esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
Unlike conventional approaches that regulate the inverter current, the PR controller is designed to track the dynamic error of the grid current. This approach effectively simplifies the control logic while delivering favorable performance in harmonic compensation and power balancing. This paper further enhances the control strategy presented in References [22; 23] by incorporating the harmonic reference current extraction method based on instantaneous power theory, as proposed in Reference [16]. An improved PR control scheme for APFs is developed, which eliminates the need for a PLL and relies solely on voltage and current sensors located on the grid side. The key innovations are as follows:  
1. Harmonic reference extraction based on instantaneous power theory: In the proposed scheme, the reference current is derived by applying frequency domain instantaneous power theory to the measured grid-side voltage and current. This allows the fundamental active power to be isolated and used as the reference, enabling precise extraction of the desired current component. Compared to the time-domain SCEA method adopted in [22; 23], which suffers from intrinsic delay due to quarter-cycle accumulation, the proposed approach offers faster response and improved real-time performance. 2. Sensorless control strategy without load current measurement: The control architecture utilizes only grid-side voltage and current measurements to both generate and track the reference current, completely eliminating the need for load-side current sensors. Such a design simplifies the sensing configuration and improves system robustness, especially in contrast to [22; 23], where both grid-side and load-side current sensors are still required. 3. Reduced PR controller demand through instantaneous power theory: Instead of assigning a dedicated PR controller for each harmonic order, the proposed method performs harmonic compensation through instantaneous power theory, with only a single PR controller used for tracking the fundamental frequency current. The compensation effect for higher-order harmonics is achieved indirectly through accurate shaping of the inverter current. This reduces controller count and computational burden while maintaining effective multi-order harmonic suppression. The remainder of the paper is organized as follows: Section 2 presents the system topology and mathematical modeling of the APF; Section 3 details a PLL-less load current sensorless improved PR control; Section 4 provides simulation results; Section 5 discusses experimental validation; and Section 6 concludes the paper.
## 5 Discussion
### Implications of Submarine Channels' Morphologies for Paleoenvironment Interpretation
Implications of Differential Compaction, Horizon Interpretation and Burial Effects on the Petroleum Prospectivity  
The presence of submarine channels in the study area (Figures 4, 9 and S1) with their fine- and coarse-grained sediments could have an impact on the differential com  
Figure 10: Recent New Zealand elevation and bathymetry (Source: NIWA Open Data License v1.0). Red rectangle is the study area that located in a marine environment (Continental shelf). See Figure 1a for coordinate location. paction, horizon interpretation pitfalls, and burial effects that might influence petroleum prospectivity in the study area. In the study area, low amplitude and continuous reflections elements of the channel-filled and surrounding deposits (Figures 5 and 6) indicate that the channels are mostly filled with a fine-grained suspended material [28; 35; 38; 49]. Meanwhile, the high amplitude reflections depict the presence of coarse-grained sediments transported by high energetic flows [19; 62]. Laterally, this lithological variation caused differential compaction between the stratigraphy below the channel features and its surroundings (Figure 11a). This could lead to hydrocarbon trap formation [64] and pore size reduction [65]. In addition, overpressure--fluid expulsion [66; 67]--and sea-floor slope instability [68] could also be triggered. These overburden lateral variations, low-velocity anomalies (incised channels), and sea-floor incisions (causing variation on the water columns) on the final post-stack time-migrated 3D image (see Section 3.1) could also lead to a pitfall on horizon interpretation, namely velocity push-down (Figure 11a). This pitfall gives rise to an apparent feature that is mapped as channels (reservoir) or syncline (traps). However, post-stack depth-migration (PSDM) with careful velocity analysis will minimize these artificial features [69]. Figure 11: **(a)** Three-dimensional view of Seabed horizon and seismic amplitude, and **(b)** Thickness map between seabed and top Neogene (study interval). See Figure 1a for coordinate location. Furthermore, the studied interval has a stratigraphic thickness ranging from a few meters to hundreds of meters with zero thickness when the seabed has incised the base horizon (Figure 11b). As sediments become buried, this thickness variation could also be contributing to the late-phase burial and generation of a petroleum system in the study area, underlying the Cretaceous and Paleocene coaly rocks [70], where the thinner area (south and SE) might provide less impact compared to other areas (Figure 11b). These implications could also be a petroleum prospectivity analogue for an area with a similar geological setting in a frontier basin worldwide.
## 6 Methods
### Vessel Enhancement Filters
#### 6.1.2 Hessian Vesselness Filter  
By integrating the directional gradient and the Hessian matrix, [PERSON] et al. [32] suggested a modified multi-scale Hessian filter. It is assumed that the brightness of a hepatic vessel is high in the middle and progressively declines toward the end, allowing it to be described as a Gaussian structure transverse to its axis. The convolution between the image gradient field and the Gaussian kernel is the basis of each approximation in the Hessian matrix \(H\) at a specified scale \(\sigma\). The eigenvalues \(\lambda_{1}\), \(\lambda_{2}\) are calculated and utilized to compute the curve derivation \(R\) and similarity measure \(S\). The curvilinear likeliness \(\mathcal{E}\) is given by  
\[\mathcal{E}(x,y,\sigma)=\begin{cases}0&if\lambda_{2}<0\\ e^{\frac{-R}{2\beta_{1}^{2}}}[1-e^{\frac{-R}{2\beta_{2}^{2}}}]&otherwise\end{cases} \tag{2}\]  
The response of the filter L is set as a maximum of different scales that approximate the size of the ridges. It is expressed as  
\[L(x,y)=\max_{\sigma_{\text{min}}\leq\sigma\leq\sigma_{\text{max}}}[\mathcal{ E}(x,y,\sigma)] \tag{3}\]  
Figure 4: Comparison of different vessel enhancement filters.  
The parameters \(\beta_{1}\), \(\beta_{2}\) control the sensitivity of the filter to the measure \(R\) and \(S\), respectively.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 2 Related Work
Other works such as [20; 23] aim to build anomaly-based systems to detect IoT botnets. These works present techniques that model the legitimate behaviour of IoT devices. This type of approach can be useful due to the systematic behaviour of IoT devices, which usually perform specialised tasks. Thus, behaviour deviations are detected as malicious, regardless of the botnet. In one of these works, [PERSON] et al. [20] proposed the N-BaIoT system, which uses deep autoencoders to detect botnets in IoT devices. They use the network traffic of IoT devices to build a model of legitimate behaviour and detect anomalies. The authors tested the approach on two botnets, Mirai and BashLite, and without using any labelled data, they achieved great results in detecting the attacks, with low false positives rate. Despite the great results, the use of deep autoencoders can be computationally costly even to a gateway and demands large amounts of data to train the model. The work proposed by [23] explores the construction of an IDS to protect Linux routers, a very popular target in recent years. They tested three different types of anomaly detection techniques, PCA, OSVM, and a naive detector using n-grams, to analyse syscall data from routers. They used two botnets to test their approach, MrBlack and Mirai, in simulated routers, and all tested methods presented good results. However, it can be challenging to find the best features among syscalls during data pre-processing, due to the large number of syscalls generated by multiple programs running in the system. Overall, different issues were found in these works such as the lack of testing in real devices or with different botnet samples, dependency on third-party systems, use of computationally costly methods, and need for large amounts of data to train the models. Also, the analysed works are mostly network-based approaches and did not explore resource consumption data to detect botnets. Considering these gaps, we proposed an approach for botnet detection in IoT devices that do not need malicious labelled data to build a detection model and relies on host-based data such as CPU and memory utilisation. Additionally, our solution does not compromise the device's performance or requirements since it is designed to be used in more robust IoT systems.
## 5 Experimental and Simulation Setup Analysis
### Binary Classification Result Analysis  
We use binary classification metric performance evaluation, such as the best-trained model's precision, accuracy, and f1-score. We show this with our proposed combined machine learning and deep learning models with intrusion detection capabilities, known as ML and DLMIDPSMs. ML and DLMIDPSMs are applied and evaluated with the methods [26] DT (decision tree) and SVM (support vector machine), and the results are shown in Table 4 below. Thus, the DT and the SVM models are initially tested and validated with the combined ML and DLMIDPSMs. The DT has a precision/accuracy of 74% in 100 epochs. Table 4 shows the simulation results of performance metrics used.  
Figure 20: Confusion matrix of proposed models’ binary classification between methods [31; 32] that result from the DLMLP.  
Consequently, we modify the ML and DLMIDPSMs to investigate the SVM using the linear kernel function in state 1. When we consider detecting botnet attacks like Mirai, we validate the newly developed SVM model at 100 epochs with a precision/accuracy of 83%. To develop and implement the DT and SVM models more precisely and accurately, we develop the proposed combined ML and DLMIDPSM methods for [31,32]. The combined model has a significant precision/accuracy which is 99% higher in the detection of Mirai attacks compared to the model in method [32], DT, SVM ML models separately, and method [31], when considered in the detection of all intrusion attacks of all kinds, as shown in Table 5. This also presupposes that our proposed model is capable of being used for producing a standard IoT security solution for many internet of things applications, including intelligent transportation systems (ITS) for internet of vehicles (IoV), smart health applications, and smart city development.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 4 LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES
### Lshade
#### 4.1.6 Semi-Parameter Adaptation (SPA) of Scaling Factor (F) and Crossover Rate (Cr)  
First Part of SPA  
SPA's idea is focused on adapting \(Cr\) using original LSHADE adaptation when the current number of iterations is less than half of the maximum number of iterations. While the \(F\) parameter is tuned using uniform distribution randomly within a specific limit as given in Equation (20) [26].  
\[F_{k}=0.45+0.1*rand \tag{20}\]  
However, \(Cr_{k}\) values are adapted based on the following Equation [26]:  
\[Cr_{k}=randn(Mcr_{k},0.1) \tag{21}\]  
where \(Mcr_{k}\) is a randomly chosen memory slot that stores successful means of preceding generations. The memory index \(k\) is randomly selected from the range \([1,h]\). \(h\) is the size of memory. All \(M_{cr}\) initial values are 0.5, and one memory slot \(M_{cr}\) is updated at the end of each generation using the arithmetic mean of \(Cr_{k}\) values, that succeed to generate new individuals.  
Second Part of SPA  
In this part, the adaptation is concentrated on \(F\), and LSHADE adaptation is used to adjust the \(F_{k}\) parameter as follows [26]:  
\[F_{k}=randc(MF_{k},\sigma) \tag{22}\]  
where \(\sigma\) is the standard deviation for Cauchy distribution and equals 0.1, \(M_{F}\) is a memory slot randomly chosen to store successful means of preceding generations. One memory slot \(M_{F}\) is modified using the Lehmer mean of \(F_{k}\) values at the end of each generation to successfully generate new individuals. The \(F_{k}\) value of the previous five generations of the first part of the SPA was used to initialize the memory slot \(M_{F}\) for the second part of SPA. The \(Cr\) parameter adaptation process remains as is during the second part. However, due to the nature of the LSHADE parameter adaptation, the \(Cr\) parameter is gradually frozen to the adapted values. According to the SHADE parameter adaptation, when successful individuals do not produce all \(Cr\) values in a generation, the corresponding memory slot is adjusted to the terminal value. Therefore, \(M_{Cr}\) will not be updated until the search is over.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 7 Multi-Agent Control and Consensus Algorithm  
The obtained control laws (25) and (31) are applied separately to each satellite of the constellation and use information about the relative position in satellite form. To improve the accuracy of the formation positioning, you can apply multi-agent control based on the consensus algorithm (protocol) [37; 38].
## 2 Results and Discussion
According to the previous report [16], Mg Al-HT contains Lewis acidic sites due to Al\({}^{3+}\) and the Brensted basic sites due to (OH\({}^{-}\)), which exert their catalytic action first via the adsorption of the amine lone pair to acidic sites, accompanied by a deprotonation/re-protonation process that assists the nucleophilic addition to endo nitrogen and affords the Michael adduct which is subjected to cyclization via dehydration and produces azolo[1,5-a]pyrimidine derivatives (Scheme 4). Figure 4 clarifies the reusability of Mg Al-HT for the synthesis of compound **3a** tested under a Q-tube system in which it was found that the reaction was repeated seven times using a regenerated catalyst. Each time the catalyst was removed after the reaction finished (as examined by TLC) via filtration, it was washed with hot ethanol and sonicated in ethyl acetate for 10 min to desorb all adsorped organic materials before drying under a vacuum. The recovered catalyst was reused seven times and the time required to accomplish the reaction, and the % yield were taken as an indication of catalytic activity. It is noteworthy  
that this reusability study not only addressed the catalytic activity of the used heterogeneous catalyst but also provided insights inon the reproducibility of the Q-tube system. Finally, we succeeded in confirming and ascertaining the economic and reproducible high-pressure green system \"Q-tube\" for organic synthesis.
## 2 Materials and Methods
### Observed Crop Growth Stages  
As detailed in Section 2.1, the observation of crop growth stages at the CN-SQ site is conducted in collaboration with the National Agro-Ecological Observation and Research Station of Shangqiu, Institute of Farmland Irrigation of the Chinese Academy of Agricul  
Figure 1: The map of the study site (CN-SQ), marked with the green diamond in this figure. Shangqiu has been marked red in the map of Henan Province, with other cites painted royal blue. The reference map is at the up-right corner, indicating the location of the Henan Province is at the heart of North China Plain as the center of one main grain-producing area in China.  
tural Sciences. The dedicated team from this institute undertakes comprehensive crop growth stage observations, encompassing not only the experimental farmland within the study site but also the broader agricultural landscape in the vicinity. Ground-based spectrum measurements, or in situ measurements, were conducted within the experimental farmland of the study site. These observed growth stages within the station served as reference data for estimating crop growth stages at the site level (site-OBs). Additionally, considering the broader coverage of satellite data over a larger agricultural landscape surrounding the study site, the average of the observed growth stages from both within the site and the broader agricultural area in proximity (area-OBs) were utilized as reference data for satellite-based estimations. The observations are meticulously recorded to document various agronomic growth transitions and milestones. The recorded crop growth stages and their respective definitions are presented in Table 1, sourced from ground observers' expertise and aligned with the Integrated Crop Management Handbook and Best Management Practices for Wheat Production [49; 50].  
For summer maize, the tasseling and silking growth stages occur in close succession, typically within a 1- to 3-day window. Given the potential margin of error in observation, this study simplifies this by treating these stages as one, referred to as tasseling and silking (T&S). Similarly, the maturity and harvest stages, often temporally intertwined, are combined into a single stage named maturity and harvest (M&H).  
Moreover, not all growth stages have an equal impact on crop yield. For instance, in maize cultivation, the silking stage is highly sensitive to water stress, necessitating meticulous irrigation management [51; 52]. Additionally, the V5 and jointing stages, when the stem of maize grows fast described in Table 1, demand increased attention due to maize's heightened need for nitrogen fertilizer and water during these phases [53; 54; 55; 56; 57]. Similarly, both wheat and maize assign critical importance to the milk stage [49; 58]. During this phase, kernels undergo crucial filling, exerting a substantial influence on the overall yield potential. Recognizing the pivotal nature of these growth stages, this study designates them as key growth stages (KGSs), which have been bolded in Table 1.
## 2 System Model
### Delay  
In addition to reducing backhaul load, caching also benefits from reducing the time delay of transmissions. In this paper, we consider the average time delay of UEs, which consists of two major parts: Wireless propagation delay and backhaul delay. Let \(d_{1}\) denote the average wireless propagation delay of a UDN which is related to the size of a file \(F_{\text{max}}\) and the data rate of transmission.  
\[d_{1}=\frac{1}{|M^{\Omega}|}\sum_{m\in M^{\Omega}}\frac{F_{\text{max}}}{R_{m}}. \tag{12}\]  
Backhaul delay, denoted by \(d_{2}\), is related to whether the files are hit by the associating BSs of UEs. Due to joint transmission of CoMP, the backhaul delay for a specific UE \(m\) occurs when the requested file is not cached on all its associated BSs. That is, the minimum operation should be applied on backhaul delays due to file transmission between the associated BSs and the core network. More specifically, \(d_{2}\) can be represented as follows,  
\[d_{2}=\frac{1}{|M^{\Omega}|}\sum_{m\in M^{\Omega}}\left(\min\left\{\frac{F_{ \max}}{U_{\text{back}}},\sum_{b\in\Omega_{m}^{\Omega}}(1-\mathbf{q}_{m}\mathbf{y}_{b}^{ T})\frac{F_{\max}}{U_{\text{back}}}\right\}\right). \tag{13}\]  
As a result, the total time delay of the network is:  
\[D=d_{1}+d_{2}. \tag{14}\]
## 2 Related Works
### Decision Boundary Maps (DBMs)  
Decision Boundary Maps (DBMs) [21] closely follow the above workflow. While the original DBM used t-SNE [26] and, alternatively, UMAP [27] for the projection \(P\), any user-chosen projection method can be used, such as PCA [28], LAMP [29], Least-Square Projection (LSP) [30], or Piecewise Laplacian Projection (PLP) [31]. For the inverse projection \(P^{-1}\), DBM evaluated two techniques, namely iLAMP [32] (the inverse of the LAMP projection technique mentioned above) and NNNiv [33]. Compared to iLAMP, which constructs the backprojection by interpolating between the samples of \(D\) and \(P(D)\) using linear or radial kernels, NNNiv deep learns a regressor to output \(D\) using \(P(D)\) as input. NNNiv achieves inverse projections with a lower mean absolute error than P and is also simpler to implement and significantly faster [21; 33]. Note that the same deep learning idea has been used to construct direct projections with similar speed, quality, and implementation simplicity advantages [34].  
A DBM is simple to implement and allows one to use any direct and inverse projection techniques. Also, a DBM works without requiring label information, which means that constructing decision maps only depends on the distribution of points in \(D^{\prime}\) in the feature space. In our subsequent evaluation, we use DBM with UMAP for the direct projection \(P\) and NNNiv for \(P^{-1}\), as this combination led to the best results in earlier DBM evaluations [21]. More information about DBMs is available at [[https://mespadoto.github.io/dbm/](https://mespadoto.github.io/dbm/)]([https://mespadoto.github.io/dbm/](https://mespadoto.github.io/dbm/)) (accessed on 1 September 2023).
## 4 Experimental Validation
### Data Source Description and Experimental Settings  
The experiments are launched based on data captured by a Kinect RGB-D camera, containing daily human activities. Captured sequences include both RGB frames and normal depth frames captured by Kinect. Kinect extracts human skeleton automatically based on the combined information of RGB frames and depth frames [15]. However, SSSE is deployed only on RGB frames captured by the monocular RGB camera on Kinect.  
In each sequence captured for effective range validation, Kinect is set up at a static distance from the human subject. The photographic distance increases from 1 m to 20 m with a fixed step of 1 m. Sampled skeletonization results based on both methods at different distances are presented in Figure 6a.