esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 4 Our Proposed Scheme
### Data Upload Phase  
In this phase, \(DO\) encrypts the data and uploads it to the cloud server. At this time, \(DO\) creates a bloom filter to verify whether the keyword is included in the document set. \(DO\) encrypts the keyword set \(CK_{i}\), generates a public auxiliary value \(\triangledown_{i}\) for index, and sends them to the cloud server. This phase is briefed in Figure 3 and detailed steps are given below.  
**Step 1:** First, \(DO\) picks a random number \(t\in Z_{q}\) as the actual searchable encryption key and generates a bloom filter for keyword set \(W_{i}\), where \(i\in\{1,\ \ldots,\ n\}\) is file index. The bloom filter is computed as \(BF_{i}=BFGen(\{H_{1}^{\prime},\ \ldots,\ H_{k}^{\prime}\},W_{i})\).  
**Step 2:** Then, \(DO\) randomly chooses a \(M\in G_{2}\) and computes a public auxiliary value \(\triangledown_{i}\) for index \(i\). The \(\triangledown_{i}\) comprises \(c_{1},c_{2},c_{3}\) and \(c_{4}\). They are computed as \(c_{1}=g^{t}\), \(c_{2}=(g_{i}\cdot PK_{do})^{t}\), \(c_{3}=h_{2}(M)\oplus BF_{i}\), and \(c_{4}=M\cdot e(g_{1},g_{n})^{t}\). Then, \(DO\) computes \(CK_{i}=\frac{e(g_{1}(w))^{t}}{e(g_{1}g_{n})^{t}}\) for each keyword \(w\) in this set's keyword set \(W_{i}\).  
Figure 2: Setup phase.
## 3 Thin Film Cathodes
### Oxide Heterostructure Interface
Vast majority of research has been performed on porous LSCF\({}_{113}\) electrodes, which lead to ambiguous structure and geometry, and therefore the physical origin responsible for enhanced cathodic performance associated with surface decoration of perovskites is not yet completely understood. However, [PERSON] et al. [66] recently demonstrated using new forms of heterostructure oxide interfaces--(1) LSCF6428 thin films with a single layer decoration of mixed LSC\({}_{214}\) and LSC\({}_{113}\) and (2) LSCF6428 thin films with a double layer decoration of stacked LSC\({}_{214}\) and LSC\({}_{113}\) - significantly enhanced ORR activity of LSCF6428. More recently, [PERSON] et al. [155] showed the improved electrocatalytic activity and durability of LSCF6428 thin films by coating PrO\({}_{2}\). The authors claimed that PrO\({}_{2}\) coating facilitates the rate of oxygen dissociation due to increased oxygen vacancies. Figure 11 shows the influence of heterointerfaces on \(k^{*}\) for LSC\({}_{113}\) and LSCF\({}_{113}\). Figure 10: (**a**) The calculated Sr/La substitution energies in the bulk LSC\({}_{214}\), LSC\({}_{113}\), and LSCF\({}_{113}\) (all relative to that of LSC\({}_{113}\), which is set to 0). (**b**) The heterostructured interface model used in the DFT simulations and the results of Sr/La substitution energies. The elements are represented as: La/Sr (dark blue), Fe/Co (light blue, center of the octahedra), and O (red). Reprinted from [86] with permission of The Royal Society of Chemistry.
###### Abstract  
Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions.  
Keywords:simultaneous wireless information and power transfer (SWIPT); ambient backscatter communication systems (ABCSs); wireless sensor networks; Internet of Things (IoT) +
Footnote †: journal: Applied sciences
## 3 Experiments and Analysis
### Ablation Study  
The S2 Looking and LEVIR-CD+ valid sets validated our proposed algorithm's effectiveness. These studies investigated various design choices: the number of UDDRes repetitions (\(m\)) for the LS module, the large-kernel Conv size (\(k\)), loss hyperparameters (\(\lambda_{1},\lambda_{2}\)), and the representation consistency integration within encoder-decoder stages. These experiments ensured a rational and well-justified algorithm structure.
## 3 Tunnel Detection Method
### GAFEM System and Data Acquisition  
The basic composition of the GAFEM system is shown in Figure 4. The system is composed of three parts: an airborne magnetic field recording system, a ground high-power transmission system, and a ground reference base station. The airborne magnetic field recording system is composed of a cabin receiver, a radar altimeter, a global positioning system, and other auxiliary equipment, such as a z-component hollow magnetic induction sensor outside the cabin and an x-y-component magnetic induction sensor that is used to collect the magnetic field information of the system under the 3D coordinates of the survey area. The ground high-power transmission system includes three-phase alternating current generated by the generator. The high-power multifrequency pseudorandom wave transmitter forms an electrical source transmission system through two electrodes and the Earth. The ground reference station is composed of a receiver and a three-component magnetic induction sensor that is responsible for recording the changes in the magnetic field of the surrounding space. In contrast with the HTEM system, the ground artificial excitation field source is deployed 3.8 km southwest of the survey line entrance for the ground-airborne GAFEM detection method.  
The ground high-power transmission system takes the Earth as the load and uses a superpowered electrical source with an electrical dipole moment of 3 km for detection. The transmitting current is a bipolar pseudorandom wave with a fundamental frequency of 16 Hz, the peak current is 18 A, the grounding resistance is 68 \(\Omega\), and the working voltage is 1200 V. One flight can simultaneously acquire the signals of 27 frequencies. The sampling  
Figure 3: HTEM system on survey. (**a**) External system of helicopter cabin. (**b**) Internal system of helicopter cabin.  
frequency of the airborne magnetic field recording system is 192 kHz, and the diameter of the receiving coil is 8 m. The data collected by the receiving system are electrical signals, and the magnetic field is obtained through data preprocessing.
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Performance Analysis of Over-Optimized Algorithm and Various Techniques  
The performance analysis of the RP-WHEVO-HSCN-based channel estimation for estimating the channel coefficient in the MIMO system model against various optimization algorithms and existing techniques is demonstrated in Tables 3 and 4. The RMSE of the proposed RP-WHEVO-HSCN-based channel estimation model for the MIMO system is improved by 4.8% versus HHO-HSCN, 1.4% versus RSO-HSCN, 2.6% versus WHO-HSCN, and 3.7% versus EVO-HSCN. Thus, the RMSE value of the proposed RP-WHEVO-HSCN-based channel estimation model for the MIMO system is reduced to improve the efficiency of the channel estimation scheme.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Measures** & **EVO-HSCN** & **WHO-HSCN** & **RSO-HSCN** & **HHO-HSCN** & **RP-WHEVO-HSCN** \\ \hline MEP & 28.408 & 28.003 & 27.605 & 28.762 & 27.20 \\ SMAPE & 42.311 & 41.518 & 40.765 & 43.018 & 39.994 \\ MASE & 107.53 & 10.435 & 10.127 & 11.011 & 97.987 \\ MAE & 28.408 & 28.003 & 27.605 & 28.762 & 27.22 \\ RMSE & 42.614 & 42.102 & 41.622 & 43.07 & 41.121 \\ L1-NORM & 71020 & 70.006 & 69.013 & 71.906 & 67.999 \\ L2-NORM & 21.307 & 21.051 & 20.811 & 21.535 & 20.561 \\ L-INF-NORM & 95.776 & 95.776 & 95.776 & 95.776 & 95.307 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Analysis of the Implemented Serial Cascaded Deep Learning-Based Channel Estimation Model against Various Algorithms.  
Figure 7: Performance assessment of the serial cascaded deep learning-based channel estimation model in terms of various measures such as (**a**) BER, (**b**) BLER, (**c**) MSE, (**d**) NMSE, (**e**) Spectral efficiency, (**f**) Sum rate, and (**g**) Effective achievable rate.
## 4 Results and Validation
### Determination of CKH with Pointwise Fitting
At reasonable total strain values for the first ramp, a flat zone is observed; this means that, due to the parameter values obtained from the fitting, the first and the second backstress components immediately saturate to values that are not high enough to reach the maximum value of the imposed stress. The third backstress, due to the low values of \(C_{3}\) and \(\chi_{3,0}\) obtained from the fit, cannot contribute to the total modeled stress for \"low\" total strain values. As a consequence, only very high total strain values, then very high plastic strain values, could allow for the total stress to reach the target. This last point has no physical meaning, and nothing would change by using the parameters obtained by fitting Cycle II, given that, as shown in Table 2, the values of \(C_{3}\) and \(\chi_{3,0}\) are still too low. The values of Chaboche parameters obtained with a pointwise fitting are reported in Table 2.  
Figure 7: Comparisons between experimental and modeled stabilized cycles of strain-controlled tests using a pointwise fitting: (**a**) cycle I, used for validation, (**b**) cycle II, used for calibration  
In Figure 10, comparisons between the experimental and the simulated stabilized cycles executed at \(R_{\varepsilon}\approx-1\) are reported. The simulated curves were reproduced by parameters \(C_{1}\), \(C_{2}\), \(\gamma_{1}\), \(\gamma_{2}\), \(C_{3}\) and \(\chi_{3,0}\) obtained with Equations (17)-(21). The simulated models tend to not properly reproduce the zone near the elastic limit, especially in Figure 10b. The determination of \(\gamma_{2}\) and the corresponding validation of the ratcheting rate need a particular focus. The experimental strain-controlled test conducted at \(R_{\varepsilon}\
eq-1\) is not fully relaxed, and this means that a high value of \(C_{3}\) resulted from the proposed procedure. If the value of \(C_{3}\) is high, then the modeled average plastic strain of the stabilized cycle is low, and can be reached after few cycles, resulting in an early plastic shakedown. This could not allow one for one to reproduce the linear trend of Figure 3b, given that Equation (23) is still not accurate enough to compute the value of \(\gamma_{2}\). Nevertheless, to show that the procedure can reproduce the ratcheting-rate, the value of \(C_{3}\) can be set as equal to 0 and its value, obtained from the proposed procedure, can be switched to \(C_{2}\), obtaining a final value of \(C_{2}\), which is \(C_{2}^{\prime}=C_{2}+C_{3}\). The results are then reported in Figure 11.  
Figure 11: (**a**) Comparison between the experimental and simulated ratcheting rate using the proposed procedure, (**b**) a comparison between the experimental and simulated stress-controlled loading cycles. Figure 10: Comparisons between the experimental and simulated stabilized cycles using the suggested procedure: (**a**) Cycle I, (**b**) Cycle II. The values of CKH parameters obtained following the proposed procedure are presented in Table 3.
## 4 Experiment Results
### SHAP Value Analyses
#### 4.1.3 Replay Attack Scenario Dataset Analysis (Types 05 and 06)  
Table 18 shows a graph of the SHAP value analysis results for scenario type 05, which represents replay attacks. In the SHAP value distribution plot of the sixth, seventh, ninth, and eleventh data bytes, the blue and red dots are more clustered than the other previous datasets. This is presumed to be a result of the replay attack's principle of injecting the same payload repeatedly. Therefore, the normal driving data in various forms can be classified into blue dots with low SHAP values and clustered in a straight line.  
\begin{table}
\begin{tabular}{c c} \hline SHAP Value Distribution by Feature determined & Feature Importance determined using Tree \\ using Tree Explainer (Type 04 Dataset) & Explainer (Type 04 Dataset) \\ \hline \hline \end{tabular}
\end{table}
Table 17: SHAP analysis of scenario type 04.  
Figure 10: SHAP force plot for 3 random rows of data (type 04).  
The pattern of the above replay attack can be seen in the SHAP force plot in Figure 11. It is observed that the SHAP value for some features is not high due to the repeated injection of the replay's payload, and instead, the SHAP value is evenly distributed for several features. This model analyzes whether it is a replay payload by studying the data bytes of the entire message rather than finding attack patterns in other features.  
Table 19 consists of a SHAP value distribution plot, a feature importance plot, and a distribution graph showing the SHAP and feature values for each element for scenario type 06, which contains the replay attack data presented in Table 19. We can see the clustering of blue and red dots in the SHAP value distribution plot of the sixth and seventh data bytes.  
Figure 11: SHAP force plot for 3 random rows of data (type 05).  
\begin{table}
\begin{tabular}{c c} \hline SHAP Value Distribution by Feature determined & Feature Importance determined using Tree Explainer (Type 05 Dataset) \\ \hline Doral1\_net & Doral1\_net \\ Doral2\_net & Doral3\_net \\ Doral3\_net & Doral  
In Figure 12, unlike Figure 11, the features with high SHAP values can be observed, which suggests that scenario type 06 collected more diverse normal driving data than scenario type 05 did. This is because the dataset of scenario type 06 consists of more rows of data, and the quantity of attack data is also higher.
###### Abstract  
_Background:_ The pharmaceutical industry is faced with the problem of how to design and conduct tests to assess photostability during drug use and storage. In this study, the aim was to analyze the changes in total hemispherical reflectance (THR) and homogeneity in two preparations of tablets with clindamycin or tablets with phenoxymethylpenicillin stored under UV radiation. _Methods:_ The analysis was performed for coated tablets with two types of antibiotics in and out of the direct package (i.e., non-blister and blister). The condition of UV radiation was maintained over 7 days in a Solarbox 1500 chamber. THR values were assessed after 3 and 7 days using a SOC-410 reflectometer. Hyperspectral evaluation was carried out with Specim IQ hyperspectral camera every 3 nm from 400 nm to 1030 nm. _Results:_ THR values for both blister and non-blister tablets with clindamycin decreased significantly for the ranges 400-540 nm, 480-600 nm, 590-720 nm, and 700-1100 nm on day 3 of UV exposure. For non-blister clindamycin tablets, THR increased slightly on day 7 of the experiment compared to day 3, while THR continued to decrease for blister tablets. THR values for non-blister phenoxymethylpenicillin tablets decreased slightly on day 3 of UV exposure for the ranges 400-540 nm, 480-600 nm, 590-720 nm, 700-1100 nm, and 1000-1700 nm, and then on day 7 of UV exposure THR values increased to near baseline. In addition, non-blister clindamycin tablets showed a tendency to increase in a difference between max-min reflectance in the total spectral range as well as in visible and infrared light (\(p<0.001\) each). Similarly, day 7 blister tablets with clindamycin had a significantly higher max-min reflectance difference compared to day 3 blister tablets but only in the range of visible light (\(p=0.034\)). Thus, the lowest homogeneity was demonstrated for 7 day tablets. On the contrary, the lowest homogeneity was observed for phenoxymethylpenicillin tablets on day 0 of experiment. _Conclusions:_ UV radiation affects the total hemispherical reflectance values for clindamycin and phenoxymethylpenicillin tablets, but to a different extent and within a different spectral range for each type of tablet. The homogeneity of the tablets may also change over time with UV exposure.  
photostability; hemispheric directional reflectance; hyperspectral analysis; UV radiation; antibiotics +
Footnote †: journal: Applied sciences
# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study
### Experimental Comparison Analysis
#### 4.4.2 Experiments and Analysis with Different Existing Methods  
For further validation that the proposed method in this paper is superior, it is compared with the method [15], method [23] and method [24], using B1_1 and B1_2 of working condition 1 as the training set and B1_3 and B1_4 as the test set. The results of the comparison of their MAE and RMSE values are illustrated in Table 4.  
In test bearing B1_3, MAE values were reduced by 50.0%, 41.7% and 22.2%, respectively, and RMSE values were reduced by 74.2%, 50.0% and 33.3%, respectively. In test bearing B1_4, MAE values were reduced by 52.0%, 84.7% and 29.4%, respectively, and RMSE values were reduced by 52.8%, 34.6% and 26.1%, respectively. It fully illustrates the advantages of the suggested approach in this paper for the RUL prediction of bearing.
## 3 Key Challenges
### Localization  
Localization is the key function of the CPNT system [36]. However, the most appropriate method for underwater application is acoustic localization considering the demand for long distance positioning [37; 38; 39]. For the localization service, it should be accurate, robust, and have an acceptable update frequency [40; 41; 42]. For traditional underwater localization, the system is designed only for localization, which contains little additional information. However, communication service can help transmit depth, velocity [43], attitude, and high precision time stamp, which will greatly improve positioning accuracy [44]. Based on the analysis above, the localization calculation should take advantage of as much information as possible.
## 2 Related Works
### Comparison of AR Frameworks  
There have been studies comparing the functionalities and features of diverse AR frameworks [16; 17; 18; 19]. However, there is no research that actually measures and compares their performance under specific application scenarios. The main comparative criteria of the previous studies were similar, including license type, supporting platform, and features related to object recognition and tracking (e.g., text, image, markerless, marker-based, 2D/3D recognition and tracking). The AR frameworks used for comparison differed slightly between studies. In Reference [16], six frameworks, such as Vuforia, Metaio, Wikitude, and ARToolKit, were compared for marker generation, recognition target types, tracking feature, and supported content overlay. In Reference [17], seven AR frameworks were compared to develop an AR application to provide geospatial information, such as housing addresses. Thus, their main interest was location-based AR, unlike other studies. Accordingly, they used some comparison criteria specific to their application requirements, including the display radius of AR content and whether it supports integration with other GIS applications. Some studies aimed to compare AR frameworks for the purpose of developing AR-based educational applications. In Reference [18], 11 AR frameworks were compared, and one of the main comparison criteria was whether to provide AR content creation and editing features for application development. According to the results, Wikitude and Vuforia have been reported to meet their requirements best. The latest study [19] compared three AR frameworks (ARKit, ARCore, and Vuforia) to assess recognition and tracking features similarly to the previous studies. They also introduced an example of chemistry education application. To our knowledge, this study is the first work to examine and compare multiple commercial AR frameworks in terms of target recognition and tracking performance in a grocery store environment.
## 2 Specim Preparation and CT Image Acquisition
### Three-Dimensional Rendering Model  
The gray value of the same substance has a large range and is not a single value, and the key to threshold segmentation lies in how to accurately find a suitable threshold. The gray value of the pixel is repeatedly compared with the selected threshold, so as to distinguish the target area into different parts. In order to avoid errors in quantitative analysis due to improper selection of thresholds, the optimal threshold was determined by referring to the porosity ratio obtained from the experiment. In the original 2D CT grayscale image, the black ones are pores, the highlighted white material is minerals, and the rest of the gray ones are soil-like matrices. In order to render the tailings model more clearly, the mineral composition contained in it was tested. XRD diffraction tests were performed on three different types of tailings using the D8 Advance X-ray diffractometer (Bruker AG, Berlin, Germany), and the mineral composition was mainly composed of quartz, chlorite, dolomite, illite, and trace amounts of other components, such as calcite, metallic minerals, etc. The test results can be seen in Table 3, which is plotted as a five-dimensional radar distribution map, as shown in Figure 5.  
Figure 4: Three-dimensional visualization of tailings accumulation reconstruction model.  
Figure 3: Binarized comparison chart: (**a**) original grayscale map (**b**) binary processing effect (**c**) original grayscale 3D model (**d**) binary 3D model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tailings Category**} & \multicolumn{5}{c}{**Mineral Composition (\%)**} \\ \cline{2-6}  & **Quartz** & **Illite** & **Dolomite** & **Chlorite** & **Other Minerals** \\ \hline Stickyt tailings & 45.2 & 30.3 & 7.6 & 11.8 & 5.1 \\ Silty tailings & 59.4 & 31.2 & 1.4 & 4.6 & 3.4 \\ Sandy tailings & 72.4 & 16.8 & 1.2 & 5.3 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of X-ray diffraction test of tailings.  
As can be seen from Figure 5, the highest mineral content in tailings is quartz, and a three-dimensional rendering model of the tailings was established on this basis, as shown in Figure 6. Figure 6 illustrates in detail the process of tailings from 2D grayscale (Figure 6a) to color rendering (Figure 6b) and then 3D reconfiguration into a model (Figure 6c) and the meaning of the different colors (Figure 6d). Clay, quartz, and pores are rendered green, red, and blue, respectively.