esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Experimental Results
### Comparative Analysis of MR Isolation and LRB Isolation  
For the same model, the isolation test with lead rubber bearing is completed in the literature [32]. Four lead rubber bearings (diameter 100 mm) were used in that shaking table experiment. General yield force for bearings is 8.652 kN. The shaking table test results are shown in Table 5 under different conditions.  
Comparison with the displacement response of the isolation layer can be seen, wherein the isolation with MR dampers is significantly less than the passive isolation with the lead rubber bearing, both displaying maximum displacement of the isolation layer and the amplification factor of the upper acceleration. In this way the isolation system with MR dampers has the ability to resist the larger earthquakes. This shows that the MR isolation system has better vibration control performance with the passive lead rubber isolation.  
Figure 14: Damper hysteretic curves for Taft wave with (**a**) fortification intensity, and (**b**) rare intensity of earthquake.  
Figure 13: Damper hysteretic curves for El Centro wave with (**a**) fortification intensity, and (**b**) rare intensity of earthquake.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**El-Centro Wave**} & \multicolumn{2}{c}{**Taft Wave**} \\ \hline
**The Earthquake Wave** & **Fortification Intensity** & **Rare Intensity** & **Fortification Intensity** \\ \hline Table acceleration peak & \(-\)0.351 & 0.727 & \(-\)0.368 \\ \hline Acceleration peak value of the first layer & \(-\)0.281 & \(-\)0.596 & \(-\)0.503 \\ \hline Acceleration peak value for fifth layer & \(-\)0.440 & \(-\)0.662 & \(-\)0.662 \\ \hline Acceleration amplification factor for structure fifth layers(LRB) & 1.254 & 0.91 & 1.79 \\ \hline Acceleration amplification factor for structure fifth layers(MR) & 0.89 & 0.64 & 0.69 \\ \hline Isolation layer displacement(LRB) & 8.38 & 24.15 & 14.36 \\ \hline Isolation layer displacement(MR) & 3.80 & 15.18 & 7.04 \\ \hline \end{tabular}
\end{table}
Table 5: Peak value for structure acceleration and isolation layer displacement response under lead rubber bearing (LRB) isolation and seismic level 8.
## Materials and Methods
### Remote Sensing Methodology  
Remote sensing is routinely applied to the measurement of chlorophyll production, predominantly at the near infrared wavelengths. Droughs reduce chlorophyll production, which results in lower radiometric values at the near infrared. The established normalized difference vegetation index (NDVI) is widely used to quantify the photosynthetic capacity of vegetation. In addition, the bare soils index (BI) is also used to measure the density of vegetation as a comparison against completely bare soil, sparse canopies, and dense canopies. Finally, the normalized difference moisture index (NDMI) is a measure that is highly correlated with canopy water content [11; 12]. To these indices, we add tasseled cap transformations (TCT) which facilitate direct comparisons across multiple scenes. TCT components which measure brightness, greenness, and wetness account for more than 97% of the spectral variability in a typical scene [13]. An advantage of TCT is that they are scene independent and accurate for monitoring land use/cover changes [14]. Lastly, we add change vector analysis (CVA); a measurement of the intensity (or magnitude) and direction between land use/cover variables in multidimensional space [13; 15]. CVA determines small variations in class reflectance as a result of high intra-class differences caused by landscape heterogeneity [15; 16]. By interpreting CVA intensity and the direction of change, the accumulated error that often occurs due to class-to-class comparisons between two image dates can be avoided. CVA also minimizes the need for collecting training and reference data for historical images, as unchanged locations can be used as reference data [13]. In the literature, a 'from-to' land use/class change analysis was used as a nested hierarchy of land use/cover to assess changes on the Pearl River Delta in China from increased economic activity [14]. Another study found that CVA was an effective method for monitoring changing ecosystems in wetland and riparian areas in southwest Montana, USA [13]. In addition, Landsat TM and ETM+ imagery was applied to map liana forests in the Bolivian Amazon by measuring changes in the spatial distribution of liana patches over a 14-year period from 1986 to 2000 [14]. Several other studies have shown the effectiveness of using CVA and TCT in unison for measuring forest disturbance as a result of events, such as deforestation, hurricanes, or tsunamis [15; 16; 17; 18; 19]. However, there is a dearth of studies that use a methodology that combines CVA and TCT for measuring land use/cover changes under severe, short-term drought events.
## 2 Physically Distributed Logically Centralized (PDLC) SDN Architecture and Components
### Connectivity  
The connectivity infrastructure must facilitate bidirectional communication between the control plane instances, illustrated by the green dotted arrows connecting the red boxes that represent the distribution I/E module in Figure 2. Moreover, bidirectional communication between switches and control plane instances is required, depicted by the green dashed arrows between the grey boxes representing the switch Import/Export module in Figure 2, with the capability to support convergence in case of link failure. Standard routing protocols such as OSPF or IS-IS are employed in the connectivity architecture to establish and maintain the forwarding state [22].  
The eastbound interface, indicated by the red boxes in Figure 2, serves as a means for SDN controllers to communicate and exchange information among themselves. Conversely, a westbound interface facilitates information exchange between distributed legacy control systems and the SDN control plane [4; 5].  
In PDLC SDN architecture, controllers are interconnected horizontally, often referred to as flat connections [6; 7]. However, the cost of intercommunication among controllers in a distributed SDN environment remains a significant challenge [23].
# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 4 Discussion
Previous studies showed the severity classification accuracy for dandruff (85.03%) [1] or hair loss (75.73-95.75%) [10; 21; 22]. Although some studies achieved comparatively high accuracy by applying CNN with attention blocks [1] or an ensemble model [22], they focused on predicting the severity of only one scalp hair disorder. On the other hand, we established deep learning models for diagnosing the severity levels of six common scalp hair disorders, applied XAI techniques to represent suspected problem regions for each disorder, and also developed user-friendly Scalp checker software incorporated with the trained ViT-B/16 models. We expect the Scalp checker software to help users perform an inspection using their typical PC without a cloud-based AI server and network connection (e.g., internet, Wi-Fi, and Bluetooth), visually check their hairy scalp conditions, and easily monitor the changes in the inspection results over time. AI technologies have been extensively applied in medical fields including diagnosis, treatment, surgery, screening, and epidemiology analysis due to their effectiveness and usefulness [14; 30; 31; 32; 33; 34; 35; 36; 37]. However, they have some potential concerns in clinical practice. AI model performance significantly depends on data quality [14; 36; 38]. If the dataset is too small and includes inaccurate annotation or inequities, there is a risk of producing incorrect or biased results [14; 33; 36]. AI models are trained and evaluated on datasets that cannot include the entire population and complex clinical environments so they might have inherent biases [37; 39]. In addition, the inevitable intrinsic uncertainties of medical interpretations are not usually considered to train AI models [40]. As a result, the performance and reliability of AI models affected by observer variability can be underrated in clinical decision-making. Therefore, in the current state, AI cannot replace the role of clinicians in medical fields, and rigorous validation through real-world clinical trials is still required to ensure generalizability, robustness, reliability, and safety [33; 36; 37]. So far, AI can offer good support for the decision-making of experts and understanding the diagnostic results of non-experts [14; 32; 36; 37; 41]. In the future, we will apply other state-of-the-art deep learning models, their ensembles, data augmentation techniques, and methods for reducing noisy label problems (e.g., label transition matrix method, MentorMix, and DivideMix), and increase the number of datasets to enhance severity classification performance, effectiveness, and reliability [22; 42; 43; 44]. Moreover, we also plan to develop an efficient and lightweight model that can be used on mobile devices.
# DC2 Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
## 1 Introduction
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{1}{c}{**CT Scans**} & \multicolumn{1}{c}{**MRI**} \\ \hline \multirow{2}{*}{Principle} & Uses multiple X-rays, taken at different & Uses powerful magnetic fields and \\  & angles to produce cross-sectional images & radiofrequency pulses to produce detailed images \\ \hline Radiation & Minimal & None \\ \hline \multirow{2}{*}{Uses} & Excellent for observing bone and very & Excellent for detecting very slight differences \\  & good for soft tissue, especially with the & in soft tissue \\  & use of intravenous contrast dye & \\ \hline Cost & Usually less expensive than MRI & Often more expensive than CT scans \\ \hline \multirow{2}{*}{Time taken} & Very quick, taking only about 5 min, & Depends on the part of the body being examined \\  & depending on the area being scanned & and can range from 15 min–2 h \\ \hline \multirow{2}{*}{Application} & Produces a general image of an area such & Produces more detailed images of soft tissue, \\  & as internal organs, fractures, or head trauma & ligaments, and organs \\ \hline \multirow{2}{*}{Benefits} & Faster and can provide images of tissue, & Produces more detailed images \\  & organs, and skeletal structure & \\ \hline \multirow{6}{*}{Risks} & \multirow{6}{*}{*} &  • Possible reactions to metals due to magnets \\  & & (e.g., artificial joints, eye implants, \\ \cline{1-1}  & & intrauterine devices, pacemakers) \\ \cline{1-1}  & &  • Loud noises from the machine can cause \\ \cline{1-1}  & &  • Loud noises from the machine can cause \\ \cline{1-1}  & &  • A potential reaction to the use of dyes & hearing issues \\ \cline{1-1}  & &  • Increase in body temperature during \\ \cline{1-1}  & &  • Long MRIs \\ \cline{1-1}  & &  • Claustrophobia \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between CT scans and MRI. * The dual cycle-consistent adversarial network (DC\({}^{2}\)Anet) is proposed as a general synthesis system for semi-supervised learning. Due to its dual cycle-consistent structure, DC\({}^{2}\)Anet can be applied to both supervised and unsupervised learning. This paper first summarizes previous research on the synthesis of medical images in Section 2. The proposed algorithm is outlined in Section 3, while Section 4 reports the experimental results and discussion. A conclusion is presented in Section 5.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Cross-Site Scripting (XSS) Attacks: Characteristics and Analysis
#### 1.1.2 Main Types of XSS Attacks  
_Reflected (non-persistent) XSS:_ malicious script is reflected off a web server, typically through URL parameters, and executes immediately when the page loads [6].  
_Stored (Persistent) XSS:_ the malicious script is permanently stored on target servers (in databases, message forums, visitor logs, etc.) and executes when victims access the stored content [4].  
_DOM-based XSS:_ vulnerability exists in client-side code rather than the server-side code, with the attack payload never reaching the server.  
_Universal XSS (UXSS):_ exploits vulnerabilities in browsers or browser extensions rather than websites [7].
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 3 Evaluation of the Receiver Performance in Simulations
### Comparison of the Proposed Decoder to Reference  
The Manchester decoder proposed has so far been compared in performance to a standard decoder, similar to those described in some papers [24; 25], but also present in several books and application notes. The standard decoder here considered (see Figure 7a) was based on a 2-level analog-to-digital converter realized by a tracking comparator, followed by a digital filter and logic for the decoding. A Numerical Oscillator (NO) reconstructed the data clock. Similarly to what we did to test the proposed decoder, Manchester-coded signals with added noise at increasing power were generated and processed through the decoder. The BER was obtained by comparing the output and ground-truth data.  
Figure 6: BER measured by receiving a data packet of 1 Mbit when frequency error of 1% (**a**) and noise jitter (**b**) are present. SNR is reported in \(x\)-axis, different curves refer to receptions obtained with different saturation levels \(Z_{\%}\).  
Figure 7: (**a**) Manchester reference decoder. (**b**) BER comparison between the proposed (blue continuous curve) and the reference decoder (red-dashed curve).  
Figure 7b reports the outcome of the comparison for BER in the range 5-25 dB. The proposed decoder was set to \(Z_{\%}\)= 2% to compare better with the low bandwidth of the NO of the reference decoder. Being equal the BER, the proposed decoder gains about 10 dB over the SNR. For BER higher than \(10^{-2}\) the advantage reduces, but this error range finds scarce interest in practical applications.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 5 Results and Discussions
### Comprehensive Comparison across All Road Segments
#### 5.1.3 Statistical Analysis of Predictive Performance  
To validate our findings, we conducted a statistical analysis comparing the MAEs and RMSEs of the presented models for statistical significance. We selected the Mann-Whitney U test due to its robustness in handling non-normally distributed data [31]. Our analysis utilized data from multiple prediction horizons, as presented in Table 5, to assess the stability and consistency of the SLIT model in long-term prediction. Compared with the best competitive method (PASS2S), for MAE, our analysis indicates that SLIT statistically outperforms PASS2S. This result demonstrates a significant advantage across the prediction horizon. Conversely, the RMSE analysis showed no significant difference, suggesting that SLIT and PASS2S manage larger errors similarly and that SLIT does not have a significant disadvantage compared with PASS2S. Given these outcomes, we extended our analysis to include tests against other competitive models, hypothesizing that SLIT's design capabilities are likely to result in lower RMSE values. This evidence represents that SLIT's RMSE performance is not only statistically significant but also superior to these competitors under long-term prediction conditions. The details of this statistical analysis are described in Appendix B.
## 3 Results and Discussion
### Photocatalytic Mechanism  
The Effect Scavengers  
During the photocatalytic reaction, various reactive radicals, such as H\({}^{+}\), HO\({}^{-}\), O\({}_{2}\)\({}^{-}\) and e\({}^{-}\) are produced which are responsible for the degradation of organic contaminants. Trapping of these reactive radicals using various scavengers was performed to evaluate how  
Figure 10: Removal of KP in drinking and groundwater using CTG–500 membrane.  
the various reactive species influenced the removal of KP using the CTG-500 photocatalytic membrane under visible light irradiation. This was done in a batch study and the results are plotted in Figure 11 Ascorbic acid was used as an H\({}^{+}\) active species while isopropanol and AgNO\({}_{3}\) were used as scavengers for HO\({}^{-}\) and e\({}^{-}\) species, respectively. These scavengers were spiked into the KP test solutions.  
It was observed that in the absence of any scavenger, 91.4% removal efficiency of KP was achieved within 5 h under visible light irradiation. However, the removal efficiency KP decreased in the presence of AgNO\({}_{3}\), iso-propanol, and ascorbic acid yielding 85.4%, 51.3%, and 21.9%, respectively. These results indicated that electrons played an insignificant role in the degradation of KP while HO\({}^{-}\) ions played a moderate role and H\({}^{+}\) ions were the most active.
## 2 Related Work
### Object Detector  
The current object detectors can be divided into two-stage detectors and one-stage detectors according to different structures. The two-stage detectors generate region proposals in the first stage and then the corresponding features of the region proposals are used for classification and regression in the second stage. Generally, the two-stage detectors use a coarse-to-fine detection process. R-CNN [26] applies a selective search algorithm [27] to extract region proposals, and then the corresponding features of region proposals are extracted by CNN for identification and classification. Fast R-CNN [28] firstly extracts the features of the entire image, and then maps the region proposals obtained by the selective search algorithm with the extracted feature maps. It can avoid repeated feature extraction and significantly reduce processing time. However, Fast R-CNN still needs an additional search algorithm module to generate region proposals and cannot be trained end-to-end. Therefore, Faster R-CNN [29] employs a region proposal network (RPN) to directly generate region proposals, which can perform end-to-end training and detection. After the above improvements, the detection speed and accuracy of Faster R-CNN are greatly enhanced.  
The one-stage detectors can be considered as regression analysis models, which directly predict the category and location of the objects. As a typical one-stage detector, YOLO [30] has good performance in detection accuracy and speed. SSD [31] improves the multi-scale objects detection ability of the model by using feature maps of different scales. RetinaNet [32] is proposed with a new loss function to address the imbalance of positive and negative samples during training.  
One-stage detectors can detect fast, but the accuracy is relatively unsatisfactory. Comparatively, two-stage detectors exhibit high detection accuracy, but the amount of computation is slightly large. In the manufacture of steel strips, the high detection accuracy of defects can effectively prevent defective steel strips from entering the next manufacturing process. Therefore, a two-stage defect detection method is proposed in this paper to achieve high-precision detection of steel strip defects.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 5 Conclusions  
Human pose estimation is a hot research area in computer vision that evolved recently along with the blooming of deep learning. Although commercial products such as Kinect with depth sensor, and TheCaptrury with multiple cameras have been employed for 3D body pose estimation, all these systems work in very constrained environments or need special markers on the human body. A monocular camera, as the most widely used sensor, is very important for 3D human pose estimation. Deep neural networks have the capability to estimate the dense depth and sparse depth points (joints) as well from monocular images. Due to limitations in depth ambiguity problem, early networks were inaccurate on 3D HPE. This review summarizes recent progress in disambiguation schemes from monocular RGB images or videos. However, the overreliance on the 2D detector remains extremely challenging. As for the case of camera parameter constraints, camera perspective models are less developed. We point out that kinematic and image cue constraints that clearly work are still far from being established. Most recently, weak supervision and unsupervision have drawn significant attention. Furthermore, multi-view images effectively solve this problem, so we can expect many innovations in the next few years, especially when sensor technologies are applied to this field. In addition, we conjecture that research on interaction and reconstruction between scene objects and the human body will also be a promising direction in the future.  
**Author Contributions:** Conceptualization, S.Z. and C.W.; methodology, S.Z.; software, C.W.; validation, S.Z. and C.W.; formal analysis, S.Z.; investigation, S.Z.; resources, B.F.; data curation, C.W.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z. and C.W.; supervision, W.D. and B.F.; project administration, W.D. and B.F.; funding acquisition, W.D. and B.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by \"Research on artificial intelligence cardiopulmonary resuscitation training and assessment system, TJUWY2022014\", the \"National key research and development plan project of China, 2018 YFC1504405-3\" and the \"Tianjin University Independent Fund Project, 2020 XZS-0029\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiment Simulation and Data Analysis
### The Comparison of Different Forecasting Models  
In order to verify the validity of the proposed method in this paper, GRU, TCN, LSTM, Prophet, VMD-GRU, and VMD-TCN were selected for experimental comparison, and the forecasting results are shown in Table 3. It can be seen that after the VMD decomposition, the forecasting errors of the GRU and TCN neural networks are lower than that without VMD decomposition. The performance of the forecasting is significantly improved, which indicates the feasibility of using VMD to decompose the original load sequence. Among the individual algorithms, the forecasting error of the GRU model is the smallest with the RMSE of 50.38 MW and MAPE of 1.12% in data set 1.  
Figure 10: The forecasting results of GRU and TCN with high-frequency component.  
Figure 9: The forecasting results of GRU and TCN with low-frequency component.  
Due to the high randomness and non-stationarity of the original load profile, the forecasting model constructed by a separate algorithm cannot fit the actual load situation very well. The results show that the hybrid model combined with the GRU and TCN network is the smallest both in data set 1 and data set 2. The RMSE of the forecasting result of VMD-GRU is reduced by 36.20% and 10.8%, respectively, while the MAPE of the forecasting result of VMD-GRU is reduced by 62.5% and 19.8%.  
At the same time, the training and operation time of different prediction models is shown in Table 3. It can be seen that the operation time of the model proposed in this paper is slightly longer than that of the other non-decomposition prediction. The time-series load data are decomposed into eight components by VMD, and the process