esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
DCNN was then used to extract the spatial representation, but the regional similarity tended to ignore single node differences. STGODE [17] and STFGNN [29] used DTW (Dynamic Time Warping) to compare similarities between time series, but it is computationally expensive. AST-GCN [1], MS-net [13], and DMVST-Net [26] comprehensively modeled actual traffic data by integrating various external information such as POI (Point of Interest) distribution, weather, and holiday notifications, but these are difficult to obtain in many scenarios. Traffic prediction models can be divided into two categories according to the acquisition of temporal dependencies, which are either RNNs-based [1; 2; 8; 18] or CNNs-based [10; 16; 17; 29]. Models based on RNNs extract temporal dependencies through RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). AST-GCN [1] and DCRNN [8] use the graph convolution operation to replace the original linear transformation in the recursive unit to obtain spatio-temporal information. GST-GAT [2] and TGC-LSTM [30] use LSTM [31] to extract temporal information. AST-MTL [18] uses GRU to obtain spatio-temporal information after graph convolution. However, when a traffic prediction data set is relatively large, the complex gating in RNNs will generate a large computation workload. As RNNs rely on the memory of the previous step, this makes it difficult to capture large traffic flow fluctuations during peak traffic conditions [9]. Some studies tend to use CNNs for temporal relationships in traffic prediction. CNNs-based studies use convolution operations across the temporal information to model temporal dependencies and expand the receptive field through dilated convolution to obtain a wider range of temporal relationships [20]. GWnet [10] and HGCN [28] use stacked 1D and 2D dilated convolutions after graph convolution to increase the temporal receptive field of the model and show better short-term prediction results. STGODE [17] uses 1D dilated convolution after graph convolution to extract temporal information. STGNN [29] uses graph convolution and 1D dilated convolution with a large dilation rate to extract spatio-temporal information in parallel. HetGAT [16] uses graph attention convolutional after 1D and 2D dilated convolutions to extract temporal information. Spatial and temporal modalities in traffic prediction usually use different methods to extract information, so the mutual fusion of spatio-temporal information plays a crucial role. STGCN [9] and HetGAT [16] use separate modules to sequentially capture spatial and temporal relationships, thereby splitting the association between spatial and temporal dimensions. STSGCN [11] and MS-Net [13] concatenate the separately obtained spatio-temporal relationships, and GWnet [10], STGODE [17], and AST-MTL [18] add the separately obtained spatio-temporal relationships, STGNN [29] multiplies the separately obtained spatio-temporal relationships, but these simple methods are not conducive to establishing long-term spatio-temporal fusion.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol
### Route Building in Downward Routing  
In order to reach the destination through multiple hops, the sink needs to build a route before sending a packet. Algorithm 1 depicts the route-building process. We use \(ID_{d}\) to stand for the ID of the destination node which is put into the route first. The sink extracts the preferred next-hop of \(ID_{d}\) from the dynamic next-hop table. If \(N(ID_{d})\) equals \(ID_{0}\), this means that the sink can directly reach node \(ID_{d}\), and the building process immediately stops. In case \(N(ID_{d})\) is not \(ID_{0}\), the sink continues the route-building process. For any entry in the first column of dynamic next-hop table, the entry \(ID_{i}\) that equals \(N(ID_{d})\) is put into the route, and its next-hop \(N(ID_{i})\) needs to be compared to \(ID_{0}\). The building process stops when the next-hop of item \(ID_{i}\) is \(ID_{0}\). The building process of a route is done from the destination to the sink, the route we get is in reverse order, and it is thus reversed before it is used as a path.  
Figure 4 shows how Algorithm 1 works. \(ID_{d}\) is put into the route first. During this process, the numbers of \(ID_{i}\) are put in the route until the next-hop of \(ID_{x}\) is found to be \(ID_{0}\). At the end, the route needs to be reversed.  
We consider that there are \(m\) nodes in the route. Before sending a data packet, the sink needs to store the IDs of these nodes in the data packet as shown in Figure 5. Every time the data packet is relayed, the route offset will be increased to help relay nodes to find the next-hop within \(m\) bytes until reaching the destination.  
Figure 3: Dynamic next-hop table of the sink node.  
Figure 2: Fields of upward data packets.  
```
Input:\(ID_{d}\) Output:\(Route\) begin Put \(ID_{d}\) in the \(Route\); \(Nexthop=N(ID_{d})\); while\(Nexthop\) does not equal to \(ID_{0}\)do forach item \(i\) in \(ID_{i}\)do if\(ID_{i}=Nexthop\)then \(Nexthop=N(ID_{i})\);  Put \(ID_{i}\) in the \(Route\);  end if  end for Reverse(\(Route\));  end for
```  
**Algorithm 1**Route building.  
Figure 4: Route-building process.  
Figure 5: Fields of downward data packets.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 4 Results and Discussion  
### Summary Statistics  
Table 3 presents the descriptive statistics of the variables used in the analyses. On average, 769,139 individuals are subscribed to fixed telephones in the 32 African countries. With regards to the crop production index, the minimum recorded throughout the period under study (2004-2019) for the aforementioned African countries is approximately 40, whereas the maximum production is approximately 172. Moreover, the average livestock production index recorded for these countries is approximately 94. We observed an uneven patronage of internet across countries, with some countries being very high, whilst other remained very low. Thus, one country among the 32 African countries recorded high 74.4% (maximum) internet patronage, whereas another recorded less than 1% (i.e., 0.20%) of its populace using the internet. On average, internet patronage stands at 16.3%, as shown in Table 3. This gives a clear indication of the low subscription to the internet in the majority of the African countries.  
Although the maximum adjusted net income per capita recorded from our summary statistics is USD 11,114.26, most of the countries studied belong to the low-income economy. From the description given by the World Bank of the gross national income per capita, a country whose gross national income per capita is less than USD 4255.00 falls under the low-income or lower-middle-income category [78]. Hence, the average adjusted net income per capita (USD 1915.03) and the median (USD 1151.36) reported in the summary statistics table indicate that most of the countries under study fall in the low-income or lower-middle-income category [79].  
As it is widely acknowledged, the private sector is one of the most important sectors that drive the economic growth of every country [79]. Per the results in Table 3, the maximum domestic credit given to the private sector by banks in the countries studied is 106.3%, whilst the lowest domestic credit given to the private sector by local banks is approximately 1.1%. On average, domestic credit given to the private sector by banks in these 32 African countries is approximately 25.9%, while the median is 18.91%.  
We observed that the maximum rural population recorded in the summary statistics results is 90.86%, whilst the minimum recorded is 10.26%. The average rural population of the studied countries is 53.71%, while the standard deviation is 17.66%.
# Remediation of Copper Contaminated Soils Using Water Containing Hydrogen Nanobubbles
###### Abstract  
This basic research study was undertaken to use ecofriendly nanobubbles that can improve the electrokinetic remediation of copper-contaminated soil, as well as to determine that remediation efficiency. The nanobubbles were generated by using pressurized hydrogen gas, and the quantity of hydrogen gas bubble that remained over 14 days was measured. The generated nanobubbles were used as an enhancer to remove a heavy metal on contaminated soil, and their applicability was confirmed. A batch test was used to compare the remediation effects of nanobubbles and distilled water on copper-contaminated soil. The results proved that the nanobubbles are a proper desorption agent for copper-contaminated sand and clay specimens. The solid-liquid ratio and the contact time for desorption of the sand and clay were then respectively determined. A large amount of effluent was obtained from electrokinetic remediation of the sand sample after applying the nanobubbles as an enhancer. The remediation efficiency demonstrated with sand proved to be higher than that for clay. This greater efficiency was attributed to a wider specific surface area, demonstrating the potential use of the nanobubbles as an enhancer for soil contaminated by copper with a large amount of effluent outflow. It was also assumed to be affected by the moving capability of the nanobubbles in the soil layer. Thus, the nanobubbled water can be used to improve the removal of heavy metals from contaminated soils. An ecofriendly enhancer for electrokinetic remediation with a relatively large void ratio and fast flowrate was confirmed by the nanobubbles.  
nanobubbles; contaminated soil; electrokinetic; in situ; remediation Article
## 6 Concluding Remarks  
In conclusion, this article summarizes the capability of in situ AE monitoring in the context of SHM based on the results of monitoring projects in mines. The in situ AE method is capable of detecting microcracking, in high resolution and sensitivity, which is caused by very small deformation processes at high deviatoric stresses. This means that in situ AE monitoring provides detailed insights into the ongoing deformation processes.  
In contrast to in situ AE monitoring, microseismic monitoring is used to measure large-scale deformations in mines, which may cause rock bursts or roof falls. Due to limitations in frequency range and sensitivity, microseismic networks are not able to detect microcracks. Therefore, small AE events are very often not considered for stability assessment and interpretation of geomechanical conditions of the rock. This work clearly shows that in situ AE monitoring is able to detect very small AE events in zones of weakness related to dynamic processes like dilatation. Therefore, in situ AE monitoring is a useful tool to monitor the geomechanical conditions of the host rock.  
Real-time processing gives direct information on the location of AE events as well as on clustering, migration of AE activity or aftershock sequences of microseismic events. Recent advances in computer storage capacity allow recording of continuous data streams with 1 MHz sampling in addition to trigger mode recording, which makes advanced post-processing techniques possible.  
The results shown here also demonstrate that monitoring of larger rock volumes with in situ AE measurements is possible in various rock types. Detections of AE events from distances much greater than 100 m is possible in rock with low wave attenuation like salt rock or hard rock. In this case, rock volumes far away from the AE network can be monitored. On the other hand, the in situ AE monitoring method is able to identify \"aseismic\" zones because AE activity is expected during significant damage processes. Finally, in situ AE monitoring is capable of detecting zones in mines where instability may appears long before macroscopic damage becomes visible, which is the objective of SHM.  
[PERSON] and [PERSON] wrote the article.  
The authors thank [PERSON] and two anonymous reviewers for their comments and suggestions to improve this article. The authors are grateful to [PERSON] and [PERSON] (BGR in Hannover), [PERSON] (GFZ in Potsdam), and [PERSON] (Tohuko University in Sendai) for providing updated figures for this review.  
The authors declare no conflicts of interest.  
Figure 22: Perspective view of AE events detected during hydraulic fracturing tests HF1 to HF3. The continuous line indicates the injection well with the position of the injection intervals [54].
## Materials and Methods
### Determination of Rutin and TP Contents, and Antioxidant Capacity of Rye-Buckwheat Biscuits  
An aliquot of 0.10 g of powdered biscuit samples was extracted with 1 mL of 80% (_v/b_) methanol. The mixture was sonicated, vortexed each for 30 s, repeated three times and centrifuged for 5 min (5000\(\times\)\(g\) at 4 \({}^{\circ}\)C). That step was repeated five times and the supernatants were collected into 5-mL flasks. The final extract concentration was 20 mg/mL. Extraction of each sample was performed in triplicate. The concentrated 80% methanol extracts from rye-buckwheat biscuits for rutin and total phenolic content (TPC), and antioxidant capacity determinations were used.  
The content of rutin in rye-buckwheat biscuits was determined with HPLC (Shimadzu, Japan) with UV detector (SPD-10A) set up 330 nm as it was recently described by [PERSON] et al. [35]. For quantitative analysis, rutin standard was prepared in triplicate at five concentrations within the range of 1.0-40 \(\upmu\)M. All solutions were filtered through a 0.45 \(\upmu\)m nylon membrane before use. The results were expressed in \(\upmu\)g/g of dry weight (DW).  
The TPC content and antioxidant capacity by DPPH of rye-buckwheat biscuits with increasing rutin addition was carried out as previously described in detail by [PERSON] et al. [36]. The OxHLIA method was performed as described by [PERSON] et al. [19] to measure antioxidant activity in-vitro. Both measurements were performed in microplate reader (Infinite M1000 Pro Multimode Microplate Reader, Tecan GmbH, Grodig, Austria), using transparent 96-well microplates for absorbance readings (Porvair, TK Biotech). TPC content was expressed in mg of Gallic acid equivalents (GAE)/g of DW whereas results of DPPH and OxHLIA methods were expressed as \(\upmu\)mol Trolox/g of DW.
## 3 Results and Discussion
### Linearity
#### 3.2.1 Statistical Data Checks  
For the selection of the appropriate regression model, tests of normality (Grubbs test) and homoscedasticity (Bartlett test) of the data were performed. Furthermore, based on the fact that the matrix does not have any significant impact as confirmed in Section 3.1.2, we can confidently conclude that the simple linear regression model is appropriate [18].  
Figure 4: Least-squares curves, for matrix effect check: Ouzo and ethyl alcohol 40% \(\
icefrac{{\circ}}{{\
u}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Concentration Level** & **Duplicate Per Level** & **DEHP Concentration, mg/L** & **Area** \\ \cline{2-5}  & 1 & 1 & 0.3 & 34.65 \\ \cline{2-5}  & 1 & 2 & 0.3 & 41.72 \\ \cline{2-5}  & 1 & 3 & 0.3 & 39.98 \\ \cline{2-5}  & 2 & 1 & 0.7 & 96.47 \\ \cline{2-5}  & 2 & 2 & 0.7 & 99.12 \\ \cline{2-5}  & 2 & 3 & 0.7 & 95.86 \\ \cline{2-5}  & 3 & 1 & 1.1 & 148.80 \\ \cline{2-5}  & 3 & 2 & 1.1 & 153.69 \\ \cline{2-5}  & 3 & 3 & 1.1 & 150.97 \\ \cline{2-5}  & 4 & 1 & 1.5 & 205.14 \\ \cline{2-5}  & 4 & 2 & 1.5 & 207.12 \\ \cline{2-5}  & 4 & 3 & 1.5 & 203.34 \\ \hline \multicolumn{5}{c}{SUMMARY} \\ \cline{2-5}  & **DEHP Concentration, mg/L** & **Mean Value Area** & **Standard Deviation, s** & **RSD,\%** \\ \cline{2-5}  & 0.3 & 38.78 & 3.684 & 9.498 \\ \cline{2-5}  & 0.7 & 97.15 & 1.733 & 1.784 \\ \cline{2-5}  & 1.1 & 151.15 & 2.450 & 1.621 \\ \cline{2-5}  & 1.5 & 205.20 & 1.891 & 0.921 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of measurements to estimate the linearity of the method.
# Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials
## 3 Results and Discussion
### Structure Analysis
#### 3.1.2 Raman Spectroscopy  
Another structural study in annealing effect of BiOI materials was also investigated by Raman spectroscopy analysis. The results of the Raman analysis are shown in Figure 2. It has been confirmed in many reports that BiOI has Bi-I characteristic in Raman spectra which can be identified by its vibration mode at 147-149 cm\({}^{-1}\)[38; 39]. This information was also obtained in the annealed BiOI at 100 \({}^{\circ}\)C and 300 \({}^{\circ}\)C. Similar to the XRD analysis results (Figure 1), the Raman spectra of annealed BiOI in those temperatures had a nearly identical pattern without the marked impurity peak from other bismuth-based materials.  
After the samples were heated at a higher temperature, the vibration mode of Bi-I shifted to the higher frequency. It is shown like in the Raman spectra of Bi\({}_{4}\)O\({}_{5}\)I\({}_{2}\) and Bi\({}_{5}\)O\({}_{7}\)I. It was also same as the reported work by [PERSON] and co-workers [40]. The annealing treatment of BiOI at 550 \({}^{\circ}\)C can result in a different trend in its Raman spectra, as it can result in a change of BiOI chemical structure. We found the peak signals around 280-350 cm\({}^{-1}\) and 445-485 cm\({}^{-1}\), which are close to the characteristic of \(\beta\)-Bi\({}_{2}\)O\({}_{3}\)[41]. By observing the XRD patterns and Raman spectra in Figures 1 and 2, we note that among the structure of BiOI and its derivations, Bi\({}_{5}\)O\(\text{I}\) and Bi\({}_{2}\)O\({}_{3}\) are different from others.  
The aforementioned reactions in Equations (1)-(3) were considered as thermal decomposition reactions, whereas the deiodination reaction results in new materials. During the reaction, the gasifying and evaporation of I\({}_{2}\) happened due to the heating treatment of BiOI to bring out the derivation of oxygen-rich and iodine-deficient bismuth oxyhalides materials [30].
## 5 Discussion
### Lue  
Many researchers have demonstrated that LUE is positively related to crop growth and, ultimately, to biomass production [54; 58]. LUE is approximately constant for forests and natural ecosystems, particularly for crops, when their growth is not limited by water or nutrient shortages or adverse climatic conditions [10; 59]. However, in practical cases, water, nitrogen, and climate conditions are the main factors that impact wheat growth, all of which finally influence LUE. [PERSON] and [PERSON] reviewed several studies and reported that the seasonal wheat LUE ranges from 0.73 to 1.62 g\(\cdot\)MJ\({}^{-1}\)[60]. The LUE for wheat has been found to have tremendous variations. [PERSON] et al. and [PERSON] et al. revealed LUE values of 2.38 and 1.81 \(\pm\) 0.05 g\(\cdot\)MJ\({}^{-1}\) in Western Australia and New Zealand, respectively [61; 62]. [PERSON] et al. and [PERSON] et al. adopted the SCEM-UA algorithm to calibrate LUE, which ranged between 1.5 and 3.5 g\(\cdot\)MJ\({}^{-1}\) for winter wheat in central Morocco and Southern Canada, respectively [54; 63]. [PERSON] et al. found that the LUE values in northwestern Mexico were 2.2-2.4 g\(\cdot\)MJ\({}^{-1}\) and were similar to those determined in previous studies of wheat, which generally discovered LUEs in the range of 2-3 g\(\cdot\)MJ\({}^{-1}\)[64; 62; 64]. [PERSON] et al. calibrated LUE as 4.23 g\(\cdot\)MJ\({}^{-1}\) with a temperature and moisture down-regulator and 3.92 g\(\cdot\)MJ\({}^{-1}\) without a moisture down-regulator to estimate crop production across China [65]. Some studies have proven the significant dependence of LUE on photosynthesis pathways, such as those of C3 and C4 crops [66; 46].  
In this study, the adoption of a constant LUE value might be a potential source of error in the biomass and yield estimations because LUE has been proven to vary with different nutritional conditions and growth stages. Therefore, more field experiments over all the growth stages of wheat should be carried out to identify appropriate LUE values. Generally, wheat LUE can either be calculated as the ratio of the total aboveground dry weight to the intercepted radiation or can be measured using a portable gas exchange system, such as an LI-6200 (LI-COR, Inc., Lincoln, NE, USA) [59; 67; 68].
## 4 Numerical Application
### Evaluation Metrics  
#### 4.1.1 Coefficient of Determination (\(R^{2}\) Score)  
The R\({}^{2}\) score was used to measure the linear correlation between the inversion results and the true model, which is defined as follows:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}\left(v_{i}^{inv}-v_{i}^{true}\right)^{2}}{ \sum\limits_{i=1}^{N}\left(v_{i}^{true}-\overline{v}^{true}\right)^{2}} \tag{17}\]  
where v\({}_{i}^{\text{inv}}\) represents the velocity value of the ith grid point obtained from the inversion, v\({}_{i}^{\text{true}}\) represents the velocity value of the ith grid point in the true model, \(\overline{v}^{\text{true}}\) represents the average velocity value of the true model, and N represents the total number of grid points. The range of an R\({}^{2}\) score is \((-\infty,1]\), where values closer to 1 indicate a stronger linear correlation between the inversion results and the true model, and vice versa.
## 3 UWB Characteristics
### Advantages of UIWB  
* **Large Channel Capacity:** According to [PERSON]'s capacity formula, the channel capacity increases linearly with bandwidth [66]. In such a case, the availability of some bandwidth which operates in typical gigahertz for UWB signals suggests that data rates of gigabits per second (Gbps) can be achieved. UWB technology transmits very short pulses within an extremely large bandwidth from 3.1 to 10.6 GHz, which provides a significant bandwidth advantage and a short duty cycle. As a result, UWB offers a larger capacity and higher data rates, making it an excellent choice [68; 69; 71].
* **Simple transceiver architecture and low cost:** UWB uses carrierless waves to transmit data [68; 69; 71; 72]. As a result, carrier oscillators are not required in order to transmit the carrier frequency for the signal transmission. This eliminates the requirements for a carrier recovery stage for the receiver side, and the UWB transceiver does not require modulators, demodulators, or intermediate frequency components [68; 69; 71; 72]. This simplicity in the UWB transceiver architecture makes it more lightweight and beneficial compared to narrowband signals. Furthermore, the system power consumption is significantly reduced due to these characteristics. Additionally, the low complexity of the UWB system and the smaller chip sizes reduce the cost of the system.
* **Multipath Immunity and Low Power Spectral Density (PSD):** Multipath refers to the phenomenon in which an electromagnetic signal travels through various paths during transmission due to factors such as signal reflection, signal absorption, diffraction, and scattering of energy by the presence of objects in the environment [65; 66; 67]. UWB communication systems have a large bandwidth, which allows them to operate at high data rates, making them highly robust. They are also capable of performing well in the condition of low signal-to-noise ratio (SNR) communication channels, providing immunity against multipath conditions. This factor makes UWB communication ideal for indoor positioning applications under NLoS conditions. Furthermore, UWB systems have good anti-multipath performance and are not sensitive to channel attenuation. The signal transmitting of UWB is of a low average power spectral density because of the short-pulse nature of the transmission, which places it within the noise floor (typical \(-40\) dBm/MHz), thus allowing for less transmitter power consumption, increased power efficiency, and resistance against jamming and interception as shown in Figure 3.