esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In terms of physicochemical parameters, meat colour is principally affected by myoglobin, which can be found in three different states (deoxymyoglobin, metmyoglobin and oxymyoglobin) and in distinct concentrations, depending on the heat treatment applied [12]. Also both water holding capacity and texture profile can be affected due to modifications in the structure of proteins, mainly myofibrillar [13] and the connective tissues due to changes caused by cooking. Moreover, other chemical reactions could also influence the appearance, flavour and nutrient contents of final cooked meat, transforming its quality and its consumer's acceptability [13]. In this regard, the profile of volatile compounds is principally affected by the lipid oxidation routes, since these generate a large amount of desirable aromatic compounds, which are the most important source for taste and odour formation [9]. Additionally, the products derived from the Maillard's reaction are also important in the meat flavour [14]. All the reactions explained above occur differently depending on the cooking method used; that is to say, they depend on the temperature reached by meat sample and on the time employed for the treatment [15]. Thus, different common cooking methods such as roasting, grilling, frying, and microwaving will have different effects on the nutritional, physicochemical parameters and volatile profile of the cooked meat. On this matter, several studies have been accomplished. [PERSON] et al. [9] observed that grilled pig steaks were the least affected by lipid oxidation with respect to roasted, frying and microwaved pig fillets. In addition, [PERSON] et al. [15] displayed that cooking losses were significantly affected by heat treatment in foal meat, being higher after microwaving and lower after grilling. At the same time, thiobarbituric acid reactive substances (TBARS) value was significantly higher when foal steaks were roasted or microwaved. A similar trend was observed by [PERSON] and [PERSON] [14] in the same type of meat. Besides, [PERSON] et al. [11] determined that frying provided a lower content of saturated fatty acids, due to the incorporation of monounsaturated fatty acids from frying oil compared to other thermal treatments (roasted, grilled and microwaved). Finally, several works determined that the generation of volatile compounds also seemed to be directly related to cooking treatment [11; 14; 16]. Therefore, the aim of the current work was to know the effect of both, the inclusion of chestnuts (_Castanea sativa_ Mill.) in the finishing diet of Celta pigs and the use of four common cooking techniques (roasted, grilled, fried and microwaved) on chemical composition, colour parameters, cooking loss, lipid oxidation, shear force and volatile compounds of pork _Biceps femoris_ slices.
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Multiple Methods to Build Models Based on Vegetation Index
#### 3.3.5 Random Forest Method  
Using the Python programming language in PyCharm, the Random Forest method was employed for modeling. After importing necessary libraries such as numpy, pandas, matplotlib and sklearn, the experimental data was imported. The model was established with two variables, yield and vegetation indices. The data was sliced, with 70% allocated for training and 30% for testing. A Random Forest regressor was employed to build the regression model, and various evaluation parameters were set. Once the regression model was constructed, the predicted values of yield were compared to the actual values through a linear regression analysis, and a scatter plot was generated to visualize the relationship. Various evaluation parameters were also generated to assess the model's accuracy. The final result demonstrated that the model achieved a validation accuracy of 0.935 and K-fold cross-validation accuracy of 0.897, indicating a highly accurate and effective model.
# Production of Cellulosic Ethanol from Enzymatically Hydrolysed Wheat Straws
## 3 Results and Discussions
### Determination of Organic Acids  
The presence and amount of organic acids depend on the nature of the material and the conditions of the solvent pretreatment. The significant parameter that improves the formation of organic acids are the pH with high values and acid pretreatment which imply the risk of production of furaldehydes (FF and HMF) and aliphatic acids (formic and acetic acid) [62]. [PERSON] et al. (2010) founded 3300 mg/L acetic acid, which are 10-15 times higher concentrations in comparison with other acids [63]. Also, [PERSON] et al. (2010) identified a concentration of 1.7 g/L acetic acid in liquid (prehydrolysate) fractions in steam-pretreated (temp. 190 \({}^{\circ}\)C for 10 min) WS slurry [64] compared to that of [PERSON] et al.'s (2008) who obtained 0.04-1.01 g/L acetic acid (temp. 190-210 \({}^{\circ}\)C, residence time 2-10 min, sulfuric acid 0.2%) [65]. In the study conducted by [PERSON] (2015), the highest concentration for acetic acid and formic acid was 11.04 g/L and 6.08 g/L, respectively [66]. [PERSON] et al. (2014) pretreated WS at 140 \({}^{\circ}\)C with 10 dm\({}^{3}\)/m\({}^{3}\) H\({}_{2}\)SO\({}_{4}\) concentration for 30 min and have obtained concentrations of formic acid and acetic acid of 32.37 \(\pm\) 4.91, 7.98 \(\pm\) 1.02 g/kg, respectively [67]. In another study [PERSON] et al. (2014) pretreated rice straws at 220 \({}^{\circ}\)C for 52 min, pH 7.0 and have obtained concentrations of formic acid and acetic acid of 6.32 \(\pm\) 1.46 and 8.45 \(\pm\) 0.59 g/L, respectively [68].  
In this study, organic acids from WS samples pretreated with H\({}_{2}\)SO\({}_{4}\) of different concentrations (1, 2 and 3% (_v/v_)) were analysed and observed that the highest content of acetic acid (0.94 \(\pm\) 0.01 mg/g) was founded at the correlation between 3% H\({}_{2}\)SO\({}_{4}\) (_v/v_) and 1/30 ratio, but the lowest values of acetic acid (0.40 \(\pm\) 0.02 mg/g) was identified at the pretreatment with 1% H\({}_{2}\)SO\({}_{4}\) (_v/v_) and 1/30 ratio. The content of organic acids are shown in Table 5 (the chromatograms obtained for standards and one of the samples can be found in Supplementary Materials).  
The lowest value of gluconic acid was obtained at 1% (_v/v_) H\({}_{2}\)SO\({}_{4}\), s/l ratio 1/20--13.77 \(\pm\) 0.05 mg/g, but highest value at 3% (_v/v_) H\({}_{2}\)SO\({}_{4}\), s/l ratio 1/30--22.59 \(\pm\) 0.05 mg/g. Gluconic acid is present in various plants, fruits, wine and honey. The obtained concentration of gluconic acid can be explained by the oxidation of aldehyde group (C1) of D-glucose to a carboxyl group [69; 70].
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 7 Evaluation of IS Weights  
We here report the results considering the IS weight sets investigated during the grid search procedure. The aim of this section is to evaluate the best setup in order to maximize the IS value and generate retweets cascades with high CAS. This step of the analysis is common for both ISN and ISN-AF models which will be evaluated in Section 8. Hence, this preliminary analysis was conducted without considering friend networks, in order to remove external factors in the weights evaluation.  
Before investigating the properties of the proposed approach, we aimed to analyze the relations, in terms of correlation, among the considered metrics. Figure 3 shows that the CAS values, which represent the average cascade strength, are positively correlated with the RFR. In fact, the higher the concentration of nodes around the \(root_{author}\), the higher the IS between each node and the \(root\). Interestingly, there is a high negative correlation with the depth variable, which corresponds to the number of levels in the cascade tree. In fact, Figure 3 shows that the CAS is strongly negatively influenced by the depth, whereas the node count variable has a smaller incidence. It follows that even big cascades with few levels might achieve high CAS scores. At the same time, the higher the GRC, the smaller the CAS. Recalling that the GRC provides an average measure of all cascades' nodes centrality, when the nodes in the cascade are central and might be reached by several paths, the strength of the cascade decreases since multiple edges exist among users, and thus the selected relations (maximum IS criterion) are weaker.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
###### Abstract
Our algorithm assumes that it can predict their destination and demand traffic amount to be delivered to the nearest edge server in advance of their arrival. * The validity of the proposed scheme is elucidated through a sophisticated computer simulation, which accurately modeled the user mobility and association/handover to macro/small cells. The rest of this paper is organized as follows. Section 2 describes the system architecture of mmWave Edge Cloud with its network topology and newly developed user mobility and traffic models to describe realistic scenarios. Section 3 proposes a prefetching algorithm \begin{table}
\begin{tabular}{c l l} \hline \hline
**Year** & **Authors** & **Key Contribution** \\ \hline
1998 & [PERSON] et al. [11] & Web **content prefetching** based on access probability \\ \hline
2002 & [PERSON] et al. [15] & Adaptive modulation and coding for access probability-based web **content prefetching** \\ \hline
2012 & [PERSON] et al. [16] & Web **content prefetching** and acceleration for extremely large \\ \hline
2016 & [PERSON] et al. [22] & Delay-based macro/small-cell association under **limited backhaul** \\ \hline
2019 & [PERSON] et al. [25] & Joint optimization of **content prefetching** in microwave-band **ultra-dense small-cell network with **limited backhaul** \\ \hline
2020 & [PERSON] et al. [23] & 4G-based machine-type communication performance on **limited backhaul** \\ \hline
2020 & [PERSON] et al. [19] & DASH **content prefetching** by machine learning-aided prediction of user association and video segment \\ \hline
2021 & [PERSON] et al. [14] & Predictive web **content prefetching** utilizing user interaction \\ \hline
2022 & [PERSON] et al. [26] & Mobility-aware distributed proactive **content prefetching** strategy for **vehicular network** \\ \hline
2022 & [PERSON] et al. (This Work) & MEC aided **content prefetching** to fully utilize **mmWave** Capacity **with **limited backhaul** in macro/small-cell heterogeneous networks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contributions to content prefetching and cellular network under constrained backhaul. to enhance both the user-experienced data rate and latency. Numerical evaluation results are presented in Section 4 to confirm effectiveness of the proposed mmWave Edge Cloud with prefetching algorithm. Finally, Section 5 concludes this paper.
## 4 Results
### Wound Segmentation
#### 4.1.2 Hyperparameters Grid-Search  
The results for the hyperparameter combinations across the five validation sets for the three experimented number of epochs are shown in Table 2. This table shows the variation in the model's performance for the different parameters. There is an increase of the mean IoU along with the number of epochs and a slight increase of the metric from 40 to 50 epochs, with a stabilization around the gap between 50 and 60 epochs, where the values settled.  
Regarding the optimizer, it is clear that the adaptive moment estimation (Adam) optimizer performs better for every experiment with the same parameters than stochastic gradient descent (SGD). The optimizer's performance does not alter with the increase in the number of epochs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **MobileNet-SegNet** & **MobileNet-Unet** & **ResNet50-SegNet** & **ResNet50-Unet** \\ \hline Mean IoU\% & \(77.48\pm 0.61\) & \(81.03\pm 0.17\) & \(55.43\pm 0.16\) & \(44.65\pm 0.73\) \\ Dice\% & \(87.17\pm 0.41\) & \(89.51\pm 0.11\) & \(71.27\pm 0.13\) & \(61.40\pm 0.65\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the DL segmentation models for the evaluation metrics, mean IoU, and mean Dice coefficient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Epochs** & **Optimizer** & **Batch Size** & **Mean IoU\%** \\ \hline \hline \multirow{4}{*}{40} & \multirow{4}{*}{[PERSON]} & 16 & \(81.3\pm 0.19\) \\  & & 32 & \(80.7\pm 0.14\) \\  & & 64 & \(80.0\pm 0.09\) \\ \cline{2-4}  & & 16 & \(76.8\pm 0.08\) \\  & SGD & 32 & \(73.6\pm 0.11\) \\  & & 64 & \(69.4\pm 1.06\) \\ \hline \multirow{4}{*}{50} & \multirow{4}{*}{[PERSON]} & 16 & \(82.1\pm 0.13\) \\  & & **32** & \(82.5\pm 0.04\) \\  & & 64 & \(80.9\pm 0.40\) \\ \cline{2-4}  & & 16 & \(77.8\pm 0.10\) \\  & SGD & 32 & \(74.8\pm 0.09\) \\  & & 64 & \(71.1\pm 0.12\) \\ \hline \multirow{4}{*}{60} & \multirow{4}{*}{Adam} & 16 & \(80.8\pm 0.31\) \\  & & 32 & \(81.7\pm 0.20\) \\ \cline{1-1}  & & 64 & \(82.1\pm 0.17\) \\ \cline{1-1} \cline{2-4}  & & 16 & \(78.0\pm 0.07\) \\ \cline{1-1}  & SGD & 32 & \(75.5\pm 0.10\) \\ \cline{1-1}  & & 64 & \(72.5\pm 0.11\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the MobileNet-Unet performance with the various hyperparameters combinations for 40, 50, and 60 epochs.  
Regarding the batch size for the SGD optimizer, there is a minor reduction in the mean IoU when the batch size increases. However, the same is not reported for the Adam optimizer, in which the score increases almost every time with a 32 batch size and decreases with 64. Nonetheless, with 60 epochs and the Adam optimizer, the score improves with the gradual increase of batches.
## 3 Results and Discussion
When comparing Test 7 and Test 8 in Table 4, we found that methane sulfonic acid (MSA) could protect UBM metals, but the solution's ability to remove contaminants was significantly reduced (possibly because without dissociation of ammonium bifluoride, silicon dioxide and polysilicic acid cannot be dissolved) [31]. Moreover, it was found that MSA did dissolve the dicing glue and ink marks, as expected (Test 7 in Table 4). Table 4 lists a summary of the tests performed on the alkaline formula (Test 1), solvent base formula (Test 2), and formula with surfactant additive (Test 5), in this study of acidic formulas for cleaning agent. When comparing the results of Test 3 and Test 8 in Table 4, we found that MSA could reduce the corrosion of UBM (Ti/TiW) during cleaning (Figure 8). This result means MSA may absorb on under-bump metal (Ti/TiW) surfaces during the cleaning process and prevent H\({}^{+}\) and F\({}^{-}\) ions from penetrating and corroding the UBM. This result may be applied to the development of a UBM etchant. Figure 7: Bump peeled off (at the red arrow) after 15 min of applying the cleaning agent in Test 3 of Table 4, with UBM being etched completely by such acidic formula. The results of Test 9 in Table 4 suggested the MSA concentration could prevent ink mark attack and lower ammonium bifluoride concentration would lead to lower under-bump metal (UBM) under cut.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm
### Extended Kalman Filter
#### 3.2.4 Interaction with the Pre-Processing Measurement Block  
Before each EKF operation, the EKF needs to adjust the state variables and covariance matrices based on TOA and carrier phase measurements.  
If at time \(t=t_{k}\), it is detected that there is a cycle slip for the phase measurements from \(i\)-th cell, the corresponding state, and covariance of the cell need to be reset. \(N_{ru}^{ij}\) can be reset based on the TDOA measurement \(\hat{T}_{r}^{ij}\) and the SD carrier phase measurement \(\Phi_{r}^{ij}\). The diagonal element \(\mathbf{P}(k|k)\) corresponding to \(N_{ru}^{ij}\) will be set based on the maximum assumed integer ambiguities measurement error.  
Suppose at time \(t=t_{k}\), the measurements associated with an existing \(i\)-th cell are no longer available. In that case, the corresponding state \(N_{ru}^{ij}\) needs to be removed from EKF, and so the elements of covariance matrix \(\mathbf{P}(k|k)\) related the \(N_{ru}^{ij}\). The dimension of the EKF will be reduced correspondingly.  
If \(t=t_{k}\), the measurements associated with a new cell are available, the EKF will add a new state of integer ambiguity for that cell. The corresponding state of the cell is calculated based on the TDOA measurement \(\hat{T}_{r}^{ij}\) and the SD carrier phase measurement \(\Phi_{r}^{ij}\). The diagonal element of \(\mathbf{P}(k|k)\) corresponding to \(N_{ru}^{ij}\) will be set based on the maximum assumed integer ambiguities measurement error.  
Figure 2 shows the signal processing diagram for the real-time kinematic positioning based on TOA and carrier phase measurements.
## 3 Results
### Application to UPHES Management
As for \(n_{batch}=8\) (Figure 6), the difference between methods become cleaner, mic-\(q\)-EGO still provides better profit than contestant approaches or equivalent with half of the simulations, and in particular, better than KB-\(q\)-EGO which indicates that relying on a complementary AF efficiently improves the AP for larger batch sizes. However, the difference in final profit and standard deviation becomes smaller between methods, and no statistical difference can be observed in favor of a better average profit for mic-\(q\)-EGO against all others. Finally, Figure 7 clearly illustrates different behaviors of the investigated algorithm when increasing the batch size to 16. While BSP-EGO and MC-based \(q\)-EGO have similar performances, the three remaining have not. Classical KB-\(q\)-EGO is again on average less efficient than its multi-criteria counterpart, and both of them are significantly (\(p<0.1\)) different from TuRBO. Looking at the final objective values in Table 7, we observe an improvement of the final average and worst-case profit, along with the increase of the batch size up to \(n_{batch}=4\). Nevertheless, similar values are observed for \(n_{batch}=8\) compared to \(n_{batch}=4\), despite the larger batch size and higher number of simulations. Additionally, regarding results with \(n_{batch}=16\), we observe a clear deterioration of the final outcomes except for TuRBO results which remain equivalent to \(n_{batch}=8\). The breaking point defined in the benchmark analysis is clearly visible also for the UPHES application. Except for TuRBO, \(n_{batch}=4\) seems to be the best compromise between speed of execution and quality of the candidates provided by the AP. Either by improving the average profit, the minimum value of profit, or reducing variance, TuRBO benefits from a larger batch size. Even though TuRBO is the only method managing to improve the quality of the final outcomes when increasing the batch size, it does not compete with mic-\(q\)-EGO (\(n_{batch}=4\)) at any batch size. To put these observations into perspective, consider Figure 9a,b, which shows the number of cycles (resp. simulations) performed in the dedicated time as a function of the batch size. The latter indicates how efficiently the algorithms use parallel processing capabilities. In accordance with the observations of the benchmark section, all algorithms reach a breaking point between \(n_{batch}=8\) and \(n_{batch}=16\), which results in a clear loss in the final profits (see Table 7). More precisely, looking at Figure 9b, we can see that the number of cycles for \(n_{batch}=1\) or \(n_{batch}=2\) is equivalent, and close enough to the maximum of 120 in both cases. It must be noted that for those batch sizes, TuRBO achieves almost 110 cycles and is the fastest method of our test bed. It can be observed as well in Figures 3 and 4 where the TuRBO curve continues beyond other curves. The quickness of TuRBO compared to MC-based \(q\)-EGO, which are using the same inner optimization, can be explained by the smaller search space: TuRBO focuses on a trust region.
## 4 Monetary Analysis  
The following mathematical formulas given by [28,29] were used to assess the monetary analytical modelling, i.e., equations from (1)-(9).  
\[\text{CRF (Capital recovery }\cos\text{t}=\frac{\text{i}(1+\text{i})^{y}}{[(1+ \text{i})^{y}-1]} \tag{1}\]  
The following assumptions have been made:  
Life of solar still (y) = 10 Years  
Interest rate (i) = 12%  
Number of sunny days (n) = 250  
\[\text{FAC (Fixed annual }\cos\text{t})=\text{P(Capital }\cos\text{t})\times\text{CRF} \tag{2}\]  
\[\text{S (Salvage value)}=0.2\times\text{ P} \tag{3}\]  
\[\text{SFF (Sinking fund factor )}=\frac{\text{i}}{[(1+\text{i})^{y}-1]} \tag{4}\]  
\[\text{ASV (Annual salvage value })=\text{SFF }\times\text{ S} \tag{5}\]  
\[\text{AMC (Annual maintenance operational }\cos\text{t})=0.15\times\text{ FAC} \tag{6}\]  
\[\text{AC (Annual }\cos\text{t})=\text{FAC}+\text{AMC}-\text{ASV} \tag{7}\]  
\[\text{M (Average annual productivity in liters )}=\text{c }\times\text{ n} \tag{8}\]  
where  
n = sunny days per year  
c = yield/day  
\[\text{CPL (Distilled water }\cos\text{t per liter )}=\frac{\text{AC}}{\text{M}} \tag{9}\]  
The cost of fabricating the CSS and the MSS, which includes all of the materials needed for production which are listed in Table 2, was USD 59 and USD 61, respectively.  
Figure 6: Variation in cthe cumulative yield.  
The financial analysis parameters included in the mathematical modelling are listed in Table 3. Due to its higher daily distillate output (934 mL/day) than the CSS, the MSS greatly increased its yearly productivity by 36.25%, further demonstrating its superiority in terms of productivity. The CSS and MSS's respective CPLs were USD 0.0665 and USD 0.0503. The MSS's CPL was 32.21% smaller than that of its equivalent CSS. As a result, the MSS became affordable in the perspective of the CPL.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results
### Ebro River--(Zaragoza) Spain 2015  
The CEMS system was activated for this event on 2 February 2015 [62]. For this case study, we considered a flood delineation map generated by analyzing a Sentinel 1 image acquired on 3 February 2015. The flood map was produced by CEMS using a semi-automatic approach, also considering visual interpretation, with an estimated geometric accuracy (RMSE) of 15 m and a thematic accuracy of 85% [62]. The SAR-RST-FLOOD map was produced using the same Sentinel 1 image (Figure 6).  
The user accuracy reached 0.76, Paf reached 0.98, and the Kappa index reached 0.84 (Table 5). This notable accuracy is related to the similarity between the maps (Figure 6), and both were corrected topographically. In addition, in this case, the visual analysis of the two images shows some discrepancies between the two outputs, which cannot be better investigated in the absence of a ground truth.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Event 2: Italy Inundation 3 October 2020** & \multicolumn{2}{c}{**Reference Data**} & \multicolumn{4}{c}{**Accuracy Matrix**} \\ \hline  & Flood & Not Flooded & UA\({}_{\text{f}}\) & UA\({}_{\text{nf}}\) & PA\({}_{\text{f}}\) & PA\({}_{\text{nf}}\) & K \\ RST-SAR-FLOOD & Flood & 340,879 & 148,082 & 0.58 & 0.95 & 0.96 & 0.97 & 0.68 \\ Not Flooded & 23,669 & 4,672,132 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for Sesia River event (3 October 2020).  
Figure 5: Flood map of the Sesia River event (3 October 2020).