esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Approach
#### 2.3.3 Support Vector Machine  
SVM is frequently employed in ML modeling of regression or categorization issues [59]. SVR employs similar categorization techniques as the SVM, along with some minor modifications [59; 60; 61; 62; 63]. Throughout the regression instance, a range of tolerance (\(\varepsilon\)) is given to the SVM as a prediction of what the issue might have previously required. In addition, an additional intricate factor should be considered because the technique is more complex. The SVM technique determines the optimal hyperplane for splitting data into many groups with the greatest margin. \(f(x)\) is employed to, as closely as possible, mimic the training dataset (\(x_{i}\),\(y_{i}\)), where \(i\) = 1, 2,..., \(N\), and \(x_{n}\) is a multivariate collection. In its simplest form, the function \(f(x)\) is represented as [59]:  
\[f(x)=wx+b \tag{5}\]  
Minimizing the following formula [59] yields optimality for the values of \(w\) and \(b\):  
\[\min\frac{1}{2}\|W\|^{2}+C{\sum_{i=1}^{N}}(\xi_{i}+\xi_{i}^{*}) \tag{6}\]  
Subject to:  
\[\left\{\begin{array}{l}y_{i}-wx_{i}-b\leq\varepsilon+\xi_{i}\\ wx_{i}+b-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i}\xi_{i}^{*}\geq 0\end{array}\right. \tag{7}\]  
where \(\xi\) is an \(\varepsilon\)-insensitive tube expressing error tolerance and \(C\) is a balance among the empirically-predicted error and generic term. Using the optimal constraints along with Lagrangian multipliers, the regression function may be stated as below [59]:  
\[y={\sum_{i=1}^{N}}(\alpha_{i}+\alpha_{i}^{*})K(x_{i},x)+b \tag{8}\]  
where \(K(x_{i}x)\) is referred to as the kernel function. The linear, polynomial, sigmoidal, Gaussian, and radial basis functions are some of the most well-known kernel functions that are commonly used with SVR.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 2 Imaging System Based on Tandem SAR Satellites  
The proposed system consists of two satellites flying successively in the same orbit. The distance between two satellites is far enough to ensure that they do not interfere with each other. Both satellites adopt a scanning mode, but the scanning methods are different. The introduction is presented in detail as follows.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 3 Numerical Analysis
### Mistuned Case
#### 3.3.1 Mistuning Parameter  
Suppose the random blade mistunings are distributed uniformly with zero mean and 3% standard deviation for main blades and 1% standard deviation for splitter blades. The random mistuning pattern is shown in Table 2.  
In the finite element model of the impeller, as shown in Figure 2, the mistuning is introduced by modification of the Young's modulus of each blade. The mistuned elasticity modulus \(E_{j}^{i}\) of the \(j\)th blade of the \(i\)th sector can be expressed as:  
\[E_{j}^{i}=E_{t}\big{(}1+\delta_{j}^{i}\big{)}, \tag{30}\]  
where \(E_{t}\) denotes the tuned elastic modulus and \(\delta_{j}^{i}\) denotes the relative deviation of the \(j\)th blade of the \(i\)th sector.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Sector (\(j\))**} & **Main (\(i\)=1)** & **Splitter (\(i\)=2)** \\  & \(\mu\)=0; \(\sigma\)=3\% & \(\mu\)=0; \(\sigma\)=1\% \\ \hline
1 & \(-\)0.01888 & \(-\)0.01663 \\
2 & \(-\)0.02435 & \(-\)0.01169 \\
3 & 0.02238 & \(-\)0.01838 \\
4 & \(-\)0.0248 & \(-\)0.00623 \\
5 & \(-\)0.00794 & 0.01857 \\
6 & 0.02415 & \(-\)0.01397 \\
7 & 0.01329 & \(-\)0.01736 \\
8 & \(-\)0.00281 & \(-\)0.00715 \\
9 & \(-\)0.02745 & \(-\)0.01031 \\
10 & \(-\)0.02789 & \(-\)0.00973 \\
11 & 0.02054 & 0.00431 \\
12 & \(-\)0.02824 & \(-\)0.00622 \\
13 & \(-\)0.02743 & 0.01315 \\
14 & 0.00088 & \(-\)0.00824 \\
15 & \(-\)0.01802 & 0.01873 \\
16 & 0.02149 & 0.00892 \\
17 & 0.00469 & 0.01815 \\ \hline \end{tabular}
\end{table}
Table 2: Random mistuning pattern (\(\delta_{j}^{i}\)).  
Figure 6: Harmonic content of the RMV of mode 94.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 14--Heterogeneous Infrastructure  
Unlike the emergence of the Internet, which focuses solely on online services and applications, the IIoT integrates various infrastructures and services in a more scalable and usable paradigm to better serve the society beyond its connectivity. An IIoT system can include IoT, cloud computing [127], fog computing [128], mobile computing [129], and industrial networks [130]. This helps with the more widespread distribution of information not only for human users but also for the billions of pervasively interconnected smart devices.  
Notably, IoT device by themselves, in isolation, cannot compute, analyze, or store the massive amounts of data collected from the physical environment. This, in other ways, advances the dependency of the IoT over the infrastructures mentioned above. The integration of IoT and such infrastructures are challenging and introduce numerous security and privacy issues for them. For example, in a heterogeneous IoT-based healthcare system, a patient's health-related records are captured via a blipcare (a Wi-Fi blood pressure monitor) and transfer information to the cloud-based infrastructure in the hospital. Doctors can view the record using their smartphone, which uses a mobile cloud infrastructure. Further, a doctor can generate health alerts to the patient's family members, who are connected over a social network infrastructure with the doctor. Therefore, any of these stages of data collection, their transmissions, processing, analysis, storage, and sharing of medical information with others are vulnerable to attackers who can breach user's personal information by penetrating any of the communication networks between the infrastructures [131; 132].  
This heterogeneous infrastructure and service introduce trust-related attacks for users as well as for organizations by making possible the disclosure of a user's identity and unauthorized data access. We argue that, while global connectivity is important for IIoT, it is also significant to ensure security in local areas to minimize the overall impact of an attack [133].
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 5 Results  
### Qualitative Results  
Based on the pre-trained weights of TBD and STAVOS, we continued training for an additional 400 epochs on the Data-medaka-Ventral and Data-medaka-Lateral-right training sets, with the learning rate adjusted to \(1\times 10^{-5}\). Similarly, we conducted 400 epochs of training using U-Net [28] on the entire medaka dataset.  
We conducted a qualitative comparison of our method with U-Net and TBD. As shown in Figure 6, it can be observed that U-Net can only identify the central position of the medaka ventricle. This limitation arises from U-Net being a static image segmentation network incapable of utilizing information features from consecutive frames in the video, resulting in poor segmentation performance. TBD shows a significant improvement in segmentation compared to U-Net, but the accuracy of segmenting the ventricular edge is not high enough. In contrast, STAVOS achieves further improvements in segmentation. It not only accurately identifies the position of the ventricle but also achieves precise contour segmentation. This is attributed to the STA module's ability to effectively extract edge features of the target.  
\begin{table}
\begin{tabular}{c c c} \hline
**Video Sequences** & **J\&F of TBD** & **J\&F of STAVOS** \\ \hline blackswan & 0.954 & 0.955 \\ bmx-trees & 0.622 & 0.698 \\ breakdance & 0.617 & 0.741 \\ camel & 0.838 & 0.839 \\ car-roundabout & 0.801 & 0.829 \\ car-shadow & 0.938 & 0.920 \\ cows & 0.949 & 0.951 \\ dance-twirl & 0.717 & 0.780 \\ dog & 0.943 & 0.946 \\ drift-chicane & 0.850 & 0.821 \\ drift-straight & 0.784 & 0.867 \\ goat & 0.875 & 0.897 \\ horsejump-high & 0.840 & 0.767 \\ kite-surf & 0.711 & 0.790 \\ libby & 0.887 & 0.878 \\ motorcross-jump & 0.760 & 0.757 \\ paragliding-launch & 0.656 & 0.735 \\ parkour & 0.917 & 0.918 \\ scooter-black & 0.640 & 0.836 \\ soapbox & 0.817 & 0.827 \\ \hline mean of all videos & 0.806 & 0.838 \\ \hline \end{tabular}
\end{table}
Table 2: Segmentation performance of TBD and STAVOS on video sequences from the DAVIS2016 validation set.
Sensors_**2013**, _13_, 16424-16450; doi:10.3390/s131216424  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
A Near-Optimal Distributed QoS Constrained Routing Algorithm for Multichannel Wireless Sensor Networks  
**[PERSON] 1, [PERSON] 1*, [PERSON] 2 and [PERSON] 2**  
1 Department of Information Management, National Taiwan University, No. 1 Section 4, Roosevelt Road, Taipei City 106, Taiwan; E-Mail: [EMAIL_ADDRESS]  
2 Department of Information Management, Shih Hsin University, No. 1, Lane 17, Mu-Cha Road, Section 1, Taipei City 116, Taiwan; E-Mails: [EMAIL_ADDRESS] (H.-H.Y.); [EMAIL_ADDRESS] (Y.-J.H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-2-2799-5768; Fax: +886-2-2799-2058.  
_Received: 28 October 2013; in revised form: 20 November 2013 / Accepted: 22 November 2013 / Published: 2 December 2013_  
**Abstract:** One of the important applications in Wireless Sensor Networks (WSNs) is video surveillance that includes the tasks of video data processing and transmission. Processing and transmission of image and video data in WSNs has attracted a lot of attention in recent years. This is known as Wireless Visual Sensor Networks (WVSNs). WVSNs are distributed intelligent systems for collecting image or video data with unique performance, complexity, and quality of service challenges. WVSNs consist of a large number of battery-powered and resource constrained camera nodes. End-to-end delay is a very important Quality of Service (QoS) metric for video surveillance application in WVSNs. How to meet the stringent delay QoS in resource constrained WVSNs is a challenging issue that requires novel distributed and collaborative routing strategies. This paper proposes a Near-Optimal Distributed QoS Constrained (NODQC) routing algorithm to achieve an end-to-end route with lower delay and higher throughput. A Lagrangian Relaxation (LR)-based routing metric that considers the \"system perspective\" and \"user perspective\" is proposed to determine the near-optimal routing paths that satisfy end-to-end delay constraints with high system throughput. The empirical results show that the NODQC routing algorithm outperforms others in terms of higher system throughput with lower average end-to-end delay and delay jitter. In this paper, for the first time, the algorithm shows how to meet the delay QoS and at the same time how to achieve higher system throughput in stringently resource constrained WVSNs.  
keywords: Wireless Sensor Networks; Wireless Visual Sensor Networks; system perspective; user perspective; QoS; Near-Optimal Distributed QoS Constrained (NODQC) routing algorithm; Lagrangian Relaxation +
Footnote †: journal: Journal of Computer Science
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 5 Opportunities for Further Research
### More Machine-Learning-Based Models  
With the rapid development of machine learning, new algorithms emerge to improve the model accuracy and computational efficiency. More models and parameters should be taken into consideration to solve the path loss prediction problem.
## 2 Adaptive Sensor Arrays
### LMS Algorithm  
The LMS algorithm is a method based on gradient search techniques, applied to mean square error functions, employing optimum solution of the Wiener-Hopf equation. The algorithm is based on the steepest descent method [31], in which changes in the weight vectors are made along the contrary direction of the estimated gradient vector. This can be described by:  
\[\mathbf{w}_{i,K}(n+1)=\mathbf{w}_{i,K}(n)-\mu\hat{\mathbf{\mathcal{V}}}(n) \tag{7}\]  
where \(\mu\) is a scaling constant that controls the rate of convergence and stability of the algorithm (adaptation step), and \(\hat{\mathbf{\mathcal{V}}}(n)\,\) is the vector gradient estimated from the quadratic error in relation to \(\mathbf{w}_{i,K}(\mathbf{n})\).  
The error is obtained between the output of the filter, (\(\gamma_{i}(n)=\mathbf{w}_{i,K}^{H}(n)\mathbf{x}(n)\)), and the reference signal, \(d_{i}(n)\), so that:  
\[\varepsilon_{i}(n)=d_{i}(n)-\,\mathbf{w}_{i,K}^{H}(n)x(n) \tag{8}\]  
The stop criterion or convergence criterion of the weights using LMS algorithm is the variation of the mean square error at each iteration, _i.e._:  
\[|\varepsilon_{i}{}^{2}(n)-\varepsilon_{i}{}^{2}(n-1)|<\xi \tag{9}\]  
where \(\xi\) ia threshold defined by the designer based on their application that limits the number of iterations.
## 3 Results and Discussion
### Yield and SDS-PAGE Pattern  
Acid-solubilized collagens were produced with a yield of 3.8%, 2.5%, and 1.6%, corresponding to _COLRp_I, COLRp_II_, and _COLRp_III_ (Figure 2A), showing yield differences related to the methodology used. The highest collagen yield was obtained with method I, which showed a similar yield to the one obtained with other marine species, namely _Diodon holocanthus_ balloon fish (4%) [46] and _Paracentrotus lividus_ sea urchin (4.93%) [50]. The purity of extracted collagen was evaluated by SDS-PAGE, showing a similar profile between the isolated fish collagen and the bovine collagen standard used as a reference for type I collagen. Figure 2B displayed the patterns of the isolated collagens from the _R. hippoglossoides_, confirming the presence of two alpha (\(\alpha\)1 e \(\alpha\)2) chain subunits with protein molecular weight nearby 130 kDa, a characteristic feature of type I collagen, and also observed a \(\beta\) dimer band with a protein weight of around 250 kDa. Typically, the \(\alpha\)1 chain had the highest intensity band, compatible with the ratio of 2 \(\alpha\)1 chains for 1 \(\alpha\)2 chain in each type I collagen molecule. However, _COLRp_II_ and _COLRp_III_ suggest having a decrease in the prevalence of \(\beta\) band compared to the _COLRp_I_ collagen with all fish collagens showing a more intense \(\beta\) band than bovine collagen, associated with the presence of intermolecular crosslinking. In the literature, the fish collagen (codfish) had a similar collagen pattern with investigational collagens (_R. hippoglossoides_) and weight of \(\alpha\) (\(\alpha\)1 e \(\alpha\)2) and \(\beta\) chains [(20)], a phenomenon also identified in other marine species as barramundi (_Lates calcarifer_), tilapia (_Oreochromis niloticus_) [(51; 52)], striped catfish (_Pangasianodon hypophtalamus_) [(53)], and small-spotted catshark (_Scyliorhinus canicula_) [(48)].
## 5 Discussion
### Problems in Applying the MBSE Approach  
MBSE is suitable for creating custom models of complex technical systems that can select and describe the relationships between components, functions, processes, and requirements according to the concepts of systems engineering [148]. MBSE supports an effective approach for providing definitions, communication, clarity, and adaptability within systems engineering projects, as well as assessing the cost, evolution, implementation time, and security requirements [51]. These factors work together to reduce errors and facilitate faster decisions throughout the entire life cycle of the product.  
Regarding the problems in applying the MBSE approach to the digital representation of enterprises, existing reviews primarily indicate issues with verifying and validating models, maintaining a balance between the complexity of models and the need to maintain their performance, and problems with semantics and the universalization of modeling languages. There is a significant gap in the data regarding successful implementation of the MBSE approach in digital transformation practices, as well as in the ability to identify potential problems related to such implementations [46]. According to a recent study [85], an effective examination of the behavior of a real production system requires the use of substantial resources and significant computation time by models, but there are also uncertainties due to simplifications in model development. Single simplifications and idealizations may not have a significant impact on the operation of a particular model. However, when combined in a single model, they can have a multiplicative effect and result in errors. The solution to this problem can be the use of the MBSE approach when developing approaches to validation, verification, and calibration when integrating simplified models into a single model, including the possibility of considering indirect influences on results from different factors. Thus, one of the most important tasks, which has been analyzed in many publications, is verifying the models used. A recent study [69] established ten paths to successful verification using the MBSE approach.
## 3 Materials and Methods
### Global Positioning Distance Error Expression Derivation
rule will apply to the longitude. The total increments of longitude and latitude can be mathematically expressed as follows:  
\[ \begin{array}{l}\Delta\psi\approx\mathrm{d}\psi=\frac{\frac{\partial\psi}{ \partial A_{\mathrm{s}}}}{\partial A_{\mathrm{s}}}\Delta A_{\mathrm{s}}+\frac{ \partial\psi}{\partial A_{\mathrm{s}}}\Delta h_{\mathrm{s}},\\ \Delta\eta\approx\mathrm{d}\eta=\frac{\partial\eta}{\partial A_{\mathrm{s}}} \Delta A_{\mathrm{s}}+\frac{\partial\eta}{\partial A_{\mathrm{s}}}\Delta h_{ \mathrm{s}}.\end{array}  \tag{9}\]  
However, total increments of longitude and latitude do not represent the final positioning distance error (\(\Delta L\)). If the measured longitude and latitude values at a point (\(\eta\), \(\psi\)) on the Earth's surface are (\(\eta+\Delta\eta\), \(\psi+\Delta\psi\)), the measurement deviations of the two independent variables will be \(\Delta A_{\mathrm{s}}\) and \(\Delta h_{\mathrm{s}}\), respectively. In addition, assuming that the Earth's surface is a standard sphere, the spherical distance between the two points can be calculated as follows:  
\[\Delta L=\arccos(\cos(\psi)\times\cos(\psi+\Delta\psi)\times\cos(\Delta\eta) +\sin(\psi)\times\sin(\psi+\Delta\psi))\times R_{\mathrm{L}}, \tag{10}\]  
where \(R_{\mathrm{L}}\) is the Earth's radius, and is set to 6400 km in this study. Therefore, the global positioning distance error calculated by the proposed error model is obtained by:  
\[\mathrm{d}L=\arccos(\cos(\psi)\times\cos(\psi+\mathrm{d}\psi)\times\cos( \mathrm{d}\eta)+\sin(\psi)\times\sin(\psi+\mathrm{d}\psi))\times R_{\mathrm{L}}. \tag{11}\]  
In conclusion, the proposed error model is defined by Equations (8), (9), and (11). This model enables the calculation of positioning distance errors of the autonomous global positioning method for any longitude and latitude values on the Earth's surface.
[18], which presented an acceptable absolute reliability for all strength assessments (CV \(<\) 10%) and an extremely high relative reliability (ICC = 0.92-0.99) in assessments of peak isometric and isometric/vibratory muscle strength in young university students. In the study by [PERSON] et al. [19], the FEMD was found to have high absolute (CV = 2.22-2.51%) and relative (ICC = 0.94-0.95) reliability for peak muscle strength in twenty-seven male collegiate athletes for an isometric mid-high pull-up exercise. In the study by [PERSON] et al. [20], relative reliability classified as \"high\" (ICC = 0.95-0.98) and absolute reliability classified as \"stable\" (CV \(<\) 10%) were observed for the muscle strength assessment protocols used and for mean and peak muscle strength in the concentric phase of five sit-to-stand measurements using three incremental loads controlled by an FEMD in sixteen healthy young adult subjects. [PERSON] et al. 's study [21] reported that the absolute reliability was \"acceptable\" (CV = 5.12-8.27%) and relative reliability was \"extremely high\" (ICC = 0.81-0.98) of the FEMD for mean muscle strength in a strength test of the shoulder internal and external rotators in standing position in the concentric and eccentric phases in thirty-two young university students. In a previous study, [PERSON] et al. [22] analyzed the reliability of a FEMD on eccentric swing exercise measurements of a hamstring strength movement in male soccer players, presenting a \"high\" absolute reliability (CV = 2.80%) and \"extremely high\" relative reliability (ICC = 0.94) in mean muscle strength for a velocity of 0.4 m\(\cdot\)s\({}^{-1}\). In addition, high absolute reliability (CV = 4.66%) and \"extremely high\" reliability relative (ICC = 0.91) were obtained for peak muscle strength. However, for the motor action of knee flexion and extension in the concentric and eccentric phases in female soccer players, the absolute and relative reliability for peak muscle strength with FEMD has not yet been demonstrated. Considering that most FEMD studies have been performed on young male college students and little evidence exists in women and none in female soccer players, the aim of this study was to analyze the absolute and relative test-retest reliability of maximal muscle strength in knee flexion-extension as measured using a FEMD in a group of young competitive female soccer players. The research hypothesis is that this test will be a reliable method for the evaluation of concentric and eccentric strength in knee flexion and extension,and the information obtained can be used to better control muscle strength and sports performance.