esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Survey on Offline Automatic Algorithm Configuration
## 1 Introduction
Because this ad-hoc, manual process is often boring and insufficient, automatic methods for finding appropriate parameter values have become available. These automatic methods lead to important improvements in the performance of target methods for solving different computationally hard optimization problems [8]. Because of its importance, a considerable number of automated parameter tuning techniques have been developed over the past few years, and parameter values found by these methods showed better results than default values identified based on human experts. Furthermore, studies have shown that using these algorithm configuration methods not only assists practitioners in identifying the best-performing parameter sets, but also in gaining a deeper understanding and analyzing the interaction between the target algorithm performance, problem instances, and selected parameter values [4]. Moreover, a successful tuning method will motivate algorithm designers to better parameterize their algorithms [9]. A comprehensive review of these studies is an important requirement because parameter tuning has become commonly utilized in the industry and academic community, and there has been a significant advancement in this area. Although there is a wide body of literature about the algorithm configuration problem, a detailed survey analysis has not been executed yet. This paper aims to give a general overview of the parameter tuning methods and shortly discuss applications of these offline algorithm configuration techniques. It also aims to discuss their main advantages and disadvantages to help researchers or practitioners select the best method for their specific problem. We also mentioned comparative studies of these methods, related research topics with the algorithm configuration problem, and other studies that consider different aspects of this problem. In the end, we provided recommendations and possible future directions for this topic. This article is designed as follows; a brief introduction to the framework of the algorithm configuration problem and different parameter types is provided, and also a survey about offline algorithm configuration approaches and related works with these methods is provided. As a conclusion of the paper, we will summarize what has been conducted in this study, and we will mention open research directions for future work.
## 2 Data and Methods
### Datasets
#### 2.2.1 Soil Moisture Datasets
It is noteworthy that at least six SMAP grids with valid data were used to calculate the areal mean soil moisture to ensure its representativeness (nine SMAP grids totally in the study area).
## 2 Design  
Basic polarization manipulation properties of metasurfaces are achieved due to cross-coupling between electric and magnetic fields' resonances in the presence of an incident wave. In order to achieve dual-band LP-to-CP operation, the unit-cell structure of the metasurface needs to be designed deliberately to tailor the cross-coupling effects in two separate bands of interest. The same structure will behave differently under different frequencies. Each frequency band will correspond to a different Eigenmode. In the past, many diagonal symmetry/semi-symmetric anisotropic structures have been proposed for single band LP-to-CP conversion [41; 42; 43; 44; 45]. Dual band LP-to-CP operation has been achieved using center-connected [38] and semi-diagonal symmetric [37] structures. Symmetric structures with horizontal and vertical axis symmetry cannot be a choice for LP-to-CP operation because the electric response for such components at normal incidence and horizontal polarization will not generate a vertical component [42]. A good choice is to have a diagonal symmetric structure so that incident wave can be divided into two equal orthogonal components. The first design consideration for the unit cell is to have two differently sized square patches, with each square corresponding to a single band LP-to-CP operation. They are arranged to have diagonal symmetry. The second consideration is a square ring to have closed form electric field distribution between the top and bottom surfaces. In a nutshell, the proposed structure is a diagonal symmetric structure which consists of multiple resonating structures to have wider bandwidths in two operational bands. Figure 1 shows the design of the proposed converter. It consists of two identical sheets of conductor patches having substrate layer sandwiched between. Gold was used as a conductor and flexible polyimide having \(\varepsilon_{\text{r}}\) of 3.5 was used as a substrate. The shaded region in Figure 1b shows the conductor layer, which consists of two parts: the outer part is a square ring consisting of a conductor with width \(w\) and an inner part consisting of three squares diagonally intersecting each other. Top right and bottom left squares have dimensions \(c\times c\), while the middle square has dimension \(c1\times c1\). The bottom layer is identical to the top layer. The design was optimized to have the best results and optimized parameters for the unit cell, as follows: \(p=50.67\) um, \(v=54.65\) um, \(c=13.4\) um, \(d=3.5\) um, \(c1=14\) um. The scheme for the operation of the dual-wide-band LP-to-CP converter is shown in Figure 2.
# The Power System and Microgrid Protection--A Review
## 3 Protection Scheme of Microgrid  
The coordination of traditional protection relays as standalone units with fixed settings is based on the operating time of the primary and backup protection (Figure 8) [64; 65; 66]. This scheme of power system protection cannot adapt to the changes in topology and operation mode. Reports have indicated that the conventional protection schemes have a main role in major blackouts of power systems [67]. Therefore, the demand and structure of the distribution network as well as the nature of the distribution system have changed with the high penetration of DGs.  
The major challenges of the distribution network are associated with the changes in the nature of the microgrid. Many schemes have been proposed to protect the distribution network with high penetration of resources.
## 2 Mechanistic Foundations of Skeletal Muscle Aging
### Prerequisites for Muscle Repair  
Effective muscle regeneration requires a temporally coordinated sequence of events. Initially, sterile inflammation following injury activates resident immune cells such as neutrophils and monocytes, which differentiate into pro-inflammatory (M1) macrophages [18; 88; 89; 90; 81; 41; 85; 84; 85; 86; 87; 88; 89; 90]. These macrophages clear debris and secrete cytokines like TNF-\(\alpha\) and IL-6 to activate satellite cells. In later stages, M2-like macrophages promote tissue repair by releasing anti-inflammatory cytokines (e.g., IL-10 and TGF-\(\beta\)) and growth factors such as IGF-1 and Hepatocyte Growth Factor (HGF) [91; 92; 93; 94; 95; 96]. Satellite cell activation is followed by proliferation, differentiation, and fusion into multinucleated myofib. This process is tightly linked to matrix remodeling mediated by fibroblasts and fibro-adipogenic progenitors (FAPs). FAPs support satellite cell function via secretion of Wnt1 and IL-33 but can also drive fibrosis under dysregulated conditions. ECM remodeling involves the balanced activity of matrix metalloproteinases (MMPs) and their inhibitors (TIMPs), facilitating proper scaffold reconstruction [18; 97; 98; 99; 100; 41; 54; 85; 86; 87]. Nutrient and oxygen supply must be sufficient to support anabolic metabolism and biosynthetic demands. Angiogenesis, mediated by VEGF and angiopoietin signaling, ensures adequate perfusion. Micronutrients like nicotinamide and pyridoxine contribute by modulating redox status, supporting NAD\({}^{+}\)-dependent enzymatic reactions, and influencing inflammatory resolution and myogenic differentiation [95; 101; 102; 103; 104; 105; 31]. Muscle regeneration is also influenced by neuromuscular and hormonal input. Reinnervation of regenerating fibers and restoration of NMJ function are critical for functional recovery. Endocrine factors such as insulin, IGF-1, and sex steroids enhance satellite cell activation and anabolism. Altogether, successful muscle repair is a highly coordinated process involving immune cell transitions, stem cell dynamics, ECM remodeling, angiogenesis, and metabolic support. Aging disrupts these components at multiple levels, leading to inefficient repair, fibrosis, and progressive decline in muscle quality. Interventions targeting redox balance, immune modulation, and stem cell support--such as supplementation with B3 and B6--offer a promising route to restore regenerative capacity [4; 7; 18; 22; 25; 38; 41; 44; 85; 106; 107; 108].
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background  
Computational and communication resilience mechanisms leverage various technologies. Important resilience challenges are associated with the interconnection between LANs and WANs, where a wide range of technologies are now available. In addition, computational resilience is accomplished using infrastructure that continues to support the transmission and processing of data under failover scenarios. This section discusses the technologies that can support resilience at both the communication and computational levels.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 4 Problem Formulation and Solution
### Computational Complexity  
In this section, the complexity of the problem is analyzed.  
The problem is deciding which cells must be activated in order to maximize the secure EE. Then the users association becomes straightforward: once the set of active BS is known, each user selects the BS that provides the highest secure EE. The problem is a 0/1 non linear programming problem where the optimization variables are the elements of the association matrix \(\mathbf{\Gamma}\) and of cells activation vector \(\mathbf{\Phi}\).  
The problem of deciding which cells must be activated is NP-hard as shown in [34]. Indeed, by assuming an homogeneous scenario, where all cells transmit with the same power, our problem is similar to the one presented in [34] even if the optimization goals are different. In particular, in [34], the goal was to find the minimum set of BSs that guarantees the the minimum achievable rate to all users. Here, we wanted to find the minimum set of BSs (i.e., if the BSs transmit with equal power this corresponds to the minimum consumed power) that provides the highest secure EE. In [34], it has been shown that this kind of problem is NP-hard. Moreover, in general, our problem is more complex, because we consider a heterogeneous scenario with different transmission powers of cells, thus we have higher degrees of freedom.  
In particular, if we consider the ES solution, we have that for each possible configuration of the vector \(\mathbf{\Phi}\), the allocation matrix that provides the maximum secure EE for each user (i.e., \(EE_{u,c}^{[sec]}\)) must be derived. Then, among all the possible values of the vector \(\mathbf{\Phi}\), the one that provides the maximum utility, \(EE^{[sec]}\), is selected. Unfortunately, in UDNs, often this search requires too high complexity. Indeed, the vector \(\mathbf{\Phi}\) can assume \(2^{\mathcal{N}_{C}}-1\) possible configurations (assuming that at least one BS is active). Hence, the utility function must be calculated \((2^{\mathcal{N}_{C}}-1)\) times.  
Differently, if we consider the proposed heuristic, the computational complexity is significantly lower. At the \(i\)-th iteration the algorithm calculates \((\mathcal{N}_{C}+1-i)\) times the utility function. In the worst case (i.e., all cells are activated and \(\mathcal{N}_{C}\) iterations are performed) the complexity of the procedure is the calculation of the utility function \(\sum_{i=1}^{\mathcal{N}_{C}}(\mathcal{N}_{C}+1-i)=\frac{\mathcal{N}_{C}( \mathcal{N}_{C}+1)}{2}\) times. However, we have to stress that in a dense environment the number of activated cells is lower than the maximum; hence, in actual system the number of iterations is lower than \(\mathcal{N}_{C}\) and, hence, the complexity is significantly lower. This is shown in the numerical results section.
## 4 Experimental Results and Discussion
### Discussion  
Based on the experimental results described above, the two-level training method designed in this study demonstrated favorable results in terms of overall model accuracy when there are many classes of objects to be detected. This also proves that our method can be effectively applied in the detection of various objects. The images in the training set were mostly captured from the dashcam footage of moving vehicles. However, we noted that the training set contained few images of poor weather conditions, which is why we added Gaussian noise to simulate poor weather. We also added several random rotation angles to simulate obscured, slanted, or twisted objects. These treatments made up for the lack of images in these categories and expanded the variety of images. We also added Canny edge  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Images That Did Not** & **Images That** & **Images That** & **Number of** & \\  & **Contain Class A or Class** & **Contained Class** & **Contained Class B** & **Incorrectly** & **Error Rate** \\  & **B Objects** & **A Objects** & **Objects** & **Detected Images** & \\ \hline Video 1 & 59 & 3 & 0 & 1 & 1.69\% \\ \hline Video 2 & 54 & 0 & 6 & 0 & 0\% \\ \hline Video 3 & 50 & 0 & 0 & 7 & 14\% \\ \hline Video 4 & 20 & 0 & 0 & 0 & 0\% \\ \hline Video 5 & 62 & 0 & 0 & 3 & 4.84\% \\ \hline Video 6 & 74 & 0 & 0 & 14 & 18.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 21: Error rates of M4 tested using different videos.  
detection to depict the contours of objects and accentuate their features, thus enhancing the model's recognition rate. The detection accuracy (expressed as the mAP) of M4 was 88.20%--significantly higher, by 8.6 percentage points, than that of M1 (79.60%), which only used a single model for detection. Furthermore, for a detection model, in addition to object type detection, it is also important to reduce incorrect detections. We performed tests using videos of different scenarios that had not been used in the training process, finding that M4 also had a quick response time, and the overall mean error rate was only 6.58%. This shows that M4 maintained a certain level of preventing incorrect detections. Overall, M4 is a good candidate model for driver safety detection.
## 5 Co-Validation of Simulation and Test  
### The Method of Co-Validation  
Compared with the mechanical transmission, the efficiency of the hydraulic transmission is relatively low, and the efficiency constantly changes during variable speed. To more accurately analyze the efficiency of the transmission system, this paper adopts the method of co-validation of the simulation and test, as shown in Figure 16.  
It is the method that combines the test of the hydrostatic transmission with the simulation of the HMCVT by the data acquisition and control unit. It mainly realizes the exchange of simulation and test data through the data acquisition unit and controls the hydrostatic transmission test bed through the control unit. Torque and speed of VP and FM are obtained through simulation analysis, and the loading system is controlled by the control unit according to the simulation date. Torque and speed of VP and FM are measured through sensors and their dates are obtained through the acquisition unit; eventually, the actual efficiency of the hydrostatic transmission can be calculated. Then, the efficiency  
Figure 16: The diagram of co-validation of simulation and test.  
Figure 15: The efficiency curves of Schemes 1 and 3: (**a**) Scheme 1; (**b**) Scheme 3.  
data are input to the simulation model to achieve more accurate simulation. Therefore, it can realize validation in the early stage of HMCVT design to avoid the irrationality of the structure after the developed prototype.  
The rotational speed and pressure of VP and FM are constantly changing, therefore hydrostatic CVT efficiency, \(\eta_{\text{CVT}}\), is also constantly changing. Its efficiency characteristic is obtained by testing on the test bed. The physical diagram of the hydrostatic transmission test bed is shown in Figure 17.  
The power source of the test bed is driven by a motor, and the hydraulic loading system is used to load the output shaft of the hydrostatic transmission. Its input and output shafts are equipped with speed and torque sensors.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ2: What Types of Malicious Activity and Which Scenarios Involve IoT Botnets?
#### 5.2.2 Downloading IoT Malware  
IoT malware is malicious software designed to access, exploit and compromise an IoT device; it is different from other malware in that it has the ability to adapt to various CPU architectures, including MIPS, ARM, Intel x86 and PowerPC [56]. IoT malware has various essential models to finish its functions, including a scanner, an attacker and a killer [86]. After scanning and receiving the information of a vulnerable IoT device, the attacker uses a downloader server to download the bot. After that, the new bot starts its functions and communication with the C&C. Some of the selected studies concentrated on proposing techniques for detecting whether an input executable file is malware or benign [49; 56; 69; 79]. As Figure 8 explained, 19% of the studies concentrated on this type of attack--nine studies, as Table 10 displays.
## 3 Results  
### Microhardness Analysis Findings  
There was no statistically significant difference between the median microhardness values of the toothpastes measured at the beginning, post demineralization, and post remineralization (\(p>0.05\)). In the intragroup comparisons, the microhardness values of the toothpastes measured at the beginning, post demineralization, and post remineralization were significantly different from each other (\(p<0.05\)). The Splat, Eyup Sabri Tuncer, and Logodent toothpastes had higher initial microhardness values than those at other stages. For the Naturalive and Buccotherm toothpastes, the difference in microhardness values was observed in all measurement periods, ranking from high to low at the beginning, post-remineralization, and post-demineralization stages. After applying these two toothpastes, the microhardness values that had decreased due to demineralization increased. The remineralization effects of these two toothpastes are shown in Table 2.  
There was no statistically significant difference between the median surface microhardness recovery values of the toothpastes (\(p>0.05\)) (Table 3.)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Toothpastes**} & \multirow{2}{*}{\begin{tabular}{c} **Measurement** \\ **Stages** \\ \end{tabular} } & \multicolumn{6}{c}{**Microhardness Values**} \\ \cline{3-10}  & & **N** & **Median** & **Mean** & **Sd** & **X\({}^{2}\)** & \(p\)** & **Bonferroni** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Splat\({}^{\text{\tiny{B}}}\) \\ \end{tabular} } & B & 12 & 276.48 & 275.88 & 32.39 & 22.167 & 0.000 * & R \(<\) B \\  & D & 12 & 9.85 & 22.19 & 28.84 & & & D \(<\) B \\  & R & 12 & 15.45 & 36.84 & 47.13 & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Logodent\({}^{\text{\tiny{B}}}\) \\ \end{tabular} } & B & 12 & 303.60 & 280.49 & 61.09 & 22.167 & 0.000 * & R \(<\) B \\  & D & 12 & 9.05 & 20.71 & 25.50 & & & D \(<\) B \\  & R & 12 & 16.75 & 29.90 & 27.75 & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} EST\({}^{\text{\tiny{B}}}\) \\ \end{tabular} } & B & 12 & 284.60 & 274.58 & 35.81 & 20.468 & 0.000 * & R \(<\) B \\  & D & 12 & 9.50 & 21.89 & 43.71 & & & D \(<\) B \\  & R & 12 & 21.35 & 37.35 & 35.16 & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Naturalive\({}^{\text{\tiny{B}}}\) \\ \end{tabular} } & B & 12 & 326.25 & 279.82 & 90.14 & 24.000 & 0.000 * & R \(<\) B \\  & D & 12 & 16.20 & 22.74 & 20.78 & & & D \(<\) B \\  & R & 12 & 23.83 & 33.85 & 31.14 & & & D \(<\) R \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Buccotherm\({}^{\text{\tiny{B}}}\) \\ \end{tabular} } & B & 12 & 299.45 & 277.33 & 89.15 & 24.000 & 0.000 * & R \(<\) B \\  & D & 12 & 10.85 & 23.10 & 39.99 & & & D \(<\) B \\ \cline{1-1}  & R & 12 & 29.65 & 45.15 & 46.88 & & & D \(<\) R \\ \hline \hline \end{tabular}  
\({}^{\text{\tiny{X}}}\)2: Friedman test statistic, * \(p<0.05\), N: Number of samples, Sd: Standard deviation, EST: [PERSON], B: Beginning, D: Demineralization, R: Remineralization.  
\end{table}
Table 2: Comparison of the microhardness values of the toothpastes measured at the beginning, after demineralization, and after remineralization.
# Low Temperature Plasma Strategies for
## 2 Materials and Methods
### Emission Spectroscopy  
To perform the spectroscopic measurement, a slightly modified version of the reactor was used to place UV grade quartz frontal windows allowing direct visual control of the discharge area and optical emission diagnostics. Optical emission spectroscopy was performed by collecting light from the SDBD plasma surface through a UV fused silica single-lens fibre optic bundle model LG-455-020-3 (3 m, 190 to 1100 nm, with 19 fibres (\(\phi\) = 200 \(\upmu\)m), 10 mm ferrule at slit end) equipped with UV grade quartz lenses system on the entrance slit of a monochromator. The imaged area collected light is limited by the entrance slit dimension and can reach a maximum size of 3 (l) \(\times\) 5 (1) mm\({}^{2}\). The light is spectrally resolved by a 30 cm spectrometer (Acton Spectra Pro 2300), equipped with a multiple-grating turret with a 300/600/1200 grooves mm\({}^{-1}\), blazed at 300 nm and covering the ranges 200-1400 nm. The spectrum is acquired with a Princeton Instruments PI-MAX4 1024i ICCD camera equipped with a 1024 \(\times\) 1024 pixel sensor (size 12.8 um, active area 13.1 \(\times\) 13.1 mm\({}^{2}\)). One CCD image of a spectrum covers a range of approximately 144/65/30 nm, respectively, for the three different gratings. The intensities of the emission spectra acquired by the ICCD detector were spectral, and intensities were corrected utilizing Halogen (oriel) and calibration lamps (oriel). The spectra were used to evaluate rotational temperatures and nitrogen vibrational distributions. Spectra were simulated to infer rotational temperature information using the freely available spectroscopic tool Massive OES [74; 75; 76].