esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Temperature Threshold Results  
The map of September temperatures below 15 \({}^{\circ}\)C shows that, for much of Slovenia, there may be sufficient hours below this threshold to begin and possibly complete aeration in August or early September (Figure 1A,B). However, aeration could not be accomplished in the coastal region until late September or possibly October. The hours below 15 \({}^{\circ}\)C in August and September for each of the 10 selected weather sites are shown in Table 1. Note the low number of hours below 15 \({}^{\circ}\)C for Portoroz, chosen to represent the coastal area, because no weather data were available for the port of Koper. In addition, the mountain site of Kredarica was chosen for geographic representation for Figure 1 and is not representative of a site where wheat would typically be stored. The average date by which 120 h are accumulated below 15 \({}^{\circ}\)C at each of these sites and the number of temperature hours are also shown in Table 1. The degree day approach predicts that a generation could be produced, starting on 1 August, in Portoroz, Murska Sobota, and Novo Mesto (Table 1 and Figure 1C). Partial generations could be completed in all other sites except Vojsko.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **Lat.** & **Long.** & **August** & **September** & **120-h Date** & **Degree-Days** \\ \hline Portoroz & 45.5 & 13.6 & 0 & 89 & 3 October & 1471 \\ Murska Sobota & 46.7 & 16.2 & 38 & 264 & 13 September & 1091 \\ Novo Mesto & 45.8 & 13.2 & 26 & 272 & 14 September & 1018 \\ Maribor airport & 46.5 & 13.7 & 67 & 287 & 8 September & 953 \\ Postojna & 45.8 & 14.2 & 150 & 334 & 27 August & 915 \\ Ljubljana airport & 46.2 & 14,5 & 123 & 356 & 31 August & 870 \\ Lesce & 46.4 & 14.2 & 136 & 359 & 30 August & 857 \\ Koč veje & 45.6 & 14.9 & 181 & 364 & 23 August & 901 \\ Vojsko & 46.0 & 13.9 & 247 & 532 & 18 August & 525 \\ Kredarica 3 & 46.4 & 13.9 & 720 & 744 & 5 August & \\ \hline \hline \end{tabular}  
* 1 August was used as a starting date for wheat storage, although wheat may be loaded into a bin before or after that date, depending on the region and the climatic conditions of a given year. This date was used for standardization.
* 2 Most of the weather sites with data were from central and western Slovenia. There was a lack of weather station data from the northeast quadrant of the country. 3 There is no wheat storage in northern mountain region of Slovenia, and the site was chosen only for geographic representation.  
\end{table}
Table 1: Hours below 15 \({}^{\circ}\)C in August and September for 10 sites in Slovenia, from lowest to highest in September. The airports in Ljubljana and Maribor are well outside the city limits, so they were used. Kredarica is in the mountains and is not representative for wheat storage but was used to make the climate map 1,2.
## 2 Materials and Methods
### Up-Flow Percolation Column Test
The pH, electrical conductivity (EC), and redox potential (Eh) of the collected eluates (at various L/S ratios) were measured immediately after collection. The eluates were preserved by the addition of nitric acid and stored in a refrigerator at 4 \({}^{\circ}\)C until required for analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Control**} & \multicolumn{5}{c}{**Stabilization Types**} \\ \cline{3-6}  & & **AMDS** & **Steel Slag** & **CMDS** & **Cement** \\ \hline Column height (cm) & 30 & 30 & 30 & 30 & 30 \\ Column diameter (cm) & 5 & 5 & 5 & 5 & 5 \\ Sample dry mass in the columns (g) & 616.8 & 539.6 & 549.4 & 533.7 & 519.4 \\ Height of the soil in the columns (cm) & 21 & 21 & 21 & 22 & 23 \\ Bulk density (g/cm\({}^{3}\)) & 1.46 & 1.33 & 1.33 & 1.32 & 1.20 \\ Flow rate (mL/min) & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Initial equilibrium time (h) & 24 & 24 & 24 & 24 & 24 \\ \hline Fluent & & & 0.001 M CaCl\({}_{2}\) solution in deionized water & & \\ \hline Sampling cumulative L/S & & & & & \\ (L/kg; eluent collection) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Column dimensions and sample size within the different columns. Sample packing involved light tamping of five layers from the top to the bottom of the column. The experimental setup followed the EPA Method 1314 with slight modifications (see the methods section for details). The percolation column test is usually carried out until a 10 L/S ratio is reached [17; 19; 23]. However, we also investigated long-term leaching (using a long exposure time) of a 50 L/S ratio. The exposure time or temporal horizon (years) was calculated using the formula provided in Equation (1) [25]:  
\[\frac{\mathrm{L}}{S}\left(\frac{\mathrm{L}}{\mathrm{kg}}\right)\ =\frac{\mathrm{net\,\, infiltration\,\,(mm/year)\,\times\, temporal\,\,horizon\,\,(years)}}{\mathrm{bulk\,\,density\,\,(kg/m^{3})\,\times\, thickness\,\,of\,\,the\,\,layer\,\,(m)}} \tag{1}\]  
The following parameters were employed in the estimation: L/S ratio for the collected eluate = 0.2-50 L/kg-dry soil; the average net infiltration of the water through the stabilized soil = 20% of annual precipitation (assumption based on a road scenario); the average precipitation was based on average annual rainfall in Korea = 1350 mm per year [26]; the dry bulk density in column = 1200-1460 kg/m\({}^{3}\); and the thickness of the layer = 0.21-0.23 m (Table 2). A literature review revealed the assumed average net infiltration of water passing through a road containing the soil was approximately 20% [27]. Most studies have estimated an infiltration rate in the range of 10% of the annual precipitation [25; 28], but our assumption has an infiltration rate two-fold higher. Using Equation (1), the long-term exposure time was calculated to be 51.1 to 56.8 years for the different soil columns (see Table S2).
## 3 Materials and Methods
### Land Cover Classification  
During this study, a methodology based on the calculation of NDVI from raster images using Landsat 8 and Sentinel-2 images of the same period (2021/2022) in order to compare the precision of the supervised classification of each one and choose the best images for the mapping of the vegetation cover and land use.  
The Landsat program was developed in the mid-1960s by the American space agency, NASA at the instigation of the United States Geological Survey (USGS) and the Department of Agriculture. LANDSAT 8 OLI (Operational Land Imager) was Launched in 2013. Is part of a family of high-resolution satellites to better identify developing environments. The satellite covers the earth every 16 days and produces 2 to 4 images every month with 30 m spatial resolution. The databases of LANSAT 8 OLI used in this paper were downloaded from [46].  
Sentinel-2 is a series of satellites of the European Space Agency and is part of the Earth observation and monitoring program developed under the Copernicus program, the first two satellites circulate with the phasing of 180\({}^{\circ}\) to allow a revisit frequency of 5 days at the equator which offers rich collections of geographical content around the world. The Sentinel-2 satellite incorporates a range of technologies, including radar and multispectral imaging instruments for land, oceans and atmospheres, enabling it to monitor vegetation, land and water cover, pathways inland waterways, and coastal areas. For land classification, the National Geographic Society used billions of human-labeled pixels to train a deep learning model.  
The databases of Sentinel-2 from Esri's beta Sentinel-2 image service for the last five year from 2017 used in this paper are with 10 m spatial resolution were downloaded from ArcGIS Living Atlas of the World [47]. The map values of the 11 classes were coded as: 0: No Data, 1: Water, 2: Trees, 3: Grass, 4: Flooded vegetation, 5: Crops, 6: Scrub/shrub, 7: Built area, 8: Bare ground, 9: Snow/ice, and 10: Clouds.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 6 Experiment
### Confirmation Latency  
The confirmation latency is a key indicator for evaluating the performance of the blockchain. Figure 8 provides comparative data on confirmation latency between traditional sharding techniques and multi-shard storage solutions. It is evident from the data that, compared to traditional sharding systems, the latency in multi-shard storage has been reduced by 40% to 84%. This significant improvement can be attributed to the mechanism for handling cross-shard transactions in multi-shard storage structures, which reduces transaction confirmation latency by eliminating the need for additional communication with nodes in other shards.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 5 Conclusions  
This paper introduces a renovated workflow of the target detection-tracking framework. Some improvements are proposed for the target detection and target tracking module. We use the convolution features extracted by target detection algorithm for the target tracking process. Using convolution features during target tracking improves the effect of target tracking. We propose a target matching strategy based on SIFT. The predetermined target can be selected from results of the target detection algorithms using this strategy. We propose a tracking score as a criterion for tracking failure detection. This criterion can be used to determine whether the tracking process is failed. The target tracking module is tested using the target tracking dataset. The target detection-tracking framework is tested by aerial videos. Evaluation shows that YOLOv3 has high precision in accomplishing target detection tasks and strong detection ability for small targets. Tdarknet has high accuracy in accomplishing target tracking tasks. However, because of its complexity due to the complexity of the Darknet network, the processing speed is slightly slower.  
In fact, we have built a small-scale UAV to undertake the test. The onboard computer is an ARM-core low-cost development board. It can obtain 6-8 frames per second with even very low-resolution images. In the future, we will develop lightweight neural network structure and model quantization skills, so that the detection-tracking architecture could work well in the small-scaled testbed in real time.  
Conceptualization, Z.M.; Data curation, Z.M.; Investigation, Z.W.; Methodology, K.W. and Z.M.; Project administration, Z.W.; Software, K.W.; Supervision, Z.W.; Writing--original draft, K.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation (NSF) of China (No. 61976014). Not applicable. Not applicable. Not applicable. Not applicable.  
All the data in this paper are available on request from the first author. The authors declare no conflict of interest.
## 1 Introduction  
In recent times, the application of technology-driven practices in agricultural food production vis-a-vis crop production, harvesting, handling, processing, transportation, storage, and distribution has not only ensured food security [1] but also affordability [2].  
Efficient use of irrigation, research on developing high-yielding varieties of seeds, and better pest control mechanisms have resulted in greater food production. Though the world population is increasing, food has become more affordable for everyone than ever before as the supply has dramatically increased. Technology has also enabled the markets for food products to expand beyond geographical boundaries; for example, food produced in tropical countries is now available in polar countries, and vice versa, because of increased maritime trade, better storage capabilities, etc. Since harvested raw products undergo multiple stages of processing and often traverse multiple geographies on their journey to reach the plates of the end consumers, the risk of contamination is increased severalfold when compared to food that is produced in the backyard. Governmental agencies across the world have recognized the importance of food safety processes [3] and have imposedregulatory measures, such as food recalls, resulting from investigations [4], voluntary disclosures by food processors/retailers [5], observed illness among the populace [6] after consumption of unsafe food, consumer complaints of contamination, and so on.  
Apart from the health hazards of contaminated foods if not recalled at the right time, at times, companies will face insurmountable financial losses [7; 8; 9] along with the loss of reputation, brand value, the costs of litigation, insurance claims, etc.  
Ensuring food safety across all phases of the food distribution process, from the source to the consumer's plate, is as important as the food itself [10]. As most food production processes are mechanized/automated, food quality assessment at every phase also needs to be automated to identify emerging food safety risks and to provide early warning signals in a timely manner [11].
## 2 Experiments and Results
### Picosecond Amplifier System at 1 kHz
#### 2.1.2. Regenerative Amplifier  
The seed pulse was first amplified by the Nd:YVO\({}_{4}\) regenerative amplifier shown in Figure 4. The laser medium was a 4 \(\times\) 4 \(\times\) 10 mm\({}^{3}\), a-cut, 0.3 at. % Nd:YVO\({}_{4}\) crystal, which was wrapped with indium foil and mounted tightly on a water-cooled copper heat sink at 14 \({}^{\circ}\)C. The surfaces of the crystal were antireflection-coated at 808 nm and around 1 \(\upmu\)m (\(T>\) 99%). Measures were taken in the regenerative amplifier to reduce the thermal effect. Firstly, the crystal is of low doping concentration and long length. It had high absorption for the pump laser (more than 85%) and good cooling capacity. Besides, the pump laser was a fiber-coupled quasi-CW laser diode with a 400 \(\upmu\)m core diameter centered at 808 nm (DILAS, Germany), which produced less heat in the crystal than a CW laser diode. The pump laser was controlled by a trigger signal with a gate width of 120 \(\upmu\)s at 1 kHz and focused into the crystal by an imaging system with a magnification of two. Finally, a thermal compensation cavity was constructed as shown in Figure 4. Since pulse energy in the cavity was very high, laser beams in the crystal and Pockels cell were relatively large to avoid damage. The beam diameters in the crystal and Pockels cell were about 1.2 mm and 1.4 mm. The intra-cavity transverse mode between the convex mirrors did not change when the focal length of the thermal lens changed from 1000 mm to infinity, which was caused by the increasing pump power, for ensuring the stability of the laser cavity. The stretcher and compressor were not used since the pulse duration of 15 ps was long enough in our laser system. In order to improve the contrast of the amplified pulses, the seed pulses passed through a Pockels cell to reduce the repetition rate from 80 MHz to 1 kHz before being injected into the amplifier. The seed pulse with about 1 nJ energy centered at 1064.4 nm was then amplified to 1.5 mJ at 1 kHz under the pump energy of 8 mJ in the regenerative amplifier. Figure 5 shows the pulse building process in the regenerative amplifier. Figure 6 shows the beam quality of the output pulses measured by a commercial instrument (M2-200S-FW, Ophir-Spiricon Inc., North Logan, UT, USA). The result (\(M^{2}<1.15\)) proved a good quality of the laser beam from the regenerative amplifier.  
Figure 4: The scheme of the regenerative amplifier operating at a repetition rate of 1 kHz. M1, M6: Concave mirrors, \(R=\) 900 mm; M2, M5: Convex mirrors, \(R=-\)1000 mm; M3, M4, M7–M10: Plane high-reflection mirrors; PC: Pockels cell; TFP: Thin-film polarizer; FR: Faraday rotator; GL: Glan prism.
## Appendix A  
**Proof of Lemma 1.** First of all, we use \(x_{m_{t}}\) and \(x_{c_{f}}\) to denote the symbols transmitted by D2D TX \(m_{t}\) and CUE \(c_{f}\), respectively, and we assume that \(E[|x_{m_{t}}|^{2}]=E[|x_{c_{f}}|^{2}]=1\). Thus, the received signal of D2D RS \(r\) is:  
\[y_{r}=\sqrt{P_{m_{t}}}h_{m_{r}}x_{m_{t}}+\sqrt{P_{c_{f}}}h_{c_{f}}x_{c_{f}}+n_ {0}, \tag{1}\]  
where \(n_{0}\) is the noise terms with complex Gaussian distribution as \(\mathcal{CN}(0,\sigma^{2})\). The received signal \(y_{m_{r}}\) of D2D RX \(m_{r}\) can be: expressed as  
\[y_{m_{r}}=\sqrt{P_{r}}h_{rm},\beta_{r}y_{r}+\sqrt{P_{c_{s}}}h_{c_{s}m_{r}}x_{ c_{s}}+n_{0}, \tag{2}\]  
where \(x_{c_{s}}\) are the symbols transmitted by CUE \(c_{s}\), and the power normalization factor \(\beta_{r}\) of D2D RS \(r\) can be expressed as:  
\[\beta_{r}=1/\sqrt{P_{m_{t}}h_{m_{t}}^{2}x_{m_{t}}+P_{c_{f}}h_{c_{f}}^{2}x_{c_{ f}}+N_{0}}. \tag{3}\]  
Thus, by substituting \(\beta_{r}\) into (2), the received SINR of D2D TR \(m\) can be expressed as \(\gamma_{m_{t},m_{r}}=\gamma_{r}\gamma_{m_{r}}/(\gamma_{r}+\gamma_{m_{r}}+1)\), where \(\gamma_{r}=P_{m_{t}}h_{m_{t}r}^{2}/(P_{c_{f}}h_{c_{f}}^{2}+N_{0})\) and \(\gamma_{m_{r}}=P_{r}h_{m_{r}}^{2}/(P_{c_{s}}h_{c_{s}m_{r}}^{2}+N_{0})\form a blocking pair. That is, TR \(i\) and RC unit \(RC_{j,k}\) have not been matched with each other, but prefer to be matched, and we have \(\hat{\mathcal{X}}(i)\
eq RC_{j,k}\), \(RC_{j,k}\succ_{i}\hat{\mathcal{X}}(i),i\succ_{RC_{j,k}}\hat{\mathcal{X}}(RC_{ j,k})\).  
During the matching process, since every TR aims to maximize its individual payoff, which is the different between matching benefit and cost, the payoff optimization problem for each TR can be formulated as \(max_{i\in R,k\in C}(w_{i,j,k,c_{s}}-\hat{F}_{j,k}^{RC}),\forall i\in M\). With the assumption that \(RC_{j,k}\succ\hat{\mathcal{X}}(i)\), TR \(i\) must have already proposed to RC unit RC\({}_{j}\) based on the matching rules. However, according to the pricing strategy given in Algorithm 2, there is no \(\hat{\mathcal{X}}(i)=RC_{i,j}\) in the matching result, which represents that TR has given up the request to \(RC_{i,j}\) during the process of rising prices because the final payoff for TR \(i\) matched with \(RC_{i,j}\) is zero. Moreover, the winner in the contention for \(RC_{j,k}\) is \(\hat{\mathcal{X}}(RC_{j,k})\), i.e., \(\hat{\mathcal{X}}(RC_{j,k})\succ_{RC_{j,k}}i\). Therefore, if \(RC_{j,k}\succ_{i}\hat{\mathcal{X}}(i)\), the condition \(i\succ_{RC_{j,k}}\hat{\mathcal{X}}(RC_{j,k})\) cannot hold, and this means that TR \(i\) and \(RC_{j,k}\) cannot form a blocking pair. The analysis result above contradicts the assumption. Thus, the matching \(\hat{\mathcal{X}}\) obtained from Algorithm 2 is stable.
as two separate units where the MECs were used for in-situ hydrogen gas injection into AD reactors for biogas enrichment [20; 21; 22; 23]. Several studies on biogas upgrading technologies using in situ and/or ex situ hydrogen gas injection have been discussed by [PERSON] et al., 2018 [PERSON] et al. (2018). Bioelectrochemical upgrading has also been reviewed in detail by the authors where small scale batch and continuous setups have been discussed. The authors suggest further research in the areas of long-term electrode stability, high current densities, electron transfer mechanism and reactor design and configuration [24] and some of these issues are addressed in the present article.  
Direct Electron Transfer:  
\[\mathrm{CO_{2}+8H^{+}+8e^{-}\to CH_{4}+2H_{2}O\qquad E=-0.244~{}V~{}vs.~{} NHE} \tag{1}\]  
Indirect Electron Transfer:  
\[2H^{+}+2e^{-}\to H_{2}\qquad E=-0.414~{}V~{}vs.~{} NHE \tag{2}\]  
\[\mathrm{CO_{2}+4H_{2}\to CH_{4}+2H_{2}O} \tag{3}\]  
From the above-mentioned research studies and chemical reactions, the concept of electrochemical CO2 reduction has been successfully established in theory and in practice albeit in short-term and laboratory scale experiments. The process stability with long-term experiments carried out on actual wastewater, as a post processing step for AD, is one of the least explored. Here, we describe a method to treat reject water from a local wastewater treatment plant using an MES, specifically as a biogas upgrading technology by placing it in the recycling loop of an AD system. This system allows efficient direct interspecies electron transfer (DIET; Reaction (1)) that is much more efficient than electrolysis (Reactions (2) and (3)) and is able to produce biogas with less than 10% carbon dioxide. The article also analyses process stability in terms of cathode potentials and pH in continuous flow MES systems over 1-month operation periods.
## 3 MG Communication Protocols and Standards
### IEEE Standards
#### 3.2.2 IEEE 2030  
This standard is a series of standards clarifying the SG Interoperability Reference Model (SGIRM). In part seven it proposes specification of MG controllers and represents MG control system functional framework [43; 44].
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
## 6 Numerical Simulations and Analysis
Differing from reference [53], the transfer strategy in this paper takes the departure time as one of the optimization parameters. Therefore, once the spacecraft leaves DRO, it does not need to perform station-keeping while entering directly into the Moon-Earth transfer orbit. Compared with the total time of flight of the return trajectory in reference [53], it can be seen that the flight time in this paper is lower (12.89 days). Reference [54] presents a strategy to build a lunar constellation from the same launch trajectory in the Sun-Earth-Moon system. First, the launch vehicle approaches the region of L\({}_{1}\) equilibrium via a low-energy orbit. Then, the satellites are released and transferred to low-energy lunar capture orbits. Although this method is designed for lunar constellations, the LEO-Moon transfer is nearly identical to that transferring back when no perturbations are considered. Comparing the Moon-LEO transfer in this paper with the LEO-perilune transfer in Reference [54], it can be seen that although the transfer time of this paper is longer than that of Reference [54], the impulse near the perilune of this paper is 0.043 km/s less than that of Reference [54] (0.228 km/s).