esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we examined the uplink connection from a ground station to an LEO satellite constellation using code division multiplexing (CDM). Due to the rapidly changing number of visible satellites, summing CDM signals introduces challenges in maintaining an appropriate back-off without significantly compromising output power. We showed that the best performance is achieved when the power amplifier operates in saturation (where we have maximum efficiency, too). In this case, we can directly transmit the sign of the sum of the CDM signals sent to the individual satellites. We then provided an analytical calculation of system performance. Simplified expressions were also derived, and both the exact and approximate error probability expressions were validated through simulations. Additionally, we presented an analytical method for calculating system loss as a function of the number of satellites. A number of results were presented and discussed. This study offers valuable insights for assessing the performance of a ground station  
Figure 19: Relationship between the maximum number of satellites \(N_{S}\) at \(P_{b}(e)^{*}\) for the CDM system with saturated HPA and the linear system.  
operating with CDM in saturation mode when serving LEO constellations, eliminating the need for extensive simulations. Additionally, it offers useful guidelines and quantitative understanding for operating the ground station when using CDM for the uplink. Future investigations could focus on the impact of the proposed approach on the initial acquisition process and the phase error at the receiver. The acquisition stage is critical for synchronizing the receiver with the transmitted signal, and further research could provide valuable insights into how the sign approach and the non-linear amplifier affect the accuracy and speed of this process. Additionally, analyzing the phase error at the receiver would be important to understand its influence on overall system performance.  
Methodology, M.V., R.S. and R.G.; software, M.V. and R.S.; writing, M.V., R.S. and R.G.; supervision: M.V. and R.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency, grant number AO10694, project name Multiple Access Sequences for Satellite Constellations without Centralised Management.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Proposed Methods
### Hyperspectral Data Acquisition and Preprocessing  
#### 3.2.1 Spectral Data Acquisition  
The FieldSpec 4 surface feature spectrometer was used to collect soil hyperspectral data. To eliminate the interference of scattered light, a light source and a black box were used, as shown in Figure 2. The light source was provided by two 50 W (650 lx) quartz tungsten halide lamps. The lamp source was 130 cm high from the soil sample to be measured and irradiated the measurement target at a depression angle of about 50\({}^{\circ}\). The measurement target was located at the center of the facula of the light source to ensure that the light source was uniform in the field of view, and the light source spectrum was as close to the solar spectrum as possible. The soil sample was placed in a culture dish (diameter: 10 cm, depth: 1.5 cm) with all the inside blackened, and its surface was scraped flat with a ruler. The ASD optical fiber probe was vertically fixed at a height of about 3 cm from the measured soil sample. The sensor probe adopted a 25\({}^{\circ}\) field of view angle probe. The area where the probe received the soil spectrum was a circle with a diameter of 1.33 cm. The field of view of the spectrometer was much smaller than the area of the Petri dish and was a circle with a diameter of 0.67 cm; these specifications ensured that the probe received the reflection spectrum of the soil sample. White board correction was performed before the test. Ten spectral curves were collected from each soil sample, and the arithmetic mean was taken as the actual reflection spectrum data of the soil sample.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Samples** &
\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & **Minimum (g kg\({}^{-1}\))** & **Maximum (g kg\({}^{-1}\))** & **Mean (g kg\({}^{-1}\))** \\ \hline Soil moisture & 800 & 124.831 & 181.295 & 142.804 & 24.081 \\ Soil organic matter & 800 & 2.016 & 5.107 & 3.209 & 0.487 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of moisture and organic matter content of soil samples.
## 3 Proposed Methodology
### Heuristics Algorithm in DSS
#### 3.2.3 The SA Procedure and Parameters  
Algorithm 1 described the procedure of the proposed SA. The proposed SA algorithm requires five parameters: \(I_{iter}\), \(T_{0}\), \(T_{f}\), \(\alpha\), and \(K\). \(I_{iter}\) denotes the number of iterations with which the search proceeds at a particular temperature. \(T_{0}\) represents the initial temperature. \(T_{f}\) is the final temperature and is a coefficient used to control the speed of the cooling schedule. Finally, \(K\) is the Boltzmann constant used to calculate accepting a worse solution.  
The proposed SA methodology starts with a random initial solution. Then, the current temperature \(T\) is set to the initial temperature \(T_{0}\). The best solution \(X_{best}\) and the current solution \(X\) are the initial solution. The best objective value is set to be the objective value of solution \(X\). The initial probabilities are used at the first temperature.  
The process generates a new solution \(Y\) from the current solution \(X\) using one of the neighborhood's moves between swap and insert. This process is applied for several iterations at a particular temperature. Let \(\Delta\) be the objective solution difference between \(Y\) and \(X\). In this case, there will be three \(\Delta\) solutions consisting of \(\Delta_{swap}\) or \(\Delta_{insert}\). If \(\Delta>0\), then \(Y\) is better than \(X\); thus, \(Y\) replaces \(X\) as the current solution. Otherwise, the new neighborhood solution is accepted with a probability calculated by the Boltzmann function; The temperature is updated based on the current temperature (\(T\)) decreasing to \(\alpha T\). The SA algorithm is terminated if the current temperature is below or equal to the final temperature \(T_{f}\).  
```
1:Input:\(I_{iter}\), \(T_{0}\), \(T\), \(\alpha\), \(K\),
2:Output:Obj;
3:InitialSolution \(\leftarrow\) Generate randomly;
4:\(I\gets 0\); \(T\gets T_{0}\); \(F_{best}\leftarrow\)Obj (\(X\)), \(X\leftarrow\) InitialSolution; \(X_{best}\gets X\);
5:\(R_{t}\gets 1/3\) for all \(t\) in {swap, reverse, insert};
6:While \(T<T_{f}\);
7:\(I\gets 1\); \(Nt\gets 0\) and \(Ot\leftarrow\mathcal{O}\) for all \(t\) in {swap, reverse, insert}; W\(\leftarrow\) 0;
8:While \(I\leq I_{iter}\);
9:\(r\leftarrow\) random (0,1);
10:If (\(r\leq 0.5\)) then
11:Generate a new solution \(Y\) from \(X\) by random swap neighborhood;
12:Else
13:Generate a new solution \(Y\) from \(X\) by inserting neighborhood;
14:Endif
15:\(\Delta\leftarrow\) obj (\(Y\)) - obj (\(X\));
16:If (\(\Delta>0\)) then \(X\gets Y\);
17:Else \(r\leftarrow\) random (0,1);
18:If (\(r<\exp(\Delta/\text{KT})\)) then \(X\gets Y\); Endif
19:Endif
20:If (Obj(\(X\)) \(<F_{best}\)) then \(X_{best}\gets X\); \(F_{best}\leftarrow\) Obj(\(X\)); Endif
21:\(I\gets I+1\);
22:Endwhile
23:\(T=\alpha T\);
24:Endwhile
```  
**Algorithm 1**Simulated Annealing Algorithm Procedure
# Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis
## 2 Related Work
\begin{table}
\begin{tabular}{l c} \hline [PERSON] et al.’s flow [32]: Tested & [PERSON] et al.’s flow [30]: Tested \\ on two SemEval Twitter datasets & on five manually annotated Twitter \\ from 2015 [38] and 2016 [39]. & datasets (reported in [30]). \\ \hline In order to keep only significant & Replace negative mentions—that \\ information, remove URLs, hashtags & is, replace _won’t, can’t,_ and _n’t_ \\ (for example, \#happy) and user & with _will not, cannot,_ and _not,_ \\ mentions (for example, \#[PERSON]). & respectively. \\ \hline Replace tabs and line breaks & Remove URLs. Most researchers \\ with a blank, and quotation & consider that URLs do not carry \\ marks with apostrophes. & any valuable information regarding \\  & the sentiment of a tweet. \\ \hline Remove punctuation, except for & Revert words that contain \\ apostrophes (because apostrophes & repeated characters to their \\ are part of grammar constructs, & original English forms. For \\ such as the genitive). & example, revert _coooooo_l to _cool_. \\ \hline Remove vowels repeated in sequence & Remove numbers. In general, \\ at least three times to normalise & numbers are of no use when \\ words. For example, the words _cooooool_ & measuring sentiment. \\ and _cool_ will become the same. & \\ \hline Replace sequences of \(a\) and \(h\) with a & Remove stop words. Multiple \\ _laugh_ tag—laughs are typically & lists are available, but the classic \\ represented as sequences of the & [PERSON] stop list [40] was \\ characters \(a\) and \(h\). & selected by [PERSON] et al. [30]. \\ \hline Convert emoticons into corresponding & Expand acronyms to the original \\ tags. For example, convert :) into & words by using an acronym \\ \(\mathtt{smile\_happy}\). The list of emoticons & dictionary, such as the _Internet \&_ \\ is taken from _Wikipedia_[41]. & _Text Shang Dictionary \& Translator_[42]. \\ \hline Convert the text to lower case, and & \\ remove extra blank spaces. & \\ \hline Replace all negative constructs (_can’t,_ _isin’t, never,_ etc.) with _not_. & \\ \hline Use \(\mathtt{PyEnchant}\)[43] for the detection & \\ and correction of misspellings. & \\ \hline Replace insults with the tag bad\_word. & \\ \hline Use the _Iterated Lovius Stemmer_ & \\ [44] to reduce nouns, verbs, and adverbs & \\ which share the same radix. & \\ \hline Remove stop words. & \\ \hline \end{tabular}
\end{table} 
Table 2: Two text pre-processing flows evaluated in the relevant literature.
# Harnessing Artificial Intelligence for Wildlife Conservation
## 3 Applications and Case Studies
### Biodiversity Monitoring  
Conservation AI plays a pivotal role in monitoring biodiversity and assessing habitat health. By continuously collecting and analysing data, the platform enables conservationists to gain a deeper understanding of wildlife population dynamics and their interactions with the environment. For example, in a case study from Mexico, the South American Mammals Model is being utilised to monitor and track jaguars, while in California, the North American Mammals model is used to observe the movements and behaviours of mountain lions. The data collected through these initiatives have provided critical insights into migration patterns, feeding habits, and the impact of human-wildlife conflict. These findings not only enhance our understanding of these species but also inform strategies for mitigating threats and promoting coexistence between human and wildlife populations.
# Assessing Voltage Stability in Distribution Networks:
## 2 Modeling of Stochastic Variables Considering Correlation
### Probability Model for Stochastic Variables
#### 2.1.2 Probabilistic Model of Load  
Research has indicated that fluctuations in the base load of power systems can be effectively described using a normal distribution. To better understand and predict changes of power system loads, a probability model of power based on normal distribution can be established [30]:  
\[f(P)=\frac{1}{\sqrt{2\pi}\sigma_{P}}\cdot\exp\left[-\frac{(P-\mu_{P})^{2}}{2 \sigma_{P}^{2}}\right] \tag{6}\]  
\[f(Q)=\frac{1}{\sqrt{2\pi}\sigma_{Q}}\cdot\exp\left[-\frac{\left(Q-\mu_{Q} \right)^{2}}{2\sigma_{Q}^{2}}\right] \tag{7}\]  
Here, \(f(P)\) and \(f(Q)\) are probability density functions of active power and reactive power of stochastic load, respectively; \(\mu_{P}\), \(\sigma_{P}^{2}\) and \(\mu_{Q}\), \(\sigma_{Q}^{2}\) are the expected and variance of the power, representing the average level and fluctuation range of the power load, respectively.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Distance Transformation  
Image distance transformation, also known as distance mapping or distance transform, is a technique used in image processing to compute the distance of each pixel from the nearest contour pixel within an image. This distance is typically calculated using a defined metric, such as Euclidean or Manhattan distance.  
The result of distance transformation is a new image in which each pixel value represents the distance from that pixel to the nearest contour pixel. This transformation is useful for various tasks, including object segmentation, shape analysis, and feature extraction.  
One common application of image distance transformation is in morphological operations, such as skeletonization or thinning, where it is used to identify the medial axis or centerline of objects within an image.  
After obtaining the distance image, local maxima within the image were identified. Each local maximum corresponded to a point where the distance to the nearest border was maximized. These local maxima served as key reference points within the image. To visualize these points, circles centered at each local maximum were drawn, with a radius equal to the value of the local maximum (as shown in Figure 5). This process effectively  
Figure 4: The weed contour: (**a**) mask contour; (**b**) convex hull area.  
outlined the boundaries of the objects in the image, with each circle's radius extending to the nearest border. By visually representing these contours, we gained valuable insights into the spatial distribution and shape characteristics of the objects present in the image.  
The local maximum values were organized in ascending order. This categorization allowed us to rank the objects according to their size, with larger local maximum values typically corresponding to larger objects. By systematically analyzing and categorizing these values, we gained valuable insights into the spatial attributes within the image. This approach enabled us to effectively classify and differentiate between objects of varying sizes, providing valuable information for further analysis and interpretation of the image data.
## 2 Study Area, Data and Methodology
### Local Spatial Autocorrelation Analysis  
Although global spatial autocorrelation analysis can reveal the overall dependence of the spatial distribution of ESV grid cells in the study area, it does not sufficiently reflect the spatial autocorrelation of local regional units. The local Moran's I\({}_{i}\) index [47] was used to measure the ESV spatial heterogeneity between grid cells \(i\) and \(j\). The calculation equation is given in Equation (10):  
\[\text{Moran}^{\prime}\text{s I}_{i}=\frac{(x_{i}-\overline{x})}{m_{0}}\sum_{ j}w_{ij}(x_{i}-\overline{x}) \tag{10}\]  
where \(m_{0}=\sum_{i}(x_{i}-\overline{x})^{2}/n\). The meanings of \(x_{i}\), \(x_{j}\), \(w_{ij}\) and \(w_{ij}\) are the same as above. When [PERSON]'s I\({}_{i}>0\), it indicates that the spatial distribution of elements presents high-high-type agglomeration or low-low-type agglomeration. When [PERSON]'s I\({}_{i}<0\), it indicates that the spatial distribution of elements presents a high-low-type interlaced or low-high-type interlaced distribution.  
The significance test equation is given in Equation (11):  
\[Z(\text{I}_{i})=\frac{\text{I}_{i}-E(\text{I}_{i})}{\sqrt{VAR(\text{I}_{i})}} \tag{11}\]where \(Z(I_{i})\) represents the level of spatial autocorrelation, \(E(I_{i})\) represents the mathematical expectation of [PERSON]'s I\({}_{i}\), and \(VAR(I_{i})\) represents the variance.
Another category belongs to mathematically synthesized networks derived from broadband matching theory [22; 23; 24]. While these approaches are straightforward to implement, they are constrained by the equivalent circuit model of the load, generate MNs with fixed or patterned topologies, and require the bandwidth, impedance transformation ratio (ITR), and ripple of the frequency response to be balanced during the design procedure [18; 19; 25]. This paper presents a 0.1 \(\upmu\)m GaN-on-Si MMIC PA designed through a combination of analytical network synthesis and numerical optimization methods. Special attention is given to the output matching network (OMN), which employs a synthesized transformer network to absorb the transistor's output capacitance (\(C_{\text{out}}\)). A simple method is offered for selecting the optimal load impedance (\(Z_{\text{opt}}\)) that enables a favorable compromise between \(\text{P}_{\text{out}}\) and PAE. Furthermore, a dual-objective optimization strategy guided by impedance rather than performance metrics is developed, along with an error function incorporating a tunable factor \(\beta\) to manage matching quality across different frequencies. The proposed parameter optimization procedure operates efficiently, as it avoids the need for time-consuming harmonic balance (HB) simulations. The implemented two-stage PA exhibits consistent small- and large-signal characteristics across the 24-31 GHz band. The electromagnetic (EM) simulation outcomes are summarized and benchmarked against those from recent work.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis
### Ablation Experiment  
To quantitatively evaluate the contribution of each component in the Pi-GRU model, this section designs an ablation experiment, comparing three groups of models:  
1. Only GRU branch: Remove the BP branch and geological feature input, and retain the temporal path;
2. Only BP branch: Remove the GRU branch, and only use non-temporal features;
3. Complete Pi-GRU: Include the dual-path fusion architecture.  
The experimental results are shown in Figure 15. The ablation experiment confirms that in the dip angle prediction, the R\({}^{2}\) of the only BP branch is negative (\(\approx\)\(-\)2.0), indicating  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **MSE (Dip)** & **MSE (Azi)** & **R\({}^{2}\) (Dip)** & **R\({}^{2}\) (Azi)** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Memory Usage** \\ **Increment (MB)** \\ \end{tabular} &
\begin{tabular}{c} **Total Number of** \\ **Model Parameters** \\ \end{tabular} \\ \hline BP & 0.21 & 0.78 & 0.18 & 0.22 & 2.05 & 51.7 & 6882 \\ LSTM & 0.09 & 0.34 & 0.66 & 0.63 & 2.44 & 97.05 & 27,690 \\ GRU & 0.08 & 0.24 & 0.68 & 0.75 & 2.95 & 95.96 & 22,650 \\ Pi-GRU & 0.01 & 0.06 & 0.95 & 0.93 & 3.89 & 154.87 & 91,394 \\ CNN- & & & & & & \\ BiLSTM & 0.03 & 0.26 & 0.87 & 0.74 & 3.26 & 137.72 & 87,346 \\ Transform & 0.22 & 0.82 & 0.11 & 0.18 & 3.33 & 111.23 & 21,106 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of comparative experiments involving different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Index** & **Pi-GRU** &
\begin{tabular}{c} **Hardware Capabilities** \\ **(Jetson TX2)** \\ \end{tabular} \\ \hline Reasoning delay & 0.21 ms & \(\leq\)80 ms \\ Train computing power & 0.29 GFLOPS & 1.3 TFLOPS \\ Reasoning computing power & 0.093 GFLOPS & 1.3 TFLOPS \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of computing efficiency in engineering scenarios.  
that its predictive ability is lower than the constant baseline model, proving that non-temporal geological features themselves do not have independent modeling capabilities. After removing the BP branch, the azimuth MSE increased by 47%, proving that geological features are indispensable for trajectory modeling. And through the dual-path fusion, the complete Pi-GRU achieved an R\({}^{2}\) of 0.92 for the dip angle, compared to the only GRU branch (0.78), an increase of 18.7%, highlighting the adaptability breakthrough of heterogeneous features' synergy for complex strata.  
This indicates that by effectively fusing sequential and non-sequential features, Pi-GRU can ensure high prediction accuracy while avoiding complex mathematical derivation processes, providing a practical and feasible solution for real-time wellbore trajectory correction in intelligent drilling systems.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 4 Analytical Energy Consumption Model
### Analytical Model  
Under the above assumptions, our modelling process relies on two fundamental calculations: the energy wasted by a node to transmit a single packet, and the energy wasted by a node to receive and forward a single packet too. For this purpose, we start by considering the scenario shown in Figure 2, where node A is a leaf node that generates and transmits a packet and node B is a relay or intermediate node that receives and forwards the packet from A. As it can also be noticed, we distinguish between forward and backward transmission distances in order to account for the general case where power control is enabled. In general, for an arbitrary node \(X\), \(d_{f}(X)\) is the distance between node \(X\) and the node to which it transmits packets (forward transmission distance), whereas \(d_{b}(X)\) is the distance between node \(X\) and a node from which it receives packets (backward transmission distance). For the detailed analysis, we can use the time diagram shown in Figure 3, which also helps to understand the operation of the LPL TinyOS mechanism in a transmitter-receiver pair. Based on this diagram, the next two subsections are devoted to the analysis of the energy wasted by nodes A and B in their respective roles. Then, we develop a general expression for the energy wasted by an arbitrary node that receives and forwards multiple packets from other nodes, in addition to generating and transmitting an own packet. Since the results take form of statistical distributions, in the last subsection we obtain the corresponding expectations.  
Figure 2: Scenario under analysis: node \(A\) generates and transmits a packet and node \(B\) receives and forwards this packet to its next-hop node.
## 4 Results and Discussion
### Effect of Fin Design Parameters
#### 4.3.2 Fin Spacing  
Figure 8 shows the effect of fin spacing on the heat transfer area and pressure drop. For the fin spacing on the hot side (solid curves in Figure 8), when fin spacing varies from 1 mm to around 3.2 mm, the heat transfer area increases at an accelerating rate and pressure drop descends first then rises to the starting value. When fin spacing varies from around 3.2 mm to around 5 mm, the heat transfer area increases linearly but pressure drop increases slightly. For the fin spacing on the cold side (dash curves in Figure 8), the situation is simpler, both pressure drop and heat transfer area go up in a straight line. Obviously, the spacing of fins on both sides should be near the lower limit, which corresponds to the result of the optimization.  
Figure 5: Effect of number of fin layers on heat transfer area and pressure drop of PFHE. (**a**) Heat transfer area, (**b**) heat transfer coefficient on hot side.  
Figure 6: Effect of plate width on heat transfer area and pressure drop of PFHE. (**a**) Heat transfer area, (**b**) heat transfer coefficient on hot side.  
Figure 7: Effect of fin length on heat transfer area and pressure drop of PFHE.