esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 3 Problem Formulation
### Greedy Approach to Multi-Hop Optimization Problems
```
Input :Area: area needs to be covered, for example: 1000 m * 1000 m  Potential_nodes: List of potential nodes  Network_nodes: List of initialized network nodes  Backhaul_radius: Radius for backhaul coverage, for example: 300 m  Grid: updated coverage area  Potential_nodes: Updated list of potential nodes  Network_nodes: Updated list of network nodes  Status: Boolean indicating if all nodes are connected
1 Identify non-donor nodes and store them in non-donor-list;
2(First Hop: Connecting non-donor nodes to donor nodes) foreach donor in Network_nodesdo
3 foreach node in non-donor-listdo
4 ifnode is within Backhaul_radius of donor and data rate constraints are satisfied then
5 Connect the node to the donor forming a first-hop connection;
6 Update backhaul data rates of donor and node;
7 else
8 ifnode is not yet in waiting_listthen
9 Add node to waiting_list;
10
11 end if
12
13 end if
14
15
16 end for
17 Multi Hop: Establish connections for nodes in the waiting list using nodes already connected in the first hop, to_connect_hops means all connected nodes, initial value is all first hop nodes. ;
18 whilewaiting_list is not emptydo
19 Create an empty new_hops list;
20 foreachother_node in to_connect_hopsdo
21 foreachnode in waiting_listdo
22 ifnode is within Backhaul_radius of other_node and meets data rate requirementsthen
23 Establish a connection between node and other_node;
24 Update their data rates;
25 Add node to new_hops;
26 Remove node from waiting_list;
27 else
28
29 end
30 end
31 ifnew_hops is emptythen
32 Break;
33
34 end
35 Update to_connect_hops with new_hops;
36
37 end
38 return Grid, Potential_nodes, Network_nodes, Status;
```  
**Algorithm 2**Backhaul Stage  
The algorithm persistently assesses the nodes within the waiting list, striving to bridge them to the most proximal node or donor that aligns with the data rate specifications. In the event that a compatible connection is unattainable, the specific node is extracted from the network. This necessitates an update of the covered grid points, and consequentially, the algorithm reverts to the coverage stage to ensure that all grid points maintain their coverage integrity. The algorithm continues through these stages until all grid points are covered and all data rate constraints are satisfied, which indicates that a solution has been found. While the greedy algorithm does not guarantee to find the global optimum due to the non-convex nature of the problem, it is a practical choice for obtaining a feasible solution in a reasonable amount of time. Its performance can potentially be improved by incorporating additional strategies, such as local search or heuristics, depending on the specific characteristics of the problem at hand.
# An Innovative Low-Cost Equipment for Electro-Concentration of Microalgal Biomass
## Materials and Methods
### Response-Surface Methodology for Variable Evaluation  
The interaction between three critical variables in the process was evaluated using a 3\({}^{3}\) (3 factors, 3 levels) nonfactorial response-surface design with two central points on STATISTICA 7.0 software (7.0, Statsoft, Tulsa, OK, United States, 2004) (Table 1).  
Each of the experiments was performed using 300 mL of 30-day-old algae culture in a 600 mL beaker. Aluminum electrodes (13 cm long, 5 cm wide), with a distance of 5 mm between electrodes and an electric current of 50 W (50 V, 1 amp), were employed. To avoid deviations by electrode degradation, every single experiment was performed using new electrodes. All the samples were mixed using an Arduino-based magnetic stirrer described by [13].  
The efficiency of cell concentration was determined by optical density (550 nm) of the culture. Each experiment was measured five times (original and four replicates). The efficiency was obtained by replacing the values obtained in Equation (1).  
\[\%Efficiency=100-\{\left(final\ OD_{550\mathrm{nm}}\times 100\right)/Initial\ OD_{550\mathrm{nm}}\} \tag{1}\]  
Once the factors affecting the process were obtained, the stability of the method was evaluated by increasing the reaction volume to 2000 mL. In this stage, the electrodes were reused up to 20 cycles.  
At the end of every cycle, the electrodes were washed with deionized water, dried in an oven (100 \({}^{\circ}\)C, 24 h) and stored in a desiccator until a constant weight was obtained. After the process, the weight of each electrode was recorded, and it was used again until 20 cycles were achieved.  
From the results, a system was designed to fit the needs of a microalgae-culture laboratory. The following parameters were taken into account:  
* maximum efficiency of cell concentration.
* minimum working volume of 500 mL and maximum of 2000 mL.
* Easy cleaning and maintenance.
* Final cost of less than 300 USD.
# Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks
## 4 Experiments
### Experimental Results and Discussion
#### 4.3.1 Textual Sentiment Analysis  
The results of our text based methods against baseline methods are listed in Table 1. While we expected some performance gains because of the use of pre-trained vectors, we were surprised at the magnitude of the gains. For example, in the Two-Class evaluation, the method of CNN_W2V_Char based only on the textual information performs remarkably well, giving competitive results of 81.1% and 74.8% compared to the textual results of 76% and 65% chieved by the CBM_Text model [23].  
As shown in Table 1, segmenting the text messages into Chinese characters can obtain better performance (the enhancement amounts to approximately 2 percentage points) than into Chinese phrases. The learning features from word vectors of Chinese characters preserve more information for the CNN model because Chinese characters can express richer meanings than Chinese phrases. The relatively smaller number of Chinese characters greatly reduces the size of the word vector vocabulary and thus increases time and space efficiency when extracting features.  
In this study, we have described two experiments with convolutional neural networks built on top of pre-trained vectors trained by _word2 vec_. These results suggest that _word2 vec_ is an efficient and effective feature extractor when applied for the purpose of Chinese sentiment prediction.
## 4 New \(\rho\) Selection Method in the Sparsity Regularization Constant \(\gamma_{k}\)  
Section 2 shows that the regularization constant \(\gamma_{k}\) in Equation (5) needs \(\rho\) to be set as \(\rho=\left\|\text{true system impulse response}\right\|_{1}=\left\|\mathbf{w}_{true} \right\|_{1}\). However, we need a new method in the constant selection because Equation (5) is not practical. Therefore, Section 4 proposes a new method to set this constant.  
For a practical method for the constant selection, we can consider using the estimated vector \(\hat{\mathbf{w}}\) instead of using the true vector \(\mathbf{w}_{true}\) because \(\hat{\mathbf{w}}\), the solution with \(l_{1}\)-norm, will be closer to the sparse true vector than the solution of the conventional RLS. The more iteration is repeated, the more \(\hat{\mathbf{w}}\) converges to the true value. Conventional RLS also converges to the true value; however, the solution with \(l_{1}\)-norm, is closer to the sparse true value. Therefore, we can use sparse estimate \(\hat{\mathbf{w}}\) instead of \(\mathbf{w}_{true}\) when we set \(\rho\), and the uncertainty arising from this is compensated through a TLS solution in the next section. When we determine \(\rho\) using the estimated \(\hat{\mathbf{w}}\), we choose between the average \(\rho\) and the current estimate \(\left\|\hat{\mathbf{w}}\right\|_{1}\). Table 1 summarizes the \(\rho\) selection steps.  
The determination method for \(\rho\) value shown in Table 1 is as follows. In Step 1, the sparsity of the estimated \(\hat{\mathbf{w}}\) is calculated. The sparsity represents the sparseness of \(\hat{\mathbf{w}}\) as a number [23]. In Step 2, \(l_{1}\)-norm of the estimated \(\hat{\mathbf{w}}\) is scaled and the value is averaged with the previous \(\rho\) value. The scaling value approaches 1 as the sparsity, \(\chi\), gets close to 1. However, the scaling value gets close to \(e^{-1}\simeq\) 0.37 as the sparsity, \(\chi\), gets close to 0. Therefore, the scaling does not change \(l_{1}\)-norm of \(\hat{\mathbf{w}}\) for the sparse \(\hat{\mathbf{w}}\).  
Figure 1: Sparsity (\(\chi\)) vs. the percentage of none zero coefficients in the channel impulse response.  
Instead the scaling changes the \(l_{1}\)-norm smaller for the dense \(\hat{\mathbf{w}}\). In Step 3, the smaller one between the averaged \(\rho\) and the \(l_{1}\)-norm of the estimated \(\hat{\mathbf{w}}\) is selected as the new \(\rho\) value. In this case, the \(\rho\) value becomes completely new if the \(l_{1}\)-norm of the estimated \(\hat{\mathbf{w}}\) is selected, otherwise the previous trend is maintained. In Figure 1, the reference value 0.75 used in Step 3 means that less than 16% of all the impulse response taps are not zero.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 2 Related Work
### Vision Transformer-Based Image Dehazing Methods  
To address the receptive field limitations of CNNs, Vision Transformer-based [19] methods have recently emerged as a powerful alternative in image dehazing, owing to their ability to model long-range dependencies through multi-head attention and large effective receptive fields. Dehamer [6] incorporates the Dark Channel Prior (DCP) with 3D positional embeddings and Transformer layers, effectively incorporating prior knowledge into the Transformer framework.  
Similarly, DehazeFormer [7] leverages the Swin Transformer [20] for hierarchical feature extraction using shifted window-based self-attention. GTM-Net [21] proposes a Guided Transmission Map (GTM) that combines DCP-based guidance with Transformer modeling and enhances local details through an SOS boost module. MB-TaylorFormer [8] introduces a novel architecture that combines multi-scale patch embedding with Taylor Self-Attention, which approximates Softmax Attention via Taylor series expansion to reduce computational complexity. Additionally, Gating Attention is incorporated to improve the modeling of local information. Although Transformer-based approaches achieve state-of-the-art performance, their high parameter counts and computational requirements often limit their practicality in real-time or resource-constrained environments.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Equipment of Ground Station  
Based on the design in Section 5.1, the actual development was carried out with details for each system as follows:
## 3 Results  
### Effect of Hyper-Parameters on Model Performance  
Hyper-parameters play a crucial role in shaping the model's learning process of LSTM models. Their tuning can significantly impact the model's accuracy, generalization ability, and computational efficiency. The set of key hyper-parameters includes the number of LSTM units, learning rate, dropout rate, activation function, optimizer, and batch size. In brief, increasing the number of units initially enhanced model performance, but beyond a certain point overfitting risks increased, with 50 units balancing the model's capacity and generalization. Moreover, a learning rate of 0.001, a dropout rate of 0.4, the Tanh activation function, the Adam optimizer, and a batch size of 200 provided stable and efficient training.  
Indicatively, Figure 4 illustrates the relationship between the batch size and two key metrics; F1-score and generalization gap when all types of sensors (i.e., accelerometers, gyroscopes, and magnetometers) were placed in all available body positions. F1-score is a measure of model performance with higher values indicating better overall accuracy, whereas the generalization gap is the difference between a model's performance on training data and its performance on unseen test data. A smaller generalization gap implies better model generalization, meaning that the model is less likely to overfit to the training data. Based on Figure 4, choosing a batch size of 200 is justified as it achieves a strong balance between high F1-score and effective generalization. F1-score seems to reach a plateau or slightly decrease after a batch size of around 200. This suggests that increasing the batch size beyond 200 might not yield significant improvements in model performance. In turn, the generalization gap is significantly lower than smaller batch sizes like 32, 64, and 128. Additionally, while larger batch sizes can improve training speed, they also require more memory and computational efficiency [48]. Hence, a batch size of 200 offers an optimal trade-off between performance, generalization, and computational efficiency making it a well-rounded choice.
## 6 Scenario and Methodology
### Performance Metrics
#### 6.1.1 Network Throughput  
Network throughput is the amount of traffic flowing from the source towards the destination. It indicates the performance of a network and determines how many data packets have been successfully delivered from source to destination. Low throughput results in poor performance for the end-users. Packet loss, jitter, and latency are the three important factors that affect throughput of a network.  
\begin{table}
\begin{tabular}{c l} \hline
**Parameter** & **Value** \\ \hline Learning Rate (LR) & \(5.0\times 10^{-6}\) \\ \hline curr\_step & 14 \\ \hline Batch Size & 32 \\ \hline envStepTime & 0.01 \\ \hline Interaction Period & 10 ms \\ \hline Reward Discount (\(\gamma\)) & 0.9 \\ \hline History Length (h) & 300 \\ \hline Episode Count & 15 \\ \hline Scenario & Convergence \\ \hline SimTime & 60 \\ \hline Steps per episode & 6300 \\ \hline TAU & 0.001 \\ \hline UPDATE EVERY & 1 \\ \hline Replay Buffer & 18,000 \\ \hline \end{tabular}
\end{table}
Table 1: DCWO Hyperparameters.  
Network throughput is calculated as  
\[NetworkThroughput=\frac{N_{t}}{\Delta t} \tag{1}\]  
In Equation (1), \(N_{t}\) is the number of transmitted frames and \(\Delta t\) is the interaction period.
# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
###### Abstract  
The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.  
Keywords:s: sugarcane stem nodes; complex natural environment; YOLO v4; visual identification +
Footnote †: journal: Medical Engineering
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 1 Introduction
Therefore, it is important to identify the inputs that significantly influence the price and cost variations of corn and soybean crops. This study focused on data obtained from companies in the state of Sao Paulo due to the significant presence of both crops in this region. The variation and correlation among inputs, prices, and production costs of corn and soybeans were analyzed, as well as how these factors impact predictions for the next harvests. Simulation and mathematical models have been used to optimize soybean production and business processes, price predictability, and other factors. Indeed, simulation applications in agribusiness have shown that models based on intelligent systems can be highly suitable for a wide range of applications in various fields [19; 20]. Unlike equations structured in linear, quadratic, or other predetermined formats, these models offer significantly greater adaptability to response data. They surpass the limitations of traditional statistical models, for instance. Using a Monte Carlo Simulation (MCS), several possible scenarios of production costs can be considered, including uncertainty and variability in the prices of selected key variables. By integrating the cost trend analysis with an MCS, it can be possible to understand and predict how fluctuations in input and labor markets influence the final production cost of corn and soybeans. Rather than providing static estimates, a Monte Carlo Simulation accounts for the uncertainties surrounding each variable, conducting multiple simulations to derive a probabilistic distribution of potential outcomes, thereby taking into consideration market volatility and uncertainties [5; 21; 22]. It is worth noting that this methodology is still relatively underexplored in the context of agricultural products. However, it is of crucial importance because it enhances the accuracy of estimates. The model proposed in this paper supports decision-making by enabling better economic and financial planning and strategic decision-making. For instance, it helps select the best crops to plant and facilitates negotiations and contracts involving the purchase of inputs and labor. Furthermore, it reduces the risk of financial losses and allows for the adoption of strategies that may contribute to a higher competitiveness in the production and export of these crops. The production of corn and soybeans has been growing significantly in recent years asking for a better understanding of its price dynamics. In an industry highly dependent on fluctuations in input prices and market volatility, the ability to accurately predict costs is essential to ensure economic sustainability. Indeed, forecasting cost risks and their causes support better decision-making in agribusinesses.
## 3 Background
### Transformer-Based NMT  
Transformer [4] has an encoder and a decoder. The input of encoder is the source sentences, the output of encoder is the context matrix of the source language. The decoder takes the target tokens and the context matrix of the source language as input, and gives the probability of next word in target language. Both encoder and decoder are composed of multiple identical layers. For the encoder, every layer has a self-attention sublayer and a position-wise feed-forward sublayer. For the decoder, every layer has a self-attention sublayer, an encoder-decoder attention sublayer, and a position-wise feed-forward sublayer. The self-attention sublayer and encoder-decoder attention sublayer have the same attention mechanism. The formula of the attention mechanism is as follows:  
\[Attention\left(Q,K,V\right)=softmax\left(\frac{QK^{T}}{\sqrt{d_{model}}} \right)V \tag{1}\]  
where \(d_{model}\) is the dimension of hidden representations. The difference between the self-attention sublayer and encoder-decoder attention sublayer is that the parameters for calculating attention mechanism are different. In the self-attention sublayer of the encoder, \(Q=K=V=[x_{1};\ldots;x_{n}]\), \(x\) is a token vector in the source sentence, \(n\) is the length of source sentence. In the self-attention sublayer of the decoder, \(Q=[y_{1};\ldots;y_{m}]\), \(y\) is a token vector in the target sentence, \(m\) is the length of target sentence. For the j-th token in the target sentence, \(K=V=[y_{1};\ldots;y_{j}]\). In the encoder-decoder attention sublayer of the decoder, \(Q=[y_{1};\ldots;y_{m}]\), \(K=V=[x_{1};\ldots;x_{n}]\), where \(x\) is the token vector by the last layer output of the encoder. Please refer to [PERSON] et al. [4] for more details.  
The optimization object of transformer-based NMT is as follows:  
\[\operatorname*{arg\,max}_{\theta}\sum_{t=1}^{m}logP\left(y_{t}=k|x,y_{<t},\theta\right) \tag{2}\]  
where \(\theta\) is the parameter of the transformer, \(k\) is the t-th token in the target sentence.
# Towards Flexible Distribution Systems: Future Adaptive Management Schemes
## 4 Market Schemes for Adaptive Control and Management Methods Collaborative TSO-DSO Use
### Sector Coupling and Energy Systems Integration Through Flexibility Market Participation  
Flexibility services provision by the integration of different sectors, for example, by use of EV batteries, electric heating/electric heat storages as well as electrolysis to produce greed hydrogen [108; 109], is increasingly important in future power systems. However, the coordinated operation of an integrated energy system and a distribution network is also increasingly crucial [110]. Slow-charging of EVs at home could be used to participate in the frequency control market, e.g., at evolution stage 2 by disconnecting or stopping charging at level 2 under-frequencies (Figure 8). On the other hand, fast EV charging stations (Figure 8) could participate in frequency control at frequency level 3 deviations (priority on fast EV charging stations).  
Figure 8: DSO network-connected DER participation on the provision of TSO frequency control services at evolution stage 2 through different markets also considering the location and type of DER as well as possible DSO network congestions (see Tables 1–5 and Figure 3).  
charging). This could be realized by adaptive charging with one-directional chargers during frequency level 3 under-frequencies or adaptive charging/discharging with bi-directional chargers at level 3 under- and over-frequencies. Regarding different types and sizes of BESSs' use for frequency control support, it would be beneficial from a DSO, and TSO needs a point of view if centralized BESS units at HV/MV substations could be used for _Pf_-control at frequency deviation level 1 and distributed BESSs at MV/LV substations and LV networks at level 2 (Figure 8). Respectively, distributed BESSs could be used primarily for DSO needs (through _P-/PU-_ and _QU-_control) during frequency level 1 deviation (Figure 8), such as for mitigation of voltage level fluctuations and voltage or thermal congestions, e.g., due to large-scale integration of PVs and EVs. In practice, the active _Pf_-control potential of EVs and BESSs during charging is also dependent on different factors (e.g., battery chemistries, SOC, and charging principle (CC/CV, i.e., constant current/constant voltage)), which should be considered when they are increasingly used for flexibility services provision during evolution stages 2-3. In the future, EVs or EV one-/bi-directional charging stations could also have a similar _QU-_droop to other DER units.