC building.
## 2 Material and Methods
### Top-of-Canopy Spectral Measurements of Reflectance and Sun-Induced Fluorescence
\begin{tabular}{c c c c c} \hline \hline
**Target** & **Coordinates** & **Dominant Species** & **LAI * & **fAPAR * \\  & & & **(m\({}^{2}\) m\({}^{-2}\))** & **(-)** \\ \hline V1 & \(52.75933^{\circ}\)N & & _Carex gracilis_ & \(4.8\pm 0.5\) & \(0.93\pm 0.03\) \\  & \(16.30989^{\circ}\)E & & _Carex fasci_ & \(4.8\pm 0.5\) & \(0.93\pm 0.03\) \\ \hline V2 & \(52.76022^{\circ}\)N & _Carex fasciorpa, Meraunthes trifoliaata, Oxyooccus_ & \(1.7\pm 0.5\) & \(0.68\pm 0.19\) \\  & \(16.30969^{\circ}\)E & _palustris, Equivalent fluvialé, Sphagnum tres_ & \(1.7\pm 0.5\) & \(0.68\pm 0.19\) \\ \hline V3 & \(52.76067^{\circ}\)N & _Typha tafifoliaa, Carex rostrata, Lycopus europaeus,_ & & \\  & \(16.30986^{\circ}\)E & _Lythrum salicaria, Calliergoellu cuspidata,_ & \(0.8\pm 0.4\) & \(0.18\pm 0.09\) \\  & _Drepanocdulus polycarpos, Sphagnum tres_ & & & \\ \hline V4 & \(52.76086^{\circ}\)N & _Carex rostrata, Comarum pulsatre, Meraunthes_ & \(1.4\pm 0.4\) & \(0.20\pm 0.12\) \\  & \(16.30975^{\circ}\)E & _trifoliata, Sphagnum angustifolium, Sphagnum tres_ & \(1.4\pm 0.4\) & \(0.20\pm 0.12\) \\ \hline V5 & \(52.76086^{\circ}\)N & _Carex rostrata, Comarum pulsatre, Meraunthes_ & \(1.4\pm 0.4\) & \(0.20\pm 0.12\) \\  & \(16.30975^{\circ}\)E & _tafolia, Sphagnum angustifolium, Sphagnum tres_ & & \\ \hline V6 & \(52.76136^{\circ}\)N & _Sphagnum tres, Carex rostrata, Comarum pulsatre,_ & \(0.9\pm 0.3\) & \(0.12\pm 0.07\) \\  & \(16.30969^{\circ}\)E & _Drosar rotundifolia_ & & \\ \hline V7 & \(52.76136^{\circ}\)N & _Carex rostrata, Comarum pulsatre, Sphagnum_ & & \\  & \(16.30969^{\circ}\)E & _angustifolium_ & & \\ \hline V8 & \(52.76178^{\circ}\)N & _Sphagnum tres, Carex rostrata, Oxyooccus pulsatris,_ & \(0.4\pm 0.1\) & \(0.06\pm 0.04\) \\  & \(16.30964^{\circ}\)E & _Drosar rotundifolia_ & & \\ \hline V9 & \(52.76178^{\circ}\)N & _Sphagnum tres, Carex rostrata, Oxyooccus pulsatris,_ & \(0.4\pm 0.1\) & \(0.06\pm 0.04\) \\  & \(16.30964^{\circ}\)E & _Sphagnum angustifolium,_ & & \\ \hline \hline \end{tabular}   
* LAI measured by means of SunScan system (Delta-T Devices, Cambridge, UK). FAPAR calculated as a ratio between Absorbed PhotostFive measurements over each plot were recorded and then averaged to reduce the noise in the sample. Spectral VIs indicated in Table 1 were calculated from TOC reflectance spectra. The fluorescence was estimated in the red and far-red regions (F\({}_{687}\) and F\({}_{760}\)) using the spectral fitting methods (SFM) introduced by [PERSON] (2009) [PERSON] and others (2009) and [PERSON] et al. (2018), and optimized by [PERSON] et al. (2013). The spectral interval used for F\({}_{760}\) estimation was set in between 759.00 to 767.76 nm for a total of 439 spectral channels, while the spectral range between 684.00 and 696.00 nm for a total of 200 spectral channels was used for estimating the F\({}_{687}\).
## 4 Experiments and Analysis
### Visual Comparison  
To intuitively demonstrate the efficacy of the YOLOv8-E network in eggplant disease detection in complex scenarios, we tested both YOLOv8 and YOLOv8-E in various complex scenarios, with the detection results given in Figure 15. When detecting yellow spots alone,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **mAP50:95 (\%)** & **GFLOPs** & **Params (MB)** \\ \hline Faster R-CNN [34] & 55.9 & 31.9 & 370.2 & 314 \\ SSD [35] & 71.2 & 48.2 & 162 & 60.3 \\ YOLOv3-tiny [36] & 72.5 & 45.3 & 18.9 & 18.9 \\ YOLOv5n & 71.9 & 48.8 & 7.1 & 5.3 \\ YOLOv6n [37] & 72.6 & 49.7 & 11.8 & 11.8 \\ YOLOv7-tiny & 69.8 & 41.6 & 13.2 & 13.2 \\ YOLOv9c [38] & 75.2 & 50.3 & 103.7 & 51.6 \\ RT-DETR-R50 [39] & 71.0 & 42.7 & 125.6 & 86.0 \\ Ours & 79.4 & 51.6 & 7.0 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Healthy** & **Fruit Borer** & **Fruit Rot** & **Yellow Spot** \\ \hline Faster R-CNN & 0.624 & 0.523 & 0.588 & 0.537 \\ SSD & 0.730 & 0.724 & 0.706 & 0.687 \\ YOLOv3-tiny & 0.713 & 0.673 & 0.775 & 0.739 \\ YOLOv5n & 0.702 & 0.679 & 0.784 & 0.712 \\ YOLOv6n & 0.704 & 0.682 & 0.781 & 0.733 \\ YOLOv7-tiny & 0.68 & 0.672 & 0.782 & 0.66 \\ YOLOv8n & 0.778 & 0.735 & 0.723 & 0.72 \\ YOLOv9c & 0.775 & 0.714 & 0.765 & 0.637 \\ RT-DETR-R50 & 0.727 & 0.758 & 0.687 & 0.670 \\ YOLOv8-E & 0.795 & 0.816 & 0.786 & 0.793 \\ \hline \hline \end{tabular}
\end{table}
Table 5: [EMAIL_ADDRESS] comparison of different algorithms for all disease types.  
YOLOv8 missed some targets, while YOLOv8-E detected those missed targets with a high confidence score. In scenarios with overlapping targets, YOLOv8 exhibited confusion between individual disease targets and background factors, whereas YOLOv8-E accurately identified various diseases with higher confidence. In scenarios with dense targets, YOLOv8 still missed some targets, whereas YOLOv8-E could more comprehensively detect those overlooked by YOLOv8. Furthermore, in reflective scenarios, YOLOv8 failed to detect all borers, whereas YOLOv8-E accurately identified these targets, indicating that YOLOv8-E maintains excellent detection performance and robustness even under environmental interference. These detection results conclusively prove that the improved YOLOv8-E manifests outstanding performance in detecting and classifying eggplant diseases in complex scenarios, rendering formidable technical aid in preventing and controlling field diseases.
# Developing the Smart Sorting Screw System Based on Deep Learning Approaches
## 2 The Proposed Method
### Anomaly Detection Techniques
#### 2.3.1 Convolutional Autoencoder (CAE)  
A convolutional autoencoder model has the ability to learn the hidden features from the input images without labeled ground truth. The architecture of the CAE model mainly consists of encoding and decoding. The main purpose of the encoder is not only to extract the potential features from the original input datasets, but at the same time, to reduce their dimensions. The output of the encoder is represented as a compression correlated to the input data. The decoder reconstructs the original image that is generated by the encoder. Both the encoder and decoder are trained together at the same time to attain meaningful represented features, and to be capable of restoring the original image without losing a large amount of feature information. In the CAE model, the encoder is integrated with a convolutional and pooling layer, while the decoder is coupled with deconvolutional and upsampling layers. The architecture of the CAE model is shown in Figure 2.  
The definition of the encoder and decoder can be expressed as in Equations (4) and (5). The loss function of the CAE model can be obtained from the mean square error, which minimizes the error resulting from the reconstruction of the image from the original, which can be expressed as Equation (6).  
\[\varphi :I\to F \tag{4}\] \[\rho :F\to I^{\prime}\] (5) \[\min\bigl{(}L\bigl{(}I,\ I^{\prime}\bigr{)}\bigr{)}=\min\left(\sum_{ i=1}^{n}\bigl{(}I-I^{\prime}\bigr{)}^{2}\right) \tag{6}\]  
where \(\varphi\) and \(\rho\) are the encoder and decoder, respectively; \(I\) and \(I^{\prime}\) are the input image of CAE encoder and the output of CAE decoder, respectively; and \(F\) is the latent feature of the CAE model. The processing of the encoder and decoder is shown in Equations (7) and (8).  
\[H=a_{e}(I*W_{e}+b_{e}) \tag{7}\] \[I^{\prime}=a_{d}(H*W_{d}+b_{d}) \tag{8}\]  
where \(a_{e}\) and \(a_{d}\) are the activation functions of the encoder and decoder, respectively; \(W_{e}\) and \(W_{d}\) are the weighing of the encoder and decoder, respectively; and \(b_{e}\) and \(b_{d}\) are the network biases of the encoder and decoder, respectively.
# Advances in Random Fiber Lasers and Their Sensing Application
## 3 Control of Output Characteristics
### Line-Width  
Narrow line-width RFL performs excellent coherence properties, and is widely used in ultra-high-precision laser radar, inter-satellite communication, as well as spacecraft docking. Utilizing filer involving fiber F-P (FFP) or FBG could obtain an RFL with 0.05 nm line-width [111]. Moreover, combining phase-shifted FBGs and regular FBGs is a feasible method to obtain spectral narrow line-width RFL (Figure 15 shows the setup of the multi-wavelength RFL) [29]. Although these setups could be constructed easily and narrow down the line-width of the RFL, the coherence cannot be improved, due to the intrinsic factor of the RFL, and the line-width is limited to narrow down further.  
The gain mechanism is an intrinsic factor for narrowing the line-width, which is mentioned in Section 2.2. Moreover, random feedback is an inner factor that can influence the line-width of the RFL. In 2014, 2.1 kHz narrow line-width RFL was proposed, which was based on random gratings that can strengthen random feedback [112]. In 2016, an RFL with the line-width of 1.17 kHz was realized by using Rayleigh-enhanced TOF [113]. In 2017, an RFL with the line-width of 1 kHz was presented, in which a 5 km non-uniform fiber provided an enhanced feedback strength for the random lasing resonance [108]. In 2018, enhanced random feedback from RFGs along the 25 km long-span half-open-cavity RFL was proposed (Figure 16), obtaining more Stokes channels with sub-kHz line-width [114].  
Figure 14: Generation of 24 channels within 3-dB flatness [109].  
Figure 15: Setup of the multi-wavelength RFL. PS FBGs, phase-shifted fiber Bragg gratings; VOA, variable optical attenuators; MUX/DMUX, multiplexer/demultiplexer.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 4 Experiments
### Qualitative Comparison  
Figure 7 qualitatively compares our method with the method based on conventional \(\epsilon\)-_greedy_ reinforcement learning using the OTB dataset. Although the test sequences contained abrupt motions (e.g., Deer, Shaking, MotorRolling, and Biker sequences), severe deformation (e.g., Ironman, Diving, Jump, Skiing, and Surfer sequences), occlusion (Soccer sequence), and illumination changes (e.g., Matrix, Shaking, and Skating1 sequences), our method accurately tracked the targets. However, conventional reinforcement learning with \(\epsilon\)-_greedy_ frequently failed to track the targets when there were abrupt motions occurred because it could not sufficiently explore unvisited states.  
Figure 8 shows qualitative visual tracking results of the proposed method with and without the Wang-Landau algorithm using the LaSOT dataset. The video sequences include tiny objects (e.g., boat-12, crocodile-3, drone-13, elephant-18, fox-3, and flog-9 sequences), background clutter (e.g., chameleon-6, cram-18, and fox-3 sequences), nonrigid objects (e.g., bear-17, bird-17, cattle-7, crocodile-3, fox-3, frog-9, and giraffe-10 sequences), motion blur (e.g., bus-5 and crab-18 sequences), and rotation (e.g., bottle-1 sequence). Despite these challenging visual tracking environments, the proposed method accurately tracked the targets. These results indicate that the proposed Wang-Landau-based reinforcement learning is helpful for finding unexplored states and recovering missed trajectories. As shown in Figure 8, the proposed Wang-Landau algorithm helps our visual tracker to recover inaccurate bounding boxes.  
Figure 6: Comparison on the recovery rate. Blue, orange, and gray bars denote the average number of interruptions, the average number of recovered trajectories, and recovery rate (i.e., average number of recovered trajectories \(\times\) 100), respectively.  
In summary, the proposed method works better than other state-of-the-art methods, as follows. Our method can predict the target state with greater accuracy by defining multiple actions in a reinforcement learning framework and moving the current state according to the selected action. In addition, we further enhance prediction accuracy by improving reinforcement learning performance  
Figure 8: Qualitative evaluation. White and green boxes present the predicted bounding boxes of the proposed method with and without the Wang–Landau algorithm.  
Figure 7: Qualitative comparisons. White boxes present the estimated bounding boxes of our method, while red boxes represent the results of our method without Wang–Landau sampling.  
using Wang-Landau sampling, in which exploitation and exploration compete against each other in a sampling framework and attain the equivalence status.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 3 Webmail XSS  
### Principle  
The XSS (Cross-Site Scripting) is an attack method that injects malicious script codes into web pages viewed by users, which can control the behavior of the user's browser. Hackers often use XSS attacks to steal user's cookies for identity theft. In theory, websites with XSS vulnerabilities can be executed with all front-end code functions. For example, executable functions include keyboard click records, screen captures, page source code, phishing pages, and others. For the XSS attack of Webmail, attackers tend to obtain the login credentials of the victim and use it to login to Webmails and steal personal information. In Webmail, a complete attack process is shown in Figure 1.  
In step 1, the attacker attempts to control a device through the intranet. The control method may be a virus infection or a device connection. Because Webmails have the ability to remember passwords or stay logged on for a prolonged session, an attacker may easily access the user's Webmail [22].  
Figure 1: Email Cross-Site Scripting (XSS) attack principle.  
In step 2, the attacker edits the email with the XSS code and sends the email to the victim through SMTP Server. In step 3, Mail Server saves the malicious email. Actually, malicious email detection and prevention is usually deployed on Mail Server. In step 4, POP3 Server will obtain emails from Mail Server. Once the victim opens the malicious email on the browser side, the malicious XSS code will be released and the cookie will be sent to the XSS receiving server established by the attacker (steps 5-7). Lastly, the attacker will use the cookies to log in to the Webmail and steal information. If the Webmail is open to the external network, the attacker will be to construct the email directly to proceed the assault. However, relatively speaking, a Webmail opened to the external network will generally set more protective measures. The attacker's initiation to break the environment will be freer, but is often more restricted by the Webmail server.
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration
### Eigenvalue Analysis Results  
#### 2.4.1 Slab Model  
For all slab cases (Case 0, 1, 2, and 3), eigenvalue analysis was conducted to obtain their modal frequencies and mode shapes. Table 3 shows the first two bending modal frequencies (Modes 1 and 3) and the first torsion mode (Mode 2). As expected, the three modal frequencies in Case 1, 2, and 3 were all smaller than those in Case 0; when damage occurred somewhere in the slab (Case 1, 2, and 3), the modal frequency dropped.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Young’s Modulus [GPa]** & **Poisson’s Ratio** & **Density [kg/m\({}^{3}\)]** \\ \hline Intact concrete (Panel) & 30 & 0.2 & 2450 \\ Steel (I shape girder) & 210 & 0.3 & 7850 \\ Deteriorated concrete (Panel) & 7.5 & 0.2 & 2450 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties in FEM for the slab model.  
Figure 6: FEM models for the pier.  
Figure 7: Riverbed geometry with/out simulated scour.  
However, it is difficult to localize the damage in the slab merely from the frequency change. Comparing the three modal frequencies from Case 1, 2, and 3, no clues are found to localize the damage. This is because modal frequencies do not provide any spatial information. To localize damage while focusing on mode shapes (e.g., see Figure 8 for Case 0), it is proposed to install the MEH devices on multiple panels to catch the relative change in energies harvested from different locations. The proposal is further discussed in Section 3.