esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Coordination of PSS and BESS Controllers to Enhance Oscillatory Stability Using Proposed Method
### Power-System-Controller Modeling
#### 4.1.2.1 Battery Model  
Figure 7 shows the equivalent model of a sodium-sulphur (NaS) battery. Battery voltage varies linearly with depth of discharge (DOD), as well as affects charging and discharging resistance [34]. DOD and state of charge (SOC) can be computed as:  
\[DOD=\frac{1}{Q_{max}}\int\,tdt \tag{9}\] \[SOC=1-DOD\]  
where \(Q_{max}\) is the maximum battery capacity at fully charged/discharged condition \((Ah)\). Charging resistance \(R_{ch}(\Omega)\) is given by [34]:  
\[R_{ch}=\ c+dDOD^{2}+eDOD^{3}+fDOD^{4}+gDOD^{5}+hDOD^{6}+iDOD^{7}+jDOD^{8}+kDOD ^{9}+lDOD^{10} \tag{10}\]  
and discharging resistance \(R_{dis}(\Omega)\) can be found as: [34]:  
\[R_{dis}=\ c+dDOD^{2}+eDOD^{3}+fDOD^{4}+gDOD^{5}+\ hDOD^{6}+iDOD^{7}+jDOD^{8}+kDOD ^{9} \tag{11}\]  
where \(c\), \(d\), \(e\), \(f\), \(g\), \(h\), \(i\), \(j\), \(k\), and \(l\) are polynomial coefficients.  
Figure 6: Developed model of NaS-based BESS with controllers in DIGSILENT.  
Figure 7: Equivalent model of NaS battery [34].  
Open-circuit battery voltage \(V_{oc}(V)\) can be computed as [34]:  
\[\begin{array}{ll}V_{oc}=&2.076,\hskip 56.905512 ptDOD\leq 0.56\\ &2.076-0.00672 DOD,\hskip 56.905512 ptDOD>0.56\end{array} \tag{12}\]  
Finally, battery output voltage \(V_{batt}(V)\) for discharging mode can be calculated as:  
\[V_{batt}=V_{oc}-R_{dis}I_{b}-R_{lc}I_{b} \tag{13}\]  
and for charging mode as:  
\[V_{batt}=V_{oc}+R_{ch}I_{b}+R_{lc}I_{b} \tag{14}\]  
where \(R_{lc}\) is life-cycle resistance \(\Omega\), and \(I_{b}\) is battery current A.
# GazeRayHand: Combining Gaze Ray and Hand Interaction for Distant Object Manipulation
## 4 User Study
### Conditions
#### 4.1.2 Modified Gaze Beam Guided  
The Gaze Beam Guided [17] technique selects an object that the gaze ray is pointing at when the user makes and releases a pinching gesture. Initially, an invisible gaze ray is used which starts from the user's forehead and ends at a gazed point. The gaze ray becomes visible when a user makes a pinching gesture, and the ray is controlled by the pinching hand afterwards. This changes the end point of the ray according to the hand movement. Moving the hand up, down, left, or right makes the end point of the ray move up, down, left, or right, respectively. When releasing the pinching gesture, the object at the end point of the ray is selected and the ray becomes invisible again with being controlled by eyes, and the object turns yellow indicating that it is selected for manipulation.  
There are two steps for object translation, allowing users to handle horizontal/vertical and depth translations separately. The first step is for horizontal/vertical translation. With another pinching gesture, the selected object instantly relocates to the point where a user is looking at, and the gaze ray becomes visible, being controlled by the pinching hand again (see Figure 4a). The selected object is attached to the end of the ray; hence, moving the hand to control the end point, as in the selection, moves the object. Releasing the pinching gesture completes the first step of translation. The ray remains visible but fixed after this step is completed, and the object turns green.  
Another pinching gesture triggers the second step in translation for a depth control. Moving the pinching hand further or closer makes the object move further or closer along the fixed ray (see Figure 4b). The distance of depth translation is controlled in the same way as with Gaze&Pinch. Upon releasing the pinch, the second translation step finishes, and the object turns purple.  
While the original Gaze Beam Guided technique does not support object rotation, we modified it to add the feature so it would be comparable to other techniques in this study. Since the Gaze Beam Guided technique repeatedly uses the pinching gesture, we also adopted it to start a rotation step. After the second translation step, the fixed ray remains visible, and it becomes a reference line for the rotation, like in our GazeRayHand technique. The user can rotate the object left, right, up, or down by moving the pinching hand in these directions. The rotation speed is controlled in the same way as in our techniques  
Figure 3: Gaze&Pinch technique [19] as condition of our study: object translation (**a**), and object rotation (**b**).  
(see Figure 4c). Releasing the pinching gesture completes all steps of object manipulation by the Gaze Beam Guided technique; so the gaze ray becomes invisible, and the object returns to its original color. We note that object manipulation with the Gaze Beam Guided technique is performed sequentially, and we modified it by adding a rotation step at the end of the sequence.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.2 The Node Layer  
The node layer on PaaS-BDP, a predecessor of MC-BDP developed in the early stages of this research, was composed of physical or virtual machines to which containers were deployed [60]. MC-BDP was developed in an effort to reduce entry barriers for SMEODs to big data analytics. Taking into account that some of the biggest entry barriers perceived by such companies are the initial investment required and the risk of vendor lock-in [32], MC-BDP recommends an infrastructure based on commercial clouds and, where possible, it recommends that multiple clouds be utilised simultaneously. It is worth noting that MC-BDP is based on a minimal platform-as-a-service (PaaS) model where virtual machines with an operating system and a container runtime are provided. Any other dependencies needed for containerised services to run are specified in their respective container images and imported from public or private repositories at runtime, thus decoupling these services from the environment where they run and allowing for a very lightweight PaaS model [60].  
One of the advantages of a lightweight PaaS model is that virtual machines have the potential to be cheaper, since no middleware or runtime technologies other than the container runtime need to be provided, maintained, or kept up to date. Additionally, there is a greater chance that different cloud providers' offerings will be equivalent, since fewer components are involved, thus enabling consumers to compare them in terms of price, SLAs, or other non-technical criteria. Since lack of standardisation amongst providers has been identified as one of the challenges in the field of cloud computing [72], research developments such as MC-BDP must be careful to account for it in their design. The proposal of a lightweight PaaS systems thus conforms with the state of current research in the field.
## 3 Aeronautical Application
### Mesh Generation  
The mesh generation was carried out using the commercial software Pointwise V18.4 R4, by following an iterative grid refinement procedure, as is usual for industrial CFD applications [29]. First, the various surfaces of the aircraft model were meshed using triangular elements. For instance, the tessellation of the main wing suction-side, including the winglet, is depicted in Figure 3, where the mesh resolution is artificially reduced for illustration purposes. Note that the surface grid was refined at the leading and trailing edges of the wing. For the production mesh, fine surface grid cells with a linear size close to 1% MAC were used. Then, three-dimensional viscous-type unstructured meshes were constructed by employing prism layers along the solid walls and tetrahedral cells in the  
Figure 2: Schematic illustration of the hemispherical computational domain for \(\mathrm{Y}<0\) (not real scale).  
rest of the computational domain. The discretization of the BL regions was performed using the T-Rex meshing algorithm, with the first cell spacing dictated by the desired near-wall resolution. The FV mesh had two prismatic layers with constant cell spacing in the wall-normal direction with a subsequent growth rate that was less than or equal to \(5/4\). For illustration, the slices through the three-dimensional mesh at \(Y=0\) and \(X=0\) are drawn in Figure 4.  
The production mesh used for investigating the GEEO procedure was selected after a preliminary grid convergence analysis, which was performed for the baseline SST RANS. Three different (similar) grids were tested with varying near-wall mesh resolution, corresponding to \(y^{+}\cong 30\) (coarse), \(10\) (medium), and \(1\) (fine), for the first wall spacing. Practically, given the target resolution to be achieved, a tentative simulation was initially carried out by prescribing the first cell size based on the theoretical estimation of the local skin-friction for the flat-plate turbulent BL flow. Then, by measuring the actual value of \(y^{+}\) resulting from the simulation, the first wall spacing was suitably adjusted. Finally, based on this procedure, a sufficiently small first wall distance for the whole mesh was selected.  
The convergence of the SST RANS solution toward the high-fidelity SBES, for increasing spatial resolution, is demonstrated by looking at the results obtained for \(\alpha=19^{\circ}\), for example. Figure 5 shows the normalized pressure and skin-friction coefficient distributions for the wing section at the semispan fraction \(\eta=y/\left(b/2\right)=0.63\), with \(b\) representing the wing span. Moreover, the errors for the predicted aerodynamic force coefficients for the different solutions with varying mesh resolution are reported in Table 2.  
Figure 4: Slices through the prismatic/tetrahedral three-dimensional mesh at \(Y=0\) (**left**) and \(X=0\) (**right**).  
Figure 3: Surface mesh on the main wing, including the winglet.
## 3 Critical Technical Approach
### Stray Light Suppression Analysis and Structural Design Based on BRDF Modeling
#### 3.3.1 Scattering Characterization Modeling and Energy Attenuation Mechanism Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Description** & **Variables** & **Description** \\ \hline \(E(x,y)\) & optical field distribution & \(\Delta n\) & refractive index difference between incident and scattering media \\ \(D\) & diaphragm diameter & \(\gamma\) & light trap absorptance \\ \(U_{E}\) & irradiation uniformity & \(\eta\) & light trap residual energy ratio \\ \(I(x,y)\) & optical intensity distribution & \(\lambda\) & wavelength of the incident light \\ \(A\) & cross-sectional area of the fiber core & \(g(\theta_{i},\varphi_{r})\) & polynomial fitting function \\ \(T(\lambda)\) & filter’s transmittance & \(F\) & Fresnel reflection term \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variable nomenclature for mathematical symbols.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### GNN-Based Spatial Representation Module
#### 3.6.3 Node Feature Updates and Message Passing  
The two-layer GCN architecture allows information to propagate beyond immediate neighbors. Each node aggregates features from its direct one-hop neighbors in the first layer. In contrast, in the second layer, each node incorporates information from its two-hop neighborhood, allowing our model to learn more contextual representations while preserving local spatial relationships. The GCN output retains the graph structure but contains refined node embeddings that integrate information from the surrounding regions. After graph convolution, we reshape the output node features into a 2D feature map to ensure compatibility with the Vision Transformer (ViT) for final classification. Unlike more complex graph-based architectures, such as Graph Attention Networks (GATs), which assign different importance to neighbor nodes, the GCN in our model applies uniform neighborhood aggregation, making it computationally efficient while capturing local dependencies in the image. In structuring the graph with spatial adjacency, we maintain the hierarchical feature representation obtained from the CNN while allowing information to flow across spatially connected nodes. The refined features provide a better representation of the image data before being processed by ViT for classification.  
The GNN module adds a relational inductive bias. That is, it forces feature propagation in ways that reflect image topology, which can improve the learned representation for the subsequent classifier. CNNs model local patterns well but fail to capture long-range or topological relations. In contrast, GCNs easily capture contextual relationships, combining them and incorporating local features and long-distance dependencies [46].  
In summary, the proposed framework uses a staged pre-training approach for feature extraction (Figure 1). In Stage 1, a CNN is pre-trained on the whole training set to extract localized visual features from the CT scan. The CNN comprises convolutional blocks with ReLU activations, batch normalization, and max-pooling, followed by dense layers used only during pre-training. Once trained, the convolutional layers serve as a fixed feature extractor, and their output feature maps are retained for use in downstream applications. In Stage 2, these feature maps are converted into a grid-structured graph where each spatial region corresponds to a node and edges encode local spatial adjacency. A GraphConvolutional Network is then applied to propagate contextual information across the graph. This integration allows the GCN to operate on rich, pre-trained local features, enabling the model to learn both local and global representations for robust downstream classification.
## 9 Conclusion  
In this paper, we surveyed a multitude of techniques that can be incorporated to achieve adaptive communications in order to meet the challenges of the Internet of Things (_IoT_) [2]. For the problem of spectrum scarcity and competition, the spectrum-sensing techniques at the physical layer (PHY) and the cooperative mechanisms at the media access control (MAC) layer play arguably the most important role. The resultant adaptive channel access allows _things_ to exploit the under-utilized frequencies and avoid interference. Routing and flow control can also take advantage of the spectral knowledge to improve the rate of successful packet delivery.  
The challenge of interoperability in _IoT_ is addressed at both the network and application layers. Whilst 6 LoWPAN enables the seamless integration of various intelligent artifacts and the existing Internet at a connectivity level, the service abstraction and context awareness provided by _IoT_ middleware facilitate the interoperation from a user-oriented angle. The delay- and disruption-tolerant overlay at the application layer is also reviewed. Such technology is particularly important in case of highly unstable connectivity and distant transmissions.  
Despite the conventional structure of the review, it becomes clear that a cross-layer ideology is either implicitly or explicitly employed, as the stringent distinction between layers has already lagged behind the advanced requirement of _IoT_. Such a trend is inevitable for designing adaptive communications for _IoT_[128] and more forthcoming studies in this area are anticipated.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 3 The Proposed Schemes
### The Initialization Phase and Two-Level Composite Hashing
#### 3.1.2 The Registration and Initialization Phase  
The registration is conducted in a secure channel. Each device \(D_{i}\) registers its identity \(D_{i}\) (here we used the same notation to denote both the entity and its identity), the secret key \(K_{i}\), and its seed \(N_{i}\) (for pseudonym generation) at the \(S\).  
\(N_{i}\) is the seed for generating the pseudonyms. When \(D_{i}\) initiates an authentication request, it computes \(PN=h_{2}(N_{i})\) as the current pseudonym and updates \(N_{i}=h_{1}(N_{i})\) for the next session. This is formulated as the algorithm PN_Seed_Update(\(N_{i}\)) called by \(D_{i}\) in Figure 2.  
When S accepts \(D_{i}\)'s registration (\(D_{i}\), \(N_{i},K_{i}\)), it initializes \(D_{i}^{\prime}s\) pseudonym vectors \(PV_{i}=\left\{PN_{i,0},PN_{i,1},\ \ldots,PN_{i,k},\ \ldots,PN_{i,w1}\right\}\), where \(PN_{i,j}=h_{2}\big{(}h_{1}^{\prime}(N_{i})\big{)},j=0\sim w1\). This is depicted in Figure 1a and in Figure 2. This arrangement of preparing the vectors of one current pseudonym and the future \(w_{1}\) pseudonyms is for deterring possible desynchronization-based DoS attacks and for handling the unreliable connection issues. When either desynchronization-based DoS attacks or unreliable connections happen, the requests from \(D_{i}\) might be blocked or be lost, and \(D_{i}\) would repetitively retry its requests. In such situations, [PERSON] who prepares additional \(w_{1}\) pseudonyms can properly tackle the issues. This initialization of \(D_{i}^{\prime}s\) pseudonym vectors is formulated as the algorithm PV_Initialize (\(D_{i},N_{i}\)) called by [PERSON] in Figure 2.  
Device_init(\(D_{i}\)) is for initializing \(D_{i}^{\prime}s\) initial parameters. \(D_{i}^{\prime}s\) PN_Seed_Update(\(N_{i}\)) method is executed when each time \(D_{i}\) initiates a D2S authentication request; in this method, \(D_{i}\) first generates the current pseudonym \(PN=h_{2}(N_{i})\) and then updates its seed \(N_{i}=h_{1}(N_{i})\).
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Thermal Decomposition Techniques
#### 2.2.1 Simultaneous Thermal Decomposition  
Among the simpler methodologies involving thermal decomposition as a synthetic route for magnetic-plasmonic structures is the thermal decomposition of both the magnetic and plasmonic moieties, either together or in tandem. Au-Fe\({}_{3}\)O\({}_{4}\) dumbbells were obtained through the simultaneous thermal decomposition of Fe(CO)\({}_{5}\) and HAuCl\({}_{4}\).3H\({}_{2}\)O in oleylamine and 1-octadecane [39]. The precursor solution was heated to 300 \({}^{\circ}\)C, and the resultant nanoparticles were exposed to air for 30 min to allow oxidation of the iron. Both 16 nm and 20 nm iron oxide dumbbell nanostructures were formed through variation in the amounts of precursor added. PEG and DMF (dimethylformamide) were used to produce interesting morphologies through further reduction of the Au\({}^{3+}\) ions onto the surface of the gold dumbbells. The Au-Fe\({}_{3}\)O\({}_{4}\) dumbbells are then used to achieve directional growth of asymmetric gold nanostars using PEG and DMF (Figure 5).  
Thermal decomposition was used as the basis for making magnetic-plasmonic nanoclusters by [PERSON] et al. [40]. Fe(acac)\({}_{3}\) was used as the organometallic precursor in this case. Iron oxide nanoparticles were first synthesised, and these iron oxide nanoparticles were then added to gold acetate and further heated to 180 \({}^{\circ}\)C in organic solvents with vigorous stirring. Nanoclusters were then formed using SDS and the water in oil microemulsion method. However, the resultant nanoclusters had a broad size distribution.  
Figure 5: TEM images of the Au-Fe\({}_{3}\)O\({}_{4}\) asymmetric nanostars and dumbbells obtained by varying the ratio of dumbbell seeds to gold salt. Reprinted with permission from [39]. Copyright RSC, 2017.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### Load-Aware Strategy  
The maximum timeout can be reduced if the flow table occupation exceeds a predefined threshold [15]. It can invalidate the previously constructed state transition diagram. To deal with that, we need to send testing packets (using the _compare_timeout_ operation) after triggering the flow rule to validate if it has the expected timeouts. If the testing packet cannot be matched, the attacker can detect the timeout adjustment. Then, the attacker can use the coarse-state-based method instead of following the previously constructed graph to trigger malicious flow rules. Since the coarse-state-based method triggers a new rule installation by generating a table-miss just slightly after the previous one expires, it can prevent the interference of load-aware adjustment.  
Figure 7: An example of the coarse-state transition diagram. Each state represents whether the flow rule has a timeout value larger than a specific value.
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 3 Materials Fabrication
### In-Situ Nanoparticle Reinforced Steel
#### 3.1.2 Fabrication Technique  
As schematically shown in Figure 4, in-situ nanoparticle reinforced steel was fabricated by a special casting process with combination of multipoint dispersion supply and electromagnetic stirring [33]. Two key factors should be considered. Firstly, when Ti wires were added into molten steel, the tips began to melt and reacted rapidly with the dissolved O in the steel to form titanium oxide, resulting in large amounts of nanoparticles. As the growth mode of these nanoparticles is typical diffusion control growth [60; 61], the main control factor is the solute content of O and Ti in steel. The dissolved O content in steel is rather low (generally lower than 40 PPM), and thus the growth process of generated titanium oxide nanoparticles is mainly controlled by the solute element Ti. Therefore, uniform multipoint dispersion supply processing was carried out by addition of many Ti wires which are arranged uniformly in the space. As a result, excessive concentration of solute elements will not happen and the growth rate of titanium oxide nanoparticles can be controlled well. Secondly, electromagnetic stirring (or argon blowing) was applied in the smelting process, resulting in flow field and force field existed in the melt. In this case, the newly formed in-situ nanoparticles were quickly taken out of initial area and dispersed homogeneously inside the steel melt. Thus it is very difficult for nanoparticles to grow with sufficient supply of solute element Ti.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Preprocessing  
The first step in preprocessing is to obtain the terrain data of the disaster struck region. This information is obtained in JSON format and then converted to the Cartesian Coordinate system required by the region-filling algorithm. The elevation profile of a region is commonly available in _Degree Minute Second (DMS): Altitude_ format [41].  
In our work, the Universal Transverse Mercator (UTM) coordinate system is used instead of the latitude-longitude coordinate system in order to precisely determine the distance between two points. If the two points are inside a UTM zone, the distance between the two points can be easily determined using the UTM coordinates [42].
## 3 Hybrid Method
### GPU Kernel Routines
#### 3.3.3 Chien Search Kernel  
The CS algorithm is the final step within the decoder. The primitive element \(\alpha^{pos-1}\) is checked if it is a root for the error locator polynomial \(\Lambda(x)\) as specified in [9]. This kernel routine is an ideal candidate for the GPU because of the parallelism it offers. Each element of the finite field is evaluated in the equation \(\Lambda(x)\) as shown in Algorithm 3. This evaluation kernel routine is independent for each GF element; hence these routines can be launched in parallel threads. Similar to the SK routine, the memory within the GPU device is shared between threads, so the _atomicXOR_ operation is used to avoid writing overlap by different CSK routines. Once the error vector is formed, the error is masked with the data in the memory to yield corrected data.  
```
1:procedureChien kernel(\(\Lambda,pos,err\))
2:\(sum\gets 1\)\(\triangleright\) Always KEQ is minimal
3:for\(j\gets 0,deg(\phi_{i}(x))-1\)do
4:\(sum\gets sum+\Lambda_{j}(\alpha^{pos-1})^{j}\)
5:endfor
6:if\(sum=0\)then\(\triangleright\)\(\alpha^{pos-1}\) is a root
7:atomicXor(\(err[pos],sum\)\(\triangleright\) Prevent overlap write
8:endif
9:endprocedure
```  
**Algorithm 3** Chien search. Kernel