esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 2 Related Works and Applications
### Application Scenario  
Ref. [1] covered secure medical records in on-road emergencies. However, emergencies often occur not only on-road but also off-road. In other words, numerous incidents, such as a vehicles rolling down a cliff or a natural disaster on a beach, often occur in places where vehicles cannot approach. In preparation for this situation, we propose a security system in which drones participate by assuming the following specific accident scenarios:  
1. CC receives and checks reports from a citizen or a drone monitoring an accident situation in the air.
2. CC orders [PERSON] to take a detailed shot, and [PERSON] to respond first.
3. According to CC's orders, the drone takes some actions, encrypts and transmits the video taken in detail to the CC in real time. [PERSON] also notifies the CC of the processing status.
4. CC analyzes it and transmits the information about nearby E to the available F or vehicle, which transfers the patient to E. ** The patient's condition is measured with the sensor device of the smartphone and vehicle, and the record is encrypted and transmitted to the arriving E.
5. In the meantime, CC generates secret key values for re-encryption/decryption and transmits them to CS and E.
6. CS re-encrypts the user's medical records encrypted by H and transmits them to E.
7. E decrypts the record that has been re-encrypted by CS using the decryption key sent by CC.
8. After decrypting the records, E proceeds to treat the patient. Following the treatment, the results are encrypted and sent to H, where they are subsequently stored in CS.  
Figure 1 shows the communication between each participant according to the application scenario.
# Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity
## 4 Methods  
As [31] pointed out, researchers are usually confronted with the kind of hypotheses that might be formulated and the models that need to be applied in the analysis of these hypotheses. The profound debate and controversy created in academia is rooted in the legitimacy of applying different statistical methods to data that are commonly used in different disciplines in which the measurement theory plays a determinant protagonist. The measurement theory, the nature of the research questions, the transformation of variables, and the statistical models are all intertwined while obtaining meaningful results.  
[PERSON], in the discussion of the paper by [PERSON] [31], emphasized that the majority of scientists saw the philosophical debates of little practical importance, and insisted that social measurement needed a much broader foundation. He finally highlighted that social measurement suffers from an inevitable arbitrariness regarding the choice of indicators, the sampling selection, and the model adequacy. In any case, the ultimate test for the social measurement is based on whether the results translate the qualitative idea into quantitative terms. On the other hand, [PERSON] discussed the paper by [31] with a focus in psychometrics and concluded that there are two important issues to handle in developing social measures: (1) how best to combine values on many variables to produce an overall aggregated value; and (2) how to derive a scale or at least an ordering from multidimensional points.  
The aim of multidimensional scaling is similar to that of multi criteria decision making (MCDM) which has benefitted greatly from the research related to Fuzzy Logic. MCDM is considered a subfield of operations research that aims to find a synthetic evaluation of multidimensional performance criteria. [PERSON] et al. [32] found a total of 413 papers published in 150 journals since 1994 that had applied fuzzy techniques in MCDM. From this total, 79 were based on a hybrid method that also applied TOPSIS proposed by [33].  
In this study, we compared two well-known methods that have been used in different fields for constructing operational concepts: the latent variable model and the fuzzy hybrid analysis. Both models aim to obtain a construct which is based on the information provided by multiple observed manifests or indicators. Several constructs such as quality of life [34; 35], national identity [19; 36], or institutional trust [37; 38] have been already built in terms of their constituent components using both methods.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 2 Phase Calibration Method
### Reflection Coefficient Measurements  
The reference ports of the RF switches are calibrated using the ECal module. After calibration with the ECal module, the calibration plane of the VNA does not match with the measurement reference plane due to different electrical lengths for different ports. Next, the reflection coefficients \(S_{11}(\omega,n_{\text{tx}})\) and \(S_{22}(\omega,n_{\text{rx}})\) of both the Tx and Rx ports are measured. This reflected signal includes the reflected signal from the phase origin, which indicates the difference in electrical length from the reference port. This signal is used for the phase calibration. However, the reflected signal also includes reflections at connector junctions (e.g., switch connections, or antenna connections). In the next step, the reflected signal at the phase origin is extracted from the reflected signal.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 3 Simulation Test
### Case 1: Nanhua District
#### 3.1.3 Elevation Difference in the Same Image  
The difference between the highest and lowest elevation in each image was calculated. The change was calculated in 10 m steps, as shown in Figure 5c. Figure 5c shows that 100% of the contour-type images have less than a 30 m elevation difference. In contrast, only 56% of the grid-type images have less than 30 m elevation differences. This shows that the contour-type is more effective in controlling the elevation difference in the images and can maintain the stability of aerial photography and image consistency.
## 4 Results
### Multilingual Transfer  
Our next goal is to test the effect of mixing multiple languages in the training set. To compare the results with the previous ones, we form a 1000-word sample, taking 250 words from four languages randomly from 1000 most frequent ones. Next, we extrapolate them to a 1000-word list (the same as in the previous experiment) and calculate the correlation coefficient. The results are shown in Table 5.  
Figure 4: Results of transfer from different languages.  
Figure 3: Results of transfer from English dataset to four other languages with respect to dictionary size (‘all’ corresponds to a full dictionary, ‘1000’ corresponds to dictionary with 1000 words).  
The results of using mixed data of four languages in three cases were higher than the arithmetic average of four transfers from each language separately, including for one language--Dutch--noticeably higher. For one language, Croatian, the result remained practically unchanged, and for Russian, it even somewhat worsened.  
The results presented in Table 5 do not allow us to come to a conclusion about the advantage of using mixed data from different languages. The improvement in the results in the first three rows of Table 5 takes place in cases where the source data are taken from languages of different structures (different branches of the Indo-European family of languages). It is possible that this factor is essential, but limited data do not yet enable us to draw this conclusion. An improvement is not observed (last two rows of the Table 5) in cases where most of the source data relate to languages of a different branch than the target language.  
Other neural network models produce, as described above, the worst result, except for the multilingual BERT model, which, when applied to English, provided the highest correlation coefficient of 0.843.
## 4 Results and Discussion
### Adsorption Data
#### 4.3.3 pH Effect  
The studies on the pH effect were evaluated in the pH ranges of 2-10. Figure 6a shows the results for Cr(VI) and the adsorption trend for CPW, APWN, APWH, and APWP; as the pH increases, the adsorption capacity decreases. These results can be explained as follows. Cr(VI) ions have different ionic forms in alkaline and acidic aqueous solutions. Under acidic conditions, hydrochromate anions (HCrO4\({}^{-}\)) mainly dominate, and it is assumed that the surface of the oxygen-rich adsorbent changes its ionic state to a positively charged state and is, therefore, protonated. The predominant hydrogen chromate form of Cr(VI) ions forms a complex with the protonated surface of the adsorbent through electrostatic attraction and ion exchange, which is why adsorption was strongly favored at the lowest pH. With increasing pH, the results showed a rapid decrease in adsorption, which may be due to the alkaline conditions in which the main form of Cr(VI) in water is the chromium tetraoxide anion (CrO4\({}^{2-}\)) [33]. The increase in pH leads to a deprotonated adsorbent surface, increasing the number of hydroxide ions that cause a strong repulsive force between the negatively charged surface and the CrO4\({}^{2-}\) ions [34]. The highest adsorption capacity was achieved at a pH of 2 for all the adsorbents, with APWN having the highest value of 36.93 mg/g and CPW having the lowest value of 15.10 mg/g. The adsorption trend for MB differed from that of Cr(VI). Figure 6b shows that as the pH of the aqueous solution increases, the adsorption capacity for MB also increases. The results show that the
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### The Measure-Resend Attack  
[PERSON] intercepts and measures the sequence \(S_{B}\) sent by [PERSON]\({}_{\text{n}}\) to [PERSON], then prepares and sends fake qubits to [PERSON] based on her measurement results. This allows [PERSON] to steal  
Figure 2: Schematic diagram of the MCSQD protocol.  
[PERSON]'s bit string \(M_{B}\) without being detected. However, in Step 3 of the protocol, [PERSON] randomly selects a sufficient number of particles from \(S_{B}\) to serve as decoy particles for the second security check and shuffles their order. Consequently, [PERSON] cannot determine the exact positions of the decoy particles or the required measurement bases. When considering the polarization DOF, [PERSON] randomly selects either \(Z_{p}\) or \(X_{p}\) for measurement. The probability that [PERSON] remains undetected is calculated as: \(\frac{1}{2}\times 1+\frac{1}{2}\times\frac{1}{2}=\frac{3}{4}\). In a system involving both polarization and spatial-mode DOF, the final probability of detection is: \(1-\left(\frac{3}{4}\right)^{2d}\), where d represents the number of decoy particles. When d is sufficiently large, the error rate will exceed a predefined threshold, ensuring detection. Based on the above discussion, the protocol effectively defends against the measure-resend attack.
## 4 Conclusions  
A HS-SPME/GC-MS method for detecting the volatile compounds in sweet potato was developed in this study. The optimized fiber was first selected with a DVB/CAR/PDMS coating that could absorb the largest number of identifiable peaks and highest peak intensity. The optimum HS-SPME conditions for the extraction were determined by studying the effect of incubation time, incubation temperature, and extraction time. The equilibrium condition among the sample, headspace, and fiber coating was provided by incubating the samples at 80 \({}^{\circ}\)C for 30 min and headspace extraction for 30 min at the same temperature. The proposed method was applied for the qualitative and semi-quantitative characterization of the volatile compounds from four sweet potato cultivars: Anna, Jieshu95-16, Ayamursaki, and Shuangzai.  
Overall, 68 compounds in 11 chemical classes were identified in these four sweet potato cultivars using the optimized method. Aldehydes, alcohols, terpenes, and ketones dominated over the other classes. Among them, 23 volatiles were identified as common compounds of the four sweet potato cultivars. Differences in the kinds and contents of volatile compounds were revealed on the basis of their volatile profiles. In particular, Anna and Ayamurasaki contained higher levels of compounds with sweet, fresh, and fruity flavors whereas Jieshu95-16 and Shuangzai were more abundant in compounds that contributed to green and fat odor notes. The proposed method is simple, fast, and precise, and could serve as a useful tool to assess the aromatic profile of sweet potato. Furthermore, understanding the volatile components of sweet potato will be a good start point to explore the aromatic active compounds of sweet potato, which will be beneficial to the flavor improvement in sweet potato breeding. Nevertheless, the role of volatile compounds detected by GC-MS in sweet potato was unclear in sensory evaluation, so further study on the aroma impact of each compound and precise quantification are necessary.  
Conceptualization and methodology, R.Z. and Z.W.; Formal analysis, R.Z. and X.M.; Data curation, R.Z.; Writing--original draft preparation, R.Z.; Writing--review and editing, Z.W., C.T. and B.J.; Visualization, R.Z. and C.T.; Funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Key R&D Program of China (nos. 2019 YFD1000700 and 2019 YFD1000701), China Agriculture Research System of MOF and MARA, the Guangdong Modern Agro-industry Technology Research System (2020 KJ111; 2021 KJ111). Presidential Foundation of Guangdong Academy of Agricultural Sciences (BZ202009).  
Not applicable.  
The data presented in this study are available in article and Appendix A.  
We thank [PERSON] for the GC-MS technical support from the Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences.  
The authors declare no conflict of interest.
# Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis
## 6 Setup for Conformance Analysis
### Difference between Causality Matrices  
This section presents the distinction between instances that are referred to as successful and instances that are referred to as failed. Figure 14 shows the difference between the direct relationships of both successful Figure 11 and unsuccessful cases Figure 13. Values in Figure 14 are calculated as a difference between causality scores in both matrices of successful and failed instances. Details of the scores in Figure 14 are as follows:  
* A value of score 0 shows that there is no difference in sequence for the corresponding activities. These values are represented as dark blue in the matrix.
* A value of score 2 presents the maximum difference between corresponding actives in successful and unsuccessful matrices. This means that there exists a totally opposite behaviour of events to each other such that, if \(A\to B\) relation in one class of instances, its \(B\to A\) exists in the other class.  
Figure 11: Causal activity matrix for successful cases. Matrix’s strengths scaled between \(-1.0\) (weakest-red) to \(1.0\) (strongest-green).  
Figure 12: Closer view of the activity matrix for successful cases. Matrix’s strengths scaled between \(-1.0\) (weakest-red) to \(1.0\) (strongest-green).  
Figure 13: Causal activity matrix for failed cases. Matrix’s strengths scaled between \(-1.0\) (weakest-red) to \(1.0\) (strongest-green).  
* Values between 0.01 and 1.99 are represented with different shades of blue to red, dependent upon the scores. Higher scores show a greater difference in the causality of the corresponding events, and vice versa.  
The dark blue colour presents that there is no difference between the direct relationship of events, whereas the colour ranging from white to red shows the difference in the causal relation ranging between 0.01 and 1.99. For instance, a dissimilarity of 1.99 is observed between 'Event-8' and 'Event-12'. The reason for this divergence is that the former event never directly followed the later event in any of the successful cases but always followed in the failed cases.  
Although causality analysis provides a difference in directly followed relationships between two variants of cases, there are still several limitations. First, to compare two causality matrices belonging to different categories of instances (successful and failed), it is essential that both matrices are of equal size. It means that the same number of event classes should be there in both types of instances. This is very rare in a business process environment as one set of cases may have fewer or more event classes than another. The next two sections present techniques to interpret such complex event data.
## 3 Method
### Model Validation
#### 3.3.2 Comparison with the V\({}_{\rm m25}\) Obtained by BESS  
In the BESS model, seasonal variation in V\({}_{\rm m25}\) is taken into account [(56; 82)]. It is assumed that the seasonal pattern of V\({}_{\rm m25}\) followed the seasonal pattern of LAI [(83)]. V\({}_{\rm m25}\) experiences a rapid increase during leaf development, reaching its peak in early leaf maturity, followed by a decline during senescence, irrespective of species. The date corresponding to the peak LAI value is identified, and the V\({}_{\rm m25}\) for that date was quantified (PeakV\({}_{\rm m25}\)). Subsequently, V\({}_{\rm m25}\) over the season is calculated as:  
\[\rm V_{m25}=V_{m25,min}+\frac{LAI}{LAI_{max}}\times(PeakV_{m25}-V_{m25,min}) \tag{18}\]  
where LAI\({}_{\rm max}\) and LAI represent the maximum and current leaf area index, respectively, throughout the growing period. In the original BESS model, the PeakV\({}_{\rm m25}\) value of C4 crops was uniformly set as 33 \(\mu\)mol m\({}^{-2}\)s\({}^{-1}\), without distinction among crop types. V\({}_{\rm m25,min}\) = 0.3 \(\times\) PeakV\({}_{\rm m25}\).  
Figure 3: Schematic diagram of the Two-stage linear model.  
In the original BESS model (Equation (18)), the PeakV\({}_{\rm m25}\) value of C4 crops was uniformly set at 33 \(\upmu\)mol m\({}^{-2}\)s\({}^{-1}\), without differentiation among crop species. In our model, the calibrated PeakV\({}_{\rm m25}\) value was substituted into the original BESS model to calculate V\({}_{\rm m25}\) (hereafter referred to as \"BESS_P\"), and then the performances of the original BESS, BESS_P and our Two-stage linear model in simulating V\({}_{\rm m25}\) were compared against the V\({}_{\rm m25}\) inversely solved by coupling the BESS and LRC using EC data.
## 3 Results
### Discussion of Results
#### 3.2.1 Comparison of Different Indicators of the Models  
Different models adopt their own optimal parameter combination training models. In Figure 8, the average classification accuracy of the datasets on the different models and the number of model parameters are illustrated. The results show that the ESERNet model proposed in this paper has little difference in classification accuracy compared with the other models in our dataset of the images of blast furnaces, but the number of parameters is reduced by 6.23-56.71%. This is because channel fusion is carried out in the SCB module of the ESERNet model, and the number of channels is halved, leading to a significant reduction in the number of model parameters. The accuracy of the ESERNet model is only 0.28% lower than that of the SERNet model, and the number of parameters is reduced by 26.62%, which not only achieves higher classification accuracy, but also greatly reduces the model complexity. Compared with the repVGG model, although the average accuracy of the ESERNet model decreases by 0.01%, the number of parameters decreases by 56.71%. Compared with the ResNeXt model, the ESERNet model improves the classification accuracy by 0.04% and reduces the number of parameters by 6.23%. The average running  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Batch Size** & **Learning Rate** & **Average Accuracy** \\ \hline ESERNet & 32 & 0.1 & 96.73\% \\ SERNet [22] & 16 & 0.1 & 97.01\% \\ repVGG [23] & 32 & 0.1 & 96.74\% \\ ResNeXt [24] & 32 & 0.1 & 96.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Optimal Parameter Combination of Different Models.  
Figure 7: K-fold cross-validation results of different models.
# Hybrid Graph Models for Traffic Prediction
## 4 Methodology
### STA Module  
In this paper, we follow the idea of [38] to design a spatial-temporal attention module, which mainly includes temporal attention and spatial attention, to extract features of historical traffic conditions. In the spatial-temporal attention, the HGM obtains spatial embedding \(E^{S}_{v_{i}}\in R^{D}\) and temporal embedding \(E^{T}_{t}\in R^{T+q}\) by using word2 vec and one-hot encoding methods, respectively. To obtain both temporal and spatial information, the method obtains the spatial-temporal embedding \(STE_{v_{i}t}=(E^{T}_{t}+E^{S}_{v_{i}})\in R^{(T_{p}+T_{Q})\times N\times D}\) through linear projection and concatenation operations, where \(v_{i}\in V\), \(T\in(t-P+1,\dots,t,t+1,\dots,t+Q)\) and \(D\) represents the output dimensions of the model. Then, the proposed method uses a multi-head attention to simultaneously extract spatial correlations \(h^{A}_{S}\) and temporal dependencies \(h^{A}_{T}\), and the extracted information is dynamically fused through a novel gated function.
## 3 Underwater-Air Channel Model
### Seawater and Air Channel  
The simulation presumes a homogeneous seawater column. The primary factors influencing the seawater channel are the absorption coefficient, \(a\), and the scattering coefficient, \(b\)[45]. In this study, a chlorophyll concentration of 0.5 mg m\({}^{-3}\) is selected to be the counterpart of the \(JII\) classification (\(a\) = 0.0523 m\({}^{-1}\), \(b\) = 0.255 m\({}^{-1}\)), which can effectively encompass a broad range of open ocean environments [46; 47].  
The Monte Carlo simulation approach underwater can follow the process (publicly available from Refs. [17; 22]): at each geometric transmission path \(r\), where \(r=-\frac{\ln(\zeta)}{a+b}\), and \(\zeta\) is a random number between 0 and 1. Then, the weight and direction of ray propagation are determined based on Inherent Optical Properties (IOPs):  
\[w_{i+1}=w_{i}\frac{b}{a+b} \tag{13}\]  
Figure 4: Property statistic from simulated sea surface topography based on finite differences: (**a**) significant wave height, \(H_{s}\), (**b**) total mean square slope, \(\sigma^{2}\). This plot uses the same legend as used for the spectrum of Figure 1. The solid and dashed orange and dashed grey lines are reprinted from Equation (3) and Equation (4), respectively. Wind speeds range from 1 m s\({}^{-1}\) to 15 m s\({}^{-1}\).  
\[\widetilde{\beta}\left(\vec{\xi}_{i+1}\rightarrow\vec{\xi}_{i}\right)\mathrm{d} \Omega\left(\vec{\xi}\right)=\widetilde{\beta}(\theta_{i},\varphi_{i})\mathrm{ sin}\,\theta_{i}\mathrm{d}\theta\mathrm{d}\varphi \tag{14}\]  
where \(w_{i}\), \(\vec{\xi}_{i}\) and \(w_{i+1}\), \(\vec{\xi}_{i+1}^{\rightarrow}\) are the ray weights and directions before and after traversing a transmission path, respectively.\((\theta_{i},\varphi_{i})\) represent the scattering angles of the incident ray of \(\vec{\xi}_{i}\) in a coordinate system, and \(\widetilde{\beta}\) is the scattering phase function (SPF).  
The primary mechanism by which the air channel influences laser transmission is similar to that of seawater. This study concentrates solely on the impact of aerosol components, deliberately excluding factors such as clouds and turbulence, thereby establishing a cloudless standard atmosphere model [48]. Table 3 presents the optical thicknesses of aerosol stratifications with a visibility of 20 km. The scattering phase function for these layers is calculated via Mie scattering theory.