esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cryogenic Photoluminescence Characterization
Note that in (**d**), the peak height of the PL spectrum of the position d measured at RT in air is divided by 5, and in (**e**), \(I_{\text{A+A-}}\) at RT in air is divided by 10. Only the series of \(I_{\text{A+A-}}\) measurements at RT in air in (**e**) was carried out in the ambient, while all the other PL experiments were conducted on the sample in the cryostat. As summarized in Figure 5d, a collection of PL spectra was measured at 77 K in a vacuum focusing the laser beam into the five different positions of the ML-MoS\({}_{2}\) in the cryostat along the white dashed line in Figure 5b across the irradiated region. For comparison, the PL spectra measured in position d at RT in the air are also plotted together. At the low temperature, the defect-activated PL peak (D peak) was revealed clearly, and its PL intensity was observed to be stronger near the irradiation spot. Simultaneously, the main PL peaks were observed to be blue-shifted, possibly due to the temperature-dependent band structure of ML-MoS\({}_{2}\)[25]. Moreover, its intensity became stronger in comparison to the PL at RT in a vacuum, probably due to the suppression of thermal effects such as phonon scatterings which hinder the radiation process [25]. In Figure 5e, the variations in \(I_{\text{A+A^{-}}}\) at RT in air and at 77 K in a vacuum and the integrated intensity of D peak, \(I_{\text{D}}\), were plotted as a function of \(d\). Considering that both the defect-activated excitation in the low temperature and the defect-driven PL emission of A become more active with the higher concentration of defects, the strong correlation between \(I_{\text{A+A^{-}}}\) and \(I_{\text{D}}\) shown in Figure 5e are naturally accepted. These results also imply that the enhancement of air gas sensitivity is caused by the increase in S-deficiency via the FLI process. In addition, the profile of \(I_{\text{A+A^{-}}}\) at RT in air in Figure 5e shows the well-defined peak shape. By the Gaussian fitting, the full width at half maximum (FWHM) of the fitted peak curve is determined to be \(\sim\)2.4 \(\upmu\)m, which is quite consistent with the laser spot size for the FLI process of \(\sim\)2.3 \(\upmu\)m. This agreement indicates that the distribution of defects related to the PL emission would follow the Gaussian profile of the laser beam intensity (Figure S3) [32].
## 4 Results and Analysis of Three-Dimensional Forward Modeling
### Single-Anomaly Model
#### 4.2.4 Two Anomalies' Response and Localization  
The previous calculations demonstrate the significant impact of the spatial positioning of anomalies on the electric field responses. In this section, we explore two scenarios involving two anomalies: (1) anomalies of 10 \(\Omega\)-m positioned along the centerline between the grounded-wire source and the survey line (Figure 12a) and (2) anomalies located near the survey line (Figure 12b). For the latter configuration, two cases were considered: (a) both cubes had a resistivity of 10 \(\Omega\)-m and (b) the left cube had a resistivity of 10 \(\Omega\)-m while the right cube had a resistivity of 1000 \(\Omega\)-m.  
Figure 10: The cubes (**a**) close to the transmitters; (**b**) on the inner side and close to the survey line; and (**c**) on the outer side and close to the survey line.  
Figure 9: Multi-channel electric field curves and differential curves for cube located at (**a**,**e**) 25 m (resistivity of 10 \(\Omega\)-m), (**b**,**f**) 25 m (resistivity of 1000 \(\Omega\)-m), (**c**,**g**) 50 m, and (**d**,**h**) 75 m in the midline of the transmitters and receivers.  
In Figure 13a, the anomaly on the left side is closer to both the transmitter and receiver, leading to a wider range and larger amplitude in the electric field. The proximity of the two anomalies causes the response from the right anomaly to be overshadowed by the left anomaly, making it nearly indistinguishable. In contrast, as shown in Figure 12b, both anomalies are positioned near the survey line but are farther apart. In the case where both anomalies are of low resistance (Figure 13b,e), or one anomaly is low-resistance and the other high-resistance (Figure 13c,f), the proposed configuration successfully distinguishes and identifies the anomalies. These results reinforce the importance of anomaly proximity to the survey line: the closer anomalies are to the survey line, the stronger their electric field responses. Therefore, maximizing the number of survey lines in our cross-hole exploration is essential to improve the detection and localization of surrounding anomalies.
## 5 Comparative Evaluations  
We investigated whether the proposed method satisfies the four requirements of inaudibility, robustness, blind-detectability, and confidentiality by comparing it with the LSB, DSS [4], and MC [14] methods.
# Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
## 2 Methods
### Wavelet Transform Method  
The basic concept of wavelet transform is to express a signal by a cluster of wavelet function. Therefore, wavelet function is the core of wavelet transform, which refers to a type of function that has oscillatory nature with the ability for rapid decay to zero [21,22].  
The wavelet function is defined as the equation:  
\[\int_{-\infty}^{\infty}\psi(t)d_{t}=0 \tag{2}\]  
\[\psi(t)=e^{-\frac{t^{2}}{2}}e^{i\omega t},\ w\in R \tag{3}\]  
where \(\psi(t)\) is called the fundamental wavelet and can compose a transform function by scaling and shifting:  
\[\psi_{m,n}(t)=|m|^{-\frac{1}{2}}\psi\left(\frac{t-n}{m}\right) \tag{4}\]  
where \(\psi_{m,n}(t)\) is sub-wavelet function; \(m\) is a non-zero real number; \(n\) is a real number.  
The discrete wavelet transform is applied to the hydrological series of rainfall and runoff by the convolution of a wavelet function with awaiting signal:  
\[W_{f}(m,n)=|m|^{-\frac{1}{2}}\Delta t\sum_{k=1}^{N}f(k\Delta t)\overline{\psi }\left(\frac{k\Delta t-n}{m}\right),\ k=1,2,\cdots,N \tag{5}\]  
where \(W_{f}(m,n)\) is the wavelet transforms coefficients; \(N\) is the number of discrete points.  
The real part and mode square of wavelet transform coefficients are used for hydrological period determination. Additionally, the period of the hydrologic time series was reflected by the wavelet variance with different scales:  
\[Var(m)=\frac{1}{N}\sum_{k=1}^{N}W_{f}^{2}(m,n) \tag{6}\]where \(W_{f}\) is the signal to be processed; \(\overline{\psi}\) is the complex conjugate function of \(\psi\).  
The discrete inverse transform can reconstruct the signal to produce a smoothed component \(W_{f}(t)\) as:  
\[W_{f}=W_{f}(t)+W_{f}(m,n,t) \tag{7}\]  
where \(W_{f}(t)\) is an approximate sub-signal and can represent the trends in the original series as a whole; \(W_{f}(m,n,t)\) are detailed sub-signals on the time scale and can capture small features of explanatory value in time series.  
When the original signal is decomposed multiple times, the approximate sub-signal and detailed sub-signal will be generated as:  
\[W_{f}=A+D_{1}+D_{2}+\ldots D_{N} \tag{8}\]  
where detailed sub-signal \(D_{N}\) depends on the magnitude of the signal.
## 5 Discussion
However, for precise analysis, especially for climate and dating analysis, samples should be analysed with a laboratory device in an optimal environment with dry conditions. Original samples taken in the field were analysed by XRD without previous chemical treatment procedures to enhance the visibility of some XRD peaks. Nevertheless, the results are reliable, and some questionable interpretations are successfully solved (i.e., goethite and hematite were distinguished by observing the color of the mineral powder). Finally, the density of the measurement points along the profiles should be planned carefully to obtain reliable results. This preliminary research suggests that the measurability of magnetic susceptibility in Croatian caves within the Dinaric Karst can be performed at a satisfactory level. Future findings could provide information about the depositional history of the investigated caves. Furthermore, systematic and dense investigations of magnetic susceptibility values, and thus observations of the magnetic material changes through time, could unravel a timeline of climatic and tectonic changes.
## 3 Research Methodology
### QPSO Process
#### 3.3.1 MLP-ADAM-Based Localization
Two constants, \(\beta_{1}\) and \(\beta_{2}\), are introduced, as well as a 'first' and a'second' set of momentum matrices, \(\mathbf{\eta}\) and \(\mathbf{\mu}\), respectively. The equations for the update of the first and second momenta are:  
\[ \begin{split}\mathbf{\eta}_{k}^{(n+1)}&=\beta_{1}\mathbf{ \eta}_{k}^{(n)}+(1-\beta_{1})\Delta\mathbf{W}_{k}\\ \mathbf{\mu}_{k}^{(n+1)}&=\beta_{2}\mathbf{\mu}_{k}^{(n)}+(1- \beta_{2})(\Delta\mathbf{W}_{k})^{2}\end{split}  \tag{11}\]  
and the weights are updated according to the expression:  
\[\mathbf{W}_{k}^{(n+1)}=\mathbf{W}_{k}^{(n)}-\lambda\frac{(1-\beta_{2}^{n})\mathbf{\eta}_{ k}^{(n+1)}}{\big{(}1-\beta_{1}^{n}\big{)}\sqrt{\mathbf{\mu}_{k}^{(n+1)}}} \tag{12}\]
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 2 Framework Design
### Long-Term Dependency Learning  
Another key for detecting the vulnerable patterns is to capture the long-term dependencies. In an ANSI C function, a data flow which starts from the declaration of a local variable (which is the source) to the corresponding sink can consist of multiple statements spanning across the whole function body. To recognize the potentially vulnerable patterns manifested in the data flow, our embedding framework implements a one-layer bidirectional LSTM (Bi-LSTM) network followed by the ELMo embedding to ensure the learning of long-term contextual dependencies.  
The LSTM implementation was introduced to handle long-term dependencies of input sequences. Compared to a vanilla recurrent neural network (RNN) or the gated recurrent unit (GRU), which is another variant of an RNN, the LSTM performs better at tracking long-term dependencies, due to having both the gates and a memory cell for keeping/updating  
Figure 3: A plot of the ELMo representations of three vulnerable sinks and their corresponding patched versions. The representations are 1024-dimensional vectors output from the mean-pooling layer of the ELMo module, which are projected onto a 2D plane using t-SNE (with \(\text{perplexity}=2.2\)). The red rectangles are the representations of the vulnerable sinks missing proper validation. The green circles are the representations of the patched sinks using _if_ statement for validation. The figure depicts that the ELMo module could produce distinct representations for vulnerable sinks and non-vulnerable ones based on their context (i.e., having _if_ statement for validation or not.)  
states from the previous and the next candidate activations. As mentioned previously, a potentially vulnerable data flow usually encompasses a number of code statements, either preceding or subsequent, or even both. Therefore, the bidirectional LSTM architecture is implemented to enhance the ability to learn the long-range dependencies in both forward and reverse directions, which can effectively capture the vulnerable data flow that is context dependent.
## 4 Experiment Results  
### Experiment Setting  
The CTDI experiment program is written in Python, and the experiment software platform is Keras. The experiment is running on a desktop computer with a 2.9 GHz Intel Core i5-10400 processor and 32 GB of RAM.  
AWID is the experiment dataset that has one normal class and three major attack classes: flooding, injection, and impersonation. In the adopted AWID training set, AWID-CLS-R-Trn, here are the distributions of four classes: (1) normal: 1,633,190 (91%), (2) flooding: 48,484 (3%), (3) injection: 65,379 (3%), and (4) impersonation: 48,522 (3%). To avoid training bias, the number of normal samples should be the same as the total number of attack samples in the training set. As Table 3 shows, the size of the normal class in the training set is reduced by random sampling. The AWID testing set, AWID-CLS-R-Tst, does not need to be balanced. Table 3 shows the sizes of four classes in the testing set, and their sizes remain the same. For validation purposes during the training, the training set is divided into the training and validation samples in the ratio of 8:2, and their sizes are 259,816 and 64,954, respectively.  
The experiment adopts two primary evaluation metrics: accuracy (ACC) and F1 score, which are based on the four major parameters in the confusion metrics: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). Accuracy measures the overall detection capability and is defined as follows: Accuracy = (TP + TN)/(TP + TN + FP + FN). The F1 score integrates precision and recall to evaluate the detection of attack behaviors and is defined as follows: F1 score = (2 \(\times\) TP)/(2 \(\times\) TP + FP + FN). In comparison to accuracy, the F1 score directly considers both FP and FN against TP without TN. Therefore, both accuracy and F1 scores are valuable for evaluating the effectiveness of intrusion detection models.  
The AWID dataset has 154 features, which are designed to detect the three wireless attacks: flooding, injection, and impersonation. These features are mainly related to Wi-Fi  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Training** & **Test** \\ \hline \multirow{3}{*}{Normal} & Unbalanced & 1,633,190 & 530,785 \\  & Balanced & 162,385 & - \\ \hline \multirow{4}{*}{Attack} & Impersonation & 48,522 & 20,079 \\  & Flooding & 48,484 & 8097 \\ \cline{1-1}  & Injection & 65,379 & 16,682 \\ \cline{1-1}  & Total & 162,385 & 44,858 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of normal and attack data samples.  
major parameters for frames, radio channels, wireless LAN control, and management. From these original features, CTDI generates 154 Auto Encoder features. Then, CTDI selects the effective features from both the original and Auto Encoder data samples.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 2 Light Field Camera and Imaging Modeling
### Imaging Modeling  
As shown in Figure 2a, each light ray is illustrated by its interactions with two parallel planes--the \(uv\) plane describes the ray's angular direction and the \(xy\) plane describes the ray's spatial position. Therefore, for the 4D light field function, \(L(u,v,x,y)\), \((u,v)\) denotes the index of different angular viewpoints, and \((x,y)\) denotes the spatial coordinates on each sub-aperture view [4].  
Figure 2: (**a**) Two-plane representation of 4D light fields \(L(u,v,x,y)\). (**b**) The imaging system for a micro-lens array (MLA) light field camera. \(f_{mla}\) is the focal length of the micro-lens, and \(f\) is the focal length of main lens. \(d\) is the macro-pixel size. \(A\) is the aperture size of the main lens. \(z\) is the object/scenedistance. \(a\) is the distance between the main lens and micro-lens array. \(b\) is the distance between the micro-lens array and photosensor. (**c**) The capturing system based on the lab-designed camera. The top right is a zoom-in of the captured light field raw image (.raw). (**d**) Left: zoom-in processed light field color raw image. It is easy to see the micro-lens array structure. Middle: \(9\times 9\) decoded sub-aperture images with a narrow baseline. Right: the edge macro-pixel detection results.  
As shown in Figure 2b, light field imaging can be viewed as capturing rays within a scene through a grid of lenses from different viewpoints. For the Plenoptic 1.0 light field camera (such as the Lytro Illum and the lab-designed camera), the distance between the micro-lens array and the photosensor equals the focal length of the micro-lens, i.e., \(b=f_{mal}\). The range of each macro-pixel in the \(x\) dimension is \(d\), and the FoV (field of view) is \(\frac{d}{f_{mal}}\). Furthermore, the aperture size of the main lens is \(A\), and the distance between the main lens and the micro-lens array is \(a\). To maximize the utilization of sensor pixels, the F-number of both the main lens and the micro-lens should be equal, [22]; that is  
\[\frac{a}{A}=\frac{f_{mal}}{d} \tag{1}\]  
If the angular resolution is \(N\times N\), the DoF (depth of focus) of the light field camera is \(N\times\) over the DSLR (digital singular lens reflex) camera, but spatial resolution is only \(\frac{1}{N^{2}}\times\). This is the trade-off between spatial and angular resolution for light field imaging.
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 4 Analysis
### Concern Parameter's Description
\[T_{transmission}=(Size_{partition}/\textit{Bandwidth}_{channel})\times\textit{ number}_{channels}\] (3)
* _Routing Time (\(T_{routing}\))_This is the time required to route the query to the correct channel(s). It depends on the complexity of the routing algorithm and the number of channels. \[T_{routing}=f\ (complexity_{routing},\ number_{channels}) \tag{4}\]  
* _Processing Time (Tprocessing)_ This is the time needed to process the query and retrieve the data. It depends on the complexity of the query and the efficiency of the query processing mechanism. \[T_{processing}=f\ (complexity_{query},\ efficiency_{processing}) \tag{5}\]  
Combining these components, the formula for calculating the access time \(T_{access}\) can be expressed as  
\[ \begin{array}{l}T_{access}=f\ (size_{xml},\ complexity_{xml})+[(\text{Size }_{\text{partition}}/\text{Bandwidth}_{\text{channel}})\times\text{number}_{ \text{channels}}]\\ +f\ (complexity_{routing},\ number_{channels})+f\ (complexity_{query},\ efficiency_{processing})\end{array}  \tag{6}\]  
This formula provides a framework for estimating the access time for XML querying over wireless broadcast channels. We used the complexity of XML data as a complexity factor on a scale of 1 to 3: 3: High, 2: Medium, 1: Low.
## 6 Conclusions  
In this paper, the HVR in the main urban area of Xi'an city was estimated by using nighttime and POI data, and the housing vacancy status in the main urban area of Xi'an city from 2018 to 2019 was obtained by analyzing data from remote sensing images at different times. The main conclusions and highlights of this study were that the (1) Luojia-1 nighttime light and POI data improved the spatial resolution of the housing vacancy results, and this paper provided new ideas for housing vacancy research; (2) the multi-period data were processed and calculated and the HVR was obtained by calculating the average, which represented the housing vacancy level in the main urban area of Xi'an city from 2018 to 2019, and we provided a new method to analyze the spatial distribution of housing vacancies.  
Housing vacancy is an interesting and appealing concern. The four scene images of Luojia-1 with high resolution and POI were used to estimate HVR in this paper. The combination of multisource remote sensing data with higher spatio-temporal resolution and statistical data to estimate HVR will be conducted in the next step. In addition, machine learning algorithms are also considered to estimate HVR. The method proposed in this paper makes up for the shortcomings of traditional HVR statistics and improves the accuracy of HVR spatial estimation. It is expected to become a new spatial estimation method for monitoring housing vacancy. The estimation results can provide scientific reference for the healthy development of regional real estate, government macro-control, and related research of scholars.  
**Author Contributions:** Conceptualization, P.Y. and J.P.; methodology, P.Y. and J.P.; software, P.Y.; validation, P.Y.; formal analysis, P.Y.; investigation, P.Y.; resources, P.Y.; data curation, P.Y.; writing--original draft preparation, P.Y.; writing--review and editing, P.Y. and J.P.; visualization, P.Y.; supervision, J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Natural Science Foundation of China (no. 42071216).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and statistical analysis methods are available upon request from the corresponding author.  
**Acknowledgments:** The authors thank the editors and anonymous reviewers for their thoughtful and helpful suggestions on improving the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Experiment on the Various CNN Models and ECG Beat Detection  
As described in Section 3.2 (see Table 1), five profiles were carried out for ECG-ID and MIT-BIH database, i.e., a total of 10 experiments. Table 5 shows the experimental results for various CNN models (various experimental profiles). Of the particular interest is the third profile, which has the following configuration: using manual ECG beat detection (annotated files), using both depthwise separable CNN (DSC) and residual CNN. The third experimental profile produced good results for both the ECG-ID and MIT-BIH database, i.e., 100% accuracy, for eight and six ECG beats, respectively.  
Although the third profile produced good authentication results, manual beat detection is only feasible in the lab or in offline authentication, as the beat detection needs to be conducted manually by cardiologists. Note that the first profile produced a good result as well, but it still uses manual beat detection, and the required consecutive beats per patient for the MIT-BIH database was higher (8) compared to the third scenario (6). Comparing the fourth and fifth profiles, it was found that [PERSON]'s automatic beat detection produced better accuracy compared to hybrid beat detection (training using manual beat detection, while testing using [PERSON]'s automatic beat detection). Therefore, the fourth experimental profile was found to be the optimal CNN configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of ECG Beats** & **Beat Detection** & **ECG-ID Accuracy (\%)** & **MIT-BIH Accuracy (\%)** \\ \hline \multirow{2}{*}{1} & Manual & 83.33 & 89.58 \\  & Hamilton & 83.33 & 89.58 \\ \hline \multirow{2}{*}{2} & Manual & 37.78 & 18.75 \\  & Hamilton & 38.89 & 16.67 \\ \hline \multirow{2}{*}{3} & Manual & 85.56 & 87.50 \\  & Hamilton & 81.11 & 87.50 \\ \hline \multirow{2}{*}{4} & Manual & 93.33 & 93.75 \\  & Hamilton & 94.44 & 91.67 \\ \hline \multirow{2}{*}{5} & Manual & 95.56 & 97.92 \\  & Hamilton & 96.67 & **97.92** \\ \hline \multirow{2}{*}{6} & Manual & 97.78 & 100 \\  & Hamilton & 96.67 & 95.83 \\ \hline \multirow{2}{*}{7} & Manual & 97.78 & 100 \\  & Hamilton & **98.89** & 97.92 \\ \hline \multirow{2}{*}{8} & Manual & 100 & 100 \\  & Hamilton & 98.89 & 97.92 \\ \hline \multirow{2}{*}{9} & Manual & 100 & 100 \\  & Hamilton & 97.78 & 97.92 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment results of a various number of ECG beats.  
From measurement results using the computing platform described in Section 3.1, automatic ECG beat detection in the ECG-ID database using [PERSON]'s method required around 141,318 \(\upmu\)s to detect around 2412 ECG beats or 59 \(\upmu\)s per beat on average. In contrast, in the MIT-BIH database, beat detection required 10,827,746 \(\upmu\)s to detect around 108,709 beats, or 100 \(\upmu\)s per beat on average. Therefore, the maximum detection time required to segment 10 beats is around 1750 \(\upmu\)s. This is much faster than acquiring 10 ECG beats from someone's finger.