esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Management for Drones in Future Mobile Networks--A Survey
## 2 Drone Networks
### IoT-Equipped Drone Networks  
The IoT network is the system of connecting everything around us to the Internet. The focus has recently shifted to drones. IoT supports numerous applications that drones can provide in addition to additional services. However, hindrances are present in turning drones into \"flying IoT\", such as the large amounts of data needed for some applications and the communication mode selection in both LoS and NLoS. A trade-off exists between cost and efficiency since wireless communication has bounded accessibility while satellite communication, by comparison, is more expensive.  
The Internet of Drone Things (IDT) is an emerging technological innovation with the potential to revolutionize AI computing and big data analytics. Ref. [54] presented a novel approach to detecting contagious disease pandemics based on AI-enabled IDT infrastructure and blockchain technology. The proposed system captures real-time geo-located data from various sensors on each drone and creates a unified IDT database for combined computational processing, storage, and retrieval. Algorithms are then employed for the identification of disease outbreak hotspots based on multi-source surveillance data collected from drones by combining visual meaningful images extracted from video streams with deep learning approaches. The combination of AI and Blockchain provides an efficient way to obtain authentic, reliable, and secure information about the contagion situation. As the IoT evolves, there is a growing need for new approaches to addressing IoT security, privacy, and scalability challenges. The authors in [55] introduced a federated learning-based Blockchain-embedded data accumulation scheme for remote areas where IoT devices encounter network supply shortages and potential cyberattacks. The proposed model consisted of a two-authentication process that validates requests first with a cuckoo filter, then with a timestamp nonce. Hampel filters and loss checks are used to ensure secure accumulation. Finally, model training is carried out in a suitable environment, and the results validate the possibility of the introduced model.
## 3 Results and Discussion
### Effect of Pretreatment on the Capacity of YBMCs  
Both native _S. cerevisiae_ yeast and autolyzed yeast were used in the present study to obtain YBMCs with oil blends with a nutritionally favorable fatty acid ratio. A significantly (\(p<0.05\)) higher increase in EY was observed in YBMCs obtained from autolyzed yeast compared to capsules based on native yeast (Figure 1). The structure of the cell wall determines its permeability; in NY, the passive transport was much slower hence the observed lower oil loading yield [41]. The biggest difference was observed in the case of RWBg encapsulation, and EY increased in AY versus NY by about 35.46%. [PERSON] et al. [42] indicate in their work that the encapsulation of oils in yeast cells is a process consisting of two sub-processes: releasing the yeast cell components into the reaction environment and the simultaneous extraction of oil from the environment, immobilized inside the cell. [PERSON] et al. [23] indicate that the pre-treatment of yeast biomass by means of plasmolysis may result in an increase in encapsulation yield by as much as 50%. However, other authors in the published report indicated that cell plasmolysis did not result in a significant increase in the oil loading into capsules [19]. The calculated EY ranged from 8.25 to 13.98%, depending on the oil blend used. This may result from the conditions of the encapsulation process carried out in the aquatic environment. The high water content causes the walls of yeast cells to become hydrophilic due to the polysaccharides present in them, which limits the process of diffusion of hydrophobic molecules into the cells [43]. Nevertheless, [PERSON] et al. [44], in their studies, showed similar EY values using yeast as an encapsulation carrier. In other studies, however, [PERSON] et al. [45] showed that EY can be as high as 55%, depending on the encapsulated material.
## 3 Network Anomaly Detection
### Header and Payload Analysis
#### 3.2.3 Transport Layer  
The same IP address accessing several ports in a short period of time indicates a probe where an attacker is seeking for open ports and possible vulnerabilities to be exploited.  
TCP flags can be used with an illegal combination to determine whether a port is open or not. For instance, packets that are used to start and finish a connection (SYN and FIN) should never be set at the same time. FIN should also not be the only flag set in TCP packet. Packets with these combinations or no TCP flags set indicate an anomaly and should be dropped. In addition, SYN flood is a common DoS attack that consists of the transmission of many TCP packets with the SYN flag set, opening many connections with the victim's machine to overload it. Table 4 reviews some of the other TCP flags' anomalies used in port scanning. Such TCP packets can be transmitted by bots using tools like Nmap.
## 4 Discussion
### Behavior of the Optimal EMS
The reason for this is that the shorter the window, the more reliable the demand prediction will be and, consequently, the more accurate the scenario characterization. This, in turn, will lead to a more accurate selection of the operating pattern. Indeed, in general, the probability of making an accurate prediction is higher for the near future than for the distant future--for example, as is the case with weather forecasting. Finally, it must be said that a prediction horizon of eight hours is suitable for the micro-CHP system and the demand profiles considered in this study. These were chosen to represent the standard case [10; 28], so that the results would have a certain generality. However, for systems with different TES sizes or in applications with significantly different energy demands, the appropriate prediction horizon could be longer or shorter than eight hours.
## 3 Results and Discussion
### Characterization of Mortars Containing Cork Waste
As expected, the increase of cork percentage in mortars involved a strength decrease for both three-point bending and compressive strength tests (Figure 7). This has also been observed by literature and is mainly attributable to the poor mechanical strength and stiffness of cork [15, 16]. However, the decrease in mechanical properties was not as pronounced as expected. This might result from the promotion of the binder hydration phenomenon by a slow but progressive release of the water absorbed by the cork (wextra). Remarkably, this could be an interesting phenomenon for mortar application in dry conditions; indeed, it would involve better binder hydration, contrast the water evaporation, and avoid shrinkage cracking [16, 32]. Despite the presence of high cork content (Table 1), all mortar mixes demonstrated good mechanical performance. Referring to results from the compressive strength test and prescriptions by UNI EN 998-2, different strength classes were assigned to the involved mixes. The reference mortar without cork was classified in the M15 class, to be used for  
Figure 6: Correlation between apparent density (bar chart) and thermal conductivity (black line) of mortars containing increasing cork content. The error bars stand for standard deviation error. Figure 7: Mean failure strength from mechanical testing of mortars containing increasing cork content: three-point bending (bar chart), compressive strength (black line). The error bars stand for standard deviation error. structural recovery or anti-seismic reinforcement. The mortars containing 1 wt% and 2 wt% of cork were classified in the M10 strength class, so they were considered to have a good mechanical strength; these can be used both for seismic and insulating applications. The mortar containing 4 wt% of cork was classified in the M5 class, so it could be primarily used for lightweight and insulating applications.
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 2 Mechanical Simulation Analysis of Hybrid Laser INS Structure
### Mechanical Simulation Analysis of the Hybrid Laser INS Structure
#### 2.2.2. Finite Element Modeling of Hybrid INS Structure  
(1) Simplification of the Hybrid INS Structural Model  
For CAE analysis, it is essential to reduce simulation time and improve mesh quality in the finite element model by simplifying the 3D models of various components in the hybrid INS according to simplification principles. Figures 4 and 5 shows a simplified diagram of the outer frame and shaft end component model.  
Figure 4: Outer frame model simplification.  
Figure 3: Outer frame assembly model diagram. 1: shaft end encoder assembly; 2: Outer frame; 3: Platform simulation counterweight; 4: shaft end motor assembly.  
Figure 2: Hybrid INS structural model diagram. 1: Base assembly; 2: Inner frame; 3: Rotation locking system; 4: Outer frame assembly; 5: Platform assembly; 6: Damper assembly.  
(2) Contact Settings of the Hybrid INS Structure Model  
The simplified Pro/E model of the laser INS structure was imported into FEA software for finite element analysis (FEA) (the finite element analysis software used in this article is the commercial software ANSYS 2017). Contact settings were applied between components, including bonded contact and frictional contact. Figure 6 shows the contact setting between the screw and the shaft end assembly  
(3) Mesh Generation of the Hybrid INS Structure Model  
Grid partitioning is an important step in finite element analysis, which discretizes a continuous solution area (such as structure, field, etc.) into many small elements that are connected to each other through nodes, forming a grid composed of a finite number of elements. In reality, objects are continuous, while finite element analysis requires transforming them into discrete computational models.  
During the grid generation process, certain parts are manually controlled to improve grid quality. In finite element analysis software, the Solid186 element type is used to simulate components with complex geometric shapes and large thicknesses in the system. By dividing irregularly shaped components with large volumes into regular structures, the mesh quality is improved, and the geometric characteristics of each component in the system can be well adapted. This element type can accurately reflect the mechanical behavior of materials, especially the characteristics of isotropic and anisotropic materials. To reduce computational costs, including computation time and memory usage, and ensure a certain level of computational accuracy, this article sets the grid division accuracy to medium and imposes corresponding limitations on grid size based on analysis needs and the size characteristics of components. A \"medium\" grid can greatly reduce computation time while meeting basic engineering accuracy requirements. Figure 7 shows the finite element mesh division of the entire inertial navigation system structure.  
Figure 5: Shaft-end component model simplification.  
Figure 6: Contact setting of screw and shaft end assembly.
## 5 Adaptive Rectifier Design for Short/Long Range  
Since a single diode cannot cover a wide input power range because of voltage breakdown limitations in the semiconductor properties, diodes with a low threshold voltage and a high breakdown voltage can be combined to achieve a rectifier operating over a wide range of input powers. The proposed rectifier, shown in Figure 6, contains three shunt Schottky diodes (\(D_{1}\) to \(D_{3}\)) and a Field-Effect Transistor(FET) \(T_{1}\) (NE3210S1) [4], which aims at the rectifier working as a switch amongst low and high input powers. The operation procedure of the adaptive/reconfigurable rectifier can be explained as follows: once the input power level is low, the gate-source voltage across the transistor \(V_{gs}\) is close to zero, thus turning the transistor ON and allowing the current to pass through the channel, hence short-circuiting \(D_{2}\) and \(D_{3}\). In this case, the current pathway passes through \(T_{1}\) and \(D_{1}\). Once the input power rises, the transistor is turned OFF and \(D_{2}\) and \(D_{3}\) are working with \(D_{1}\). Therefore, by carefully selecting the proper diodes to operate at low and high input power levels, a low threshold voltage and a high breakdown voltage can be achieved [4; 10].  
At low input power,  
\[V_{Gs}>V_{th,T},\left\{\begin{array}{c}T_{1}\textit{ is ON, }V_{D1}<\textit{ V}_{th,D1}\\ D_{2},D_{3}\textit{ are shorted}\end{array}\right.,\textit{ the current is OFF }\Rightarrow V_{out}=V_{in} \tag{6}\]  
\[V_{Gs}>V_{th,T},\left\{\begin{array}{c}T_{1}\textit{ is ON, }V_{D1}>\textit{ V}_{th,D1}\\ D_{2},D_{3}\textit{ are OFF}\end{array}\right.,\textit{ the current is ON} \tag{7}\]  
and the output voltage can be expressed as in (8)  
\[V_{out}=Vin-(V_{D1}+\textit{ V}_{DS,T}). \tag{8}\]  
As the power increases, the voltage increases and \(T_{1}\) turns OFF  
\[V_{out}=Vin-(V_{D1}+\textit{ V}_{D2}+V_{D3}). \tag{9}\]  
The outline of the adaptive rectifier concept can be illustrated through the I-V diode characteristics and Power Conversion Efficiency. As seen in Figure 7a,b, stacking two or three diodes will increase the breakdown voltage; however, the threshold voltage will increase as well. Therefore, by applying the configuration of the adaptive system in Figure 6c, a low threshold and high breakdown can be accomplished, which leads the rectifier to achieve a high efficiency while operating over wide range of input powers as can be seen in Figure 6d,e.  
Figure 6: Adaptive rectifier configuration [4]. Reproduced with permission from [4], Copyright IEEE, 2016.
# Lester: Rotoscope Animation through Video Object Segmentation and Tracking
## 3 Methodology
### Segmentation and Tracking  
In order to segment the different visual traits of the character the Segment Anything Model (SAM) [1] is used. SAM is a promptable segmentation model which is becoming a foundation model to solve a variety of tasks. Given an input image with three channels (height \(\times\) width \(\times\) 3) and a prompt, that can include spatial or text information, it generates a mask (height \(\times\) weight \(\times\) 3), with each pixel assigned an RGB color. Each color represents an object label from the object label space \(L=l_{1},l_{2},\dots\), inferred from the prompt, corresponding to the predicted object type at the corresponding pixel location in the input image. The prompt can be any information indicating what to segment in an image (e.g., a set of foreground/background points, a rough box or mask or free-form text). SAM consists of a image encoder (based on MAE [24] a pre-trained Vision Transformer [25]) that computes an image embedding, a prompt encoder (being the free-form text based on CLIP [26]) that embeds prompts, and then the two information sources are combined in a mask decoder (based on Transformer segmentation models [27]) that predicts segmentation masks.  
Applying SAM at the frame level would result in different labels for the same object in different frames. SAM does not start from a closed list of object labels but rather creates and assigns labels during execution. Therefore, there is no guarantee that the same labels are assigned to the same object in each frame. To do it consistently, we use SAM-Track [2]. SAM results for the first frame are propagated to the second frame, with the same labels, with DeAOT [28], a method of hierarchical propagation for semi-supervised video object segmentation. The hierarchical propagation can gradually propagate information from past frames to the current frame and transfer the current frame feature from object-agnostic to object-specific. SAM is applied to subsequent frames to detect new objects, but the segmentation of already seen objects is carried out by DeAOT.  
We have chosen keyword-based prompts to specify the desired segmentation. We provide some default prompts for common scenarios (e.g., 'hair','skin','shoes', 't-shirt', 'trousers') but it is anticipated that in many scenarios, the user will need to specify an alternative. These prompts are not processed with the free-text capabilities of SAM because of their limitations, instead they are processed with Grounding-DINO [29], an open-set object detector, which translate them into object boxes that are sent to SAM as box prompts. One problem we have identified is that certain object combinations lead to incorrect masks when they are specified in the same prompt. To solve this, we allow specifying multiple prompts that are processed independently. Figure 3 shows the workflow of the segmentation and tracking stage.
## Materials and Methods
### Elemental Analyzer (EA-Isolink)  
For 8\({}^{13}\)C and 8\({}^{15}\)N bulk isotopic determination, biomass and compost samples were weighted (c. 0.7 mg) and enveloped into tin capsules. The samples were analyzed in duplicate (\(n=2\)). The analysis was performed using an EA IsoLink(tm) HRMS System (Thermo Fisher Scientific, Bremen, Germany). The EA microanalyzer was equipped with a combustion furnace set up at 1020 \({}^{\circ}\)C and coupled via a ConFlo IV Interface unit to a continuous flow Delta V Advantage isotope ratio mass spectrometer (IRMS) for measuring 8\({}^{13}\)C and \(\delta^{15}\)N. Isotopic values were corrected by using the appropriate standards recognized by the International Atomic Energy Agency (IAEA).  
The stable isotope abundances are reported in the delta (8) notation (e.g., \(\delta^{13}\)C) in variations relative to an international measurement standard. The isotope value is defined in [44], according to the Equation (1).  
\[\delta^{i}E_{sample}=\frac{R\ \left(\frac{iE}{jE}\right)sample}{\left(\frac{iE}{jE }\right)standard}-1, \tag{1}\]  
where \"\(R\)\" is the molar ratio of the heavy (\(iE\)) to light (\(jE\)) most abundant isotope of chemical element \"\(E\)\" (\({}^{13}\)C/\({}^{12}\)C for \(\delta^{13}\)C values; \({}^{15}\)N/\({}^{14}\)N for \(\delta^{15}\)N values). The \"\(\delta\)\" values are reported in per mil (%o). The stable isotope standard for reporting carbon measurements is the Vienna Pee Dee Bellemite limestone (VPDB scale) and for nitrogen measurements atmospheric nitrogen (Air). The analytical precision and accuracy of bulk \(\delta^{13}\)C and \(\delta^{15}\)N was typically less than \(\pm\)0.15 and 0.70 %o, respectively.  
The total C and total N contents were determined by the dry combustion method using the same instrument specified above (EA IsoLink) in the CN analyzer mode. The C/N ratio was calculated and used as a supplemental information to study the evolution of the decomposition process of biomass into composting material.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 2 Characteristics of Magnetic Field Detectors
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SQUID** & **Optical Pumping** & **Hall** & **Flugate** & **GMR/TMR** & **AMR** & **Induction** \\ \hline Detection limit & fT & pT & \(\mu\)T & pT & nT & nT & fT-T \\ Power supply & Line & Line & Battery & Battery & Battery & Battery & Battery \\ Size & Medium & Medium & Ultra small & Small & Small & Ultra small & Ultra small-large \\ Measured value & Vectorial & Scalar & Vectorial & Vectorial & Vectorial & Vectorial & Vectorial \\ Sensor noise & Ultra-low & Ultra-low & High & Low & High & Medium & Low \\ Bandwidth & Small & Small & Medium & Medium & Medium & Large & Small \\ Saturation by Earth’s magnetic field & Yes & Yes & No & No & No & No & No \\ Operating temperature & Ultra-low & Normal & Normal & Normal & Normal & Normal & Normal \\ Cost & High & High & Low & Medium & Low & Low & Low \\ Suitability for mobile applications & No & No & Yes & Yes & Yes & Only small coils \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of magnetic sensors regarding mobile communication purposes.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 6 Numerical Results
### Reconstructing a Shape from Its 3D Zernike Moments: Importance of the Order \(N\)
Compared to the example of calmodulin shown above, TIM is a more globular protein. The reconstruction of its shape for \(N=20\) shows similar characteristics than the corresponding reconstruction for calmodulin: the overall shape is conserved, without information on local details. In contrast, Reconstructions at \(N=50\) and more significantly at \(N=300\) show the local features of the surface of TIM, namely the local spherical patches corresponding to its atoms. The final example, illustrated in Figure 11, corresponds to a brain surface. We use the brain surface data of one human subject from the Images and Data Archive of the USC Stevens Neuroimaging and Informatics Institute data set (accessed on 23 September 2022) ([[http://ida.loni.usc.edu](http://ida.loni.usc.edu)]([http://ida.loni.usc.edu](http://ida.loni.usc.edu))). The brain surface obtained there is represented with a high quality oriented triangular mesh of genus zero, containing 65,538 vertices and 131,072 triangles. We computed its Zernike moments using the finite precision algorithm with a tolerance of \(10^{-8}\), up to order 300. We then reconstructed the shape of the brain with orders 20, 50, and 300. Sulci, the grooves, and gyri, the folds, make up the folded surface of the brain. As the surface area of the brain increases from the formation of those sulci, more functions are made possible. A smooth-surfaced brain is only able to grow to a certain extent. Therefore, neurobiologists focus on those surface features when they study a 3D image of a brain. As shown in Figure 11, reconstruction of the surface of a brain from Zernike moments of orders up 20 only provides the general shape of this surface, with all sulci being smoothed out. The full geometry of those sulci, as observed in the original  
Figure 11: Reconstruction of the surface of a brain from the Zernike moments computed from a triangle mesh representing its boundary. We compare the reconstructions generated from three different maximum order \(N\), \(N=20\), \(N=50\), and \(N=300\). The original brain surface is shown on the left. All figures were generated with MeshLab [64]. surface, is only restored with high orders moments are considered for the reconstruction, as illustrated in Figure 11 with \(N=300\). All examples shown above highlight the same conclusions: at low order, up to \(N=50\), the Zernike moments only capture the global features of a shape. Order 50 is usually the limit to which Zernike moments can be computed for a shape represented by triangular meshes. The new algorithms presented in the paper allow for computing Zernike moments at much higher order. With \(N=300\), we observe reconstructed meshes that capture the local features of the original shapes.