esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Equipment and Methods
### Equipment
The application relies on an iOS-based client and a worldwide network of high-speed servers to measure throughput in three steps: (1) the client establishes multiple connections with the closest throughput server, then (2) the client application down- or uploads a certain amount of data, and finally (3) the time needed to complete the down- or upload is used to calculate throughput. We used an internet-based throughput test in order to emulate real-world industrial environments, where traffic needs to leave the local network. The inclusion of a much wider network ecosystem, as well as type of IEEE 802.11 client and access point used, may introduce variability, and prevent accurate determination of absolute down- and upload rates, but our focus was on _differences_ between data transfer rates in the presence or absence of interference. We wanted to investigate whether interference from the wireless audio transmitter could be a dominant factor in degrading IEEE 802.11 network throughput.
# CentralBark Image Dataset and Tree Species Classification Using Deep Learning
## 2 Materials and Methods
### Deep Neural Model for the Tree Species from Bark  
In recent studies based on Convolutional Neural Networks (CNNs), researchers demonstrated that accurate identification can be achieved by utilizing a quantitative approach to exploring unique features in each tree's bark. Several studies have successfully used CNNs for bark identification, reporting equally good or better accuracy when compared to texture classification methods, with the added benefit of easy implementation and end-to-end training. Deep learning methods require a large dataset of labeled bark images to be effective. A few datasets have already been created, such as BarkTex, TRUNK12, and BarkNet. They contain bark images of hardwood species throughout the US, Canada, and parts of Europe to supplement the deep learning methods, but each dataset varies in its usefulness due to its availability and content of similar species. The datasets were upscaled in the number of images by cropping them. However, this does not improve variability, as the images are from the same trees.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Species** & **Common Name** & **\#Trees** & **\#Images** \\ \hline
1 & _Tilia americana_ & American basswood & 67 & 283 \\ \hline
2 & _Fagus Grandifolia_ & American beech & 280 & 1.132 \\ \hline
3 & _Ulmus Americana_ & American elm & 156 & 640 \\ \hline
4 & _Platanus occidentalis_ & American sycamore & 239 & 981 \\ \hline
5 & _Cary cordininis_ & Bitternut hickory & 149 & 609 \\ \hline
6 & _Prunus serotina_ & Black cherry & 284 & 1.173 \\ \hline
7 & _Robinia pseudoacacia_ & Black locust & 154 & 631 \\ \hline
8 & _Quercus velutina_ & Black oak & 211 & 843 \\ \hline
9 & _Juglans nigra_ & Black walnut & 286 & 1.174 \\ \hline
10 & _Populus deltoides_ & Eastern cottonwood & 279 & 1161 \\ \hline
11 & _Celtis occidentalis_ & Hackberry & 202 & 846 \\ \hline
12 & _Gleditsia triacantlnos_ & Honeylocust & 36 & 171 \\ \hline
13 & _Quercus rubra_ & Northern red oak & 278 & 1158 \\ \hline
14 & _Aesculus glabra_ & Ohio buckeye & 32 & 155 \\ \hline
15 & _Maclura pomifera_ & Osage-orange & 154 & 616 \\ \hline
16 & _Carya glabra_ & Pignut hickory & 142 & 574 \\ \hline
17 & _Sassafras albidum_ & Sassafras & 96 & 400 \\ \hline
18 & _Carya ovata_ & Shagbark hickory & 278 & 1156 \\ \hline
19 & _Acer saccharium_ & Silver maple & 150 & 642 \\ \hline
20 & _Ulmus rubra_ & Slippery elm & 27 & 121 \\ \hline
21 & _Acer saccharium_ & Sugar maple & 290 & 1238 \\ \hline
22 & _Liquidambar styracifflua_ & Sweetgum & 48 & 201 \\ \hline
23 & _Frazimus americana_ & White ash & 150 & 610 \\ \hline
24 & _Quercus alba_ & White oak & 322 & 1327 \\ \hline
25 & _Liriodendron tulipifera_ & Yellow poplar & 315 & 1305 \\ \hline  & _Total_ & & 4697 & 19,147 \\ \hline \end{tabular}
\end{table}
Table 1: Species list and dataset statistics for the CentralBark dataset. The #Trees column indicates how many individual trees were used while capturing the images, while the #Images column indicates the total number of training data images captured for each species.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Medium/Large Satellite Constellations
#### 3.1.3 TanDEM-X
For the first time, pan-Arctic InSAR is made possible with TDX and was applied to observations of a thaw slump in the Mackenzie River Delta, Canada whose aerial photo is provided in Figure 12A [70; 71]. The swamp whose DEM was taken in 2015, shown in Figure 12B, has grown in size compared to 2011, which is represented by negative height changes (depression) in Figure 12C. In addition to this differential InSAR (DInSAR), polarimetric InSAR (Pol-InSAR) by TSX/TDX enables multi-temporal, detailed vertical mapping of croplands and wetlands, which is further discussed in Section 4[72; 73; 74]. Lastly, 3D SAR images can be constructed from multitudes of 2D SAR images taken at slightly different angles, which reduces ambiguity in pixels with the identical range from satellite but different terrain elevations. This technique of SAR tomography is being applied to deformation analysis of rigid bodies including glaciers and urban infrastructure, as shown in Figure 13[70; 75]. Figure 11: Illustration of TDX-TSX flight formation: (**left**) Formation-building procedures (from left to right): identical orbits, horizontal plane rotation, eccentricity offset, and perigee rotation. (**right**) Helix trajectory in the relative coordinates (numbered axis: angular position in orbit, unnumbered axes: along-track and cross-track position). Source: DLR. Figure 12: Mackenzie River Delta, Canada: (**A**) Aerial snapshot of a thaw slump. (**B**) Sentinel-2 L1C. Image containing the slump in August 2017. (**C**) Digital elevation model (DEM) processing results after single-pass TanDEM-X observation in June 2015. Source: ETH Zurich. The nominal design life of TSX and TDX spacecraft has ended in 2012 and 2015, but as of March 2016, the two spacecraft were still operational with similar amounts of residual propellant (TSX: 55%, TDX: 56%) [76]. The fact that calibration of radar instruments did not show any signs of degradation implies the future possibility of extending the lifetime of a medium-sized SAR spacecraft if desired; on-orbit servicing was first demonstrated to refuel a communications satellite in 2020 [77]. The residual capacity of onboard lithium-ion batteries is a function of time, gauging 74% and 82% of the initial 108 Ah for TSX and TDX, respectively. The available spacecraft power has decreased from the peak power of 1.8 kW at the beginning of life (BOL) to the orbital average of 800 watts at the end of life (EOL), which could affect the capability of radar radiation and data communications. The design life of smaller SAR satellites is significantly shorter (<3 years) where the degraded performance should be taken into account for constellation deployment and maintenance because refueling them would not be economical [52; 78].
# A Multi-Carrier Waveform Design for 5G and beyond Communication Systems
## 1 Introduction
It is also shown that each of the \(k\) users consumes the same frequency band yet it has distinct power levels to increase the system capacity and throughput. There are many users who are multiplexed simultaneously. This multiplexing may cause interference with the co-channel. In order to reduce this interference, successive interference cancellation has to be used at the receiver end. In order to correctly decode the received signal, utmost attention must be paid to the allocation of the power to multiplexed users. To achieve this, the power is allocated to each user as the individual link condition (i.e., channel state or channel gain) [9; 10]. The topics discussed in this paper are as follows; Section 2 presents a short review of the recent related work reported in the literature, and Section 3 briefly explains the newly designed waveform. In Section 3, the mathematical equations are derived and analyzed thoroughly. Subsequently, the numerical results are explained in Section 4 and finally, the concluding remarks about the presented work are provided in Section 5.
## 3 Link Level Parameters and Channel Coefficient Generation  
In this section, we describe all involved link level parameters such as propagation conditions, pathloss, Large Scale Parameters and Small Scale Parameters, and finally we illustrate how to generate channel coefficients for the considered scenario.
## 4 Discussion
### Challenges and Outlook  
In this study, we did not separate the data into training data and evaluation data. This was because of the limited number of flux sites and the short coverage time of Sentinel-2 in Northern Europe, and because our main concern was the ability of Sentinel-2 and MODIS to drive the GPP models but not the absolute accuracy of the prediction model. With more data in the future, it will be possible to fine-tune an accurate model by dividing the data into training data and evaluation data. The high \(p\)-values in the test indicated that the conclusion of no significant differences between the data sets may change with a larger sample size.  
Although the empirical regression models were generally accurate, further GPP model development may need to be carried out when developing upscaling methodology from Sentinel-2 MSI. The fact that model selection had more impact on the accuracies than the choice of input data highlights the importance of further studying the input environmental drivers and the model formulations for estimating GPP, e.g., [PERSON] et al. [66]. To further improve the match between flux tower EC data and remote sensing, applying daily rather than annual footprint climatologies is a possibility that should be pursued in future studies [38; 39]. This is a rather complex analysis and given the small differences observed between the different data sets it is not likely that this would radically change the conclusions in the current study. For upscaling across large areas, a practically applicable methodology needs to be developed (such as the abovementioned study by [PERSON] et al. [11]). It may also be fruitful to further investigate the drivers behind the spatial variation of GPP, e.g., canopy openness or leaf area index, and find suitable remote sensing variables that directly or indirectly represent these drivers. Another factor to consider is the contribution of the understory vegetation to the ecosystem carbon exchange, which is not usually taken into account in satellite-derived GPP models. This affects remotely sensed data [67] and could account for errors in GPP estimates at the beginning and the end of the growing season [68]. Along with EC towers, flux chambers may be useful for mapping spatial heterogeneity in GPP in low vegetation areas [69].  
With regard to satellite data processing, we suggest analyzing more efficient vegetation indices, e.g., the Plant Phenology Index, which has shown to correlate more strongly with GPP than EVI or NDVI [70; 71]. Though efficient, this index requires angular effects to be corrected for, which precluded us from using the index with the available Sentinel-2 data.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Analysis and Discussion
#### 4.3.4 Language Commonality Not Fully Used  
From the technical summary, we can see that the ASR research on Uyghur, Kazakh, and Kyrgyz has almost been independently developed. However, we know that the three languages belong to the same language family and that they share many commonalities. Unfortunately, this commonality is seldom utilized in ASR research. A few exceptions include: [PERSON] et al. [132], who developed a general morpheme analysis method based on the agglutinative property of Altaic languages; [PERSON] et al. [150], who transferred a Uyghur model to a Kyrgyz model, assuming that the transfer between languages in the same language family is easy. In spite of these few exceptions, the commonality of the three languages has been ignored by most researchers working on each language.  
One possible reason for this situation is the lack of \"homologous data\". Notice that THUYG-20, KSC, and Common Voice are the most commonly used databases right now for the three languages, respectively. These databases come from different sources and possess different properties in their quantity, quality, and acoustic features. The discrepancy among the datasets prevents a comparative or collaborative study.  
From this point of view, the M2 ASR corpus may provide a valuable solution, as the data of the three languages were collected using similar data collection pipelines, so the quality and speaking style are similar. Therefore, we expect the M2 ASR data to be suitable for conducting a comparative analysis among the three languages and performing language collaborative learning.
Using the solution, [PERSON] obtains the rainfall intensity to find the amount of flow due to rainfall and then determines inlet spacing for a commonly used Singapore inlet. [PERSON] and [PERSON] [19], using the results of [PERSON] [22], analyze the effect of allowable flow spreads on the inlet spacing; they find an exponential relationship for which an increase in the spread results in an exponential increase in the inlet spacing. The concentration time models were then tested against experimental data in [PERSON] [23] and found to be applicable; however, these studies consider flow direction normal to the curb face, despite nonzero road grades. While distributed models may be of interest, due to the uniformity of the road surface and relatively small size of basins, it is also accepted that a lumped model should work. Most roadway drainage models focus on inlet capacity [24; 25; 26; 27; 28; 29; 30; 31] but few give precedence to inlet spacing. However, this may limit the comfort that the users acquire. In this study,we obtained the maximum allowable inlet spacing values based on gutter flow depth restrictions for roadway configurations free from hydroplaning risks (with the goal to raise the standards for multiple user types). The conventional maximum spread criteria were still in place as a secondary concern. The results are presented for various roadway slopes, widths, and a range of rainfall intensities. We test the impact of slope orientation in inlet spacing; we speculate whether slope orientation governs the inlet spacing or if other factors rule. In general, the standards vary with the purpose of roadway, so the analyses were performed using two groups and the results were analyzed for slope variations in both groups. Those are for high-volume-high-speed roads with no tolerance to flow accumulation or high water-film thicknesses and for low-speed-high-pedestrian and bicyclist volume roads that also require lower flow depths, though encroachment into the lanes could be tolerated to some extent. We then determine the inlet spacing for conditions that fit best for the given geometrics (especially cross slopes) and environmental conditions. Hydroplaning-safe configurations were determined as a first step using [PERSON] and [PERSON] [17]'s over-lane flow depth solution as a mandatory step; configurations that pass this were used to determine fully capturing inlet spacings with the Rational Method and [PERSON]'s equation combined.
# Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique
## 2 Materials and Methods
### The Combination of the Ant Colony Algorithm and Finding the Shortest Path Algorithm  
The ACOD algorithm steps:  
The specific steps of the ACOD algorithm are as follows:  
Step 1: each node sends its current energy and position coordinates to the Sink node.  
Step 2: We use the WSN shortest distance algorithm to calculate the initial optimal path from the Sink node to each sensor node in the network, and reverse plan the routing path from each node to the Sink node. And we send the routing path to each sensor node.  
Step 3: each node adopts the optimal path routing generated by the Sink node.  
When a node in the network fails or its energy reaches a threshold, the local node that uses this node for route forwarding uses an improved ant colony algorithm to dynamically plan the routing path.  
Step 4: k ants start from each local node and select the next hop node based on Equations (1) (ant colony) and (2) (ant colony), until the Sink node is reached.  
Step 5: When reaching the Sink node, the Sink node generates a return ant that returns along the original path, and during the return process, it updates the pheromones of each node according to Equation (3) (ant colony).  
If the maximum number of iterations is reached, the algorithm ends. Otherwise, we return to step 4 to continue searching for the optimal path.  
Step 6: When the algorithm reaches the maximum number of iterations Ite, we re-evaluate the local nodes.  
We generate a new routing path and route according to the routing path.  
Using Matlab2023a experimental simulation, we obtain the wireless sensor area: the black lines represent obstacles or WSN fault nodes, and the red lines represent WSN optimal paths.  
As shown in Figures 7 and 8, \"*\" represents the wireless sensor node, and the black line represents the obstacle. When the wireless sensor node encounters the obstacle, the signal transmission is blocked and the network topology changes. The shortest path has been found, with a total distance of 76.8112 and a time of 0.24689 s.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Five-Step Interactive Quality Control Based on Knowledge
#### 3.7.2 Step Two: Interactive Quality Control Based on Available Thematic Maps  
The available thematic maps provided a lot of useful reference information, such as the predominated land cover class, the area ratio and spatial distribution of each land cover class, which were useful for discovering potential classification errors in the preliminary classification result. To effectively use this information, a series of comparisons between the CLC-SW2010 product and available thematic maps were conducted. Once any inconsistencies occurred and were caused by the classification errors, the mapper would further revise classification errors by an automatic or manual method according to the area of classification errors, until any significant differences did not appear in the comparison results.  
The location map of the township government (hereinafter referred to as township map) was used to discover the omitted residential land in the preliminary classification result in this study. It is difficult to extract automatically the residential land only relying on the spectral features, because the residential land in mountainous areas are usually composed by dispersed rural houses. The township governments are an important indicator for the residential land because they are usually situated in the concentrated regions of residential land. In this study, under the guidance of the township map and leaf-on satellite images, the omission residential lands were added artificially.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 4 Design
### Network Topology Improvement
#### 4.1.3 Optimization of the Liveness Protocol Between Domain Gateway Nodes and Intra-Domain ICN Nodes  
By leveraging the historical heartbeat protocol information exchanged between ICN nodes during the local betweenness centrality evaluation phase, the liveness status of both domain gateway nodes and intra-domain ICN nodes can be indirectly determined. This optimization reduces the network communication overhead associated with the conventional heartbeat protocol, thereby enhancing the efficiency of the liveness protocol between domain gateway nodes and intra-domain ICN nodes.  
Figure 6 illustrates a flowchart of the optimized heartbeat protocol. Domain gateway nodes determine the liveness of intra-domain ICN nodes indirectly by matching heartbeat messages exchanged between ICN nodes and other inter-domain nodes. Conversely, intra-domain ICN nodes infer the liveness of domain gateway nodes by receiving proxy requests from them. When a domain gateway node A sends a slow heartbeat request to an ICN node G and receives a reply, both parties transition to the slow heartbeat protocol. By reducing the frequency of direct heartbeat messages exchanged between domain gateway nodes and intra-domain ICN nodes, this optimization effectively minimizes network communication overhead while maintaining liveness detection reliability.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 4 Pre-Processing of Data and Selection of Neural Model
### Analysis and Pre-Processing
The missing values of the selected sets were imputed by using the algorithm MICE [48], considering the same variables at different sites. The time series were generated according to the number of hours necessary for the prediction of future hours. In this particular case, and after some preliminary analysis, we decided to take the observations of the last 24 h in order to predict the values of the following hours. The time series used for the input and output of the neural network model were generated as follows: first, the entry and output periods were selected, corresponding to the first 24 h available and 25 th hour, respectively. The variables to be considered for each hour were extracted and concatenated in an array. Then, the generation process started from the following hour, taking the interval 2-25 for the input period and the 26 th hour for the output period. Again, the variables were extracted and a list was generated. This process was repeated, concatenating the arrays in a data matrix, which was used for both training and test data. This procedure is called sliding window [49] and can be seen in Figure 4. It should be noted that the data obtained in each hour correspond to those given by each station. To denote each station, its coordinates (latitude and longitude) were added. Given that we have 24 h, 11 stations and 7 selected variables (defined in Section 4.3.1), in addition to the two location variables of each station, we have that the number of input variables in each time series corresponds to 11 \(\times\)(2 + 7 \(\times\) 24) = 1870. In the output only one variable is required, \(PM_{2.5}\). In the experiments where the number of stations varies, the number of data inputs will also be changed. The generated layout can be viewed in the header of the Table 3.  
Figure 4: Visualization of sliding window procedure for the generation of time series considering 7 h of input and 1 h of output. The variables presented in Table 3 refer to those mentioned in the Table 2 during a certain period of time; this means that, for example, columns E(Es-01) and E(Es-02) refer to the easting (longitude) coordinates for stations 01 and 02, respectively, and N(Es-01) is the northing (latitude) coordinate for station 01. The letters A, B and C indicate the UTM coordinates. In general, we differentiate the coordinates by unique letters of the alphabet. Additionally, column PM\({}_{2.5}\) (Es-01)(t \(-\) 23) refers to the PM\({}_{2.5}\) concentration 23 h before the reference time t for station 01, and column PM\({}_{2.5}\) (Es-02)(t + 24) refers to the value of PM\({}_{2.5}\) at 24 h after the reference time \(t\) for station 02. The pre-processed dataset with the data from the 11 stations is available to the community ([[https://ldrv.ms/u/s](https://ldrv.ms/u/s)]([https://ldrv.ms/u/s](https://ldrv.ms/u/s))!AnkU8l4 kGM9 whlWy22 fexCGzHByp?e=ZQ9 Hdq, accessed on 1 January 2020).