esa-sceva/satcom-chunk-collection huggingface dataset:
# Green IoT: A Review and Future Research Directions
## 4 Energy-Efficient M2M Communications
### Offloading Computation/Task Offloading
workload on the M2M device's processor can lower the device's energy consumption [37]. Another benefit is that offloading computation can allow M2M devices to perform tasks that they would not be capable of performing on their own, due to limited resources such as processing power or memory [38]. This can enable M2M devices to perform more complex tasks, such as data analysis and machine learning, which can improve the overall functionality of the M2M system. There are several ways in which offloading computation for energy-efficient data transmission in M2M communications can be classified. One way is based on the location of the resources or computing used for offloading [39] (as shown in Figure 5):  
1. _Edge-based offloading_: In this type of offloading, computation-intensive tasks are transferred to a nearby edge device, such as a gateway or a fog node. This can be done over a local area network (LAN) or a wireless network. 2. _Fog-based offloading_: A decentralized computing paradigm that gathers computing and data storage closer to the devices and users that need them, enabling more efficient and effective use of resources. 3. _Cloud-based offloading_: In this type of offloading, computation-intensive tasks are transferred to a remote server or cloud computing infrastructure. This can be done over the internet or a wide area network (WAN). Another way to classify offloading is based on the types of tasks that are offloaded [39]:  
1. _Data-intensive offloading_: In this type of offloading, tasks that involve large amounts of data, such as data analysis and machine learning, are transferred to external resources. 2. _Computation-intensive offloading_: In this type of offloading, tasks that require significant processing power, such as image processing and video encoding, are transferred to external resources. Figure 5: Architecture of computing layers.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 1 Introduction
** We propose _IPPNN_, a non-interactive and privacy-preserving image classifier based on ANN over distributed image data, which only requires communication between the users and the servers as a one-way interaction. Compared to interactive privacy-preserving ANN systems, our approach introduces less computation and communication overheads for the users. * **High-Precision. **_IPPNN_ enables the building of high-precision image classification as it does not require the use of polynomials or linear piecewise function approximations to handle out the non-linear functions. _IPPNN_ supports lossless classifier construction which does not alter any of the computation of the original ANN training algorithm. * **Efficiency. ** We have implemented and evaluated the performance of _IPPNN_. For two popular MNIST datasets with a large amount of image data and high dimensionality, the experiments validate that _IPPNN_ is several orders of magnitude faster than the state-of-the-art privacy-preserving ANN schemes without compromising the accuracy of classifiers. The rest of paper is organized as follows. We review the related work in Section 2. In Section 3, we introduce ANN model and cryptographic primitive (functional encryption for inner-product). We overview the proposed privacy-preserving framework of _IPPNN_ in the first part of Section 4, and the core idea and construction details of _IPPNN_ is described in the latter part of Section 4. The security analysis and evaluation are presented in Section 5. The conclusion of our paper are presented in Section 6.
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 4 Discussion  
### Comparison with Similar Systems  
Although some systems had achieved higher accuracies of more than 90%, they were based on 30 min or longer records [12]. Table 2 shows a comparison of similar PAF prediction systems in the literature, in which predictions were made based on 5 min HRV sequences extracted from the PAF Prediction Challenge Database (AFPDB). We note that the second to the last group in the table claimed to achieve an accuracy of 90, but, in their paper, they indicated that such an accuracy was only attained when the HRV signals were 2.5-7.5 min prior to the onset of AF, whereas other episodes could only achieve around 85% accuracy [12]. The most competitive and promising result in the table thus leads to [PERSON]'s system with an accuracy of 87.7% [11]. Such an accuracy is very close to our result of 87.9% in the validation set and 87.2% in the testing set. However, the accuracy of [PERSON]'s system was obtained from a 10-fold cross-validation, in which the data were only drawn from the AFPDB. The same database was used as our training and validation sets. Yet, we tested our system on a much larger dataset with 4885 HRV sequences from two other databases and achieved a similar accuracy of 87.2%.  
Furthermore, the features used in other systems in Table 2 consisted of time-domain and/or frequency-domain nonlinear features. These features are usually computationally intensive and their medical implications are rarely interpretable. In comparison, our system was able to perform prediction of 4885 400-point HRV sequences in the test dataset in an average (from 10 tests) of 6.1 s or 1.25 milliseconds per sequence under the MATLAB(r) environment with an Intel(r) Core(tm) i7-9750H CPU at 2.60 GHz and 32 GB RAM. In addition, because our features can directly link back to the original HRV sequence, their medical implications can be traced easily. Some of the implications from the designed inter- and intra-features are discussed next.  
Figure 4: Confusion matrices of the (**a**) training, (**b**) validation, and (**c**) testing datasets.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 3 System Model
### Environment Modeling  
In Figure 1, we present a dynamic resource sharing scenario where multiple V2V links strive to use the spectrum occupied by the V2I links. This competitive environment underscores the suitability of a MARL approach, thereby treating each V2V link as an autonomous agent navigating the uncertain landscape to optimize both individual and collective performance.  
Expanding on the resource sharing scenario in Figure 2, we conceptualize the interactions among numerous V2V links as a Multi-Agent MDP (MA-MDP). This framework views each V2V link as an independent agent, where the system reward is accessible to each V2V agent, which then adjusts its actions toward an optimal policy by updating its own DQN. The MA-MDP approach captures the inherent complexity of this dynamic environment, thus facilitating the use of MARL techniques for efficient resource allocation and coordination among V2V links.  
Figure 2: Multi-Agent Reinforcement Learning [21].  
In this multi-agent setting, at each timestep \(t\), each Vehicle-to-Vehicle (V2V) agent \(k\) explores the unknown environment. They receive their observations and then take action, thereby collectively forming a joint action. This joint action leads to a transition of the environment to a new state. Subsequently, the agents receive new observations based on this new state. To promote cooperation among agents, all agents share the same reward signal.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 5 Discussion  
Table 2 shows the detection rates of ten targets--the average detection rate was 96.5%. The detection rate of Target 5 was particularly low, at 74.5%. Target 5 was located far away from the drone, as shown in Figures 8b and 11e. Therefore, its relative lower speed caused detection to be missed during frame subtraction. The false alarm rate was 1.17 per frame. The false alarms were mostly generated when the drone was swung by the wind or the objects passed through a complex background. Table 3 shows the RMSE of position and velocity. The average RMSE of the position was about 0.8 m, and the average RMSE of the velocity was about 0.586 m/s (\(\approx\)2.1 km/h) for the two-mode IMM filter. The minimum RMSEs were obtained when the two-mode IMM filter was used. It turns out that the process noise standard deviations (0.6 and 1 m/s\({}^{2}\)) were properly chosen, because similar results were obtained from the Kalman filter with the average standard deviation (0.8 m/s\({}^{2}\)). The two-mode IMM filter provided slightly better results than the Kalman filter. It was especially good for Target 7 (car), which moved to higher maneuvers than other targets. It is noted that the IMM filter with three modes did not provide better results in this scenario.  
The position RMSE varied from 0.458 m for Target 5 to 1.284 m for Target 10. The average RMSE (about 0.8 m) was close to half the human height. Except for Target 7 (car), Targets 9 and 10 generated higher position errors than other targets; there were biases between the measurements and the position estimates. The velocity RMSE varied from 0.342 m/s (1.23 km/h) for Target 1 to 0.959 m/s for Target 2 (3.45 km/h). The speed of human movement is important because we can recognize the threats from unexpected movements.
## 1 Introduction
Luminescence methods for detecting adulteration of milk fat with vegetable oil are summarized in [19]. New fluorescent methods such as differential fluorometry [20] or obtaining a complete fluorometric \"map\" [21] are very informative. In the latter case, the luminescence intensity is measured as a function of the wavelength of both excitation and emission. But such methods are not very convenient for a quick determination of the degree of falsification since they require, firstly, an expensive spectrofluorometer and, secondly, complex processing of a three-dimensional spectral pattern. However, the luminescence emission spectra of the butter and spread using certain excitation wavelengths can give simple dependences of different spectral components on the concentration of vegetable oil in the spread. We managed to find such a wavelength of excitation. The purpose of this article is to propose two convenient and non-expensive methods of quantitative analysis of butter adulteration with palm oil. The first one is based on the dynamic light scattering (DLS) method. The second one deals with determining the ratio of the intensities of certain spectral components in the luminescence spectrum of oil or spread upon excitation at a wavelength of 266 nm. However, we do not attempt to study the chemical composition of oil and palm oil but offer practical ways to determine falsification based solely on experimental results. These DLS and luminescent methods for determining the proportion of palm oil give errors of no more than 10% and 6%, respectively, but this inaccuracy can be reduced due to better standardization of sample preparation and additional collection of measurement statistics. Moreover, measurements together with sample preparation can be performed in a relatively short time, \(\sim\)30 min (including sample preparation), and require inexpensive equipment. Particularly, DLS has a number of advantages, such as a short measurement time (about 60-120 s), non-invasiveness, and accuracy. Additionally, in this work, we use the method of laser phase microscopy to experimentally confirm the formation of submicron fat droplets in an alcohol emulsion, which was intended for studies by the DLS method. In practice, to determine the falsification of butter, each company uses a method for which it has the necessary equipment and specialists. At the same time, many researchers tend to suggest using the method that they are accustomed to employing. The proposed two methods for detecting and evaluating adulteration are physically independent but successfully supplement each other.
## 3 Active Fault-Tolerant Control System Design
### Adaptive Sliding Mode Fault-Tolerant Controller Design
#### 3.2.2 Adaptive Sliding Mode Controller Design with Input Saturations  
In real applications, the controller presented in Equation (39) might not be implementable because of the saturation limit. Let us denote \(v_{\max}>0,\ v_{\min}>0\) as the maximum and minimum control inputs. Then, the rotation movement in Equation (30) can be rewritten in presence of actuator saturation as:  
\[\left\{\begin{array}{ll}\dot{\overline{x}}_{2i-1}=\overline{x}_{2i}\\ \dot{\overline{x}}_{2i}=f_{i}(\overline{x})+g_{i}\text{sat}(v_{i}(t))+\xi_{i} (\overline{x},t)+\zeta_{i}\end{array}\right. \tag{44}\]  
**Theorem 2**.: _Let us consider the faulty operation with actuator saturation presented in Equation (44), if modified adaptive sliding mode backstepping control (MASMBC) is designed as:_  
\[v_{i}=v_{if}-\delta_{i}s_{2i} \tag{45}\]  
_where \(v_{if}\) is designed as the same as Equation (39), and \(\delta_{i}>0\), then the close loop system can global asymptotically track the desired trajectory in presence of saturation limit, disturbances, and actuator faults._  
**Proof.** With the proposed control law in Equation (45), we obtain  
\[g_{i}\text{sat}(v_{i})=g_{i}v_{if}-g_{i}\Big{[}\text{sat}(\delta_{i}s_{2i}-v_{if} )+v_{if}\Big{]} \tag{46}\]  
Considering the Lyapunov function in Equations (41) and (45), we obtain:  
\[\dot{V}_{2i}\leq -A_{i}s_{1i}^{2}-(\Gamma_{i}-\zeta_{i})|s_{2i}| \tag{47}\] \[-g_{i}s_{2i}\Big{[}\text{sat}(\delta_{i}s_{2i}-v_{if})+v_{if} \Big{]}\]  
The function \(\text{sat}(\delta_{i}s_{2i}-v_{if})+v_{if}\) can be evaluated as:  
\[\text{sat}(\delta_{i}s_{2i}-v_{if})+v_{if} \tag{48}\] \[= \left\{\begin{array}{ll}\delta_{i}s_{2i},&\text{if}\ \big{|} \delta_{i}s_{2i}-v_{if}\big{|}<v_{\max}\\ v_{\max}+v_{if},&\text{if}\ \big{(}\ \delta_{i}s_{2i}-v_{if}\big{)}>\ v_{\max}\\ v_{\min}+v_{if},&\text{if}\ \big{(}\ \delta_{i}s_{2i}-v_{if}\big{)}<v_{\min} \end{array}\right.\]  
Substituting Equation (48) into Equation (47), we have:  
\[\dot{V}_{2i} \leq-A_{i}s_{1i}^{2}-(\Gamma_{i}-\zeta_{i})|s_{2i}| \tag{49}\] \[-g_{i}s_{2i}\Big{[}\text{sat}(\delta_{i}s_{2i}-v_{if})+v_{if} \Big{]}\] \[\leq-A_{i}s_{1i}^{2}-(\Gamma_{i}-\zeta_{i})|s_{2i}|\]  
According to Assumption 2, it can be shown that \(\dot{V}_{2i}\ \leq 0\). Therefore, MASMBC in Equation (45) will asymptotically stabilize the system.  
**Remark 3**: _The actuator faults are compensated adaptively with the help of the proposed control law in Equation (45). To be more specific, with the fault estimation information in Equation (12), the adaptive sliding mode backstepping control is designed to address actuator faults, disturbances, and chattering problem through Equations (39), (40), and (43). Finally, with the control law in Equation (45), the saturation problem can be eliminated by tuning the parameter \(\delta_{i}\)._
## 2 Methods
### Path-Planning Improvement
#### 2.2.3 Fusion of the FGA-RRT and PRM Algorithms  
The improvement of the RRT algorithm solves the problem of random tree expansion, but RRT is a single-query algorithm, and the goal is to find a feasible path as soon as possible, so the planned path has more redundant nodes. This document aims to minimize the redundancy of the planned path by shortening the path through the incorporation of the PRM algorithm's strategy. The basic idea of PRM is to use a random network to represent the running space of the robot system and then find a feasible path in this network based on a graph search. The random network consists of a series of nodes and their connections. The nodes correspond to the robot pose without obstacle collision in space, and the connection between the two nodes corresponds to a feasible region between the two poses. The general PRM path-planning method is divided into two stages, the preprocessing stage and the search stage. In the preprocessing stage, a large number of points are evenly and randomly scattered into the configuration space C, and then the nodes in the space without obstacle collision are retained to form the node set M. Then, a local planner is used to find its adjacent nodes for each node in N and connect them to form a connection set N. In the search phase, the starting point and the target point are added to the node set M, and then the local planner finds its adjacent nodes and connects them, so that the map becomes several connected graphs. Ultimately, the graph search technique is employed to determine the most efficient route from the beginning to the goal point on the map. The PRM algorithm obtains the optimal path by calculating the connection of random uniform nodes in the search space. The algorithm has poor completeness, the search time is long when the number of sampling points is too large, and the solution may not be found in complex scenarios. However, the RPM algorithm plans fewer redundant nodes in the path. Therefore, this paper combines the RPM and FGA-RRT algorithms to make full use of their respective advantages, learn from both, and plan a better path. Utilizing the PRM concept, the fusion algorithm enhances the path designed by the FGA-RRT algorithm: Initially, numerous points are distributed uniformly and randomly along the path planned by the improved RRT, and then the nodes are connected to perform collision detection on the connection. If there is no collision, it is added to the path to detect whether it is a shorter path, and the shorter path is updated. The above procedure is repeated, and multiple iterations are performed until the maximum number of iterations is reached, and the optimal path at this time is output. Integrating with the PRM algorithm decreases the planned path's inflection points and shortens its length through the optimization of the superfluuous path.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
###### Abstract  
We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging _Hyperledger Fabric_, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines _Ciphertext-Policy Attribute-Based Encryption (CP-ABE)_ with temporal, spatial, and controlled revelation constraints to grant data owners precise control over shared intelligence. To ensure scalable decentralized storage, encrypted CTI is distributed via the _IPFS_, with blockchain-anchored references ensuring verifiability and traceability. Using _STIX_ for structuring and _TAXII_ for exchange, the framework complies with the _GDPR_ requirements, embedding revocation and the right to be forgotten through certificate authorities. The experimental validation demonstrates that TrustShare achieves low-latency retrieval, efficient encryption performance, and robust scalability in containerized deployments. By unifying decentralized technologies with cryptographic enforcement and regulatory compliance, TrustShare sets a foundation for the next generation of sovereign and trustworthy threat intelligence collaboration.  
distributed ledger technology; cyber threat intelligence; encryption; GDPR
## 3 Results
### Effects of HR2302-30E and C3G on the FccRI Signaling Pathway
Figure 6: \(Cont.\)  
Figure 6: Effects of HR2302-30E and C3G on mitogen-activated protein kinase (MAPK), phospholipase C (PLC)-\(\gamma\), and protein kinase C (PKC)-\(\delta\) levels in RBL-2H3 cells. IgE-primed RBL-2H3 cells were incubated with 250 ng/mL of DNP-HSA and HR2302-30E (1000 and 2000 mg/mL) and C3G (5 and 10 mg/mL). The protein expression of (**a,b**) p38, ERK, and JNK, (**c,d**) PLC\(\gamma\) and PKC\(\delta\) were determined using immunoblotting. Data are represented as the mean \(\pm\) SEM of the three independent experiments. The statistical analyses were performed using the Dunnett \(t\)-test. PP2 is a general Src family kinase inhibitor. * \(p\) \(<\) 0.05, ** \(p\) \(<\) 0.01, and *** \(p\) \(<\) 0.001 vs. anti-DNP IgE plus DNP-HSA (IgE/Ag); *** \(p\) \(<\) 0.001 vs. control. –, No treatment, +, Treatment. Figure 7: HR2302-30E and C3G suppress mast cell degranulation by inhibiting Fc\(\varepsilon\)RI signaling pathway. HR2302-30E and C3G inhibit Fc\(\varepsilon\)RI signaling by suppressing Src family kinases (Syk, Fyn) and downstream pathways (PLCy, PKC\(\delta\), MAPK), thereby reducing the release of allergic mediators.
# Force Fight and Its Elimination in a Prototype of a Redundant Direct-Drive Avionic Actuator
###### Abstract  
Nowadays, in aeronautical applications, rigorous reliability requirements are high constraints for embedded systems. Therefore, material redundancy is adopted to ensure safety whatever operation conditions may be. In the design of a direct-drive actuator for a specific application, redundancy is taken into account using a compound of four three-phase permanent magnet synchronous machines (PMSM). Each two electric machines are mounted in series sharing the same rotor with a common through shaft. The stators of each two electric machines are designed and manufactured as a six-phase machine. For each machine, power supply, sensors, and control loops are independent. In the characterization of the designed actuator and in the validation of its specific control in healthy operation mode and in faulty operation mode, taking into account the force fight phenomenon between the motor lanes, a specific test bench is developed. The aim of this paper is to highlight this phenomenon in faulty operation mode and to develop an easily implementable monitoring architecture, in order to eliminate it. Finally, with the proposed approach of monitoring, the force fight phenomenon is eliminated in faulty operation mode keeping the same performance as in healthy operation mode.  
redundancy; reliability; actuator; permanent magnet synchronous machine; position control; sensors; faults; force fight; monitoring; performance
## 2 Materials and Methods
### Human-Assisted Feature Extraction Methods
#### 2.2.5 Wavelet Decomposition and Power Spectral Density  
The Power Spectral Density (PSD) of the wavelet decomposition of the ECG signals was computed as shown in Figure 5. The outcome provided two different feature sets. For this purpose, the signals were decomposed up to the sixth level using a quadratic spline wavelet, which extracts features of rhythm classification by a mother wavelet, see Figure 5b. The coefficients of this wavelet method's finite impulse response filters are detailed in the paper by [PERSON] et al. [33]. The six detailed and one approximation sets of coefficients obtained from the ECG signal after wavelet decomposition of a five-second ECG rhythm are shown in Figure 5a,c. Figure 5d shows the PSD of each of the approximation (A6) and detailed (D1 to D6) wavelet coefficients. The PSD of each wavelet coefficient was computed using the Welch  
Figure 4: Higher-Order Statistics (HOS) cumulants example of the merged database, which was created dividing each beach into five intervals and then computing the kurtosis and skewness values for each of them as the results give morphological ECG description features of the signal.  
method. The blue dotted lines delimited the sub-bands of one set of features, the average values of sub-bands.  
The final output is two major sets of features extracted from the signal. The first set is based on the computation of the average PSD values over the predefined frequency sub-bands. The predefined frequency bands are [0, 2]; [2, 4]; [4, 8]; [8, 16]; [16, 32]; and [32, 64] Hz. This set contains 42 extracted features (six values for each of the seven signals. The second set of features is the integral over the frequency range [0,5] Hz. In total, in this group, there are seven features representing each of the patterns.