esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wire rope will have defects such as local faults (LF) and loss of metal area (LMA) during the long-term using process. The nondestructive testing method of magnetic flux leakage (MFL) has been widely used in wire rope defect detection. Currently, the detecting distance between magnetic sensors and wire rope with the MFL method is relatively small (2-5 mm). Considering the inner surface of the sensor head is close to the wire rope, it quickly leads to the sensor head scraping off the surface oil of the wire rope or being stuck by a cut wire in the course of MFL detection. Therefore, it is challenging to realize the sensor with MFL detection of wire rope obtaining the weak signal at a large lift-off (>30 mm). This study used finite element analysis to verify if the MFL signal exists at the large lift-off (>30 mm). Meanwhile, the sensor head was improved using an open magnetizer to make the wire rope reach saturation and excite enough magnetic leakage field. By combining magnetic sensing and coupling and a weak analog signal processing method, not only was the signal effectively detected, but also the signal-to-noise ratio (SNR) was improved. Finally, experiments verify the feasibility of detecting defects at a large distance. The method also has been applied in the high-speed detection of wire rope, which can detect broken wire of 1 mm diameter.  
magnetic flux leakage (MFL); large lift-off; coil array coupling; analog signal processing; high-speed; signal-to-noise ratio (SNR)
## 4 Experiment Results and Analysis
### Subjective Evaluation  
We conducted this experiment with 10 subjects (8 males, 2 females) with normal vision without visual disabilities. The goal of this subjective evaluation was to compare the visual inspection of the results of the three saliency cuts methods because every person has a unique visual system. It is a very important experiment in terms of generating tactile graphics for visually impaired because tactile graphics must be simple and understandable. We provided 100 various types of images that had single and multiple objects to 10 subjects. Each image file consisted of the result of the three saliency cuts methods and ground truth image. The subjects were asked to assess the results of the three saliency cuts methods with respect to ground truth images by providing scores on \"foreground extraction accuracy\", \"clear edges of objects\", \"multiple object detection\", \"amount of noise introduced\", and \"false object detection\". The subjects scored each item on a scale from 1 to 5--1 (very bad), 2 (bad), 3 (average), 4 (good), and 5 (very good) in 15 s overall, with 5 s per each of the saliency cuts methods. We informed the subjects that the answer to the \"amount of noise introduced\" and \"false object detection\" should be \"very good\" for only the best results. Furthermore, we prevented any help or discussion with each other by experimenting with the individuals one by one. As the results of subjective evaluation, Table 2 shows that foreground extraction accuracy of the proposed approach was very high. This was because locally adaptive triple-thresholding produced a reliable binary mask by assigning a different threshold value to each pixel. The proposed method and ATT_Cuts were able to detect multiple objects in a scene, whereas RC_Cuts failed to do so. In summary, the proposed method achieved the best overall results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **RC\_Cuts [17]** & **ATT\_Cuts [16]** & **Proposed Method** \\ \hline
**Precision** & 0.93 & 0.90 & **0.94** \\ \hline
**Recall** & 0.77 & **0.82** & 0.80 \\ \hline
**F-measure** & 0.89 & 0.88 & **0.90** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative analysis of three saliency cuts methods performed using MSRA 10k dataset.  
Figure 12: Quantitative comparisons of saliency cuts approach using MSRA 10k dataset.
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 4 PALANTIR General Architecture for Threat Remediation
### Coordination of the Remediation Procedure  
When a threat is detected and classified by an external detection tool, the IR component is notified with the main characteristics of the threat to handle. The devised reactions may comprise either the reconfiguration of existing SCs in the managed networks or the deployment of new ones, as well as the exploitation of other services in the PALANTIR infrastructure. The IR component is able to adapt the generic operations defined in the recipes to the status of the network, such as the presence of a specific SC needed to deploy a recipe (e.g., the iptables packet filter). The PALANTIR-protected infrastructure hosts multiple IR instances that are responsible for triggering mitigation policies, right after a threat/attack related to data breach is detected. The objective of the IR is to handle remediation policies for threat mitigation, especially those that cannot be handled by, e.g., the cross-system or stakeholder notifications, which may be specific to an individual entity (SME/ME). IR implements predefined Finite State Machines (FSMs) to tackle specific incidents. This approach, and those handling personal policies, are explained in Section 5.2.  
When a threat is detected and classified by an external detection tool, the IR component is notified with the main characteristics of the threat to handle. Reactions may comprise the reconfiguration of existing SCs in the managed networks, the deployment of new SCs, or the exploitation of other services in the PALANTIR infrastructure. The tool is able to adapt the generic operations defined in the recipes to the status of the network, such as the presence of a specific SC needed to deploy a recipe, e.g., the iptables packet filter. PALANTIR-protected infrastructure hosts multiple IR instances that are responsible for triggering mitigation policies when a threat/attack related to data breach is detected. The objective of the IR is to handle remediation policies for threat mitigation, especially those that cannot be handled by, for instance, the cross-system or stakeholder notifications, which may be specific for an individual entity (SME/ME). IR implements the predefined FSMs to tackle with the specific incident. The usage of the FSM approach and how IR handles personal policies is explained in Section 5.2.
# Combination Pattern Method Using Deep Learning for Pill Classification
## 3 The Proposed Pill-Classification System
### Color and Shape Detection  
The identification of pills also heavily relies on distinguishing features such as color and shape. For example, Amaryl pills, used in diabetes management, share the same shape and size but are differentiated by color based on dosage. We employ a CNN model to detect these features, focusing mainly on color because of its significance in the identification of the pill. The CNN's ability to process and classify images based on learned features allows it to effectively separate pills that appear similar but differ in critical attributes like color. This section details how the CNN model is specifically trained to recognize and categorize pills based on these attributes, providing a foundational step in the classification process.  
The CNN model utilized for color detection has also been adapted for shape identification, leveraging the robust feature-detection capabilities of CNNs. This approach is supported by extensive research in the field. References [42; 43] applied neural-network-based methods to detect pills in images. References [44; 45] focused on identifying pills in images portraying multiple different pills. Reference [46] aimed to detect the presence of pills in blister images for pill tracking, as well as identify pills in images of the mouth. Reference [47] attempted to learn potential associations between pills using information extracted from prescriptions. Although the problems covered in previous studies differ from the present study's objective, our work aims to complement the results of these studies by presenting an accurate and efficient pill-identification method.  
In this study, two primary methods are implemented to classify pills using the CNN framework. The first method processes entire images as input, dividing them into multiple  
Figure 5: YOLOv5-based pill-detection step for pill classification.  
layers and applying a specific filter to each layer. This technique demonstrates high accuracy but requires extensive processing time and significant data. The second method focuses on extracting key features from pill images and using these as direct inputs to the CNN. This method significantly reduces processing time and increases accuracy by concentrating on essential information extracted directly from the pills.  
After verifying the accuracy of both methods, we introduced an additional feature-extraction process tailored for pills that are difficult to classify based on standard attributes. This enhancement improves the robustness of our pill-classification system, allowing for more precise identification across a broader range of pill types and conditions.
## 3 Results  
### Engine Performance Characteristics  
#### 3.1.1 Brake Power (BP)  
The performance of CI diesel engines relies on the characteristics of the fuel utilized for the testing engine and fuel injection system. The fuel characteristics include kinematic viscosity, density, oxygen content, and calorific value [48,49]. Figure 2 shows the brake power (BP) of _Moringa oleifera_ biodiesel blends and diesel at different engine speeds. According to the results, BP increases progressively with engine speed until 2200 rpm and then decreases. Consequently, diesel fuel has the highest BP (5.43 kW) at 2200 rpm.  
In contrast, the MOB20 blend has the lowest BP (4.68 kW). The average BP is higher for diesel than MOB10 and MOB20 by 12.18% and 17.32%, respectively. The average BP is lower for MOB20 than MOB10 and diesel by 6.85% and 7.17%, respectively. This may be attributed to the larger HHV of biodiesel blends [50]. The MOB10 blend has the highest HHV in comparison with other biodiesel blends examined in this study. Besides, the fuel's physicochemical properties affect the spray formation during fuel injection, which in turn, affects combustion [51]. Lower viscosity and density of the MOB10 blend may result in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Description** & **Specifications** & **No.** & **Description** & **Specifications** \\ \hline
1 & Engine Model & TF 120M & 5 & Compression ratio & 17.7:1 \\
2 & Displacement (cm\({}^{3}\)) & 638 & 6 & Maximum power & 8.8 kW/2400 rpm \\
3 & Bore (mm) & 92 & 7 & Cooling system & Water cooling \\
4 & Stroke (mm) & 96 & 8 & Fuel system & Pump line nozzle \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of diesel engine [50] (Adapted with permission from the Elsevier).  
Figure 2: Variation in BP with engine speed for biodiesel blends and diesel.  
loss of engine power due to more significant fuel pump leakage than other fuel blends [52]. Generally, fuels with higher viscosities can reduce fuel pump leakages [53].
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 6 Conclusions  
In this paper, the element phase condition is determined when the MIMO system's n-order channel capacity reaches its maximum value. Moreover, the optimal placement of the 2 \(\times\) 2 and 3 \(\times\) 3 transceiver antennas is proposed in the LoS non-scattering environment, which provides basic understanding of \(n\times n\) optimal antenna placement. The impact of different model parameters on the channel capacity is investigated, including array element spacing, wavelength, and the distance between transmitted and receiving antenna arrays. This theory applies to a channel without scattering and multipath, which is mainly used in short-range LoS communication and a non-scattering environment communication, such as coupling train communication and inter-satellite communication. The simulation results show that we can choose an appropriate signal wavelength and the array element spacing at a certain propagation distance. Moreover, the 2 \(\times\) 2 antenna array is more suitable for the communication of coupling trains, while the 3 \(\times\) 3 antenna array is more suitable for virtual coupling trains according to proposed antenna configurations. Furthermore, the 2 \(\times\) 2 antenna rectangular configuration proposed in this paper has reached the optimal channel in inter-satellite communication, which lays a foundation for the deployment of communication systems. However, according to the theory proposed in this paper, the optimal channel can still be achieved in larger scale MIMO systems, but it needs to correspond to different antenna configurations, which needs to be studied further.  
Conceptualization, K.J. and G.Z.; data curation, K.J. and X.W.; formal analysis, K.J.; funding acquisition, G.Z. and Y.J.; investigation, K.J. and X.W.; methodology, K.J. and G.Z.; project administration, K.J. and A.S.; resources, K.J. and G.Z.; software, K.J.; validation, K.J.; writing--original draft, K.J.; writing--review and editing, K.J., A.S., G.Z. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61871261) and Natural Science Foundation of Shanghai (22 ZR1422200).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Preliminary Theory and Related Work
### Time-Delay Embedding  
The Takens Embedding Theorem states that for a \(k\)-dimensional dynamical system, if the time series of a single variable \(x(t)\) is measured, a delay embedding space with an appropriate embedding dimension \(m\) (where \(m\geq 2k+1\)) can be constructed as an m-dimensional Hankel-type delay embedding matrix:  
\[\mathbf{H}=\begin{bmatrix}x(t_{1})&x(t_{2})&\cdots&x(t_{n})\\ x(t_{2})&x(t_{3})&\cdots&x(t_{n+1})\\ \vdots&\vdots&\ddots&\vdots\\ x(t_{m})&x(t_{m+1})&\cdots&x(t_{m+n-1})\end{bmatrix}. \tag{3}\]  
Here, the data matrix \(\mathbf{H}\) can be viewed as an m-dimensional trajectory measured at n different time snapshots. Given an attractor, a sufficiently high-dimensional time delay embedding can produce an embedding that is diffeomorphic to the original attractor, meaning that the two are geometrically equivalent. This implies that a sufficiently high-dimensional time delay embedding can almost completely capture the dynamic characteristics of the original system.  
In many related studies, there is limited discussion on the selection of embedding dimension. Some papers have uniformly chosen an embedding dimension of 100 in their experiments [17]. These experiments have found that this embedding dimension can indeed balance computational speed and the general applicability of the embedding dimension. In this paper, we will select the embedding dimension based on reconstruction accuracy (MSE) and reconstruction performance.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
###### Abstract  
Over the past few years, the modeling of wireless channels for radio wave propagation over the sea surface has drawn the attention of many researchers. Channel models are designed and implemented for different frequencies and communication scenarios. There are models that emphasize the influence of the height of the evaporation duct in the marine environment, as well as models that deal with different frequencies (2.5, 5, and 10 GHz, etc.) or the impact of various parameters, such as antenna height. Despite the increasing literature on channel modeling for the over-the-sea marine environment, there is no comprehensive study that focuses on key concepts that need to be considered when developing a new channel model, characteristics of channel models, and comparative analysis of existing works along with their possible improvements and future applications. In this paper, channel models are discussed in relation to their operational principles and key features, and they are compared with each other in terms of major characteristics and pros and cons. Some important insights on the design and implementation of a channel model, possible applications and improvements, and challenging issues and research directions are also discussed. The main goal of this paper is to present a comparative study of over-the-sea channel models for radio wave propagation, so that it can help engineers and researchers in this field to choose or design the appropriate channel models based on their applications, classification, features, advantages, and limitations.  
over-the-sea communication; radio wave propagation; wireless channel; channel model; wireless channel characterization +
Footnote †: journal: Applied Sciences
## 2 Linear or Linear-like Formulations of Nonlinear Systems
### Takagi-Sugeno Fuzzy Model  
The Takagi-Sugeno fuzzy (TSF) model is a technique that locally linearizes a nonlinear system to linear subsystems. After that, fuzzy if-then rules are applied to choose and blend those linear subsystems for particular cases. The Takagi-Sugeno fuzzy models are given as [25]  
The \(i\)th rule is expressed as  
If \(\xi_{1}\) is \(M_{i1}\) and \(\dots\) and \(\xi_{q}\) is \(M_{iq}\), then  
\[\dot{\mathbf{x}}=\mathbf{A}_{i}\mathbf{x}+\mathbf{B}_{i}\mathbf{u},\ \mathbf{y}= \mathbf{C}_{i}\mathbf{x},\ i=1,2,\cdots,p \tag{13}\]  
where \(\xi_{i}\) are premise variables which are functions of the state variables; \(M_{ij}\) is the fuzzy set; \(p\) is the number of If-then rules; \(q\) is the number of the premise variables; \(\mathbf{A}_{i}\), \(\mathbf{B}_{i}\), and \(\mathbf{C}_{i}\) are state, input, and output matrices of the linear state-space of the \(i\)th rule.  
Thus, the fuzzy systems are inferred as Equation (2), where:  
\[\mathbf{A}=\sum_{i=1}^{p}h_{i}(\xi)\cdot\mathbf{A}_{i},\ \mathbf{B}=\sum_{i=1}^{ p}h_{i}(\xi)\cdot\mathbf{B}_{i},\ \mathbf{C}=\sum_{i=1}^{p}h_{i}(\xi)\cdot\mathbf{C}_{i} \tag{14}\]  
in which  
\[h_{i}(\xi)=(\prod_{j=1}^{q}M_{ij})/(\sum_{i=1}^{p}\prod_{j=1}^{q}M_{ij}) \tag{15}\]  
Note that the stability of the system is proved using Lyapunov function [26].
# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 2 Related Works
[21] propose to combine the proxy of ISP and image evaluation network to achieve end-to-end tuning for HDR algorithms. [PERSON] et al. [22] use an attention mechanism to tune specific images or scenes. By utilizing the correlation between various parameters in ISP and their structural information, the parameters are divided into disjoint groups to achieve tuning under grouped parameters. Nonetheless, none of the aforementioned methods incorporate the prior information of ISP into the training process of the proxy. Specifically, ISP is treated solely as a black box that only exposes parameters, without integrating the relationships between modules within the pipeline and between modules and parameters into the design process of the proxy network. Instead, they solely utilize an end-to-end methodology for training. In the AI-ISP field [23], the staged network fitting method [14] outperforms the end-to-end network fitting method [22] in terms of image processing, as it takes into account the correlation between different components of the ISP. Therefore, by considering the relationship between ISP modules, the ISP is divided into two steps: reconstruction and enhancement. This approach facilitates separate learning of the image processing effects across different types of modules by the proxy, thereby avoiding parameter interference and proxy training difficulties caused by end-to-end methods. To compare the impact of staged proxy on image quality with that of end-to-end proxy, it is necessary to evaluate image quality from multiple dimensions, including color, noise, structure, and features. Metrics such as histogram correlation(HC) and color coherence vector (CCV) are commonly utilized to evaluate chromaticity differences between images [24; 25; 26]. Metrics like structure similarity index measure (SSIM) and feature similarity index measure (FSIM) are commonly utilized to evaluate structural and feature differences between images [27; 28]. Metrics like peak signal-to-noise ratio (PSNR) and mean squared error (MSE) are commonly utilized to evaluate pixel-level differences, encompassing multi-level information such as structure and color [29; 30]. The advantage of phased proxy over end-to-end proxy lies in the ability to optimize image structure and color information separately during the reconstruction and enhancement phases. Therefore, when evaluating proxy performance, optimized SSIM [31] for assessing structure and HC for evaluating color differences are used as evaluation criteria. This paper is based on the proxy method and decouples the ISP pipeline into two parts: reconstruction and enhancement. Using ReEn-GAN, the influence of style transfer on downstream CV tasks and human subjective perception is controlled by adjusting ISP hyper-parameters to achieve ISP tuning for certain downstream tasks, and the proxy performance can be evaluated by the above-mentioned optimized SSIM and HC.
## 3 Experimental Setup and Results
### Measurement System
#### 3.1.4 WirelessHART Transceivers  
To continuously monitor PD, it is pivotal to have robust interfacing of PD sensors to the wireless network. For this reason, WirelessHART IEC 62591 has been used as wireless communication technology. Wireless HART provides a continuous PD monitoring with the option of scalability if the scope of the system deployment gets bigger. It is a low power, low cost and easy to install communication system that is based on IEEE 802.15.4. It is a self-forming multi-hop mesh technology. The technology is specifically designed for harsh industrial environments.