esa-sceva/satcom-chunk-collection huggingface dataset:
# The Intra-Class and Inter-Class Relationships in Style Transfer
## 3 Method
### Cov-Matrix Method  
In [4], they used _Gram matrix_ to represent the style of an image. Here, we use covariance matrix, which can be applied to many fields such as principle component analysis (PCA) and spectral analysis [27; 28], to represent the style of an image and can achieve good results (see Section 4). This method is termed as _Cov-Matrix_ method in this paper.  
In probability statistics, the variance describes the average distance from the sample point to the mean for each sample set. When faced with two-dimensional data, the covariance is often used to measure the relationship between two random variables (see Equation (9)). Here, \(n\) is the number of samples. When \(cov(X,Y)>0\), this shows that X and Y are positively related. When \(cov(X,Y)<0\), this shows that X and Y negatively related. When \(cov(X,Y)=0\), this shows that X and Y are independent of each other.  
\[cov(X,Y)=\frac{\sum_{i=1}^{n}(X_{i}-\bar{X})(Y_{i}-\bar{Y})}{n-1} \tag{9}\]  
Covariance can only handle two-dimensional problems; when faced with multi-dimensional data, we usually need to calculate multiple covariances. We can use covariance matrix to represent these data (see Equation (10)).  
\[C^{l}_{N_{l}\times N_{l}}=(c_{i,j},c_{i,j}=cov(Dim_{i},Dim_{j})) \tag{10}\]  
Here, \(N_{l}\) is the number of feature maps of layer \(l\), which is also called the number of dimensions of layer \(l\). \(Dim_{i}\) and \(Dim_{j}\) represent the \(i\)th feature map and the \(j\)th feature map of layer \(l\), respectively. The value of \(cov(Dim_{i},Dim_{j})\) can be computed by:  
\[\begin{split} cov(Dim_{i},Dim_{j})\\ =&\frac{\sum_{k=1}^{M}(Dim_{ik}-\bar{Dim}_{i})(Dim_{ jk}-\bar{Dim}_{j})}{M-1}\end{split} \tag{11}\]  
where \(M\) is the width times the height of the feature map and \(Dim_{ik}\) is the \(k\)th element of the \(i\)th feature map. The style loss \(\mathcal{L}^{l}_{style}\) can be computed by:  
\[\mathcal{L}^{l}_{style}=\frac{1}{4N_{l}^{2}M_{l}^{2}}\sum_{i=1}^{N_{l}}\sum_{j =1}^{N_{l}}(C^{l}_{ij}-H^{l}_{ij})^{2} \tag{12}\]  
where \(C_{l}\in R^{N_{l}\times N_{l}}\) is the covariance matrix between the vectorized feature maps of \(x^{*}\) in layer \(l\), i.e., \(C^{l}_{ij}=cov(F^{l}_{i},F^{l}_{j})\). Similarly, \(H_{l}\) is the covariance matrix corresponding to \(S_{l}\). Apparently, this method contains both intra-class and inter-class relationships of the statistical distributions of feature maps.
## 2 Materials and Methods
### Analysis of Antioxidant Activity
#### 2.3.6 FRAP Method  
Ferric reducing antioxidant power (FRAP) assay uses antioxidants as reductants in a redox-linked colorimetric reaction, wherein Fe\({}^{3+}\) is reduced to Fe\({}^{2+}\). Ferric (Fe\({}^{3+}\)) to ferrous (Fe\({}^{2+}\)) ion reduction at low pH causes formation of a colored ferrous-probe complex from a colorless ferric-probe complex.  
The FRAP analysis was performed in accordance with [22]. TPTZ (2,4,6-Tris(2-pyridyl)-s-triazine), HCl, iron chloride and reagents for acetic buffer (sodium acetate and glacial acetic acid) were purchased from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO, USA). Samples were combined with the reaction mixture previously mixed in the ratio of 10:1:1 (acetic buffer, TPTZ solution in 0.40 M HCl, iron chloride solution in distilled water). They were then measured after 10 min at 593 nm with a RayLeigh UV-1800 spectrophotometer (Beijing, China).  
The values obtained for each sample in ABTS and FRAP method, after their comparison with the concentration-response curve of the standard Trolox solution, were expressed as \(\mu\)mol Trolox equivalents per gram of dry weight (TEAC) (\(\mu\)mol Trolox/g dry weight).
## 2 Modeling Foundation of the Digital Twin  
### Basic Working Principle of the BTS  
The BTS is an essential carrier for the train to get the information of the current running state in time. The BTS consists of onboard equipment (balise transmission module (BTM) and OAU which includes transmitting antenna and receiving antenna) and ground equipment (passive balise, active balise, and lineside electronic unit). The working diagram of the BTS is shown in Figure 1.  
During the train operation, the onboard transmitting antenna continuously sends telepowering signals to the ground. When the train approaches the passive balise, the balise is activated from dormant state. After entering the active mode, the balise sends the stored telegram signals according to the specified format, modulation, and bit rate. The received tele-powering signals are converted into voltage supply signals for the passive balise. When the tele-powering signal is too weak to ensure the operation of the passive balise, the process of sending telegram signals ends. During balise activation, telegram signals are sent in a circular manner. For active balise, its power supply is provided by lineside electronic unit. The BTM obtains telegram signals through the onboard receiving antenna. The received signals whose amplitude exceeds the threshold level of BTM will be sent to the subsequent circuit module for processing. BTM performs the following functions: balise power supply, signal acquisition, filtering, amplification, demodulation, and decoding, and finally reports the decoding results to the kernel of automatic train protection (ATP). Thus, the ATP kernel could adjust the train operation state in time by combining the information of telegram and other sensors.  
In the communication process of the BTS, the transmission quality of uplink telegram signal is related to whether the ATP kernel can obtain effective telegram signals successfully. In SUBSET-036, several parameters that affect the signal transmission characteristics were discussed. The vertical distance between the OAU and the balise should range from 220 to 460 mm. The balise can be installed in the middle of the track in longitudinal or transverse  
Figure 1: Working diagram of the BTS.  
ways. In addition, the specification does not put forward specific requirements for the shape and size of the onboard antennas. The current intensity of the tele-powering signal on the onboard transmitting antenna is also limited indirectly.  
In fact, according to the previous research, the change of uplink telegram signal power with the train movement direction directly affects the effective transmission distance and effective transmission time of the uplink telegram signal [5]. Therefore, the electromagnetic coupling characteristic discussed in this paper refers to the variation of the uplink telegram signal power received by the onboard receiving antenna with the antenna displacement.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods
### Parameter Estimation
#### 3.2.1 Simulating the Indoor Temperature  
Only the measured heat demand and outdoor temperature were used for the simulation of the indoor temperature in the buildings. The parameters \(a\), \(b\), \(x_{1}\) and \(x_{2}\) of the indoor temperature model (Equation (1)) were estimated by applying constraints to the simulated indoor temperature. By analyzing known indoor temperatures in different buildings the simulated indoor temperature was constrained between 16 \({}^{\circ}\)C and 23 \({}^{\circ}\)C and the standard deviation of the simulated indoor temperature was constrained between 0.2 \({}^{\circ}\)C and 1.1 \({}^{\circ}\)C. The parameters were first estimated using one week of measured heat demand and outdoor temperature data. Then, using the estimated parameters, the indoor temperature was simulated for another two days. In both cases, the indoor temperature and its standard deviation were checked to ensure it was between the defined limits. If in bothcases this was true, the parameters would be accepted and the model was used for the optimization simulations. Otherwise, the parameter estimation was performed again. For the parameter estimation, the _simulated annealing_ algorithm from the Global Optimization Toolbox of MATLAB(r) was used. As there were no real indoor temperature measurements available, the assumption for the optimization studies was made that the simulated indoor temperature is the set point temperature in the buildings. Furthermore, as the indoor temperature model defines the heat storage capacity of the buildings and as there were no measured indoor temperatures to calibrate the model, multiple sets of parameters for the indoor temperature model were estimated for each building.
# Dynamic Energy Efficient Control of Induction Machines Using Anticipative Flux Templates
###### Abstract  
Energy efficiency optimization techniques for steady state operation of induction machines are the state-of-the-art, and many methods have already been developed. However, many real-world industrial and electric vehicle applications cannot be considered to be in steady state operation. The focus of this contribution is on the efficiency optimization of induction machines in dynamic operation. Online dynamic operation is challenging due to the computational complexity and the required low sample times in an inverter. An offline optimization is therefore conducted to gain knowledge. Based on this offline optimal solution, a simple and easy to implement template based solution is developed. This approach aims at replicating the solution found by the offline optimization by resembling the shape and anticipative characteristics of the optimal flux trajectory. The energy efficiency improvement of the template based solution is verified by simulations and measurements on a test bench and using a real-world drive cycle scenario. For comparison, a model predictive numerical online optimization is investigated too.  
energy efficiency; induction machines; optimization; dynamic operation; loss minimization
# Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
## 3 Evaluation and Discussion
### Basic Evaluation
The proposed method assigned a high-priority QoS level to UE1 having content with a short delay tolerance from the generation of the content until 30 s. From this trend, we confirmed the tendency of the proposed method to determine the priority of the bandwidth allocation while considering a delay tolerance. Consequently, we confirmed that the proposed method can control the priority and balance the load while considering the delay tolerance in a scenario wherein the method of assigning priorities is clear. Moreover, we showed that an agent can learn the load balancing method while considering a delay tolerance by using the designed state, action, and reward.
## 2 Materials and Methods
### Instruments and Methods
#### 2.2.5 Gas Chromatography-Mass Spectrometry (GC-MS)  
The GC-MS analysis was performed on samples A2 and A3 by a 6890 N GC system gas chromatograph equipped with a split/splitless injection port and coupled with a 5973A mass selective detector mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). A Varian FactorFour (Varian Inc., Palo Alto, CA, USA) VF-5 fused-silica capillary column (30 m \(\times\) 0.25 mm \(\times\) 1 \(\upmu\)m) coated with a 0.25 \(\upmu\)m film of methyl silicone (5% Phenyl) was used for the separation. The mass spectrometer operated in electron impact positive mode (70 eV), and the mass spectra were acquired in the scan range of 40-500 \(m/z\). The MS transfer line temperature was set to 280 \({}^{\circ}\)C.  
Following the methodology reported in a previous work [17], fatty acids were extracted from 20 \(\upmu\)g of selected samples treated with HCl (4N) in methanol (1 mL) and n-hexane (1 mL) for 2 h at 50 \({}^{\circ}\)C. Heptadecanoic acid (50 \(\upmu\)L of a 0.1 mg/mL \(w/v\) solution) was added as the internal standard. After centrifugation, the hexane phase containing fatty acid methyl esters was taken, and 1 \(\upmu\)L of extract was analyzed.  
For the amino acid extraction, after evaporation to dryness of the methanol phase, the residue was dissolved in 6N hydrochloric acid (2 mL), and Norleucine (50 \(\upmu\)L of a 0.1 mg/mL solution \(w/v\)) and Norvaline (50 \(\upmu\)L of a 0.01 mg/mL solution \(w/v\)) were added as internal standards. The hydrolysis was performed for five hours at 100 \({}^{\circ}\)C. After evaporation to dryness, the hydrolysate was esterified using 3 mL of HCl (2N) in propan-2-ol at 90 \({}^{\circ}\)C for one hour. Then, the solvent was vacuum evaporated until dry, and the obtained residue was dissolved in 0.2 mL of dichloromethane and derivatized with 0.2 mL of trifluoroacetic anhydride at 60 \({}^{\circ}\)C for one hour and again brought to dryness. The final residue was then redissolved in 0.2 mL of dichloromethane, and the solution, containing N-trifluoroacetyl-O-2-propyl esters amino acids derivatives, was used for GC-MS analysis (1 \(\mu\)L).  
The mass spectrometer was operated in the selected ion monitoring mode (SIM). The following target ions for the fatty acid methyl ester were considered: \(m/z\) 153 for azelaic acid, \(m/z\) 270 for palmitic acid, and \(m/z\) 298 for stearic acid, and for amino acid derivatives, \(m/z\) 140 for alanine (Ala), \(m/z\) 168 for norvaline (Nval), \(m/z\) 182 for leucine (Leu) and norleucine (Nleu), \(m/z\) 126 for glycine (Gly), \(m/z\) 166 for proline (Pro), \(m/z\) 164 for hydroxyproline (Hyp), \(m/z\) 184 for aspar acid (Asp), \(m/z\) 198 for glutamic acid (Glu), and \(m/z\) 91 for phenylalanine (Phe) were selected.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### Average Energy Consumption  
The average energy consumption from the OLSR and AODV protocols is shown in Figure 7. The results show that OLSR has a higher average energy consumption than AODV. The default AODV protocol's energy consumption has an average value of 78.38 J, while that of the OLSR protocol is 145.82 joules. Hence, increasing the value of RREQ_RETRIES to 4 (AODV 3) increases the average energy consumption by 0.83%. Regarding reducing and adding up to the value, MAX_RREQ_TIMEOUT does not always reduce the average energy consumption. The reduction in the value of MAX_RREQ_TIMEOUT to 6, 8, and 9 s increases the average energy consumption to 0.24%, 0.26%, and 0.004%, respectively,  
Figure 5: Packet loss results.  
Figure 6: Delay Results.  
whereas increasing the MAX_RREQ_TIMEOUT value to 11 and 12 s reduces the average energy consumption to 0.39% and 0.17%, respectively. The lowest average energy consumption is obtained with the RREQ_RETRIES value of 2 s (AODV 2).
## 2 Materials and Methods
### Data Analysis
#### 2.2.2. Bivariate Analysis  
Bivariate analysis focused on correlation analysis where Pearson's product-moment and Spearman's rank correlations were used to evaluate the degree of association between two continuous variables and determine the direction and strength of the relationship [38]. The significance of both correlations was tested using the \(p\)-value, where it represents the probability of rejecting the null hypothesis when the \(p\)-value is less than the significance level of 5%, \(\propto\) = 0.05 [39]. The null hypothesis refers to the hypothesis of the correlation coefficient of two continuous variables that are not significantly different from zero and no statistical significance exists in the population. By rejecting the null hypothesis, two continuous variables are statistically significant. The significance of the \(p\)-value and the highest correlation coefficient were used as the final output. The only variables that will undergo correlation analysis are between continuous inputs and outputs. There was no computation of association within the inputs or outputs because the most important thing to explore is the dependency of output variables towards the input variables.  
The Pearson's product-moment is denoted as \(r\); and [PERSON]'s rank correlations, \(R\) were computed according to Equations (1) and (2) [40]:  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\left[ \sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\right]\left[\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)  
significant correlation between inputs and outputs. If the \(r\); value is close to zero despite having a \(p\)-value \(<\) 0.05, the significant correlation might be nonlinear. Therefore, the [PERSON]'s rank coefficient was used instead. The correlation coefficient of both the slurry and sand pack tests were visualized using a heatmap, as shown in Figure 4.  
Figure 4: Heatmap plot of SRT variables. (**a**) Correlation analysis of continuous slurry test variables; (**b**) correlation analysis of continuous sand pack test variables.  
The white box (no value) in Figure 4 represents the hypothesis tests that failed to reject the null hypothesis for both the Pearson and Spearman correlation tests due to the \(p\)-value, which is greater than the significance level. Thus, the correlation coefficient is not significant for use. The slurry test's highest correlation coefficient is between D50_B (input) and RETAINED_PRM (output) with a \(-0.66\) value. It means that there is a moderate negative correlation between PSD of \(d_{50}\) with the retained permeability. Meanwhile, the highest correlation coefficient of the sand pack test was observed between SIZE_CR2 (input) with SAND_PROD_PER_AREA (output) with a 0.36 value, which indicates a low positive correlation between the sizing criteria 2 with the amount of sand produced per unit area.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 4 Challenges and Limitations
Identifying which specific LoRaWAN and NB-IoT functionalities should be onboard satellite systems and which should remain on the ground are critical areas of research and development. This decision will significantly influence the effectiveness and efficiency of satellite-based LPWAN architectures in managing the unique challenges of space-ground network dynamics. * Doppler effect: This phenomenon and its effects are explained in Section 3.2.3.
## 2 Materials and Methods  
### Sample Preparation  
Intact human molars (n = 140), stored in 0.5% chloramine solution to inhibit microbial growth, were used in this study. The teeth were examined for lack of caries and restorations under 10\(\times\) magnification optical microscope and used within 2 months of extraction. After being separated from roots, crowns were sectioned mesio-distally in precision micro-cutting machine (Mecatome T210 Prezi, France). Two samples, buccal and lingual, were obtained from each tooth. The enamel of each sample was removed and dentin was exposed and ground (Mecatome T330, Prezi, France) under water lubrication. Silicon carbide papers, 180-grit followed by 600-grit were used to create standardized smear layer on dentin [25]. The dental surface was examined for residual enamel under 10\(\times\) magnification optical microscope. Institutional Ethical Committee approval was obtained for this study (RNN/435/18/KE).  
Dentin samples (n = 280) were randomly assigned to 4 groups (n = 70) according to the dental adhesive used. The study used two self-etch and two universal dental adhesives (Table 1.).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adhesive** & **Manufacturer** & **Composition** & **pH** & **Mode of Etching** \\ \hline \multirow{8}{*}{AdperTM Easy One} & \multirow{8}{*}{3M ESPE, Germany} & 6-methacryloloxyhexyl dihydrogen phosphate (MHP Phosphate Monomer), Dimethacrylate resins, 2-hydroxyethyl methacrylate (HEMA), Bis-GMA, Methacrylate functionalized Polyalkenoic acid (VitrobondTM Copolymer), Nanofilier (silica), Ethanol, Water, Initiators based on camphorquinone & \\ \hline \multirow{8}{*}{Xeno V} & \multirow{8}{*}{Dentsply DeTrey GmbH, Germany} & Bifunctional acrylic amides, Acrylamido alkylsulfonic acid, “inverse” functionalized phosphoric acid ester, Acrylic acid, Camphorquinone, Coinitiator Burbylated benzenediol, Water, tert-Butanol & 1.5 [28] & SE \\ \cline{1-1} \cline{2-5}  & & & MDP Phosphate Monomer, Dimethacrylate resins, HEMA, VitrebondTM Copolymer, Nanofilier, Ethanol, Water, Initiators, Silane & 2.7 * & MM 1 \\ \cline{1-1} \cline{2-5}  & & & Ethanol, Water, Initiators, Silane & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dental adhesives used in the study.  
For each tested adhesive, five study groups (n = 14) were designed. The tested adhesives were applied on the dentin surface according to the research model presented in Table 2. The adhesives were light-cured with LED curing lamp (Elipar(tm) S10 LED Curing Light, 3M ESPE, Germany). Next, silicone ring with the inner diameter of 3 mm and height of 4 mm was placed on the sample surfaces. Flowable composite (Flow-Art, Arkona, Poland) was applied inside the ring and light-cured for 40 s in 2 equal increments, to ensure complete polymerization. Within each study group, 11 samples were tested for SBS, while 3 samples underwent SEM-EDS analysis of dentin-resin interface.
## 4 Methodology
### Test Procedure  
As general rules for conducting the tests, it was specified that participants should know the visual characteristics of the target element and be unaware of its location. For this, a visual element model required to be presented for searching in all the locate tasks performed by the participant.  
This configuration was defined based on the taxonomy defined in [16], where different types of search tasks are specified. For this study, the type of search task defined was locate, which consists in having prior knowledge about visual characteristics of the target element but without knowing any information about its location.  
Based on the preliminary tests and the initial results [15], it was possible to define some specifications regarding the composition of the visual variables and the time to complete each task, as described below:  
* The visual coding values for each visual variable (Figure 4);
* Maximum time of 30 s to complete each task.  
The test was designed as a mixed-design study [25] with three independent variables:  
* Visual variables: hue, lightness, saturation, texture, orientation, shape, and text;
* Partial overlap levels: 0%, 50%, 60% and 70%;
* Number of different visual encoding values: 3, 4 and 5.  
Figure 4: The visual variables analyzed in this study are represented by their respective subsets of visual coding values (3, 4, and 5 values).  
Figure 3: Examples of evaluation scenarios used in the tests: (**A**) Hue visual variable with 70% partial overlap, (**B**) orientation visual variable with 60% partial overlap, (**C**) texture visual variable with 50% partial overlap, and (**D**) shape visual variable with 0% partial overlap.  
The participants were divided into four groups of 12 individuals, as suggested in [25], for a between-subjects design. The independent variables (visual variables and the number of distinct values) were permuted in 21 evaluation scenarios (visual variable \(\times\) number of different visual encoding values \(\times\) partial overlap level) and organized in a within-subject design [25]. For each evaluation scenario (21), the participant performed six (6) tasks, resulting in 126 tasks per participant. Figure 5 illustrates the distribution of the independent variables over the tasks.  
The following data were captured automatically for each test scenario:  
* The level of partial overlap applied;
* The number of different visual encoding values per visual variable;
* The visual variable type;
* The target visual element and its characteristics;
* The participant answer: click on the correct item, click on the erroneous item, or no answer (time out);
* Task resolution time, which was quantified in seconds.