esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 1 Introduction
In this context, the present work proposes a novel hybrid approach that integrates fuzzy techniques with DDPG. By fuzzifying the input states to the actor network, the method significantly reduces the number of training episodes required, leading to faster convergence and improved robustness compared to conventional methods such as Perturb and Observe, Incremental Conductance, and standalone DDPG [33; 34]. The principal contributions of this paper are the following:  
1. Development of a novel fuzzy-based reinforcement learning framework: A hybrid MPPT strategy that combines fuzzy logic with DDPG to enhance global MPP tracking under partial shading. 2. Comprehensive validation: Detailed simulation and experimental results that corroborate the improved convergence speed, tracking accuracy, and robustness of the proposed method. Comparative analysis: A systematic comparison with conventional MPPT techniques, highlighting the advantages of the proposed approach in real-time PV system control.
## 4 Experiment Results  
### Datasets  
We evaluate our framework on five public datasets: FI, Flickr [28], EmotionROI [25], Twitter I [29], Twitter II [28]. Figure 5 shows examples of these datasets. FI dataset is collected by querying with eight emotion categories (i.e., amusement, anger, awe, contentment, disgust, excitement, fear, sadness) as keywords from Flickr and Instagram, and ultimately gets 90,000 noisy images. The original dataset is further labeled by 225 Amazon Mechanical Turk (AMT) workers and resulted in 23,308 images receiving at least three agreements. The number of images in each emotion category is larger than 1000. Flickr contains 484,258 images in total, and the corresponding ANP automatically labeled each image. EmotionROI consists of 1980 images with six sentiment categories assembled from Flickr and annotated with 15 regions that evoke sentiments. Twitter I and Twitter II datasets are collected from social websites and labeled with two categories (i.e., positive and negative) by AMT workers, consisting of 1296 and 603 images. Specifically, we conducted training and testing on the three subsets of Twitter I: \"Five agree\", \"At least four agree\" and \"At least three agree\", which are filtered according to the annotation. For example, \"Five agree\" indicates that all the Five AMT workers rotate the same sentiment label for a given image. As shown in Table 1.  
Figure 5: Some examples in the five datasets.  
According to the affective model, the multi-label datasets EmotionROI and FI are divided into two parts: positive and negative, to achieve the sentimental polarity classification. EmotionROI has six emotion categories: anger, disgust, fear, joy, sadness, and surprise. Images with labels of anger, disgust, fear, sadness are relabeled as negative, and those with joy and surprise are labeled as positive. In the FI dataset, we divided [PERSON]'s eight emotion categories into binary labels based on [30], suggesting that amusement, contentment, excitement, and awe are mapped to the positive category, and sadness, anger, fear, and disgust are labeled as negative.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 3 Preliminaries
### Paillier Cryptosystem  
The Paillier cryptosystem is a probabilistic asymmetric encryption scheme with homomorphic properties. It consists of key generation, encryption, decryption, and homomorphic addition operations.  
Key Generation: To generate a Paillier key pair, select two large prime numbers \(p\) and \(q\). Compute \(n=pq\) and \(\lambda=\text{lcm}(p-1,q-1)\), where lcm denotes the least common multiple. Choose a random integer \(g\) such that \(g^{\lambda}\equiv 1\pmod{n^{2}}\) and \(\gcd(g,n^{2})=1\). The public key is \((n,g)\), and the private key is \(\lambda\).  
Encryption: To encrypt a plaintext message \(m\), choose a random integer \(r\) such that \(0<r<n\). Compute the ciphertext as \(c=g^{m}\cdot r^{n}\mod n^{2}\).  
Decryption: To decrypt a ciphertext \(c\), compute the plaintext message as \(m=L(c^{\lambda}\mod n^{2})\cdot\text{modinv}(L(g^{\lambda}\mod n^{2}),n)\), where \(L(x)=\frac{x-1}{n}\) and modinv is the modular inverse.  
Homomorphic Addition: Given ciphertexts \(c_{1}\) and \(c_{2}\) encrypted with the same public key, the homomorphic addition of \(c_{1}\) and \(c_{2}\) results in the encryption of the sum of their plaintexts: \(E(m_{1})\cdot E(m_{2})\mod n^{2}\).
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 3 Simulation of Ship's Wake Bubble Laser Dual-Mode Detection Based on Monte Carlo
### Design of Ship's Wake Laser Dual-Mode Fusion Detection System  
The laser dual-mode fusion detection system for a ship's wake mainly includes a laser transmitting device, an APD receiving system, an imaging CMOS detection receiving system, a synchronous control system, and an information processing system. The structure design is shown in Figure 10.  
The laser emitting device uses a semiconductor pump laser with an emission wavelength of 532 nm, a pulse width of 8-10 ns, a repetition rate adjustable in the range of 1-5 KHz, and a laser energy adjustable between 1 and 20 mJ. The laser APD receiving system consists of an optical receiving mirror group, an optical fiber, an APD detector, and a filter device. The APD receiving gain is adjustable from 0 to 30 dB. The light reflected by the target is focused on the optical fiber through the convex lens and the filter device, and is coupled into the APD detector through the optical fiber. The imaging detection receiving module selects an industrial camera with a maximum frame rate of 814 fps and a minimum exposure time of 40 us. The imaging detection is performed by external trigger, and the lens can be replaced according to the actual detection needs. Based on a GD32 single-chip  
Figure 10: System construction of the detection and receiving module: (**a**) overall design of the detection system; (**b**) physical picture of laboratory pool and equipment.  
microcomputer, a high-precision synchronous control trigger circuit is designed, which triggers the CMOS industrial camera with low exposure and high frame rate synchronously with the high peak power and high repetition frequency pulse laser to realize laser-gated imaging and effectively improve the detection image quality. Based on the MS1003 high-precision time measurement chip, a real-time ranging panel is designed to output the target distance information in real time, so that the dual-mode detection results can support each other and improve the detection reliability.  
The laboratory indoor pool is shown in Figure 11. The length \(\times\) width \(\times\) height of the laboratory pool is 18 m \(\times\) 2 m \(\times\) 1.5 m. A towing vehicle is installed above the pool, which can drag the molybdenum wire plate and ceramic tube to move. The two ends of the pool are equipped with optical transmission windows, and the laser detection system can transmit and receive laser, which can simulate the underwater experiment equivalently. The bubbles generated by the molybdenum wire plate are about 30-70 \(\upmu\)m, which can simulate the far-field wake, and the bubbles generated by the ceramic tube are about 100-300 \(\upmu\)m, which can approximately simulate the near-field wake and simulate the occurrence of false wake.
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.4.1 Time Distribution Mechanism  
Synchronization is one of the most important issues for telecommunications. Reliable and low-latency deterministic communications require accurate timing information [103].  
Multiple standards related to 5G have very strict time error requirement. In fact, 5G radios use Time Division Duplex technology, where the uplink and downlink, respectively, use the same frequency band. There is a space between transmission time intervals called a guard band, which is a period of silence. Guard bands are implemented to ensure that there is no overlap in the timeslots transmitting and receiving information, otherwise the frequency overlaps and causes interference between adjacent timeslot channels. To increase the data transmission rate, guard bands should be as small as possible but also consider interference mitigation. This is the reason why accurate time information is essential in 5G.  
Carrier Aggregation (CA) is a key technology in 5G, enabling the simultaneous use of multiple carriers across the same or different frequency bands to enhance mobile data throughput [104]. The time alignment error (TAE) requirement for CA is specified in 3 GPP TS 36.104, not exceeding 130 ns or 260 ns [105]. This is the alignment requirement between links, which is the relative time error. For absolute time error, within \(\pm 1.5\) us drift to UTC is required [105].  
Since 5G already has an accurate time information network for communication purposes, this time information can be used in any applications that need time synchronization. In this sense, the 5G network can be treated as a time distribution network.  
Why can the 5G network achieve a high time accuracy? This is determined by PTP. In terms of time synchronization, 5G can be explained as an extension of PTP. The time synchronization needs in 5G are defined in separate PTP Profile standards. The International Telecommunication Union Telecommunication Standardization Sector (ITU-T) has defined two important telecom profiles for PTP: G.8275.1 and G.8275.2. Among these, G.8275.1 offers the highest level of accuracy [106]. Additionally, IEEE provides key PTP standards relevant to mobile networks. One of these is IEEE 802.1 AS, which specifies a \"generalized\" PTP (gPTP) profile and forms part of the Ethernet Time-Sensitive Networking (TSN) standards, as shown in Figure 12.
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 2 Problem Description and Theoretical Background
* Wasserstein distance: \[W_{p}(\mu,\
u):=\left(\inf_{\psi\in\overline{\Psi}(\mu,\
u)}\int_{X\times Y}\|x-y \|^{p}d\psi(x,y)|\psi\in\Psi(\mu,\
u)\right)^{\frac{1}{p}},\] (1) where \(\psi\) is a probability measure, \(\Psi(\mu,\
u)\) indicates the collection of all probability measure with marginals \(\mu\) and \(\
u\) on spaces \(X\) and \(Y\), respectively and \(c(x,y)=\|x-y\|^{p}\) is the Euclidean distance with \(p\)th order (\(p\geq 1\)) between \(x\in\mathbb{R}^{2}\) and \(y\in\mathbb{R}^{2}\) (for two dimensional case). This Wasserstein distance (1) describes the least amount of effort required to convert one distribution \(\mu\) into another one \(\
u\). For the transportation problem in the discrete marginal case with \(\mu\) and \(\
u\) indicating particles of the given two distributions, the following linear programming (LP) formulation is equivalent to the Wasserstein distance where the given distributions are represented by the sample points. * Linear Programming problem: (for \(p=1\)) \[\underset{\pi_{ij}}{\text{minimize}} \sum_{i,j}\pi_{ij}\|x_{i}-y_{j}\|\] (2) subject to \[\pi_{ij}\geq 0,\quad\sum_{j=1}^{N}\pi_{ij}=m(x_{i}),i=1,2,\ldots,M,\] \[\sum_{i=1}^{M}\pi_{ij}=n(y_{j}),j=1,2,\ldots,N,\]  
where \(x_{i},y_{j}\in\mathbb{R}^{2}\) are the locations sample points of the ensemble (for two-dimensional scenarios), \(m(x_{i}),n(y_{j})\in\mathbb{R}\) are some non-negative constants representing the mass or weight assigned to each particle in the ensemble. The variable \(\pi_{ij}\) denotes the transportation plan which indicates the amount of weight that needs to be delivered from \(x_{i}\) to \(y_{j}\). Hence, the optimal transport plan \(\pi_{ij}^{*}\) can be interpreted as the minimum effort required to transport the mass from each \(x_{i}\) to \(y_{j}\). The Wasserstein distance in the LP form will be employed to measure the similarity between the two distributions, one from the trajectories of the UAVs and the other from the given reference distribution. For the second research question, which is how to control the UAVs to achieve the similarity, the OT-based multi-UAV exploration strategy is proposed in Section 3.2 The formal procedure to generate a multi-UAV trajectory based on the OT theory is briefly describe in Figure 2). For the time-varying spatial distribution case associated with the third research question, we extend our results to the spatio-temporal distribution case in Section 3.3. Prior to further discussions on the proposed multi-UAV exploration scheme, the animal movement modeling is discussed first in the following section. Figure 2: Procedure to generate the multi-UAV trajectory using the optimal transport theory.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 7 Security Evaluation  
Security is a major concern when implementing healthcare applications using various technologies; where sensitive data are protected from unauthorized access, the integrity of the data is enhanced, and the privacy of the users is enhanced. These assertions were used to demonstrate the trustworthiness of the proposed approach.  
In this section, a security analysis of the proposed framework is conducted using different attack situations. In addition, the advanced features of the suggested data exchange method are addressed to emphasize the utility and practicality of our approach.  
Theorem 7.1: _If an adversary has access to information in the cloud platform without permission granted by the EHR administrator, retrieving and reading health information through our systems is exceptionally difficult._  
Proof: All health records were encoded with the public key of EHR management before being uploaded to decentralized cloud services based on our proposed architecture. To receive data from the cloud, recipients must have access to the e-health owner's secret key to decode original information. This secret key is unique, and only the EHR management can access it. Consequently, guessing the secret key to decode and acquire information in cloud storage is extremely difficult for an adversary. \(\square\)  
Theorem 7.2: _Assume that an attacker can request cloud e-healthcare data through suspicious transactions. It is difficult for an attacker to gain authorization by tampering with the access control architecture._  
Proof: To demand an approved transaction, a mobile node might need a private key (SK), public key (PK), and request ID, which are required to authenticate the transaction when it is sent to cloud services using the authorized (Tx) and SignSK syntax (rawTx PK, requestID, and timestamp). Only the user's public key is accessible to many individuals throughout the blockchain network, whereas the user's secret key is accessible only to the owner/user. Consequently, an attacker cannot authenticate transactions for database access using such a secret key. Additionally, because miners would remove any erroneous records from the Ethereum blockchain, the access management system would be impervious to any potential dangers. \(\square\)  
Figure 11: Decryption Time Comparison against Traditional RSA.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 3 Thermodynamic Analysis Model
### Performance Criteria  
The proposed ST-CAES system has two operational modes: energy storage and energy release. Different from conventional CAES, the energy storage mode can be divided into air storage and solar thermal collection and storage processes. It is worth mentioning that the solar thermal collection and storage process is independent of air compression. Therefore, these two processes can be conducted simultaneously. In the energy release mode, the stored high-pressured air in SPT and thermal energy in HOT are released simultaneously and drive the air turbine to generate electric energy. The electricity storage efficiency (ESE), round-trip efficiency (RTE), and exergy efficiency are important indices to analyze the performance of the proposed system. The ESE is defined as the electricity generation in the discharging process divided by power consumption in the charging process. The power consumption of the water and oil pumps is neglected because of their low values. Therefore, the ESE can be expressed as  
\[\eta_{ESE}=\frac{W_{TUR}\cdot\tau_{dch}}{W_{COM}\cdot\tau_{ch}}. \tag{25}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Parameters of PTC** & **Corresponding Data** \\ \hline Mirror type & Ultra-white float glass \\ Optical efficiency at 0 degrees incidence & 75\% \\ Reflector length per row (m) & 13.12 \\ Number of reflector rows & 8 \\ Aperture width (m) & 1 \\ Specular reflectivity & 0.94 \\ Absorber tube emissivity & 0.15 \\ Glass envelope emissivity & 0.9 \\ Glass tube support fin efficiency & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key design factors of PTC.  
RTE is defined as the ratio of total thermal and electrical energy output to total solar and electrical energy input in a full charge/discharge cycle. It can be expressed as  
\[\eta_{RTE}=\frac{W_{TUR}\cdot\tau_{dch}+Q_{heat}\cdot\tau_{hs}}{W_{COM}\cdot \tau_{ch}+Q_{u}\cdot\tau_{c}}. \tag{26}\]  
The exergy efficiency can be represented as  
\[\eta_{EXE}=\frac{W_{TUR}\cdot\tau_{dch}+Ex_{heat}\cdot\tau_{hs}}{W_{COM}\cdot \tau_{ch}+Ex_{HTF}\cdot\tau_{c}}, \tag{27}\]  
where \(Q_{heat}\) is the thermal energy supply to the heat load, \(Q_{u}\) is collected and stored solar thermal energy, \(Ex_{heat}\) is enthalpy exergy of \(Q_{heat}\), and \(Ex_{HTF}\) is the HTF enthalpy exergy absorbed by the PTC.  
For each subsystem \(j\), the exergy destruction and exergy efficiency can be calculated as follows:  
\[L_{j}=E_{j,in}-E_{j,out}, \tag{28}\]  
\[\eta_{EXE,j}=\frac{E_{j,out}}{E_{j,in}} \tag{29}\]  
where the subscripts in and out represent the input and output states, respectively, of subsystem \(j\). The expressions for input and output exergy of each subsystem are listed in Table 2.
# A Wideband Circularly Polarized Filtering Dipole Antenna
## 4 Conclusions  
In this paper, a wideband CP filtering crossed-dipole antenna has been proposed. By vertically extending the four arms of the crossed dipole and introducing four parasitic patches parallel to the dipole arm ends, the design has achieved a wide 3 dB axial ratio (AR) bandwidth of 46.9% (2.25-3.63 GHz) while creating two upper-band radiation nulls. A square-ring slot has been etched on the ground plane to generate an additional lower-band radiation null, collectively producing a bandpass filtering response. The high-selectivity bandpass performance has been realized through three controllable radiation nulls: the lower-band null has been primarily induced by the ground's square-ring slot, the 4.2 GHz null has been mainly generated through mutual coupling between parasitic patches, and the 5 GHz null has been dominantly created by loop currents formed between the crossed-dipole arms. The measured results have shown good performance, with an impedance bandwidth of 73% (1.825-3.925 GHz), an AR bandwidth of 46.2% (2.33-3.73 GHz), and a stable gain of approximately 7 dB. Without additional filtering circuits, the antenna has achieved three radiation nulls, with suppression levels reaching 20 dB in both the lower and upper stopbands, thereby realizing a favorable bandpass filtering effect.  
Conceptualization, X.L. and R.H.; methodology, X.L. and M.Z.; software, R.H.; validation, X.L., R.H., and A.Y.; formal analysis, M.Z.; investigation, A.Y.; resources, X.L.; data curation, R.H.; writing--original draft preparation, X.L. and M.Z.; writing--review and editing, R.H. and A.Y.; visualization, M.Z.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Guangdong-Taiwan Technology Cooperation Projects in Guangdong Province Granted No. 2024A0505050044.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results
### The Analysis of Test Result on the SIRI-WHU Dataset
The method of features integration [65] fuses low-level visual features with middle-level visual features to improve the accuracy of scene classification, but the accuracy is still lower than the method proposed in this paper. Because there is a semantic gap between low-level visual features and scene semantic categories, the scene classification methods based on low-level visual features often fail to achieve good results. Although middle-level visual features are abstract expressions of low-level visual features, there are still some gaps between middle-level visual features and semantic categories of scenes. High-level visual features extracted by deep learning methods can simulate the human brain mechanism to interpret data and obtain better features. Therefore, the classification accuracy of the deep learning method is generally higher than that of the traditional methods. Among deep learning methods such as LPCNN [59], WMS-RFPNet, MRNet, MFPNet, WD-RFPNet, WPNP-RFPNet, WMAM-RFPNet, and RFPNet, RFPNet has the highest classification accuracy. Further analysis can be made through Table 3:  
Figure 14: Test result of accuracy, precision, recall, F1-score on the SIRI-WHU dataset. Figure 15: (**a**) Confusion matrix of the SIRI-WHU dataset. (**b**) Image categories that can be easily misclassified. (1) The classification accuracy of RFPNet is 8.3% higher than that of WMS-RFPNet, which proves that the effectiveness of the multi-view scaling strategy for high-resolution remote sensing image scene classification. (2) The classification accuracy of RFPNet is 4.98% higher than that of MFPNet, which proves the effectiveness of the adoption of the residual block in scene classification of high-resolution remote sensing images. (3) The classification accuracy of RFPNet is 2.83% higher than that of MRNet, thus proving the effectiveness of the fusion strategy of pooling layer feature maps. (4) The classification accuracy of RFPNet is 3.5% higher than that of WD-RFPNet, thus proving the effectiveness of using the Dropout strategy. (5) The classification accuracy of RFPNet is 0.87% higher than that of WPNP-RFPNet, thus proving the effectiveness of using the parameter norm penalty strategy. (6) The classification accuracy of RFPNet is 1.07% higher than that of WMAM-RFPNet, thus proving the effectiveness of using the moving average model. Even compared with other methods of deep learning like LPCNN [59] and so on, the proposed method still has very good results.