esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 3 Downlink Scheduling in LTE
### Frame Level Scheduler (FLS)  
This scheduler uses the two-level structure to ensure a specified delay value interval between the real-time data flow. The transmission of the total data in the actual time flow is determined at the highest level of the scheduler structure. The lower level works on every TTI and assigns RBs per flow. In this algorithm, the bandwidth requirements were estimated at the upper level. Then, the PF method was employed for sharing the reserve spectrum amongst the users showing the best efforts [24]. The researchers calculated the amount of data transmitted using Equation (4).  
\[u_{i}\ (k)=h_{i}(k)*q_{i}(k) \tag{4}\]where \(u_{i}(k)\) refers to the quantum of data moved to correspond to the \(i\) flow and the \(k\) frame. This was acquired by transmitting a \(q_{i}(k)\) queue level signal over a linear time-invariant filter with response \(h_{i}(k)\); denoting discrete-time convolution [22].
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 2 All-in-Focused Image Combination  
In this paper, we propose an image combination method that detects in-focused regions in the light field images and merges them into the all-in-focused image. Figure 1 represents the procedure of our proposed method. After dividing the input stack images into blocks of \(8\times 8\) pixels and calculating the DCT coefficients of each block, we calculate SUML and HM as the in-focus measures and perform the image combination procedure. Based on the final in-focused maps, we reconstruct the all-in-focused image by applying the inverse DCT and mitigating blocking artifacts.
## 2 Materials and Methods
### Questionnaire for Intensive Care Unit Medical Staff  
Medical staff in the St. Vincent's Hospital ICU were requested to complete a first questionnaire (Table 2) regarding their prior experience with conventional ICU monitoring systems before deploying the proposed system. After implementing the proposed system, a second questionnaire (Table 3) related to the experience with the new device will be requested from staff members.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Survey for ICU Medical Staff** & \
\begin{tabular}{c} **Strongly** \\ **Agree** \\ \end{tabular} & **Agree** & **Neutral** & **Disagree** &
\begin{tabular}{c} **Strongly** \\ **Disagree** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{**Awareness of ICU monitoring equipment currently in use**} \\ \hline I think the current ICU monitoring equipment is optimized for patients & & & & \\ and medical staff. & & & & \\ \hline It is convenient to use the current ICU monitoring equipment. & & & & \\ \hline I am satisfied with the current ICU monitoring system; there is nothing to & & & & \\ supplement. & & & & \\ \hline \multicolumn{6}{c}{**The inconvenience of the currently used ICU monitoring equipment**} \\ \hline There are many unnecessary alarms (device noise, false alarms, incorrect & & & & \\ settings, and so on). & & & & \\ \hline Many cables are connected to the sensor, which is inconvenient when & & & \\ \hline Interlocking or compatibility with other devices is difficult (ventilator, DC & & & \\ device, and EMR linkage). & & & & \\ \hline It is difficult to use patient monitoring equipment (it takes learning time, & & & \\ responding to alarms is inconvenient, and so on). & & & \\ \hline \multicolumn{6}{c}{**I hope that ICU monitoring equipment will be improved as follows.**} \\ \hline Using a wireless sensor (without cable) & & & \\ \hline Unnecessary alarms are reduced. & & & \\ \hline In-hospital remote monitoring system (mobile phone/tablet PC) & & & \\ \hline Other ideas? & & & \\ \hline \multicolumn{6}{c}{**What are your thoughts on using new digital equipment to make ICU**} \\ \multicolumn{6}{c}{**monitoring more straightforward and accurate?**} \\ \hline I want to try it as soon as possible. & & & \\ \hline \multicolumn{6}{c}{I would like to apply new equipment more slowly (skilled use seems } \\ \multicolumn{6}{c}{complicated).} \\ \hline New digital equipment is unlikely to be trusted yet. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Questionnaire for intensive care unit medical staff regarding their experience of the conventional monitoring system.
## 3 GNSS Spoofing Signal Power Control Algorithm
### Building the Objective Function  
Under the premise of satisfying the above constraints, the following three points should be optimized: 1 the noise variance estimation \(\partial^{2}\) should be minimum; 2 the relative acquisition probability of each branch spoofing signal should be optimal; 3 the overall acquisition performance of the multi-channel spoofing signal achieved should be excellent. The overall acquisition performance of the multi-channel signal is represented by the sum of the relative acquisition performance of each spoofing signal: \(\sum\limits_{i=1}^{n}P_{i}\). Therefore, the objective function can be defined as:  
\[\left\{\begin{array}{l}F_{\theta^{2}}=\frac{N_{0}}{2N\Gamma_{s}}+\left(\sum \limits_{h=J^{\mu}}P_{h}^{a}+\sum\limits_{m=J^{\
u}}P_{m}^{s}\right)\bar{\sigma }_{\psi}^{2}\\ F_{P_{t}}=\frac{P_{t_{d}}^{2}}{P_{d_{d}}^{2}+P_{d_{d}}^{2}}\\ F_{P_{total}}=\sum\limits_{i=1}^{n}P_{i}\end{array}\right. \tag{20}\]  
where \(F_{\theta^{2}}\), \(F_{P_{t}}\), and \(F_{total}\) represent three objective functions, respectively, to minimize \(F_{\theta^{2}}\), \(F_{P_{t}}\) and \(F_{total}\) reach the maximum.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 2 Theory
### Calibration Theory  
In practice, the system error leads to a frequency shift and broadening of spectral lines, resulting in errors in spectral range and resolution. Wavelength calibration can obtain the actual spectral range and resolution. Generally, the wavelength can be calibrated using a light source with a known spectral range. In this study, a mercury lamp light source was used to calibrate the wavelength of the optical system. After measurement of the fringe frequencies of two known emission lines, the Littrow wavelength of the system can be obtained through the following formula:  
\[\lambda_{L}=\frac{f_{2}-f_{1}}{(f_{2}/\lambda_{1})-(f_{1}/\lambda_{2})} \tag{11}\]  
where \(\lambda_{L}\) is the Littrow wavelength, \(\lambda_{1}\) and \(\lambda_{2}\) are the known wavelengths of the calibration light source, and \(f_{1}\) and \(f_{2}\) are the fringe frequencies of the known emission lines. According to Equation (2), the Littrow angle of the two diffraction gratings on the interference arms can be deduced:  
\[\theta_{L}=\text{arcsin}\bigg{(}\frac{\lambda_{L}\text{-}\delta}{2}\bigg{)} \tag{12}\]  
The spectral resolution is:  
\[\delta\sigma=\frac{1/\lambda_{1}-1/\lambda_{2}}{f_{1}-f_{2}} \tag{13}\]  
The spectral range of the instrument can be obtained from Equation (7).
## 8 Conclusions  
In this paper, we have proposed a low-cost, low-power, high-speed satellite IP route lookup architecture. The proposed architecture is based on the cuckoo Bloom hybrid filter (CBHF) structure and the PR-Trie algorithm. We have evaluated the performance of the proposed architecture using C++ language at the behavioral level and Verilog HDL at the hardware level. The behavioral simulation and hardware implementation results show that the CBHF structure can achieve better lookup performance with less storage space, and the single-port throughput of our proposed lookup architecture is over 13 Gbps on an FPGA board with one single DDR3 SDRAM when operating at 200 MHz. In other words, we provided evidence for the current viability of our approach for satellite IP route lookup.  
In addition, if implemented with multiple lookup engines, our proposed satellite IP route lookup architecture could achieve 40 Gbps throughput with three DDR3 SDRAM chips. In comparison, TCAM-based or SRAM-based solutions could not satisfy the requirements of space applications due to their massive power consumption and costs. Therefore, algorithms such as ours that employ SDRAM devices and pipelining technologies to achieve comparable or better performance will continue to be future research directions in satellite-borne applications.  
Conceptualization, Y.Z. and L.Q.; methodology, L.Q.; software, L.H. and Q.C.; hardware, Y.Z. and L.Q.; validation, Y.Z.; investigation, L.Q.; resources, L.H. and X.X.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, L.Q. and X.X.; visualization, Y.Z.; supervision, Q.C.; project administration, X.X.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 62171466.  
Not applicable.  
All research data are publicly available.  
The authors wish to thank [PERSON] (who is now with Key Laboratory of Information Systems Engineering, China) for insightful discussions on the cuckoo filter.  
The authors declare no conflict of interest.
# Antimicrobial Activity of L-Lysine and Poly-L-Lysine with Pulsed Electric Fields
## 3 Results
albicans_, where (**A**) 15 kV/cm treatment; (**B**) 30 kV/cm treatment. Asterisk (*) represents statistically significant (_p_ < 0.05) difference versus Lys or PLL treatment, respectively. CTRL—samples with Lys or PLL without PEF treatment. It can be seen, that both Lys and PLL in combination with PEF are effective against Gram-positive bacteria _S. aureus_. Duration-wise the tendency observed with previous microorganisms persists. Similarly, the increase in the number of pulses (2.5-fold) did not improve the treatment efficacy significantly. However, as was expected, an increase of the PEF amplitude to 30 kV/cm (Figure 4B) significantly improved the inactivation efficacy of shorter duration pulses (500 ns and 1 us). Lastly, the efficacy of the treatment on the Gram-negative bacteria _E. coli_ was investigated. The results are presented in Figure 5. The antimicrobial response against _E. coli_  
Figure 4: The antimicrobial efficacy of L-lysine and poly-L-lysine with pulsed electric field treatment against _S. aureus_, where (**A**) 15 kV/cm treatment; (**B**) 30 kV/cm treatment. Asterisk (*) represents statistically significant (_p_ < 0.05) difference versus Lys or PLL treatment, respectively. CTRL—samples with Lys or PLL without PEF treatment. was the weakest compared to all the other microorganisms used in this study. The increase of the number of pulses was not effective; however, the treatment with 30 kV/cm field in combination with Lys and PLL improved the inactivation several-fold. The scaling with the number of pulses follows a predictable manner, indicating that longer duration pulses are more efficient compared to the sub-microsecond procedure. An increase of the burst energy via an increase of the number of pulses (2.5-fold) positively  
Figure 5: The antimicrobial efficacy of L-lysine and poly-L-lysine with pulsed electric field treatment against _E. coli_, where (**A**) 15 kV/cm treatment; (**B**) 30 kV/cm treatment. Asterisk (*) represents statistically significant (\(p<0.05\)) difference versus Lys or PLL treatment, respectively. CTRL—samples with Lys or PLL without PEF treatment. affects the treatment with 1 \(\upmu\)s pulses and 30 kV/cm treatment (Figure 5B). The inactivation efficiency scales proportionally (more than 2-fold).
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Attack Layer Network  
Digital medical images are vulnerable to channel noise pollution, unauthorized use, and tampering during transmission through public networks. These intentional or unintentional attacks can cause a certain degree of distortion to the protected medical images, leading to the failure of correctly extracting watermark information embedded in the images, thus undermining copyright authentication. To improve the robustness of the  
Figure 8: The proposed reversible information embedding process.  
watermark algorithms, common attack methods can be incorporated into the model training process to enhance the robustness of the watermark extraction networks. We selected eight common image attack methods to form an attack layer network: Median filtering (MF), Gaussian filtering (GF), salt and pepper noise (SPN), Gaussian noise (GN), rotation, cropping, JPEG compression (JPEG), and scaling. Figure 9 visualizes the distortion losses caused by these eight image attack methods.
## 2 Materials and Methods
### Data Analysis  
Based on the national-scale statistical data, a methodological framework was employed to analyze the relative contributions of FDS and the environmental variables to LST variability. The analytical procedures were as follows.  
First, linear regression analysis was implemented to elucidate the temporal trends of individual variables across two distinct periods. The goodness-of-fit for these linear models was evaluated using the coefficient of determination (R\({}^{2}\)), and \(p\) values were calculated to determine the statistical significance of the trends [48; 49]. Additionally, bivariate linear regression was applied to examine the correlations between variables.  
Subsequently, a random forest (RF) algorithm, a robust ensemble learning technique based on decision trees [50], was utilized to assess the relative influence of FDS and the environmental variables on LST variations. RF employs bootstrap aggregation (bagging) by constructing multiple decision trees on different training subsets generated via sampling with replacement. The importance of each predictor variable was quantified using metrics based on Gini index reduction and out-of-bag (OOB) error evaluation. All RF computations were executed using the scikit-learn package (version 0.22.1) within the Python (version 3.7.6) environment.
## 5 Discussion and Conclusions  
This study describes a new method for the construction of shipping routes using the FDA. The proposed method starts by detecting anomalous trajectories and removing them. Then, the median curve from the functional data analysis is identified; this is a shipping route.  
Regarding the detection of trajectory outliers, this study compared the functional data analysis approach with the Mahalanobis distance method, which is a multivariate outlier detection method. FDA detects abnormal movement patterns. In contrast, the Mahalanobis distance method cannot detect them. It is necessary to look at the pros and cons of both methods, based on a dedicated application.  
Furthermore, the resulting shipping-route constructions are compared with those of a density-based approach. It is significant that the grid cell size resolves the resolution of the map construction because small grid cell sizes cause irregular shipping routes that are not naturally present.  
However, it is worth noting that the proposed method cannot be applied to all types of map construction, such as street-map construction. For example, it is unlikely that FDA can accurately represent all street maps at intersections. The proposed method is good at representing simple trajectories, such as shipping routes in the ocean.  
Ultimately, further investigation should focus on the design of a robust algorithm for a variety of map-construction applications based on the FDA. Besides, the current research does not consider locational circumstances or natural environment. Such geographic contexts should be incorporated. This research will lead to increased knowledge and a better understanding of vessel movement dynamics, which improves maritime monitoring and security.  
Conceptualization, [PERSON]-H.J.; methodology, [PERSON]-H.J. and [PERSON]-[PERSON]; software, [PERSON]-H.J., [PERSON]-[PERSON] and T.-Y.L.; validation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, [PERSON]-H.J., [PERSON]-[PERSON] and T.-Y.L.; investigation, [PERSON]-H.J., [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, [PERSON]-H.J., [PERSON]-[PERSON] and [PERSON]-[PERSON]; data curation, [PERSON]-[PERSON] and [PERSON]-Y.L.; writing--original draft preparation, [PERSON]-H.J.; writing--review and editing, [PERSON]-H.J. and M.K.Y.; visualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; supervision, [PERSON]-H.L.; project administration and funding acquisition, [PERSON]-H.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1C1B5043892). The authors declare no conflict of interest.
## 3 Results and Discussions
[PERSON], [PERSON] [67] reviewed the SVM-based model of EMG signal classification and reported that a large number of sounds, vibration signals, and images have been classified using the SVM classification algorithm, achieving more accuracy without feature selection and 5% less accuracy with feature selection. Therefore, to determine if the SVM model performs better without feature selection, the models were evaluated with the 12 features, and the loss function was determined. The performance of both models for 5-fold cross-validation with all the feature vectors is illustrated in Figure 10, which shows that, for SVM models, the performance improved greatly compared to when feature selection was implemented. The overall average error loss when 5-fold cross-validation was performed on all the features was 0.1668, compared to 0.5031 when feature selection was performed. However, for KNN models using 5-fold cross-validation with feature selection, the overall average error loss increased from 0.0318 to 0.2202. These results show that, although feature selection improves the performance of KNN models in classifying the conditions of the tool, this is not the case with the accuracy and performance of the SVM model. The result shown in Figure 11, for the 10-fold error loss for SVM and KNN classification models developed without applying feature selection, also indicates that the performance of SVM models was more accurate without feature selection. The error loss for 10-fold cross-validation for SVM models when feature selection was applied was 0.5009, while it reduced to 0.1578 without feature selection. On the other hand, the performance of KNN models when feature selection was adopted was 0.0343, while it increased to 0.2172  
Figure 9: Error Loss for SVM vs. KNN classification with 10-fold Cross-validation. without feature selection. Therefore, the KNN algorithm performed better in classifying the condition of the cutting tool during the machining operation. Figure 4.0 Error Loss for SVM vs. KNN classification with 10-fold cross-validation without feature selection. Figure 4.1 Error Loss for SVM vs. KNN classification with 5-fold Cross-validation without feature selection.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 2 Voltage Control Model Based on MPC
### Optimization Model  
The proposed scheme aims at minimizing the system voltage deviation and the corresponding control cost. The optimization problem can be formulated as follows:  
\[\min(\sum_{k=1}^{N_{\text{P}}}{(\mathbf{V}_{r}-\hat{\mathbf{V}}_{k})\mathbf{Q}(\mathbf{V}_{r} -\hat{\mathbf{V}}_{k})}^{\text{T}}+\sum_{k=1}^{N_{\text{c}}}{\Delta u_{k}\mathbf{R} \Delta u_{k}^{\text{T}}}) \tag{6}\]  
Subject to:  
\[\mathbf{u}_{k}=\mathbf{u}_{0}+\sum_{l=1}^{k}{\Delta\mathbf{u}_{l}} \tag{7}\]  
\[\mathbf{V}_{\min}\leq\hat{\mathbf{V}}_{N_{\text{P}}}\leq\mathbf{V}_{\max} \tag{8}\]  
\[\mathbf{u}_{\min}\leq\mathbf{u}_{k}\leq\mathbf{u}_{\max} \tag{9}\]  
Figure 2: Voltage prediction based on trajectory sensitivity analysis.  
\[|\Delta u_{k}|\leq\Delta u_{max} \tag{10}\]  
where \(\mathbf{V}_{\mathbf{r}}\) is the vector consists of the control references. \(\hat{\mathbf{V}}_{k}\) is the predicted voltage at sampling point \(k\). \(\Delta u_{k}\) is the vector consists of the control variable changes at sampling point \(k\). \(\mathbf{Q}\) and \(\mathbf{R}\) are both diagonal matrices which define the weight coefficients of the controlled voltages and the cost of the control actions respectively.  
The voltage constraints in the prediction horizon is shown in Equation (8), \(\hat{\mathbf{V}}_{N_{\mathrm{P}}}\) represents the predicted voltage at the sampling point \(N_{\mathrm{P}}\). The objective function sets a target of minimizing the voltage deviations at every sampling point under the constraints of control input (9) and (10), and the voltage constraints are only enforced at the end of the prediction horizon. The inequality constraints (9) stipulates the upper and lower bounds of the control inputs.  
When undesirable bus voltage magnitudes are detected, appropriate voltage control actions should be applied as soon as possible to reduce the negative influence to the consumers and avoid the deterioration of system voltages. The single-step optimization approach or large control variations in one control step may suffers from deficiencies such as low optimization efficiency or undesirable control results [21]. Thus the constraint of control variation in one control step (10) is included in the model.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.2 Dynamic Part  
The mobile part of the cloud is composed of vehicles. A vehicle is considered as being a part of the cloud if it has access, via the G5 channel, to the static part. This communication channel and the ephemeral nature of the connection raise issues. First, because the bandwidth is limited (750 kB/s) and is not duplicable (wireless network) and because a vehicle is not permanently connected to the cloud. Additionally, for data to be useful by a vehicle, it is important that they are received rapidly.
# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Qualitative Analysis
#### 3.2.7 Cybersecurity  
Local explanations based on XAI techniques in cybersecurity are mainly applied to tabular data. This is evident from the industrial sector analysis presented in Table 2, where most methods employed are based on SHAP.  
An ML-based intrusion detection system (IDS) employing an ensemble trees technique was proposed in work by [PERSON] et al. [123]. The method employs decision trees and random forest classifiers, and it does not require much computational power to train the models. The SHAP approach was utilized to explain and interpret the models' classification conclusions, allowing cybersecurity specialists to optimize and evaluate the validity of their judgments swiftly. [PERSON] et al. [122] developed an ML-based detection strategy for anomalous pods in a Kubernetes cluster in a different study. To identify and explain crypto-mining applications, the system uses auto-encoding-based techniques for LSTM models, SHAP, LIME, and LIME. The system's explainability is critical for system administrators to grasp system-level rationales for supporting disruptive administrative decisions. [PERSON] et al. [119] also used SHAP to improve the interpretation of IDSs by integrating local and global explanations. Their proposed architecture can improve IDS transparency, allowing cybersecurity experts to make better decisions and optimize IDS structure. Finally, [PERSON] et al. [120] employed RFC (random forest classifier), XGBoost, and the sequential algorithm to study two big cybersecurity datasets and used three SHAP approaches to explain the feature contributions. The study emphasizes the need to understand the value of data in order to increase the explanatory capabilities of cybersecurity threats data using SHAP methodologies, which can lead to future data collection operations in cybersecurity or other fields.  
The benefits of employing SHAP for XAI in cybersecurity include improved model transparency and interpretability, the ability for cybersecurity specialists to make better decisions, and the optimization of model structures. However, drawbacks include the method's intricacy and the possibility of misleading explanations if the model is not thoroughly understood.