esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 4 Our Proposed PCAC Scheme  
### Overview  
Our paper proposes an automatic, fast, and effective access control model both in the data collection stage and the data obtainment stage. The flow chart of access control is shown in Figure 3. DS encrypts the collected data with symmetric encryption algorithm, and sends the ciphertext to PC for storage, while the symmetric key is sent to CKA for encryption in the encrypt phase. Since attribute-based encryption consumes a large amount of time, this encrypt phase reduces the computational burden by encrypting the symmetric key instead of the full file. CKA will distinguish each DS by the proof of identification, and then construct an interaction to generate evidence for an action audit, which will be executed by the PC. After the audit, CKA will generate the corresponding access structure, and exchange the access structure into the LSSS-shared matrix for an easy operation. When passing the audit, CKA will generate the DS's access structure and transform the access structure into the sharing-generating matrix, which is easy to calculate. After that, CKA will execute the encrypt algorithm and distribute the ciphertext to KSs and PC. This marks the end of the data collection stage. If U wants to get the data saved in PC, U needs to send his/her own identification proof to CKA, and CKA will find the attribute set of U and verify it with access structure. After passing the verification, CKA will construct an interaction to generate evidence for action audit, which is like the data collection stage. After the audit is passed, CKA will communicate with the KSs and the PC to obtain the secret key and send it to U. The ciphertext of the data saved in PC is accessible to every U. This marks the end of the data obtainment stage.
# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 3 Results
### Prediction of Low Frequency Sampled Variables Starting from Argo Collected Input Data  
An important application of the proposed deep learning model is the prediction of nutrient concentrations and carbonate system variables starting from the input provided by the BGC-Argo floats mounting temperature, salinity and oxygen sensors (as mentioned in Section 1), in order to contribute to tackling the undersampling problem between low-frequency and high-frequency observed variables.  
Additionally, besides the potential use of our model to provide reliable estimates for non-observed variables, it can be used as a powerful quality check for the observed variables, such as nitrate. In fact, the comparison of our model prediction with BGC-Argo profiles can spot inconsistencies and biases and provide information, if necessary, to adjust the raw data. An example of profiles predicted by our model is shown in Figure 5. We provide as input vectors the time and the geolocation of the BGC-Argo floats together with the profiles of the temperature, salinity, and oxygen density. The plot shows the comparison between the nitrate profile predicted by our model and the two different floats selected for two different areas in the Mediterranean Sea:  
* _SD6902954_ : located at 42.89 N 7.66 E, with samples for the date 13 June 2019.
* _SD6903250_: located at 41.85 N 17.88 E, with samples to the date 3 December 2019.  
As a general comment, the model predictions are fairly good (Figure 5), in the sense that both the typical shape of nitrate profiles and the typical values of nitrate in the deeper layers (i.e., 8 mg m\({}^{-3}\) and 4 mg m\({}^{-3}\) in _SD6902954_ and _SD6903250_, respectively) are very satisfactorily reproduced. A closer inspection of the plots reveals a potential weakness of our model: the reconstruction profiles are not as smooth as would be expected. This is probably a consequence of the fact that the model is trained on punctual data, resulting in unawareness of the typical shape of the profiles of the biogeochemical variables that they try to infer.  
Figure 5: Comparison of the profile measured by the float instrument (in blue) and the profile predicted by our model (in orange).  
Finally, the skill metrics of the reconstructed BGC-Argo nitrate profiles confirm the rather good performance of our model. In fact, the MAE and RMSE computed on about 2200 nitrate profiles for the period 2015-2020 are, respectively: 0.75 and 0.87. These values are similar to the ones computed on test set data, demonstrating the quality of the model even with an independent and much bigger dataset.
## 4 Security Assumptions
### Indistinguishability  
We define an attacker \(\mathcal{A}\) on the experiment **EXP** of our symmetric encryption/decryption functions (**SE**), which is a game controlled by the simulator \(\mathcal{S}\). We also define two pseudo-random hash functions (\(\omega_{1}\) and \(\omega_{2}\)), which are satisfied with the property we call \"indistinguishability\" (**Ind**), due to which the attacker \(\mathcal{A}\) can make a hash query to \(\omega_{1}\) and \(\omega_{2}\) on the message \(M^{\prime}\), which is chosen by \(\mathcal{A}\). These functions act as real functions as our hash functions (\(H_{1}\) and \(H_{2}\)), where \(i\in\{U,V\}\). The simulator also can switch this function pairto respond to each query made by \(\mathcal{A}\) during the simulation rounds of the above experiment. Finally, the simulator \(\mathcal{S}\) is given a challenge message target \(M\) chosen by the \(\mathcal{A}\).  
At this time, \(\mathcal{S}\) makes a coin flip on \(b\). If \(b=0\), \(\mathcal{S}\) randomly chooses \((\omega_{1},\omega_{2})\) to generate the hashed value of \(M\) and return it to \(\mathcal{A}\). Otherwise, \(\mathcal{S}\) forwards \(M\) to \((H_{1},H_{2})\) to ask for the hash value. \(\mathcal{A}\)'s goal is to guess correctly the hashed value that is from \((\omega_{1},\omega_{2})\) or \((H_{1},H_{2})\) with non-negligibility probability.  
**Lemma 2** (Indistinguishability).: _In this lemma, our symmetric encryption/decryption functions satisfy the indistinguishably property if there is no attacker \(\mathcal{A}\) that can guess the hashed value from the chosen (\(M\)) with more than \(\frac{1}{2}\) with negligible probability \(\varepsilon^{*}\) under the \(t^{*}\) polynomial time bound. That is,_  
\[|Pr[b^{\prime}\longleftarrow\ \mathcal{F}^{(\omega_{1},\omega_{2},H_{1},H_{ 2})}(M)|b=b^{\prime}]-\frac{1}{2}|\leq\varepsilon.\]  
_Therefore, we concluded that_  
\[Adv^{Ind}_{\mathcal{A},\boldsymbol{SE}}(\theta,t^{*})\leq\frac{1}{2}+ \varepsilon^{*}.\]
## 4 Experiments and Results
### Interaction with Nursing-Care Assistant Robot  
HMT gesture recognition was applied to the intelligent interaction with the nursing-care assistant robot at home. Based on the control system and interaction mode of the nursing-care assistant robot proposed in Section 2, we improved the HTM gesture recognition system to a real-time HMT gesture recognition system, which was more efficient for the interaction application with the robot. Different from the algorithm framework proposed in the theoretical verification part of Section 3, the real-time HMT gesture recognition system's algorithm framework had the specified training templates based on the LOSO cross-validation, which is more practical and faster during the interaction progress.  
The interaction application with the nursing-care assistant robot was carried out under two different interaction modes of the robot. As shown in Figure 12a, under the man-in-seat interaction mode, the user wore a wearable camera on his right wrist, and sat on the seat part in front of the nursing-care assistant robot. Then, the user made corresponding predefined gestures to guide the robot. Due to the additional background velocity of the robot when the user interacted with the robot in the man-in-seat interaction mode, there was a deviation between the experimental results in sitting conditions and the actual control interaction process in theory. However, the subsequent application shows that this additional velocity hardly affected the recognition accuracy, which illustrates the robustness of the proposed HMT gesture recognition system. The HMT gesture recognition in standing conditions corresponds to the remote interaction mode of the nursing-care assistant robot. The remote interactive state is shown in Figure 12b. The user remotely guides the robot to a specified condition, and then the command mode is switched for the control of the dual manipulator by performing the gesture command. After that, the manipulator is remotely operated to complete the action of assisting in grabbing the specified objects. During the progress of the interaction, the original video data of the gesture collected by the wearable camera is transmitted to the host computer through Wi-Fi for processing and recognition, and the recognition result communicates with the robot's control system through the predefined control commands corresponding to the predefined HMT gesture.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 2 5G Reference Signals  
5G utilizes various reference signals for distinct functions: the Synchronization Signal Block (SSB) for time-frequency synchronization, the Sounding Reference Signal (SRS) for uplink angle-of-arrival estimation, and the Positioning Reference Signal (PRS) for downlink ranging. We elaborate on these three reference signals in detail.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**VOCs** & **Retention Time, min** & \multicolumn{4}{c}{**Similarity Search \(\star\), \(\star\)/Pack Area, TIC \(\times\)10\({}^{3}\)**} \\ \hline Acetaldehyde (1) & 4.98 & \(98/140\pm 30\) & \(98/140\pm 30\) & \(98/110\pm 20\) & \(98/160\pm 30\) & \(98/160\pm 30\) & \(98/220\pm 40\) & \(98/160\pm 30\) \\ \hline Trimethylamine (22) & 5.55 & \(94/11\pm 2\) & \(93/20\pm 4\) & \(87/16\pm 3\) & \(80/45\pm 1.4\) & \(85/62\pm 1.9\) & \(82/42\pm 1.3\) & \(88/24\pm 0.7\) \\ \hline Ethanol (5) & 6.88 & \(98/63\pm 13\) & \(98/75\pm 15\) & \(98/180\pm 40\) & \(98/160\pm 30\) & \(98/270\pm 90\) & \(98/950\pm 110\) \\ \hline Verquan (23) & 7.75 & \(\mathrm{ND}1\) & \(87/026\pm 09\) & \(91/25\pm 08\) & \(92/44\pm 13\) & \(95/11\pm 3\) & \(88/49\pm 15\) & \(85/31\pm 09\) \\ \hline Acetone (4) & 7.95 & \(84/38\pm 1.1\) & \(86/42\pm 13\) & \(87/43\pm 13\) & \(92/59\pm 18\) & \(89/101\pm 30\) & \(80/42\pm 13\) & \(90/70\pm 21\) \\ \hline Isopropyl Alcohol (17) & 8.21 & \(86/56\pm 1.7\) & \(88/24\pm 0.7\) & \(88/61\pm 1.8\) & \(85/28\pm 0.8\) & \(\mathrm{ND}\) & \(87/34\pm 10\) & \(\mathrm{ND}\) \\ \hline Propual,2-methyl (6) & 10.20 & \(86/3.2\pm 10\) & \(87/89\pm 2.7\) & \(87/24\pm 0.7\) & \(91/26\pm 0.8\) & \(83/21\pm 0.6\) & \(85/21\pm 0.6\) & \(93/13\pm 04\) \\ \hline \(23\)-butbutation (\(\mathcal{O}\)) & 11.64 & \(95/16\pm 3\) & \(95/12\pm 2\) & \(93/96\pm 2\) & \(93/17\pm 3\) & \(94/14\pm 3\) & \(95/18\pm 4\) & \(93/17\pm 3\) \\ \hline Butanal,3-methyl-(9) & 14.28 & \(80/3.2\pm 10\) & \(87/32\pm 10\) & \(80/23\pm 0.7\) & \(87/85\pm 1.7\) & \(84/59\pm 1.8\) & \(78/44\pm 13\) & \(79/41\pm 12\) \\ \hline Ethylylcoppropanol (24) & 15.60 & \(87/21\pm 0.6\) & \(86/14\pm 0.4\) & \(88/25\pm 0.8\) & \(87/66\pm 20\) & \(88/61\pm 1.8\) & \(87/63\pm 19\) & \(88/73\pm 22\) \\ \hline Pentanal (11) & 15.79 & \(83/31\pm 0.9\) & \(\mathrm{ND}\) & \(80/10\pm 0.4\) & \(83/85\pm 26\) & \(86/61\pm 1.8\) & \(81/65\pm 20\) & \(80/51\pm 15\) \\ \hline Pentanal (19) & 17.87 & \(84/11\pm 0.3\) & \(79/21\pm 0.6\) & \(86/07\pm 0.25\) & \(79/02\pm 0.8\) & \(85/074\pm 0.26\) & \(88/16\pm 0.5\) & \(91/11\pm 03\) \\ \hline Heanal (12) & 19.80 & \(86/6\pm 2.0\) & \(90/49\pm 15\) & \(89/59\pm 18\) & \(91/23\pm 0.7\) & \(93/24\pm 5\) & \(93/21\pm 4\) & \(92/19\pm 4\) \\ \hline Nonanal (15) & 29.80 & \(81/16\pm 0.5\) & \(84/32\pm 10\) & \(80/39\pm 10\) & \(84/19\pm 0.6\) & \(87/36\pm 1.1\) & \(\mathrm{ND}\) & \(90/44\pm 13\) \\ \hline \end{tabular} \({}^{1}\) ND–Not detected. Similarity search \(\star\)–the degree of agreement between the library and experimentally obtained mass spectra.  
\end{table} 
Table 10: Volatile organic compounds detected in irradiated and non-irradiated gallon samples (\(n=3,p=0.95\)).
## Case Study
### Results and Analysis of DR Potential and Willingness  
According to the latest questionnaire survey feedback, the LA obtains the residents' DR awareness level. Due to the large resident base, if the LA uses each resident as a control variable, it will cause the disaster of the variable dimension, so the LA divides residents into different groups according to the awareness level, each group contains 100 households. The LA simulates the response plan of each resident according to the flexible load DR strategies proposed in Section 3 and obtains the response quantity of each resident group during two periods of load increase and load decrease. The response quantity and awareness level of some residential groups are shown in Figure 12. It can be found that different residents have different response quantity and awareness during the period of DR. The overall response quantity during the load increase period is greater than that during the load decrease period, because the LA considers the residents' electricity comfort when estimating their response potential, which is not detailed in this paper due to space limitations. Therefore, during the peak period of electricity consumption, reducing a large amount of load has a greater impact on the electricity consumption of residents. While in the low period of electricity consumption, the load such as EWH and EV can be transferred to this period while meeting the needs of residents.  
Based on the DR willingness evaluation model in Section 4 and Equation (12), set the incentive unit price of the LA in period 1 and period 2 to 0.8 yuan/kWh and 1.3 yuan/kWh, and the average willingness evaluation results of some residential groups in the two periods are as shown in Figure 13. Combining with Figure 12, it can be found that when there is little difference in the response quantity of the residential groups, the awareness factor largely affects the willingness to participate in DR.  
Figure 11: The load forecast curve of the residential community.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Load Type**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Value**} \\ \cline{3-4}  & & **Ranges** & **Precision** \\ \hline \multirow{4}{*}{HVAC} & \(R_{1}\) & 3.0–8.0 & 0.0001 \\  & \(C_{a}\) & 600–800 & 1 \\  & \(\mathcal{Q}_{\text{EWH}}^{c/h}\) & 6–10 & 0.1 \\  & \(\mathcal{T}_{\text{HVAC}}^{\text{set}}\) & 24–26 & 1 \\  & \(\mathcal{Q}_{\text{HVAC}}\) & 0.8–1.2 & 0.1 \\ \hline \multirow{4}{*}{EWH} & \(V\) & 50–100 & 10 \\  & \(\epsilon^{24}\) & 0.6–0.8 & 0.1 \\ \cline{1-1}  & \(\mathcal{Q}_{\text{EWH}}\) & 2–4 & 0.5 \\ \cline{1-1}  & \(\mathcal{T}_{\text{EWH}}\) & 55–60 & 1 \\ \cline{1-1}  & \(\mathcal{T}_{\text{EWH},\rho}\) & 24–26 & 1 \\ \cline{1-1}  & \(\mathcal{T}_{\text{EWH},i}\) & 15–20 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model Parameters of HVAC and EWH.  
For example, the awareness of the 19 th and 21 th resident is low, and the corresponding DR willingness is also low.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 3 Quadrotor Attitude Control System Design  
The attitude controller receives the desired attitude \(att_{d}\), the current attitude \(att\), and the current angular velocities \(\omega_{x},\omega_{y},\omega_{z}\) and then outputs the control values \(a_{x},a_{y},a_{z}\) of each axis according to the states. The CGPs of each axis \(b_{0x},b_{0y},b_{0z}\) are estimated or tuned manually. The disturbance of roll and pitch axes \(\xi_{x},\xi_{y}\) is estimated by the ESOs. Finally, the control distributor transfers \(a_{x},a_{y},a_{z}\) and \(th_{d}\) to the \(\mathit{ctr}_{motors}\) (\(\mathit{ctr}_{motors}\in[0,1]\) and, in return, sends the attenuated control values \(a_{xa},a_{y},a_{za}\) to the ESOs because the actual control value of each axis may be attenuated by the control distributor if \(th_{d}\) is too large or too small. Figure 2 presents the control diagram.  
The typical ADRC comprises three major components: the nonlinear tracking differentiator (TD), extended state observer (ESO), and state error feedback (SEF) [14]. In the traditional ADRC, the transient process is set to avoid a sharp deviation between reference signals, and the process is realized through the TD. In this paper, the transient process is automatically employed by the RL method. The CGP is the estimation of the system response capability, and the accuracy of CGP is critical to the performance of both the ESO and SEF. In previous studies, CGPs were the manually adjusted parameters. We propose a method to estimate the CGP value for each quadrotor platform with a TD. With ESOs and CGPs, the RL controller is able to control different quadrotors with the same actor network. In order to optimize the final performance, the CMA-ES algorithm is presented to tune the parameters of ESOs, and succeeded eliminating the high frequency vibration problem.
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 5 Packet Generators  
### Evaluation Purpose and Method  
One relatively crude way of testing a DUT is to flood it with as much traffic as possible and evaluate how many packets the device is able to process successfully versus how many of them get dropped. By contrast, more sophisticated testing techniques require much finer control of the inter-packet interval, not only in terms of ensuring that a given number of input packets per second hits the DUT on average but also to guarantee that the inter-packet interval stays as stable as possible during the measurement.  
In fact, it has been shown [33] that, for at least some kinds of DUT like firewalls, any input traffic burstiness may trigger an internal change of state in the DUT. In turn, this leaves the DUT temporarily unable to process even the number of packet per seconds it would be able to under normal circumstances. If the measurement goal is, for instance, to accurately measure the maximum number of packets per second the DUT can handle without any drops, burstiness may clearly lead to misleading results.  
In our approach, measurements involve several experiments with different input stream characteristics. Each experiment is characterized by the packet type being used (always UDP in this paper), the packet length \(l\), the nominal inter-packet interval \(\omega\), and the number of packets \(m\) (\(m_{\omega}\), when we refer to a specific inter-packet interval \(\omega\)). In the following, \(l\) represents the total Ethernet packet length, including the trailing CRC but excluding the L1 preamble, SFD, and IPG. The way to express \(\omega\) varies from one tool to another. Namely, some tools compute \(\omega\) indirectly from the given target bandwidth, link speed, and packet length. However, for clarity here we will still reason in terms of \(\omega\) regardless of the tool at hand.  
As discussed previously, in order to determine the quality of a packet generator for our purposes, we need a good knowledge of the statistical distribution of \(\omega\). We evaluated the following three different tools: Iperf3 [21], MoonGen [23; 24], and our custom s/w tool called Rtgen. The first two are the most promising tools among those surveyed in Section 2, while the third has been developed in-house specifically for this work.
# Treatment Selection for Early to Intermediate Hepatocellular Carcinoma
## 3 Current Status of RFA in Asia  
The multicenter trial committee of the Asian Conference on Tumor Ablation (ACTA) examined the current status of RFA in Asia. Thirteen hospitals in five regions (China, India, Japan, Korea, and Taiwan) provided their data. The number of hospitals was not enough to represent the status of the entire nation, but each hospital in this study had enough cases of HCC treated with RFA to show the trend in each region. The study protocol complied with the ethical guidelines of the World Medical Association Declaration of Helsinki and was approved by our institutional review boards (Approval codes: Okayama University, 1908-011; Okayama City Hospital, 1-63).  
No statistical difference was observed between regions. When we stratified the patients by the status of the hepatitis virus infection and BCLC stage, no difference in overall survival (OS) was observed between regions (Figure 1). This finding indicates that uniform effectiveness of RFA was achieved in the collaborative hospitals regardless of the region. However, the median size of the tumor treated with RFA differed from one region to another (Figure 2). The median tumor size reported in Korea (Asan Medical Center, Samsung Medical Center, and Seoul National University Hospital) was the smallest (15 mm). In addition, 99.6% (254/255) of tumors in Korean hospitals were less than or equal to 3 cm in diameter. The median tumor size reported in India (TATA Memorial Centre and Rela Institute and Medical Center) was the largest (3.6 cm). Furthermore, 55.4% (31/56) of tumors in the Indian hospitals were greater than 3 cm in diameter.  
RFA was selected not only for the treatment of BCLC-0 and BCLC-A patients but also for the treatment of BCLC-B and BCLC-C patients in some regions (Figure 3). The numbers of patients in China, India, Japan, Korea, and Taiwan were 19, 56, 635, 255, and 185, respectively.  
Figure 1: Recurrence-free survival of the patients in different regions.  
Figure 2: Box plot of the tumor size treated with radiofrequency ablation in different regions. Horizontal bars in the boxes and the numbers beside indicate median values. The numbers of patients in China, India, Japan, Korea, and Taiwan were 19, 56, 635, 255, and 185, respectively.