esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 2 Materials and Methods
### Neck Feature Fusion Optimization  
In target detection algorithms, the Pyramid Attention Network (PAN) has been widely recognized as a classical feature extraction method that enhances the performance of target detection algorithms [26]. One crucial step in PAN is the lateral connection, which fuses low-level features with high-level features. This fusion process aims to extract fine-grained target features from feature maps at different scales and combine them into a pyramidal feature map, thereby improving the algorithm's accuracy. To address this issue, Figure 3 depicts the proposed Spatial Pyramid Attention Network (SPAN) structure in this paper. The SPAN structure extends the original PAN structure by adding a layer of feature maps. This augmentation introduces richer target feature information to the model. Furthermore, a 3D non-parametric SimAM is introduced to adjust the weights of the feature maps at the PAN lateral connections. This attention mechanism enables the feature to adjust and reduce the impact of high semantic noise during the fusion process, thereby preserving the crucial features of small targets.  
Figure 2: D-ELAN module and D-MP module visualization.  
SimAM is an attention mechanism inspired by neuroscience theory [27]. It leverages the understanding that neurons exhibit distinct firing patterns based on the abundance or scarcity of information, allowing for the assessment of neuron importance. In the context of target detection, neurons deemed more important exert stronger inhibitory effects on surrounding neurons, which carry critical information and should be assigned greater weight. In remote sensing image target detection, these crucial neurons often play a role in extracting significant features of the target. SimAM combines the energy function with the attention mechanism, as illustrated in Figure 4. This combination enables the weighing of each neuron's input, thereby enhancing the model's attention toward important information. The SimAM attention mechanism is mathematically represented by Equations (1)-(4). In these equations, \(t\) represents the target neuron, \(x\) represents the neighboring neuron, \(\lambda\) is a hyperparameter, and \(e_{t}^{*}\) denotes energy. Lower energy values indicate a higher distinction of the neuron \(t\) from the surrounding neurons, signifying greater importance. Equation (4) demonstrates how the neurons are weighted based on their importance. Compared to mainstream attention mechanisms like SENet [28] and CBAM [29], SimAM offers greater explanatory power and does not require the introduction of learnable parameters.  
\[u_{t}=\frac{1}{M-1}\sum_{i=1}^{M-1}x_{i} \tag{1}\]  
\[\sigma_{t}^{2}=\frac{1}{M-1}\sum_{i=1}^{M-1}\left(x_{i}-u_{t}\right)^{2} \tag{2}\]  
\[e_{t}^{*}=\frac{4{\left(\sigma^{2}+\lambda\right)}}{{\left(t-\hat{u}\right)}^{ 2}+2\sigma^{2}+2\lambda} \tag{3}\]  
\[\widetilde{X}=sigmoid(\frac{1}{E})\odot X \tag{4}\]  
Figure 4: Diagram of 3D attention weight.  
Figure 3: Multi-scale feature fusion structure introducing SimAM attention mechanism.
## 4 Simulation Experiment Analysis
### CG-TSA High-Dimensional Function Finding Performance Comparison Analysis  
To further validate the processing capability of the CG-TSA for high-dimensional optimisation problems, the basic TSA, WOA, GWO algorithm, and SCA were selected and compared with the CG-TSA for 500-dimensional and 1000-dimensional simulation experiments of the benchmark test functions. For each test function, the dimensionality \(d\) = 500/1000, the population size \(N\) = 30, and the number of iterations \(T_{\text{max}}\) = 500. Each algorithm was run 30 times independently, and the mean and standard deviation were taken to judge the performance of the optimisation algorithm. The optimisation search results of each algorithm for the high-dimensional benchmark test functions are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fun No.** & **Name** & **Range** & **Dim** & **Optimal Value** \\ \hline F1 & Sphere & [\(-100\), \(100\)] & \(30/500/1000\) & 0 \\ F2 & Schwefel2.22 & [\(-10\), \(10\)] & \(30/500/1000\) & 0 \\ F3 & Schwefel1.1 & [\(-100\), \(100\)] & \(30/500/1000\) & 0 \\ F4 & Schwefel2.21 & [\(-100\), \(100\)] & \(30/500/1000\) & 0 \\ F5 & Step Function & [\(-100\), \(100\)] & \(30/500/1000\) & 0 \\ F6 & Quartic Function & [\(-1.28\), \(1.28\)] & \(30/500/1000\) & 0 \\ F7 & Rastrigin & [\(-5.12\), \(5.12\)] & \(30/500/1000\) & 0 \\ F8 & Ackley & [\(-32\), \(32\)] & \(30/500/1000\) & 0 \\ F9 & Criewank & [\(-600\), \(600\)] & \(30/500/1000\) & 0 \\ F10 & Penalized 1 & [\(-50\), \(50\)] & \(30/500/1000\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction to benchmark functions.  
Figure 4: _Cont._  
It is clear from the comparison results in Table 3 that the CG-TSA had clear advantages when handling high-dimensional optimisation problems. The CG-TSA results were similar in the 500-dimensional and 1000-dimensional search spaces for the single-peaked test functions F1-F4 and the multipeaked test functions F7 and F8, and globally optimal values were found for F1-F4 and F7. The search results for the other 1000-dimensional test functions were remarkably similar to the data at 500 dimensions. The search results were superior to those of other algorithms, despite the fact that the theoretical optimal value was not found. It can be concluded that the CG-TSA had superior search capability and better robustness than the other algorithms for the search for high-dimensional functions. The proposed CG-TSA is a better optimiser for solving global optimisation problems.  
Figure 4: Partial convergence curves of the proposed CG-TSA and other metaheuristic algorithms of the benchmark test functions: (**a**) Convergence curve of F1; (**b**) Convergence curve of F2; (**c**) Convergence curve of F3; (**d**) Convergence curve of F4; (**e**) Convergence curve of F5; (**f**) Convergence curve of F6; (**g**) Convergence curve of F7; (**h**) Convergence curve of F8; (**i**) Convergence curve of F9; (**j**) Convergence curve of F10.
## 4 Experiments and Results
### TRIDEC Drilling Support Components
#### 4.1.2 Data Analysis Component  
The purpose of the data analysis component is to provide and prepare information and know-how on historical items such as data, inferences, and relations between them. It features the analysis of these items in a fashion similar to conventional online analytic processing techniques. Unlike such systems, it additionally provides analysis methods for generating new knowledge.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
###### Abstract
A detailed explanation of Convolutional Neural Networks, the Support Vector Machine and the proposed scheme NSVM for Automatic Classification of Wireless Signals is presented in Section 3. Section 4 outlines the simulation experiment results and discussion. Finally, the overall study is concluded in Section 5.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms
### Process Description
#### 5.1.1 Source Application Process  
1. After the initialization of the source node is finished, the RREQ packet is sent by setting self-interrupt trigger. In addition to the initialization phase, the network also allows nodes to send applications when it is running normally.  
2. All source nodes initialize RREQ packages. The fields in the packages include unit traffic, priority and coordinates of Source, which are sent by the shortest radial sink.  
3. After receiving RREQ packets, the sink is inserted into the source node queue from large to small according to the angle of the source relative to the sink. Set a self-interrupt because there are multiple source nodes. The interrupt time ensures that all node requests are processed only after RREQ messages have been received from all source nodes.  
4. The sink processes the source node queue, calculates the node weight according to a certain algorithm, assigns angles to each source node in the queue according to the weight ratio, and then sends RACK packets to all source nodes in turn.  
5. After all sources receive RACK packets, they start sending data.
## 3 Results and Discussions
### Degree of Torrefaction  
The relative energy gain of the torrefied biomass is expressed in terms of the degree of torrefaction, which is calculated using Equation (2), obtained from Nithithitron and Suthum [34]. The degree of torrefaction is directly correlated with the calorific value of the torrefied product:  
\[\text{Degree of torrefaction}=\frac{\text{Calorific value of torrefied biomass (MJ/kg)}}{\text{Calorific value of raw biomass (MJ/kg)}} \tag{2}\]  
One of the major advantages of torrefaction is the gain in energy density of the torrefied biomass, which is also supported by Figure 1. The graph in Figure 4 shows the degree of torrefaction with respect to the torrefaction residence time. Nithitron and Suthum [34] indicate that the greater the degree of torrefaction over unity, the greater the relative energy gain per unit mass. Moreover, the authors also  
Figure 3: Thermogravimetric Analysis-Differential Thermal Analysis (TG-DTA) analysis of sewage sludge in a nitrogen atmosphere as a function of the torrefaction temperature.  
suggested that the increase in the degree of torrefaction is due to the increase in the calorific value of torrefied biomass with an increase in the torrefaction temperature. It can be seen from the graph that the degree of torrefaction was more pronounced for 250 \({}^{\circ}\)C at 50 min torrefaction residence time. While the increase in the degree of torrefaction is consistent for 200 \({}^{\circ}\)C, the overall increase is not great. With the increase in the torrefaction temperature from 200 to 250 C, the rate of increase of the degree of torrefaction increases. However, after 300 \({}^{\circ}\)C, the rate starts to decline and a gradual decrease in the rate of the degree of torrefaction is observed at 350 \({}^{\circ}\)C from the inception of torrefaction. In addition, at 300 \({}^{\circ}\)C, the rate of degree of torrefaction increases until 20 min residence time, after which the rate starts declining on a further increase in the torrefaction residence time. This increase, up to 300 \({}^{\circ}\)C, may be attributed to the removal of the oxygen. In addition, sewage sludge is non-lignocellulosic, constituting of the thermally-degradable organic component that degrades easily on higher torrefaction temperature, resulting in a decrease in the degree of torrefaction. [PERSON] et al. [35] and [PERSON] et al. [36], in their report, also established that an increase in the torrefaction temperature increases the calorific value which, in turn, increases the degree of torrefaction. A study by [PERSON] et al. [37] conducted a microwave-assisted torrefaction where the increase in calorific value was observed with an increase in the torrefaction residence time. [PERSON] et al. [38], in their study, showed that the calorific value improved only at 290 \({}^{\circ}\)C. These results can be correlated with the degree of torrefaction attained in this study as the degree of torrefaction increases with the increase in the calorific value.
# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 2 Methods  
To conduct a systematic review on segmentation methods for acne images, a systematic literature review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This standard is commonly used for reviews on clinical trials. The analysis included all studies published until September 2022. The studies data collection was performed by exploring four different databases: Scopus ([[https://www.scopus.com](https://www.scopus.com)]([https://www.scopus.com](https://www.scopus.com)), accessed on 1 January 2022), PubMed Central ([[https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)]([https://pubmed.ncbi.nlm.nih.gov/](https://pubmed.ncbi.nlm.nih.gov/)), accessed on 1 January 2022), Web of Science ([[https://www.webofscience.com/wos](https://www.webofscience.com/wos)]([https://www.webofscience.com/wos](https://www.webofscience.com/wos)), accessed on 1 January 2022), and Google Scholar ([[https://scholar.google.es/](https://scholar.google.es/)]([https://scholar.google.es/](https://scholar.google.es/)), accessed on 1 January 2022).  
The searching strategy was defined according to the PICO model, and it was focused on diagnosis question type. A summary is showed in Table 1. The population included in the studies was defined by 'humans', the main intervention was defined as'segmentation of face images of acne vulgaris patients', the comparison was made with'manual segmentation', and the outcome or measure of the algorithms utility included 'diverse results'.  
Search terms were'acne images segmentation'. Due to the large number of results obtained with Google Scholar, only relevant publications (those that were cited at least once) were considered. This was proven to be a valid criterion, because for the search terms employed, only the first results page included papers related to the topic of interest. Duplicates were removed using Mendeley.  
All the studies describing a segmentation method for acne vulgaris images (applied to any image modality) were included. The references of these studies were also analyzed following the PICO model to prevent relevant papers being excluded. The exclusion criteria were the following:  
* All the implemented methodologies that use enlarged images, showing only one lesion per image, for example, dermatoscopy.
* Studies not published in English.
* Those publications which are neither open access nor available to the Polytechnic University of Valencia members.
* Studies that are a continuation of a previous one, in which the segmentation methodology is yet described. For instance, publications about acne lesions classification after image segmentation, when the applied segmentation method has been presented in a previous study and has not suffered substantial changes.
## 4 Simulation and Experiment Results and Discussion  
### Computing Parameter Setting  
The LiB parameters, and the maximum and minimum values of the charge time and temperature required for the fitness value function are listed in Tables 1 and 2, respectively. The weighting index \(\gamma\) in (16) was set to 0.5, indicating an equivalent significance in the FC assigned to both the charge time and charge temperature. Each current (\(I_{c1}\sim I_{c5}\)), set between 0C and 1C, affects the charging time and temperature. The cut-off voltage (\(V_{cut-off}\)), set as 4.2 V, determines the transition threshold. The ranges of the five objective current parameter values are listed in Table 3. To verify the efficacy and feasibility of the various MOAs, eight MOA methods were considered, in addition to the formula method: PSO, WSO, JSA, GWO, BWO, LFDA, and AGTO. The key considerations for selecting these MOAs include their up-to-date versions, high citation indices among various MOAs, and their applications in other MSCC studies. Most of the chosen MOAs are recent developments. The simulation parameters for each MOA are provided in Table 4.
# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 3 TOSM-Based Autonomous Navigation Framework
### Semantic Autonomous Navigation
#### 3.3.5 Reinforcement Learning  
The main usage of the mental simulation is to generate experience tuples \((\mathcal{S},\mathcal{A},\mathcal{R},\mathcal{S}^{\prime},\mathcal{D})\), where \(\mathcal{S}\) is the current state, \(\mathcal{A}\) is the chosen action, \(\mathcal{R}\) is the reward and \(\mathcal{S}^{\prime}\) the next state after performing \(\mathcal{A}\), and \(\mathcal{D}\) represents whether or not the episode has ended. Those simulated episodic memories are generated whenever the robot is idle, such as when charging. Those memories are then sampled in batches to train a policy using the DDPG algorithm [90]. The RL structure is shown on Figure 9. Three consecutive sparse laser scans and relative distances to the goal are concatenated as the algorithm input. This input goes through an actor-network which outputs two different values. A _sigmoid_ function is used to encode the robot forward speed between 0 and 1, while a _tanh_ function encodes the angular speed between -1 and 1. Those outputs are concatenated with the input and served to the critic-network, which outputs the Q-value (i.e., the expected future reward) for the action chosen on this given state. Finally, the environment rewards are defined as  
\[\begin{cases}r_{completion}-t*r_{time},&\text{when arriving to the goal at time t},\\ r_{closer},&\text{if getting closer to the goal},\\ -r_{collision},&\text{if too close to an obstacle},\end{cases}\]  
where \(r_{completion}\), \(r_{time}\), \(r_{closer}\) and \(r_{collision}\) are positive integers defined trivially.  
As the training step only needs to sample the experience tuples, it can be performed either on-board or by using a central cloud service, provided that such structure is accessible by the robot.  
Figure 8: Example environment generated by the mental simulation building algorithm.
## 5 Experiments
### Analysis of Communication Dropout  
In practical wireless networking applications, communication losses commonly occur in forms of delay, congestion or packet losses. To better cope with such real-world demanding communication conditions, we assume a \(p=0.5\) probability of communication dropout between interconnected UAVs during the training phase. Theoretically, this setting can improve the robustness of our model when implemented in different conditions. Therefore, we have trained two ms-SDRGN models in environments with and without communication dropout, respectively. Then, we test them in SAG-MCS, where the random communication dropout rate \(p\) varies in \([0,1]\), with an interval of 0.1. The evaluation result is shown in Figure 6.  
From Figure 6a, it is observed that as the dropout rate grows in evaluation environment, the reward of the model trained w/o dropout continuously decreases. While the model trained w/ dropout achieves more stable evaluated reward and outperforms the other when the dropout rate \(p\) is larger than 0.4. In terms of the major metric CFE score in Figure 6b, ms-SDRGN trained w/ dropout continuously surpasses ms-SDRGN trained w/o dropout. When the evaluating communication dropout rate changes from 0 to 1.0, the CFE score of ms-SDRGN trained w/ dropout drops around 0.05 point. By contrast, ms-SDRGN trained w/ dropout gets 0.19 point of degradation on CFE score.  
Random communication dropout can affect the stability of timing correlation in GRU memory unit. However, after trained in environment with 50% probability of communica  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Reward** & **CFE Score** & **Coverage** & **Fairness** & **Energy** \\ \hline ms-SDRGN & **6025.82**\(\pm\) 911.13 & **0.8911**\(\pm\) 0.1699 & **1.5345**\(\pm\) 0.1509 & 0.5205 \(\pm\) 0.0423 & 0.9067 \(\pm\) 0.0347 \\ DGN & 4730.38 \(\pm\) 840.32 & 0.8104 \(\pm\) 0.1802 & 1.4636 \(\pm\) 0.1600 & 0.5032 \(\pm\) 0.0514 & 0.9226 \(\pm\) 0.0514 \\ MAAC & 4670.71 \(\pm\) 783.61 & 0.8587 \(\pm\) 0.1799 & 1.4496 \(\pm\) 0.1519 & **0.5255**\(\pm\) 0.0484 & **0.8992**\(\pm\) 0.0373 \\ DQN & 3291.03 \(\pm\) 631.22 & 0.6273 \(\pm\) 0.1367 & 1.3332 \(\pm\) 0.1554 & 0.5027 \(\pm\) 0.0521 & 1.0819 \(\pm\) 0.0227 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of DRL Baselines.  
tion losses, our ms-SDRGN is proved to be more robust, and will not result in significant performance loss even under unreliable communication conditions.
## 2 Experimental Setup and Methods
### Signal Processing and System Calibration
Intensity channel and R-Pdop at 85\({}^{\circ}\) are demonstrated in Figure 4; both parameters show the stability of continuous measurements and a significant difference between samples with different sizes. However, the polarization signal and the light-intensity signal show completely different behaviors. Figure 3: Calibration results of monodisperse polystyrene spheres. (**a**) Measurements of PSL before calibration. (**b**) Measurements of PSL after calibration. As shown in Figure 4c, the standard deviation of original R-Pdop data at 85\({}^{\circ}\) for three different-sized PSL spheres is greater than 0.08. With the increase of the average points, the standard deviation first declines sharply and then becomes smooth at 300 points. The single-scattering signals of suspended particles can be influenced by the spatial orientation and position of particles. Therefore, to mitigate the effects mentioned above, in the follow-up studies, our polarization scattering results are treated by 300-point average processing for a single-component aerosol sample. In this work, we defined polarization feature resolution (PFR), as PFR = 2 \"standard deviation, to reflect the stability of the measured polarization index and to quantitatively evaluate how much difference between polarization indices is enough to distinguish different samples. In the following section, several types of aerosols with various complex refractive index values and micromorphology were analyzed to assess the aerosol recognition capability based on our multidimensional polarization index system.
# OTFS-Based Handover Triggering in UAV Networks
## 6 Mathematical Analysis of the Channel Estimation Error Effect  
Although the proposed scheme provides stable communication links and low handover rates, it is vulnerable to channel estimation errors. This channel estimation error mainly depends on the channel estimation strategy used. In the following mathematical analysis, we will investigate the effect of the channel estimation error on the achievable spectral efficiency in bps/Hz of the proposed handover strategy. Towards that, we analyzed the most dominant OTFS channel estimation techniques, which are LS [27] and MMSE schemes [28]. Following the procedure given in [29], let us assume that the channel estimation error in the DD domain is defined as follows [29]:  
\[\mathcal{H}_{i_{q}}-\mathcal{\hat{H}}_{i_{q}}=a, \tag{37}\]  
where \(\mathcal{H}_{i_{q}}=h_{i_{q}}e^{-j2\pi a_{i_{q}}\hat{P}_{i_{q}}}\) and \(\mathcal{\hat{H}}_{i_{q}}=\hat{h}_{i_{q}}e^{-j2\pi\hat{a}_{i_{q}}\hat{P}_{i_{q}}}\) are the actual and estimated channel coefficients of path \(i_{q}\). \(a\sim\mathcal{N}\big{(}0,\sigma_{\mathcal{H}}^{2}\big{)}\) is a Gaussian random number of zero mean and variance of \(\sigma_{\mathcal{H}}^{2}\), where \(\sigma_{\mathcal{H}}^{2}\) is equal to the MSE between the actual and estimated channel path, as defined in [29]. The spectral efficiency \(\psi_{\mathcal{H}}\), including both AWGN and channel estimation errors, becomes the following [29]:  
\[\psi_{\mathcal{H}}=\log_{2}\left(1+\frac{P_{\mathrm{Tx}}|\mathcal{H}|^{2}}{MSE _{\mathcal{H}}+\sigma_{g}^{2}}\right), \tag{38}\]  
where  
\[MSE_{\mathcal{H}}=\frac{1}{MN}{\sum_{l=0}^{M-1}{\sum_{K=0}^{N-1}}}{\left|h_{DD }[l,k]-\hat{h}_{DD}[l,k]\right|^{2}}, \tag{39}\]  
In the following, we will evaluate the value of \(MSE_{\mathcal{H}}\) for both LS [27] and MMSE [28] OTFS channel estimators.