esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### General Procedure for Ethylbenzene Oxidation  
The oxidation reactions were performed in a gasometric apparatus (Figure S17), as described in [45]. Ethylbenzene, AIBN, and the appropriate catalyst were placed in a 10 mL flask connected to a gas burette filled with dioxygen under atmospheric pressure. The oxygen uptake (nO2) was measured and recalculated for normal conditions (273 K, 1 atm), and this value was used to calculate the total ethylbenzene conversion (\(\alpha\)) using the equations given below:  
\[n_{O_{2}}=\frac{V_{O_{2}}\cdot 273\cdot p}{101325\cdot 22.415\cdot T}\ (\text{mol}) \tag{1}\]  
\[\alpha=\frac{n_{O_{2}}}{n}\cdot 100\% \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & **Loding** & **Amount of** & **TA** & **EaN** & **Dioxane** & **NH\_OH HCI** & **Pyridine: C\_H\_ClCl** & **Imobilized** & **Amount of** \\  & **(mmol C/g)** & **resin (g)** & **(mmol)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **NHPI** & **immobilized NHPI (g)** \\ \hline PS-Cl-1 & 5.5 & 2 & 44 & 110 & 60 & 110 & 220 & PS-NHPI-1 & 3.34 \\ PS-Cl-2 & 2.5-4.0 & 2 & 32 & 80 & 60 & 80 & 160 & PS-NHPI-2 & 3.42 \\ \hline PS-Cl-3 & 3.5-4.5 & 2 & 36 & 90 & 60 & 135 & 270 & PS-NHPI-3 & 4.68 \\ \hline PS-Cl-4 & 3.5-4.5 & 2 & 36 & 90 & 60 & 90 & 180 & PS-NHPI-4 & 3.10 \\ PS-Cl-5 & 2.0-2.5 & 2 & 20 & 50 & 60 & 50 & 100 & PS-NHPI-5 & 1.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantities of reagents used in NHPI immobilization on chloromethyl polystyrene resins.  
The quantity of ethylbenzene hydroperoxide (EBOOH) produced was determined iodometrically according to the method described in [46], and the result was used to calculate the reaction selectivity (S\({}_{\text{EBOOH}}\)) using the equation below:  
\[S_{EBOOH}=\frac{n_{OOH}}{n_{O_{2}}}.100\% \tag{3}\]  
EBOOH is thermally unstable and easily decomposes to 1-phenylethylanol (PEOH) and/or acetophenone (AP). Thus, before GC analysis, the hydroperoxide was quantitatively reduced to PEOH by adding triethyl phosphite ((EtO)\({}_{3}\)P), which gets oxidized to triethyl phosphate ((EtO)\({}_{3}\)PO) [47]. The quantity of PEOH measured by GC was equal to the sum of the alcohol and hydroperoxide formed. In order to calculate the PEOH selectivity, we simply subtracted the amount of ethylbenzene hydroperoxide that was determined independently by iodometric analysis. The quantity and selectivity related to acetophenone were determined based on analogous GC analysis.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 7 Conclusions  
This work introduces the problem of separate waste collection in smart cities, where it is assumed that each station contains a limited number of bins; each bin is specialized for a single type of waste. The uncertainty is considered regarding the amount of waste in each container at each station by installing sensors capable of real-time reading and transmitting the stations' fill levels. This makes it possible to consider the problem as a multi-compartment vehicle routing problem to collect only the stations having at least one more attractive bin, which improves the efficiency of operations. For that, we proposed a mathematical model whose objective is to minimize total transport costs, including crossed distance and penalties caused by bin overflowing. To validate our model we tested the model using the CPLEX Optimizer on a small example, since the problem was a very complicated one.  
We have proposed a static M-CVRP resolution approach based on genetic algorithm hybridization. Afterward, we adapted this approach to solve the M-CVRP in the dynamic case; in the latter, during the execution of the planned route, we integrated new stations triggering the alarm for at least one waste type.  
To validate our approaches, we used a real dataset from Valorsul with some modifications to adapt these data to our problem, where we dedicated to the collection of two types of recyclable waste Paper/Cardboard (PC) and Plastic/Metal (PM) using multi-compartment vehicles. We were able to ensure the effectiveness of our approach based on the results of the static case, which are very encouraging. Moreover, in the dynamic case, we used the definition of the degree of dynamism with such significant results established.  
Conceptualization, Y.B. and A.E.A.; methodology, Y.B. and A.E.A.; software, Y.B.; validation, Y.B. and A.E.A.; formal analysis, Y.B. and A.E.A.; investigation, Y.B. and A.E.A.; resources, Y.B.; data curation, Y.B.; writing--original draft preparation, Y.B.; writing--review and editing, Y.B.; visualization, Y.B. and A.E.A.; supervision, A.E.A.; project administration, A.E.A.; funding acquisition, Y.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 2 Materials and Methods
### Setting up the GNU Radio Environment  
The experiments were performed using GNU Radio Companion (GRC), a graphical tool for designing and executing signal processing flowsheets. This environment is essential for the modular and interactive development of signal processing functionalities, especially when dealing with complex systems such as OFDM with channel aggregation. The GNU Radio flowgraphs were developed with reference to Figures 1 and 2, which illustrate the transmitter and receiver, respectively.
## 4 Discussion
### The Synergistic Effects of Other Factors on the GPP-SIF Relationship Under Drought Stress
At around midday, plants often partially or completely close their stomata to minimize or halt stomatal transpiration, thereby preventing water loss [19]. Stomata regulate the carbon assimilation process of photosynthesis by controlling CO\({}_{2}\) exchange, while the light reaction is less sensitive to stomatal regulation. Under drought stress, a decrease in canopy conductance (gc) will lead to a reduction in the CO\({}_{2}\) partial pressure parameter in the chloroplast stroma (Cc) and q\({}_{\rm L}\), thereby resulting in a weakening reduction in the linear relationship between GPP and SIF (GPP/SIF) [16]. We did not specifically investigate the influence of gc on the relationship between GPP and SIF. However, gc is mainly regulated by VPD, which explains the crucial role of VPD as a drought synergistic effect. Soil moisture was considered an essential factor in controlling photosynthesis and had direct or indirect effects on SIF and GPP (Figure 7). However, the results of the path analysis revealed that the effect of SWC on the relationship between GPP and SIF was weak (Figure 7), a finding that may have been related to the thin soil layer of the plantation examined. Our correlation analysis showed that the PWSI exhibited a strong correlation with both SWC and VPD, with the PWSI-VPD relationship being stronger (Figure S3). The path analysis showed that VPD had a greater direct effect than SWC on the PWSI (Figure 7). VPD describes the intense evaporative effect of the atmosphere and promotes drought occurrence. The results of the random forest analysis showed that VPD could explain more than 40% of the variation in K and R\({}^{2}\) (Figure 6). Based on the results, we consider VPD the key factor with respect to its synergistic effect in terms of drought stress. We attempted to incorporate VPD into the GPP-SIF relationship to offset the weakening effect of drought stress and improve the accuracy of SIF in capturing GPP under drought stress for the specific introduction method given in Table S3. Eventually, we chose the approach with better verification results: \"GPP = c + a \(\times\) ln(SIF) + b \(\times\) VPD\({}^{2\prime}\)\" Our results significantly alleviated the negative impact of drought stress on the estimation accuracy of GPP.
## 3 Results and Discussion
### Ca-Dominant Clinoptilolite  
The arrangement of Ca atoms in Ca-clinoptilolite configurations was as follows: two Ca atoms in two distinct 8 MR of the small channel B and another Ca atom in the big channel A. The addition of two Ca atoms in the big 10 MR channel or the small 8 MR channel was not considered to be likely due to cation-cation repulsion, while not placing one Ca atom in 10 MR would introduce instability due to the presence of Al at T2 and/or T3 sites that require the presence of any cation.  
Unlike Na-clinoptilolites, the situation where clinoptilolites were dominated by Ca unfolded slightly differently. When Ca atoms were added to our models, the initial SCF iterations did not converge; in other words, the system struggled to meet the convergence criteria in the presence of Ca atoms. The reasons for this are multifactorial: the first contributing factor could stem from the incomplete basis sets. The second factor could be the presence of only three Ca atoms to balance the negative framework charge induced by the six Al atoms. The system is charge-balanced; however, there the local charge surrounding the Al atoms had no contact with any cation, as is evident in the least stable configurations. The third factor is definitely the positions of the cations. For instance, adding Na to any channel impacted the SCF iteration behavior but not in a negative way, provided that the correct basis sets were used; for Na atoms, as mentioned earlier, the most efficient choice was the MOLOPT SZV basis set. However, the Ca atoms required a different approach. The alternatives were to adjust the cation positions and improve the basis sets.  
The DFT calculations in this work confirm that Ca prefers to occupy the 8 MR of channel B, as suggested by the XRD experiments and contrary to what was stated in previous computational studies [10; 22]. Adding Ca to the 10 MR of channel A not only increased the energy of the whole system and led to very high energy configurations but also made it very hard for the SCF cycle to converge. Notably, the iterations of the diagonalization process within any SCF cycle are designed to capture the behavior of the KS system but provide some freedom since the intermediate steps were not created to determine the actual physics but to aid in convergence. Therefore, while the wavefunctions may not converge at each SCF cycle, and this can be considered normal (although not in all cases), it is worth investigating why this behavior happens in the first place. Consequently, the first logical attempt after adjusting the positions of cations was to optimize the basis sets used for Ca. All details on basis set optimizations can be found in the Supplementary Material, along with the optimized basis sets.
# Quality and Safety of Dried Mushrooms Available at Retail Level
## 2 Materials and Methods  
### Collection of Mushrooms  
In total, 61 samples of packed dried mushrooms (at least 100 g/sample) were randomly purchased online (37 samples), in Asian supermarkets (18 samples), in a whole foods store, and in a supermarket (2 samples each) in Vienna, Austria. The samples originated from China (33 samples), Bosnia and Herzegovina (6 samples), Bulgaria (5 samples), Spain (3 samples), Greece (2 samples), Montenegro, Poland, and Vietnam (1 sample each). The country of origin was not provided for nine of the samples. The set of dried mushrooms consisted of 11 different mushroom species, which were mainly offered as pure species. Twenty-one different suppliers were included in the study. Details are presented in Table 1. Samples were stored in a dark and dry place until microbiological analysis (1-7 days).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Classification A–E** & **Mushroom Species F** & **Samples (\%) G** \\ \hline _Agaricomycotina_ species without gills B & total 25 (41.0) \\ \hline _Aurticulariales_ & Wood ear (_Auricularia auricula-judae_) & 10 (16.4) \\ _Tremellomycetes_ & Jelly mushroom (_Tremella fuciformis_) & 2 (3.3) \\ _Polyporales_ & Maitake (_Grifola fondosa_) & 1 (1.6) \\ \hline _Cantharellales C_ & total 10 (16.4) \\ \hline \multirow{6}{*}{_Agaricomycotina_ species without gills B _} & Chanterelle (_Cantharellus chiratius_) & 6 (9.8) \\  & Horn of plenty (_Craterellus corunicopioules_) & 2 (3.3) \\  & Mixed mushroom species with _Cantharellales_ & 2 (3.3) \\ \hline _Boletales D_ & total 10 (16.4) \\ \hline \multirow{6}{*}{_Pezizomycotina E_} & Porcini (_Boletalus edules_) & 9 (14.8) \\  & Mixed mushroom species with Porcini & 1 (1.6) \\ \hline \multirow{6}{*}{_Pezizomycotina E_} & total 3 (4.9) \\ \cline{2-2}  & Morel (_Morchella_ spp.) & total 61 (100) \\ \hline \hline \end{tabular} Abbreviations: Classification A,C,DE according to taxonomic position or B common characteristics of the group; F common species name and scientific name in brackets; G number of samples and percentage of the total.  
\end{table}
Table 1: Sample description of dried mushroom species included in this study.
# Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror
## 2 Related Work  
### Use of Smart Mirror  
IoT technology is receiving a lot of attention, and many technologies are being developed [10] and widely used such as UAV [11]. The Smart Mirror is one of those Internet-of-Things (IoT) devices made by inserting a board such as a mini PC into the basic structure of a mirror. Depending on the purpose, a display and speaker can be used as a visual and audio output device, and various visual, tactile, audio, or other sensors-such as RGB camera, RGB-D camera, touch panel, microphone, or RFID sensor-can be inserted as an input device [12; 13].  
Examples of roles suggested in a thesis that focused on the utility of smart mirrors [14] include information delivery and entertainment. An example of a smart mirror used as a window for information transmission is a smart mirror that acts as a secretary [15], such as delivering daily information in a bathroom, and a smart mirror that can be used for academic purposes [16], in this case, anatomy.  
Smart mirrors used to transmit information are also called magic mirrors. In [PERSON] et al. [16], magic mirrors were used for learning anatomy. By processing visual input of the target user through an RGB-D camera, that system can show estimated 3D models of organs in the user's abdomen region, text information, and additional reference images about anatomy. [PERSON] et al. [15] designed a bathroom smart mirror that estimates the weight of the user and displays simple information such as mail, weather, and calendar in the bathroom.  
As mirrors are essential furniture in the home, smart mirrors are easier to access than any other IoT device, and this is no different for elderly households. Considering that it is a fixed mirror, a smart mirror with an RGB camera can play the role of a surveillance system similar to a CCTV surveillance camera. In this case, a system that detects the safety of the elderly or a slow-motion detection system that allows one to exercise while watching is more appropriate than a CCTV system that detects pedestrian movement or monitors the presence of a suspicious wanderer.
## 3 Methods
### Model Structure  
In this paper, we design a Pyramid Hierarchical-Convolution-based U-Net model with a Mix Global Attention Module (MGAM), Edge Feature-Extractor Module (EFEM) and Supplementary Attention Module (SAM), named PHCNet.  
As shown in Figure 1, our proposed PHCNet is a fully convolutional neural network. It has a symmetric encoder (left side)-decoder (right side) structure like U-Net. We can see that the encoder includes several Pyramid Hierarchical Convolution Modules (PHCMs) and several max-pooling layers. The decoder also includes several PHCMs and several up-sampling layers. The PHCM is used to simultaneously extract features with different-size convolutions. In addition, several direct connections are used to concatenate feature maps from the encoder to the corresponding features of the decoder for the purpose of feature-fusion between layers. It is noted that feature maps generated from the encoder would input into the Mix Global Attention Module (MGAM) proposed by us before concatenating with the corresponding features of the decoder, since the MGAM is used to fuse global feature information, guiding the model to restore high-resolution features. These feature maps generated from the encoder would also be input into the Supplementary Attention Module (SAM) to solve the interference of various stains in crack images. In addition, the Edge Feature Extractor Module (EFEM) is used to increase the model's ability to learn the edge features of cracks.
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
## 1 Introduction
### Background
Therefore, there is a need for more intelligent and efficient methods to cope with the ever-expanding count of mobile devices, with the aim of enhancing network performance and reducing resource wastage. In consideration of the aforementioned issues, this study introduces an innovative optimization algorithm based on common node density, known as CND-AODV. This algorithm not only effectively addresses the challenges posed by a high neighbor node density, but also takes into account the location of the receiving nodes, thus further reducing wastage of network resources and enhancing network performance.
## 5 Conclusions  
In this paper, a structural impact damage monitoring method based on Lamb waves and a multi-task 2D-CNN is proposed. Within the detection framework, a new image processing method is proposed for multi-sensor data fusion by converting the raw Lamb wave signals into grayscale images. A multi-task 2D-CNN is established for feature extraction, impact location classification, and impact energy quantification. According to the experimental results, the impact damage detection method has excellent performance in monitoring the impact damage to aluminum alloy. The specific conclusions are as follows:  
1. A new image processing method is proposed to closely link the sensor signals from multiple channels and finally convert the original Lamb wave signal into a grayscale image to realize multi-sensor data fusion. The constructed grayscale image contains rich impact information, so it is used as the input to the multi-task 2D-CNN.
2. The multi-task 2D-CNN can automatically extract deep-level damage features without physical a priori information, and the accuracy of the impact damage location identification and impact energy identification are higher than traditional machine learning methods.
3. The multi-task 2D-CNN can understand the commonality and characteristics of each task by sharing the network structure and parameters. Experimental results show that the model can effectively recognize the location and severity of impact damage simultaneously. Compared with single-  
has a strong advantage in the impact damage identification of irregular structures under severe service environment exposure.  
Conceptualization, J.Y.; methodology, [PERSON] and J.Y.; software, [PERSON] and X.Z.; validation, J.Y. and T.W.; formal analysis, J.X.; investigation, J.X.; resources, T.W.; data curation, J.Y. and Z.G.; writing--original draft preparation, J.Y. and Z.G.; writing--review and editing, J.Y. and Z.G.; supervision, T.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Joint Funds of the National Natural Science Foundation of China (Grant No. U2268205).  
Not applicable.  
Data Availability Statement: Data requirements can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 7 Results
### Effect of the Fitness Thresholds  
As described in Section 5, the \(\alpha\) parameter is a value that determines to what extent some of the connections are immediately discarded as next hop candidates. Intuitively, a very small value would mean that only a small portion of the current connections are discarded, so most of them have a chance to be chosen (although in descending order with respect to their fitness values). The limit is \(\alpha=0\), and since \(1\geq f\geq 0\), the condition \(f\geq\alpha\) means in this case that all of the connections are considered as potential candidates. Similarly, a very large value of \(\alpha\) will result in a strong limiting condition, meaning that only the very best hosts (the ones with considerably large fitness) will be considered as possible next hops. As we can infer from this explanation, the dynamics of the environment are strongly influenced by the \(\alpha\) value. To better understand the effect of this fitness threshold, we run simulations changing this parameter with \(\alpha=\{0,\ 0.05,\ 0.1,\ 0.15,\ 0.2,\ 0.3,\ 0.5,\ 0.65,\ 0.8,\ 0.95,\ 1.0\}\) and a msg TTL varying from \(TTL=\{10,25,50,75,100,150,200,300\}\). A similar reasoning than that for \(\alpha\) was made for the \(\beta\) fitness threshold, so we considered \(\beta=\{0,\ 0.05,\ 0.1,\ 0.15,\ 0.2,\ 0.3,\ 0.5,\ 0.65,\ 0.8,\ 0.95,\ 1.0\}\) in the simulations as well. We distinguished two main differentiators in both the \(\alpha\) and \(\beta\) values: \(\alpha=0\) and \(\alpha>0,\ \text{and}\ \beta=0\) and \(\beta>0\). In the first case, with \(\alpha=0\), we can see that the cases \(\beta=0\) and \(\beta>0\) resulted in noticeable different dynamics (see Figures 5 and 6). We notice that for \(\alpha=0\), for TTL values smaller than 60, the performance of DLR+ is better with \(\beta=0\) for PDR. For ADD, in turn, \(\beta=0\) is the choice, as it showed better results than for other \(\beta\) values.  
In any case, however, for OVH and HOP (Figure 6) the choice is any value different than 0 for \(\beta\). As we can see, there is a tradeoff mainly between network overhead and delivery ratio or delivery delays, and the final choice of the parameters ultimately depends on the final application of the router  
Figure 5: Effect of the fitness thresholds in packet delivery ratio (**a**) and average delivery delay (**b**).  
Figure 6: Effect of the fitness threshold in network overhead (**a**) and hop count (**b**).  
in delay-tolerant networks (i.e., if we are interested in minimizing latency, at the expense of some overhead, or we have limited resources, such as in mobile sensor networks).  
For \(\alpha>0\), we did not notice any significant difference in the values of \(\beta\). Finally, for \(\alpha>0.5\) there was a slightly improvement in overhead and number of hops. For this version of DLR+, we decided to use \(\alpha=0.65\) and \(\beta=0\).
## 2 Data and Methods
### Vertical TEC from GNSS Stations in Africa  
Slant TEC (STEC) from ground-based GNSS Africa Geodetic Reference Network (AFREF, [[http://afrefdata.org](http://afrefdata.org)]([http://afrefdata.org](http://afrefdata.org)), accessed on 29 January 2021) is used to study the low-latitude ionospheric variations in the African sector during the multiphase storms of February, 2014. AFREF stores input GNSS data for sTEC calculation in Receiver Independent Exchange Format (RINEX) [48] with 30 s cadences. To characterize the response to the storm, sTECis converted into vTEC using GPS-TEC analysis software tools [1]. Table 1 shows the coordinates of the GNSS stations.  
The median-average of February is used as a background vTEC for the analyses of disturbances to obtain the relative deviation of the daily values. The TEC deviation from the TEC background is referred to as relative TEC change (rTEC). Here, the threshold was set at \(\left|\text{rTEC}\right|\geq 30\%\) to detect the disturbed TEC during geomagnetic storms. The rTEC is calculated as follows:  
\[\text{rTEC}=\frac{\text{TEC}_{\text{obs}}-\text{TEC}_{\text{med}}}{\text{ TEC}_{\text{med}}}, \tag{2}\]  
where \(\text{TEC}_{\text{obs}}\) and \(\text{TEC}_{\text{med}}\) denote the observed and the background TEC, respectively.