esa-sceva/satcom-chunk-collection huggingface dataset:
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.7 Hardware Security  
Wireless sensor networks are targeted by hackers and attackers. Most of the sensor nodes reviewed in this paper do not include adequate security features, since the end devices are power-constrained. The nodes are vulnerable to various malicious attacks. The architecture of the node proposed by [35] is based on Fog computing to improve security and privacy. A pre-cloud link is introduced to safeguard the data. Transceivers such as the XBee modules offer advanced encryption standard (AES) encryption security features. Introducing other security algorithms incurs extra energy costs. Besides, the wireless sensornodes have limited storage capacities to accommodate security algorithms. In an attempt to mitigate hardware security threats, ARM recently introduced the TrustZone technology to the microcontroller level (Cortex-M). The approach regards the hardware as the point of trust and then enables other resources of the sensor node to be trusted. Again, introducing additional security features is hindered by memory availability and energy constraints.
By using carbon steel (S20C), as well as applying a novel hybrid PM structure, the output power (225 W) is much higher than that obtained from the coreless models. An optimal 1-phase, 8-slot, 8-pole machine can even increase the output power by more than two times (339.5 W). The idea is to rearrange the relative position between the excited flux density and slots by changing the slot-pole combination. In this way the power on each slot is maximized, hence, the total power can be dramatically increased. Compared with conventional tubular machines designed for vehicle suspension systems, the maximum and average power density (0.311 and 0.087 W/cm\({}^{3}\)) from the 8-slot 8-pole model are superior. However, when output power is increased, detent force and electromagnetic force are also increased. It was reported that maximum force in 16-slot 8-pole and 8-slot 8-pole model obtained from FEA are 2789.6 and 2040.4 N, respectively. According to suggestions from manufacturers, in a mid-size car (such as an SUV) and in a heavier vehicle (such as a school bus), the maximum force value has to be smaller than 2000 and 3000 N, respectively. In real driving conditions, this high force has a negative impact on the safety and driving comfort of the driver due to ripple and mechanical vibrations. Because of the specific configuration, in which electrical components are attached to the inner and outer frame of the shock absorber, the detent force (under no-load condition) and electromagnetic force (under the load condition) have to be minimized. In this paper, the main objective is to reduce the detent and maximum electromagnetic force of the tubular PM machine proposed in [4], while ensuring other performances still achieved their design targets. The first classical approach involves skewing the PM array by different angles. Although the skewing effect has been widely investigated for rotating machines, its impact on the tubular machine has not been deeply considered. Secondly, a multi-objective optimization approach based response surface method (RSM) can be implemented to find the trade-off between the machine's performance and force. Finally, a novel approach based on an unbalanced model (or iron teeth arrangement) can be applied to the optimal 8-slot 8-pole model above. Although the skewing PM approach and unbalanced model can significantly reduce the detent force and electromagnetic force, the output power is decreased accordingly. Hence, using multi-objective optimization seems to be the best solution to satisfy the various physical responses. It should be noted that the design targets for maximum and average power are 250 and 100 W, and the targets for maximum and average power density are 0.250 and 0.100 W/cm\({}^{3}\), respectively. To verify the validity of the FEM analysis, a prototype was fabricated and tested.
# The \"Sling\" Technique for Horizontal Guided Bone Regeneration: A Retrospective Case Series
###### Abstract  
The aim of this retrospective case series was to present a new technique, called the \"sling technique\", for massive horizontal alveolar ridge augmentation through the innovative use of an expanded polytetrafluoroethylene (e-PTFE) membrane to reduce the risk of biological complications related to membrane exposure. Materials and Methods: Eligible patients for implant rehabilitation with a horizontal bone defect were enrolled in our study. All patients were treated with our new GBR approach, called the \"sling technique\" (ST), which consisted of covering the vestibular side of the bone defect with a stretched e-PTFE membrane. This technique allowed the amplification of the membrane surface as long as the compression and the immobilization of the underlying graft material remained (1:1 autologous bone and bovine xenograft). The membrane was placed far from the surgical incision of the mucosa to avoid infective complications. Pre- and post-regenerative surgery CBCT scans were used to assess the tissue gain amount after the GBR. Results: A total of 10 patients (4 females, 6 males; age range: 45-60 years old) were enrolled in our study and 33 implants were placed in regenerated bone. An average of 4 mm bone gain was achieved using the ST. No membrane exposures or biological complications were registered during the post-operative healing period. After a 24-month follow-up, a 100% implant survival rate was observed without hard or soft tissue dehiscence. Conclusion: The e-PTFE membrane positioned with the \"sling technique\" gave satisfactory results in horizontal bone augmentation. Considering the small sample, further confirmations with larger studies are needed.  
guided bone regeneration; sling technique; case series series
## 2 Materials and Methods
### Methodology
In addition to the artificial neural network (ANN) models, two gradient-boosting ensemble methods were employed: CatBoost (developed by Yandex) and XGBoost (developed by [PERSON] and [PERSON]) [38; 39; 40]. While both models leverage gradient-boosted decision trees, CatBoost uses ordered boosting and special handling of categorical features, whereas XGBoost primarily focuses on computational efficiency and effective tree pruning. For the CatBoost model, hyperparameters were optimized via cross-validation, resulting in a learning rate of 0.05, 150 iterations, a depth of 7, and a random strength of 2. Similarly, the XGBoost model was tuned with a learning rate of 0.05, 150 boosting rounds, a maximum depth of 7, and a subsample ratio of 0.8 to improve generalization. These configurations were chosen to maximize predictive performance, as determined by the specified evaluation metrics. Alongside the Mean Squared Error (MSE), other metrics considered for evaluation included the coefficient of determination (R\({}^{2}\)), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE). The performance of time series models such as ARIMA, LSTM, Prophet, and MAGRU has been previously evaluated on various datasets in earlier studies [41; 42; 43]. In many cases, the predictive accuracy of these models was highly dependent on the nature and characteristics of the data being analyzed, including its complexity, nonlinearity, and seasonality. In this study, we focus on a dataset representative of the time series of influent wastewater quality, which presents unique challenges such as irregular patterns, potential outliers, and noise. By leveraging these models, we aim to determine their relative effectiveness in forecasting wastewater quality parameters and analyze their suitability for this specific application. The comparison provides insights into the adaptability of these models to environmental datasets, characterized by variability and the need for precise predictions to support decision-making in wastewater treatment processes.
## 4 Experimental Results
### Learning Parameter  
The dataset is randomly split into training and testing. The k-fold cross-validation technique is applied to obtain the optimized parameter in the training dataset. For the experimental purpose, the five-fold validation is conducted and the radial basis function (RBF) kernel function is chosen to optimize the parameters like gamma and epsilon for SVM regression. The model learns the hyperparameter values performed by a five cross-fold validation method. The optimal value for the support vector machine for regression was chosen based on different values tested with k-fold validation.
# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## 4 Conclusions  
The paper presents a framework to apply the multi-attribute utility theory (MAUT) for the categorization of the condition of railway embankments using multiple sources of data available to the infrastructure managers. The overall methodology proposes the use of five attributes. These include frequency of tamping activities, extent of external irregularities obtained from visual inspections, frequency of internal irregularities obtained from GPR lower frequency data, ballast quality (fouling), and ballast depth (pockets). Ballast quality and depth data were obtained from higher frequency GPR antennas. The selection of these attributes is made because of their relevance to the problem and that they are readily available. By weighting the importance of each of the attributes and by evaluating them on the investigated railway lines, the MAUT-based methodology provides calculation of the overall utility function values used to form a ranking list of the condition for the investigated railway embankments. To verify the usefulness of the methodology, the paper presents its application on 181 km of railway embankments in Croatia. The calculated overall utility function for each section provided the categorization of embankments in five categories, ranging from very poor to very good. The developed MAUT model provides a transparent and comprehensive procedure that can support decision-makers to plan maintenance works and further detailed investigation works and monitor programs and remediation measures. The framework is flexible and methodology could be expanded to consider additional attributes.  
Conceptualization, M.S.K. and M.B.; Data curation, M.S.K.; Formal analysis, M.S.K. and M.B.; Investigation, M.S.K. and M.B.; Methodology, M.S.K. and I.S.; Project administration, M.S.K.; Validation, M.B., I.S. and K.G.; Visualization, M.B. and K.G.; Writing--original draft, M.B. and I.S.; Writing--review & editing, M.S.K. and K.G.  
The authors gratefully acknowledge the support from the H2020 Programme for DESTination RAIL project, funded under MG-2.1-2014 I\({}^{2}\)I Intelligent Infrastructure call, grant agreement No 636285.  
The authors declare no conflicts of interest.  
Figure 8: Overall utility function—overall percentage categorization summation results.
# Effect of Fermentation Humidity on Quality of Congou Black Tea
###### Abstract
Additionally, the rolling and cutting process of broken black tea results in heavier cell fragmentation, larger specific surface areas, and full exposure of the inner components to air, thus accelerating the rate of chemical processes such as oxidation of polyphenols and hydrolysis of glycosides in the tea leaves, and completing the fermentation in a shorter period of time (1 to 2 h) [11]. In contrast, the fermentation time of congou black tea is longer, generally 3 to 6 h [12]. This suggests that fermentation humidity has a much greater effect on congou black tea than on broken black tea, but the relevant information is not yet clear. Against this background, this study intended to optimize the fermentation humidity conditions of congou black tea by setting different fermentation humidity gradients and analyzing their effects on the sensory quality and chemical composition, as well as on the antioxidant activity against DPPH and inhibitory effects on the activities of \(\alpha\)-amylase and \(\alpha\)-glucosidase, which may offer a theoretical foundation to improve congou black tea's quality.
# Fusing Appearance and Prior Cues for Road Detection
## 2 Proposed Algorithm
### Preprocessing of the Input Image  
#### 2.1.1 Superpixels  
Conventional road detection algorithms using monocular color images usually define features by exploiting each pixel in the input image independently. However, in this paper, we estimate the appearance model of the road surface via superpixels. Superpixels are kinds of mid-level visual cues, which have been applied to image segmentation and object detection with demonstrated success [31; 32]. We choose to over-segment the images into superpixels as one of the preprocessing steps in road detection due to the following reasons: Firstly, superpixels are obtained by over-segmentation of the image into atomic regions which are ideally similar in color and texture. As a consequence, features can be well defined over a superpixel. Secondly, using each pixel independently will lead to errors in road detection resulting from various noise. On the contrary, using superpixels as elementary units can reduce the amount of noise. Thirdly, superpixels can increase the chances that the boundaries of different semantic classes are extracted. In other words, superpixels can be used to preserve boundaries, which boosts the performance of our road detection algorithm. Finally, pixel-based methods increase the complexity of inference algorithms owing to the large number of variables in high resolution images. Conversely, superpixel-based representations can reduce the computational complexity of the algorithm, because by treating each superpixel as one elementary unit, the number of total variables will be largely reduced.  
In this paper, we generate superpixels as shown in Figure 1 using the SLIC (simple linear iterative clustering) method, which was proposed in [33]. In addition, for each superpixel in the input image, we compute the average feature value of all the pixels composing the corresponding superpixel.
## 8 Use Cases and Applications
### Other Applications of Drones and Autonomous Ground Vehicles  
In addition to the aforementioned applications, both drones and autonomous ground vehicles showcase their versatility and impact on various industries. Some noteworthy applications include the following:  
* Tourism, commerce, and cinematography: Drones play a pivotal role in the realms of tourism [181], commerce, and cinematography [182]. They provide high-quality images, thereby offering new perspectives to attract tourists and promote destinations at a whole new level. In cinematography, drones offer a cost-effective means to capture special shots that might be physically and financially challenging with alternative equipment.
* Underwater operations: Both aerial drones and unmanned underwater vehicles (UOVs) contribute significantly to monitoring marine life. Aerial drones can be used to survey coastlines and assess environmental conditions, while underwater drones are employed in surveys, checking underwater infrastructure, and searching for oil and gas. The advantage of underwater drones lies in their ability to withstand the dangerous and harsh conditions beneath the water's surface. Advancements in this technology are anticipated, thereby enabling more sophisticated research and exploration of deep sea locations [183].  
* Traffic monitoring: Drones and autonomous ground vehicles have proven effective in traffic and accident monitoring, thus quickly providing necessary footage through high-quality cameras [184]. They find applications in areas with a high frequency of accidents, thereby assisting in assigning blame or facilitating decision making. Beyond traffic, these autonomous systems are utilized to monitor crowds at protests or refugee movements, thereby contributing to public safety [185].
* Military applications: In military contexts, both drones and autonomous ground vehicles serve various purposes, from simple surveillance and reconnaissance to more complex tasks. They play a role in targeted killing through artificial intelligence, thereby reducing the risk to personnel. However, concerns about the misuse of such technologies for personal gain, potentially leading to property damage or harm to innocent lives, have been raised.
## 5 Results  
We run the Coverage Path Planning (CPP) node insertion algorithm for a variety of surfaces. Two of them are panels whose hardware is available to us from the Aerospace Industry (ISRO). One is of an octant shape (part of a propellant tank) and a cylindrical shape with cutouts, part of a launch vehicle structure. These two are listed as doubly curved convex and singly curved convex shapes, respectively. By rotating them upside down, we also obtain a doubly curved concave and singly curved concave shape, respectively. This is also a realistic use case in the industry as we need to do this kind of NDT inspection (thermography) for both sides of the panel. Three more panel shapes are considered here in mesh form (although we have no physical panels of that shape available to us)--A highly doubly curved convex surface (hemisphere), highly single curved in one direction and slightly curved in the other (nose cone shape), and a concave-convex structure (saddle shape). These examples are used to illustrate extensively the shapes of panels used in aerospace applications (see, e.g., Figure 18). Flat or near-flat shapes (e.g., airfoils, wings, empennage) and full cylinders (e.g., fuselage, engines) are not listed as they are basic variations of listed shapes. Note that all results here are produced with a default value of \(p_{f}=1.1\). See Section 2.6 to see how the results change based on the projection factor.  
The complete list of panel shapes is thus:  
1. Singly curved convex: Partial cylinder panel with cutouts, part of a launch vehicle.
2. Singly curved concave: The same panel as singly curved convex but rotated 180 degrees about the Y-axis.
3. Doubly curved convex: Octant shape, part of a propellant tank.
4. Doubly curved concave: The same panel as doubly curved convex but rotated 180 degrees about the Y-axis.
5. Doubly curved convex highly curved: Hemispherical shape.
6. Nose cone: Highly curved in one direction and slightly curved in the other, with a singularity at the tip.
7. Concave-convex: Saddle shape.
## 3 Theoretical Analysis
### The Principle of an MSC Working in the Visible Band  
The MSC is made by fusing two fibers of the same material, but with different diameters. In order to achieve a high \(\gamma\) for MSC, the dispersion effect of the fiber is taken into account for a given doping ratio, and the \(\gamma\) is increased by optimizing structural parameters of the MSC without adjusting the fiber doping ratio to change the RI of the fiber. Assuming that the optical fiber used is only doped with GeO\({}_{2}\), the core is doped with 13.28%, and the cladding is doped with 9.78%. The dispersions of the two fibers, taken from the Sellmeier equation, are expressed as  
\[n_{co}=\sqrt{1+\frac{0.7108675759\times\lambda^{2}}{\lambda^{2}-0.0684800426^{ 2}}}+\frac{0.4491392689\times\lambda^{2}}{\lambda^{2}-0.1219252124^{2}}+\frac{0. 8917276872\times\lambda^{2}}{\lambda^{2}-10.1760054835^{2}} \tag{5a}\] \[n_{cl}=\sqrt{1+\frac{0.7069927717\times\lambda^{2}}{\lambda^{2}-0.0684600885^{2} }}+\frac{0.4383817131\times\lambda^{2}}{\lambda^{2}-0.12045325052^{2}}+\frac{0. 8932435754\times\lambda^{2}}{\lambda^{2}-10.1030035705^{2}} \tag{5b}\]  
where \(n_{co}\) is the RI of the core, \(n_{cl}\) is the RI of the cladding, and the unit of wavelength (\(\lambda\)) is micrometers (\(\upmu\)m).  
The key to designing the MSC is that the wave-vector of different modes should be matched, which means that different modes have an equal RI [11].Under such conditions, the fundamental modes propagating in one fiber can all be converted into higher-order modes propagating in the other fiber, as shown in Figure 1. During propagation, the fundamental mode gradually passes through the fusion area into another fiber and propagates for a certain length to satisfy \(\gamma=1\). If the fusion area is too long, energy will propagate from one fiber to another periodically which will lead to the fluctuation of \(\gamma\).  
However, because of the dispersion effect caused by the RI of the fiber, not all the power of the fundamental modes in a wavelength range totally propagates to another fiber and converts into the power of the higher-order modes, resulting in \(\gamma<1\). Given the RI of  
Figure 3: The proportion of enhanced power as the normalized frequency increases.  
the fiber, the variation in the RI of the \(\mathrm{LP_{nm}}\) mode can be calculated by Equation (2) and Equation (5), as illustrated in Figure 4. Considering reasonable structural parameters of the MSC, the mode effective RI is set as 1.482248. Then, the radius of the fiber core for the \(\mathrm{LP_{01}}\) mode is identified as 2.99 \(\upmu\)m; this parameter for the \(\mathrm{LP_{11}}\) mode (\(\mathrm{LP_{21}}\) mode) is 5.15 \(\upmu\)m (7.15 \(\upmu\)m) at \(\lambda=500\) nm.  
As shown in Figure 4, the dispersion effect caused by the RI changing with wavelength leads to a mismatch of the effective refractive indices for different modes within a wavelength range. Therefore, \(\gamma<1\). To achieve the overall maximum value of \(\gamma\) in the visible band, the structural parameters of MSC are optimized in Section 4.1.