esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Biochar Structure and Morphology  
Figure 1 displays the scanning-electron-microscope images of coconut shell biochar produced through the pyrolysis of coconut shell at 800 \({}^{\circ}\)C in an inert atmosphere. The figure shows that the biochar particles form irregular shapes with sharp edges, after size reduction, Figure 1b. In order to avoid clogging of biochar particles during printing, the particles were sieved using a 400 mesh-size.  
The XPS survey spectra, Figure 2, reveal that the major elements present in biochar are carbon (\(\sim\)93.54%) and oxygen (\(\sim\)6.46%) without any significant impurities. Here, the high-resolution C1s quantitative spectra of biochar (inset of Figure 2) show a binding energy peak at 284.4 eV, corresponding to sp\({}^{2}\) hybridised carbon. Moreover, the deconvolution of the C1s peak reveals peaks at \(\sim\)285.2 eV, attributed to an epoxy group (C-O-C), and at \(\sim\)289.3 eV, corresponding to a carbonyl group (C=O). The percentages of epoxy and carbonyl groups were calculated as 31.83% and 17.65%, respectively. A major contribution to the total percentage was sp\({}^{2}\) hybridised carbon, of \(\sim\)50.52%, which was much higher than sp\({}^{2}\) hybridised carbon in biochar produced at lower temperatures [42]. In this work, the coconut shell biochar showed a higher carbon content with a high purity, compared to those in other precursors and pyrolysis conditions. For example, rice husk biochar contains silicon dioxide as an impurity, while date palm biochar produced at 900 \({}^{\circ}\)C has a lower carbon concentration with impurities, when compared to the value for coconut shell biochar [18; 42; 43].  
The coconut-shell-biochar-powder crystallographic structure was determined through XRD analysis, Figure 3, which was performed in the range of 2\(\theta\) angle from 10 to 60\({}^{\circ}\). The broad peaks at 2\(\theta=24.9^{\circ}\) and 43.4\({}^{\circ}\) correspond to the reflections from (002) and (100) planes in the disordered amorphous carbon structures. The sharp peak at 26.5\({}^{\circ}\) in the (002) plane is attributed to the ordered graphitic structures [44]. The coconut shell pyrolysis at 800 \({}^{\circ}\)C in an inert atmosphere favoured the formation of graphitic structure along with amorphous carbon, due to the peculiar pyrolysis conditions. Several other studies have shown that the biochar produced from other precursors and with other pyrolysis conditions favour the production of amorphous carbon over graphitic carbon [45; 46; 47]. According to [PERSON] et al., coconut shell carbon produced through autogenic pressure pyrolysis at a  
Figure 2: X-ray photoelectron spectra of coconut shell biochar.  
pyrolysis temperature of 800 \({}^{\circ}\)C promoted the production of amorphous carbon which was verified through XRD analysis [33].
## 2 Materials and Methods
### Methodology of FOA-LS-SVM Model  
#### 2.2.1 Support Vector Machine (SVM)  
As a supervised machine learning method, SVM aims to finite data points based on statistical learning theory [31]. Details of the SVM algorithm can be found in the literature [32; 33].  
Assume a training data set is defined as \(\{x_{k},y_{k}\}_{k=1}^{N}\), and the input data and the corresponding output are \(x_{k}\in\mathbb{R}^{n}\) and \(y_{k}\in\mathbb{R}^{m}\), respectively. The feature space of SVM model is expressed as Equation (2).  
\[y(x)=\omega^{\mathrm{T}}\varphi(x)+b \tag{2}\]  
where \(\omega\) represents the weight vector of the same dimension as the feature space, superscript T is transpose matrix, \(b\) is bias terms, and \(\varphi(\cdot):\mathbb{R}^{n}\rightarrow\mathbb{R}^{m}\) represents the nonlinear mapping function which is not explicitly constructed.  
To solve the solution of SVM model, quadratic programming method is usually adopted. However, this method is difficult to implement and is time consuming. Moreover, it is often confronted with some problems such as long CPU time and large memory requirement when trained SVM model [18], and so on.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation  
This section addresses the accuracy of the displacement measurement and power consumption rate of the system while also analysing the data obtained from applying the system in real-world field conditions.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 4 Our Proposed PCAC Scheme
### Detail Procedures in PCAC
#### 4.2.3 Data Obtainment Stage  
The data obtainment stage shows the flow when U requests data from PC. There are three phases. First of all, [PERSON]'s identity should be audited by PC and then, CKA generates a secret key for [PERSON]. At last, U can decrypt the data with a secret key.  
**Act_Audit**: Action audit phase in the data obtainment stage is executed by three parties, CKA, U, and PC. This phase is similar to the Act_Audit phase in the data collection stage. First of all, U needs to uploaded _Pro_, to prove its identity. In addition, U also needs to select a random number \(r\in Z_{p}\) and upload \(R=g^{r}\) to CKA. And the message U needs to send to CKA is \(\{\textit{Pro},R\}\). Then, CKA randomly chooses \(r^{\prime}\) and sends it to U. U gets \(r^{\prime}\) and calculates \(m=\frac{r-r^{\prime}}{\Pi\omega}\). U generates the time stamp \(TS\) and sends a message, which contains \(m\) and \(TS\), to CKA. CKA sends an audit message \(AM=\{\textit{Pro},\textit{R},\textit{m},\textit{TS},\textit{rr}\}\) to PC for audit. PC may judge if the U's action is in overtime. In addition, the audit is operated according to Equation (1).  
If the audit is passed, it means that [PERSON] has caught the identification associated to himself/herself. Then, the CKA generates a secret key for [PERSON]. The communication network between U and CKA is a wide area network, which has a risk of being wiretapped, so our Act_Audit phase contains randomness \(r^{\prime}\) and \(U_{id}\), which is difficult to be discovered by the listener, in multiple communications.  
**Gen_key**: The secret key generation phase is executed by the CKA, aimed to generate the secret key, based on the CT. When [PERSON] requests files, he/she needs to upload \(\textit{Pro}_{U}\), as long as some proof of his/her attribute set is obtained, in order to express their identity. CKA receives the message from U  
Figure 4: The Process of Generating the Access Tree.  
and verifies the attribute set. If the verification is passed, CKA constructs an interaction to achieve specific message for Act_Audit phase. After passing the action audit, the PC calculates the SK with Equation (5), otherwise the PC sends _REJ_ to the CKA. CKA makes further calculations to generate a secret key, which is used to decrypt the symmetric key. In order to resist replay attacks, the PCAC scheme introduces a random factor to further hide the shared secret \(s\). CKA selects a random number \(d{\in}Z\), and combines \(d\) with the time stamp \(TS\) and the user's \({\textit{Pro}}_{U}\) to generate a random factor \(A\) in the secret key, denoted as \(A~{}=~{}H(d||\text{TS}||{\textit{Pro}}_{U})\). The
## 3 System Model
#### 3.1.2. Energy Consumption Factor  
Here, we consider the energy model of the node \(i\) as follows:  
\[E_{r}^{i}=\min\Bigl{\{}E_{0}^{i}+E_{h}^{i}-I(e(i))\Bigl{(}E_{tx}^{i}+E_{rx}^{i} \Bigr{)}-E_{s}^{i},E_{m}\Bigr{\}} \tag{2}\]  
where \(I(\cdot)\) is a binary marking function and \(e(i)\) denotes the event that node \(i\) receives or sends data. If node \(i\) receives or sends data, \(I(e(i))\) returns 1, otherwise, returns 0. Let the transmission power and reception power of nodes be \(P_{t}\) and \(P_{r}\), respectively. Then, the energy consumed by sending and receiving a packet can be expressed as:  
\[E_{tx}^{i}+E_{rx}^{i}=(P_{t}+P_{r})\frac{L}{R} \tag{3}\]  
Since the channel conditions are constantly changing, the total signal gain follows the Rayleigh distribution after reflective, refractive and scattering in multiple paths [25; 26]. [PERSON]'s experiment proves that Rayleigh fading channel is more suitable for signal attenuation calculation models in a realistic environment with interference factors [27]. Therefore, the received power can be further expressed as follows [28]:  
\[P_{r}=\ell P_{t}Cd_{ij}{}^{-2} \tag{4}\]  
\[C=\frac{G_{t}G_{r}\lambda^{2}}{(4\pi f_{c})^{2}} \tag{5}\]  
where \(G_{t}\), \(G_{r}\) represents the transmitting and receiving antenna gain, \(\lambda\) denotes a wavelength, \(f_{c}\) indicates the carrier frequency, \(\ell\) represents Rayleigh gain factor following index distribution, in which probability density function is \(f(\ell)=e^{-\ell}(0\leq\ell\leq\infty)\).
## 4 Experiments  
### Experimental Data Set and Experimental Environment  
The experimental data set includes two parts: a synthetic image sample data set obtained through component synthesis and a real sample data set obtained by segmenting Tibetan ancient manuscript document images. For commonly used Tibetan ancient manuscript characters, a Tibetan character component synthesis data set was created. The sample set consists of 519 classes, with 1000 samples per class, totaling 519,000 synthetic samples. Based on the character baseline information, synthetic image samples were generated for the parts above and below the baseline of Tibetan ancient manuscript characters. Above the baseline, there are 13 classes of synthetic samples, with 70 samples per class, totaling 910 samples. Below the baseline, there are 273 classes of synthetic samples, with 300 samples per class, totaling 81,900 samples.  
Additionally, 111,932 real-character image samples were obtained through segmentation. In the training process, only synthetic samples are used to train the model parameters, and real unlabeled samples are used to test the final model performance. Among these samples, the 519-class samples are used for training the whole-word CNN model. The 13-class samples are used for training the Up-CNN model, and the 273-class samples are used for training the Down-CNN model. These three models are employed for classifying and recognizing the real image samples.  
All experiments were conducted in the following software and hardware environment: The CPU used was Intel(R) i9-10885H, with 32 GB of RAM, and the graphics card was NVIDIA GeForce GTX 1650. The operating system was Windows 10 Professional Edition and the software used was MATLAB 2020b.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 4 False Data Injection Attacks Detection Modelling and Methods
### Detection Methods of FDIA
\\ \cline{3-3} \multirow{6}{*}{**Power**} & IEEE 2030-2011, IEEE 1686-2007, IEEE 1402-2000 & IPSec, VPN, TCP/IP, Smart Energy Profile Protocol (ICNP), IETF with Power Systems \\  & 1686-2007, IEEE 1402-2000 & Interoperability architectural perspective (PS-IAP) & \\  & & Interoperability architectural perspective (PS-IAP) & \\* NIST and FERC standards' discussions about IPv4 and IPv6 continue. When it is needed to install or change the equipment, usage of two protocols can cause more issues and require more complex infrastructure. Furthermore, against upper-layer protocols attacks such as SQL injection and FDIA, IPv4 or IPv6 stack can be used to communicate with the client. Organizations will need time to achieve solutions for IPv6, since they have been working on IPv4 over the years [41; 42; 193]. * FERC does not indicate the adoption of standards or how effective they are, but given the increasing use of communication and information technology in the field of electricity and energy and the evolving nature of cyber threats, it tries to offer solutions that will help reduce the risk posed by these threats on the electricity grid, which require constant attention [41; 42]. * ISO/IEC 15,408/EAL, ITSEC, TCSEC, CTCPEC Standards are built to be used as the reference for evaluation of Internet security, but they are insufficient for complex infrastructures. Mainly, ISO/IEC 30,111 Standard describes processes for potential vulnerabilities in IoT services [43; 194]. * In the Information Security Management System (ISMS) with ISO/IEC 27,000 Series, following requirements can be used to provide access to facilitate organization's data. When the ISMS allow to access the information security requirements of customers and other stakeholders, meet the data and manage information assets to facilitate improvement and adjustment to current organizational goals [193]. * ISO/IEC 62,351, IEC 60870-5 and DNP, IEC 563's IP usage causes devices to be vulnerable to IP-based network attacks such as IP spoofing, DoS, and others. In the usage of TCP/IP, Adequate standardization has not been achieved for the implementation of consistent security solutions. Since the security level of different wireless protocols also changes, it becomes difficult to adjust the security level of IEEE 802.11.i and IEEE 802.16.e, IEEE 61,850 standards, and it can be concluded that IEEE standards working with different protocols are more vulnerable to MitM attacks [194; 195]. * AES is confirmed from many organizations because of its strong security and high performance. However, encryption technologies' choice depends on the criticality and risks of the communication system that needs to be protected [196]. * Traditional physical access approach in NERC-CIP standard needs to be revised to address unauthorized access issues [42; 200]. NERC1300 is dedicated to identification and mitigation of cybersecurity vulnerabilities of critical assets [43; 69].
# Circularly Polarized Vivaldi Antennas Integrated with Septum-like Polarizer
###### Abstract  
In this paper, two orthogonally placed Vivaldi antennas with a septum-like polarizer to generate circular polarized (CP) waves are presented. Septum polarizers have garnered attention due to their simple structure and high quality of CP waves. While a typical septum polarizer has been applied to various types of waveguides, its applicability to the substrate integrated Vivaldi antenna is demonstrated here for the first time. A pulse train-shaped polarizer is used, which is placed on one of the two Vivaldi antennas. The contours of the polarizer are optimized using a genetic algorithm to provide an equal amplitude and 90\({}^{\circ}\) phase difference between the two orthogonal electric fields. In contrast to typical feed networks with a 90\({}^{\circ}\) phase shifter, any unwanted loss caused by an electronic circuit can be greatly mitigated. The antenna prototype was fabricated, and its radiation pattern and impedance matching were measured and compared to the simulated results.  
circular polarization; Vivaldi antenna; septum polarizer; genetic algorithm; feed network design; co-planar antenna; full-wave simulation 202424346
## 2 Materials and Methods
### SBF Immersion  
The degradability of scaffolds was evaluated through the SBF soaking test. The SBF was prepared using the Kokubo method [24]. Inorganic aqueous solution (pH = 7.4) with ion concentration near human blood plasma was used [25]. Scaffolds with different contents of nHAp were soaked in SBF and kept at 37 \({}^{\circ}\)C. The ratio of scaffold surface area to SBF volume was kept at 1 cm\({}^{2}\) to 10 mL. The SBF was refreshed every two days. After finishing each immersion period, the scaffolds were taken away from solution and dried for 12 h at 60 \({}^{\circ}\)C.  
The weight of scaffolds was measured before (\(W_{0}\)) and after soaking (\(W_{t}\)) to determine their degradation degree. The degradation (weight loss) was calculated using the following equation: Degradation = \((W_{0}-W_{t})/W_{0}\times 100\%\). Meanwhile, the pH of soaking solution was measured every other day through an electrolyte-type pH meter (PHS-2C, Jingke Leici, Shanghai, China). Moreover, the apatite formation on the scaffolds was detected by SEM, EDS, and Fourier-transform infrared (FTIR, Thermo Scientific Co., Madison, WI, USA) technology.
# Experimental and Numerical Studies on the Structural Performance of a Double Composite Wall
## 4 Analysis Results and Discussions  
### Comparison of Test and Analysis Results  
Figure 11 compares the test and analysis results of the DCW-F specimen. It appears that the proposed non-linear flexural analysis model was able to estimate the initial stiffness and post-cracking stiffness of the specimen very closely and predict the flexural strength of the specimen and the displacement at failure with a high level of accuracy. Therefore, it is ascertained that the non-linear flexural analysis model proposed in this study can be applied in the design of DCW and prediction of its flexural behavior.  
Figure 10: Finite element model for shear analysis: (**a**) schematic description of the finite element model; (**b**) bond–slip relationship between bolts and concrete.  
Figure 12 compares the test and analysis results of the DCW-SN and DCW-SB specimens. The finite element model adequately evaluated the shear behavior of the DCW-SN specimen without bolts and provided very approximate estimations not only on the shear strength of the specimen, but also on the displacement at failure. In particular, it was found that for the DCW-SB specimen with bolts placed in the shear span, the member behavior under high loads is greatly affected by the bond performance between the PC panel and bolt elements, as shown in Figure 12b. When a perfect bond was assumed between the PC panel and bolt, the finite element model was found to overestimate the shear strength of the specimen. On the contrary, when the bond between the PC panel and bolt was not considered in the shear analysis (i.e., non-composite condition), the finite element model provided conservative shear strength estimation results. The analysis model to which the constitutive laws for the bond-slip relationship were applied, considering the partial interaction between the PC panel and bolt, estimated the shear behavior of the specimen most closely. In particular, it showed relatively good simulations not only of the shear strength of the specimen, but also of the behavior of the bolt according to the load, as shown in Figure 12c. From the experiment and analysis results, it appears that the bolt exerted approximately 65-70% of its yield strength (\(f_{yb}\)) at the shear failure of the specimen, based on which the following section aims to propose a practical shear strength equation for DCW members with CIP concrete and bolts.  
Figure 11: Comparison of the test and analysis results (DCW-F specimen).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Peak** & **RT** & **[M-H]-** & **[_m/z_)** & **Compound** & **Refs.** \\ \hline
1 & 0.984 & 305.1158 & Glutathione (oxidized form) & [34,35] \\ \hline
2 & 1.123 & 129.1799 & UI organic acid & [35] \\ \hline
3 & 1.206 & 215.2558 & UI & \\ \hline
4 & 1.361 & 191.2826 & Quinic acid & [36] \\ \hline
5 & 2.069 & 337.2717 & 5-Aminoimidazole-4-carboxamide-1-beta-D-ribofuranosyl & \\  & & & 5\({}^{\prime}\)-monophosphate & [34,35] \\ \hline
6 & 2,325 & 191.2458 & Quinic acid & [36] \\ \hline
7 & 2.791 & 321.2785 & UI & \\ \hline
8 & 3.154 & 377.3181 & UI & \\ \hline
9 & 3.619 & 393.3092 & UI flavonoid & [35] \\ \hline
10 & 3.808 & 377.3181 & UI & \\ \hline
11 & 3.997 & 211.2830 & UI & \\ \hline
12 & 4.307 & 431.2463 & Apigenin-7-O-glucose & [34,35] \\ \hline
13 & 4.428 & 279.3191 & p-Coumaric acid cinnamyl ester & [37] \\ \hline
14 & 4.583 & 361.3319 & UI & \\ \hline
15 & 4.806 & 375.3049 & Loganic acid & [38] \\ \hline
16 & 5.082 & & 707.1758 & Chlorogenic acid & [39] \\ \hline
17 & 5.358 & 577.2130 & Pelargonidin 3-O- robinobioside & [34,35] \\ \hline
18 & 5.547 & 707.1758 & UI & \\ \hline
19 & & 579,2114 & UI & \\ \hline
20 & 5.945 & & 707.1758 & Chlorogenic acid & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The phenolic compounds identified in guelder rose fruit.
## 4 mobiDOM Design
### Malware Detector
#### 4.1.2 Real-Time Malware Detection in The FTL  
The MDetector is implemented in the flash translation layer so that even if the malware can compromise the operating system, it cannot subvert the MDetector. We only consider the data corruption malware (Section 3.2), which will always create issues with I/Os on the external storage in order to corrupt user data. Our intuition is that special behaviors of malware at upper layers will eventually cause special access patterns [12] on underlying flash memory, and by capturing and extracting those patterns in the FTL, it is possible to detect the malware. To function correctly, the MDetector relies on a classifier, which is able to classify any I/Os as malicious and non-malicious in real-time. The classifier can be trained using the I/Os generated by running a set of pre-collected malware (Section 6.1). Once the classifier has been trained and loaded, MDetector will monitor all the I/O requests issued from the upper layers, analyze them in real-time, and decide whether there is malware present. If any malware is detected, the MDetector will work with the TApp to get the user aware of the instance (i.e., via the QUERY command issued by the TApp periodically).  
**The design details of the classifier**. Each malware/software may generate a sequence of I/Os in the FTL. Each I/O typically contains the following information: (1) a flag indicating whether this I/O is corresponding to a read or a write operation, (2) the address of this I/O, and (3) the data length of this I/O. Our objective is to differentiate the I/O sequence of the malware from that of the regular software. To achieve this objective, we use k-Nearest Neighbors [13] (kNN), a non-parametric supervised learning algorithm. We choose the sequence of fixed-length I/Os captured for each software/malware sample as the feature. An important step for kNN is to measure the distance between any two software/malware samples. However, it is challenging to measure the distance between two I/O sequences. To address this challenge, we utilize the dynamic time warping algorithm [14], which has been designed to measure the similarity between two temporal sequences. Given two sequences \(S_{1}\) and \(S_{2}\), we compute its distance following these steps:  
1. We view each sequence as a collection of points, and each point corresponds to an I/O of this sequence. We also initialize empty queues \(Q\).
2. For \(S_{1}\), we compute the euclidean distance from its first point to each of the points in \(S_{2}\). The minimal distance will be used as the distance between the first point to \(S_{2}\) and added to \(Q\).
3. We repeat Step 2 for each of the remaining points in \(S_{1}\).
4. For \(S_{2}\), we compute the minimal distance between each of its points to \(S_{1}\) by repeating Step 2 and 3.
5. The distance between \(S_{1}\) and \(S_{2}\) will be the sum of all the values in \(Q\).
## 3 Use-Case Definition
### RADAR Significance  
Automotive RADAR furnishes the essential range and speed data for driver assistance systems, including Long-Range RADAR (LRR) adaptive cruise control, automatic emergency braking, cross-traffic alert, lane change assist, and Short-Range RADAR (SRR) parking aid, as well as pedestrian detection. The signal processing functions involved are range estimation, Doppler frequency estimation, direction of arrival estimation, and tracking. In normal cases, multiple RADAR channels are required for angular information or to compensate for any information loss. In our case, we adopt ocalization-based data collection from multiple vehicles for data fusion and extract any valuable information. The significance of the automotive RADAR market is increasing at 40% year-on-year, especially in the premium or mid-segment vehicles. Prominent Automotive RADAR Original Equipment manufacturers include Bosch [27], Continental, NXP Semiconductors or STMicroelectronics for object detection even at 200 km/h. Also, for vehicle safety concerns, as we evolve from L2 autonomous vehicles to fully autonomous L5 vehicles, RADAR is necessary for safety optimization while driving.