esa-sceva/satcom-chunk-collection huggingface dataset:
# The NIRS Brain AnalyzIR Toolbox
## 3 Statistical Modules
### Parametric Models  
The _StimulusEvents_ data class, which holds the onsets and durations of task events, is also used to encode amplitude information that can be used in parametric regression models. In the parametric model, the amplitude of each task block is varied. This can be used to test for brain regions where the amplitude of the brain response covariates with, e.g., reaction time or some other block-dependent variable encoded in the amplitude of the stimulus events. The tool function _nirs.design.split_parametric_ is used to construct and manipulate these models. This function requires a formula to be specified, for example \"<condition> * (1 + amp + amp^2)\" would encode three regressors for (i) a term that is constant for all blocks; (ii) a term that linearly varies by the amplitude parameter across blocks, and (iii) a quadratically varied term. This can be used to specify a condition-specific model where \"<condition>\" is replaced by the name of the condition in the files. The notation \"?\" is used to apply the formula to all conditions. The formula supports higher order (including negative) power models as well. The formula can also use the keywords \"dur\" [duration] and \"time\" in addition to \"amp\" [amplitude] to allow models where the regressors vary over the experiment time (within each scan) or by task duration. The function also takes a flag for centering the variables (amplitude, time, or duration) in the model, which subtracts the mean of the variable before encoding the model such that the DC-term (e.g., <condition>*1) part of the model represents the average response. An example of the design model returned by the _split_parametric_ function is shown in Figure 2. In this example, a subject repeats a blocked task three times and we wish to test the hypothesis of \"did learning occur?\" over the blocks. To do this, we use a parametric model modulated by time (e.g., \"<condition> * (1 + time)\") which creates two regressors in the GLM model. The first regressor shown in Figure 2a represent the conventional stimulus information produced by a box-car function and models brain activity that stays the same for all three trials. The second regressor shown in Figure 2b is modulated by time and models brain activity is higher for the first block then the last block. Figure 2c shows the example of hemodynamic response generated by convolving the parametric stimuli pattern in Figure 2b and the simulated raw signal. When this model is solved, the second regressor can be used to test the null hypothesis of whether the event blocks did not change linearly over time (e.g., was there a learning effect?). The toolbox has the option to mean center the variables (default is true as used in this example).  
Figure 2: Illustration of the parametric stimulus design: (**a**) Conventional box-car function as the stimulus design; (**b**) parametric stimulus design with time varying models; (**c**) evoked signal from parametric stimulus design in (**b**).
## 3 Results
### Hyper-Spectral Data  
For 224 bands Sook Lake (Figure 5b, 256 \(\times\) 256, 16 bit [26; 27]) AVIRIS hyperspectral images, the WFCRMCA crosses out heavily disturbed bands, such as 1-6 th, 33 rd, 107-114 th, 153-168 th, 222-224 th bands, and uses the remaining 190 bands for algorithm analysis.  
In Table 3, when \(T_{\text{peak}}\) = 0, the number of classes is 133. If \(T_{\text{peak}}\) is increased, the number of classes nonlinearly reduces, and clustering time depresses accordingly. When \(T_{\text{peak}}\) > 7, the number of classes begins to fluctuate, so the WFCRMCA usually chooses \(T_{\text{peak}}\) = 5.0, which could realize a fairly accurate classification.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Band Number** & \(T_{\text{cr1}}\) & \(T_{\text{cr2}}\) & **Class Number** \\ \hline
6 (\(S_{\text{calc}}\) = 2) & 0.8 & 0.8 & 4 \\
39 & 0.7 & 0.7 & 36 \\ \hline \end{tabular}
\end{table}
Table 1: Expanded bands number comparison (3 \(\times\) 3 sampling, \(S_{\text{calc}}\) = \(S_{\text{calc2}}\) = 5, \(T_{\text{signal}}\) = 0.1, \(T_{\text{step}}\) = 0.01).  
Figure 5: (**a**) Mississippi TM 4 th band image after gray balance. (**b**) Sook Lake AVIRIS 60 th band image after gray balance.  
In Table 4, with more minutiae attending clustering, the number of clustering classes increases sharply: two of five scale components cluster eight categories; obviously, that does not separate the objects. However, four-scale components cause objects to disperse and expand the class number. Depressing \(T_{\text{end}}\) could effectively decrease the class number.  
If high three-scale or five-scale wavelet decompositions are chosen to attend clustering and \(T_{\text{peak}}\) = 5.0, 17 classes are created (the main clustering results are seen in Figure 7), the time is 21s, and the division result is favorable; here, class 1 is the basin (Figure 7a), class 4 is for the mountain peaks (Figure 7d), and class 5 is the water body of the Sook Lake (Figure 7e).  
\begin{table}
\begin{tabular}{c c c} \hline \(\mathbf{T_{\text{peak}}}\) & \(\mathbf{T_{\text{end}}}\) & **Class Number** \\ \hline
0 & 0.4 & 133 \\
2 & 0.4 & 65 \\
5 & 0.4 & 38 \\
7 & 0.4 & 26 \\
10 & 0.4 & 22 \\
15 & 0.4 & 24 \\ \hline \end{tabular}
\end{table}
Table 3: Sook lake AVIRIS hyper-spectral image, WFCRMCA clustering parameter \(T_{\text{peak}}\) comparison (5\(\times\)5 sampling, band number 190, \(S_{\text{cale}}\) = 5, \(S_{\text{cale}2}\) = 4, \(T_{\text{start}}\) = 0.9, \(T_{\text{step}}\) = 0.05).  
Figure 6: Mississippi PM image, WFCRMCA clustering results: (**a–i**) are the eight significant signals.
## 4 Secure and Precise DM Scheme Based on A Multi-Carrier Multiple Antenna Arrays Model
### The Architecture of the BS Based on a Multi-Carrier Multiple Antenna Arrays Model  
The architecture of the BS based on a multi-carrier multiple antenna arrays model is shown in Figure 5. Compared with the architecture in Section 3.1, the difference is that \(K\) transmit antenna arrays emitting different carriers. For the \(k\)th transmit antenna array, the carrier frequency can be expressed as (\(f_{ck}\) (\(k=1,2,\cdots,K\))).  
Before up-converting to RF, the modulated symbols are processed with the same manner in Section 3.1. Therefore, for the \(k\)th transmitted symbol, we still have  
\[\mathbf{u}_{k}=\beta_{1}\sqrt{P_{s}}\mathbf{h}(\theta_{dk})s_{k}+\beta_{2} \sqrt{P_{s}}(\mathbf{I}_{N}-\mathbf{h}(\theta_{dk})\mathbf{h}^{H}(\theta_{dk}) )\mathbf{z}_{k}. \tag{22}\]  
Figure 5: The architecture of the BS for the proposed DM based on a multi-carrier multiple antenna arrays model.  
Then, for the purpose of matching the \(N\) antenna elements for the \(k\)th transmit antenna array with carrier \(f_{ck}\), the radiating signal \(\mathbf{x}_{k}=[x_{1k},x_{2k},\cdots,x_{Nk}]^{T}\) at time \(t\) can be obtained by  
\[\mathbf{x}_{k}(t)=\mathbf{u}_{k}(t)\cdot e^{2\pi f_{ck}t}. \tag{23}\]  
Eventually, all \(K\) radiating signal vectors for each symbol frame \(\mathbf{s}\) can be expressed as  
\[\mathbf{x}(t)=\sum_{k=1}^{K}\mathbf{x}_{k}(t){=}\sum_{k=1}^{K}\left[\mathbf{u }_{k}(t)\cdot e^{2\pi f_{ck}t}\right]. \tag{24}\]  
It is also noted that the BS sends each symbol of \(\mathbf{s}\) towards the LU from distinct directions with different carriers concurrently. Obviously, just the LU can receive the complete information without contamination. Meanwhile, due to the introduction of AN, the Eve can hardly intercept any useful signals seriously interfered by AN.
## 5 Results and Discussion
### Unit Load Segment Results and Discussion
This inverse relationship has been explained by the comparing smaller packages to a more uniform load transfer from payload to the pallet, instead of the discrete loading observed for larger packages. For comparable payloads, this study mostly supports this hypothesis. The box size does affect pallet deflection, but the other identified factors can also influence the magnitude and direction of this change. Most of the previous studies related to load bridging [4, 15, 19, 20, 21, 22] conducted physical experimentations to measure the change in deflection when different payload characteristics were modified. These studies were particularly complex to conduct and validate given the multiple elements that must be controlled. The model developed here can allow for the detailed study of each one of the potentially influencing factors, such as box size, coefficients of friction, number of package layers, the stiffness of the pallets and the pallet support conditions. The model presented here is the first attempt to validate the load bridging effect without the need for a more comprehensive and complex model. The finite element model developed can help in the study and application of the load bridging effect without the need for extensive data collection. A practical application of the model can be generated when an interested party requires the evaluation of a pallet design for multiple payloads. Current design and evaluation methodology requires for the involved party to design the pallet assuming uniform loading, which will provide the lowest possible load carrying capacity, or to study individually each one of the payloads to determine the minimum pallet carrying capacity. The presented model can help decide which payload will the pallet present the lowest carrying capacity. Pallet design can be greatly improved with this consideration. Additional studies can be conducted to fully characterize the effects of each design factor in unit loads. Knowing which factors are significant can help designers make decisions that will impact pallet carrying capacity from early on in the process. Current design practices require the development of prototypes and physical experimentation to determine how the payload configuration will affect the pallet carrying capacity. The presented tool can streamline and greatly reduce the unit load design process time and effort. Overall, the model can simulate, with acceptable margins of error, the deflection of unit load segments and the box movements for unit loads with three and four columns of boxes. This can translate into better understanding of the load bridging effect by conducting further simulations with a wider range of variables, such as different numbers of columns, layers of boxes, payload heights, pallet stiffnesses, and all of the friction characteristics of each contact.
## 4 Simulation Results and Dissections
### Commercial Load  
In this case, the overall reactive and real load demand during 24 h are 37.82 MVAr and 53.35 MW, respectively. Without integrating BESS and PV in RDS, the overall reactive and real loss during 24 h are 0.99 MVAr and 2.17 MW, respectively. The system power loss is reduced to 1.12 MW with installing one PV alone at bus 61. The optimal placement and sizing of two PV alone at buses 61 and 17 decreases the system loss to 1.04 MW as shown in Table 6. Additionally, the optimal sizing of three PV alone at buses 61, 18, and 11 with total energy of 22.81 MWh decreases the system loss to 1.02 MW. From Figures 26 and 27, the total energies of one and two PV and three PV alone during the day are presented in Figure 26, Figure 27, and Figure 28, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Position (Size (kW))**} & \multirow{2}{*}{**PV Energy (kWh)**} & \multirow{2}{*}{**\begin{tabular}{c} **Total PV Energy (kWh)** \\ \end{tabular}**} & \multirow{2}{*}{**\begin{tabular}{c} **Ploss (kW)** \\ \end{tabular}**} & \multirow{2}{*}{**\
\begin{tabular}{c} **Ploss (kW)** \\ \end{tabular}**} \\  & & & & & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Commercial \\ Load \\ \end{tabular} } & Without & - & - & - & - & 2173.851 \\  & 1-PV & 61 (2168.2) & 61 (16,319) & 16,319 & 1124.5 \\  & 2-PV & 17 (611.6) & 17 (4603) & 20,130 & 1038.2 \\  & & 61 (1991.5) & 61 (14,989) & & \\ \cline{1-1}  & 3-PV & 18 (439.4) & 18 (3306.8) & 22,809.30 & 1021.7 \\ \cline{1-1}  & 11 (599.7) & 11 (4513.5) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The obtained results with and without installing PV alone in commercial system loads. Figure 26: PV output during the day by installing one PV alone in commercial system load. Installing one, two, and three PV with BES decreases the system losses to 0.83 MW, 0.71 MW, and 0.69 MW, respectively, as shown in Table 7. The total energy of PV and the charging and discharging energies of BES by integrating one PV with BES in RDS are illustrated in Figures 29 and 30. Figures 31 and 32 show the energies of two PV and the charging and discharging energies of BESS during the day, respectively. By incorporating three PV with BESS, the injection energies from PV to BESS and the grid during the day are shown in Figure 33, and the charging and discharging energies of BESS are shown in Figure 34. The results proved which the presented algorithm is an efficient to obtain the best global results when compared with modified HGSO algorithm and HGSO algorithm. This comparative study is illustrated in Table 8.  
Figure 28: PV output during the day by installing three PV alone in commercial system load. Figure 27: PV output during the day by installing two PV alone in commercial system load. Figure 30: BES output during the day by installing one PV with BES in commercial system load.
## 3 Drone Routing Algorithms
### Trajectory Planning  
One of the major concerns of the drone-based delivery systems is to design the optimal trajectory of drones with maximum possible energy efficiency because drones have limited flight time owing to their limited battery capacity. The term 'trajectory' is often interpreted as routing path, waypoint design, and path planning across various literatures. The trajectory of the drone-based delivery system is often formulated as a TSP, where the drone visits each of the customers or waypoints based on the requirement imposed by the proprietor. For drone-based delivery problems, several variants of TSP are formulated to meet the customers' demand as well as the proprietors' profit margin. These variants are dependent on payload, battery capacity, and traveled trajectory. In most cases, the trajectory planning of the drone can be classified under three categories: independent single-customer-based drone-only delivery, truck-drone collaborative delivery, and multi-drone-based delivery with nearby CSs.  
In independent single customer-based delivery, drones deliver the parcels to the designated customers, one at a time. After the completion of delivery, the drone either comes back to the depot to make a new delivery or goes to a CS for charging. This method is relatively inefficient as drones must charge frequently or return to the depot after making a delivery with available energy to spare. Thus, this approach is drawing relatively less attention from researchers as well as industries. However, new research methodologies and modifications continue to open new pathways toward this short-haul delivery system.  
In the case of truck-drone collaboration, trucks carry high payloads to long-haul destinations. Next, to make 'last-mile' delivery where the truck is unable to go any further, a drone is deployed from the back of the truck to deliver the parcel to the customer. These problems are classified as flying sidekick traveling salesman problem (FSTSP) [39], vehicle routing problem with drones (VRPD) [40], and last-mile delivery (LMD) problem [37]. Insome cases, the truck or the medium of transport itself acts as a CS to provide the drone with battery backup through a charging facility [18].  
For multi-drone-based delivery with nearby CSs, drones leave the depot with the parcel and deliver it to customers, where the customers are grouped or clustered based on the nearby CS. The trajectory is designed so that the drone can make the deliveries and returns to nearby CS for recharging before its battery is completely discharged. This method is discussed further in the drone charging methodologies subsection.
# Digital Workflow to Fabricate Complete Dentures for
## 2 Materials and Methods  
The digital intraoral scan data of both the maxillary and mandibular arches were obtained using an intraoral scanner (TRIOS 3; 3 Shape A/S, Copenhagen, Denmark) (Figure 1A,B). The scanned data were saved in the standard tessellation language (STL) file format. Modeless trial denture bases were designed using a CAD software program (Meshmixer; Autodesk, San Rafael, CA, USA) and additively manufactured using a 3D printing material (NextDent Try-In TI2; 3D Systems, NextDent B.V., Soesterberg, The Netherlands) and a 3D printer (NextDent 5100; 3D Systems, NextDent B.V., Soesterberg, The Netherlands). A wax rim was subsequently placed on each of the trial denture bases (Figure 1C). The trial denture base and wax rim assemblies were placed in the patient's mouth. They could be adjusted, if needed, to achieve better esthetic and functional outcomes. The interarch relationship was recorded using an occlusal registration material (O-Bite; DMG, Hamburg, Germany). The obtained, united assembly was scanned using the same intraoral scanner (TRIOS 3; 3 Shape A/S, Copenhagen, Denmark) (Figure 1D).  
The STL file of the scan data of the united assembly with a registered interarch relationship was imported into a metrology/inspection software program (Geomagic Control X version 2018.0.1, 3D Systems, Rock Hill, SC, USA) to reverse the maxillary and mandibular portions individually. Subsequently, each portion was saved as an individual STL file (Figure 2A,B). The maxillary STL file obtained using the intraoral scanner was superimposed onto the reversed maxillary STL file. The same procedure was performed with the mandibular STL file, thus, establishing the interarch relationship for the original STL files (those obtained using the intraoral scanner) (Figure 2C).  
The artificial teeth were arranged using the transformed maxillary and mandibular STL files as well as the STL file (or the color format file (.dcm)) of the united assembly. The occlusal plane of the assembly could be viewed by adjusting the translucency of the.dcm file (Figure 2D). Try-in dentures were additively manufactured with a 3D printing material  
Figure 1: Impression making and bite registration for edentulous arches. (**A**) Digital intraoral scan data of a maxillary arch. (**B**) Digital intraoral scan data of a mandibular arch. (**C**) Trial denture base and wax rim assemblies. (**D**) Scan data of the united assembly with registered interarch relationship.  
(NextDent Try-In TI0; 3D Systems, NextDent B.V., Soesterberg, The Netherlands) and the 3D printer (NextDent 5100; 3D Systems, NextDent B.V., Soesterberg, The Netherlands) to evaluate esthetics and function (Figure 2E). Thereafter, the gingival and tooth sections were additively manufactured individually and characterized (Figure 2F). The digitally fabricated complete dentures were placed in the patient's mouth and evaluated for esthetics and function.
# Space Weather Effects on Satellites
## 2 SW Drivers and Their Effects on Spacecraft Technology
### Energetic Particles  
SEPs, SEEs, heavy ions, and neutrons which are energized in an SF reconnection event and/or a CME-driven shocks [22; 51] may have a direct impact on the Earth and near-Earth environment as well as an immediate impact on satellite operations [52; 53]. They can cause many deleterious effects and accelerate material aging by altering their electrical and optical properties, which may gradually reduce the performance of solar cells by up to \(\sim\)10%. Also, they may cause device leakage and power consumption and, eventually, can decrease the overall functionality of the space systems [32]. The particle effects as a function of energy, species, and origin are given in more detail in [8], in their Table 3, whereas the spacecraft anomalies as a function of the their orbits are explored by [54] with high-altitude and high-inclination being the most vulnerable ones.  
The physical principle of the high-energy particle interaction with various substances is based on the energy deposition (e.g., total ionizing dose effects) and the creation of a charge in the material of the spacecraft (or in the semiconductor lattice structure, e.g., due to displacement damage dose effects) or its electronics, such as the single event effects. The latter are mostly software related and in the extreme cases may require power reset of the system, but some of the particle-related effects can be destructive and irreversible. The surface charging effects can cause charge build-up leading to high-voltage potential differences between different parts of the spacecraft and thus are considered as a serious risk. Alternatively, the deep dielectric charging effects are due to high-energy particles causing discharges inside the spacecraft components. These and other engineering aspects have been investigated in detail, e.g., [52; 53; 55; 56], either for the preparation of space-hardened materials or for the development of various mitigation strategies.  
Below are given the main spacecraft-related effects due to energy deposition of energetic particles, as already described above:  
* Single-event upsets/single-event effects;
* Cumulative radiation effects (total ionizing dose and displacement damage dose);
* Surface discharges;* Deep dielectric charging;
* Solar cell degradation, material aging/surface damage to materials.