esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses
### Information Entropy Analysis  
Information entropy can reflect the indeterminacy of image information. For an ideal encryption image, its information entropy is close to 8 [23]. For the gray image \(I\), we describe the information entropy as:  
\[H(m)=-\sum_{\mathrm{i=0}}^{255}P(m_{\mathrm{i}})\log_{2}P(m_{\mathrm{i}}) \tag{19}\]  
where \(m_{\mathrm{i}}\) is the \(i\)th gray level, and \(P(m_{\mathrm{i}})\) is the emergence probability of \(m_{\mathrm{i}}\).  
For the plain image, its entropy value is 7.1206. For the encrypted images of the proposed algorithm, [PERSON]'s algorithm, [PERSON]'s algorithm and [PERSON]'s algorithm, their entropy values are given in Table 5. Simulation data display that all these four algorithms have the ability to resist the statistical attack.
## 2 Internet of Vehicles and Smart Cities
### Architecture
#### 2.2.3 Five-Layer Architecture  
The five-layer architecture, proposed by [PERSON] et al. [10], is divided as follows:  
* _Perception layer_ that handles data gathering, data digitization, and transmission, and energy optimization at lower layers.
* _The coordination layer_ is not only responsible for transferring data to the artificial intelligence (AI) layer in a secure manner but also being responsible for dealing with the heterogeneity of the network structure, unifying received information.
* _The artificial intelligence layer_ holds the cloud infrastructure, which stores, processes, and analyzes the data from layers below, utilizing this analysis for decision making while also managing cloud systems' services.
* _The application layer_ is responsible for providing intelligent services to end-users, based on the processed and analyzed information of the AI layer, which serves for service discovery from smart applications.
* _Business layer_, a novelty from the last two architectures, which is responsible for giving foresight on the development of business models, based on data analysis and statistics, which come from the _application layer_ and is later transformed by analysis tools, while utilizing decision making for budgeting and optimization usage of resources.  
Figure 2: Comparison between the various layered architectures for IoV.
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 3 Experimental Results and Analysis
### Crab Detection
#### 3.2.3 Ablation Experiments  
To evaluate the contribution of each proposed module, we conducted ablation experiments by sequentially adding ODConv (A), SlimNeck (B), and LSCD (C) to the YOLOv10n baseline. As shown in Table 2, the baseline achieved an mAP50-95 of 86.5%, with 2.26 M parameters, 6.5 GFLOPs, and 317 FPS. When only the ODConv module was introduced, the parameter count increased slightly to 2.31 M, while the computational cost was notably reduced to 4.4 GFLOPs, indicating an improvement in feature extraction efficiency. However, the mAP50-95 experienced a marginal drop to 86.0%, possibly due to the absence of high-level semantic aggregation. In contrast, the integration of the SlimNeck module yielded a significant improvement in both accuracy (87.2%) and FPS (334), while maintaining a moderate model size (2.39 M) and relatively low computation (5.9 GFLOPs), indicating its effectiveness in lightweight multi-scale feature fusion. The LSCD module alone also contributed positively, raising mAP50-95 to 87.5% and increasing FPS to 375, while simultaneously reducing the model size to 1.94 M, highlighting its capability to enhance detection performance with reduced parameter overhead. The full model achieved the best overall performance with an mAP50-95 of 88.5%, a parameter size of 2.11 M, 4.2 GFLOPs, and an inference speed of 309 FPS. Compared to the baseline, this model achieves a 2.0% improvement in accuracy, a 35.3% reduction in GFLOPs, and a significant enhancement in detection efficiency.
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 2 Background Knowledge
### Output Signal of Fiber Optic Gyroscope  
Through a long investigation, it was found that the fiber-optic gyroscope (FOG) has an output signal containing the angular velocity information of FOG, as well as the angular velocity information of the earth and various noises. Therefore, under the condition of a static pedestal, when the FOG measurement axis is perpendicular to the horizontal plane, the mathematical model of the gyro output is as follows:  
\[\omega(t)=\omega_{1}\sin L+\varepsilon_{d}+D_{t}+W(t). \tag{1}\]  
In the above equation, \(\omega_{1}\) is the angular rate of the earth's rotation, \(L\) represents the local latitude, and \(\varepsilon_{d}\) is a constant zero offset (i.e., when the input angular rate is zero, the output constant of the  
Figure 1: Photoelectric platform system.  
gyroscope can be regarded as a constant in a short time). In addition, \(D_{t}\) is the drift error, which has strong randomness and weak trend and periodicity. \(W(t)\) is white noise with a mean value of zero.  
The original gyro output signal is shown in Figure 2.
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 3 The Proposed System Architecture
### The IoT WSANCS Architecture  
Figure 1 presents the updated system architecture introduced in [27]. The system consists of two RESs including a micro-wind turbine and a photovoltaic system (PV Array), both with their respective charge controller. It is worth noting that the architecture allows the system to increase the number of RESs as needed. The produced energy is stored in a battery bank. In addition, an inverter and a DC-to-DC converter are required to supply the loads. The WSANCS is composed by two types of nodes: the sensor-actuator nodes (SA-N) and the sink node (S-N).  
The SA-N are included in every load of the system while the S-N is responsible for receiving the sensed data, process them, and control the network by sending wireless-control signals to the SA-N. The current and voltage are measured at different points in the microgrid using the SA-N. XBee modules are included in the WSANCS for wireless transmission purposes. Switches are connected to the Arduino micro-controller to control the loads (turn-on/turn-off) through a digital signal. The SA-N architecture (seen in Figure 2) is composed of a current sensor, an Arduino micro-controller, and a XBee Pro module as end device using the IEEE 802.15.4 protocol [28] to monitor the grid generation and consumption. A voltage sensor is also included to measure the quality of voltage levels at different points in the microgrid.  
The S-N architecture (see Figure 3) includes the XBee coordinator connected to the Arduino 2 MEGA microprocessor for reading the signal of all the sensor nodes, making the necessary calculations, and printing those values to the corresponding serial port. A Raspberry-PI3 B+ (RPI3+) computer collects these values using a Python script to store data in both local and cloud databases using Sqlite3 and Firestore, respectively. This information is accessible for the user through the Android mobile application explained in detail in Section 3.2.3. In addition, the user can send the command to switch the state of the loads connected in the microgrid test bench through the mobile application, and then the S-N will send the signal to the corresponding SA-N to achieve what the user desires.  
Since there are different XBees frequently attempting communication, the implemented module includes an acknowledgment scheme that allows the user to verify if data were received and from which device. The system was designed as a centralized coordinator-end device system with a coordinator XBee in the S-N that receives data from the SA-N and delivers them to a RPI3+.  
Figure 1: System architecture.  
Figure 2: Sensor-actuator node architecture.
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 3 Test and Result Analysis
### Performance Test of Filtering Algorithm for Invalid Targets
#### 3.1.3 The Vehicle Was Driving on an Uneven Road Surface  
Three trials of the rototilling operation were carried out at a set speed to verify the effect of the vehicle vibration operation scenario on the filtering effect while keeping the position of the obstacle and the radar angle constant. The results before and after the treatment in the driving condition of the rotary tillage operation are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Timestamp** & **Vertical Coordinate (m)** & **Horizontal Coordinates (m)** & **Category** \\ \hline
2 & 17:14:41:301 & 24.6 & \(-0.4\) & experimenter A \\
9 & 17:14:41:456 & 21.2 & 4.8 & Point \\
11 & 17:14:41:523 & 66.4 & 0.2 & Trolley \\
12 & 17:14:41:6162 & 50.2 & 1.0 & Point \\
13 & 17:14:41:783 & 37.8 & \(-1.8\) & Point \\
14 & 17:14:41:845 & 78.0 & 4.0 & Point \\
18 & 17:14:41:966 & 83.0 & 2.6 & Point \\
19 & 17:14:42:086 & 29.8 & 0.4 & experimenter B \\
20 & 17:14:42:168 & 39.8 & \(-1.2\) & Point \\
21 & 17:14:42:236 & 70.8 & 1.2 & Point \\
22 & 17:14:42:301 & 141.0 & 44.8 & Point \\ \hline \end{tabular}
\end{table}
Table 5: Unfiltered partially-resolved data of millimeter-wave radar at static state.  
Figure 7: Before and after processing at res.  
Figure 8: Before and after processing in motion.  
Figure 9: Results before and after processing in the condition of vibration accompanied by driving.  
As the vibration of the vehicle increased, this factor increased the number of false targets. Compared to the steady state, at the same speed, the filtering rate of invalid targets decreases, but after 9 trials, the invalid target filtering algorithm achieves a filtering rate of more than 86% for the 3 types of invalid targets.
# Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing
###### Abstract  
Cloud computing has become popular in data sharing mainly because it has huge storage capacity and computing power. Securing the privacy of sensitive data for cloud-based data sharing is vital. Currently, there are various conditional proxy re-encryption (UPRE) schemes that have been proposed to resolve the privacy issue. Nevertheless, the existing UPRE schemes cannot allow the proxy (e.g., the cloud server) to transfer the outsourced encrypted data under the data owner's public key of any homomorphic encryption scheme into the encrypted data under the data user's public key of a homomorphic encryption scheme (possibly different from the data owner). The transformation of outsourced encrypted data between homomorphic encryption schemes is more suitable for the real data sharing in clouds. Consequently, we present the notion of universal conditional proxy re-encryption (UCPRE) to solve the issue of flexible transformation of outsourced encrypted data between homomorphic encryption schemes in cloud-based data sharing. UCPRE is lightweight in the sense that it only requires the re-encrypted key generation and re-encryption algorithms. We give the definition of UCPRE and prove that it is HRA secure without random oracle. Finally, we show that our UCPRE is efficient and rational compared to other existing CPRE schemes by instantiating our UCPRE.  
universal conditional proxy re-encryption; grained access control; homomorphic encryption +
Footnote †: journal: Applied  
to solve the issue of access control about data delegation. CPRE permits the proxy to convert the ciphertexts holding a condition specified by the data owner Alice into the ciphertexts for Bob. Therefore, the CPRE scheme is a more flexible PRE scheme since it enables fine-grained delegation of decryption rights in practical scenarios.  
Although the existing CPRE schemes achieve fine-grained delegation for outsourced encrypted data. However, these schemes are not suitable for real data sharing scenarios. In the actual applications, the data owner has encrypted his all data via a popular homomorphic encryption scheme (e.g., the Elgamal encryption) and stored the encrypted data in the cloud server before deciding to share the data with the data users. When conditional PRE schemes are used in the above case, the data owner needs to download the encrypted data and decrypt the ciphertext using his secret key, and re-encrypt the data using the data users' public key. In other words, the data owner needs to keep online in actual data sharing. Therefore, the challenge is how to design a mechanism that allows the proxy to transfer the ciphertext under a homomorphic encryption scheme into the ciphertext under a (possibly different) homomorphic encryption scheme.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Implementation Details  
We implemented our proposed attention Res-UNet depth network using the Python programming language. The workstation setup is summarized in Table 4. The code was built and implemented using Python programming language with CUDA TensorFlow 2.2.0.  
Our encoder was composed of four original Res-Net-blocks with no pretrained weights. All the network training was started from default random weights. In all experiments, we used the Adam optimizer [54] with a learning rate of 0.001 with default parameter values of alpha and beta. The batch sizes were set to 8 and 16. The total number of trainable parameters for the entire network was 32.6 M.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 4 Experiments
### Analogous Experiment for Very High Frequency  
#### 4.2.1 Experimental Design  
To verify the performance of the SR and CD-SR methods in the very high frequency range (30-300 MHz), the thickness of the silicon die is required to be tens of microns, which undoubtedly has high requirements on the production accuracy. Considering the analogy between acoustics and electricity, the propagation of ultrasonic waves in a die is analogous to the propagation of electromagnetic waves in a coaxial cable, and the reflection of ultrasonic waves caused by the difference in acoustic impedance on the surface of the die is analogous to the reflection of electromagnetic waves caused by the difference in electrical impedance at the two ends of the coaxial cable. Therefore, echoes with different degrees of overlap can be obtained simply by changing the length of the coaxial cable. The experimental device is shown in Figure 14. The CTS VF428 200 MHz acoustic lens-focused transducer (CTS, Lisle, IL, USA) was excited with a high-frequency transmitting circuit. The receiving circuit and the Keysight MSO9254A oscilloscope (Keysight, Santa Rosa, CA, USA) were used to amplify and acquire the AMI signal (lens echo), respectively. The coaxial  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(d\) (\(\mu\)m) & \(\delta\) (\%) & \(A_{err(x1)}\) (\%) & \(P_{err}\) (\%) \\ \hline
250 & 50 & 4.12 & 8.48 \\
200 & 60 & 2.68 & 3.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconstruction errors of CD-SR.  
cable was located between the transducer and the receiving circuit. When the length of the coaxial cable is zero, the reference echo \(x_{0}\) was obtained. The FWTM duration \(T\) of the electromagnetic wave is 12.0 ns, and the properties of the coaxial cable are shown in Table 4. Three cases were designed by changing the length \(d\) of the coaxial cable. Figure 15a-c are the cases where the overlap degree is \(-25\%\), \(50\%\) and \(60\%\), respectively.  
Figure 14: The 200 MHz experimental device.  
Figure 13: Comparison of reconstructed echoes: (a) \(\delta=50\%\); (b) \(\delta=60\%\).
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.3 Mass Sensing
The doubly clamped nanomechanical resonators with mass as small as 25 fg were embedded in the middle of a 2D PhC nanocavity. Due to the strong optomechanical interaction in the nanoscale slot nanobeam cavity, a displacement sensitivity of 0.94 fm/\(\sqrt{\text{Hz}}\) was realized. Therefore, this scheme was favorable for the high-resolvable mass sensor due to its reduced mass of the resonator itself.
## 4 Discussion  
In this study, the different electrochemical performances of Co\({}_{3}\)O\({}_{4}\)-1, Co\({}_{3}\)O\({}_{4}\)-2 and Co\({}_{3}\)O\({}_{4}\)-3 are determined by their own structural features. For Co\({}_{3}\)O\({}_{4}\)-1, mass collapsed structures accompanied the loss of opening pores, which were caused by ultrathin shells, while severely reducing the available surface area, as observed from a SEM image (Figure 2g). In contrast, during the fabricated process of Co\({}_{3}\)O\({}_{4}\)-2 and Co\({}_{3}\)O\({}_{4}\)-3, morphologies were well inherited not only in synthesis of Co-LDH from a ZIF-67 precursor but also in conversion of Co-LDH to Co\({}_{3}\)O\({}_{4}\) (negligible aggregation). The hollow nature as well as the highly porous architecture (the shell is assembled by numerous nanoparticles) provide a large electrochemical active area with increased active sites for redox reactions. In addition, the unique morphology also significantly facilitates electron transportation and ion diffusion and permeation in electroactive material. The relatively lower electrochemical performance of Co\({}_{3}\)O\({}_{4}\)-3 electrode is possibly due to the decreased accessibility of electrolyte ions into material produced by the large size and thick shell [38; 39; 40]. Table 1 compares this work with other Co\({}_{3}\)O\({}_{4}\) based electrodes reported in previous literature. It clearly demonstrates that the specific capacitance of hollow Co\({}_{3}\)O\({}_{4}\) nanoboxes electrode in this research is competitive, and even higher than some Co\({}_{3}\)O\({}_{4}\) hybrid materials. More importantly, the Co\({}_{3}\)O\({}_{4}\) electrode reaches a specific capacitance of 1324.5 F/g at a high current density of 20 A/g. The value at 20 A/g is superior to some Co\({}_{3}\)O\({}_{4}\) electrodes at 1 A/g. Except for the two criterions, a slight capacitance decay of 14.1% after 5000 cycles is also worth noting.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 4 Experiments
### Experimental Settings  
We implement our method by using Pytorch framework and one GTX 3080 Ti GPU. The MGDFormer network was trained with the batch size of 256 for 50 epochs. The initial learning rate is set to \(5.0\times 10^{-4}\) and, decreasing it to \(5.0\times 10^{-5}\) after the 25 th epoch, we choose the Adam optimizer [37] and use the default parameters (\(\beta_{1}=0.9\) and \(\beta_{2}=0.99\)). In addition, we applied a weight decay of 0.0001 to avoid the overfitting problem. We evaluated our proposed MGDFormer on the LIDC-IDRI dataset using a 10-fold cross-validation method, where 9 folds were used for training and the rest for testing. With regard to evaluation criteria, we also adopt the unified standard as previous works for a fair comparison, namely AUC, accuracy, precision, and sensitivity. The specific metrics are listed as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{11}\]  
\[F1-Score=\frac{2 TP}{2 TP+FN+FP} \tag{12}\]  
\[AUC=\int_{0}^{1}\eta_{tp}(\eta_{fp})d\eta_{fp}=P(X^{l}>X^{f}) \tag{13}\]  
As shown in Equations (9)-(13), where \(FN,FP,TP\), and \(TN\) indicate the number of false negative, false positive nodules, true positive, and true negative, \(\eta_{fp}\) is the false positive rate and \(\eta_{tp}\) is the true positive rate. \(X^{t}\) and \(X^{f}\) denote the confidence scores for a positive and negative sample, respectively.
## Priority-Based Protocol Extension Method
### Data Priority Delineation  
After the frame type is extended, different data can be transmitted through different kinds of frames and pipes, and the priority of the data is determined according to the timeliness of the data. The priority of different data is shown in Table 5. The output delay in the table represents the time difference between the data time stamp and the data output time. Figure 4: Application extension frame structure. Figure 3: (**a**) GB/T-26865.2-2011 data frame structure; (**b**) GB/T-26865.2-2011 command frame structure. The priority 1 (P1) data includes PHASORS, FREQ, DFREQ, and analog quantities requiring time synchronization. Based on the national grid enterprise standard Q/GDW 11202.6-2014 \"The test specifications for automation equipment in smart substation Part 6: Phasor measurement unit\", the output delay should be within 50 ms. The priority 2 (P2) data includes the remote control command. As can be seen from Table 1, the one-way communication delay of vertical network protection as the protection application is less than 100 ms, and the one-way communication delay of advanced distribution automation as the control application is less than 500 ms. The communication medium for protection and control applications should use Ethernet Passive Optical Network (EPON) (delay value 1.5 ms) or an industrial Ethernet (delay value 10 ms) with a low network delay, and the output delay should be within 90 ms. However, if there is only a control application and the communication mode is a wireless private network (delay value 30-300 ms), the output delay should be within 200 ms, and the table is set to 90 ms. The priority 3 (P3) data includes telemetry and telesignalization summoned by the main station, changed data, and Sequence Of Event (SOE). Generally, when the voltage changes by 1%, the current changes by 1% or 5%, it will be actively sent, and the output delay is within 200 ms. \begin{table}
\begin{tabular}{c l l} \hline \hline  & **Types of Files** & **Description** \\ \hline
1 & Dynamic data & Generate one per minute, record real-time data \\ \hline
2 & Transient recording & Common format for transient data exchange (COMTRADE) format, recording transient data \\ \hline
3 & Continuous recording & COMTRADE format, 3-h continuous recording data \\ \hline
4 & Statistical data & Daily statistics, monthly statistics \\ \hline
5 & Alarm data & Device abnormal alarm and line abnormal alarm \\ \hline
6 & Extremum data & Daily extremum data, monthly extremum data \\ \hline
7 & Reactive power & Record the relevant data when the capacitor is operated when the device has the reactive switching function \\ \hline
8 & Remote operation data & Record the remote operation process and remote control point number and time when opening/closing the switch \\ \hline
9 & Fault record data & Record the fault voltage, fault current and time of the corresponding line when the line fails \\ \hline
10 & energy data & Fixed point storage of electric energy data \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Types of files.