esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model
### Transmission Model  
We assume that the transmission distance of a TP is limited by the available optical power budget and the attenuation of the fiber and optical components that are introduced on the TP. Namely, the transmission distance (\(L\)) of a TP is estimated as follows:  
\[L=\frac{P-(\sum_{m\in\mathcal{M}}N_{\text{M}}(m)A_{\text{M}}(m)+\sum_{o\in \mathcal{O}}N_{\text{O}}(o)A_{\text{O}}(o)+2A_{\text{C}}+A_{\text{MM}})}{ \alpha}. \tag{1}\]  
Figure 2: Considered packet-optical xHault network deployment scenarios; a 4-channel WDM system is assumed in scenarios Fix-O and Fix-D.  
where \(P\) is the power budget, \(N_{\text{M}}(m)\) and \(A_{\text{M}}(m)\) are, respectively, the number of MUXs and the attenuation of a MUX of type \(m\in\mathcal{M}\), \(N_{\text{O}}(o)\) and \(A_{\text{O}}(o)\) are, respectively, the number of OADMs and the attenuation of an OADM of type \(o\in\mathcal{O}\), \(2A_{\text{C}}\) represents the attenuation of end connectors, and \(A_{\text{MM}}\) is the maintenance margin. We consider that the attenuation of optical devices (MUXs and OADMs) includes the attenuation of their connectors. The power budget (\(P\)) is the difference between the minimal transmitter outer optical modulation amplitude (\(P_{\text{TX}}\)) and receiver sensitivity (\(P_{\text{RX}}\)), as shown in Figure 2-1 in [38].  
Equation (1) calculates the maximum allowable length of the optical fiber on a TP. The calculation is based on the standard optical power budget model, which accounts for the available power budget (\(P\)) and the attenuation of optical components that are present on the TP (see the components parameters in Section 5), as explained in Chapter 4.2 in [39]. Due to the short assumed distances, less than 10 km, factors other than the power budget affecting transmission, such as dispersion, are not considered.
## 2 Materials and Methods
### Determination of the Optimal Spectral Configuration of the Hyperspectral Camera
#### 2.6.2 Simulation Based Method  
For all the possible \(P\) combinations for a group of \(N\) spectral bands (see Equation (9)), the quantification errors in \(\Delta C_{Hb}\), \(\Delta C_{Hb\circ 2}\), or \(\Delta C_{oxCCO}\) were computed:  
\[E_{\Delta C_{n}}(N,p)=\left|\frac{\Delta C_{n}^{Expected}-\Delta C_{n}^{Estimated }(N,p)}{\Delta C_{n}^{Expected}}\right|\times 100. \tag{10}\]  
Note that this quantification error is represented in absolute values. \(\Delta C_{n}^{Expected}\) is the concentration changes of the chromophore \(n\) that were expected to be measured in the tissue; see Section 2.4. \(\Delta C_{n}^{Estimated}(N,p)\) is the concentration changes of the chromophore \(n\) estimated from the simulated data using the combination \(p\) (\(p\in[1;P]\)) for a group of \(N\) spectral bands; see Equation (9). The computation of \(\Delta C_{n}^{Estimated}(N,p)\) and \(E_{\Delta C_{n}}(N,p)\) was repeated \(10^{3}\) times to get their mean and the standard deviation values. Different noises were added to the simulated reflection spectra \(\phi\), mean path length \(L\), and camera intensities \(R\) for each iteration. For each group of \(N\) spectral bands, the optimal spectral combination among the \(P\) possibilities was determined using Equation (11):  
\[\underset{p}{min}\left(\sqrt{m\left(E_{\Delta C_{n}}(N,p)\right)^{2}+\sigma \left(E_{\Delta C_{n}}(N,p)\right)^{2}}\right). \tag{11}\]  
\(m\) and \(\sigma\) designate the mean and standard deviation functions, respectively. This metric was chosen to identify the spectral combination of \(N\) spectral bands that minimize the average quantification errors while ensuring the reproduction of measurements. Equation (11) leads to the determination of the combination \(p_{n}^{N}\), which is the best combination of \(N\) spectral bands for the deconvolution of the chromophore \(n\). Finally, the optimal spectral configurations of the hyperspectral camera for the quantification of \(\Delta C_{Hb\circ 2}\), \(\Delta C_{Hb}\), and \(\Delta C_{oxCCO}\) were obtained using Equation (12):  
\[\underset{p_{n}^{N}}{min}\left(\sqrt{m\left(E_{\Delta C_{n}}(N,p_{n}^{N}) \right)^{2}+\sigma\left(E_{\Delta C_{n}}(N,p_{n}^{N})\right)^{2}}\right). \tag{12}\]  
Note that the spectral configurations determined by Equation (12) are not necessarily the same for the deconvolution of \(\Delta C_{Hb\circ 2}\), \(\Delta C_{Hb}\), and \(\Delta C_{oxCCO}\). In other words, this means that three distinct configurations may be used.
# Material Characterization for Sustainable Concrete Paving Blocks
## 3 Results and Discussion
### Abrasion Resistance  
Figure 11 demonstrated the relationship between materials' replacement levels and the blocks' maximum groove lengths that were obtained from abrasion resistance tests. According to the British standard BS EN 1338: 2003 [15], the higher value of groove length represents a lower performance of block's abrasion resistance. In general, all the samples' test results were higher than 23 mm, which fall short according to the standard requirements for class 3 blocks [15]. The impacts of different replacement levels of materials on the samples' abrasion resistance are shown in Figure 11.  
Figure 9: Relationship between the different replacement levels of materials and the tensile splitting strength of specimens.  
29.34 mm to 25.58 mm. Then, with increasing content of RCCA, the value of groove length also started to rise and peak at 35.73 mm. A low percentage of RCKA can increase the blocks' abrasion resistance. The value of groove length dropped from 28.74 mm to 28.14 mm when the content of RCKA changed from 10% to 20%. Then, at a 30% replacement level, the length returned to 28.81 mm. To sum up, adding RCKA in the concrete mixture had a relatively positive impact on the blocks' abrasion resistance, and the impact was not significant. For the CG, the values of groove lengths were 29.55 mm, 29.33 mm, 28.12 mm, and 29.77 mm when the replacement levels were 10%, 20%, 30%, and 40%, respectively. The abrasion resistance only had a sharp increase at 30%. The variation of abrasion resistance of the blocks mixed with different percentages of GGBS was significant. At a 30% replacement level, the groove length was only 24.49 mm, which was the lowest in this series of experiments. The value of length rose to 25.98 mm when the replacement level was 50%, and that result was still much lower than 29.34 mm (replacement level: 0%). However, at a 70% replacement level, the result peaked at 30.23 mm. For the blocks mixed with CR, the groove length was very high (30.96 mm) at a 1% replacement level, then declined to 29.02 mm at 2%, and finally reduced to 28.36 mm at 3%.
## 3 Results and Discussion  
### High Temperature Tests  
#### 3.1.1 Exposure of Concrete Specimens to High Temperatures  
In the non-cycling tests, temperatures of 200, 300, 400, 500, and 600 \({}^{\circ}\)C are applied to the concrete specimens respectively. The specimens exposed to elevated temperatures are shown in Figure 7. It can be found that the specimens demonstrate a change of color after exposure to high temperatures. There is no visible crack in the concrete specimens until the temperature elevated to 500 \({}^{\circ}\)C. Some micro-cracks form on the surface of the specimens after exposure to 500 \({}^{\circ}\)C and more appear at 600 \({}^{\circ}\)C. It is considered that different thermal strains for hardened cement matrices and aggregates will produce a stress between them, resulting in the development of the micro-cracks.  
Mass loss of the specimens after exposure to high temperatures is shown in Figure 8. The mass loss of three specimens at a target temperature is close. The average value of mass loss for three specimens gradually increases from 3.0% at 200 \({}^{\circ}\)C to 7.5% at 600 \({}^{\circ}\)C. Since no spalling of concrete occurs, the mass loss is attributed to both leakage of water vapor and thermal decomposition accompanied by release of carbon dioxide.  
Figure 8: Mass loss of the specimens after exposure to high temperatures.  
Figure 7: Concrete specimens before (**left**) and after (**right**) exposure to high temperatures. (**a**) Exposure to 300 \({}^{\circ}\)C; (**b**) Exposure to 400 \({}^{\circ}\)C; (**c**) Exposure to 500 \({}^{\circ}\)C; (**d**) Exposure to 600 \({}^{\circ}\)C.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Model Training and Evaluation  
Once the feature vector was split into a training and test set, the deep learning model was trained over the training set for recognizing the human activities. After the extraction of the different features, the Deep Neural Networks (DNN) method was implemented with the DeepLearning4j framework [29]. During the hyper parameter tuning with a grid search approach [31], we considered the following values for each of the parameters: learning rate (\(10^{0}\), \(10^{-1}\), \(10^{-2}\), \(10^{-3}\), \(10^{-3}\), \(10^{-5}\), \(10^{-6}\), \(10^{-7}\)) with an adaptive learning rate approach [32], number of hidden layers (1-4), regularization (L1, L2), normalization (min-max normalization), and mean and standard deviation. The following parameters were selected with the grid search and were configured for the final DNN model:  
* _Activation function_: Sigmoid;
* _Learning rate_: 0.1;
* _Maximum number of training iterations_: \(4\times 10^{6}\);
* _Number of hidden layers_: 3;
* _Weight function_: Xavier;
* _Backpropagation_: Yes;
* _Regularization_: L2 [33];
* _Normalization_: By mean and standard deviation [30].  
Finally, the evaluation of the performance of the trained model was performed, and it was tested over the unseen data, i.e., the test set. Note that the experiments were repeated five times with different seeds causing different training and test splits, as well as different initializations of the DNN network. During the experiments, the hyperparameters were fixed to the above values. Based upon the testing results, the confusion matrix was constructed, which is further used to evaluate the performance of the trained model with respect to different performance metrics. The results and metrics are discussed in Section 3, and they represent the averages of the five repetitions.
## 3 Numerical Simulation Results
### Analysis of Numerical Results
#### 3.4.1 Plastic Zone Analysis  
It is worth noting that the bolts were herein adopted together with the concrete for supporting the roadway. The plastic region in the surrounding rocks had evident discrepancies under different supporting schemes. The distribution of the plastic zone is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Elastic Modulus (GPa)** & **Poisson Ratio** & **Cohesion (MPa)** & **Friction Angle (\({}^{\circ}\))** \\ \hline A2 & 3.5 & 0.22 & 14.25 & 15 \\ B2 & 2.2 & 0.28 & 6.45 & 28 \\ C2 & 1.5 & 0.31 & 5.18 & 20 \\ D2 & 1.0 & 0.35 & 3.54 & 20 \\ PC & 4.3 & 0.20 & 3.02 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mechanical parameters of various supporting materials.  
Figure 8: The stress–displacement curve of PC under uniaxial tests.  
Figure 9: Plastic zone distribution of six cases. (**a**) case 1; (**b**) case 2; (**c**) case 3; (**d**) case 4; (**e**) case 5; (**f**) case 6.  
As shown in Figure 9, the plastic zone in the surrounding rocks under unsupported conditions (Case 1) was more extensive compared to other cases with support, and its range may approximately extend to 3 m, and the roof and floor of the roadway had exhibited some damaged area. It was found from Case 2 to Case 6 that the maximum extension of the plastic zone in Case 2, Case 3, Case 4, Case 5, and Case 6 was about 1.82 m, 1.42 m, 1.41 m, 1.73 m, and 1.96 m, respectively. The plastic zones of Case 3 and Case 4 were smaller compared with those of other cases; therefore, when the mass fractions of basalt fibers and rubber particles in BFRGC were 0.4% and 5-10%, respectively, the BFRGC could effectively reduce the plastic zone of surrounding rock of deep high-stress soft rock roadways, achieving the optimal performance.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 3 Construction Model of Power Fault Knowledge Graph Based on Deep Learning
### Entity Extraction Model Based on BBIGRU-GA-CRF  
Both the BiLSTM entity extraction model and the BiLSTM-CRF model can capture pre and post two-way information, but its model structure is slightly complex and requires more parameters during training, resulting in a long training time. The BiGRU-CRF model simplifies the structure of the BiLSTM model and BiLSTM-CRF model, requires fewer parameters for training, and achieves faster training speed. However, the BiGRU-CRF model cannot extract global features, which makes it difficult for the model to screen important information, resulting in low accuracy of model recognition and poor overall recognition effect.  
To solve the above problems, this paper proposes an improved entity extraction model composed of four parts, BERT Bidirectional GRU with Gated Attention Mechanism, and Conditional Random Fields (BBiGRU-GA-CRF). With the powerful text feature representation ability of the BERT model, the power fault text is represented, the global features are extracted, and the corresponding semantic vector sequences are obtained as the input of the BiGRU network layer. At the same time, the Gated Attention Mechanism is introduced to further mine the local features of fault text. Finally, the CRF module is used to complete the sequence decoding and annotation, and the optimal annotation sequence is obtained.  
The BBiGRU-GA-CRF entity recognition model proposed in this paper is shown in Figure 5.  
The whole entity extraction model is mainly composed of the BERT layer, bidirectional GRU layer, Gated Attention layer, and CRF layer. Next, each module is introduced in detail.
## 3 Results and Discussion
### GC-MS Analysis of Volatile Profile of Dried Citrus Peel as Effected by Drying Methods
#### 3.1.5 Aldehydes  
The aldehydes detected in dried citrus peel in this experiment include aromatic aldehydes (furfurfural, benzaldehyde), cyclic aldehyde (perillal), saturated aliphatic aldehydes (octanal, decanal, undecanal, dodecanal and tetradecanal), and unsaturated aliphatic aldehydes (\(\beta\)-sinensal, \(\alpha\)-sinensal, 7-pentadecene-7-carbaldehyde). Aliphatic aldehydes, such as octanal, decanal, dodecanal, and sinensal were reported to show significant aroma activity in citrus fruit oil [32; 33]. It can be found from Table 1 that dried Ponkan peel showed higher content of both aromatic and aliphatic aldehydes than dried Chachi peel except for \(\alpha\)-sinensal and perillal, and dried Chachi peel contains much higher content of \(\alpha\)-sinensal (the most aldehyde detected in dried Chachi peel) than dried Ponkan peel; thus, total aldehydes were observed to be higher in dried Chachi peel. For the peel of both cultivars, FD was the best to preserve aromatic aldehydes while AD was the best to preserve aliphatic aldehydes.
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 6 Experimental Evaluation
### Numerical Results
#### 6.4.1 Analysis of the Computed Solution  
The computed solution is described in Tables 3-5. Table 3 reports the production schedule (in tons), i.e., the quantity of products to be processed each week, in each line, and for each product, in order to meet the formulated demand, subject to the defined constraints for production capacity of the machinery and the storage volume of the facilities. In addition, additional information is presented describing the hours each production requires, if a cleaning must be performed after the product has been processed, if a clean was avoided, and the stored stock of the product at the time of finalizing the corresponding production, after subtracting the withdrawals for the week (if any).  
\begin{table}
\begin{tabular}{r r r r r r r r} \hline \hline _Week_ & _Line_ & _Product_ & _Production (Tons)_ & _Hours_ & _Cleans_ & _Cleans Saved_ & _Stockpiled_ \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Production schedule (in tons) by week for the considered case study.  
A relevant feature of the computed solution is the proposed sequencing for each production line of every product to be processed within the same week. For every week and line, if the value reported in the column _cleans saved_ takes the value 1, it means that the last product processed in the week in that line is also the first product to be processed in the following week, therefore, the machine does not need to be cleaned (and one cleaning is avoided).  
Table 4 reports relevant indicators for each of the two considered production lines: the total number of operator shifts used, the number of products produced, the total production hours per line, the number of cleanings required for machines, the number of cleanings avoided in the computed solution, and the total hours spent on effective cleanings. Results show that a significant number of cleanings are avoided when applying the proposed MILP model for solution optimization. The cleanings saved have an important impact on the overall production costs.  
Table 5 describes each week of the harvest, reporting the quantity of products stored (computed as the total production minus the withdrawn products), and the storage capacity available in the facility. According to the weekly production plan obtained in Table 3 and the demand established in Table 2, it can be seen that the occupation of the maximum storage capacity occurs in week 9, with 96.48% occupancy (2,605 tons) and the average space occupied during the entire period is 38.53%.
# Madeira Wine Volatile Profile. A Platform to Establish Madeira Wine Aroma Descriptors
###### Abstract  
In the present study we aimed to investigate the volatile organic compounds (VOCs) that may potentially be responsible for specific descriptors of Madeira wine providing details about Madeira wine aroma notes at molecular level. Moreover, the wine aroma profile, based on the obtained data, will be a starting point to evaluate the impact of grape variety (Malvasia, Bual, Sercial, Verdelho and Tinta Negra), type (sweet, medium sweet, dry and medium dry), and age (from 3 to 20 years old) on Madeira wine sensorial properties. Firstly, a comprehensive and in-depth Madeira wine volatile profiling was carried out using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-qMS). Secondly, a relation among the varietal, fermentative and aging aroma compounds, and their aroma descriptors with the Madeira wine sensorial properties was assessed. A total of 82 VOCs, belonging to different chemical families were identified, namely 21 esters, 13 higher alcohols, ten terpenic compounds, nine fatty acids, seven furanic compounds, seven norisoprenoids, six lactones, four acetals, four volatile phenols and one sulphur compound. From a sensorial point of view, during the aging process the wine lost its freshness and fruitiness odor related to the presence of some varietal and fermentative compounds, whereas other descriptors such as caramel, dried fruits, spicy, toasty and woody, arose during ageing. The Maillard reaction and diffusion from the oak were the most important pathways related with these descriptors. A relationship-based approach was used to explore the impact of grape variety, wine type, and age on Madeira wine sensorial properties based on shared number of VOCs and their odors.  
wine; VOCs; potential odorants; HS-SPME; GC-qMS Article
# A 60 GS/s Two-Stage Sampler with a Linearity Calibration Loop for PAM-8 Receivers
## 3 Linearity Calibration Feedback Loop
Then, by following the calculation shown in the block diagram we have:  
\[e(k)=[G_{r}h_{e}(1)-1]u(k)+G_{nor}[a_{3}x^{3}(k)+n(k)]\otimes h_{FFE}(k) \tag{12}\]  
\[v_{1}(k)=[G_{r}h_{e}(1)-1]u^{2}(k)+u(k)G_{nor}[a_{3}x^{3}(k)+n(k)]\otimes h_{FFE }(k) \tag{13}\]  
\[v_{2}(k)=C_{E}\{[G_{r}h_{e}(1)-1]+G_{nor}[a_{3}x^{3}(k)+n(k)]\otimes h_{FFE}(k )/u(k)\}. \tag{14}\]  
Knowing that \(u(k)\) is a random signal, which can assume eight evenly spaced values between \(-1\) and \(1\), we have \(C_{E}=E\{u^{2}(k)\}=0.4286\), and after subtracting \(v_{1}(k)-v_{2}(k)\) the filtering of the signal \(w(k)\), we obtain:  
\[v_{corr}(k)\approx 0.14 Ga_{3}C_{3}^{\prime}G_{ch}^{2}. \tag{15}\]  
This allows correct calculation of the system while being independent of the gain and the residual ISI. The error signal must be divided by one of the eight PAM-8 symbols. This means we can easily implement the division with a look-up table (LUT) to obtain the reciprocal of the symbol values followed by a multiplier. This can be performed with ease because the operation is carried on in the calibration path where the loop bandwidths can be small, and latency can be tolerated. Moreover, this elaboration block complexity does not change with the increasing ISI, while the one in [8] requires a number of delays and sums that scale quadratically with the number of nonnegligible ISI components. Therefore, in the presence of a channel difficult to equalize, the system presented in this work does not show criticality. By operating on samples coming from different interleaving TH buffers and switches, the mismatches impact is averaged out. This means the loop will converge at a \(v_{corr}\) that minimizes the overall distortions, but at the cost of a reduction in linearity compared to the ideal matching case.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Sub-Satellite Points Density along Lattitude  
To design a mega-constellation in which sub-satellite points are evenly distributed along the latitude zone, an indicator that measures the distribution density of sub-satellite points must first be established. Therefore, we define the density of a constellation's sub-satellite points along latitude as the number of sub-satellite points per unit latitude zonal area of the Earth's surface. Its expression is as follows:  
\[D=\frac{n}{S} \tag{10}\]  
where \(S\) represents the area of an arbitrary latitude zone on the Earth's surface, as shown in the shadow section in Figure 1. \(n\) is the number of sub-satellite points falling within the \(S\) region.  
Before calculating the area of the S, the necessary assumptions must be given. Consider the Earth as an ideal sphere. The radius of the Earth is taken to be 6378.137 km (Earth's equatorial radius). The area of latitude range \([\delta,\delta+\Delta\delta]\). can be computed with area integral and expressed by the following equation.  
\[S=\int_{\delta}^{\delta+\Delta\delta}2\pi rR_{e}\cdot d\theta=\int_{\delta}^{ \delta+\Delta\delta}2\pi rR_{e}^{2}\cos\theta\cdot d\theta=2\pi R_{e}^{2}\cdot [\sin(\delta+\Delta\delta)-\sin\delta]. \tag{11}\]  
Suppose the Earth's surface is divided into m latitude zones. The density of the constellation's sub-satellite points on the \(k\)-th latitude zone at time \(t\) is denoted as \(D_{k,t}\)  
Figure 1: Schematic diagram of a latitude zone on the Earth’s surface.  
Taking \(\Delta t\) as the time step, then in an orbital period \(T\), the average density of the sub-satellite point at each time in the \(k\)-th latitude zone is:  
\[\overline{D}_{k}=\frac{\Delta t}{T}\sum_{i=0}^{n_{\Delta t}}D_{k,i\cdot\Delta t} \tag{12}\]  
where \(n_{\Delta t}=T/\Delta t\), indicates the sampling size in the orbital period \(T\).  
The probability density function of \(\overline{D}_{k}\) can be expressed as:  
\[f_{k}=\frac{\overline{D}_{k}}{\sum_{i=1}^{m}\overline{D}_{i}} \tag{13}\]  
Walker constellation features high symmetry and uniform satellite phases distribution on the same orbit plane, ensuring its configuration remains stable during operation. According to these characteristics, it can be reasonably deduced that for the Walker constellations, the value of \(f_{k}\) is influenced by \((a,e,\ i)\) and is independent of \((N,P,F)\).