esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 2 Swarm Intelligence
### Cuckoo Search (CS)  
The CS [21, 24] algorithm is a nature-inspired optimization algorithm. It is inspired by the brood parasitism behavior of some cuckoo species that lay their eggs in the nests of other host birds. The CS algorithm leverages this behavior to find optimal solutions to complex problems [22]. In CS, each potential solution is represented by a cuckoo's egg,and the goal is to replace the worst solutions with new, potentially better ones. Below are detailed step-by-step instructions for the CS algorithm process.  
1. The position of a cuckoo is updated using a random walk called Levy flight, a step characterized by a heavy-tailed probability distribution. The new position \(\mathbf{x}_{i}^{(t+1)}\) of cuckoo \(i\) is given by: \[\mathbf{x}_{i}^{(t+1)}=\mathbf{x}_{i}^{(t)}+\alpha\cdot\text{Levy}(\lambda)\] (14) where: \(\bullet\quad\)\(\alpha\) is a step size scaling factor. \(\bullet\quad\)\(\text{Levy}(\lambda)\) is a Levy flight distribution.
2. The Levy flight distribution is defined as: \[\text{Levy}(\lambda)=t^{-\lambda},\quad(1<\lambda\leq 3)\] (15)
3. The fitness \(f(\mathbf{x}_{i})\) of each solution \(\mathbf{x}_{i}\) is evaluated using the objective function of the optimization problem.
4. A fraction \(p_{a}\) of the worst nests (solutions) are replaced with new solutions: \[\mathbf{x}_{i}^{(t+1)}=\mathbf{x}_{i}^{(t)}+\alpha\cdot\text{Levy}(\lambda) \quad\text{if}\quad\text{rand}<p_{a}\] (16) where \(p_{a}\) is the probability of discovering an alien egg.
5. The best solutions are carried over to the next generation based on their fitness values.  
Consider a scenario where a team of robots must navigate an environment with obstacles to reach their respective goals. Each robot uses cuckoo search to find the optimal path. Firstly, each robot (cuckoo) starts with a random initial path and evaluates its fitness based on path length and obstacle avoidance. Robots update their paths using Levy flights to explore new potential paths as formulated in Equation (15). Then, they evaluate the new paths' fitness based on the objective function (e.g., path length and safety). Next, robots replace a fraction of the worst paths with new paths found through Levy flights if they provide better fitness (Equation (16)). The best paths are carried over to the next generation. The robots refine their paths through iterations, converging on optimal solutions that minimize travel time and avoid obstacles. The decentralized nature of CS ensures robustness and adaptability in dynamic environments.
## 2 Materials and Methods
### Determining the Maximum Real Power Injection and Capacity of PV System with a Smart Inverter  
As shown in Figure 2, the decision-making process of maximum real power injection and capacity of PV system with a smart inverter can be divided into the following four stages:  
Figure 2: Flow chart to determine the maximum real power injection and capacity of a PV system with a smart inverter.  
* Stage I: Set Parameters  
Setup the parameters required in the OpenDSS software (Version 8.1.1, Electric Power Research Institute, Palo Alto, CA, USA), such as voltage levels, the standard value, phase, angle, frequency, the type and length of the wire connection on the bus, load, capacitor, power factor, etc.
* Stage II: Increase Real Power and Compensate Reactive Power  
When the real power injection is increased too much, the voltage variation percentage may become greater than 2.5%. Then, to reduce the voltage variation, the inductive reactive power Q can be added. When the inverter still has enough Q available, it will fix the real power and only compensate the inductive reactive power to reduce \(\Delta\)V. If the Q value reaches the maximum allowable value \(Q_{max}=P\times\tan(\cos^{-1}\!\!\!p/)\) where \(pf\) is the power factor control limit of the inverter and the \(\Delta\)V% is still greater than 2.5%, then it is necessary to cut down the real power output and step into Stage III.
* Stage III: Search Maximum Real Power Injection by Bisection  
Inside the dotted yellow line in the bottom of Figure 2, if the maximum allowable reactive power \(Q_{\max}\) is reached and the voltage variation is still more than the limit of 2.5%, then the real power reduction, \(\Delta\)P\(\left(\frac{1}{2}\right)^{n}\). will be carried out to reduce the voltage variation, where \(n=1\) for the first-time decrease in P. When the voltage variation percentage is below the limit of 2.5%, the real power increment, \(\Delta\)P\(\left(\frac{1}{2}\right)^{n}\), will be carried out to increase the voltage variation. Such process is repeated before reaching a near maximum point of the real power injection.
* Stage IV: Check Stopping Criterion and Calculate the Maximum PV Capacity  
1. Check if \(n\) is equal to 4. When \(n=4\), the increase or decrease of P would be small; if \(n\) continues to increase, then the impact of \(\Delta\)V can be neglected. Then, we can stop the procedure and calculate a near maximum PV capacity \(S_{PV}=P/pf\), or it can be stopped by the following criterion.
2. When \(n<4\) and \(2.5\%-\Delta\)V% \(<10^{-4}\), the error tolerance is taken to be acceptable and the procedure can be stopped. A near maximum PV capacity can then be obtained.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 3 Proposed Solution
### QoS Factors in OFQS  
\(OFQS\) with its metric \(mOFQS\) takes the quality of the links into consideration by calculating their \(ETX\) value. In Contiki Operating System, \(ETX\) is implemented in the \(MRHOF\) objective function. \(ETX\) is updated based on callbacks from the MAC layer which gives the information whether a MAC layer transmission succeeded, and how many attempts were required. Lower \(ETX\) values mean better links quality to route the packets with less re-transmissions. Alongside with the quality of links, the delay is an important factor in SG applications as already mentioned. For that, \(mOFQS\) considers the delay \(d\) between sending the packet and receiving it in the network layer between two  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Data Traffic** & **Maximum Allowed Delay** & **Reliability** \\ \hline DA-Data related to the protection of the distribution network & \textless{}3 s & \textgreater{}99.5\% \\ \hline DERs (Distributed Energy Resources)—Data related to the protection & \textless{}4 s & \textless{}99.5 \% \\ of the distribution network & & \\ \hline Critical traffic of: DA, DSM, AMI, DERs & \textless{}5 s & \textgreater{}99.5\% \\ \hline Electric transport & \textless{}10 s & \textgreater{}98\% \\ \hline Non critical traffic of DSM \& AMI & \textless{}15 s & \textgreater{}98\% \\ \hline Non critical traffic of DA \& AMI & \textless{}30 s & \textgreater{}98\% \\ \hline Network configuration traffic, normal AMI traffic & \textless{}5 min & \textgreater{}98\% \\ \hline Normal AMI traffic & \textless{}4 h & \textgreater{}98\% \\ \hline Network configuration traffic & \textless{}Hours/Days & \textgreater{}98\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: NAN requirements in terms of reliability [5].  
adjacent nodes. This allows the algorithm to choose faster links especially for critical applications considering at once transmission, queuing and interference delays. Moreover, in a SG, electricity and energy do exist, but connecting sensors to such high voltage with intermittent and ill-adapted energy levels is sometimes inappropriate or physically impossible. For that, battery-powered sensors must be deployed all over the grid alongside with the mains powered ones. Different requirements for different applications may tolerate in some cases passing by a longer route in order to preserve the remaining energy in the nodes. Hence, considering the battery level for the nodes in our metric will be beneficial in terms of traffic load balancing and network lifetime. To do so, we classify the remaining energy in the nodes into three Power States (_PS_) [35]:  
* _PS_ = 3: Full battery state (ranging between 100% and 80%) or main powered
* _PS_ = 2: Normal battery state (ranging between 80% and 30%)
* _PS_ = 1: Critical battery state (less then 30%)  
By using this classification, weak nodes become unfavorable in the route selection by penalizing the ones with a smaller _PS_. We note that these thresholds could be adjusted for other applications depending on the network characteristics.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 4 Discussion  
The software design for real-time head-wave extraction consisted of distinct parts: front-end control, digital filtering, and real-time head-wave extraction. All codes are written in the VHDL language. The front-end control program is implemented in CPLD, while the digital filtering and head-wave extraction program is implemented in FPGA by using a pipeline mode.
## 5 Results
### Low-Speed Flows over a NACA0012 Airfoil  
In order to verify whether our preconditioned high-order DG method can be used for nearly incompressible flow simulations, flows over a symmetric NACA0012 airfoil with zero angle of attack (\(\alpha=0^{\circ}\)) at four selected Mach numbers, 0.1, 0.01, 0.001, and 0.0001, are first simulated. Without loss of generality, the order of DG method is fixed to be fourth (\(p=3\)) during the simulations, and a fully unstructured mesh having only 3527 triangle elements (see Figure 4) is used in view of adopting a fourth-order accurate DG method.  
The behaviors of low speed in convergence (stiffness problem [18; 19]) at low Mach numbers can be learned from the convergence histories of three representative Mach numbers, as shown in Figure 5. It seems that the high-order DG method itself (without preconditioning) has the ability to cope with the flows at relatively low Mach numbers (see \(Ma=0.1\) in Figure 5a), which is different from low order methods [35]. However, difficulties in convergence appear as the Mach number further reduces. Such a situation is greatly ameliorated, as illustrated in Figure 5b,c, through the help of preconditioning.  
The effects of very low speed on the accuracy of the solutions can be learned from the corresponding contours of the Mach number. The typical contours of \(Ma=0.0001\) are presented in Figure 6. It can be noted that the developed code, without preconditioning, fails to obtain the correct physical solution (reaching unphysical solution [18], on the left-hand side) in view of the chaotic, asymmetric, and poor smoothness of the contours, while the code with preconditioning can capture smooth contours of the Mach number (see the right-hand side of Figure 6).  
Figure 4: Computational mesh around NACA0012 airfoil.  
Figure 5: The convergence histories of flows over NACA0012 airfoil. (**a**) \(Ma=0.1\); (**b**) \(Ma=0.01\); and (**c**) \(Ma=0.001\).
## 4 Experiments
### Evaluation of DO-YOLOv4 for Crack Detection
#### 4.2.2 Testing Results and Analysis  
Several typical testing results of the trained DO-YOLOv4 for crack detection are depicted in Figure 10, from which it can be observed that various inclined cracks, including those with individual directions, with different extension directions, and even with multiple branches, can be well-located and tightly enclosed by a series of oriented bounding boxes. This indicates that DO-YOLOv4 is capable of adjusting to a wide variety of cracks with different sizes and directions and multiple branches, even in the presence of background interferences.  
To better evaluate the performance of DO-YOLOv4, its feature extraction network and crack detection accuracy were compared with those of the traditional object detection methods, including Faster R-CNN, SSD, YOLOv3, and YOLOv4. For the feature extraction  
Figure 10: Detection results of cracks by DO-YOLOv4.  
network, the improved CSPDarknet53 is adopted in DO-YOLOv4, in which the original convolutional layers connecting different CSPblocks are replaced by the deformable convolutional layers, and a total of 15 resblocks can be reduced, as shown in Figures 3 and 4. Therefore, as shown in Table 1, the size of improved CSPDarknet53 accounts for roughly 1/10, 1/2, 1/3, and 1/2 of the size of VGG16, Resnet50, Darknet53, and CSPDarkent53, respectively, leading to the lightweight feature of DO-YOLOv4. For the detection accuracy, as multiple oriented bounding boxes are employed for crack detection, the area under the P-R curve of DO-YOLOv4 is much larger compared to those of the traditional object detection methods, as depicted in Figure 11. Correspondingly, the AP metric of DO-YOLOv4 calculated using the method stated in Section 4.2.1 reaches up to 80.43%, as also shown in Table 1, which is 21.54%, 39.39%, 35.27% and 27.33% higher than the AP metrics of Faster R-CNN, SSD, YOLOv3 and YOLOv4, respectively, indicating the high accuracy of DO-YOLOv4.
## 3 Fabrication, Measurements and Discussions
### Measurement and Results
#### 3.2.4 Lifespan  
The equipment and connection employed to test lifespan are displayed in Figure 16.  
The RF MEMS switch is placed on the microwave probe table, and the CPW transmission line port is connected to the RF signal generator and Ka band wideband detector respectively through the GSG microwave probe and the microwave coaxial line. It should be noted that when the CPW transmission line is used as the DC bias electrode of the switch, a DC \(\sim\)40 GHz block needs to be added to prevent the DC signal from entering the  
Figure 16: Block diagram of RF MEMS switch lifespan test platform.  
signal generator and detector. The oscilloscope is used to observe and record the pull in and release time of MEMS switch.  
The lifespan test of the RF MEMS switch in this paper is carried out by using the above method. We set the rectangular square wave DC voltage frequency to 1 KHz. After 13 h (the RF MEMS switch operates 4.68 \(\times\) 10\({}^{7}\) times), the resonant frequency of the switch is always in Ka band. When the switch works for 35 h (1.26 \(\times\) 10\({}^{8}\) times), the isolation of MEMS switch in Ka band is less than 10 dB, so that the Ka band detector has difficulty detecting the signal power level when the switching state is changed.  
From the test results of switch lifespan, it is found that the resonant frequency of the switch changes as the switching times increase. However, when the resonant frequency of the switch increases to the frequency above Ka band, the switch has no mechanical failure and dielectric charging.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 2 In Vivo Confocal Laser Scanning Microscopy  
RCM represents a non-invasive imaging technique permitting the observation at a quasi-histological resolution of a given tissue, in vivo, therefore avoiding a biopsy. When applied to the skin, RCM enables the exploration of different layers including epidermis, dermo-epidermal junction (DEJ) and upper dermis, reaching up to 200 \(\upmu\)m of depth, therefore impairing both the visualization of the deepest part of dermis and the evaluation of the thickness of lesions [6; 7]. However, despite this limitation, the high-resolution and the visualization of gray-scale horizontal images of cells and nuclear structures at epidermal level, collagen, and circulating blood cells at dermal layer, enable the identification of clues for diagnosis of different skin conditions. Among these, an increased diagnostic accuracy of melanocytic and nonmelanocytic skin lesions has been described, as well as an improved recognition of variations related to pigmentary disorders, inflammatory skin diseases and skin aging features [11].  
Technically, in vivo RCM has a lateral resolution of 0.5-1 \(\upmu\)m and an axial of 3-4 \(\upmu\)m. A schematic representation of optical principals of CLSM is reported in Figure 1.  
The acquisition of images with in vivo CLSM can be performed with skin-contact devices, the wide-probe (Vivascope 1500\({}^{\copyright}\), MAVIG GmbH, Munich, Germany) and the handheld RCM (Vivascope 3000\({}^{\copyright}\), MAVIG GmbH, Munich, Germany). The first one allows the collection of mosaics (Vivablock) at three different layers including epidermis, DEJ and dermis, overlapping with the dermoscopic image through the Vivacam(r) (MAVIG GmbH, Munich, Germany), with a maximum size of 8 \(\times\) 8 mm, while the latter permits the evaluation of difficult-to-access areas of the head such as the nose, the eyelids and ears, the scalp and mucosae such as the nose, ears, eyelids, scalp and mucosae [12; 13; 14]. Main applications of in vivo CLSM are summarized in Table 1.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies  
Small-scale refrigeration refers to systems with lower cooling capacity than those provided by conventional units, and its purpose is providing localized cooling at a certain place, space, system, or body demanding cooling. Compactness and light weight are important because there is a global trend towards the miniaturization of numerous devices in different areas such as mechanics, electronics, computing, and others, and many of these devices demand refrigeration for thermal management and heat dissipation to ensure proper operation and durability. Furthermore, compactness allows its use in applications where portable cooling systems are needed. Applications of small-scale cooling systems are vast and range from automotive to medical, military to aerospace, and personal to electronics cooling [9].  
Classifications for small-scale systems vary between authors. Some classify systems according to cooling capacity, while an alternative classification may be based on dimensions. In terms of cooling capacity, several studies define small systems as those with cooling power below 1000 W; however, some others consider even lower thresholds for particular applications, e.g., 500 W for electronics cooling [14]. Regarding the dimensions, [PERSON] et al. [15] consider that miniature systems are comprised of microscale (size in millimeters) and meso-scale systems (not exceeding 5 cm in any dimension), while [PERSON] et al. [16] consider the meso-scale in the range of tenths of a millimeter to tenths of a meter. An additional classification is presented by [PERSON] et al. [5], where thermal management methods are divided into two categories: direct or indirect contact of the cooling fluid with the targets.  
In this review, we define 1000 W cooling capacity as the threshold for small systems and classify systems as active and passive depending on whether they need input power to perform their function. We start with a brief description of passive technologies used in small-scale refrigeration and continue with a more detailed description of active technologies, with special focus on vapor compression.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Overview of the 3-Phase Framework  
The proposed framework, named a \"custom GAN-augmented Deep Belief Network (DBN) model,\" tackles these challenges through a three-phase approach:  
**Phase 1: Generating Realistic Ransomware Data (GAN Pre-encryption Data Augmentation)**  
* What it does: Uses a GAN to create synthetic, yet realistic, ransomware attack patterns.
* Why it is important: Real-world pre-encryption ransomware data is scarce. GANs help overcome this data insufficiency, allowing the detection model to be trained on a much larger and more diverse set of potential attack scenarios. The framework employs a \"Bi-Gradual Minimax loss function\" to make the synthetic data even more accurate.  
**Phase 2: Smartly Selecting Key Indicators IMIS**  
* What it does: Employs an IMIS technique to identify and prioritize the most important \"features\" or indicators of ransomware behavior.
* Why it is important: Ransomware constantly evolves, meaning what was a useful indicator yesterday might not be today. IMIS dynamically reassesses which features are most relevant as new data becomes available, ensuring the detection model focuses on the most current and effective signals, while also avoiding redundant information.  
**Phase 3: Optimizing the Detection Model (Deep Belief Network with Uncertainty-Aware Dynamic Early Stopping)**  
* What it does: Trains a DBN using the augmented data and selected features. It introduces a UA-DES criterion.  
* Why it is important: Training deep learning models can lead to \"overfitting,\" where the model becomes too good at recognizing the training data but performs poorly on new, unseen data. UA-DES helps determine the optimal point to stop training, preventing overfitting and improving the model's ability to accurately detect new and evolving ransomware threats. It does this by considering the uncertainty in the model's predictions.  
Essentially, our deep learning framework creates its own \"training data\" for rare ransomware behaviors, intelligently picks out the most relevant clues, and then trains a powerful detection system in a way that makes it highly adaptable and accurate against the ever-changing threat of polymorphic ransomware, especially in critical infrastructure like SCADA systems.
## 3 Results
### Biomarkers Analysis  
#### 3.3.1 Insulin Levels  
The estimated marginal means of insulin (INS) concentrations at baseline (t1), 1 h (t2), 2 h (t3), and 4 h (t4) are summarized in Table 4 and depicted in Figure 2. Baseline, INS levels were comparable across the CTRL (11.570 \(\pm\) 1.084 uU/mL), OLT (10.387 \(\pm\) 0.642 uU/mL), and OLB (10.158 \(\pm\) 0.757 uU/mL) groups. Figure 1: CONSORT schematic of participant recruitment, screening, assessment, and analysis. Postprandial INS increased in all groups, peaking at 1 h (t2) for CTRL (31.250 \(\pm\) 3.311 u/mL) and at 2 h (t3) for both OLT (31.513 \(\pm\) 3.383 u/mL) and OLB (29.773 \(\pm\) 4.471 u/mL) before declining toward baseline at 4 h (t4) in all groups. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **INS** & **INS** & **INS** & **INS** & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{\(p\)**Value**} \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **Time** & **Lower Bound** & **Upper Bound** & **(Group \(\times\) Time)** \\ \cline{3-8}  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **Time** & **Lower Bound** & **Upper Bound** & **Upper Bound** \\ \hline CTRL & \multirow{3}{*}{\(11.570\pm 1.084\)} & \multirow{3}{*}{\(31.250\pm 3.311\)} & \multirow{3}{*}{\(25.353\pm 3.936\)} & \multirow{3}{*}{\(9.654\pm 0.907\)} & \multirow{3}{*}{\(9.654\pm 0.907\)} & t1 & 9.328 & 13.812 & \multirow{3}{*}{\(13.812\)} \\  & & & & t2 & 24.401 & 38.099 & t3 & 17.211 & \multirow{3}{*}{\(33.495\)} \\  & & & & t4 & 7.778 & 11.530 & & \\ \hline \multirow{3}{*}{OLT} & \multirow{3}{*}{\(10.387\pm 0.642\)} & \multirow{3}{*}{\(24.614\pm 2.418\)} & \multirow{3}{*}{\(31.513\pm 3.383\)} & \multirow{3}{*}{\(9.840\pm 0.879\)} & t1 & 9.058 & 11.715 & \multirow{3}{*}{\(p=0.054\)} \\  & & & & t2 & 19.612 & 29.616 & & \\ \cline{3-8}  & & & & t3 & 24.515 & 38.510 & & \\ \cline{3-8}  & & & & t4 & 8.020 & 11.659 & & \\ \hline \multirow{3}{*}{OLB} & \multirow{3}{*}{\(10.158\pm 0.757\)} & \multirow{3}{*}{\(26.607\pm 3.356\)} & \multirow{3}{*}{\(29.773\pm 4.471\)} & \multirow{3}{*}{\(11.330\pm 1.529\)} & t1 & 8.593 & 11.724 & \multirow{3}{*}{\(11.320\pm 1.529\)} \\  & & & & t2 & 19.665 & 33.550 & & \\ \cline{3-8}  & & & & t3 & 20.524 & 39.021 & & \\ \cline{3-8}  & & & & t4 & 8.167 & 14.493 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Postprandial changes in serum insulin levels.