esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 3 Energy Consumption Model for Sensor Networks
### Energy Consumption Model for Power Transfer  
Line loss is the heat loss caused by the current on the resistance when it flows through the cable. For example, \(E\) (_Joule_) energy is transferred from Node A to Node B, and the distance between the two nodes is \(L\) (_meter_), as shown in Figure 4.  
Suppose that the voltage on the power line is \(u\) (\(V\)), the current is \(i\) (\(A\)), and the resistance of the line is \(\rho\) (\(\Omega\cdot\text{m}^{-1}\)). The power of energy transfer is \(P=u\times i\), and the energy transfer time of \(E\) (_Joule_) by constant current is as follows  
\[t=\frac{E}{P}=\frac{E}{u\times i}, \tag{4}\]  
The total resistance of the power line is \(R\)  
\[R=2L\rho, \tag{5}\]  
The energy loss is calculated as  
\[E_{\text{loss}}=i^{2}Rt=\frac{2\rho i}{u}\times L\times E, \tag{6}\]  
It can be seen that if energy is transferred by constant current and voltage, line loss is proportional to the transfer energy. For example, suppose the node operates at 5 V and the adjacent section delivers power at 5 mA, providing 25 mW of power. If the cable resistivity is 1 \(\Omega\cdot\text{m}^{-1}\), and the two nodes are connected with a 10 m power line. The line loss of the transmitted energy is \(E_{\text{loss}}=2\%E\). In practical applications, the voltage and current of the power transmission are selected according to the operating voltage and power consumption of the node. In any case, the energy loss caused by line loss is very limited.  
Figure 4: Energy transfer model.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
###### Abstract  
In order to improve the subjective and objective consistency of image sharpness evaluation while meeting the requirement of image content irrelevance, this paper proposes an improved sharpness evaluation method without a reference image. First, the positions of the edge points are obtained by a Canny edge detection algorithm based on the activation mechanism. Then, the edge direction detection algorithm based on the grayscale information of the eight neighboring pixels is used to acquire the edge direction of each edge point. Further, the edge width is solved to establish the histogram of edge width. Finally, according to the performance of three distance factors based on the histogram information, the type 3 distance factor is introduced into the weighted average edge width solving model to obtain the sharpness evaluation index. The image sharpness evaluation method proposed in this paper was tested on the LIVE database. The test results were as follows: the Pearson linear correlation coefficient (CC) was 0.9346, the root mean square error (RMSE) was 5.78, the mean absolute error (MAE) was 4.9383, the Spearman rank-order correlation coefficient (ROCC) was 0.9373, and the outlier rate (OR) as 0. In addition, through a comparative analysis with two other methods and a real shooting experiment, the superiority and effectiveness of the proposed method in performance were verified.  
image sharpness; no-reference; eight-neighborhood algorithm; edge width; distance factor 1  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Improved Method for Evaluating Image Sharpness Based on Edge Information. _Appl. Sci._**2022**, 12, 6712. [[https://doi.org/10.3390/app1236712](https://doi.org/10.3390/app1236712)]([https://doi.org/10.3390/app1236712](https://doi.org/10.3390/app1236712))  
Academic Editor: [PERSON]  
Received: 8 June 2022  
Accepted: 30 June 2022  
Published: 2 July 2022  
**Published:** **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 3 Proposed Method
### Extraction Process  
The extraction process is described as follows:  
1. Considering the color watermarked image \(I_{\rm w}\), perform RGB-YCbCr color model transformation and obtain the watermarked luminance \(Y^{\prime}\)(\(x\),\(y\));
2. Compute the 2D DFT transform \(F^{\prime}\)(\(u\),\(v\)) from \(Y^{\prime}\)(\(x\),\(y\)) and get the watermarked magnitude \(M^{\prime}\)(\(u\),\(v\));
3. The annular area \(A\) is computed employing the values of radiuses \(r_{1}\) and \(r_{2}\) used in the embedding process;
4. Split \(M^{\prime}\)(\(u\),\(v\)) in two parts, the upper half, and the lower half respectively;
5. By symmetrical DFT properties, considering only the information from the upper half part of \(M^{\prime}\)(\(u\),\(v\)), the embedded watermark can be extracted using the secret key \(k_{3}\), computing the linear correlation \(c_{b}\) between the normalized watermarked magnitude coefficients \(M^{\prime}\) normalized and \(b\)-\(th\) pseudorandom pattern \(g_{b}\) as follows: \[c_{b}=\sum_{b=1}^{L}\big{(}(g_{b}-\hat{g}_{b})\cdot M^{\prime}_{normalized} \big{)},\] (9) where \(\hat{g}_{b}\) is the average of all values in \(g_{b}\) and \(M^{\prime}_{normalized}\) = \(M^{\prime}-M^{\prime}_{\rm av}\), where \(M^{\prime}_{\rm av}\) is the average of all values in \(M^{\prime}\)(\(u\),\(v\));
6. Recover the watermark data bits of \(W^{\prime}\) = {\(w^{\prime}{}_{b}\)\(|b\) = 1,..., \(L\)} using the sign function as follows: \[if\ sign(c_{b})\ is\ ``+\"\ then\ w^{\prime}{}_{b}=0,\ otherwise\ w^{\prime}{}_{b}=1;\] (10)
7. Finally, rearrange \(W^{\prime}\) using the secret key \(k_{2}\).
## 4 Discussion
### EHI and EGI Assessment
#### 4.1.2 EGI Assessment  
In this study, an indicator system comprising the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), and Gross Primary Production (GPP) was developed to assess the vegetation cover of the Inner Mongolia Autonomous Region. The distribution pattern of the NDVI exhibited a decreasing trend from the northeast to the southwest, with the southwest region showing generally lower NDVI values. The only area with high NDVI values was the woodland region under the Yinshan Mountain Range, which aligns with previous NDVI studies in Inner Mongolia [58; 59; 60]. After normalizing the machine learning-derived LAI values, the results indicated higher values in the woodland areas, with all other regions displaying moderate to low values, which is consistent with the general vegetation distribution in Inner Mongolia [61]. The GPP values were also normalized, revealing low values concentrated in the desert grasslands, deserts, Gobi, and lakes. This pattern of primary productivity mirrors the findings of previous studies [62; 63; 64]. The spatial distribution patterns of the greenness index closely resembled those of NDVI, LAI, and GPP, aligning with a study that assessed the ecological quality of Inner Mongolia using REEI [65]. Unlike earlier studies, which reported null values (no data) for LAI and GPP in large unvegetated areas, such as deserts and the Gobi in western Inner Mongolia, this study assigned a value of 0 to all LAI and GPP data points in such areas for the calculation of the EGI. The resulting greenness index was compared with REEI results from related studies, and the distribution patterns were largely similar, except in the vicinity of the Yinshan Mountain Range, where land use changed from forested areas in 2020 [65] to cropland and construction land in 2022. This shift, along with forest degradation and accelerated urbanization, contributed to a decreased Ecological Greenness Index.
## 2 Materials and Methods
### Measurements  
We conducted measurements on the samples featuring Ta buffer and capping layers, as well as on those with W buffer and capping layers. We examined the crystalline structures and chemical compositions of the samples with Ta and W buffer and capping layers using X-ray diffractometer (XRD; X'Pert PRO, PANalytical, Almelo, The Netherlands) and X-ray photoelectron spectrometer (XPS; PHI VersaProbe III, ULVAC-PHI, Kanagawa, Japan), respectively. We observed the magnetic properties of each sample by varying the temperature and taking measurements. Diverse magnetic properties were characterized at different temperatures, with the samples analyzed for both static and dynamic magnetic properties. Static magnetic properties, such as \(M_{\mathrm{s}}\), were measured using a VSM (LakeShore 7404, Lake Shore Cryotronics Inc., Westerville, OH, USA). Dynamic magnetic properties, including \(\alpha\), were evaluated in the frequency range of 5-20 GHz using FMR spectroscopy. Both VSM and FMR spectroscopies were performed in the in-plane direction. VSM measurements were conducted at temperatures ranging from room temperature (293 K) to 550 K. Values at temperatures beyond 550 K were determined using the \(T^{1/3}\) power law [52], represented as:  
\[M_{\mathrm{s}}(T)=M_{0}\times\left(1-\frac{T}{T_{M_{\mathrm{s}}=0}}\right)^{ \frac{1}{3}} \tag{1}\]  
where \(M_{0}\) and \(T\) represent the \(M_{\mathrm{s}}\) values at 0 K and absolute temperature, respectively. To analyze the temperature dependence of \(\alpha\), we performed FMR spectroscopy measurements in the temperature range of 306-443 K using a field-sweep technique and a coplanar waveguide. The frequency range of 5-20 GHz was utilized for the setup. The raw FMR spectra were fitted using the derivative of a Lorentzian line shape to determine the resonance field (\(H_{\mathrm{r}}\)) and peak-to-peak linewidth (\(\Delta H_{\mathrm{pp}}\)). We obtained the values of \(\alpha\) and \(H_{\mathrm{k}}\) from the extracted \(H_{\mathrm{r}}\) and \(\Delta H_{\mathrm{pp}}\).  
Figure 1: Schematics of the configurations for (**a**) Ta buffer and capping layers sample and (**b**) W buffer and capping layers sample.
## 4 Experiment 2: Amplitude Dependence of Vibration-Weight Illusion
### Results
#### 4.6.2. Perceived Heaviness at Different Amplitudes  
The ranking of the perceived heaviness against the three stimuli with different amplitudes is shown in Figure 3B. The stimuli with higher amplitudes were perceived as heavier. The stimuli with the amplitude of 203 \(\upmu\)m were perceived as significantly heavier than the other stimuli (203 \(\upmu\)m vs. 156 \(\upmu\)m: \(t(10)=67.5\), \(p=0.0018\), 203 \(\upmu\)m vs. 177 \(\upmu\)m: \(t(10)=75.5\), \(p=0.0145\), Wilcoxon signed-rank test with Bonferroni correction), but there was no significant difference between the amplitudes of 156 \(\upmu\)m and 177 \(\upmu\)m.  
Figure 4: Grip force over time during Experiment 2. The red lines and dashed black lines indicate the means and range of standard errors, respectively. Stimuli started at 0 s and lasted 3 s. For the analysis of the change in grip force, means of 0.5 s before the stimulus (\(-\)0.5–0 s) and after the stimulus (1–1.5 s), which are surrounded by the black and green dashed lines, respectively, were used.  
Figure 5: Results of Experiment 2. Three different stimuli with different amplitudes but the same frequency (30 Hz) were used. (**A**) Increase in grip force for three different amplitudes. (**B**) Ranks of perceived heaviness. A higher rank number indicates a perception of greater heaviness. * and ** indicate significant differences at the 5% and 1% levels, respectively. There was a positive correlation between the increase in grip force and perceived heaviness (\(\rho=0.77\), \(p=1.37\times 10^{-7}\)).  
4.6.3. Correlation between Grip Force and Perceived Heaviness at Different Amplitudes  
The increase in the grip force and perceived heaviness were positively correlated (\(\rho=0.77\), \(p=1.37\times 10^{-7}\), [PERSON]'s rank correlation). The grip forces and perceived heaviness increased with the amplitude of the vibration when the frequency was constant.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 2 Related Theoretical Foundations
### Hypergraph Model Definition with Association Rules  
Combining the descriptions in Sections 2.1 and 2.2, we can correspond the hypergraph \(H=(V,E)\) with association rules. For the hypergraph \(H=(V,E)\), each vertex \(v_{i}\) corresponds to an item in the dataset, and the hyperedge \(e_{i}(i=1,2,\ldots,m)\) is a transaction that contains an itemset, the vertex set denoted as \(V(e_{i})\) (also called \(V\)), and the hyperedge set denoted as \(E(e_{i})\). Therefore, the support of the itemset \(X\) is  
\[support(X)=\sigma(X)/n \tag{2}\]  
where the support count of itemset \(X\) (\(X\subseteq E\)) is \(\sigma(X)=|\{t_{i}|X\subseteq t_{i},t_{i}\in T\}|\).  
Let the association rule \(r\) discovered in itemset \(X\) is \(A\Rightarrow B\), where \(A\subseteq X\), \(B\subseteq X\), then the confidence of this association rule is found by  
\[confidence(r)=\sigma(A\cup B)/\sigma(A) \tag{3}\]  
If the itemset \(X\) satisfies the minimum support threshold, the itemset \(X\) is denoted as a frequent itemset and a hyperedge \(e_{i}\) is constructed by a frequent itemset, so the weight of hyperedge \(e_{i}\) is defined as follows:  
\[weight(ei)=\sum_{r}confidence(r)/R \tag{4}\]  
where \(R\) is the number of association rules discovered in itemset \(X\).  
Figure 2 shows an elemental example of constructing a hypergraph based on association rules, where the dataset is represented by binary 0/1, and the minimum support is set to 60%.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Key Space and Key Sensitivity  
The key space represents the set of all possible keys that an encryption algorithm can use. To ensure a stronger level of security against brute force attacks, the key space of an image encryption algorithm should be larger than \(2^{100}\)[64].  
Key sensitivity analysis assesses the image encryption algorithm's sensitivity to small changes to the key. A robust and secure algorithm should produce two completely different cipher images when encrypted by two keys that are different by only one bit. Furthermore, it should not be able to decrypt a cipher image and retrieve the plaintext image with a key that has one bit altered [65].  
To evaluate an encryption algorithm's key sensitivity for encryption, researchers encrypt a plaintext image with two keys (\(k_{1}\) and \(k_{2}\)) that differ in only one bit and compare the resulting cipher images. For the case of evaluating key sensitivity for decryption, researchers attempt to decrypt a cipher image that is encrypted with key \(k_{1}\) using key \(k_{2}\), which differs in only one bit. If the algorithm is sensitive to changes in the key, it should not be able to decrypt the cipher image with the altered key.
# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 3 Proposed Method
### Fine Network
#### 3.3.2 Architecture of Fine Network  
As shown in Figure 1, fine network is designed as a flexible encoder-decoder architecture, with an encoder, a series of residual blocks, and a decoder. The encoder contains a convolutional layer with a stride of 1 and three convolutional layers with strides of 2, followed by several residual blocks. The decoder contains three upsampling layers, three convolutional layers with strides of 1, and three SSF modules. We use an SSF module before each upsampling layer. Given the content image \(x_{c}\) as the input of fine network, the encoder and several residual blocks generate the content feature \(f_{c}\). Then, SSF modules generate the fused features \(f_{ssf}\) by taking \(\overline{f}_{r}^{(i)}\) and \(f_{cs}\) as inputs, where \(f_{cs}\) is the output of these convolution layers in the decoder (except the first SSF module, which takes \(f_{c}\) as \(f_{cs}\)). These fused features \(f_{ssf}\) are fed into an upsampling layer and a convolution layer. Finally, the decoder generates the final stylized image \(x_{cs}\) after the last convolution layer.
## 4 Experiments
### Ablation Study  
In this section, a series of ablation studies are conducted on the DTU dataset to validate the effectiveness of each component. The official point cloud reconstruction metrics provided by the DTU dataset are employed as experimental benchmarks [38], with default input image specifications set at 864 \(\times\) 1152. Control variable methodology is employed to isolate the impact of individual components on the overall network performance, ensuring that other components are evaluated under the same experimental conditions.
## 1 Introduction
The reduction rates of the average, root-mean-square, and maximum contour errors achieved were 96.27%, 95.92%, and 94.76%, respectively. The machine tool thread milling experiment results indicated that the PCB-ILCC could provide good helical contour accuracy even though the feed drive axes suffered the adverse influence of the friction disturbance and cutting force. The reduction rates of the average, root-mean-square, and maximum contour errors achieved 90.56%, 89.68%, and 83.35%, respectively. Therefore, the experimental results validated that the PCB-ILCC can effectively mitigate the adverse influence of the friction disturbance and cutting force to significantly improve the contour accuracy of the feed drive axes while performing the helical path in thread milling operations. This paper is organized as follows. Section 2 describes the design of the PCB-ILCC and the difference between the PCB-ILCC and conventional ILCC designs. Section 3 presents the CEV estimation method and the ILCC design with previous cycle learning. Section 4 presents the PMSM driving control design for the feed drive axes with robust motion performance. Section 5 presents the experimental results performed on a PMSM testbench and a five-axis CNC machining center to demonstrate the feasibility and performance of the PCB-ILCC developed in this study. Section 6 concludes this paper.
## 2 State of the Art
### Safety and Security Engineering  
Safety and security aim to mitigate system risks but target distinct hazards. Safety addresses unintentional risks arising from system vulnerabilities, aiming to prevent accidents, and is categorized into product safety (focused on fault-free operation, including the safety of the intended functionality, SOTIF) and functional safety (addressing faults or malfunctions) [11]. Security mitigates intentional threats to E/E components of cyber-physical systems (CPS). It comprises functional security (protecting software from unauthorized access, modification, or suppression during development and operation) and information security (safeguarding data from unauthorized access) [11]. The foundational standards underlying the method to be developed are ISO 26262:2018 [9] and ISO/SAE 21434:2021 [12]. The identification of functional safety requirements for vehicle components is described in ISO 26262:2018 [9], while cybersecurity requirements are identified within the framework of ISO/SAE 21434 [12]. While the focus of this paper is on the cybersecurity framework developed specifically for the automotive industry, it is crucial to mention the General Data Protection Regulation (GDPR) [13] and the California Consumer Privacy Act (CCPA) [14] due to the fact that private data are collected during vehicle operation. [PERSON] et al. point out that the application of GDPR is required and must be carried out together with the UN ECE R155 and R156 [15]. A possible GDPR-centered analysis for CAVs is shown by [PERSON] et al. [16].
## 5 IO Data Management and Data Sharing
### Distributed SQL Databases with RPC
#### 5.1.1. Replicated Data Bases and Tables
Typically, 10-20 epochs are required to derive a suitable model. Figure 9.5 Results of the total computation times, with a distributed-parallel cluster, with shell workers processing multi-model CNN-ML training using a different number of distributed and parallel workers PN. HPG3: NC = 4, HPZ620: NC = 12. Figure 9.6 Results of the total computation times, with a distributed-parallel cluster with web workers, processed on smartphones and performing multi-model CNN-ML training with different numbers of distributed and parallel workers: (**a**) SQL data loading times; (**b**) ML training times (1 iteration = 1 epoch). In [30], the ML problem consisted of independent multi-instance learning and multi-instance inference, with independent and partitioned input data, i.e., basically a distributed and localized multi-model ML system (finally deployed in a distributed sensor network) with global model fusion. The PSciLab framework was used to train all models in parallel. Again, a linear scaling-up to the number of cores _NC_ was observed (an HPZ620 platform was also used). In this case study, the problem size was static and was fixed with a similar partition schema, as used in the parallel simulation. To summarize, multi-model ML training scales up nearly linearly with the number of worker processes, up to the number of CPU cores. Hyper-threading provides no additional improvement when using VM processing. Both web browser and node.js-based processing using the V8 core VM of ML tasks perform equally. In contrast to shared-memory-based simulation, parallel ML tasks do not rely significantly on the memory architecture of the host platform. The data exchange times (input, intermediate, output) using SQLite data bases are negligible compared with the training times of medium and large-size models (typically, a ratio of 1:100). Even one SQLite service scales up well under parallel data stream processing; multi-data-base services can improve scaling.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## The APD-TDMA  
### Network Model  
We consider a MUASN that has a centralized topology as shown in Figure 1, in which UNs transmit their sensing data to a SN periodically. We assume that each UN is equipped with half-duplex underwater acoustic modem that has a single omnidirectional sensor, and that the velocity of each UN is included in the periodical data packet, which enables the SN to track UNs. We also assume that the SN is fixed at the sea surface and equipped with the global positioning system (GPS), and that UNs are located within the communication range of the SN. This scenario is a representative one of MUASNs. To be specific, command ship and autonomous underwater vehicles (AUVs) correspond to the SN and UNs, respectively. It is essential in operating AUVs to use underwater acoustic positioning systems such as long baseline (LBL), short baseline (SBL), and ultra-short baseline (USBL). In addition, AUV can measure velocity using navigation system with the aid of a gyroscope, an inertial measurement unit (IMU), and doppler velocity logs (DVLs).  
The proposed APD-TDMA has two phases: the initialization phase and the data transmission phase. The SN gets the position of UN in the initialization phase. To this end, the SN broadcasts a very short initialization request (IRQ) packet to UNs, and UNs respond to this IRQ packet. The SN checks collisions and repeats those procedures until the SN attains all UNs' positions. In the data transmission phase, the SN broadcasts a beacon packet that contains UNs' transmission schedule, and UNs transmit their data packet complying with the schedule specified by the received beacon packet. The SN updates UNs' position and re-calculates the transmission schedule from newly received data packets before the broadcast of a new beacon packet. If the number of data packet collisions at the SN is greater than a certain value, the SN re-broadcasts an initialization signal. The procedure for these two phases is detailed in following subsections.