esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
The MATOPIBA region covers four Brazilian states: Maranhao, Tocantins, Piaui and Baha, with 73 million hectares (ha), consisting of 337 municipalities [26]--(Figure 1). Altitudes vary between 1 and 1254 m above mean sea level (MSL), with the highest altitudes at the extreme west (W) of Baha and the lowest in the north (N) of Maranhao. MATOPIBA encompasses portions of three biomes, Cerrado (90.94%), Amazon (7.27%), and Caatinga (1.64%), corresponding to areas of 66,543,540.87 ha (665,435.41 km\({}^{2}\)), 5,319,628.40 ha (53,196.29 km\({}^{2}\)) and 1,203,107.22 ha (12,031.08 km\({}^{2}\)), respectively [26].  
The rainfall regime in MATOPIBA is seasonal, with the rainy season from September/October to April/May, with values above 900 mm, and the dry season from May to September, with values below 600 mm [34; 35]. Due to its territorial extension, MATOPIBA presents a rainfall distribution according to the biomes, with the highest (smallest) records near the border with the Amazon (Caatinga) [34]. According to the evaluation of the variability of rainfall over the Brazilian Cerrado carried out by [PERSON] et al. [3] on a multiscale, the largest accumulations occur in the west (W) (Mato Grosso-MT and Goias-GO), northwest (NW) (Tocantins), and north (N) (Maranhao) sectors of the biome, with values > 1500 mm\(\cdot\)year\({}^{-1}\), mainly in the transition zone with the Amazon.  
About the terrain, 47.9% of the areas are flat (slope up to 3\({}^{\circ}\)) and 33.7% of areas have gentle slopes (slope between 3\({}^{\circ}\) and 8\({}^{\circ}\)); data from the agricultural aptitude of  
Figure 1: Geographic location of the MATOPIBA region in Brazil and South America (**a**) and elevation of the MATOPIBA region (**b**). SRTM data source: [PERSON] et al. [33].  
Matopiba reveal a significant proportion of land with high potential for intensive agriculture development-approximately 26 million hectares (35% of the total) are classified as possessing good and regular aptitude [36]. The predominance of large grain production regions in the Plateaus and Depressions areas occurs due to the ease of the mechanization of cultivation and the lower risk of erosion occurring, which is a cause of precautions due to the predominance in the region of sandy and sandy mean texture soils [37].  
In MATOPIBA, three river basins are inserted: Tocantins, Atlantic (between north and northern transitions regions), and the San Francisco River. In these river basins, the Tocantins, Araguasia, Sao Francisco River, and Parmaiba Rivers are inserted [26].
# Prediction of the Absorption Characteristics of Non-Uniform Acoustic Absorbers with Grazing Flow
## 5 Conclusions  
In this paper, a broadband BAU absorber was first constructed by using an MPP and backed cavities with dissimilar lengths to produce peak absorption at multiple frequencies. Since each cavity length is extended along the MPP surface, different cavity volumes can be adopted to yield different resonance frequencies without increasing the thickness of the structure. The IBNSE method was developed to predict the attenuation characteristics of the duct acoustic system, in which comparisons of four semi-empirical impedance models were performed. It was found that the IBNSE method with Goodrich model was sufficient to accurately predict the acoustic attenuation of the absorber under the grazing flow condition. Using the BAU structure as the basic building block, planar and the cylindrical broadband non-uniform acoustic absorbers were constructed. The acoustic attenuation characteristics of the RDPAA and ADCAA systems under different grazing flow Mach numbers were calculated using the FNSE method as well as the developed IBNSE method. It was demonstrated that under different Mach numbers, these two acoustic systems exhibited good broadband absorption performances. The IBNSE method with Goodrich model was accurate and computationally efficient, and can be used to predict the absorption characteristics of acoustically treated ducts in the presence of grazing flow.  
All authors were involved in the study design, data acquisition, data analysis and data interpretation. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 24: Transmission loss and absorption coefficient of the ADCAA system under different grazing flow Mach numbers. The solid lines represent the results computed by the IBNSE method with Goodrich model, while the circles represent the results computed by the FNSE method. (**a**) transmission loss. (**b**) acoustic absorption coefficient.
## 1 Introduction
explored the suppression of the main system by nonlinear time delay feedback dynamic vibration absorber under superimposed excitation [30]. [PERSON] et al. studied the multi-objective optimal design of an active vibration absorber with delayed feedback [31]. [PERSON] et al. applied time delay feedback to the active control of vehicle suspension [32; 33]. Time delay feedback control has been in greater development. Considering the above problems, the new wheel structure proposed in this paper is based on a conventional wheel structure equipped with a time-delay feedback-controlled dynamic vibration absorber. This new structure works by transferring the vibration energy generated by the road excitation to the tire to the mass block, reducing the vibration of the tire. The reduction in the amount of tire vibration will reduce the transfer of vibration energy to the vehicle's body through the suspension. This new wheel structure solves the contradiction between vehicle ride comfort and handling stability. In addition, based on [PERSON]'s method for analyzing the stability of the vehicle quarter model [20], it is generalized to the vehicle quarter model with the vibration-absorbing mass block, which provides a method of stability analysis for three-degree-of-freedom time-delay dynamical systems. The remainder of the paper is organized as follows: Section 2 introduces the mathematical model of the new vibration-absorbing wheel structure and designs some parameters of the new structure; in Section 3, the stability of the system is analyzed; afterwards, the objective function of the system is designed and optimized; Section 5 presents the simulation results to verify the effectiveness of the proposed new vibration-absorbing wheel structure; conclusions are drawn in Section 6.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### AI-Based Systems in Glaucoma
#### 4.3.1 Detection of Glaucomatous Characteristics  
AI-based glaucoma detection identifies, marks, highlights, or directs attention to portions of the input images that may contain glaucoma-based anatomical or functional abnormalities, and/or detects characteristics and makes measurements that can express those anomalies. Thus, specialists can use visual and quantitative information to detect glaucoma or monitor changes during patient follow-up. AI-based detection methods are usually designed for unimodal data. For example, _optic disc/optic cup_ segmentation techniques are mainly applied in fundus image processing.  
_Optic Disc/Optic Cup (OD/OC) segmentation_  
The visualization of the optic nerve head is crucial in the identification of glaucoma-based lesions. Certain AI-driven segmentation methods have been deployed for OD and OC in retinal images to compute CDR (cup-to-disc ratio).  
Prior to DL methods, contour-based and superpixel categorization methods were the most commonly used means for OD and OC segmentation. Advances in DL networks, high-performance computing (HPC), and large-scale publicly available image databases have led to the increasing use of DL-based models (e.g., U-Net) in OD/OC segmentation [76,77]. In addition to local datasets, several public databases, like RIM-ONE, Drishti-GS, ORIGA, and HRF, are often used grow and confirm OC/OD segmentation methods.  
A late attempt in this domain was the international Retinal Fundus Glaucoma Challenge (REFUGE) [78], which initiated and standardized an assessment foundation to differentiate certain methods in OD/OC segmentation and glaucoma categorization.  
_Detection of VF (view field) loss_  
Computational VF testing is essential in identifying glaucoma-induced functional modifications. [PERSON] et al. [79] evolved an unsupervised ML technique, named archetypal analysis, to leverage the sensitivity data included in VFs. [PERSON] et al. emphasized the role of AI in glaucoma analysis with a focus on detecting VF loss [80].  
Alternative AI-based attempts for glaucoma identification comprise tissue segmentation, retinal vessel segmentation, visualization, anomaly identification, and risk assessment. An important use of these models is population screening to achieve early detection of glaucoma and referral for specialized exam.  
Figure 6: Architecture of the DL model used for glaucoma screening [75].
## 5 Decision Engine for Relay
### Cost of AF  
With an AF strategy, the relay is assumed to keep the code rate unchanged, i.e., \(R_{2}=R_{1}\), so the same number of bits is sent over both links in the first transmission. Decoding the bundle at the end user costs \(bc_{D}\) plus any cost associated to IR if failures occur. Thus, the cost of AF is decomposed as  
\[c_{AF}=2\cdot\frac{bk}{R_{1}}+bc_{D}+\sum_{i=1}^{b}P_{B}(b,p_{AF},i)\delta_{ AF}(i), \tag{26}\]  
where \(p_{AF}=P_{e}(R_{1},\text{SNR}_{AF})\), \(\text{SNR}_{AF}\) is taken from Equation (10), and \(\delta_{AF}(i)\) denotes the expected future cost when \(i\) failures are present at the end user. If decoding failures do occur, the end user will request IR from the relay. We assume that the relay always reverts back to a DF strategy in this case, decoding the cached bundle with IR from the base station if necessary. If there are \(j\) failed codewords at the relay, decoding the entire bundle will cost \(V((j,\text{SNR}_{1},R_{1}),\text{SNR}_{1})\). Once the relay has succeeded at decoding the whole bundle, it can generate and transmit the IR that the end user requested. This step costs another \(V((i,\text{SNR}_{AF},R_{2}),\text{SNR}_{2})\).  
With AF, the noise accumulates over the two links. It is therefore very unlikely for a codeword that could not be decoded at the relay to be correctly decoded at the end user. Similarly, if a codeword was correctly decoded by the end user we assume that it will also be successfully decoded by the relay.  
The number of failures at the relay then follows a binomial distribution with \(i\) representing the number of failures at the end user and \(p_{R}\) representing the conditional probability that a codeword fails at the relay conditioned on it failing at the end user. Hence,  
\[\delta_{AF}(i) =bc_{D}+V((i,\mathcal{SNR}_{AF},R_{2}),\mathcal{SNR}_{2})\] \[+\sum_{j=1}^{i}P_{B}(i,p_{R},j)V((j,\mathcal{SNR}_{1},R_{1}), \mathcal{SNR}_{1}), \tag{27}\]  
where  
\[p_{R}=\frac{P_{e}(R_{1},\mathcal{SNR}_{1})}{P_{e}(R_{1},\mathcal{SNR}_{AF})}\]  
follows from [PERSON] rule.  
The values of \(c_{DF}\) and \(c_{AF}\) can now be computed for all discretized values of \(\mathcal{SNR}_{1}\), \(\mathcal{SNR}_{2}\), and \(R_{1}\) using Equations (23) and (26). A decision map is then generated by specifying whether AF or DF provides lower expected cost. According to this decision map, the relay can make the AF or DF decision by estimating the SNR on the two links and finding the rate of the received codeword in a practical situation.
## 4 Experiments and Results
### Distributed Control Strategy Comparison Experiments
#### 4.3.1 Test on Small-Scale Swarms
Figure 7: Variation of the single-step consistency evaluation metrics under the control of the two distributed learning-based strategies in a specific episode, in which the red line represents the control result of the IDGCNs-based strategy and the blue dotted line represents the control result of the DAGNNs-based strategy. (**a**) Variation of the VR. (**b**) Variation of the DR. (**c**) Variation of the GIE. The experimental results show that, on the one hand, the idea of improving swarm robustness by achieving the consistent importance of nodes and links is effective; on the other hand, it also shows that GIE is highly correlated with the swarm robustness and can be used as a quantitative evaluation metric of swarm robustness. From the evaluation results above, we can conclude that the IDGCNs-based strategy realizes better performance in swarm consistency and swarm robustness than the other two distributed strategies, and achieves the control results close to the centralized template while respecting the realistic communication constraints.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### The Structure of RFPNet
#### 3.2.2 Fusion Strategy of Pooling Layer Feature Maps  
Although the pooling layer has the effect of characteristic invariance and dimension reduction, dimension reduction will lead to the loss of useful information and the detail information will be ignored. Based on this problem, this paper proposes the fusion strategy of pooling layer feature maps, as shown in Figure 8. The features in each pooling layer are expanded into a one-dimensional vector through a fully connected layer. Features are then cascaded through the final fully connected layer. Finally, classification is carried out through the output layer. This strategy can make the semantic information contained in different pooling layers complement each other and retain the details, thus improving the classification accuracy.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 5 Multi-Gateway-Based Protocol  
The LEACH [5] protocol consumes energy quite efficiently, but there are several drawbacks associated with it that may lead to the energy holes problem in the network. e.g., the size of a cluster is not predetermined in the LEACH protocol. The size of the cluster can be large or small. If the cluster size is large, the distance between the CH and a member node becomes large and the energy consumption for intra-cluster communication will increase. If the large size clusters are formed near the sink, the energy of nodes near the sink area will deplete quickly. After some rounds the energy of these nodes will be completely exhausted and energy holes will emerge in this area. In our protocol, the clustering process is well-organized within the region and clusters are not oversized.  
In the LEACH protocol, CHs use direct (single-hop) communication towards the sink; if the distance between CH and sink is larger, more energy will be consumed in transmission. Energy holes will develop in remote areas of the network. We have reduced the transmission distance between CH and the sink by deploying a gateway between them. As the transmission distance is reduced, energy consumption in transmission is also reduced and there is an improvement in network lifetime. In LEACH, the number and position of cluster heads (CHs) are not predetermined, and it is possible that CHs are not uniformly formed over the area and some nodes do not have a nearby CH. We have solved this issue by division of the area into regions and a separate clustering process in each region.
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 3 Models and Improvements
### Feature Fusion Method Based on LDA Topic Model  
#### 3.2.1 LDA Topic Model  
The LDA topic model was first proposed by [PERSON] [24] in 2003 as a model for document topic generation based on latent semantic analysis with a Dirichlet prior distribution. In the Bayesian network structure of the LDA topic model, for any text \(d\), its topic distribution \(\theta_{d}\) is:  
\[\theta_{d}=\text{Dirichlet}\left(\overset{\rightarrow}{\alpha}\right) \tag{1}\]  
where \(\alpha\) is the hyperparameter of the topic distribution, it is a \(k\)-dimensional vector. For any topic \(k\), the distribution of the subject term \(\beta_{k}\) is:  
\[\beta_{k}=\text{Dirichlet}\left(\overset{\rightarrow}{\eta}\right) \tag{2}\]  
where \(\eta\) is the hyperparameter of the topic distribution, and also a V-dimensional vector. V represents the number of all words in the vocabulary. For the \(n^{th}\) word in any text \(d\), the distribution \(Z_{dn}\) of its topic number obtained from the topic distribution \(\theta_{d}\) is:  
\[Z_{dn}=\text{multi}(\theta_{d}) \tag{3}\]For this topic number, the probability distribution of the word \(W_{dn}\) can be obtained as:  
\[W_{dn}=\text{multi}\left(\beta_{z_{dn}}\right) \tag{4}\]  
The construction of the LDA topic model requires the setting of the number of topics. After the LDA topic model was proposed, perplexity [25] was initially used to determine the optimal number of topics. Under different numbers of topics, the smallest the perplexity is the optimal number of topics. However, the number of topics determined by this method is generally larger and the similarity between topics is also larger, which is more suitable for long text data. The text of the _Carya cathavensis_ comments crawled in this paper is short and independently distributed, so it is not suitable for this method to determine the optimal number of topics. Coherence [26] is often used to evaluate the interpretability of the generated potential topics. When a topic is easy to interpret, the words ranking high under that topic will frequently co-occur in the corresponding text corpus. Therefore, a higher coherence index means that the number of topics is better. The Formula (5) of topic coherence [27] is as follows:  
\[C\left(t;V^{(t)}\right)=\sum_{m=2}^{M}\sum_{l=1}^{m-1}\log\frac{D\left(v_{m}^{ (t)},v_{l}^{(t)}\right)+1}{D\left(v_{l}^{(t)}\right)} \tag{5}\]  
where is the number of \(M\) text, \(D(v)\) denotes the frequency of occurrence of the phrase \(v\) in the text set, and \(V^{(t)}\) denotes the M most likely words in the topic t.
## 2 Related Work
### Perception Technologies  
Lasers or 2D LiDARs have been mainly the sensors used for mapping indoor environments with robots for a long time. However, this type of sensors can only perceive features on a single plane, therefore providing limited information for object detection. For this reason, and due to new advances in sensor technology and computation resources, the use of cameras to process 3D information spread lately too. Stereo cameras or RGB-D cameras can provide 3D perception by means of processing algorithms that output high-resolution point-clouds, with a restricted FoV though (Table 1). Moreover, the availability of both depth as well as color information significantly facilitates the task of classifying different objects.  
The main limitation of RGB-D cameras resides in the short distance at which objects can be detected. Such restriction paved the way for the extensive use of 3D LiDARs in outdoor environments. A reference application scenario for this technology is, indeed, autonomous driving [14]. In Figure 1, we report a representative comparison of the output of an RGB-D camera against that of a 3D LiDAR. The color and depth information provided by the camera has a 10-time higher resolution within a smaller FoV in comparison to what a 3D LiDAR can provide for a 360\({}^{\circ}\) view.  
The resulting low-density of 3D LiDARs and their high price has promoted the belief that this technology is inefficient for indoor scenarios. Thus, the advantage of offering an extended detection range seems to be uninteresting in small spaces like the ones typical of buildings. Interestingly enough, however, a sparse point-cloud has the side effect of offering an inherent higher privacy standard, e.g., given the inability of performing face recognition. Nonetheless, the initial high price for this technology is seeing a clear decrease (the well-known Velodyne VLP-1  
Figure 1: Illustrative example of sensed data by an RGB-D camera and a 3D LiDAR: (**a**) RGB-D camera color image; (**b**) point-cloud provided by an RGB-D camera; (**c**) point-cloud provided by a LiDAR.  
a cost of around 8000 Euros and it is now available for 4000 Euros). This is due to both the larger adoption, currently mostly in outdoor scenarios, as well as the move to a solid-state technology, e.g., the Velarray from Velodyne [7], the Cube 1 from PPEP [11] or the CE30 from Benewake [12], which costs around 685 Euros [15]. The last Ipad Pro from 2020 [6] also integrated a solid-state LiDAR without an increase in price in comparison to previous versions.  
In this work, we embrace the current trends promoting the use of 3D LiDARs and investigate the potential that this technology offers for indoor scenarios. We analyze how low-density point-clouds can be processed to detect typical indoor objects without sacrificing accuracy in comparison to established methods. In particular, we highlight the potentials and show how the properties of low-density point-clouds provided by 3D LiDARs can be exploited to simplify the processing, making the technology practical also for low-end robotic platforms.
## 3 Results
### Accuracy Assessment with the Simulated Dataset
For the data measured by the QE Pro (SR = 0.3 nm, SNR \(>\) 1000) and ASD FieldSpec4 (SR = 3 nm, SNR \(>\) 4000) spectrometers, the RRMSE is less than 10%, which is sufficient for common applications. In addition, we also compared the accuracy of the full-spectrum SIF retrieval method with the commonly used 3 FLD [25] and iFLD [26] methods at the O\({}_{2}\)-A and O\({}_{2}\)-B bands under different SR or SNR conditions. The results are also shown as contour maps in Figure 8. The contours being more vertical indicates that the algorithm is more sensitive to the SR, while the contours being more horizontal indicates that the algorithm is more sensitive to the SNR. Because of the more accurate estimation of reflectance and SIF inside the absorption bands, the full-spectrum SIF retrieval algorithm is less sensitive to the SR and SNR, especially at the O\({}_{2}\)-B band. As Figure 8c shows, F-SFM is not sensitive to the SR for the O\({}_{2}\)-A band and an SR of 3 nm is sufficient here. For the O\({}_{2}\)-B band, the influence of the SR is more obvious because this absorption band is much narrower. When the SNR is higher than 1000, the accuracy of F-SFM becomes relatively stable for an SR above a certain value (Figure 8f). The accuracy of the F-SFM algorithm is comparable to that of the 3 FLD and iFLD methods when the SR and SNR are sufficiently high and the accuracy is much higher than that of 3 FLD and iFLD when the SR or SNR is relatively low. This indicates that the newly proposed PCA-based spectral fitting algorithm is not only useful for full-spectrum SIF retrieval but can, in fact, improve the accuracy of single-band SIF retrieval for data with a relatively low SR or SNR. For the configuration of the popularly used ASD FieldSpec4  
Figure 7: Contours of the full-spectrum RRMSE (%) in the SIF retrieved by the F-SFM algorithm using simulated test datasets with different SRs and SNRs. spectrometer (SR = 3 nm, SNR \(>\) 4000), the RRMSEs of 3 FLD and iFLD at the O\({}_{2}\)-B band are higher than 80% and the performance of F-SFM is still acceptable (RRMSE \(<\) 15%).