esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Use Case: UMTS Networks
### Multivariate Analysis  
As stated in the theoretical framework step, groups or _clusters_ of cells with different characteristics are generated, with similar features within the cells that belong to a cluster, and differences between cells belonging to different clusters. At this point, different criteria to group the cells can be used, e.g., cells can be grouped by area coverage, the type of cell (macocell, microcell, femtocell, etc.), or scenarios (stadium, railway, etc), among others. It is also possible to group the cells by technology, e.g., 2G, 3G, or 4G. In our use case, the clustering step will not use factor or cluster analysis [25] to group the cells, since categories and variables (KPIs) have been clearly defined in the data selection step. More precisely, the criterion here is to group the cells according to area coverage, and five clusters are defined, namely, urban macro, suburban, urban micro, rural macro, and rural micro.  
It has to be pointed out that the object of the paper is not to compare real scenarios, since these KPIs are the property of MNO and it is not allowed to use them externally. Because of this, the value of the variables should not be considered in the strict sense; they have been generated following consistent measurements that can occur in UMTS networks [26].  
Data for each cluster are obtained by considering different scenarios in the KPIs included in the quality dimension, which is, the Ec/N0 and the RSCP. In order to avoid bias in the rest of the variables and, thus, conditioning the composite indicator, the rest of variables are generated while using random distributions, obtaining possible values within the range of values that are typical for networks and guaranteed by the manufacturer. Please refer to Appendix A for detailed information on how the variables are simulated for each cluster.  
Regarding mobility KPIs, differences between clusters are created while keeping consistency in mind, assuming that the mobility between cells of the same frequency is always more successful than between cells of different frequency or even of different technology. Mobility is determined by the correct definition of neighborhood relations  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Relevance** & **Interpretability Coherence** & **Timeliness** & **Accessibility** & **Credibility** & **Accuracy** \\ \hline CSSR PS & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ CSSR CS & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ DROP HSPA & & & ✓ & ✓ & & ✓ \\ DROP R99 & ✓ & & ✓ & ✓ & ✓ & ✓ \\ DROP CS & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ THROUGHPUT & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ INTRA-HO & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ INTER-HO & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ INTER-RAT & & ✓ & ✓ & ✓ & ✓ & ✓ \\ HO & & & ✓ & ✓ & ✓ & ✓ & ✓ \\ Ec/N0 & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ RSCP & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected variables (KPIs). Fulfilling of requirements according to the methodology.  
between cells, so bad values may indicate that the cell neighbors are not properly configured for intra-freq, inter-freq, or inter-RAT HOs.
# Application of Virtual Reality for Learning the Material Properties of Shape Memory Alloys
## Appendix A Achievement Test of the Shape Memory Alloys
(A) Clearing cancer cells (B) Opening the blocked blood vessel (C) Preventing blood clotting from flowing to the heart (D) Reinforcing fibrotic vessels
* 18. During the shaping process of the Ni-Ti SMA, what is the temperature and time interval required to achieve a better shape memory effect? (A) 400 \({}^{\circ}\)C/4 h (B) 500 \({}^{\circ}\)C/5 h (C) 600 \({}^{\circ}\)C/6 h (D) 700 \({}^{\circ}\)C/7 h
* 19. During the shaping process, a heavy iron block is placed on top of the SMA wire after it is bent into a star shape. How will the shape of SMA wire change if the shaping process is conducted without the iron block? (A) It remains unchanged. (B) The star will concentrically enlarge. (C) The star will concentrically shrink. (D) The star will warp. * 20. In the manufacturing process of an SMA, many misalignments may occur to result in hardening during the cold working step such as rolling. Therefore, annealing must be performed to eliminate hardening for the SMA to have better shape memory properties. The SMA is widely used in biomedical applications, such as: vascular stents and dental braces. When the SMA is in the martensite phase and plastically deformed within the limited extent, it can be transformed into the austenite phase by heating to the transition temperature, where the SMA will restore its original shape. What is the main mechanism of stress deformation in the martensite phase? (A) Grain boundary sliding (B) Sliding of coherent twin boundaries (C) Slip displacement (D) Diffusion
## 2 State of the Art
### Die Bending
#### 2.1.1 Springback  
Dimensional inaccuracies occur during bending in the form of springback, which is caused by the state of stress in the bent component.  
Figure 1 schematically shows two characteristic stress curves in the sheet metal during a die bending process. The dotted line represents the neutral fiber, which is characterized by a stress-free state. In the overlying (underlying) area of the sheet metal, compressive stresses (tensile stresses) prevail, which are highlighted in red (blue) in Figure 1. These stresses increase in magnitude with the distance from the neutral fiber. This becomes clear in the effective stresses during the bending process (blue line). The elastic parts of the bending can regress after the component has been unloaded and the residual stresses (red line) thus cause a springback in the component. In order to be able to clearly distinguish between the different bending angles, they are defined as follows:  
* \(\alpha_{\rm des}^{\rm(unl)}\): Desired bending angle after unloading the component;
* \(\alpha_{\rm act}^{\rm(unl)}\): Actual bending angle after unloading the component;
* \(\alpha_{\rm des}^{\rm(loa)}\): Desired bending angle with loaded component (in process);
* \(\alpha_{\rm act}^{\rm(loa)}\): Actual bending angle with loaded component (in process).  
Figure 2 shows this graphically. The schematically depicted probability density functions imply that variables describing the actual state are subject to uncertainty. The springback of the workpiece can thus be defined as \(\Delta\alpha=\alpha_{\rm act}^{\rm(unl)}-\alpha_{\rm act}^{\rm(loa)}\).  
Figure 1: (Residual) Stresses.
## 2 Deformation Allowance of Loess Tunnel and Statistical Monitoring Samples
### Significance of Deformation Allowance  
Deformation allowance refers to the designated space intentionally incorporated into engineering or construction projects to provide safe and stable flexibility, allowing for the natural movement or deformation of surrounding rock or structures without compromising their overall integrity. When determining the excavation dimensions across different rock layers, it is essential to allocate sufficient space for rock deformation, while also ensuring that the excavation meets the required construction limits and safety standards. Therefore, the deformation allowance refers to the amount of space deliberately left within the surrounding rock after excavation, ensuring sufficient flexibility to accommodate any potential deformation without compromising the stability of the structure. Monitoring the apparent displacement presents significant challenges, both in terms of difficulty and cost, due to the limitations of current construction technologies. In modern construction monitoring, deformation values are typically aligned with those of the support structure to ensure consistency and reliability in tracking structural changes. Due to limitations in measurement techniques and the interference caused by construction blasting, monitoring is generally initiated at a certain distance away from the excavation face to ensure accuracy and minimize the impact of these factors. As a result, the deformation values obtained from monitoring often reflect only a part of the deformation occurring in the support structure, failing to capture the full deformation process within both the surrounding rock and the support system. Before tunnel excavation, it is crucial to carefully design the tunnel deformation allowance. An excessively large allowance may lead to over-excavation, which significantly increases the volume of earth that needs to be removed during construction, resulting in higher costs for slag disposal and backfilling. If the allowance is set too small, it could restrict the initial support system's intrusion and create safety risks by limiting the available space for secondary lining. A small intrusion limit may require only the replacement of the arch, potentially leading to construction delays, as illustrated in Figure 2. Therefore, it is essential to carefully determine an appropriate deformation allowance before rock excavation, and accurately predict the extent of deformations in loess tunnels to avoid such issues.
## 4 Discussion  
### Data Collection and Segmentation  
This study conducted a comparative analysis of the performance of the MLP and 1D-CNN models. Both models were designed for regression analysis and used to predict continuous values based on numerical data. The models were trained for 300 epochs, with the training data randomly reshuffled at each epoch to enhance their generalization performance.  
The performance of the models was evaluated using the R\({}^{2}\) score and MAE. The R\({}^{2}\) score indicates how well the model explains the variability in the actual data, while MAE represents the average absolute difference between the predicted and actual values. As illustrated in Table 5, The MLP model achieved an R\({}^{2}\) score of 88.79% and an average MAE of 7.26. In the case of the 1D-CNN model, the R\({}^{2}\) score was 88.35%, and the average MAE was 6.90. Although the R\({}^{2}\) scores for both models were similar, the 1D-CNN model showed slightly better performance in terms of MAE. This is likely because 1D-CNN is more suitable for processing sequential data.  
In this study, the predicted values for the training, validation, and test data were recorded at each epoch to evaluate each model's performance. This enabled the visualization of the R\({}^{2}\) score and MAE graphs, which were used to analyze the models' learning trends. Dropout and L2 regularization were employed to address the overfitting problem during training for both MLP and 1D-CNN, ensuring stable model performance.  
As a result, the 1D-CNN showed slightly better MAE performance compared to the MLP, likely due to CNN's ability to effectively learn important frequency bands in the PTA data. However, both models achieved high R\({}^{2}\) scores, demonstrating their ability to learn the non-linear patterns in the data effectively. These results suggest that both MLP and 1D-CNN are suitable models for predicting speech audiometry scores based on PTA data. This study provides valuable insights into the performance differences of deep learning models for the task of speech audiometry prediction, offering a key criterion for selecting the appropriate model based on the characteristics and structure of the data.
## 2 Materials and Methods
### Data Observation  
#### 2.2.1 Flux and Meteorological Observations  
The eddy covariance (EC) flux observation system includes an eddy correlation system with a fast-response infrared CO\({}_{2}\)/H\({}_{2}\)O analyzer (IRGA, Li-7500, Li-COR Inc., Lincoln, NE, USA) and a 3-D sonic anemometer (CSAT 3, Campbell Scientific Inc., Logan, UT, USA). The EC system was installed on a tower 36 m above the ground. Raw data were recorded at a frequency of 10 Hz using a data logger (CR 1000, Campbell Scientific Inc., Logan, UT, USA).  
A meteorological observation system was installed in the observation tower. The air temperature (Ta) and relative humidity (RH) sensors (HMP 155, Vaisala Inc., Vantaa, Finland), along with the anemometer (WindSonic, Gill Inc., Hampshire, UK), were mounted on platforms at heights of 5, 8, 11, 14, 18, 26, and 32 m above ground, and data from 11 m were utilized in this study. A net radiometer (CNR 4, Kipp and Zonen Inc., Delft,  
Figure 1: The location of the study region.  
The Netherlands), a rain gauge (TE 525 MM, Campbell Scientific Inc., Logan, UT, USA), and a linear photon sensor for the measurement of photosynthetically active radiation (RR-9753, RainRoot Inc., Beijing, China) were installed on the platform at 17 m above ground. Soil moisture probes (EC-5, METER Inc., Pullman, WA, USA) were placed at depths of 5 cm, 10 cm, and 20 cm within the soil. The soil water content (SWC) was the average value of the above three layers. Soil heat flux plates (HFT-3, Campbell Scientific Inc., Logan, UT, USA) were buried 5 cm beneath the surface in four directions surrounding the flux tower. Another CR1000 data logger controlled all of the above instruments, and data were recorded in 10 min intervals. The vapor pressure deficit (VPD) was determined based on Ta and RH:  
\[\mathrm{VPD}=\left(1-\frac{\mathrm{RH}}{100}\right)\times 0.611\times\mathrm{ exp}\left(\frac{17.27\times\mathrm{T_{a}}}{237.3+\mathrm{T_{a}}}\right) \tag{1}\]  
2.2 Observation of the Fraction of Absorbed Photosynthetically Active Radiation (f\({}_{\mathrm{PAR}}\))  
Four linear photon sensors (RR-9753, RainRoot Inc., Beijing, China), operating within the spectral range of 400 to 700 nm, were used to measure the fraction of absorbed photosynthetically active radiation (f\({}_{\mathrm{PAR}}\)). One sensor was positioned 3 m above the forest canopy, oriented downward, to capture the photosynthetically active radiation reflected by the canopy (PAR\({}_{\mathrm{reflect}}\)). The other three sensors were installed, facing upward, below the canopy at varying locations 2.5 m above ground level and positioned 50 to 100 cm away from tree trunks to measure the photosynthetically active radiation penetrating the canopy (PAR\({}_{\mathrm{under}}\)).  
\[\mathrm{APAR}=\mathrm{PAR}-\mathrm{PAR}_{\mathrm{reflect}}- \mathrm{PAR}_{\mathrm{under}} \tag{2}\] \[f_{\mathrm{PAR}}=\frac{\mathrm{APAR}}{\mathrm{PAR}} \tag{3}\]
# Impact of Coil Misalignment in Data Transmission over the Inductive Link of an EV Wireless Charger
## 3 Theoretical Analysis of the Coil Misalignment Effects on the Wireless Channel Capacity
The central frequency corresponds to the resonant frequency in the ICPT system. As in [15], this work assumes that there is Additive White Gaussian Noise in the channel with a power density equal to \(N_{o}\). Under this assumption, we consider that the system operates in a separate mode for the power and the data transmission. An EV wireless charger operating at realistic conditions could suffer from coil misalignments, which will impact on both \(B\) and \(P_{L}\). Consequently, the capacity is altered by these conditions. Thus, the capacity, B and \(P_{L}\) depend on the concatenated magnetic flux, that is, on the mutual inductance (\(M\)). The previous equation is expressed as:  
\[C(M)=f_{B}^{comp}(M)\cdot log_{2}\Bigg{(}1+\frac{f_{P_{L}}^{comp}(M)}{N_{o}f_{ B}^{comp}(M)}\Bigg{)} \tag{2}\]  
where \(f_{P_{L}}^{comp}(M)\) and \(f_{B}^{comp}(M)\) are the functions that relate to the power received at the load and the system bandwidth to the mutual inductance. These two functions depend on the compensation topology, \(comp\), which can be any of the single-resonant compensation networks explained in Section 2. Thus, \(comp\) can stand for SS, SP, PP or PP. In order to derive \(f_{P_{L}}^{comp}(M)\) and \(f_{B}^{comp}(M)\), a circuit-based analysis is conducted for each of the single-resonant topologies. The equivalent circuit used for the analysis is represented in Figure 2.  
By using [PERSON]'s laws, it is possible to obtain \(f_{P_{L}}^{comp}(M)\) for the four compensation topologies (Equations (3)-(6)). \[f_{P_{L}}^{SS}(M)=R_{b}\left(\frac{\omega MV_{s}}{(R_{2}+R_{b})\Big{(}R_{1}+ \frac{\omega^{2}M^{2}}{R_{2}+R_{b}}\Big{)}}\right)^{2} \tag{3}\]\[f_{P_{L}}^{\text{SP}}(M)=R_{b}\left(abs\left(\frac{\omega M}{\left(j \omega L_{2}+R_{2}+\frac{1}{\frac{1}{k_{b}^{2}}+j\omega C_{2}}\right)}\cdot \frac{1/R_{b}}{\left(\frac{1}{R_{b}}+j\omega C_{2}\right)}\cdot\frac{V_{5}}{R_{1 }+\frac{\omega^{2}M^{2}}{k_{B}+j\omega L_{2}+\frac{1}{k_{b}^{2}}+j\omega C_{2} }}\right)\right)^{2} \tag{4}\] \[f_{P_{L}}^{\text{PP}}(M)=R_{b}\left(abs\left(\frac{\omega M}{ \left(j\omega L_{2}+R_{2}+\frac{1}{\frac{1}{k_{b}^{2}}+j\omega C_{2}}\right)} \cdot\frac{1/R_{b}}{\left(\frac{1}{R_{b}}+j\omega C_{2}\right)}\cdot\frac{V_{5} }{R_{1}+j\omega L_{1}+\frac{\omega^{2}M^{2}}{k_{B}+j\omega L_{2}+\frac{1}{k_{ b}^{2}}+j\omega C_{2}}}\right)\right)\] (5) \[f_{P_{L}}^{PS}(M)=R_{b}\left(abs\left(\frac{j\omega V_{5}}{ \frac{R_{1}+j\omega L_{1}+\frac{\omega^{2}M^{2}}{k_{B}^{2}+R_{b}}}{R_{2}+R_{b }}}\right)\right)^{2} \tag{6}\]  
As can be observed, for the four compensation topologies, the dependence with \(M\) is not simple. Alternatively, a closed-form equation for \(f_{B}^{comp}(M)\) is not easy to obtain without incurring simplifications. Thus, it is numerically computed in this study.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 1 Introduction
Using their emails as training samples, we tried to construct a deep learning model with the Attention mechanism. The model could identify enterprise emails that contain malicious XSS payloads. This paper made the following contributions:  
* Collected and organized the email XSS dataset. Researchers often used general-purpose XSS datasets for machine learning training. The detection of these models were comprehensive, but the accuracy in the specific scenario were not high. For Webmail application scenario, there is not any public email XSS dataset at present. Therefore, we decided to collect the real research data for the experimental dataset. We spent two months collecting email XSS vulnerabilities datasets that were publicly available on the Wooyun community (a vulnerability submission platform). The collected dataset covers more than 20 well-known Webmail service institutes and will be published for other research. * Built an effective email XSS detection framework. The paper constructed the entire email XSS detection framework which included data processing, feature extraction, and deep learning detection. The framework is a hierarchical structure that ensures the email XSS detection is running efficiently. The experiment showed an accuracy value could rise to 0.9936 with loss value 0.03. * Added Attention mechanism to the model. We introduce Bidirectional-RNN (Bidirectional Recurrent Neural Network) algorithms to detect email XSS attack and used the Attention mechanism to improve the training effect of the model. This is the first effective model in the field of Webmail XSS detection. The rest of the paper is organized as follows. In the Section 2, the research results are introduced on Webmail XSS in recent years. In Section 3, the basic principles of Webmail XSS attacks and various attack methods are discussed. In Section 4, the framework of enterprise email XSS detection is explored. The dataset and two comparison experiments are detailed in Section 5. In Section 6 is the summary of the work.
## 4 Results and Analysis
### Characteristics of Spatiotemporal Variation in NPP  
#### 4.2.1 Temporal Trends  
On an interannual scale, the NPP variation within the research area from 2001 to 2020 showed a fluctuating upward trend between 64.13 and 79.72 g C/m\({}^{2}\) with a rate of increase of 0.7235 g C/m\({}^{2}\) (\(R^{2}\) = 0.82) and a mean value of 72.75 g C/m\({}^{2}\) (Figure 7). The trend of NPP changes during the study period was not continuous, with large ups and downs and fluctuating characteristics during 2001-2016 and leveling off during 2017-2020. The maximum value of NPP over the studied period occurred in 2016, exceeding the average by 6.97 g C/m\({}^{2}\), while the minimum value of NPP occurred in 2001 and was below the mean value of 8.62 g C/m\({}^{2}\).  
Figure 6: Box plot of the correlation between the NPP of different vegetation types for different products.  
Figure 7: Graph of the average NPP in 2001–2020.  
On a monthly scale, the NPP trend line for the study area over the studied period from January to December generally showed an inverted \"V\" shape (Figure 8), with the NPP peaking in July each year. The monthly variation features of NPP are similar to the climate characteristics, with higher NPP values in summer and lower NPP values in winter. Because the changes in vegetation NPP are influenced by the control of vegetation growing process and light energy utilization, in turn, light energy utilization is affected by different circumstantial factors, which include temperature, precipitation, and solar radiation. The hot and humid environment in summer is suitable for vegetation growth, while the cold and poor rainfall in winter is not conducive to the accumulation of vegetation organic matter.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 5 Experiments and Results
### Experimental Results  
Twelve transmissions were carried out with different SNRs at the receiver. The received bits and IQ data were saved in every test. The BER was calculated by comparing the received packed to the transmitted data. Results are shown in Figure 15. On the abscissa, the received signal power S is reported in dB after being normalized with respect to the maximum dynamics of the analog-to-digital converter of the system. We measured no errors until the input signal was higher than \(-30\) dB. For lower intensities, the BER rose and reached the \(10^{-3}\) threshold for S of \(-47\) dB.  
For a better understanding of the results, Figure 15 reports the constellations measured in four selected cases. The constellations are shown in the IQ plane by overlapping 200 IQ values. Please note that the axes scale changes among the four panels. The unity in the axes again represents the maximum dynamics of the rece  
Figure 14: In the experiments, we employed one VLC systems: one acts like TX (left) and is connected to the automotive lamp, the second like RX (right) and is connected the PDAPC2 photodetector.  
Figure 15: BER measured at different levels of input signal S. Input signal is normalized with respect to receiver dynamics. Red circles report experimental measurements. Received constellations are shown in four selected cases.  
noise is mainly due to the electrical noisy of the analog section of the receiver, and remains constant. Thus, the diameter of the circular clouds, formed by the distribution of the received IQ points, remains basically unchanged. When the power of the signal, which in the constellation is related to the distance of each point from the origin, reduces below \(\mathbb{S}<-30\) dB, the decision given by the sign of Q starts to generate errors.