esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimisation Results
### Theoretical Minimum  
As shown in Table 1, using the calculation shown in Section 2.5, the theoretical minimum operation cost for sampling day is 449 pence. To get the theoretical minimum, the cost of electricity imported from the grid is 58 pence, the gas that is used to supply the heat demand is 31 pence and the remaining cost of 360 pence is used to import gas to supply the CHP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Gas Price (Used** \\ **for CHP) (Pence)** \\ \end{tabular} & \begin{tabular}{c} **Gas Price (Used** \\ **for Heat) (Pence)** \\ \end{tabular} & \
\begin{tabular}{c} **Electricity Price** \\ **(Pence)** \\ \end{tabular} &
\begin{tabular}{c} **Total Price (Pence)** \\ \end{tabular} \\ \hline No algorithm & 293 & 293 & 309 & 602 \\ Proposed algorithm & 299 & 74 & 91 & 464 \\ Theoretical minimum & 360 & 31 & 58 & 449 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy cost for in case study without algorithm, proposed algorithm and theoretical minimum.  
Figure 14: The state of CHP and the amount of imported gas and electricity in a day.  
Figure 15: Energy Stored in hot water tank and batteries.  
By comparing two results, it is easy to find that the cost from the combined hub control algorithm is very close to the theoretical minimum, and the ratio of cost from the theoretical minimum to the combined hub control algorithm cost is over 96.8%. This ratio varied from 88.8% to 97% after testing the whole year's data and clearly strongly depends on the daily load profile. If the daily average electrical load is very high, the CHP may need to operate at the rated power for the whole day, and this will reduce the difference between the proposed algorithm's cost and theoretical minimum. Thus, in this case the ratio is very high and the proposed method is very effective. However, it is worth noting that the theoretical minimum is calculated using conservative assumptions such as perfect CHP efficiency and instantaneous output. Therefore, the whole range of ratios will be somewhat improved when real world imperfections are accounted for. It can thus be stated with some confidence that the algorithm presented here always yields better than 88% of the theoretical minimum cost.
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
### A Survey on Simulators for Unmanned Aerial Vehicles
A completely different simulation approach is found in [38]. This work presents a compendium of videos of flights with unmanned aircraft. The compendium allows simulation of visualization activities with these vehicles and provides benchmarks for testing and evaluating algorithms. Similarly, the work in [39] shows an unconventional simulator where the simulation of unmanned aerial vehicles is carried out through its dynamic model. However, this work uses a real six-degree-of-freedom robotic manipulator instead of using a 3D environment to visualize the simulation. In this way, the end-effector is positioned and oriented following the aircraft's configuration. The alternative found in [40] proposes a simulator of unmanned aerial vehicles in traffic environments (static and moving obstacles). This simulator allows specifying the physical and control parameters of the vehicles and information related to the environment. The simulation is carried out in Unity 3D and indicates the points through which each vehicle must travel. This simulator aims to facilitate route planning tasks in dynamic spaces. Up to this point, reviewed works address different aspects of UAV simulators, such as the development of the vehicle model, the generation of numerical data, the design of 3D environments, and the use of dedicated hardware. However, there are more advanced simulators whose use has become widespread in recent years for commercial, research, and educational purposes. These alternatives are characterized by simulators that integrate several aspects described in the works reviewed before, providing high-quality simulations with a high level of realism. One of the above alternatives is the DJI Flight Simulator ([[https://www.dji.com/simulator](https://www.dji.com/simulator)]([https://www.dji.com/simulator](https://www.dji.com/simulator)) (accessed on 6 December 2021)). This simulator is proprietary software that allows simulating the behavior of various UAVs and controllers of the DJI brand. The simulator provides different 3D scenarios and can customize the weather and daylight conditions. By its part, Gazebo ([[http://gazebosim.org](http://gazebosim.org)]([http://gazebosim.org](http://gazebosim.org)) (accessed on 6 December 2021)) is a 3D simulator for robotic systems. One of the main characteristics of Gazebo is its level of generality. In this sense, it is possible to simulate the kinematics and dynamics of robots and mechanisms made up of rigid bodies and provide the user with an accurate 3D representation of their movements. Finally, AirSim [41] is an alternative dedicated to the simulation of UAVs and cars. AirSim uses a simplified physics engine that allows performing fast simulations suitable for experimentation with artificial intelligence algorithms.
## 4 Experiments and Discussion
### Impact of Different Window Sizes on Image Pathes  
To find the best window size for a patched split, we designed the following comparative experiments: we gradually expanded the size of image patches from \(11\times 11\) to \(19\times 19\). Due to the salience of the central pixel position, we exclusively employed patch sizes with odd dimensions. The experimental results indicate that, considering a comprehensive  
Figure 10: (**a**) Description of the effect of the different window sizes on the OA, AA, and kappa on the IP dataset. (**b**) Description of the effect of the different window sizes on the GFLOPs on the IP dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Window Size** & **OA (\%)** & **AA (\%)** & **Kappa (\%)** & **GFLOPs** \\ \hline
11 & 96.73 & 97.80 & 96.25 & 2.38 \\
13 & 96.87 & 98.18 & 96.41 & 3.25 \\
15 & 95.77 & 97.31 & 95.15 & 4.27 \\
17 & 95.81 & 97.76 & 95.20 & 5.44 \\
19 & 96.00 & 97.63 & 95.41 & 6.75 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Different window sizes affect the performance on the Indian Pines dataset.  
trade-off between the inference speed and classification performance, a patch size of \(13\times 13\) demonstrates an optimal performance, as shown in Table 11.
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
## 2 Materials and Methods
### Methods  
Si sludge was mixed with carbon powder in various ratios by 1 h ball milling. After mixing, pellets with a 13 mm diameter and 2-4 mm thickness were made by mold and press. The pellets were prepared at 300 psi pressure. Then, the pellets were placed in an alumina crucible and heated at different temperatures in a Kanthal Super furnace (Korea Furnace Development Co., Yangju, Korea), as shown in Figure 1. The alumina reaction tube (OD 60 mm \(\times\) ID 50 mm \(\times\) L 600 mm; Samwha Ceramics Co., Seoul, Korea) was enclosed with a water-cooled jacket. High-purity argon gas was continuously fed into the reaction tube at a flow rate of 300-500 SCCM during the heat treatment. Although the holding time at the reaction temperature was the same in every synthesis (240 min), the reaction temperature was varied between 1400 \({}^{\circ}\)C and 1600 \({}^{\circ}\)C, as shown in Table 1. After holding at the maximum temperature, the pellets were cooled to room temperature. The pellets were then crushed into powder for characterization.  
Raw material as well as the obtained product were characterized by several analyses. The particle-size distribution for the starting material, i.e., Si sludge, was done with Multisizer 4e (Beckman Coulter, Brea, CA, USA). For the XRD measurement, the powder was sieved through 200 mesh and was analyzed using a RIGAKU D/MAX-2500V (Rigaku, Tokyo, Japan) and Cu K\(\alpha\) radiation under an accelerating voltage of 40 kV and a current of 200 mA. The diffraction data for the obtained product powder were recorded at 20 angles between 10\({}^{\circ}\) and 90\({}^{\circ}\). Again, for morphological  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mixing Ratio (Si Sludge/C)** & **Reaction Temp. (\({}^{\circ}\)C)** \\ \hline
1:1.2 & 1400 \\ \hline
1:1.4 & 1550 \\ \hline
1:4.0 & 1600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preliminary experimental conditions for the synthesis of SiC from Si sludge.  
Figure 1: Schematic diagram of the experimental settings.  
analysis, FE-SEM and energy dispersive X-ray spectrometry (EDS) analysis were performed using a JEOL JSM-7401F (JEOL, Tokyo, Japan) at a voltage of 10.0 kV. Similarly, attenuated total reflectance (ATR) was recorded on a combinatory vibrational spectrometer (Nicolet is 50, Thermo Fisher Scientific Inc., USA). In addition, the carbon removal treatment was performed using the same furnace employed in the heat treatment during synthesis, in the open air.
## 3 Proposed Scheme
### Encryption and Aggregation
#### 3.2.2 Storage and Aggregation  
(1). Spatial Aggregation  
Once receiving encrypted data at timeslot \(d\) from all pairwise nodes, \(user_{i}\) aggregates them and generates the local spatial data \(LS\) (\(i,d\)) as follows:  
\[LS(i,d)=\sum_{j\in ind_{i}[s]}^{n}(x^{j}_{(i,d)}+r^{j}_{(i,d)})\bmod M(1\leq s \leq k) \tag{7}\]  
Every user sends the local spatial aggregation formed as \(LS\) (\(i,d\)) to the GW at every timestamp.  
Once receiving the locally spatial aggregation \(LS\) (\(i,d\)) from the pairwise nodes, the GW adds them up together and the pairwise keys cancel out. The total spatial aggregation is denoted as:  
\[AS_{d}=\sum_{i=1}^{n}LS(i,d)\bmod M \tag{8}\]  
(2). Temporal aggregation  
Every user node receives the encrypted data from its pairwise nodes and stores it as a matrix of \(T\) rows and \(n\) columns formed as Equation (6).  
Figure 2: Example multi-region spatial and temporal aggregation.  
In every billing period \(T\), the user node aggregates every column in the Equation (6) into locally temporal aggregation after the pairwise keys cancel out. The locally temporal aggregation form is as follows:  
\[\begin{array}{ll}LT^{i}_{(j,T)}&=\sum\limits_{d=1}^{T}E^{i}_{(j,d)}\bmod M(j \in ind_{i}[s])\\ &=\sum\limits_{d=1}^{T}\left(x^{i}_{(j,d)}+r^{i}_{(j,d)}\right)\bmod M\end{array} \tag{9}\]  
Once the CC issues the temporal aggregation request for \(user_{i}\) to the GW, the pairwise nodes of \(user_{i}\) would report its local temporal aggregation \(LT^{i}_{(j,T)}\) to the GW.  
The GW aggregates them into the temporal aggregation and transmits it to the CC; the aggregation process is as follows:  
\[AT(i,T)=\sum\limits_{j\in ind_{i}[s]}LT^{i}_{(j,T)}\bmod M(1\leq s\leq k) \tag{10}\]  
We assume \(j\in ind_{i}[s];i\in ind_{j}[s]\).  
Figure 3 shows the communication process between the pairwise nodes and GW at the timestamp \(d\).
## 5 Experimental Results
### Results without Pathologies
#### 5.2.3 Results for Gradient Boosting with Evolutionary Feature Selection  
The results obtained using GB with evolutionary FS are presented below.  
**GA with _Stud_ selection for Gradient Boosting (GB-S):** Using only 12 variables, it has been possible to achieve an average accuracy of 0.7295 and 0.7797 for the best case, achieving a slight improvement compared to the version without feature selection, as shown in Table 3. In this new set of variables, sex and age continue to dominate similarly to the set with pathologies. Other variables used are those related to hours of sleep, fermented/distilled drinks, soft drinks, legume portions, and education.  
The graph of the impact of the variables corresponding to the Gradient Boosting model with _Stud_ selection for the case without pathologies is shown in Figure 9. In the highest positions we find sex, age, education, and soda with the same performance as above. A new variable to highlight is legumes, whose highest values appear in the left zone of the graph. As for alcoholic beverages, we find apparent differences between distilled  
Figure 8: Pearson Correlation Matrix of variables in Figure 7.  
and fermented beverages; for the higher values of the variable wineWEEK, the probability of being overweight/obese is lower. In the case of spirits, it seems that their intake may be associated with overweight/obesity.  
**GA with tournament selection for Gradient Boosting (GB-T):** Using the tournament selection method, the number of variables used was also 12 with an average accuracy of 0.7307 and for the best-case scenario up to 0.7763, as shown in Table 3. New variables were included: adherence to the Mediterranean diet and the population. Other variables seen previously such as education, soft drinks, distilled/fermented beverages, sex, age, and hours of sleep, among others, also appear.
# Antioxidant Activity of _Panax ginseng_ to Regulate ROS in Various Chronic Diseases
## 6 _P. ginseng_ in ROS-Mediated Diseases
### Antioxidant Activities of P. ginseng in Neurogenerative Diseases
#### 6.2.2 Alzheimer's Disease  
Alzheimer's disease (AD) is a cognitive condition defined by the accumulation of senile plaques, the development of neurofibrillary tangle, and finally the death of neurons. The improper degradation of the amyloid precursor protein (APP) is the primary mechanism causing AD progression [99; 100]. Several changes in molecular and cellular pathways including mitochondrial dysfunction, antioxidant decreases, oxidative stress increases, synaptic impairment, and amyloid A\(\beta\) clearance capacity are present in the AD brain [101; 102; 103]. Many studies have distinguished that _P. ginseng_ extract, powder, and ginseng were applied to AD in in vivo and in vitro studies. The total saponins of ginseng consumption for seven months revealed a remarkable reduction in memory loss by inhibiting oxidative stress and increasing the proteins associated with plasticity in aged mice [104]. Ginsenoside Rb1 shields neurons from A\(\beta\)1-42 neurotoxicity via an antioxidant mechanism [105]. Rb1 pre-treatment in PC12 cells for 1 day inhibits the overproduction of ROS and lipid peroxidation enhances the activation of caspase-3 and Bcl-2/box for promoting cell survival [106]. In H\({}_{2}\)O\({}_{2}\)-induced PC12 cells, Rg1 prevents NF-\(\kappa\)B/P65, ERK1/2, and Akt stimulation [107]. Rg1 can protect PC12 cells from cytotoxicity caused by A\(\beta\)25-35 by preventing \(\beta\)-secretase activities [108]. However, the ref. [109] experiment found for the first time that ginseng Rk3 can trigger the intracellular ROS level and A\(\beta\)-induced neuronal injury by stimulating the AMPK pathway and the upregulation of Nrf2. Interestingly, this study also confirmed that the pharmacological activity of ginseng Rk3 is better than the control drug donepezil in case of the treatment of AD. Recently, a new ginseng component gintonin has been discovered that is effective in reducing the severity of AD-related neuropathies [110].
## 4 Discussion
In this study, we used a pulsed wave (PW) approach to PBM, which distinguishes it from continuous wave (CW) methods in the application of optical energy. Unlike CW, which operates at higher power densities, typically in the range of 0.5 to 48 J/cm\({}^{2}\)[36], PW uses lower power densities, delivering photon energy in pulses. In this case, the PW setting of the power density of 1.91 mW/cm\({}^{2}\) at the duty cycle of 30% was used to produce an energy density of 80.22 mJ/cm\({}^{2}\). This selection was informed by previous studies showing the superiority of PW in differentiating pulp stem cells into odontoblast-like cells compared to CW [18, 34, 35]. Despite its lower energy density, PW was observed to be more effective than CW in stimulating tooth tissue regeneration, as evidenced in our study by the enhanced activity of odontoblasts and the improved condition of fibrous connective tissue within the periodontal ligament. However, our study has a few limitations. One significant limitation is the scope of the study model, which was confined to a single animal subject. This limits the breadth of the data and raises questions about the generalizability of the results. Additionally, comprehensive data on the long-term effectiveness and safety of PBM are lacking, which are crucial for understanding its viability as a long-term treatment option. Furthermore, although positive outcomes have been observed, the exact biological mechanisms by which PBM promotes tooth tissue regeneration are only partially understood. An in-depth exploration of these mechanisms is necessary to fully comprehend and optimize the use of PBM in dental health applications. In summary, the application of PBM using 300 Hz-NIR can be a promising tool in dental tissue regeneration. This not only addresses the challenge of managing dental hypersensitivity but also plays a crucial role in the regeneration and health of tooth tissue. While acknowledging certain limitations, our research has contributed significantly to the evolving landscape of regenerative dentistry, paving the way for innovative treatment strategies in the field of dental tissue management and regeneration.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 3 Quantized Precoding  
We wish to ensure compatibility with respect to LP-ZF. In other words, each receiver \(k\) should ideally see signals \(u_{k}[t]\), \(t=0,\ldots,T-1\), that were generated from the frequency-domain signals \(\hat{u}_{k}[m]\), \(m=0,\ldots,T_{F}-1\). Let \(\mathbf{u}[t]=(u_{1}[t]\ \ldots\ u_{K}[t])^{T}\) and define the time-domain mean square error (MSE) cost function  
\[G(\mathbf{x}[0],\ldots,\mathbf{x}[T-1],\alpha) =\sum_{t=0}^{T-1}\text{E}_{z[t]}\left[\left\|\mathbf{u}[t]-\alpha\mathbf{ y}[t]\right\|^{2}\right]\] \[=\sum_{t=0}^{T-1}\left\|\mathbf{u}[t]-\alpha\sum_{t=0}^{L-1}\mathbf{H}[ \tau]\mathbf{x}[t-\tau]\right\|^{2}+\alpha^{2}TK\sigma^{2} \tag{8}\]  
where \(\text{E}_{z[t]}[\cdot]\) denotes the expectation with respect to the noise \(\mathbf{z}[t]\). The optimization problem is as follows:  
\[\min_{\mathbf{x}[0],\ldots,\mathbf{x}[T-1],\alpha} G(\mathbf{x}[0],\ldots,\mathbf{x}[T-1],\alpha)\] \[\text{s.t.} \mathbf{x}[t]\in\mathcal{X}^{N},\ t=0,\ldots,T-1 \tag{9}\] \[\alpha>0.\]  
The parameter \(\alpha\) in (3.2) and (3.2) can easily be optimized for fixed \(\mathbf{x}[0],\ldots,\mathbf{x}[T-1]\) and the result is (see [18] Equation (26))  
\[\alpha=\frac{\sum_{t=0}^{T-1}\text{Re}\left(\mathbf{u}[t]^{\text{H}}\sum_{t=0}^{L -1}\mathbf{H}[\tau]\mathbf{x}[t-\tau]\right)}{\sum_{t=0}^{T}\left\|\sum_{t=0}^{L-1}\bm {H}[\tau]\mathbf{x}[t-\tau]\right\|^{2}+TK\sigma^{2}}. \tag{10}\]  
For the MAGIQ and QCM algorithms below, we use alternating minimization to find the \(\mathbf{x}[0],\ldots,\mathbf{x}[T-1]\) and \(\alpha\). For the linear MMSE precoder, we label the \(\alpha\) in (10) as \(\alpha_{\text{WF}}\).  
Observe that we use the same \(\alpha\) for all \(K\) UEs because all UEs experience the same shadowing, i.e., all \(K\) UEs see the same average power. For UE-dependent shadowing, a more general approach would be to replace \(\alpha\) with a diagonal matrix with \(K\) parameters \(\alpha_{k}\), \(k=1,\ldots,K\), and then modify (3.2) appropriately.
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 4 TI3 GER Campaign Measurements  
### Campaign Overview  
The TI3 GER campaign facilitated the execution of a series of eight flights where MTHP was operated [26]. Throughout these flights, data were collected not only with MTHP but also with other several atmospheric sensors, as well as collocated in situ measurements using balloons. The comprehensive dataset from these flights can be accessed and explored in detail at the specified reference [23]. The campaign itself was conducted in April 2022, covering locations in multiple regions within the United States, including Boulder, Colorado; Hawaii; and Alaska. These diverse geographical areas were selected strategically to capture a broad range of atmospheric conditions and provide a comprehensive perspective on atmospheric dynamics. The eight flight paths followed during the TI3 GER campaign are documented and depicted in Figure 9.  
Figure 8: Time series of estimated antenna temperature using external calibration load for several consecutive time-tagged rotations of the averaged spectrum at (**left**) 60 GHz channel and (**right**) 183 GHz channel. Color bar scale is Kelvin. Note that all spectral components have been averaged for visualization. Note that the rotor has an offset and zenith is at 55\({}^{\circ}\).  
Figure 7: (**Left**) MTHP canister integrated into the GV airplane as part of the TI3 GER campaign, and (**right**) external hot load calibration test during integration and validation of the instrument in the GV.
## 3 Results and Discussion
### E-SEM and EDS Analysis  
An environmental scanning electron microscope (E-SEM) was used to examine the microscopic morphology of the materials. SEM and EDS characterization analysis are shown in Figure 8.  
Figure 7: IR spectrum of fullerene thin film deposed by UCBD with different deposition periods.  
As shown by the E-SEM results, the structure illustrates that the morphology of the film improves as the deposition period increases. The EDS results confirm that the content of carbon increased with the deposition duration; however, oxidation may also increase. Note that the specific gravity of C\({}_{60}\) powder elements (Carbon and Oxygen) are 98.5 and 1.5 (in PP At.%) and 98.87 and 1.13 (in At%), respectively.  
Indeed, this last figure indicates that the thin film expels the chemical elements not proper to C\({}_{60}\) material and is restored as the solvent evaporates. However, as the deposition time increases, oxidation takes place. Additionally, note that C\({}_{60}\) films prepared by UCBD are dense and homogenous, and without the presence of pinholes.
###### Abstract  
The purpose of this study was to produce margarine with reduced trans and saturated fatty acid contents using 10% beeswax Oleegel and hydrocolloid-based oleogel containing 3.15% sodium caseinate, 0.5% guar gum, and 0.22% xanthan gum with a melting point, rheological and textural characteristics similar to palm oil. Oleegel samples were used as a substitute for palm oil and partially hydrogenated palm ole oil. Margarine (70% fat) formulated with these oleogels was investigated for solid fat content (SFC), melting point, and rheological and textural properties. The results showed that the replacement of 100% partially hydrogenated palm olein oil (PHPO) and 25% palm oil (PO) with beeswax Oleogel and the replacement of 100% PHPO with hydrocolloid-based oleogel resulted in the production of margarine with rheological and textural properties similar to the commercial control margarine. In addition, these samples had a lower content of SFC and a higher melting point than the commercial control sample. The amounts of saturated and trans fatty acids also decreased. These were 28% and 80% in the sample containing beeswax and 15% and 73% in the sample with hydrocolloid-based oleogels for saturated and trans fatty acids, respectively. It was concluded that it is possible to manufacture margarine using the oleogel method while maintaining its physical characteristics and improving its nutritional properties.  
margarine; oleogel; beeswax; hydrocolloid; palm oil; rheological property +
Footnote †: journal: Applied Sciences and Nutrition