esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Magnetic Field Test for Sideband Current and Harmonic Currents  
Magnetic field strength and magnetic induction intensity in the \(xy\) direction, \(yz\) direction and \(xz\) directions in a wide range of frequencies can be measured by the spectrum analyzer NF-5035. The magnetic induction intensity in different directions at 7.5 cm and 70 cm from the ground outside the vehicle were measured, as shown in Figure 24. The unit of magnetic field is pT. The red curve indicates the maximum value, the green one is the transient value, and the yellow one is the average value.  
The magnetic field peaks appear at 85 kHz, 255 kHz (3 th harmonic) and 425 kHz (5 th harmonic) respectively. These frequencies are the multiples of 85 kHz, and the magnetic induction intensity reaches the highest at 85 kHz. The spikes of magnetic field also appeared outside the basic frequency, indicating that the side-band harmonics and odd harmonics also generate corresponding magnetic fields, which may have an effect on the measurement of magnetic field strength at test points. Therefore, when analyzing the magnetic field of WCS, it is necessary to comprehensively consider the magnetic field at the basic frequency, side-band frequency and the harmonic frequency. Adopting appropriate weighting factors can be a useful method. Magnetic field strength of sideband (84.8 kHz and 85.2 kHz) and harmonic currents (255 kHz and 425 kHz) with 30% offset are shown in Table 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Point** & **Experimental Results** & **Simulation Results** & **Difference** \\ \hline A2 & 14.55 & 13.07 & 0.52 \\ \hline A3 & 17.73 & 14.15 & 3.58 \\ \hline A4 & 1.85 & 3.22 & 1.37 \\ \hline B1 & 0.40 & 0.21 & 0.19 \\ \hline B2 & 0.38 & 0.15 & 0.23 \\ \hline B3 & 0.84 & 0.72 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results are compared with simulation results (A/m).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Offset**} & \multicolumn{4}{c}{**Experimental Results**} & \multicolumn{4}{c}{**Simulation Results**} \\ \cline{2-7}  & **A2** & **A3** & **A4** & **A2** & **A3** & **A4** \\ \hline
60 mm & 15.1 & 17.9 & 3.1 & 15.4 & 18.2 & 3.4 \\ \hline
120 mm & 11.9 & 17.2 & 3.2 & 12.3 & 17.8 & 3.3 \\ \hline
180 mm & 7.5 & 20.5 & 3.3 & 7.8 & 20.8 & 3.4 \\ \hline
240 mm & 6.3 & 30.7 & 3.0 & 6.5 & 31.2 & 3.3 \\ \hline
300 mm & 4.5 & 41.2 & 3.2 & 4.7 & 41.6 & 3.4 \\ \hline Error & A2 & 1.4 (3.1\%) & A3 & 2.1 (1.6\%) & A4 & 1 (6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Magnetic field strength at different offsets (A/m).
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Hold Power  
The hold power refers to the power consumed by an SRAM while it retains stored data in the hold mode without state transition. Since the SRAM is in this mode for most of the operation, the HPWR accounts for a significant portion of the total power consumption and is used as a key indicator for evaluating energy efficiency and low-power performance [12]. The WRTU-16T cell consists of a total of sixteen transistors, eight of which are PMOS devices, which have a higher PMOS-to-NMOS ratio than conventional SRAM designs. Since PMOS transistors generally have a lower leakage current, this configuration helps to reduce static power consumption. Two of the four access transistors are PMOSs, which further suppresses the leakage current during data hold operations. Also, the PMOS transistors are placed directly along the VDD path to minimize static losses. In general, increasing the number of access transistors increases the HPWR, but the use of PMOS devices offsets this and improves the power efficiency through the effects of high-stacking structures. In the design, the HPWR is further reduced by avoiding excessive transistor sizing and simplifying the interconnects to limit parasitic leakage paths. As shown in Figure 11, the WRTU-16T achieves a low HPWR.
###### Abstract  
In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes' reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs.  
Vehicular Networks; Interplanetary File System; Edge Vehicular Nodes; Proof of Authority; Blockchain; Road Side Unit
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 3 Results  
### Electrical Conductivity of Unidirectional CFRP Composite  
Figure 3 shows the electrical conductivity of CFRP at the different fiber orientations of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\)and 90\({}^{\circ}\) (red dots). The conductivity of CFRP decreased as the fiber orientation angle increased from 0\({}^{\circ}\) to 90\({}^{\circ}\). An anisotropic CFRP composite was described as that all the fibers are highly aligned and parallel to each other in the composite. However, the fibers are not entirely straight and have a wave shape in the composite, consequently creating many contact points between adjacent fibers. This causes the composite to have specific electrical conductivity in the transverse direction of the fiber, although it is extremely lower compared with that in the fiber direction. As a consequence, the unidirectional CFRP composite shows the highest and lowest conductivity values in the fiber direction and transverse direction, respectively.  
Figure 1: Schematic of free-space set-up for electromagnetic interference (EMI) shielding measurement. CFRP: carbon fiber-reinforced polymer.  
Figure 2: Distribution of the electric field inside the coaxial tube and the specimen dimension used for coaxial transmission line measurement.  
The decreasing tendency of conductivity was not linear of CFRP, which decreased as the fiber orientation angle increased from 0\({}^{\circ}\) to 90\({}^{\circ}\). The conductivity decreased sharply from 0\({}^{\circ}\) to around 30\({}^{\circ}\), and the decreasing trend became gradual when exceeding 30\({}^{\circ}\). Since the conductivity of CFRP presents a cosine change trend, the conductivity at different angles can be calculated and predicted by the formula derived below:  
\[\sigma_{\theta}=\frac{1}{A\cos^{2}(\theta+\pi)+B}\ \ 0\leq\theta\leq\frac{\pi}{2} \tag{3}\]  
where \(\sigma_{\theta}\) is the electrical conductivity of CFRP at \(\theta\) degree, \(A=1/\sigma_{0}-1/\sigma_{90}\), \(B=1/\sigma_{90}\), \(\sigma_{0}\) and \(\sigma_{90}\) are the electrical conductivity of CFRP at the testing angle of 0\({}^{\circ}\) and 90\({}^{\circ}\), respectively.  
The curve in Figure 3 shows the predicted electrical conductivity of unidirectional CFRP as a function of the carbon fiber orientation angle from 0\({}^{\circ}\) to 90\({}^{\circ}\). It can be seen from Figure 3 that the calculated results are in good agreement with the experimental results, indicating that the proposed formula can predict the electrical conductivity of unidirectional CFRP at any angle just through the conductivity values at 0\({}^{\circ}\) and 90\({}^{\circ}\) directions.
## 4 Rationale for Each TACE Technique
### Rationale for cTACE  
Iodized oil is a semi-fluid embolic agent that can change its shape in vessels. Two types of iodized oil emulsion can be prepared: water-in-oil emulsion (WOE) and oil-in-water emulsion (OWE). WOE should be properly prepared because WOE has stronger embolic effects on tumor vessels than OWE [37; 38].  
Iodized oil injected into the hepatic artery is initially retained in the tumor sinusoids. When more than a certain amount of iodized oil is pooled in the tumor sinusoids, some iodized oil overflows into the portal veins through the drainage route of the tumor [39; 40]. Iodized oil also enters the portal veins through the PBP in the surrounding liver [40]. As a result, the peritumoral portal blood flow, including reversed portal blood flow into the tumor, can be temporarily blocked by viscosity of iodized oil. Moreover, some iodized oil can pass through the arterial communications and reach the neighboring hepatic arterial branches and/or extrahepatic arteries [10; 13; 41]. This allows embolization or identification of an occult tumor feeder and may prevent the arterial collateral supply to the tumor following TACE [13]. By adding arterial blockage with GS particles after injection of iodized oil, both the hepatic artery and portal vein surrounding the tumor can be embolized (Figure 3). Small-sized GS particles (slurry, approximately 0.2-0.5 mm in diameter) are favorable to achieve more necrosis because the reported mean diameter of tumor feeders in tumors of 7 to 63 mm (mean, \(20.3\pm 12.7\)) in diameter was \(0.41\pm 0.32\) mm (range, 0.12-1.79) [42]. This leads not only to ischemic necrosis of the hypervascular tumor portions but also to peritumoral necrosis including tumor portions supplied by a dual blood supply and induces shrinkage of the surrounding liver parenchyma [11; 12; 13; 33; 40; 43]. This also indicates that cTACE damages the normal liver; therefore, selective catheterization is essential to minimize the liver toxicity. It is also important to minimize the total dose of iodized oil, and the maximum dose in one cTACE procedure is generally 10 mL in Japan and 15 mL in Western countries [44].
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 4 An Algorithmic Metatheorem for Log-Depth Circuits and Its Applications
### Applications: Model Checking for Propositional Logic  
As an application of the first situation, where the input graph is actually a tree, consider the model checking problem for propositional logic: the input is a pair consisting of a propositional formula \(\phi\) and a variable assignment \(\beta\) that maps each variable in \(\phi\) to 0 or 1. The question is whether \(\beta\models\phi\) holds. For instance, we might have \(\phi=(p\wedge\
eg p)\vee(q\lor p)\) and \(\beta(p)=0\) and \(\beta(q)=1\) and, clearly, \(\beta\models\phi\) holds.  
Lemma 10: _The model checking problem for propositional logic lies in NC\({}^{1}\)._  
Proof Ideas.: First, given a formula \(\phi\) as input (coded as a string), one can easily construct a \"formula tree \(T^{\prime}\)\" that represents \(\phi\) (the nodes are labeled \(\wedge\) or \(\vee\) or \(\
eg\), the leafs are labeled with 0 and 1, depending on what \(\beta\) tells us about the assignment of the variables), see Figure 3 for an example.  
Now, we want to evaluate the tree using the algorithmic metatheorem. For this, we use a monadic second-order formula stating the following: \"Does there exist a subset \(X\) of the nodes of the tree such that the following conditions are met: the root is part of \(X\); a leaf is part of \(X\) if, and only if, it is labeled 1; a node labeled \(\wedge\) is part of \(X\) if, and only if, both its children are; a node labeled \(\vee\) is part of \(X\) if, and only if, at least one of its children is; and a node labeled \(\
eg\) is part of \(X\) if, and only if, its child is not\". Clearly, this lengthy formula uses just one monadic quantifier at the beginning and tests whether \(X\) contains exactly the nodes of the tree evaluating to 1.  
Proving the lemma using the algorithmic metatheorem is, of course, somewhat circular: we used the fact that the Boolean formulas can be evaluated in NC\({}^{1}\) in order to prove the algorithmic metatheorem--so perhaps we should not use it to the prove this very fact and move on to an application that feels less like cheating.
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 2 Materials and Methods
### Feature Extraction  
Despite the trigger-related nature of the data, identification of the mental fatigue states, while omitting the visual stimuli information, was decided (thus the 0.5 sec post-trigger time frame was removed, as mentioned in the previous section). However, such a configuration creates a non-phase-locked dataset. This could be a significant hindrance in time-locked analysis, since the important temporal information of the signal would be ignored and lost. On the contrary, time-frequency (TFR) analysis can harness the temporal and spectral resolution for the non-phase-locked trials [18]. Therefore, TFR analysis wasimplemented as the base analysis prior to artificial intelligence learning to address potential data inconsistencies that could prove detrimental for the predictive model performance.  
Specifically, the preprocessed data were analyzed by employing complex Morlet wavelet [19] creating the final feature dataset. The utilization of Morlet wavelets was done due to their prominence in time-frequency analysis, as they provide equal variance in time and in frequency [20]. With this approach, the raw power values for each frequency bin were computed with a varied time window (that rely on frequency), using a Gaussian taper with a specified width. The width of the Morlet taper was selected as a hyperparameter with a value of 4, resulting in a temporal resolution of 13 time bins. This analysis outputs a power spectral-temporal activation map to be used as input for the subsequent DL model. As such, a 3D TFR matrix was created (per subject and trial) incorporating the Morlet power features, with dimensions [C\(\times\)F\(\times\)T], where C is the number of channels, F is the frequency bins, and T is the time bins. The number of channels in the proposed configuration is 63, with 40 frequency bins (from 1-40 Hz, with 1 Hz frequency windows) and 13 time bins (from 0.5 s to 3.5 s with a time window of 0.25 s). Morlet wavelet calculations were performed using the FieldTrip Toolbox [21].
## 2 Search Methodology
### Refined Results Acquisition Procedure  
Figure 2 schematically shows the application of the inclusion and exclusion criteria. The process of refinement was carried out in three stages: initial search, selection and inclusion. Throughout the initial search stage, the raw results of the databases were collected. Up to 224 articles were found in total, 219 in Scopus, 5 in IEEE Xplore and none in the ACM Digital Library. In the selection stage, the duplicated documents were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Search Term** & **Description** \\ \hline defect OR flaw OR imperfection OR fault OR & Synonyms for defect \\ crack OR bug OR deficiency & \\ \hline detection OR detecting OR recognition OR & Synonyms for detection \\ recognising OR identification OR identifying & Synonyms for classification \\ classification OR classifying OR categorisation & Synonyms for classification \\ OR categorisation & \\ \hline vision OR visual OR image & Screening the articles which work with \\  & visual detection \\ \hline wafer OR semiconductor & Seeking articles in which the defects appear in \\  & semiconductor wafers \\ \hline SEM OR “scanning electron microscope” OR & Articles with SEM as inspection device \\ “scanning electron microscopy” & Articles in which defects are classified using \\ \hline “deep learning” OR “machine learning” &
\begin{tabular}{c} Articles in which defects are classified using \\ these techniques \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search terms.  
eliminated. As many as 3 documents were duplicated. Then, another 143 documents were removed after reading the abstract, title and keywords. As the terms were searched throughout the full document, those 143 articles clearly did not address the objective of the review. Finally, at the inclusion stage, 70 more papers were removed from the list after careful reading. Another 29 of them were eliminated for not using SEM images. In fact, SEM was mentioned throughout the article, but other devices were used to construct the data sets. Another 19 articles were removed because they did not perform defect detection on semiconductor materials. Again, semiconductor materials were mentioned in the full text, but defect detection and classification was devoted to other types of materials and defects. Finally, 22 articles that did not employ machine or deep learning were removed. These techniques were mentioned in the articles as future work or an alternative. Therefore, solely 9 of the 224 initial documents remained on the list. The fact is that this may seem like a really low number of articles. However, to the authors' knowledge, all papers related to the subject of the review have been collected. These 9 documents will be used for the detailed study.  
Figure 2: Search methodology.
## 4 Privacy and Security Enhanced Methods
### Credit-Heterogeneity-Based Executor Scheduling Strategy
#### 4.2.3 Dynamic Updates to Credibility and Heterogeneity  
**Heterogeneity Updates.** When common-mode attacks are detected among executors in set \(\textbf{P}_{C}\), update their heterogeneity:  
\[H^{\prime}_{i,j}=H_{i,j}-\gamma,P_{i}\in\textbf{P}_{C},P_{j}\in\textbf{P}_{C},i\
eq j, \tag{18}\]  
where \(\gamma\) is the heterogeneity penalty weight introduced by the existence of unknown common vulnerabilities.  
**Credibility Updates.** For executors operating normally, increase their credibility:  
\[R^{t+1}_{i}=\min(R^{0}_{i},R^{t}_{i}+\delta). \tag{19}\]  
For executors exhibiting anomalies, decrease their credibility:  
\[R^{t+1}_{i}=\max(0,R^{t}_{i}-\theta), \tag{20}\]  
where \(\delta\) is the credibility increment value, \(\theta\) is the credibility decrement value, and \(R^{0}_{i}\) is the initial credibility of the executor.  
It is worth noting that our proposed scheduling strategy's primary innovation lies in combining periodic scheduling with event-triggered scheduling mechanisms to address the limitations of existing approaches. The credibility and heterogeneity calculation components in our architecture are designed with modular characteristics, which allow for the integration of advanced clustering methodologies [49; 52] to enhance heterogeneity assessment and executor selection.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 4 Conclusions  
In this paper, the design of a compact dual-band microstrip patch antenna operating at 2.5 and 5.2 GHz has been presented and analyzed for various critical parameters. With the help of two different EBG ground planes, improvement in the overall performance (in terms of directivity, gain, efficiency and other parameters) of the proposed dual band conventional ground plane antenna has been achieved. A comparison on the basis of the performance of the conventional antenna with two different EBG inspired antennas has been made, as given in Table 4. It is observed that EBG-inspired antennas achieve better performance compared to the antenna with a conventional ground plane. In addition, we tested the proposed dual band conventional ground plane antenna in various bending conditions. Based on our results, we conclude that the designed antenna remains tuned all the time, irrespective of the bending radii. Furthermore, we studied the proposed antenna design with respect to its contact with various human body parts (such as flat and rounded body parts) and performed a detailed SAR analysis. The corresponding results reveal that  
Figure 24: Conventional antenna’s SAR value on a child’s arm (worst case) of radius 40 mm at (a) 2.5 GHz and (b) 5.2 GHz.  
at both the given resonance frequencies, the SAR value of the conventional antenna is well within the standard SAR limit (2 W/kg) prescribed by the European standards in both conditions when the antenna is placed over the flat body phantom (chest) or wrapped around the rounded body parts. Overall, we conclude that the proposed antenna design can be useful for a variety of applications including personal communications gadgets, equipment for combat and rescue operations, and health devices without compromising on the health of the wearer. An interesting future study is to ascertain the accuracy of the performance parameters by developing a wearable healthcare product to analyze the antenna performance practically.  
Conceptualization, F.F. and A.A.; formal analysis, F.F., A.A. and S.U.; funding acquisition, D.-Y.C. and A.A.; methodology, F.F., S.U., A.A. and D.-Y.C.; project administration, S.U. and D.-Y.C.; resources, A.A. and S.U.; software, F.F., S.U. and A.A.; supervision, S.U. and D.-Y.C.; validation, F.F., D.-Y.C. and S.U.; visualization, S.U., F.F. and. A.A.; writing--original draft, F.F., S.U. and A.A.; writing--review and editing, D.-Y.C. and S.U. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022 RIIA3064544).  
Not applicable.  
The authors declare no conflict of interest.