esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Security Guarantee Scheme and Operation of OTP\({}^{3}\)  
The proposed zero-trust stream data encryption mechanism is based on the advantages of integrating \"One-Time Password (OTP password)\" and \"One-Time Pad (OTP-key)\" for \"processing secure stream data transmission\", named OTP\({}^{3}\). This will meet the following three requirements for meeting the un-breakable security guarantee by [PERSON] [16]: (1) the key must be generated randomly, (2) promise that the key is not reused, and (3) ensure secure distribution of the key to both encryption and decryption endpoint devices. Accordingly, the operation flow of OTP\({}^{3}\) is introduced as follows:  
As Figure 4 shows, the OTP\({}^{3}\) operation process includes several steps, and each step is briefly described in sequence, as follows: Step 0: The User presets an identical Serial Number (SN) to both Device A and Device B as a communication pair, and the used SN is essentially non-repeating in other communication pairs, as defined in the previous work of D-OTP [4]. Step 1: The Server intends to have Device A receive the streaming signal detected by its paired Device B. Step 2: The server generates a non-repeating OTP password, as introduced in [4]. Step 3: The server uses public network encryption technologies (such as SSL/TLS) and symmetric-key exchange techniques like the classic Diffie-Hellman (DH) [48] to transmit this OTP password to the paired devices A and B. Step 4: Devices A and B each generate non-repeating OTP keys capable of meeting cryptographic security standards using this OTP password and the preset SN. Step 5: Device B continues to encrypt the streaming signal it inputs using these OTP keys. Step 6: Device B continues to transmit the encrypted ciphertext generated by the encryption process to Device A via the public network (no need for further encryption during the process). Step 7: Device A continues to decrypt the network data it inputs using these OTP keys. Step 8: Until the transmission is completed, stopped, or an external intrusion signal is detected, the OTP password and OTP keys used for this transmission are discarded, and the process returns to Step 1 to prepare for the start of another transmission.  
It is worth noting that, in Step 6, during the transmissions between Device A and Device B, there is no additional encryption operation required by any network equipment (such as gateway, router, and core network). Accordingly, the proposed OTP\({}^{3}\) mechanism is aligning with the principle of zero-trust network access.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
###### Abstract  
Geosynchronous Synthetic Aperture Radar (GEO SAR) is a possible next generation SAR system, which has the excellent performance of less than one-day revisit and hundreds of kilometres coverage. However, Radio Frequency Interference (RFI) is a serious problem, because the specified primary allocation frequencies are shared by the increasing number of microwave devices. More seriously, as the high orbit of GEO SAR makes the system have a very large imaging swath, the RFI signals all over the illuminated continent will interfere and deteriorate the GEO SAR signal. Aimed at the RFI impact in GEO SAR case, this paper focuses on the performance evaluation and the system design requirement of GEO SAR in the presence of RFI impact. Under the RFI impact, Signal-to-Interference-plus-Noise Ratio (SINR) and the required power are theoretically deduced both for the ground RFI and the bistatic scattering RFI cases. Based on the theoretical analysis, performance evaluations of the GEO SAR design examples in the presence of RFI are conducted. The results show that higher RFI intensity and lower working frequency will make the GEO SAR have a higher power requirement for compensating the RFI impact. Moreover, specular RFI bistatic scattering will give rise to the extremely serious impact on GEO SAR, which needs incredible power requirements for compensations. At last, real RFI signal behaviours and statistical analyses based on the SMOS satellite, Beidou-2 navigation satellite and Sentinel-1 A data have been given in the appendix.  
Geosynchronous SAR (GEO SAR); Radio Frequency Interference (RFI); Signal-to-Interference and Noise Ratio (SINR); RFI measurement +
Footnote †: journal: _Journal of the Royal Society Interface_
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 2 Research Backgrounds
### Transfer Learning  
Deep learning models based on CNNs show high performance in image classification but require significant time and resources for training [10; 24; 26; 27; 28; 29; 30; 31]. Meaningful performance is difficult to achieve if there is a lack of data, thus necessitating substantial training data [6; 7; 32; 33]. Transfer learning involves retraining a pre-trained model for a different domain. Utilizing an existing model can significantly reduce the design time, thus addressing the aforementioned challenges [22; 24; 28; 29; 30; 31].  
Transfer learning aims to extract knowledge from one or more source tasks and apply it to a target task [34; 35; 36; 37]. Unlike multi-task learning, which focuses on learning all the source and target tasks simultaneously, transfer learning prioritizes the target task. In transfer learning, the roles of the source and target tasks are no longer symmetric. Figure 2 illustrates the difference between the traditional learning processes and transfer learning techniques. The traditional machine learning methods attempt to learn each task from scratch. In contrast, transfer learning techniques aim to transfer knowledge from previous tasks to a target task, particularly when the target task has limited high-quality training data.  
In this study, the amount of data was adjusted due to the given development environment. Transfer learning is suitable for creating scalp diagnosis models with limited data because it allows the use of weights from models that were trained in areas with abundant data [32; 33]. Therefore, this study applied the method of retraining the EfficientNet model to achieve high performance even with limited input data.  
Figure 1: The architecture of EfficientNet. Source: Google AI Blog ([[https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html](https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html)]([https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html](https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html)), accessed on 21 August 2024).  
Figure 2: Different learning procedures between traditional machine learning and transfer learning.
## 3 Simulation of Sand Plane Strain Test
### Determination of Model Parameters  
The general form of hypoplastic model is as follows:  
\[\overset{o}{\boldsymbol{\sigma}}=\mathbf{L}(\boldsymbol{\sigma},\dot{\boldsymbol{ \varepsilon}})+\mathbf{N}\big{\|}\dot{\boldsymbol{\varepsilon}}\big{\|}. \tag{6}\]  
The symbols \(\mathbf{L}\) and \(\mathbf{N}\) in the equation represent, respectively, the linear and nonlinear terms of the hypoplastic model, which together describe the nonlinear mechanical behavior of soil. Meanwhile, \(\big{\|}\dot{\boldsymbol{\varepsilon}}\big{\|}\) represents the Euclidean norm of the strain rate, namely, the modulus of strain rate.  
Based on an anisotropic state variable that considers the rotation of axes of principal stress, a critical state line is established and introduced into the hypoplastic constitutive model proposed by [PERSON] (2017), resulting in an anisotropic hypoplastic constitutive model that considers the effects of axes of principal stress rotation [36]:  
\[\overset{o}{\boldsymbol{\sigma}}=C_{1}\text{tr}(\boldsymbol{\sigma})\dot{ \boldsymbol{\varepsilon}}+C_{2}\text{tr}(\dot{\boldsymbol{\varepsilon}}) \boldsymbol{\sigma}+C_{3}\frac{\text{tr}\sigma\dot{\boldsymbol{\varepsilon}}}{ \text{tr}\sigma}\boldsymbol{\sigma}+C_{4}f(e)(\boldsymbol{\sigma}+\boldsymbol {\sigma}^{*})\big{\|}\dot{\boldsymbol{\varepsilon}}\big{\|} \tag{7}\]  
\[f(e)=(\frac{e-e_{min}}{e_{\text{tr}}-\lambda_{c}(P/P_{a})^{\zeta}+tA^{\prime}- e_{min}})^{a}. \tag{8}\]  
We can introduce the critical state line by simply multiplying \(f(e)\) in the nonlinear term. In Equation (8), \(A^{\prime}\) represents the anisotropic state variables that consider the rotation of principal stress axes. Additionally, the literature has explained the basic parameters [44].  
The parameters \(C_{i}\) (\(i=1\)-\(4\)) in the hypoplastic constitutive model discussed above can be determined via triaxial compression tests conducted under constant confining pressure. Several papers have explained the detailed calculation method for \(C_{i}\)[33,44]. It is important to note that if void ratio related terms exist in hypoplastic constitutive models, the corresponding dilatancy angle should be set to zero. Additionally, the model parameters and test parameters should remain consistent. The calculated model parameters are presented in Table 2.
# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
###### Abstract  
Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed.  
machine learning; deep learning; cognitive networks; data-driven networks +
Footnote †: journal: Applied Sciences
## 3 Methodology
### GPU-Based PBD Cloth Framework (Reality Collision Version)
#### 3.2.5 Hand Collision Handling Algorithm  
Realistic collision between the virtual cloth and the user's hand is essential for enhancing immersion in XR environments. The proposed system utilizes both the OVR Skeleton and OVR Mesh functionalities provided by Unity's Meta Quest SDK to acquire real-timehand joint and mesh data, enabling accurate collision detection and response based on the user's hand movement.  
Two hand collision handling methods are considered in this work: a capsule collider-based method, shown in Algorithm 3, and a triangle mesh-based method using OVR Mesh, shown in Algorithm 4.  
```
1:for each cloth particle \(p_{i}\)do
2:for each capsule collider \((\texttt{pointA},\texttt{pointB},r)\)do
3: Compute vector \(\texttt{ab}\leftarrow\texttt{pointB}-\texttt{pointA}\)
4: Compute projection factor \(t\leftarrow\texttt{clamp}\Big{(}\frac{(\texttt{p}_{i}-\texttt{pointA}) \cdot\texttt{ab}}{\texttt{ab}\cdot\texttt{ab}},0.0,1.0\Big{)}\)
5: Compute closest point \(\texttt{q}\leftarrow\texttt{pointA}+t\cdot\texttt{ab}\)
6: Compute distance \(d\leftarrow\|\texttt{p}_{i}-\texttt{q}\|\)
7:if\(d<r\)then
8: Compute normal vector \(\texttt{n}\leftarrow\texttt{normalize}(\texttt{p}_{i}-\texttt{q})\)
9: Move particle: \(\texttt{p}_{i}\leftarrow\texttt{q}+\texttt{n}\cdot(r+\texttt{collisionMargin})\)
10: Apply friction to velocity of \(p_{i}\)
11:endif
12:endfor
13:endfor
```  
**Algorithm 3** Hand collision handling for cloth particles (using capsule collider)
## 3 New Shear Resistance Formula for the Y-Type Perfobond Rib Shear Connector
### Regression Analysis for the New Shear Resistance Formula
#### 3.2.1 Investigation of Index Value for Concrete Strength Term: \(f_{ck}\)  
In advance of the regression fit test with the whole equation (Equation (2)) to select the proper index values (alpha values) adopted in Equation (2), the trial index values are first selected based on the individual sensitivity analysis with the basic design variables.  
To investigate the trial index value for the concrete compressive strength (\(f_{ck}\)), 4 specimen groups are selected from the results in Table 5 and summarized in Table 8. The sensitivity analysis is performed with the (1) 30.4 MPa group to 42.6 MPa group, (2) 42.6 MPa group to 50.9 MPa group, (3) 52.9 MPa group to 62.4 MPa group, and (4) another 52.9 MPa group to another 62.4 MPa group with different rebar grade (SD400 and SD500 groups). All specimens in each group have the same design variables except the concrete strength. Based on the results in Table 8, the index of 0.3 is proposed for the regression fit test with the experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Variables** & **Unit** & **Design Range** \\ \hline Number of Y-ribs and transverse rebars & \(n\) & N/A & 4–10 \\ Concrete compressive strength & \(f_{ck}\) & MPa & 30–60 \\ Diameter of transverse rebar & \(d_{r}\) & mm & 16, 19, 22 \\ Yield strength of transverse rebar & \(f_{yr}\) & MPa & SD400 (\(f_{yr}\) = 400) \\ Y-rib thickness & \(t\) & mm & 10–12 \\ Y-rib width & \(w\) & mm & 80–120 \\ Y-rib height & \(h\) & mm & 80–120 \\ Yield strength of structural steel & \(f_{y}\) & MPa & SM490 (\(f_{y}\) = 315) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Application ranges for design variables.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group 1** & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{**Ratio**} & \\ \hline \(f_{ck}\)**(A)** & \(P_{u}\)**(B)** & \(f_{ck}\)**(C)** & \(P_{u}\)**(D)** & \(f_{ck}\) **(E = C/A)** & \(P_{u}\) **(F = D/B)** & **Index (G = logF/logE)** & **Test No.** \\ \hline
30.4 & 1671.9 & 42.6 & 1773.6 & 1.400 & 1.061 & 0.175 & 4R-1, 2, 4, 5 \\
42.6 & 1773.6 & 50.9 & 1936.7 & 1.195 & 1.092 & 0.495 & 4R-2, 4, 5, 6, 7 \\
52.9 & 2115.8 & 62.4 & 2228.5 & 1.180 & 1.053 & 0.314 & 4R-9, 11 \\
52.9 & 2176.8 & 62.4 & 2267.1 & 1.180 & 1.041 & 0.246 & 4R-10, 12 \\  & & & Average & & & 0.308 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Index values for the variable of \(f_{ck}\).
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### WSN and Health
The authors extensively studied posture and positioning of the body movement via a real-time sensor application. They proposed a device which can be worn as a necklace which includes the sensor nodes and the movement is detected by the sensors. Furthermore, they obtained more than an 80% sensitive result with 100% specificity. Figure 7: Wireless Body Area Network (WBAN) Architecture. [PERSON] et al., 2014 investigated the application of sensors in the health motoring system. They developed a Bluetooth module with wearable sensors. This device monitors the ECG pattern, skin humidity, and body temperature. They calculated the error and it was shown to be around 5% compared to a certified commercial medical device already available in the market. [PERSON] et al., 2015 investigated the body temperature, heartbeat, and oxygen saturation in blood via real-time sensors. The ubiquitous application of the real time sensor provides a new avenue for the diagnostics industry. Several applications are available, but are still in their infancy, but here in this study, the authors provide a comprehensive approach for the aforesaid parameters. They have developed a Desktop version with printable facilities. The results are in the form of graphs and tables for comprehension. They can be accessed in a specific area network such as from 10 m to 50 m via ZigBee without using the wire, which is more appropriate for the WBAN. [PERSON] et al., 2016 investigated a non invasive approach for the detection of the blood glucose level. The invasive approach for the detection of blood glucose is an age old technique which is generally practiced in clinics or hospitals for the diagnosis of diabetes. Of late, the authors have developed a non-invasive approach and quiet reproducible technique via the application of real-time sensors and it plays a pivotal role in the Healthcare industry. The authors have developed glucose sensor monitoring via a Glucometer sensor with an Arduino Uno board and ZigBee module system. Furthermore, in this approach, the patient does not need to be present physically for the routine examination; they can perform the test via a web-based application via the usage of mobile phones. [PERSON] et al., 2017 investigated the application of sensors based on solar influx gathering. The authors placed an emphasis on evaluating the body temperature distribution, heartbeat, and shelf life of the instrument via web-based smart-phone applications. Additionally, they enhanced the lifetime of the device via several changes in the influx of the solar power. The authors put forward the strategies for resolving the aforementioned tasks.
## 3 The Proposed Protocol
### The Proposed Scheme
#### 3.3.1 The Initialization Phase  
The initialization consists mainly of the generation and distribution of system parameters and authentication information. First, a compliant elliptic curve is selected and the points on this curve form a generating element \(G(x,y)\) of group \(E_{P}(a,b)\). The order of \(G\) is required to be a large prime number \(q\). As well as being equipped with various cryptographic functions such as digital signature algorithms, encryption and decryption functions, cryptographic hashing functions (e.g., HMAC; hash-based message authentication codes), pseudo-random number generators (PRNGs), etc. The certificate authority (CA) then generates the private and public keys and prepares the public parameter set. The steps are as follows.  
1. The CA uses a random number generator to generate a random number \(S_{CA}\) as the CA's private key and calculates the public key: \(P_{CA}=S_{CA}\times G\).
2. Select the public parameter set, set to \(\eta=(P_{CA},G,q,H)\) and publish.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 3 Analysis of Intermittent Failure Dynamic Characteristics
### Dynamic Model of Intermittent Failure
If the value of \(R_{c}(t)\) is less than \(R_{th}\), then the electrical connector is fault-free; otherwise, failure occurs. The dynamic characteristics of intermittent failures proposed in this paper mainly consist of occurred numbers denoted by \(N_{I}\), duration in due time denoted by \(D_{I}\), and the average numbers in due time denoted by \(\overline{N_{I}}\) and the average duration denoted by \(\overline{D_{I}}\). According to parameters framework of intermittent failure dynamic model, the intermittent behavior in electrical connector can be described quantitatively. The above modeling process can be summarized as follows:  
Step 1: Set the initial electrical contact state parameters, including \(z_{\rm c}(t)\), \(\overline{a}/R_{ci}(l_{i},\theta_{i})\), \(L\), \(N_{c}\) and so on. Step 2: According to (15) and \(z_{c}(t)\), calculate the initial electrical contact resistance \(R_{\rm c}(0)\);  
Step 3: Let \(t=t+\Delta t\); calculate \(R_{c}(t)\). If \(t\) reaches the cut-off time, then go to step 4; otherwise, repeat step 3;  
Step 4: Obtain the statistics \(N_{I}\) and \(D_{I}\) according to \(R_{th}\);  
Step 5: Extract the dynamic characteristics of intermittent failure using Equations (14) and (15).
## 4 Soil water and drought monitoring for early-warning applications
It is calculated as the ratio of seasonal actual evapotranspiration (ETa--or supply [=demand met]) to the seasonal crop water requirement (ETc--or demand):  
\[\mathrm{WRSI}=\mathrm{ET}_{\mathrm{a}}\ast\mathrm{ET}_{\mathrm{c}}\mathrm{ }^{-1} \tag{5}\]  
ETc is calculated from the product of the Penman-Monteith reference evapotranspiration (ETo) using the standardized FAO equation that uses short grass as the reference crop ([PERSON] et al. 1998) and crop coefficient (Kc). Crop coefficients (Kc), piecewise linear weighting functions, have traditionally been used to adjust for type and growth stage of the crop:  
\[\mathrm{ET}_{\mathrm{c}}=\mathrm{Kc}\ast\mathrm{ET}_{\mathrm{o}} \tag{6}\]  
USGS/EROS has generated an operational daily global ETo since 2001 using meteorological forcings from the Global Data Assimilation System (GDAS) ([PERSON] et al. 2007b). ETa represents the actual (as opposed to the potential) amount of water withdrawn from the soil water reservoir (\"bucket\"). The key difference between ETc and ETa is that ETa depends on soil moisture that is calculated on a daily basis to provide the daily soil stress correction factor, Ks ([PERSON] and [PERSON] 2003). \[\mathrm{ET}_{\mathrm{a}}=\mathrm{K}_{\mathrm{s}}\ast\mathrm{Kc}\ast\mathrm{ ET}_{\mathrm{o}} \tag{7}\]  
Where Kc is crop coefficient, ETo is reference ET, and Ks is soil water stress index. Whenever the soil water content is above the maximum allowable depletion (MAD) level, which varies by vegetation type, the ETa will balance the ETc resulting in no net water stress. However, when the available soil water falls below the MAD level, the ETa will be lower than ETc, in proportion to the remaining soil water content. Runoff and deep drainage out of the root zone are assumed to occur in excess of field capacity. The soil water content is obtained through a simple mass balance equation where the level of soil water is monitored in a root-zone soil layer defined by the water holding capacity (WHC) of the soil and the crop root depth, i.e.,  
\[\mathrm{SW_{i}=SW_{i\_1}+PPT_{i}-ET_{ai}-RF_{i}-DD_{i}} \tag{8}\]  
Where SW is soil water content, PPT is precipitation, RF is runoff, DD is deep drainage below the root zone, and i is the temporal index.
## 4 Concept
### Data Processing
#### 4.3.4 Use of the ML Application  
At the end of the presented workflow, the actual use of the developed ML application for the addressed use cases takes place. The results of the analysis algorithms are visualized and output to influence the behavior of the CPPS with respect to the target variables. With the acceptance of the CPPS, the effectiveness of the ML application regarding the technical and economic target variables defined in the specifications is also checked and evaluated. This also evaluates the success of the development project. The explainability of the presented effects as well as the comprehensibility of the algorithmic workflow is important for the acceptance of the ML application.  
Figure 5: Examples of context-based data quality problems, covered by context-based data analysis part of the quality assurance approach.  
During the operation of the CPPS, the data quality and the analysis quality respectively are continuously monitored with the aspects of data completeness and freedom from errors, algorithm accuracy as well as effectiveness of the ML application. Thus, being able to assess the reliability and the trustworthiness respectively at any time and to warn against risks if necessary. Based on the data quality monitoring, a system of measures is set up to deal with critical conditions of both the CPPS and in the data pipeline as well as algorithm reliability.
# 5G-MEC Testbeds for V2X Applications
## 1 Introduction
To account for the large set of parameters, testbeds have been deployed to experiment with V2X applications without the limitations of theoretical or simulation models.