esa-sceva/satcom-chunk-collection huggingface dataset:
# Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath
## 2 Methodology for Smoothing Toolpaths
### Building Smooth Curves from Temperature Field
#### 2.3.1 Hermite Quartic Spline Interpolation  
The Hermite quartic spline interpolation model (**HQS**) was presented by [32] and is a generalization of the usual Hermite cubic spline interpolation. The general form of the HQS interpolation is (Equation (5)):  
\[\begin{cases}x(u)=ea_{0}(u)x_{0}+ea_{1}(u)x_{1}+eb_{0}(u)\cos(\theta_{0})+eb_{1} (u)\cos(\theta_{1})\\ y(u)=ea_{0}(u)y_{0}+ea_{1}(u)y_{1}+eb_{0}(u)\sin(\theta_{0})+eb_{1}(u)\sin( \theta_{1})\end{cases} \tag{5}\]  
with \(P_{u}=(x(u),y(u))\), the coordinates of an interpolated point; \(u\in[0,1]\), the real interpolant; \((x_{0},y_{0})\), the coordinates of the spline first point (\(u=0\)); \((x_{1},y_{1})\), the coordinates of the spline last point (\(u=1\)); \(\theta_{0}\) and \(\theta_{1}\), the orientations of the tangent at the beginning and at the end, respectively; and \(ea_{0},\;ea_{1},\;eb_{0},\;eb_{1}\), the blending functions (\"\(e\)\" is for extended).  
The degree of blending function equals to 4 and, thereby, 5 constraints are allowed and detailed below.  
* The spline passes over two points \(P_{0}\) and \(P_{1}\): +2 constraints;
* The spline respects tangent orientations at the beginning and at the end: +2 constraints;
* An initial curvature \(C_{0}\) is fixed: +1 constraint.  
The blending functions are described with four parameters: \(k_{0},\;k_{1},\;\alpha,\;\beta\) (Equation (6), Figure 4).  
If \(\alpha\) and \(\beta=0\), blending functions are those of classical cubic Hermite splines.  
\[\begin{cases}ea_{0}(u)=1+(\alpha-3)\;u^{2}+2(1-\alpha)\;u^{3}+\alpha\;u^{4}\\ ea_{1}(u)=(3-\alpha)\;u^{2}+2(\alpha-1)\;u^{3}-\alpha\;u^{4}\\ eb_{0}(u)=k_{0}\;\big{(}t+(\beta-2)\;u^{2}+(1-2\beta)\;u^{3}+\beta\;u^{4}\big{)} \\ eb_{1}(u)=k_{1}\;\big{(}-(\beta+1)\;u^{2}+(2\beta+1)\;u^{3}-\beta\;u^{4}\big{)} \end{cases}\quad u\in[0,1] \tag{6}\]  
* \(k_{0}\) and \(k_{1}\) do not affect the orientation of the tangent, only the norm of the tangent vector;
* \(\alpha\) and \(\beta\) are called shape parameters and allow the shape modification of blending functions.  
Figure 4: Representation of blending functions of Hermite quartic splines.  
The initial curvature (\(C_{0}\)) is a complex function of \(k_{1}\), \(k_{2}\), \(\alpha\) and \(\beta\) (Equation (7)):  
\[C_{0}=C\big{]}_{u=0}=\frac{\frac{dx}{du}\frac{d^{2}x}{du^{2}}-\frac{dy}{du}\frac {d^{2}y}{du^{2}}}{(\frac{dx^{2}}{du}+\frac{dy}{du})^{\frac{2}{2}}}\Bigg{]}_{u=0}= f_{0}(k_{1},k_{2},\alpha,\beta) \tag{7}\]  
But this expression can be analytically inverted to isolate \(\beta\) by the use of the Symbolic Toolbox of MatLab, for example (Equation (8)). The final curvature is obtained in the same way but for \(u=1\) in Equation (7) and is noted as \(C_{1}=f_{1}(k_{0},k_{1},\alpha,\beta)\):  
\[\beta=f_{0}^{-1}(C_{0},k_{1},k_{2},\alpha) \tag{8}\]
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### MAC Considerations  
Besides the PHY layer, the medium access control (MAC) layer strongly impacts the scalability of the system. The MAC layer is in charge of properly assigning the medium resources to the competing nodes of the system and as such it impacts among others the overall throughput, the channel access latencies and the overall scalability of the system [24]. Two MAC protocol approaches will be considered in this paper: (i) a random access method where every node competes to gain channel control and (ii) a scheduled access method where the control over the network is granted by a central unit. Regardless the MAC and localization approaches, a significant influence on users scalability is due to frame size. As a rule of thumb, the longer it takes to transmit a packet, the less tags can be localized as the channel is kept busy for a longer period by each transmitting user. The packet duration \(T_{packet}\) is standard dependent and its computation follows the standard frame definition. The IEEE 802.15.4-2011 [25] standard determines the frame duration \(T_{packet}\) as follows:  
\[T_{packet}=T_{SHR}+T_{PHR}+T_{DATA} \tag{3}\]  
where:  
\[T_{SHR}=(L_{pramble}+L_{SFD})\cdot\tau_{SHR} \tag{4}\]  
\[L_{SFD}=\begin{cases}64&\text{if}\quad\text{bitrate}=110\,\text{kbps}\\ 8&\text{otherwise}\end{cases} \tag{5}\]  
\[T_{PHR}=21\cdot\tau_{PHR} \tag{6}\]  
\[T_{DATA}=(L_{DATA}+L_{Rend})\cdot\tau_{DATA} \tag{7}\]  
and the definition of the different variables in (3)-(7) can be found in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Variable Name** & **Unit** \\ \hline \(T_{packet}\) & Frame duration & ms \\ \(T_{SHR}\) & Synchronization header (SHR) duration & ms \\ \(T_{PHR}\) & PHY header (PHR) duration & ms \\ \(T_{DATA}\) & PHY service data unit duration & ms \\ \(L_{pramble}\) & Preamble length & symbols \\ \(L_{SFD}\) & Start of frame delimiter (SFD) length & bits \\ \(L_{Rend}\) & Reed Solomon bits & bits \\ Radio & Data length & bits \\ \(\tau_{SHR}\) & SHR symbols duration & ms \\ \(\tau_{PHR}\) & PHR symbols duration & ms \\ \(\tau_{DATA}\) & Data symbols duration & ms \\ \(PRF\) & Pulse repetition frequency & Hz \\ \(P_{TX}\) & Transmission power & dBm \\ \(S_{i}\) & Receiver sensitivity & dBm \\ \(Ch\) & Channel number & unitless \\ \(PL\) & Path loss model & unitless \\ \hline \hline \end{tabular}
\end{table}
Table 6: UWB variables: radio, MAC, standard levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Time Difference of Arrival** & **Two Way Ranging** \\ \hline Anchors & Yes & No \\ Synchronization & Low & High \\ Energy Overhead & Low & High \\ Hybrid MAC Protocol & Low & High \\ Flexibility & At Infrastructure Side & At Both Sides \\ \hline \hline \end{tabular}
\end{table}
Table 5: TDoA and TWR approaches comparison
## 4 Architecture and Implementation  
The following use case was defined to validate the proposed technologies and their integration in a smart city scenario (Figure 7):  
* A maintenance worker wishes to check the status of some city appliances.
* The worker approaches an electrical panel and points a smartphone at an AR dynamic marker.  
Figure 5: Sampling sequence for dynamic augmented reality (AR) marker LEDs.  
Figure 6: A geometrical reference is passed to the application so it can determine the relative positions of the camera and the dynamic marker.  
* The smartphone decodes the information sent by the AR marker and obtains the device ID of the electrical panel and dynamic status information (average energy consumption and status codes).
* This information is shown to the maintenance worker via the AR interface.
* If additional data are needed, the smartphone application can use the device ID obtained from the AR marker to send a query to the backend infrastructure. Note that this is not compulsory in many cases, as the status codes will quite often be enough to inform the worker of the situation.  
This use case represents a system that is capable of linking a physical resource (an electrical panel) to its logical representation (the panel ID) for AR applications and, at the same time, takes advantage of the same identification channel to exchange basic information without the need for a permanent WAN connection, saving thus both terminal power and bandwidth.  
Thus, the system in the use case consists of a combination of three main components, namely:  
* An IoT infrastructure (described in Section 4.1) capable of gathering information from several distributed monitoring points and distributing this via a low power wireless mesh communications network to several devices, where it is consumed.
* An AR user interface (described in Section 4.2), supported by the user's smartphone, that gives the user seamless access to infrastructure information.
* An AR anchor (described in Section 3) that allows a physical point of information to be dynamically linked to the logical information associated with that point.  
The combination of these three elements lays the foundations for building an application that is both more intuitive for users and more efficient in terms of network resource usage thanks to a distributed access to information.  
Figure 7: Use case for system validation.  
The core contribution of this paper is a novel interaction mechanism for smart city proximity information, with a use case focused on maintenance tasks, based on the combination of AR and IoT through the previously described dynamic AR anchor. However, for the system to be successfully validated in a testbed, an IoT communications infrastructure and an AR user interface are also required. Sections 4.1 and 4.2 describe these complementary elements.
## 4 Discussion
In the second part of this study, we compare S factor results for spheres of radius equal to 0.1 mm, 1 mm, and 5 mm based on the present DPK-based calculations against the Geant4 simulations carried out in this work. Specifically, in Figures 8-10, we study the influence of changing the simulation parameters available in the Geant4 toolkit, such as the step length (SL) and the tracking and production cut-off energies (CE). For all spheres, the differences between the Geant4 simulations and the DPK-based calculations are up to 20% irrespective of the choice of the SL and CE parameters. For all spheres, the difference in the S factor gradually increases with electron energy and becomes largest at the energywhere the range of electrons is comparable to the size of the sphere, i.e., at 100 keV for the 0.1 mm sphere, 700 keV for the 1 mm sphere, and 2 MeV for the 5 mm sphere. The underestimations of the present S factor calculations compared to Geant4 are consistent with the observation made above in relation to the other MC codes; that is, for most of the energy range investigated, the present DPK-based approach predicts lower energy deposition (see discussion in relation to Figure 5). However, at even higher energies, the DPK-based calculations become higher compared to the Geant4-based S factor. This trend is already seen against the other MC codes for the mm-sized volumes (see R = 1.3, 2.9, 6.2, and 7.8 mm in Figure 5). This observation may be explained by the fact that the DPK-based approach is based on the assumption of a continuous energy-loss rate, which neglects the production and transport of delta-rays. At sufficiently high energies, these delta rays are energetic enough so that they can leave the target volume, thus depositing part of their energy outside. So, it may be reasoned that, at sufficiently high energies and/or small volumes, the DPK-based approach should overestimate the MC-based S factors that explicitly consider the delta-ray escape from the volume. Finally, the dependence of Geant4-based S factors on the ionization model used in the simulations is shown in Figure 11. It can be seen that the influence of the ionization model is sensitive to the size of the target volume. Specifically, whereas the difference between the Standard and Livermore models is negligible for all energies and target volumes, the Penelope model results in higher S factor values by up to 20% for the 0.1 mm sphere, 5% for the 1 mm sphere, and 2% for the 1 mm sphere. These maximum differences are observed at electron energies that correspond to penetration ranges comparable to target dimensions. As already mentioned, this situation is very sensitive to the details of electron transport in terms of physics models and transport methodology.
## 4 Pattern Recognition
### Kohonen Self-Organizing Maps  
A Self-Organizing Map (SOM) [53] is an unsupervised neural network that produces a discrete representation of an input space, which is referred to as a map. This algorithm is used as a clustering or dimensionality reduction method and consists of an input layer and a computational layer (Figure 4a) conformed to nodes or neurons. Each node has a topological position and has a number of weights equal to the number of inputs (\(V=[v_{1},v_{2},\ldots,v_{n}]\), with \(n\) as the number of inputs, \(W_{m}=[w_{1},w_{2},\ldots,w_{m}]\); \(m=\) number of nodes, where \(n=m\)). The SOM method calculates the Euclidean distance between an entry vector and the weights of each node:  
\[dist=\sqrt{\sum_{i=0}^{n}(v_{i}-w_{i})^{2}} \tag{1}\]  
and chooses the lowest distance to one node as the best or winning node. This node is referred to as the Best Matching Unit or BMU.  
Once the BMU is found, the nodes in the neighbor (i.e., influence area) of the BMU and the BMU itself are selected, and their weights are updated (Figure 4b). The BMU's influence area is calculated as \(\sigma(t)=\sigma_{0}e^{\frac{-t}{\sigma_{0}}}\), with \(\sigma_{0}\) as the lattice width at the instant \(t_{0}\) and \(\tau_{\sigma}\) as the updating constant of \(\sigma\). After the area is selected, the weights are updated using the equation below:  
\[w(t+1)=w(t)+\Theta(t)\eta(t)(V(t)-W(t)) \tag{2}\]  
Figure 4: (a) Two layers of a SOM, with the lower layer as the input and the upper one as the nodes that map the input. In (b), the BMU and the area of influence are illustrated, with the area of influence decaying as the nodes move from the BMU outwards.  
where \(\eta\) and \(\Theta\) represent the learning rate and influence rate at the instant of time \(t\). As training time advances, the learning rate and influence rate diminish their effect by:  
\[\eta(t)=\eta_{0}e^{\frac{-t}{\eta_{0}}};\quad\Theta(t)=e^{\frac{-dt \eta^{2}}{2\sigma(t)}} \tag{3}\]  
where \(\eta_{0}\) is the initial learning rate, and \(\tau_{\eta}\) is the update constant of \(\eta\). With this learning technique, the inputs with similar characteristics will cluster together around a given node, while inputs with different characteristics will cluster apart in other different nodes. The steps of this method can be seen in Algorithm 1.  
```
Input network:  Training set \(S=\{X_{1},X_{2},\ldots,X_{s}\}\); Learning and influence rate \(\alpha\) & \(\theta=((0-1])\) Init network:  Initialize the weights to a small random value Train network:  Loop until \(w^{new}!=w^{old}\quad\)&& \(iter<max\quad iterations\):  Select random input: \((X_{s})\)  Compute the distances: \(dist=\sqrt{\sum\limits_{i=0}^{n}{(x_{i}-w_{i})^{2}}}\)  Select BMU  Calculate area of influence: \(\sigma(t)=\sigma_{0}e^{\frac{-t}{\eta}}\)  Update weights: \(w(t+1)=w(t)+\theta(t)\alpha(t)(X(t)-W(t))\)  Update learning and influence rate: \(\alpha(t)=\alpha_{0}e^{\frac{-t}{\eta_{0}}}\); \(\theta(t)=e^{\frac{-dt\eta^{2}}{2\sigma(t)}}\) Output network: Weights \(w\)
```  
**Algorithm 1** SOM Pseudo-code.
## 2 RPL Overview
In addition, RPL defines two different procedures for addressing node or link failure. The first is the local repair which addresses failures by enabling the immediate selection of an alternative preferred parent node to the current one. The second procedure is the global repair which requires updating the current version of the topology and initiating full DODAG topology reconstruction. The sink node initiates this process by incrementing the currently advertised VN. This results in the exchange of several DIS and DIO messages across the network after resetting the trickle timer. These failure recovery procedures are initiated upon the detection of any routing problem such as routing loops. This provides a reliable guarantee of effective failure recovery but at the cost of much more network overhead, particularly in large deployments.
## 4 Discussion
Note that the site reversal exists already in several binary systems like Au-Cu (_L12_ exists at both compositions AuCu and AuCu and crystallizes in two anti-structures). However, we could not find any ternary system showing reversal in a continuous manner. The reason is the presence of other ordered structures like ternary _L21_ for example or other intermetallic phases. Extensive homogeneity domains are not favored because of the low temperatures necessary to have ordered phases. The TCP phases are known to have broad ranges of homogeneity and may be highly non-stoichiometric therefore offering certain structural flexibility. Their stability and site occupancies are more influenced by geometry and atomic sizes than the electronic properties of constituting atoms. They are therefore prone to show these irregular homogeneity domains caused by the geometric accommodation of three atoms of different sizes. The fact that, in these phases, the atomic radius monitors the site occupancies according to the site CN also favors this behavior. It also seems to be important to have ternary configurations stabilizing the phase (\(\chi\) phase in Zr-W-Re, \(C\)14 phase in Zr-Nb-Fe). This is also typical of the TCP phases with their often complex structures. Then, one may wonder why rhenium appears in most of the systems presented here. It is because rhenium has an intermediate size and intermediate electronic properties (half-filled _d_-band). Therefore, it forms many compounds (it is the element involved in the largest number of binary \(\sigma\) and \(\chi\) phases) and it may be larger or smaller than the other element (Re is the large atom in Re-Cr-Co, the medium one in Mo-Re-Ni, and the small one in Zr-W-Re). The \(B\) atom, through switching its preference from low CN sites to high CN sites, reaches a state in which it is completely disordered. In the middle of the dotted line in Figure 2, one reaches the composition \((A_{0.5}B_{0.5})(B_{0.5}C_{0.5})_{x}\). The configurational entropy for this composition is equal to that of a binary disordered solid solution (\(-0.69\) in unit of _R_) _i.e._, not very far from a completely disordered ternary solid solution (\(-1.10\) in unit of _R_), so that one could expect a stabilization of this state at high temperatures due to entropic effects. By reference to high entropy alloys [30], we could propose the concept of high entropy intermetallic compounds. On the other hand, the site reversal should cause miscibility gaps inside the phase at low temperatures. A simple substitution can occur at low temperatures if ideal or with a negative enthalpy of mixing. This is not the case if one goes from _ABx_ to _BCx_ because the replacement cannot be done in one step and involves site reversal. This causes miscibility gaps, shown by phase diagram calculations at low temperatures from the DFT data (see, for example, the \(\sigma\) phase diagram of the Mo-Re-Cr system calculated at low temperature in [22]). Experimentally, the miscibility gap between the two \(C14\) phases is clearly shown in the Zr-Nb-Fe system (see Figure 5) and also confirmed by the calculation.
## 4 Experiment and Results
### Data Preparation
#### 4.1.1 Image Labeling  
Once images in the dataset had been completed all the operations of image preprocessing mentioned above, the structural components in the images were labeled based on their classes. In this paper, LabelImg [53] was used to label the scanned 2D drawings. The components to be annotated were selected with a box respectively, and then tags were assigned to the corresponding boxes. Finally, the labeled images were output as XML format. To increase the features of structural components, which have a significant influence on the final results of detection, when boxing the components, the semantic information corresponding to the components was also selected. Taking beam as an example, correct and incorrect labeling on the image of the structural drawing are shown in Figure 5.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
## 4 Metaheuristic-Based Approach for Team Formation
### Particle Swarm Optimization (PSO)  
Particle Swarm Optimization (PSO), a bio-inspired algorithm introduced by [11], is recognized for effectively solving many problems. PSO is distinct from other optimization methods, as it relies solely on the objective function without needing gradients or differential forms. The algorithm draws inspiration from natural phenomena such as swarming, fish schooling, and bird flocking and shares similarities with genetic algorithms and evolutionary programming [22]. Sociobiologists suggest that groups of fish or birds benefit from shared knowledge, improving their collective success in tasks like foraging. PSO's strengths include its insensitivity to variable scaling, ease of parallel processing, derivative-free nature, few parameters, and robust global search capabilities. However, its local search ability may need to be better optimized. The equations for velocity and position updates in PSO are as follows:  
\[v_{i}^{(c+1)}=v_{i}^{(c)}\oplus\alpha\otimes(p_{\text{best}_{i}}^{c}-x_{i}^{( c)})\oplus\beta\otimes(g_{\text{best}_{i}}^{c}-x_{i}^{(c)}), \tag{3}\]  
\[x_{i}^{(c+1)}=x_{i}^{(c)}\oplus v_{i}^{(c+1)}. \tag{4}\]  
Here, \(\alpha\) and \(\beta\) are learning factors, \(p_{\text{best}_{i}}^{c}\) is the best personal solution, and \(g_{\text{best}_{i}}^{c}\) is the best global solution.
## 4 Methodology
### Definition and Extraction of Features
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Tested Features** & **Description** & **Reference** \\ \hline Spectral & Mean and standard & Mean (7 bands \(\times\) 8 images) and & \\ Information & deviation (SD) & SD (7 bands \(\times\) 8 images) of each & [39] \\  & & pan-sharpened Landsat 8 band & \\ \hline \multicolumn{3}{c}{NDVI (Normalized Difference Vegetation Index)} & (NIR \(-\) R)(NIR \(+\) R) & [40] \\ \cline{2-3}  & GNDVI (green NDVI) & (NIR \(-\) G)(NIR \(+\) G) & [41] \\ \cline{2-3}  & GVI (green vegetation index) & (G \(-\) R)(G \(+\) R) & [41] \\ \cline{2-3}  & MSR (modified simple ratio) & ((NIR/R) \(-\) 1)((NIR/R)\({}^{0.5}+\) 1) & [42] \\ \cline{2-3} Indices & MSAVI & (2 \(\times\) NIR \(+\) 1 \(-\) (2 \(\times\) NIR \(+\) 1)\({}^{2}-\) 8 & [43] \\  & (Modified Soil-Adjusted Vegetation Index) & \(\times\) (NIR \(-\) R)\({}^{0.5}\))/2 & [43] \\ \cline{2-3}  & NDSVI & (SWIR1 \(-\) R)(SWIR1 \(+\) R) & [44] \\ \cline{2-3}  & (Normalized Differential Senescent Vegetation Index) & (SWIR1 \(-\) SWIR2)(SWIR1 \(+\) SWIR2) & [45] \\ \hline \multirow{3}{*}{Texture} & GLCM\({}_{h}\) & GLCM homogeneity sum of all directions from NIR & [37] \\ \cline{2-3}  & GLCM\({}_{d}\) & GLCM dissimilarity sum of all directions from NIR & [37] \\ \cline{2-3}  & GLCM\({}_{e}\) & GLCM entropy sum of all directions from NIR & [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Landsat 8 time series object-based features. GLCM, gray-level co-occurrence matrix; NIR, near-infrared; SWIR, shortwave infrared; R, red band; G, green band; B, blue band. The OBIA software eCognition v. 8.8 was employed for the extraction of the object-based features from both Landsat 8 and WV2 orthoimages shown in Tables 3 and 4[37, 38, 39, 40, 41, 42, 43, 44, 45, 46]. To this end, the chessboard segmentation algorithm included in eCognition was applied on a thematic layer composed of a previously digitized vector file with the 694 reference greenhouses. Using this approach, the software only projects the vector file on the images in order to obtain a boundary adapted to the pixels that compose them (Figure 3). One eCognition project was conducted for the eight multi-temporal Landsat 8 pan-sharpened orthoimages by halving four times the original 15-m GSD on the created project tab in order to increase the geometric resolution to 0.9375 m per pixel. Thus, the segmentation fit better to the reference greenhouses (see Figure 2(a,c,d)). In the case of the single WV2 MS orthoimage, another project was performed within eCognition, where the original GSD (1.6 m) was enhanced up to 0.8 m per pixel (Figure 2(b)). The main goal was always to attain the best possible segmentation for our 694 reference