esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Transmission Model  
The communication between vehicle and UAV is based on the IEEE 802.11p protocol, which adopts the carrier sense multiple access protocol with collision avoidance (CSMA/CA) for data transmission [56]. In the IEEE 802.11p protocol, the transmission  
Figure 2: Joint task offloading, resource allocation, and security assurance for MEC-enabled UAV-assisted VANETs.  
of computing task \(\Xi_{i}\) within a single time slot has two states: (i) successful transmission and (ii) collision and back off. More specifically, the task can be successfully transmitted when the wireless channel is idle. If two vehicles try to transmit their computing tasks simultaneously, a collision occurs. According to the Markov chain [57], we define \(S_{ij}\) to represent the probability of vehicle \(i\) transmitting the task in a certain time slot (vehicle offloads its task to UAV in a random time slot). Let \(p_{i,j}^{c}\) represent that there are at least two offloading tasks transmitted in the same time slot. Let \(p_{i,i}^{f}\) and \(p_{i,j}^{s}\) denote the probability of channel idle and the successful transmission probability between vehicle \(i\) and UAV \(j\), respectively. We can get  
\[p_{i,j}^{c}=1-p_{i,j}^{f}-p_{i,j}^{s}, \tag{1}\]  
\[p_{i,j}^{f}=\big{(}1-S_{ij}\big{)}^{\sum\limits_{i=1}^{N}x_{i,j}},\sum\limits _{i=1}^{N}x_{i,j}\leq N, \tag{2}\]  
\[p_{i,j}^{s}=S_{i,j}\big{(}1-S_{ij}\big{)}^{\left(\sum\limits_{i=1}^{N}x_{i,j} \right)-1},\sum\limits_{i=1}^{N}x_{i,j}\leq N. \tag{3}\]  
According to (1)-(3), we have  
\[S_{i,j}=\frac{2\Big{(}1-p_{i,j}^{c}\Big{)}}{\Big{(}1-p_{i,j}^{c}\Big{)}^{-1}+CW _{\min}\sum\limits_{s=1}^{b_{\max}}\Big{(}2p_{i,j}^{c}\Big{)}^{s}+CW_{\max} \sum\limits_{s=b_{\max}+1}^{b_{\max}+r_{\max}}\Big{(}p_{i,j}^{c}\Big{)}^{s}}, \tag{4}\]  
where \(b_{\max}\) represents the maximum number of times to avoid, \(r_{\max}\) represents the maximum number of retransmissions, \(CW_{\min}\) represents the minimum contention window, and \(CW_{\max}\) represents the maximum contention window.
# A Statistical Approach for Modeling Individual Vertical Walking Forces
## 5 Simulation of Human-Induced Vibrations
### Comparison with Existing Models
#### 5.4.2 Multi-Harmonic Force Model  
The multi-harmonic force model for the calculation of the structural response to a single pedestrian crossing the footbridge is proposed by [PERSON] et al. [11]. The model is based on the force spectrum of the walking force measured by [PERSON] et al. [21], who found that the human-induced walking force is not periodic but rather a random narrow band process. This means that there is a leak of energy into adjacent frequencies on either side of the fundamental pacing rate and its multiples in the amplitude spectrum of a series of continuous footsteps. Hence, the model is developed in the time domain but it is also able to represent the intra-subject variability of the walking force thanks to the characterization of the force in the frequency domain.  
The load model covers the frequency range related to the first five harmonics of the walking force. The contribution of both the five main harmonics, with frequencies as integer multiples of the step frequency, and the first five sub-harmonics, appearing between the main harmonics, is considered. The total force is the sum of the contribution of each harmonic and sub-harmonic (indicated by the superscript \(s\)):  
\[F_{Z}(t)=\sum_{n=1}^{5}F_{n}(t)+\sum_{n=1}^{5}F_{n}^{s}(t) \tag{19}\]  
The contribution of the \(n\)th harmonic to the total force is the following:  
\[F_{n}(t)=G\,\alpha_{n}\sum_{\overline{f}_{j}=n-0.25}^{n+0.25}\overline{\pi}_{ n}(\overline{f}_{j})\cos(2\pi\overline{f}_{j}f_{s}t+\Phi(\overline{f}_{j})) \tag{20}\]  
while for the \(n\)th sub-harmonic it is:  
\[F_{n}^{s}(t)=G\,\alpha_{n}^{s}\sum_{\overline{f}_{j}=n-0.75}^{n-0.25}\overline {\pi}_{n}^{s}(\overline{f}_{j})\cos(2\pi\overline{f}_{j}^{s}f_{s}t+\Phi( \overline{f}_{j}^{s})) \tag{21}\]  
In Equations (20) and (21), the product \(\overline{f}_{j}f_{s}\,[Hz]\) is a frequency line within the energy range of the analyzed (sub-)harmonic and \(\Phi(\overline{f}_{j})\,[-]\) is the phase assigned to the spectrum  
As far as the simplified model is concerned, it is observed that the walking force is a step function due to the assumption of constant footfall forces (Figure 10(a)). Moreover, as a result of the intra-subject variability, the single step forces of both the proposed and simplified models differ from each other leading to non-periodic walking forces. The same goes for the multi-harmonic force model, characterized by a non-periodic walking force such as the one shown in Figure 11(a). On the contrary, in the single step model, the gait parameters are the same for each step, resulting in the periodic walking force of Figure 11(a). Note that the force of Figure 11(a) is obtained by adding the contribution of the static weight \(G\) to the force \(F_{Z}(t)\). In Equations (19)-(21) the weight of the pedestrian is neglected, as the acceleration caused by a constant force moving at a low velocity (as for pedestrians) is negligible compared to that caused by the dynamic part of the force.
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 4 Deep Learning Models
### VGGNet  
We conducted experiments with the VGGNet model [14] as a sophisticated CNN model in contrast to the basic CNN model to compare the effectiveness of various CNN models. The VGGNet is a CNN series model developed by the Visual Geometry Group, a research team at Oxford University. It is a standard deep CNN architecture with multiple layers. We chose the VGGNet model because other models, such as the ResNet deeper than VGGNet, require more time and memory during training, resulting in practical difficulties in experiments. The VGGNet model is deeper than the basic CNN model, and the filter size is fixed as small as 3 \(\times\) 3. The idea of VGGNet is to use multiple convolution layers consisting of small-sized 3 \(\times\) 3 filters instead of a convolution layer consisting of large-sized filters. As a result, the number of parameters is reduced, which is efficient in terms of computational amount and increases the training speed. Furthermore, as the depth of the model deepens, the performance is improved because it can express high-dimensional nonlinearity. According to the number of layers, VGGNet is called VGG16 (Figure 5) if it has 16 layers (13 convolution layers + 3 fully connected layers) and VGG19 if it has  
Figure 3: LSTM AutoEncoder model [2] with best performance when processing 5 h-old time series data entering as input, and estimated fine dust value after 1–8 h is output.  
19 layers (16 convolution layers + 3 fully connected layers). The VGG research team made a total of six structures (A (11), A-LRN (11), B (13), C (16), D (16) and E (19)) and compared their performance to check the performance difference according to depth. As a result, it was confirmed that the performance improved as the depth increased from 11, 13, 16, to 19 layers. VGGNet is used to process satellite images and, in this paper, VGG16 and VGG19.  
Figure 4: Basic CNN model where number of layers does not include pooling layers. There are three convolutions and two fully connected layers, totaling five layers.  
Figure 5: VGG 16 model does not include many pooling layers, so there are 13 convolution layers and three fully connected layers, totaling 16. VGG19 has three more convolution layers than VGG16.
## 4 Discussion
Furthermore, with regard to the relationship between MPV and negative outcomes in HF patients, the results from our research support those obtained in the study conducted by [PERSON] et al. [14], which showed that increased levels of MPV were associated with worse outcomes in patients with HF, defined by cardiac death and hospitalization. In comparison to the latter study, which was a study conducted prospectively on 3289 patients, the limitations of our study are represented by our study design (retrospective study), and by the number of patients (260 patients) included in our study. Even though our research has the limitations highlighted, our results support the findings obtained in the study conducted by [PERSON] et al. [14], since our study demonstrated that increased levels of MPV were encountered in patients with HF who were rehospitalized at 3 months, 6 months, or who died at 1 year. According to the medical literature, patients with classes III NYHA HF, IV NYHA HF, or acute pulmonary edema (APE), are included in the category of decompensated HF [2]. Regarding the relationship between MPV value and the type of HF, our study revealed that, from the category of patients with decompensated HF, only patients with class IV NYHA HF and APE had higher MPV values (over 9 fl) compared to patients with class III NYHA HF, who had similar results to patients with class II NYHA HF (MPV values below 9 fl). These results are not exactly contradictory since, compared to patients with class IV NYHA HF or APE whose clinical presentation is severe, the clinical presentation of patients with class III NYHA HF is relatively similar to the clinical presentation of patients with class II NYHA HF who have minimal signs and symptoms. The strength of our study in comparison to the cited studies mentioned above is provided by the regression model analysis through which we could estimate the probability of a negative event happening (i.e., 3-month and 6-month rehospitalization, 1-year mortality, prolonged in-hospital stay), and also the probability of a patient having decompensated heart failure, based on the MPV value. Considering these results, we can state that, based on MPV value, we may be able to identify HF patients at risk of having a poor outcome, and, therefore, apply an aggressive management approach. There are several limitations to our study as follows: (1) the study was conducted in a retrospective manner, which involved data collection from the hospital's system database, and, therefore, there is the possibility of bias with respect to the selection of patients; (2) certain values of MPV could be influenced by the collection and processing of blood samples performed retrospectively, and, therefore, by human error. We intend in our future research to eliminate these limitations by conducting the research in a prospective manner and, therefore, to achieve better control of blood sample collection, and to improve the statistical strength by increasing the number of patients and studying other platelet indices as possible prognostic factors in HF patients.
# Impact of Cryogenic Condition and Drill Diameter on Drilling Performance of CFRP
###### Abstract  
Machining of carbon fiber-reinforced polymer (CFRP) is a rather hard task due to the inhomogeneity and anisotropy of this material. Several defects occur in the material when CFRP is machined and machining quality deteriorates owing to these material properties. In recent years, liquid nitrogen has been considered an environmentally safe, clean, and non-toxic coolant used to cut various materials in order to enhance machinability and prevent damage during machining. In this study, a new, eco-friendly cryogenic machining technique called dipped cryogenic machining was applied for the drilling of CFRP. This experimental study investigated the effect of feed rate and drill diameter on the thrust force, delamination factor, surface quality and drill wear. Machined surfaces were analyzed in detail using a scanning electron microscope and atomic force microscope. Results indicated that the drilling of CFRP with the dipped cryogenic machining approach greatly improved machinability by reducing the surface roughness of the drilled parts and tool wear. However, it increased the thrust force and delamination factor.  
CFRP; cryogenic; drilling; drill wear; surface quality Article
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 2 Materials and Methods  
### Screening of Cyanbacterial Strains for Toxin, Biomass and Polysaccharide Production  
#### 2.1.1 Isolation and Cultivation of Cyanobacterial Strains  
Five cyanobacterial strains were selected among those isolated from the BLCs collected at the Ruma loess section (N 45\({}^{\circ}\)00\({}^{\prime}\)43.8\({}^{\prime\prime}\), E 19\({}^{\circ}\)51\({}^{\prime}\)28.8\({}^{\prime\prime}\)) in 2006. BLCs were collected together with approximately 10 cm of sediment below them using a spatula, packed in storage bags, and transported to the laboratory where they were further used to isolate cyanobacterial strains. 1 cm\({}^{2}\) of cyanobacterial BLCs were inoculated in the BG11 N\(-\) (BG11 without NaNO\({}_{3}\)) and BG11 N\(+\) (BG11 supplemented with 1.5 g/L of NaNO\({}_{3}\)) media. Upon the initial cultivation in the liquid media, strains were isolated through cultivation on a solid (supplemented with 1.5% agar) and liquid BG11 media [69]. Separation of strains was done manually under the stereomicoscope (Motic, SMZ-168) by separating and transferring singular colonies and/or filaments from the solid to the liquid medium. Isolation and separation processes were repeated until the monocultures of cyanobacteria were obtained. Strain determination to the genus level was performed on an Olympus BX 51 microscope with the support of recognized determination keys for cyanobacterial species [70; 71; 72].  
Isolated cyanobacterial strains were cultivated in triplicate in batch cultures using the BG11 N\(+\) and the BG11 N\(-\) medium and 1 mL inoculum per 100 mL medium. The strains were cultivated under room temperature, without mechanical shaking but shaken manually on a daily basis and illuminated for 14 h a day. White cool fluorescent tubes provided a light intensity of 720 lux.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 5 Experiments
### QUIC Traffic Classification

Table 6: Results on the test set with 50 packets as input. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **10-Fold Cross-Validation Acc.** & **Training Time (s)** \\ \hline RF & 89.83 & 89.60 & 89.54 & 89.60 & 92.37 & 16.86 \\ \hline ET & 84.55 & 84.40 & 84.22 & 84.40 & 88.86 & 7.06 \\ \hline GBT & 90.92 & 90.60 & 90.55 & 90.60 & 92.41 & 239.78 \\ \hline XGBT & 93.83 & 93.60 & 93.58 & 93.60 & 94.30 & 149.52 \\ \hline LGBM & 94.03 & 93.80 & 93.77 & **93.80** & **94.80** & 49.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results on the test set with 60 packets as input. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **10-Fold Cross-Validation Acc.** & **Training Time (s)** \\ \hline RF & 91.06 & 90.60 & 90.57 & 90.60 & 93.11 & 18.35 \\ \hline ET & 89.92 & 89.60 & 89.56 & 89.60 & 91.06 & 5.88 \\ \hline GBT & 91.69 & 91.00 & 90.98 & 91.00 & 93.25 & 317.11 \\ \hline XGBT & 93.32 & 92.80 & 92.79 & 92.80 & 94.80 & 174.21 \\ \hline LGBM & 93.50 & 93.20 & 93.16 & **93.20** & **95.05** & 58.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results on the test set with 80 packets as input.
## 3 Results and Discussion  
### Phase Composition after TMT  
X-ray diffractograms of all the alloys after TMT are shown in Figure 2. In all cases, the main phase constituent is the BCC \(\beta\)-phase. In the 18-15 alloy after 3.0 + 600 (30) and in the 41-12 alloy after 1.5 + 600 (30), 1.5 + 800 (30), and 3.0 + 550 (5), a weak peak at (130)\(\alpha^{\prime\prime}\) is visible (Figure 2i,l,o,d). Since it is only one peak and it does not have a systematic appearance, it can be attributed to traces that were not completely removed by etching the \(\alpha^{\prime\prime}\)-martensite, which were strain-induced upon mechanical polishing. Weak peaks of the \(\alpha\)-phase are observed in the 22-6 and 18-15 alloys after severe CR and PDA at 550 \({}^{\circ}\)C for 5 min: there is a weak [110]\({}_{\alpha}\) peak in the first alloy and clearly visible [100]\({}_{\alpha}\), [101]\({}_{\alpha}\), [102]\({}_{\alpha}\), [110]\({}_{\alpha}\), and [112]\({}_{\alpha}\) peaks in the second one (Figure 2m,n). This could be a result of the short-term and low-temperature annealing of the well-developed nanocrystalline structure formed as a result of severe plastic deformation. Observations confirming an increase in temperature and/or changes in the kinetics of the \(\beta\rightarrow\alpha\) transformation in Ti-18 Zr-(14-15)Nb alloys subjected to severe CR were previously reported in [25]. Furthermore, significant changes in the ratios of \(\beta\)-phase line intensities can be observed due to the high variability of their crystallographic textures, necessitating additional attention.  
To evaluate the influence of the TMT on the crystal lattice structure of the \(\beta\)-phase, the lattice parameter \(a_{\beta}\) is calculated from each obtained X-ray diffractogram and presented in Figure 3a. It can be seen that the obtained \(a_{\beta}\) values are typical for each alloy and do not depend on the TMT within each alloy composition. The full width at half maximum (FWHM) (\(B_{\mathrm{h}\mathrm{f}}\)) is measured for the \([211]_{\beta}\) X-ray line since it is the only line where the profile is clearly visible after each TMT. Figure 3b shows that the \(B_{211}\) values for all three alloys, after similar TMTs, are very close within the error limits. After \(0.3+600\) (30) TMT, all alloys exhibit a systematic increase in \(B_{211}\). This could be a result of incomplete recrystallization when compared to \(0.3+800\) (30), which leads to complete recrystallization. The transition to intermediate CR does not increase the \(B_{211}\) value after PDA at temperatures of 600 or 800 \({}^{\circ}\)C. This can be explained by the earlier start of the recrystallization processes after higher CR strain due to higher accumulated strain energy. The transition to severe plastic deformation results in broadening of the \(\{211\}_{\beta}\) peak, which is most pronounced after \(3.0+550\) (5) and \(3.0+600\) (5). Such low annealing temperatures and short times can result in the formation of ultrafine-grained structures which in turn increase the FWHM [25].
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
## 3 Results and Discussion
### Response of Terpene Compounds to UV-attenuated Treatments
#### 3.2.2 Effects on the Concentrations of Free Norisoprenoid Compounds  
Overall, UV attenuation has limited impacts on the concentration of norisoprenoids, where most changes were quite small despite significance with clear vintage variations (Tables S2 and S3). A dramatical increase of \(\beta\)-damascenone was observed in the TW PETG treatment compared to control in vintage 2016, but a significant reduction was recorded in the TW PC treatment (Figure 3B). No significant differences were observed among three treatment groups in vintage 2017. Significant influences of UV attenuation time were observed, where the TI treatment from veraison to intermediate ripeness reduced the concentration of \(\beta\)-damascenone compared to treatments at other phenological stages (Figure 4).  
A previous study suggested that UV radiation could increase the concentration of \(\beta\)-damascenone as a result of carotenoid degradation [34], whereas UV attenuation during grape ripening could reduce the concentration of both free and bound norisoprenoids at harvest [33; 35]. However, other researchers reported no statistically significant differences in norisoprenoids among different UV treatments [16; 36]. Similar to monoterpenes, the concentration of free norisoprenoids at harvest may rely on their biosynthesis and the conversion between the free and bound forms. \(\beta\)-Damascenone is biosynthesis via the apo-caroenoid pathway and influenced by the _VvCCD1_ gene, which could stably express from veraison to harvest in Shiraz [37]. Therefore, this compound could be detected at most phenological stages during grape ripening [26; 38]. However, the influences of light and UV radiation on the expression of _VvCCD1_ and other carotenoid degradation genes remain inconclusive [36], thus the mechanism of UV impacts on norisoprenoid biosynthesis requires further investigation. Although some monoterpene-specific \(\beta\)-D-glucosyltransferases have been identified, it remains unclear which specific \(\beta\)-D-glucosyltransferases could be responsible for the conversion of free norisoprenoidsinto bound forms [39]. Nevertheless, the reduced free \(\beta\)-damascenone in UV-shaded grape from veraison to intermediate ripeness observed in the current study indicates a possible downregulation of carotenoid degradation genes or upregulation of relevant \(\beta\)-D-glucosyltransferasearises during this period, which requires further investigation.
## 4 Classification Based on Taxonomy and Design Drivers
### Multi-Replication  
From GHT on [1], illustrated in Section 3.1, a home node is called a hotspot if too many events with the its key are detected. To address this issue the authors have extended GHT by employing Structured Replication (SR) referred to as SR-GHT. SR-GHT introduces a hierarchical scheme where one node can have \(4^{4}-1\) mirror images of the root home node. The hierarchy depth is denoted by \(d\). As shown in Figure 6, a decomposition of two levels (\(d=2\)) is deployed having \(4^{2}-1\) (\(15\)) mirror images at different levels. A producer node can store the detected event to its closest mirror node reducing the storage cost to \(O\left(\sqrt{n}\Big{/}\! which then forwards it to all mirror images in level 1 and then level 1 nodes forward this query to level 2 mirror images. This again increases the retrieval cost to \(O(2^{d}\sqrt{n})\) from \(O\left(\sqrt{n}/2^{d}\right)\). Table 3 shows a summary of multi-replication DCS schemes based on their functionalities. In SDS [19], Section 3.2, the head node keeps a copy of all data to be replicated, which may be unrealistic due to storage space availability, and if any head node fails, then data cannot be recovered from the head node. ToW [27] also replicates the root node into \(4^{\text{d}}-1\) mirror images like GHT but the authors used a combing routing protocol, taking advantage of the grid structure (see Figure 6) to create a shorter replication tree for transferring messages among replica nodes. Like GHT and ToW, QAR also proposes replication of the home node but in a more adaptable quadratic evolution \(N_{r}=d^{2}\). QAR also provides an analytical model defining the optimal number of replicas (\(N_{r}^{*}\)), thus minimizing the