esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogel Tissue Expanders for Stomatology. Part II. Poly(styrene-maleic anhydride) Hydrogels
## 3 Results and Discussion
### Biocompatibility Test  
Determination of the sample cytotoxicity in the presence of cell line provides information about toxicity of the material for intended in vivo application. The biocompatibility test was performed using human fibroblasts. A samples weighing approx. 0.1 g were immersed in 1 mL of media with cells. The number of growing/viable cells after 12, 24, and 36 h incubation time with polySMA-0.7 sample was monitored. The time dependent influence of the sample presence on cell growing compared to the control test (cells without sample) is shown in Figure 5. It is apparent that the differences in number of viable cells between polySMA-0.7 hydrogel and control are not significant providing noncytotoxicity of tested polymers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Leaching Medium**} & \multirow{2}{*}{**Sample Status**} & \multicolumn{2}{c}{**Amounts of Leached Residual Monomer (wt \%)**} \\ \cline{3-4}  & & **Styrene** & **MA** \\ \hline \multirow{2}{*}{PBS} & Unwashed & 0.00 & 16.94 \\  & Washed in acetone & 0.00 & 0.08 \\ \multirow{2}{*}{Ethanol} & Unwashed & 1.89 & 6.22 \\  & Washed in acetone & 0.00 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The amounts of residual monomers leached into PBS and ethanol from sample polySMA-0.7 determined by HPLC.
## 2 Principle of VMD  
Variational Mode Decomposition (VMD) is a data processing technique introduced by [PERSON] et al. [12] in 2014. It is particularly effective in handling dynamic, non-stationary, and nonlinear complex signals. By decomposing the original signal under specific constraints, VMD facilitates signal denoising, enhancing the quality of the data for subsequent analysis.
###### Abstract  
Decentralized networks bring us many benefits, but as networks evolve, many nodes either actively or passively become unreachable behind an NAT or a firewall. This has become a hindrance to the development of decentralized networks, where peer-to-peer communication data transfer between unreachable nodes cannot be accomplished, whether in decentralized file systems, decentralized social, or decentralized IoT. The existing scheme requires a series of centralized servers or requires network-wide flooding for consensus data, which can lead to the loss of decentralized nature of the network and cause flooding bottlenecks, contrary to the design concept of decentralization. In this paper, our proposed scheme uses a structured P2P overlay network to store the indexes of unreachable nodes in the whole network, so that the characteristics of a decentralized network are still maintained while ensuring the efficiency of lookup. When nodes communicate, the transmission channel is established so that both nodes continuously transmit data streams peer-to-peer without relying on the central server. Moreover, the scheme guarantees the security and privacy of nodes' data transmission and the P2P overlay network without relying on centralized trusted institutions. Finally, we deploy a real cluster environment to verify the effectiveness of each module at different network sizes and prove the overall feasibility of the scheme. The scheme has certain advantages over existing solutions in terms of security, privacy, communication efficiency, device democracy, etc.  
unreachable peers; decentralized architecture; structured P2P network; centralized server-independent; device democracy Article
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
###### Abstract  
In recent years, distributed energy storage (DES) has experienced rapid growth and has been widely applied in active distribution networks (ADNs). Owing to the close correlation between the characteristics and the application scenarios, DES modeling needs to be parameterized separately for various application demands. In this paper, a parameterized model for optimal DES planning in ADNs is proposed. The typical scenarios for DES planning are generated by the clustering technique, containing the patterns of load demand, wind turbine output and photovoltaic output. Secondly, an optimal planning model of DES considering parameterized characteristics is established, which is essentially a mixed integer non-linear optimization problem. Then, the model is converted to a mixed-integer second-order cone programming model, which can be solved efficiently by available commercial software. Finally, case studies on the modified IEEE 33-node system and IEEE 123-node system verify the efficiency of the proposed method, and the effects of DES planning are validated by two evaluation indexes.  
distributed energy storage (DES); active distribution networks (ADNs); optimal planning; mixed-integer second-order cone programming (MISOCP) +
Footnote †: journal: Applied Sciences
# A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation
## 3 Our Proposed Algorithm
### Description of the Proposed Method  
In this subsection, a self-adaptive approximated-gradient-based adversarial attack is presented. The framework consists of two main steps: self-adaptive approximated gradient simulation and boundary exploration. The pseudo-code of the SAGM is summarized in Algorithm 2.  
```
Input: image: original image \(I_{0}\), parameters: \(\alpha\), \(\beta\), \(\lambda\), \(\theta_{1}\), \(\theta_{2}\), the objective function: \(f(.)\), \(F\), CR, \(T\), \(G\). Output: perturbation image \(I_{T}\).
1. Initialize perturbed vector \(\delta_{0}\);
2.for\(i\gets 1\) to \(T\)do
3.\(I_{G}^{\prime},\delta_{G},f_{G}\gets SaGS(10,\delta_{0},G,F,CR,\alpha, \lambda,\ f(.))\);
4.\(k\leftarrow\) sort(\(f_{G}\))
5.\(\Delta I^{\prime}\gets I^{\prime}(k(2))\) - \(I^{\prime}(k(1))\)
6.\(\Delta\delta\leftarrow\delta_{G}(k(2))\) - \(\delta_{G}(k(1))\)
7.\(\beta=\langle\Delta I,\Delta\delta\rangle/\langle\Delta\delta,\Delta\delta\rangle\);
8.\(I_{0}\leftarrow\Gamma(k(1))+\beta\cdot sign(\delta_{G}(k(1)))\);
9.endfor
10. Return the perturbation image \(I_{0}\);
```  
**Algorithm 2** The SAGM algorithm  
In algorithm 2, the perturbed vector \(\delta_{0}\) is initialized between \(-1\) and \(1\) according to Equation (15). \(\delta_{G}\) is the optimal gradient after \(G\) generation. The retention of the former gradient helps barrel through local optima and accelerates convergence. In line 3, the \(SaGS\) method simulates the approximate gradient of the image, and a set of temporary perturbation samples are generated which may fool DNN with a high success rate. The effect of perturbations is sorted in ascending order in line 4. In lines 5 to 7, \(\beta\) is updated by Equation (12). In line 8, permanent perturbation samples are generated by the optimal sample of the temporary perturbation samples. After \(T\) generation, the adversarial image is returned.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 5 Case Analysis
### Design of Platform Access Control Model  
In the platform, platform administrators are responsible for the design of abstract roles and permissions, while domain administrators are responsible for specific role design, user registration, and user authorization. The responsibilities of the two types of administrators do not intersect, reflecting the platform-domain isolation management feature of the RBAC-IC and solving the platform-domain management conflict problem.
## 3 Doppler OCT for Otology Applications
### Ex Vivo Studies  
Following the results of phantom simulations, ex vivo experiments, including small animals and human cadaveric samples, have proceeded, as a clinical application of Doppler OCT.
# Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles
## 2 Materials and Methods  
### Multi-Dimensional Tensor Model of Road Network Travel Time  
Generally, the travel time of a vehicle passing through two adjacent detectors \(A\) and \(B\) can be calculated by the following formula:  
\[t_{AB}=t_{A}-t_{B} \tag{1}\]  
where:  
\(t_{AB}\) is the travel time of the vehicle between road link \(AB\).  
\(t_{A}\) is the time when the vehicle passes detector \(A\).  
\(t_{B}\) is the time when the vehicle passes detector \(B\).  
However, the calculation of travel time is usually inaccurate. The ANPR equipment may record a car's license plate repeatedly for a short time. Due to factors such as misidentification, identification failures, or driver stops, vehicle travel time is not accurately obtained. The travel time obtained after data processing and the actual value may have many errors. These data need to be revised scientifically.  
The tensor model is one of the most commonly used methods to describe the multi-modal correlation of data, which has been successfully applied to signal processing, pattern recognition, and personalized recommendation [28]. In recent years, methods based on tensor models have also been proven effective for processing traffic data, such as traffic flow and travel time. Personalized TTE refers to providing travel time information, considering the traffic conditions and driving behavior and habits, such as driving speed. Essentially, the driver's driving characteristics are hidden in travel time data extracted from the ANPR data.  
Considering that the complexity of the urban road network and the travel time of road links are affected by a variety of complex factors, the relationship between travel time and its influencing factors is modeled. According to the travel time data obtained in ANPR, a model with a fourth-order tensor is obtained. As is shown in Table 1, each dimension of the tensor represents the driver \(K\), the time slot \(M\), the road link \(N\), and the number of trips \(L\). The value of each item represents each driver and the number of trips \(L\) on the \(N\)th road link in the \(M\)th time slot of the \(K\)th driver [28]. This approach is associated with the spatial correlation between different road links, the deviation between different drivers, and the correlation between traffic volume differences in time slots on travel time.
## 3 Methodology
### Bias Correction Methods
#### 3.1.1 Simple Multiplicative Bias Correction  
The principle of the mean bias correction method is operable and straightforward. It assumes that satellite precipitation and ground observation precipitation have a uniform bias in the entire watershed. It is essentially a globally used standard correction method. However, it ignores the temporal and spatial heterogeneity of precipitation distribution within the watershed. The SMBC considers the time distribution compared to the mean  
Figure 2: Framework of this study.  
bias correction method [46], and the corresponding bias factor is obtained on a month and 10-day basis. Specific methods are shown as Equations (1) and (2).  
\[BiasFactor(T_{t})=P^{S}(T_{t})/P^{S}(T_{t}) \tag{1}\]  
\[P^{S}_{corr}\left(P_{(x,y)},T_{i}\right)=P^{S}\left(P_{(x,y)},T_{i}\right)/Bias Factor(T_{t}) \tag{2}\]  
where \(P^{S}(T_{t})\) and \(P(T_{t})\) are monthly or 10-day rainfall of SPPs and gauge at watershed-scale, respectively. \(P^{S}\)\({}_{corr}(P_{(x,y)},T_{i})\) is the bias-corrected precipitation at grid location (\(x\),\(y\)) and time \(T_{i}\), which \(T_{i}\) is in the time series period \(T_{t}\).
# Chaos Based Frequency Modulation for Joint Monostatic and Bistatic Radar-Communication Systems
###### Abstract  
In this article, we propose the utilization of chaos-based frequency modulated (CBFM) waveforms for joint monostatic and bistatic radar-communication systems. Short-duration pulses generated via chaotic oscillators are used for wideband radar imaging, while information is embedded in the pulses using chaos shift keying (CSK). A self-synchronization technique for chaotic systems decodes the information at the communication receiver and reconstructs the transmitted waveform at the bistatic radar receiver. Using a nonlinear detection scheme, we show that the CBFM waveforms closely follow the theoretical bit-error rate (BER) associated with bipolar phase-shift keying (BPSK). We utilize the same nonlinear detection scheme to optimize the target detection at the bistatic radar receiver. The ambiguity function for both the monostatic and bistatic cases resembles a thumbback ambiguity function with a pseudo-random sidelobe distribution. Furthermore, we characterize the high-resolution imaging capability of the CBFM waveforms in the presence of noise and considering a complex target.  
chaos; frequency modulation; joint radar-communication; bistatic radar; high-resolution imaging; nonlinear detection 2021
# Handover Management for Drones in Future Mobile Networks--A Survey
## 8 Conclusions  
This paper mostly focused on studying handover managements in drone networks. Drones are a popular alternative to ground-based BSs or UEs. Due to limited power consumption, packet loss, or dense networks, various challenges may emerge during drone operation, making the handover process critical for effective data transfer. A comprehensive review of previous research was presented and discussed. Various research challenges were also highlighted. The proposed solutions from the literature were extensively reviewed. The main focus, however, is on handover management in future mobile networks. From this overview, several points can be highlighted. The research trends indicate that in future mobile networks, the integration of drones in mobile cellular networks, satellite networks, and other traditional technologies (such as MANETS, VANETs, and IEEE 802.11) will be part of the main solution. Drones move at higher speeds than ground network UEs and have different characterizations. Initially, it would seem that the two network behaviors have several similarities; however, drones possess greater handoff probability. This will lead to further handover issues, such as high HPPP and RLF. This is a significant challenge facing the implementation of drones. Existing handover mechanisms may not be efficient for drone networks. Machine and deep learning-based handover models have higher success rates and fewer assessment overheads in overlapping regions than traditional approaches, indicating that this technology may be a successful solution for managing the handover issue of drones.  
All authors [PERSON], P.D., M.B., [PERSON], S.A., M.A.S., [PERSON] and H.M. have contributed to the paper by adding sections or subsections, or reviewing the paper and improving it further. All authors have read and agreed to the published version of the manuscript.  
This research has benefitted from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C276) conducted at Istanbul Technical University (ITU). Also, from project under Universiti Teknologi Malaysia (UTM) RA ICONIC grant (Vot number QJ130000.4351.09G69) supported by Ministry of Higher Education (MOHE) Malaysia.  
Not applicable.  
This research has benefitted from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C276) conducted at Istanbul Technical University (ITU). Moreover, the authors wish to express their gratitude to Ministry of Higher Education (MOHE) Malaysia and Universiti Teknologi Malaysia for the financial support of this project under UTM RA ICONIC grant (Vot number QJ130000.4351.09G69).  
The authors declare that there is no conflict of interest in this paper.
## 5 Experiments and Results
### Visualization of Action Recognition  
HRNet human pose estimation performs human pose estimation on the original RGB image, the estimated skeleton is drawn above the human body in the original image, and the template matching result is written in the upper left corner of the image. Figure 6 shows the confusion matrix results on the five-category test set of whether to wear protective equipment, and Figure 7 shows the confusion matrix results on the two-category test set of smoking behavior, where the horizontal axis of the matrix represents the predicted label and the vertical axis of the matrix represents the true label. In addition, the last column represents the precision for each category, the last row represents the recall for each category, and the bottom right corner represents the accuracy for the entire test set. It can be seen from the confusion matrix that for the 64 videos of the five-category test set, the graph convolution-based method in this paper can achieve an average accuracy of 85.9%. Among them, the judgment accuracy of the coat and hat removal action is the highest, followed by the gloves removal action, and the judgment of the mask removal action is the least accurate, because the mask removal action is similar to the hat removal action. For the 23 videos of the two-category test set, the average accuracy reached 87.0%, which is a good effect.  
Figure 5: Display of object detection and joint point extraction.  
Figure 6: The confusion matrix for PPE using our method.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 4 Data Plane Programmability Using P4
### Match Action Tables  
Within the ingress and egress pipeline, packets are processed by a programmable sequence of match action tables. Their entries are called rules and each rule consists of a match part and a set of actions. Rules are installed, modified, or deleted during runtime through the API. When a packet is processed, its header or metadata fields are compared by the match part of each rule until a matching rule is found. There are different kinds of match types: exact, longest-prefix, and ternary. In case of a match, no further matching within this table is performed, and the actions in the action set of the corresponding rule are executed.  
An action set consists of pre-defined primitives like adding or removing a header, reading and writing header or metadata fields, adding or subtracting values, updating counters, or dropping the packet. Custom functions, so-called externs, may be utilized within actions. Examples are encryption or decryption of fields. While the set of supported externs is target-specific, software switches even allow the definition of new externs. It is possible to define multiple match action tables. One action is applying another match action table to a packet. Thereby, packets may be processed by a chain of match action tables. To prevent processing loops, a packet can be processed at most once by any table within a pipeline.  
As an example, IP forwarding can be implemented using a longest-prefix match for the destination IP address of an IP packet. In case of a match, actions are called to decrement the TTL field, adapt the IP checksum, and forward the packet to the appropriate egress port.  
For the sake of readability we omit technical details about P4 programming, the P4 code, and P4 syntax. For details we refer to the P416 specification [25].  
Figure 2: P4 processing pipeline.
## 5 Conclusions  
In this paper, we present a symbol-based adaptive channel estimation method in the DD domain for the OTFS system. It incorporates multidirectional scanning and noise reduction techniques by using an MA filter. The simulation results and field experimental data processing together demonstrate that the proposed channel estimation method is superior to the traditional pulse-based channel estimation method and the traditional IPNLMS method in terms of estimation accuracy. Specifically, in the simulation, the MMSE between the estimated channel matrix and the true channel matrix is reduced compared with that of traditional methods. Moreover, the channel data that were sampled in the Danjiangkou Lake experiment are employed to verify the performance of the proposed method. The results show that the BER of the proposed method is lower than that of the traditional method.  
Conceptualization, W.S., M.J., L.J. and C.H.; methodology, W.S., M.J. and L.J.; software, W.S., M.J., L.J. and N.T.; validation, W.S., M.J. and L.J.; investigation, W.S., M.J. and L.J.; material, W.S. and L.J.; data supervision, W.S., M.J. and L.J.; writing--preparation of original draft, M.J. and L.J.; writing--review, editing, and polishing, W.S., M.J. and L.J.; visualization, M.J., L.J. and N.T.; supervision, W.S., L.J. and C.H.; project administration, W.S., L.J. and C.H.; funding acquisition, W.S. and L.J. 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. 62371393 and 62471397, the Stable Supporting Fund of Acoustic Science and Technology Laboratory under Grant No. TCKYS2024604 SST3010, and the Fundamental Research Funds for the Central Universities (23 GH02027).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank anyone who supported the review of this paper.  
The authors declare no conflicts of interest.  
Figure 8: (**a**) The impulse response in the time-delay domain of the UWA channel; (**b**) The delay-Doppler Channel scattering function.
## 5 Results and Discussion
### Decision Tree Model Results  
Table 7 presents the confusion matrix for the DT model utilized for the soil classification task. The table evaluates the performance of a predictive model in classifying different soil types based on input features. The number of observations that were accurately predicted by the model (diagonal entries) and the number of misclassifications (off-diagonal entries) appear in the table. Based on the model's confusion matrix, the model performed well in the classification task, as the number of correctly predicted values are significantly higher than the number of misclassifications. The overall accuracy of the model in predicting soil types on the test dataset was 95.67%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performance Metrics** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Sensitivity & 1.00 & 0.99 & 0.99 & 0.99 & 0.99 & 0.98 \\ Specificity & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ PPV & 1.00 & 0.99 & 0.99 & 0.99 & 0.99 & 0.96 \\ NPV & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 1.00 \\ Prevalence & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Detection Rate & 0.02 & 0.04 & 0.31 & 0.51 & 0.11 & 0.01 \\ Detection Prevalence & 0.02 & 0.04 & 0.31 & 0.52 & 0.11 & 0.01 \\ Balanced Accuracy & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Class distribution summary for RF model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{5}{c}{**Actual**} \\ \cline{2-6}  & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
2 & 819 & 40 & 0 & 0 & 0 & 0 \\
3 & 20 & 1554 & 45 & 0 & 0 & 0 \\
4 & 0 & 71 & 13,240 & 591 & 0 & 0 \\
5 & 0 & 0 & 537 & 22,049 & 269 & 0 \\
6 & 0 & 0 & 0 & 274 & 4548 & 19 \\
7 & 0 & 0 & 0 & 0 & 59 & 282 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for DT model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multicolumn{5}{c}{**Actual**} \\ \cline{2-6}  & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline
2 & 835 & 2 & 0 & 0 & 0 & 0 \\
3 & 4 & 1655 & 8 & 0 & 0 & 0 \\
4 & 0 & 8 & 13,719 & 88 & 0 & 0 \\
5 & 0 & 0 & 95 & 22,763 & 59 & 0 \\
6 & 0 & 0 & 0 & 63 & 4806 & 5 \\
7 & 0 & 0 & 0 & 0 & 11 & 296 The statistics by class (Table 8) show that the DT model has a high sensitivity and specificity for all soil types. Moreover, the model exhibits a high balanced accuracy with values ranging from 0.96 to 0.99. Overall, the model performed well in the classification task, with high scores for multiple performance metrics across each soil type.