esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Interleaved LLC Resonant Converter  
In order to increase output power and reduce the current ripples, both in input and output ports, interleaved LLC resonant converter can be used. High power LLC converters with more than 3 kW power level are introduced using the multiphase technique with more components, volume, weight, cost, and complexity of current sharing [11]. Two and three phase LLC converters can achieve higher efficiency, as reported in the literature [96; 97; 98; 49]. In order to optimize the magnetic components, there is a trade-off between the winding and core loss of the transformer.  
Various arrangements have been introduced for the three phase interleaved LLC converter to achieve automatic current sharing, like Y-primary, Y-secondary, and primary delta capacitor network [100; 101; 102; 103]. Interconnecting the primary sides to a common floating Y-node and interleaving the switches provide automatic current sharing between phases even if there is unbalance between the resonant components of the phases. It also helps in reshaping the current waveform and reduction of the RMS current [19]. Moving the Y-node to the secondary side offers the benefit of automatic current sharing, but the secondary winding current would be doubled, leading to more winding loss [102; 103]. Using delta-connection for both primary and secondary side windings can reduce the current by a factor of \(\sqrt{3}\). It means that the winding loss will reduce to one third, while the voltage\"second is increased by a factor of \(\sqrt{3}\)[103].  
By using interleaved topologies, the primary and secondary side RMS current could be reduced due to the interaction between phases and flux cancellation [100; 101; 102]. [PERSON] et al. integrated all inductors and transformers into one magnetic component to reduce cost and volume [102]. Another three phase interleaved LLC converter has been proposed by [PERSON] et al. for high voltage application, but it needs an additional balancing transformer [104].  
By increasing switching frequency, integrated magnetics with PCB windings, as proposed in [11], can achieve higher efficiency and reduced common mode noise. Older LLC converters without GaN transistors were operating at around 100 kHz with bulky magnetics. Thus, implementing integrated magnetics with PCB could be very difficult and expensive [11].  
Figure 5: Most common high-side driver topologies: (**a**) isolated driver; and (**b**) half bridge driver with bootstrap.
## 3 ML Architectures and Results  
According to the earlier study [9], an artificial neural network is a reliable ML algorithm for this type of classification problem. We started by exploring the use of random forest (RF) classifiers using Matlab built-in functions [36] as an alternative to ANNs, but the average accuracy obtained was lower than when using ANNs. So, we decided not to follow this route to build our classifier and focused on ANNs. Yet, for the sake of completeness, we present the results obtained with the RF algorithm (Section 3.1).  
ANN-based classifiers were considered with cross-validation, which was achieved by using the function **fitcnet** generating different ANNs. For this, we fixed 80% of the entire dataset as a training set and 20% as a testing set. The output of the function provides the best ANN according to the options \"OptimizeHyperparameters\", setting \"MaxObjectiveE-valuations\" equal to 100. The hyperparameters taken into account included the maximum number of objective functions evaluated, the activation function, and layer sizes.  
Below, we present the results obtained for different sets of attributes with random forest and the optimized ANN. We verified that ANNs indeed produce better results that RF. This study proceeded with ANNs as basis for our classifier. We also verified that, at some variance with the conclusion of Ref. [9], the angle of incidence is an important attribute, especially if we use less attributes as input for the ANN. This can be understood from the physical point of view for well-polished surfaces (such as car paint samples), which are characterized by strong specular reflection. So, we included the AoI even in the reduced set of MME attributes.  
We also propose an ML architecture based on the ANN and the K-nearest neighbors (KNN) classification algorithm [37]. It consists in three steps. First, an ANN classifier is built by using the **fitcnet** function on the training dataset. Secondly, using the dataset on the ANN classifier, seven clusters are built, one for each class of objects obtained from the ANN, but keeping the original label. Finally, for each of the seven clusters, a KNN classifier is built (the best value of \(K\) is chosen by using the Matlab function **fitcknn**), allowing for an improvement in the success ratio for some classes when a reduced number of attributes is used. This approach is further explained in Section 3.4 of this paper.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 2 Cybersecurity Vulnerabilities in Smart Grids and Mitigation Requirements
### Cyber Security Requirements in SGs  
The term \"Cyber-Physical systems\" (CPS) relates to the currently prevalent terms Industry 4.0, Internet of Things (IoT), Machine-to-Machine (M2M), the Internet of Everything, TSensors (Trillion Sensors), and the Fog. These reflect a view of a technology that profoundly engages the physical world with the information world [30].  
Cyber means computed, communicated, and controlled but discrete, logical, and switched. On the other hand, physical means that systems are bound by physics laws and operating continuously. Cyber-Physical (CP) means the systems in which the cyber and physical systems are closely integrated at all environmental conditions and levels.  
Therefore, SG is a typical CP System which integrates a physical energy transmission and distribution system with the cyber process of communication and control [31]. As SGs grow, millions of smart assets with two-way communication ability will be integrated. This situation causes new security problems in a large geographical area [32]. More complex system security can be obtained using real-time communication standards to modify the control system between generation, transmission, distribution, and consumers in the network structure [33].  
In electrical infrastructures, various organizations have established some security standards to regulate issues such as the system's proper operation, protection of information and against attacks. Various standards are established by the organizations working within the scope of cybersecurity in SGs. Some of the organizations that constitute these standards can be listed as follows.  
International Society of Automation (ISA) [34], National Infrastructure Protection Plan (NIPP/CISA) within the Department of Homeland Security's Cybersecurity and Infrastructure Security Agency, being a shareholder with the National Institute for Hometown Security (NIHS) [35], National Institute of Standards and Technology (NIST) [36], Institute of Electrical and Electronics Engineers (IEEE), Computer Security Division (CSD), Com  
Figure 3: Illustration of cyber security in smart grids.  
puter Security Resource Center (CSRC), Organization for Standardization/International Electrotechnical Commission (ISO/IEC), Federal Energy Regulatory Commission (FERC), and The North American Electric Reliability Corporation (NERC) [37].  
In addition to these organizations, ISO 17,799 (27,000 series) security standard is fundamental in establishing more secure, consistent, and scalable systems [38].  
In this study, security standards are researched about all the SG parts, which is shown in Figure 4, and divided into three sections and then examined detail in the following section:  
* Examining the firewalls of communication systems and the vulnerabilities in the protocols;
* Based on attacks on energy transmission and distribution systems;
* Applied for remote control security of the devices connected to the system.
## 3 Results and Discussion
### Effects of Grain Boundaries and Grain Orientations
As seen in the LG1 case, the extruded pin was already curved to the left, even when all the bottom grain orientations were 0\({}^{\circ}\) to the extrusion direction. When the grain orientations of the large bottom grains were changed in the single-grain cases, the curvature directions were still towards the left, but the amount of curvature (ECI values) changed according to the set grain orientations. The results clearly showed that the initial grain boundaries of the bottom grains significantly affected the curvature directions.
## 3 Methodology of FSACM
### Hybrid Signature- and Anomaly-Based Attack Detection Technique  
While determining the attack status and type, initially the signature-based model and then the anomaly-based model are applied. The purpose of such a hybrid system is to increase the intrusion detection speed with the signature-based model and to increase the accuracy with the anomaly-based model. As is known, the signature-based model is successful in detecting known attacks quickly and accurately. The anomaly-based model is slower than the signature-based model, but it has a higher accuracy rate in detecting unknown attack types.
## 4 Fluid Dynamic Performance  
### Factors  
The dynamic performance of microbots when they move through the human body can be assessed based on the velocity field around the microbot and the size of the wake formed behind it. When the blood flow over the microbot is very slow, a laminar flow is expected, as the velocity gradient is small and adjacent layers within the boundary layer slip over each other in an orderly manner. On the other hand, at higher local velocities, the velocity gradient is likewise high and the friction between layers causes them to trip over one another, creating vortices. As in the case of aerodynamics studies, the frontal morphology of an object is such that the blood flow has a tendency to follow the shape, but as the fluid flow passes the widest cross section, the boundary layer becomes thicker and when the shape of the body departs too much from the ideal, the flow can no longer follow the shape, and the boundary layer becomes detached from the body. The object moving through the fluid flow then leaves a wake behind. Studies have shown that the drag force is approximately proportional to the size of the wake [62]. For this reason, it is crucial to diminish this wake to improve the performance of the prototype in order to get a more efficient movement, and, as a result, less injury inside the body of the patient.  
The size and shape of the object and the speed are significant factors affecting the size of this wake. However, other very important factors are the characteristics of the fluid i.e., the viscosity and the elasticity especially enhanced due to the small length scale and the type of flow. These factors will be described in detail in this section.
## 3 Results and Discussion
### The loading capacity and entrapment efficiency  
The loading capacity and entrapment efficiency were determined based on Equations (1) and (2). The results presented in Table 1 show an entrapment efficiency between 64.96 \(\pm\) 1.92% and 1.00 \(\pm\) 1.73%, with the highest value for Ga-5 HF-DPCNS2. The loading capacity showed results between 10.43 \(\pm\) 0.31% and 13.82 \(\pm\) 0.26% with the highest value for the same compound. Moreover, it can be observed that in comparing the metal complexes, higher values were obtained for Ga compounds.
## 4 Experiments
### Limitations and Future Work  
While the LFEAR model demonstrates considerable strengths in sentiment analysis, it has certain limitations that warrant further exploration. One limitation lies in the use of a single vector database for context retrieval, which could impact scalability as the volume and diversity of datasets grow. This limitation may lead to decreased retrieval efficiency and accuracy when handling large, heterogeneous datasets with varied sentiment expressions. Furthermore, data access restrictions under the Protection of Personal Information Act (POPIA) constrained our data inputs from certain mobile applications, such as WhatsApp, thus limiting the diversity and richness of sentiment sources within the financial domain. Additionally, while LFEAR effectively interprets general sentiment patterns, it may face challenges with highly nuanced or ambiguous sentiment expressions, which can limit its precision in cases where sentiments are complex or contextually specific. The model's architecture is optimised for English-language financial reviews, and its adaptability to non-English or low-resource languages remains untested, potentially limiting its transferability across different linguistic contexts. These limitations highlight areas where improvements could enhance the robustness and adaptability of the model in dynamic data environments.  
Future work should focus on several promising avenues to enhance the LFEAR model's performance and broaden its applicability. First, addressing scalability issues by incorporating advanced retrieval techniques, such as hierarchical indexing, distributed vector databases, or hybrid retrieval models, could improve its capacity to handle extensive and varied datasets. Furthermore, expanding the model's adaptability to multilingual sentiment analysis--particularly in low-resource languages such as indigenous South African languages--could significantly increase its utility in diverse financial markets. Additionally, applying clustering algorithms during preprocessing may improve document classification and retrieval accuracy by grouping similar sentiment patterns before analysis, which can enhance the model's overall efficiency and precision. Finally, exploring meta-learning and reinforcement learning approaches may allow the model to better interpret complex sentiment nuances and adapt to evolving sentiment trends, making it a more versatile tool for sentiment analysis across multiple domains and languages.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 3 Illustrative Examples  
### Example 1--Comparative Controller  
In this simulation, the original linear model used in [12] is adopted as a comparative model as follows:  
\[\dot{x}_{e}(t)=x_{e}(t)+u(t),x_{e}(0)=1 \tag{21}\]  
The initial parameter for this simulation is \(t\in[0,5]\), \(M_{L\xi}(t)=1,N_{L\xi}(t)=1\), and the cost function or criterion function is described as follows:  
\[J=50\big{|}x_{e}(T)\big{|}+\frac{1}{2}\int\limits_{0}^{T}\big{(}u^{2}(t)+x_{ e}^{2}(t)\big{)}dt \tag{22}\]  
The solution of the comparative controller is obtained as follows:  
\[\dot{x}_{e}(t)=x_{e}(t)+Q_{L\xi}{}^{*}(t)sign(x_{e}(t)) \tag{23}\]  
\[\dot{Q}_{\Delta L}=\big{|}x_{e}(t)\big{|}-Q_{\Delta L}(t) \tag{24}\]  
In this simulation, the Runge-Kutta (RK) method is applied to solve the differential equations. It is remarked that the RK method is frequently used in real time systems [16; 17; 18]. Simulation results are shown in Figure 1. In Figure 1a, the gain function \(Q_{\Delta L}(t)\) is always increased its value increases. This point indicates that the optimized gain value cannot be found. Hence, the main control \(u^{*}(t)\) is also not optimized as shown in Figure 1b. Because of the values of \(Q_{\Delta L}(t)\) and \(u^{*}(t)\), the system responses as state variables (position \(x_{e}(t)\), velocity \(\dot{x}_{e}(t)\)) are not stable as shown in Figure 1c,d. In short, the system becomes unstable after applying the optimal controller [12]. In Reference [12], the gain matrix function (24) was obtained from the general form as follows:\[\dot{Q}_{\Delta L}(t)=N_{L\xi}(t)\big{|}x_{L\xi}(t)\big{|}-G_{L\xi}{}^{T}Q_{\Delta L }(t) \tag{25}\]  
In the comparative controller, this gain matrix function has the signon (sign) function, which is not sufficient for controlling the system. This reason arises from the properties of the signon function where the default boundary in upper and lower values as \(1\) and \(-1\). In [12], the final gain matrix value has been pointed out before the progress of the control action. In the conventional optimal control law, the time of convergence of the control could be predicted. However, this property cannot apply to the gain value. Thus, the shooting method is normally used for predicting the gain value [12]. However, the shooting method cannot be used for practical systems because it takes long time to find the final solution.  
Figure 1: Simulation results of the comparative optimal controller in [12]: **(a)** gain function \(Q_{\Delta L}(t)\), **(b)** main control \(u^{*}(t)\), **(c)** state variable (position) \(x_{e}(t)\), **(d)** state variable (velocity) \(\dot{x}_{e}(t)\).
## 4 Results and Discussions
### Volumetric Estimation of Karst  
This analysis aims to provide a volumetric estimation of the karst geobodies on the Jintan and M1 platforms. Figure 17 displays the karst geobody that has been selected on both platforms. Automatic volume calculation in the software estimated a total volume of 1.45 \(\times\) 10\({}^{7}\) m\({}^{3}\) and a total surface area of 66.66 km\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Karst \% on Jintan** & **Karst \% on M1** \\ \hline STFT & 65 & 50 \\ CWT Morlet & 52 & 51 \\ CWT Ricker & 42 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative interpretation by using manual gridding calculation of the karst features delineates by short-time Fourier transform (STFT), continuous wavelet transform (CWT) Morlet, and CWT Ricker spectral decomposition techniques.  
Figure 14: (**a**) STFT method delineates the most karst features especially on Jintan platform as highlighted in the yellow arrow. (**b**) CWT Morlet wavelet delineates most karst features in M1 near to the reef rim as highlighted in yellow. (**c**) CWT Ricker wavelet delineates fewer karst features compared to STFT and CWT Morlet where the karst features were missing on the red arrow area.  
Figure 16: Evolution of the karst features on selected K1, K2, and K3 horizons show that the karst features are well-developed and more prominent on the K2 horizon, highlighting that the heavily karstified intervals falls in between SU4 and SU5 as interpreted by [10].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Platform** & **Shape** & \
\begin{tabular}{c} **Quantitative** \\ **Interpretation** \\ \end{tabular} &
\begin{tabular}{c} **Most Concentrated** \\ **Area** \\ \end{tabular} \\ \hline \multirow{3}{*}{Jintan} & \multirow{3}{*}{Dendritic} & \(\geq\)70–100 m deep & \multirow{3}{*}{Lagoon} \\  & & 3–5 km long & & \\  & & \(\geq\)500 m wide & & \\ \hline \multicolumn{3}{c}{Circular/sinkhole} & \(\leq\)1 km in diameter & \multicolumn{1}{c}{Lagoon} \\ \hline \multirow{3}{*}{M1} & \multirow{3}{*}{Dendritic} & 1–2 km long & Close to reef rim and \\  & & \(\geq\)500 m wide & lagoon \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative interpretation of the karst features observed on Jintan and M1 seismics.  
Figure 15: Horizon K1, K2 and K3 picked on different seismic unit (SU) intervals.
## 5 Conclusions  
In this study, the displacement of concrete structures was measured through LiDAR, a non-contact sensor. An algorithm was developed to extract the lateral and vertical displacement of concrete structures from point clouds measured through LiDAR. The proposed method was compared with LVDT, the conventional contact-based sensor. The conclusions of this study are as follows:  
1. With respect to the price, the LiDAR equipment applied in this study is affordable compared to contact-based measurement devices or cameras. Moreover, LiDAR has shown benefits compared to conventional devices such as saving time, being effective in structural failure conditions, and providing high-accuracy data with a resolution of approximately 1.0 mm.
2. The non-contact method was experimentally demonstrated in cases of the drift ratio of a concrete wall and the deflection of precast concrete, respectively. The results were compared with LVDT data. From the correlation analysis between LiDAR and LVDT, 99% similarity of the results was identified. This indicates that LiDAR data can be monitored to monitor specimens' deformation under loading in the same way as with the conventional method.
3. It is believed that a correlation error of 1-2% occurred in the point cloud extracted displacement value due to noise in the initial section. Further research needs to be conducted for plane fitting deformation through the advancement of the algorithm.  
Conceptualization, H.C. and U.W.; methodology, H.C.; software, D.K.; validation, T.T.P. and D.K.; investigation, T.T.P. and D.K.; resources, S.-G.J.; data curation, T.T.P. and D.K.; writing--original draft preparation, T.T.P. and D.K.; writing--review and editing, H.C.; visualization, U.W. and D.K.; supervision, H.C.; project administration, H.C.; funding acquisition, S.-G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00144050).  
Not applicable.  
The data presented and the MATLAB codes in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 15: Displacement correlation between LVDT and extraction from LiDAR: (**a**) PC-C; (**b**) PC-P.
# Automatic Roadway Features Detection with Oriented Object Detection
## 3 System Overview
### Orientation Representation
This expands the domain of the prediction from [0, \(180^{\circ}\)) to [0, \(360^{\circ}\)) and the orientation encoding discontinuity problem is avoided as demonstrated in the right region of Figure 2. Like the case of encoding with sin(\(\theta\)) and cos(\(\theta\)), six descriptors are adopted for each oriented bounding box. Both of the sin(20) and cos(20) predictors are required, because sin(20) is symmetric around \(45^{\circ}\) and \(135^{\circ}\) of orientation, while cos(20) is symmetric around \(90^{\circ}\). Therefore, a solution using orientation encoding with sin(20) and cos(20) is proposed to better represent the road objects. The proposed model adopts YOLOv3 residual skip connections and up-sampling [5] to provide predictions on three different scales, which balances the model efficiency between detecting small and big objects. The grid cells in each scale are 52 \(\times\) 52, 26 \(\times\) 26, and 13 \(\times\) 13 respectively. The network output layer tensor shape for each scale is S \(\times\) S \(\times\) 3 \(\times\) (4 + 2 + 1 + 17), where S \(\times\) S is the number of grid cells in that scale, each cell has 3 anchor boxes and each box has 4 offsets descriptors, two descriptors for sin (20) and cos (20), a confidence score, and the 17 classes in our dataset. In Figure 1, FM\({}_{\text{n}}\) is the nth output layer (feature map) of the CNN and AB\({}_{\text{n}}\) is the nth anchor box. x, y, l, w, and o are the predicted object \(x\)-axis offset, y-axis offset, length, width, and prediction confidence respectively. sin(20) and cos(20) represent the orientation encoding, where \(\theta\) is predicted object orientation angle. c\({}_{\text{n}}\) is the probability that the predicted object is classified as the nth roadway safety-related object class. The decoder translates the FM\({}_{1}\), FM\({}_{2}\), and FM\({}_{3}\) outputs into a list of undirected oriented bounding-boxes, each box is for a detected roadway safety-related object.