esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation  
In this paper, we mainly consider the data downloading problem in a polar orbit constellation. We consider the constellation with \(N\) orbits, where each orbit contains \(M\) satellites in the space segment. The angle between the orbital planes is \(\pi/N\). The LEO satellite network is modeled as a undirected weighted graph, where \(\mathbf{V}\) is the set of satellites and \(\mathbf{E}\) is the set of wireless links, including ISLs and feeder links. Let \(l_{i,j}\in\mathbf{E}\) denote the ISL established by satellites \(v_{i}\) and \(v_{j}\). Intra-orbit ISLs can always be maintained, however inter orbit ISLs in the earth's polar area will be momentarily disconnected when a satellite travels through the region and will be re-established after exiting the zone. There is an orbital seam between neighboring orbits because the satellites in these two orbits are moving in opposite directions. The relative speed of the satellites on both sides of the orbital seam is too fast, which leads to the high cost of link establishment [14], hence this paper assumes that satellites on either side of the orbital seams do not establish.  
Furthermore, two satellites in neighboring orbits will swap relative positions after traveling over the polar areas, as shown in Figure 2a. When the upper part of Figure 2a is rotated 180 degrees, the topology of the network translates to Figure 2b. In this way, the topology of the LEO satellite network can be modeled as a set two-dimensional mash structure. The dynamic nature of a network topology is principally driven by the establishment of inter-orbit ISLs, as satellites enter and exit the polar area, and by the interference that prevents laser terminals from establishing ISLs due to a high bit error rate.  
Let \(V_{ES}\in V\) represent the set of visible satellites that are located above the ESs and can establish feeder links with the ESs. When the satellite \(v_{i}\) can establish the feeder link \(W_{i}\) with the ground station, the satellite will join the set \(V\). When the feeder link of the satellite is disconnected, it will exit the set \(V\). There are many ESs scattered around the globe. Each ESs is equipped with \(k\) MIMO antenna arrays, each of which can establish a feeder link to a visible satellite. Due to the fact that all satellites follow their own orbits, their contact time windows with ESs (i.e., the start and finish times of windows) are known. On account of that the satellite travels with period \(T\), the satellite network topology is likewise regularly transformed with period \(T\). This paper separates the satellite operating period into time slots of the same size and duration of \(\tau\), such that the timeline can be represented as 0, 1\(\tau\), 2\(\tau\),..., \(T\). The network topology in each time slice is fixed so as to eliminate the influence of satellite network dynamics on routing algorithm.
## 4 Discussion
### Data Sampling in the Context of Limited Reference Data in Large-Scale Study Area  
Our study applied a sampling scheme to account for capturing different local particularities for the cocoa and non-cocoa classes. Diverse environmental conditions causing intra-class variability can be expected across large-scale areas. Although cocoa is a perennial crop, it may experience different spectral behaviour based on the health status and the local environmental conditions. For instance, stressed cocoa trees or ones suffering from drought respond spectrally differently from healthy cocoa trees [34; 120]. To achieve representative sampling, we allocated polygons from diverse and geographically scattered locations to the training, validation and test datasets. Different splitting designs were applied to each class due to their disparate distribution, i.e., cocoa polygons were unevenly distributed over the study area (Figure 1a), while non-cocoa polygons were more evenly distributed (Figure 1). We divided the non-cocoa polygons based on a gridded block scheme to ensure that the training, validation and test data sampling was performed in disjoint blocks [60]. On the other hand, we allocated the cocoa polygons into training, validation and test datasets based on spatial homogeneous groups with a minimum distance of 50 km from each other.  
Our sampling scheme's objective was also to ensure the spatial independence between the training, validation and test datasets, which is critical for unbiased model performance [59; 60; 121]. Strategic sampling rather than random sampling is suggested to address issues of spatial autocorrelation, according to which spatially adjacent observations have inherently similar characteristics [60; 121]. For our study, random sampling was deemed inappropriate because not only does it ignore spatial autocorrelation but it also requires large-sized datasets to sufficiently represent the classification classes [28]. Previous national-scale studies with millions of reference pixels available trained different random forest models at regional partitions of the study area to capture site-specific particularities and local climatic conditions [113; 117]. However, this promising technique is not feasible with limited and scattered reference data.  
Given that we did not have randomly sampled and stratified reference data, it was not feasible to calculate an unbiased cocoa area estimation [23], and therefore, we did not report such figures in our study. Pixel counting was not performed, as it would have resulted in an unreliable area estimation.
# Sorting of Fresh Tea Leaf Using Deep Learning and Air Blowing
###### Abstract  
The sorting of machine-picked fresh tea leaves after mechanized harvesting remains a challenge because of the complex morphological characteristics and physicochemical properties of fresh tea leaves. First, the recognition results of four types of models, namely, YOLOv5, YOLOv3, Fast RCNN, and SSD, were compared. It was found that YOLOv5, with guaranteed recognition accuracy, had a recognition speed of 4.7 ms/frame (about four times that of the second ranked YOLOv3). Therefore, this study presents a novel fresh tea leaf sorting system that provides rapid and high-precision multi-channel sorting for four grades of tea leaves using a tea leaf recognition model based on the You Only Look Once (YOLOv5) deep learning model. Subsequently, a raw dataset, consisting of 6400 target images of different grades and different moisture contents, was used to evaluate three different optimization methods. Among these, the Stochastic Gradient Descent (SGD) optimization method was found to provide the best model training results with an average recognition accuracy of 98.2%. In addition, the recognition efficacy of the recognition model was found to be positively correlated with the gradient coverage of tea's moisture content in the training set. Theoretical analysis was then conducted, along with the experimental investigation of the air-blowing force on the fresh tea leaves in the sorting process, with 30\({}^{\circ}\) determined to be the optimal air-blowing angle. Finally, the overall results showed that the construction of the full moisture content training set enabled a model recognition accuracy of up to 88.8%, a recall of 88.4%, a recognition speed of 4.7 ms/frame, and an overall sorting accuracy of 85.4%. This result is promising for multi-channel sorting of fresh tea leaf grades in complex situations, and as such provides a strong basis for the application of tea leaf sorting equipment.  
eta; sorting; YOLOv5s; air blowing; moisture content +
Footnote †: journal: Applied Sciences
## 6 Computational Results and Discussion
### Direct Deliveries
#### 6.1.1 Area-Based Approaches  
As stated above, mainly full-truck loads are assumed, i.e., tours are sequentially planned for all city districts. The results are given in Table 2. Column _VKT DC to the Center of the Districts and Back_ gives the distances for driving from the DC to the respective centers of the districts and back to the DC. These distances need to be added to the distances traveled on the tours within each district.  
The kilometers traveled within each district are first estimated on the basis of Equation (1) for each district, denoted as _Approx._ in Figure 8. In order to validate these results, they are compared to actually solving the TSP for respective customer locations and orders. Here, two different settings for the underlying data sets are distinguished, a random selection of customer locations, denoted as _TSP (RAND)_ and using nodes from open street map, denoted as _TSP (OSM)_.  
The results show a high impact of the choice of data sets for the example of Dortmund (see Figure 8). In all scenarios, the sum of VKT is about 23% less for the TSP approach with fixed regions when using the OSM data compared to randomly generated data sets. This is due to the relatively high degree of areas in Dortmund where no residents live.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Demand** & **Number** & **VKT DC to the Center** \\
**Scenario** & **of Tours** & **of the Districts and Back** \\ \hline H & 49 & 946 \\ S1 & 71 & 1,358 \\ S2 & 88 & 1,698 \\ S3 & 106 & 2,069 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the number of tours and kilometers traveled daily to the start of the tours within the districts and back to the DC.  
Figure 7: Demand scenarios Hermes (H) and S1.  
The choice of data for customer locations also has a significant effect when looking at the results for the approximation approach, where Equation (1) either leads to too optimistic (RAND) or pessimistic (OSM) results. When actually solving TSP instances in the respective districts, a deviation of \(11\%\) is obtained when assuming OSM data, and \(-16\%\) for randomly generated customer locations compared to the simple approximation approach. As a result, further studies should rely on more realistic data (as given by OSM), and one should always carry out tests before possibly integrating approximation approaches. Here, the original correction factor of \(0.765\) in Equation (1) could be changed in order to obtain more realistic results.
## 1 Introduction
### Band Selection and Complete Band Combinations  
Most often, the retrieval of a biophysical variable based on remotely sensed data is derived from a preferred individual spectral wavelength range, such as the red-edge band which highlights vegetation vigor. Alternatively, a spectral index is employed as a proxy for a vegetation trait [57]; for instance, the Normalized Difference Vegetation Index (NDVI) has been a popular and frequently used spectral index. However, several studies have indicated that this is not always the solution yielding the most accurate results for certain vegetation traits [58; 59]; it is often the case that information inherited in bands is overlooked, leading to the glorification of mainstream spectral indices, such as the NDVI in the case of AGB quantification.  
The current study scrutinizes the selection of the spectral information inherited in the optical and SAR satellite systems for the retrieval of AGB. We calculate the exhaustive spectral combinations from the Sentinel-1 and Sentinel-2 satellites to investigate the degree to which the full spectral information is utilized when retrieving AGB. Based on in situ ground truth information collected in a private forest in Indonesia, we compare the performance of the satellite-based retrieval approach proposed and benchmark our findings against the AGB estimation based on traditional vegetation indices. Finally, we showcase the superiority of examining the full potential of the exhaustive band combinations and suggest it as a methodology for remote sensing-based biophysical retrieval.
## 3 Results
### Mathematical Model
#### 3.2.1 Determination of Thermal Conductivity of the Vapor Phase
[15; 45] propose an empirical equation for the evaluation of fuel mixtures if their components have only slightly different molecular weights (\(\frac{M_{1}}{M_{2}}<4\)):  
\[\lambda_{mix}=\lambda_{1}^{gas}\cdot x_{1}+\lambda_{2}^{gas}\cdot x_{2} \tag{13}\]  
The inverse of thermal conductance can be obtained by employing Equation (14):  
\[\frac{1}{\lambda_{mix}}=\frac{x_{1}}{\lambda_{1}^{gas}}+\frac{x_{2}}{\lambda_ {2}^{gas}} \tag{14}\]  
[PERSON] and [PERSON] [46] have noticed that when calculating a mixture composed of two fuels according to the Equation (13), higher values \(\lambda_{mix}=\sum x_{i}\cdot\lambda_{i}^{gas}\) are obtained compared to those determined experimentally, and Equation (14), in contrast, leads to lower \(\lambda_{mix}^{\prime\prime}=\sum\frac{x_{i}}{\lambda_{i}^{gas}}\) values. Thus, the authors proposed an empirical formula that allows for obtaining values close to experimental results:  
\[\lambda_{mix}=a_{mix}\cdot\sum x_{i}\cdot\lambda_{i}^{gas}+(1-a_{mix})\frac {1}{\sum\frac{x_{i}}{\lambda_{i}^{gas}}} \tag{15}\]  
For blended fuels, [PERSON] and [PERSON] [46] recommend the coefficient \(a_{mix}=0.5\). According to their estimates, the average error for gaseous mixtures containing H\({}_{2}\) or He varies from 2.6 to 11.4%. For the assessment of the thermal diffusivity of fuel mixtures, [PERSON] and [PERSON] propose relying on the diagrams published in [47]. In the case where the molecular weights of the components are similar (\(\frac{M_{1}}{M_{2}}<4\)), the choice of the right formula for precise calculations depends on the possibility of assessing the molecular polarity that makes up a particular mixture. If the degrees of molecular polarity of the components are similar, the Additivity Rule can be applied (5.8). For mixtures containing polar and nonpolar molecules, [PERSON] and [PERSON] [47] suggest the following equation:  
\[\mu=\frac{1}{3}\cdot\rho\cdot\bar{w}\cdot\bar{I} \tag{7}\]0  
As indicated in Ref. [47], the maximum calculation error according to Equation (16) does not exceed 5.1%. Considering that the dipole moment values of diesel fuel and RME molecules are similar, and the ratio of their molecular weights is \(\frac{M_{1}}{M_{2}}<4\), then, according to [47] recommendations, the Additivity Rule can be applied to calculate the thermal conductivity of fuels (gas phase) comprising a mixture (4.8). For a fuel mixture of 80% MD + 20%RME (\(x_{1}=0.8;x_{2}=0.2\)), at \(T=293\) K, when the thermal conductivity for diesel fuel is \(\lambda_{1}^{gas}=95.72\) W/(m\(\cdot\)K), and for RME is \(\lambda_{2}^{gas}=66.46\) W/(m\(\cdot\)K), \(\lambda_{mix}=92.78\) W/(m\(\cdot\)K).
# Utilization of Yacon Damaged Roots as a Source of FOS-Enriched Sweet-Tasting Syrup
## 3 Results and Discussion
### Yacon Varieties Composition
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variety**} & \multirow{2}{*}{**Fraction**} & \multirow{2}{*}{**Yield (\%)**} & \multicolumn{6}{c}{**Carbohydrate Composition (molar \%)**} & \multicolumn{2}{c}{**Total**} \\ \cline{3-10}  & & & **Rha** & **Fuc** & **Ara** & **xyl** & **Man** & **Gal** & **Glc** & **UA** & **(g/kg)** \\ \hline
**Hualqui** & & & & & & & & & & & \\  & _EtSn_ & 49 & - & - & \(1\pm 0\) & - & \(22\pm 1\) & - & \(70\pm 2\) & \(7\pm 1\) & \(331\pm 37\) \\ Skin & _EtRe_ & 31 & tr & tr & \(3\pm 0\) & \(1\pm 0\) & tr & \(1\pm 0\) & \(9\pm 1\) & \(86\pm 1\) & \(220\pm 9\) \\ Pulp & _EtSn_ & 86 & - & - & - & - & \(23\pm 1\) & - & \(71\pm 1\) & \(6\pm 2\) & \(402\pm 15\) \\  & _EtRe_ & 9 & tr & - & \(2\pm 0\) & \(1\pm 0\) & tr & \(1\pm 0\) & \(10\pm 2\) & \(84\pm 2\) & \(335\pm 11\) \\ \hline
**Crespo** & & & & & & & & & & \\  & _EtSn_ & 64 & - & - & - & \(24\pm 1\) & - & \(71\pm 1\) & \(5\pm 0\) & \(538\pm 38\) \\ Skin & _EtRe_ & 34 & \(1\pm 0\) & \(1\pm 0\) & \(13\pm 1\) & \(1\pm 0\) & \(22\pm 3\) & \(5\pm 0\) & \(48\pm 6\) & \(9\pm 1\) & \(338\pm 47\) \\ Pulp & _EtSn_ & 56 & - & - & - & \(28\pm 2\) & - & \(67\pm 1\) & \(5\pm 1\) & \(547\pm 26\) \\  & _EtRe_ & 42 & tr & tr & \(4\pm 0\) & \(1\pm 0\) & \(28\pm 1\) & \(2\pm 0\) & \(49\pm 1\) & \(15\pm 1\) & \(500\pm 16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Neutral sugar and uronic acid composition of the ethanol-soluble material (EtSn) and residue (EtRe) of the Hualqui and Crespo yacon varieties. Rha—rhamnose; Fuc—fucose; Ara—arabinose; Xyl—xylose; Man—hamnose; Gal—galactose; Glc—glucose; UA—uronic acids. Figure 3: Representation of the (**a**) carbohydrate composition (g/kg dry matter) of the damaged roots of yacon Crespo and Hualqui varieties and (**b**) relative proportion of the different degrees of polymerization of fructoligosaccharides as determined by HPAEC-PAD. Glc—glucose; Fru—fructose; FOS—fructooligosaccharides; DP—degree of polymerization.
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 3 Physical Design of DTL Accelerator
### Longitudinal Beam Dynamics Considerations  
The designed accelerating gradient E0 is shown in Figure 8a. A ramped average E0 field ranging from 1.10 MV/m to 1.75 MV/m is adopted to more easily accept the incoming proton beam and to make the 162.5 MHz DTL length compact. The high average electric accelerating field in the 325 MHz DTL tank is held constant at E0 = 2.85 MV/m, maximizing the energy acceptance and energy gain.  
The synchronous phases (\(\varphi_{\mathrm{s}}\)), shown in the Figure 8b, are ramped in both DTLs from \(-30^{\circ}\) to \(-26^{\circ}\) and from \(-30^{\circ}\) to \(-25^{\circ}\). The initial synchronous phase starts at the same value of \(-30^{\circ}\) at the RPQ exit to capture the proton beam in longitudinal phase space, then ramping \(\varphi_{\mathrm{s}}\) to increase the accelerating efficiency.  
The longitudinal beam dynamics simulated with the TraceWin code [46] are shown in Figure 9. All macro particles are within the stable bunch widths in the whole DTLs, and no beam loss is found in the longitudinal phase space.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of the Nine Storage and Maintenance Methods on the Performance of Ni-MH Battery Modules  
The test results of Ni-MH battery modules from manufacturers A and B were averaged to obtain the self-discharge rates, capacity recovery rates, and charge-discharge power and efficiency change rates resulting from the nine different storage and maintenance methods, as shown in Figures 20-23, respectively.  
We can see from Figure 20 that the average monthly self-discharge rate of the Ni-MH battery modules with the \(DDC\) mode (methods 1, 2, and 3) was larger than the rest, since these modules were not fully charged and discharged, which deactivated the active substances after long-term storage [20]. Figure 21 shows that the minimum capacity recovery rate at room temperature was 98.7% with a capacity loss less than 1.3%. As seen from Figure 22, the charge-discharge power dropped obviously during the storage and maintenance, which indicates that the storage mainly affected the power characteristics of the Ni-MH battery modules. Moreover, Figure 23 demonstrates that the average efficiency reduction rates with \(DDC\) mode were 3.6% for charging and 5.9% for discharging, which were slightly higher than those of \(FDC\) mode (3.3% for charging and 4.2% for discharging) and those of \(FDC+10\%\) mode (2.7% for charging and 3.7% for discharging).
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 2 Fundamentals of QZS
### Dynamic Characteristics of QZS Isolation
In summary, it can be seen that for the vibration isolation system to appear chaotic, oscillatory, and bifurcated, and other unstable phenomena, it can be avoided by parameter design. However, problems such as parameter misalignment and polarization are inevitable in engineering applications. Although the introduction of nonlinear damping helps to reduce the resonance phenomenon of the system and improve the high-frequency transmission characteristics, its effect depends on a variety of factors such as the excitation amplitude of the system, the operating frequency, and so on. Different types of nonlinear damping also have different effects on the system, and more theoretical support is still needed.
## 6 Final Remarks  
In this work, we have applied the collaborative, distributed, and decentralized QL-based GF RA protocol to a massive MIMO scenario for pilot collision control, assuming realistic wireless propagation effects, such as multipath fading, shadowing, path loss, thermal noise, and ICI. As devices cannot know the exact number of pilot contenders without incurring excessive complexity and signaling overhead, our proposed approach takes advantage of the massive number of BS antennas to allow the devices to compute the QL rewards in a simplified way. We have also shown that our proposed approach is robust regarding the number of packets to transmit, which can be as small as 10 or even random, following a discrete uniform distribution, and regarding the number of active devices, which can be randomly activated following a binomial distribution. Our proposed method is also robust regarding the number of antenna variations and does not require more than \(\approx\)30 antennas at the BS to produce significantly improved performance, very close to the ideal (_genie_) cQmMTC protocol of [30]. Possible research directions involve the extension of the protocol to non-terrestrial networks or leveraging other technologies like reconfigurable intelligent surfaces and cell-free massive MIMO.  
F.A.D.B.: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing--original draft, Investigation, Validation. A.G.: Conceptualization, Methodology, Writing--review and editing, Visualization, Investigation, Supervision. T.A.: Writing--review and editing, Visualization, Investigation, Supervision, Project administration, Funding acquisition. J.C.M.: Conceptualization, Methodology, Software, Data curation, Writing--review and editing, Visualization, Investigation, Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grants: 310681/2019-7 and 314618/2023-6, and in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES, Grant ID: 88881.883505/2023-01).  
Figure 11: Average network throughput \(\times\)\(K\), for \(L_{k}\)\(\sim\)\(\mathcal{U}\)(1,10), and \(M=100\).  
Data Availability Statement: The raw data will be made available by the authors on request.
## 3 Results and Discussion
### Material Balance  
Material balances of hydrothermal pretreatment and subsequent alkaline, enzymatic and alkali enzymatic treatments are shown in Table 4. The material balance shows that the higher solid recovery of 88.9% was at the lowest severity of the hydrothermal pretreatment, and it decreased to 76.8% at the highest severity. The total amount of sugars released during HTP and alkaline treatment increased with the severity, from 2.35 (1.62 g/L) to 3.53 (8.76 g/L). It was noticed that the treatments applied showed high efficiency in releasing the sugars from the lignocellulosic materials, with the highest sugar recovery of 94% at the severity of 3.53 (180 \({}^{\circ}\)C, 15 min), obtained by considering the total amount of sugars released (glucose, xylose and arabinose) after combining all treatments applied (hydrothermal, alkaline, enzymatic hydrolysis and the alkali enzymatic). Discriminating between treatments, it was noticed that the hydrothermal pretreatment at the highest severity combined with enzymatic hydrolysis with xylanase had the highest sugar recovery of 64.86%, followed by the combined alkaline and enzymatic treatment with the enzymatic mix (Cellic(CNec2), which showed a recovery of 54.41%. In contrast, the lowest sugar recovery (18.79%) was shown at the lowest severity (140 \({}^{\circ}\)C), after enzymatic hydrolysis with xylanase.