esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results, Analysis, and Discussion
### Attenuation Coefficient
Moreover, it was observed that the standard deviations from the average values of AC were similar for both cases (0.068 and 0.067). The biggest differences between two analyzed test sections were noticed during the summer months (V-VIII). Analysis of the correlation between AC and daily temperature difference in the ventilation cavity (Figure 10) revealed that for lower daily external temperature attenuations (up to 20 K), values of AC were more differentiated, while with the increase of \(\Delta\)T7, their values became more convergent. It can be also noted that maximum AC for both cases was independent of temperature alterations. On the other side, the minimum values of AC rose for higher values of \(\Delta\)T7. Figure 10: Dependence of AC on daily external temperature fluctuations. Figure 9: The AC values calculated for two test sections: PCM—modified with PCM (P4), REF—reference one (P6).
## 4 Result and Discussion  
### Experimental Setup  
#### 4.1.1 Hardware and Software Environment  
* System: Windows 11;
* CPU: Intel Core i5-13600 KF (Intel, Santa Clara, CA, USA);
* GPU: NVIDIA GeForce RTX 3060 (NVIDIA, Santa Clara, CA, USA);
* RAM: 32 GB;
* Program Language: Python 3.8.20;
* Program Framework: PaddlePaddle.  
Figure 5: The observation space is divided based on the grid map and consists of the following four information channels: the goal layer, obstacle layer, A* layer, and neighbor AGV layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Reward** \\ \hline AGV reaches goal & 10 \\ AGV takes any action & \(-0.1\) \\ AGV follows the A* path to take action & 0.1 \\ Collision or invalid move & \(-2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reward scheme.
## 3 Materials and Methods
### Activation Functions  
The activation functions also have a very essential responsibility in designing deep learning models. The selection of the activation function depends upon the types of input data and the category of classifications. In this work, we utilized Leaky ReLU and ReLU activation functions, and we compared these with the most commonly used activation functions, sigmoid and tanh, as visualized in Figure 6.  
Figure 5: Building blocks of proposed hybrid residual/inception-based deeper model (HRIDM).  
* _Leaky ReLU:_ We employed the Leaky ReLU activation function for the present study, which offers strong benefits for ECG classification. It is important to analyze several activation functions that are particular to our task and dataset. Negative values in ECG signals are frequently indicative of certain types of cardiac activity. Leaky ReLU ensures that neurons continue to contribute to learning features from the data by keeping them from going into inactive states as a result of these negative inputs. Leaky ReLU is computationally more efficient than tanh and sigmoid, which is advantageous for training deeper and bigger neural networks using ECG data [47; 48]. Leaky ReLU, in contrast to ReLU, keeps a little non-zero gradient for negative inputs. This feature might be useful in applications where it is important to detect even minute deviations from normal cardiac rhythm in order to capture minor changes in ECG patterns. Leaky ReLU can be mathematically expressed by Equation (17) [48]: \[f(x)=\left\{\begin{aligned} x,&\quad x\geq 0\\ ax,&\quad\text{otherwise}\end{aligned}\right.\] (17)
* _ReLU (Rectified Linear Unit):_ This is simple and effective in terms of computing; it promotes sparsity by generating a large number of zero outputs. In comparison with sigmoid and tanh, it allows models to converge more quickly during training. But it has the \"Dying ReLU\" issue, which prevents learning by allowing neurons with negative inputs to be stuck at zero indefinitely. The mathematical definition of the ReLU activation function is given by Equation (18) [48; 49]: \[f(x)=\left\{\begin{aligned} x,&\quad x\geq 0\\ 0,&\quad x<0\end{aligned}\right.\] (18)
* _Sigmoid:_ The sigmoid function reduces input values to the range [0, 1]. It is especially effective for binary classification problems that require probabilistic outputs. The function has a smooth gradient over its full range, allowing for effective gradient-based optimization during training. The sigmoid activation may be mathematically described as illustrated in Equation (19) [49]: \[f(x)=\frac{1}{1+e^{-x}}\] (19)
* _Tanh (Hyperbolic Tangent)_: The tanh function converts input values to the range [\(-1,1\)]. Similar to the sigmoid function, its output is zero-centered, which can help  
Figure 6: Comparison of output responses for ReLU, Leaky ReLU, sigmoid, and tanh activation functions.  
neural networks converge. The tanh activation function is mathematically stated as follows in Equation (20) [48]:  
\[f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{20}\]
## 3 Results and Discussion
### Cylinder Pressure  
Figure 6 illustrates the cylinder pressure observed across several engine settings, namely, diesel (D100), DP20, DP20H5 (5% hydrogen), DP20H10 (10% hydrogen), and DP20H15 (15% hydrogen). According to the graphical representation, it can be seen that the cylinder pressure of the DP20H5, DP20H10, and DP20H15 engines surpasses that of the D100 engine across all test settings. The declared minimum cylinder pressure for diesel fuel is 76.7 bar; however, the DH15 sample has a maximum cylinder pressure of 90.5 bar at 23.5\({}^{\circ}\) b TDC. The experimental results indicate that the diesel fuel mix containing 15% hydrogen exhibited the greatest pressure while operating at maximum capacity, with a recorded value of around 15.2%. This study examines the impact of hydrogen addition on the fluctuation in cylinder pressure during combustion, in comparison to the use of D100 fuel. Noticeable alterations are seen in diesel fuel that includes 10% and 15% hydrogen. The cylinder pressure exhibits an increase at different engine settings when a hydrogen propellant is introduced [30; 31]. The heating value of hydrogen is 120 MJ/k, which is 64.5% more than the heating value of diesel fuel, which is 42.5 MJ/kg.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 3 U-Shaped Roadway Model and Reflection Path Validity
### Valid Mirror Points
#### 3.1.1 Mirror Point Calculation  
A three-dimensional coordinate system was established as shown in Figure 1. The coordinates of the transmitting point are T(\(x_{t}\), \(y_{t}\), \(z_{t}\)), the receiving point is R(\(x_{r}\), \(y_{r}\), \(x_{r}\)), the mirror point is I(\(x_{i}\), \(y_{i}\), \(z_{i}\)), and the equation of the reflecting plane \(m\) is set as \(Ax+By+Cz+D=0\).  
A plumb line of reflection plane \(m\) was made through emission point T, and the foot of the plumb line was F(\(x,y,z\)). I is the mirror image point of emission point T in reflection plane \(m\), as shown in Figure 2. From the coordinates of the emitting point T and the pendant point F, the linear equation of TF is  
\[\frac{x_{t}-x}{A}=\frac{y_{t}-y}{B}=\frac{z_{t}-z}{C}=k \tag{1}\]  
Because the pendant point F is the midpoint of line TI, the following relationship is satisfied:  
\[\left\{\begin{array}{l}x=\frac{x_{t}+x_{i}}{y_{t}+y_{t}}\\ y=\frac{y_{t}+y_{t}}{2}\\ z=\frac{x_{t}+z_{i}}{2}\end{array}\right. \tag{2}\]  
According to the pendant point F both on the line TI and in the plane \(m\), and using (1) and (2), the equation of plane \(m\) can be obtained:  
\[k=\frac{Ax_{t}+By_{t}+Cz_{t}+D}{A^{2}+B^{2}+C^{2}} \tag{3}\]  
Figure 1: U-shaped roadway model diagram.  
Figure 2: Calculation of mirror points.  
Substituting \(k\) into (1) yields the coordinates of the pendant point F:  
\[\left\{\begin{array}{l}x=\frac{(B^{2}+C^{2})x_{l}-ABy_{l}-ACz_{l}-AD}{A_{l}^{2} +B_{l}^{2}+C^{2}}\\ y=\frac{-ABx_{l}+(A_{l}^{2}+C^{2})y_{l}-BCz_{l}-BD}{A_{l}^{2}+B^{2}+C^{2}}\\ z=\frac{-ACx_{l}-BCy_{l}+(A_{l}^{2}+B^{2})z_{l}-CD}{A^{2}+B^{2}+C^{2}}\\ \end{array}\right. \tag{4}\]  
Finally, (4) is substituted into (2) to obtain the coordinates of the mirror point I(\(x_{i}\), \(y_{i}\), \(z_{i}\)):  
\[\left\{\begin{array}{l}x_{i}=\frac{(B^{2}+C^{2}-A^{2})x_{l}-2 ABy_{l}-2 ACz_{l} -2 AD}{A_{l}^{2}+B^{2}+C^{2}}\\ y_{i}=\frac{-2 ABx_{l}+(A_{l}^{2}+B^{2})y_{l}-2 BCz_{l}-2 BD}{A_{l}^{2}+B^{2}+C^{ 2}}\\ z_{i}=\frac{-2 ACx_{l}-2 BCy_{l}+(A^{2}+B^{2}-C^{2})z_{l}-2 CD}{A^{2}+B^{2}+C^{2} }\\ \end{array}\right. \tag{5}\]
## 4 Expert System and Decision Support System
_Distinction between Decision Support System and Expert System_  
DSS and ES applications in domains requiring decision-making have increased significantly [19] but while DSS and ES are intended as strategies for enhancing decision-making, it is also true that there are strong and substantial variations between them. To recognize  
Figure 6: Basic structure of a neural expert system. Figure 7: The architecture of a neuro-fuzzy inference system. these variations, DSS and ES are compared in four primary areas: priorities and targets; organizational differences; users; and development methodology [19]. Priorities and Targets: DSS and ES essentially have the same fundamental goal; they aim to enhance decision efficiency. Nevertheless, their fundamental philosophies and priorities are quite different. The philosophy of DSS is to help the user decide by providing fast and easy access to appropriate data and models for that decision. ES, on the other hand, provides the user with a judgment that is always right. Since this is not feasible, an ES output standard is usually acceptable, similar to or better than the experts. Organizational Differences:--DSS provides the user with the ability to modify data and templates in several ways when going through the decision-making process to solve an issue in a versatile way. With ES, relevant segments of the knowledgebase are implemented based on the inference engine, control method durations, domain-specific heuristics and knowledgebase data. Users: Another major difference exists between the typical users. DSS is primarily implemented in a company or organizational environment. These are targeted at the efficacy of individuals in decision-making within a business organization. DSS is for a limited community of users. Expert systems, on the other hand, are generally concerned with science as opposed to business activities; users are predominantly scientists or scholars in a specific field of study. Development Methodology:--DSS and ES are usually developed using a prototype or adaptive approach. Basic information and organizational specifications are defined when creating a working prototype. The selection of DSS approach is based on three fundamental principles: a high degree of user participation, offering flexibility to the user's needs, and functional early-stage decision support. ES, on the other hand, is developed through refinement and extension of the knowledgebase to advance towards higher output levels.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy-Preserving Defense Strategies
#### 4.2.5 Differential Privacy  
Differential Privacy (DP) is a technique widely used to preserve privacy in industry and academic domains. The main concept of DP is to preserve privacy by adding noise to sensitive personal attributes. In FL, DP is introduced to add noise to clients' uploaded parameters in order to avoid inverse data retrievals. For instance, the DPFedAvgGAN framework [52] uses DP to make GAN-based attacks inefficient in training data inference of other users for FL-specific environments. Additionally, [PERSON] et al. [53] improve privacy guarantees of the FL model by combining the shuffling technique with DP and masking user data with an invisibility cloak algorithm. However, such solutions bring uncertainty into the uploaded parameters and may harm the training performance.
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
###### Abstract  
In photovoltaic systems, parasitic capacitance is often formed between PV panels and the ground. Because of the switching nature of PV converters, a high-frequency voltage is usually generated over these parasitic capacitances; this, in turn, can result in a common-mode current known as leakage current. This current can badly reach a high value if a resonance circuit is excited through the PV's parasitic capacitance and the converter's inductive components. Transformers are usually used for leakage current mitigation. However, this decreases the efficiency and increases the cost, size, and weight of the PV systems. Number of strategies have been introduced to mitigate the leakage current in transformer-less converters. Among these strategies, using common-ground converters is considered the most effective solution as it offers a solid connection between the negative terminal of PV modules and the neutral of the grid side; thus, complete mitigation of the leakage current is achieved. Number of common-ground inverters have been recently presented. These inverters are different in their size, cost, boosting capability, the possibility of producing DC currents, and their capability to offer multilevel shaping of output voltage. This work introduces a comprehensive review and classification for various common-ground PV inverters. Therefore, a clear picture of the advantages and disadvantages of these inverters is clarified. This provides a useful indication for a trade-off between gaining some of the advantages and losing others in PV systems. In addition, the potentials for optimization based on different performance indicators are identified.  
photovoltaic; transformer-less; leakage current; common-ground; grid +
Footnote †: journal: Applied Sci.
## 5 Results and Analysis  
### Progressive Training  
The progressive training strategy adopted in this study is tailored specifically for remote sensing images. Its iterative approach aids the model in learning image features more effectively, enhancing both the generalization performance and training stability when handling large-scale images. The gradual increment in input image sizes entails dealing with larger and more intricate images. However, larger images often contain additional structural details, potentially leading to challenges like gradient explosion or disappearance during training. Progressive training mitigates these complexities by allowing the model to gradually acclimate to different input image sizes. This gradual adaptation avoids overwhelming the model with the intricacies of large images at the initial stages, thus enhancing training stability. Furthermore, the progressive increase in input image size compels the model to assimilate richer and higher-level features, fostering improved feature learning and enhancing the model's generalization capability. Moreover, progressive training mitigates the risk of the model converging to a suboptimal solution early in training. This approach facilitates the exploration of the global optimal solution or a solution proximal to the optimal one.  
Figure 5 illustrates the variation in PSNR and SSIM across the four-round training process. It is seen that the model exhibits consistent improvement in performance not only within each training round but also demonstrates a coherent progression across various training rounds. These training outcomes underscore the efficacy of the progressive training strategy in consistently enhancing the model's reconstruction performance.  
Figure 5: _Cont._
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Experimental Elements  
#### 2.2.1 Element Presentation  
Given the constraints of human and material resources for onsite landscape evaluation, landscape photographs were selected as substitutes for direct environmental investigations [55]. To ensure the subjects' perceptions closely align with real-world experiences, virtual scenes, which could distort visual perception, were avoided [56; 57; 58]. Consequently,  
Figure 2: Study areas and landscapes: (**a**) The location of Dalian in Liaoning Province; (**b**) The block of the three villages in the city of Dalian; (**c**) The location of the three villages in their respective blocks; (**d**) Specific locations of the three types of villages.  
original images were utilized in this study, with all images sized at 1920 \(\times\) 1080 pixels, rather than edited landscape photographs. A SONY A6000 SLR (SONY, Tokyo, Japan) camera was employed to capture landscape samples from a distance of 8 m and at a height of 1.6 m from the edge of the object space. The photos were taken from November 2023 to May 2024, resulting in a total of 360 images.  
During the photo-selection process, five experts classified the images according to the following procedure: (1) Feature Annotation: Reviewed 360 photographs, extracted salient keywords for landscape elements (such as buildings, water bodies, vegetation, roads, etc.), and annotated each image's core attributes. (2) Unrestricted Classification: Grouped images sharing similar annotated features. (3) Consolidation: Considering overlap or redundancy among the 360 photographs, provisionally sorted them into four categories--architecture, water, vegetation, and roads--with ten images per group. (4) Validation and Refinement: Experts scored and cross-referenced each category, reducing each group to five or six images. (5) Representativeness Assessment: Based on each village's characteristics, retained the three most representative images per category. This process yielded a structured library of 12 landscape images from three villages to support subsequent experimental research. To mitigate potential bias from priming effects, four additional images were chosen as warm-up stimuli for the experiment (as shown in Figure 3).
# Analysis of a Tracked In-Pipe Robot's Obstacle-Crossing Performance
## 4 Discussion  
This study addressed the challenges of pipeline maintenance by designing an in-pipe robot that integrates both active and passive variable-diameter mechanisms, in accordance with the current state of robot development. Based on the specific usage background and requirements, the pipe-diameter adaptation mechanism of the robot was meticulously designed and analyzed through mechanical modeling. The torque formula for the lead-screw motor was derived. According to the formula, when the robot adapts to the maximum diameter of 340 mm, \(\alpha\) is 69.7\({}^{\circ}\) and \(\beta\) is 76.2\({}^{\circ}\). When adapting to the minimum pipe diameter of 260 mm, \(\alpha\) is 15.8\({}^{\circ}\) and \(\beta\) is 23.4\({}^{\circ}\).  
By constructing the obstacle mechanical model and the in-pipe pose model of the pipeline robot, the obstacle-crossing performance of the robot was thoroughly analyzed. The theoretical maximum obstacle-crossing height was determined to be 7.5 mm. The obstacle-crossing performance was tested through both ADAMS simulation and prototype experiments. Results indicated that the robot's maximum obstacle-crossing height is 7.8 mm, which meets the design requirements and provides a solid foundation for further development and research of this robot.  
Conceptualization: L.Y.; formal analysis: F.L.; funding acquisition: L.Y.; investigation: P.L., L.Y. and F.L.; methodology: F.L.; resources: L.Y.; software: F.L.; visualization: F.L.; writing--original draft: P.L.; writing--review and editing: G.L., L.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 52375470 and 52005455; and the Central Guidance on Local Science and Technology Development Fund of Shanxi Province, grant number YDZJSX2022C005.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors acknowledge the technical support from the North University of China.  
The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analysis, or interpretation of data; in the writing of this manuscript; or in the decision to publish these results.
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 2 Related Work
### Decentralized Storage Network  
The cloud service is provided by a trusted third party. The data permission is controlled with a centralized mechanism. With the blockchain innovation, the recent study shows that the decentralized storage network is viable [1; 2; 3; 4]. In the decentralized storage network, the user is not required to trust any providers, just the cryptography. It brings enormous confidence to the data owner.  
The decentralized storage network is ideally built upon a permission-less blockchain. Blockchain miners provide the storage resource. The content that a user uploads to the blockchain is kept by miners. The blockchain makes the storage network permission-less. The miners can freely join or quit.  
In the decentralized storage network, permission control must be cryptographic since the storage providers are semi-trusted. The existing public-key crypto is required to specify the target user to decrypt before encryption. Beyond this, PRE allows users to add the target users by re-encrypt the existing ciphertext into a new one. It is identical to permission granting at the application level. Thus, PRE schemes are helpful for blockchain storage.  
However, a decentralized storage network must periodically check content integrity. The malicious miner may chet by fetching content from other honest miners and responding to the checking. The cheater is committing to keep the content but never spending the storage resource. A decentralized storage network must be resilient to the outsourcing attack. Proof-of-replication requires the miners to encode the user content into the replication with each miner's unique identity. It is intended to make the encoding more expensive. The miner is willing to save the replication on disk, as it is impossible to fetch the content from other miners and finish the encoding in the limited time.
# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 2 Contextualisation
### MIoT Security and Privacy
There are efforts to improve the scalability, resilience and trust mechanisms offered by DLT through a technology called IoT application (IOTA) [70; 71; 72; 73], a next-generation blockchain solution. Other noteworthy concepts associated with healthcare, and which use technologies to sustain additional communication and remote management features, include employing patient health information (PHI) and storing it under electronic health records (EHR). Some authors have also included so-called healthcare systems, such as electronic medical recording (EMR), order communication systems (OCS), and picture archiving and communication systems (PACS) [41]. These systems must comply with underlying cyber security IS, such as IDS, firewall/filtering, security information and event management (SIEM), and continuous cyber security monitoring and logging practices [74]. IoT and MIoT are targets for a host of cyber-attacks [35; 62; 75]. As [PERSON] et al. (2019) [48] commented, approximately 45% of all ransomware attacks dating 2017 were directed at the healthcare sector. In 2018, cybercriminals deployed WannaCry ransomware and Zero-day attacks in healthcare facilities in the UK's National Health Service (NHS), encrypting all data in unpatched systems [76; 77]. [PERSON] et al. (2018) [75] discussed IoT security and TM in detail, highlighting attack venues explored by threat actors. For instance, they highlighted issues such as DoS, physical threats, eavesdropping, node capture and compromisation. Their work focused on modelling IoT-enabled cyber-attacks by understanding: (i) adversaries (access, motivations, capabilities); (ii) IoT devices (embedded system vulnerabilities, network vulnerabilities); and (iii) actual targets connectivity (direct, indirect, no connection to critical infrastructure).