esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 4 Simulation Results and Analysis
### Simulation Experiment 3  
The goal of this experiment is to verify the effectiveness of the proposed algorithm for the birth and death scenarios of multiple extended targets. The strength function of the newborn target is given by the following:  
\[\begin{split}\gamma_{t}(x)&=0.1\mathcal{N}\Big{(}x;m_{ \gamma,t}^{(1)},P_{\gamma,t}\Big{)}+0.1\mathcal{N}\Big{(}x;m_{\gamma,t}^{(2)},P _{\gamma,t}\Big{)}\\ &\quad+0.1\mathcal{N}\Big{(}x;m_{\gamma,t}^{(3)},P_{\gamma,t} \Big{)}+0.1\mathcal{N}\Big{(}x;m_{\gamma,t}^{(3)},P_{\gamma,t}\Big{)}\end{split} \tag{14}\]  
where \(m_{\gamma,t}^{(1)}=100\) m, 450 m, 0 m/s, 0 m/s\({}^{\mathrm{T}}\), \(m_{\gamma,t}^{(2)}=[-450\) m, -450 m, 0 m/s, 0 m/s\({}^{\mathrm{T}}\), \(m_{\gamma,t}^{(3)}=450\) m, -209 m, 0 m/s, 0 m/s\({}^{\mathrm{T}}\), \(m_{\gamma,t}^{(4)}=[-350\) m, -350 m, 0 m/s, 0 m/s\({}^{\mathrm{T}}\). Figure (a)a shows the real tracks of multiple extended targets in Scenario 3. Figure (b)b shows the simulation results of the proposed algorithm.  
Figure (b)b shows the OSPA distance comparison of the three partition methods in scenario 3. It can be seen from the figure that the proposed method has the smallest OSPA distance. However, at the 27 th and 76 th seconds, the OSPA distance becomes larger, which is because there are newborn targets appearing at these two moments. Figure (b)b shows the comparison of target number estimation. As can be seen from the figure, the cardinality estimation of the proposed algorithm is significantly better than the other two algorithms when the number of targets in the scene is large.  
Figure (a)a,b show the average number of partitions and the average running time of the three methods. The proposed method has the lowest number of partitions and running time. Since the proposed method not only makes full use of the prior information, but  
Figure 8: (**a**) Comparison of OSPA errors for Scenario 2. (**b**) Comparison of target number estimation in Scenario 2.  
Figure 9: (**a**) Comparison of measurement set partition number in Scenario 2. (**b**) Comparison of running times of each algorithms for Scenario 2.  
also converts point target tracking into extended target tracking, the number of distance partitions increases with the number of objects in the scene.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### The Mamdani-Type Fuzzy Inference Process
#### 4.4.5 Defuzzification  
The last stage of the fuzzy inference process is defuzzification, which transforms the aggregated fuzzy set into a single crisp output value. As the reverse of fuzzification, this process converts the distributed degrees of membership within the fuzzy set back into a precise numerical value, ensuring a clear and actionable result for the output variable.  
Among the various defuzzification techniques discussed in the literature, the Centroid Method--also referred to as the center of area or center of gravity--is the most commonly used due to its intuitive nature and reliability [16; 18]. Given its effectiveness, this paper exclusively employs the Centroid Method. In the solar power system example, defuzzification converts the aggregated fuzzy set representing solar power performance into a single crisp value, which determines the final power output level and aids in making precise control decisions to optimize solar energy utilization.  
\[Z_{COA}=\frac{\int_{z}\mu_{A}(z).zdz}{\int_{z}\mu_{A}(z)}\]  
Here, \(z\) represents the output variable, while \(\mu_{A}(z)\) denotes the membership function of the aggregated fuzzy set A corresponding to z. The following figure visually presents the outcome of the solar power system example, computed using the Centroid Method. Specifically, when solar irradiance is 600 W/m\({}^{2}\) and the battery charge level is 40%, the fuzzy inference system assigns a solar power performance score of 3.75. This suggests that the system operates at a moderate efficiency level, though further optimization may enhance energy utilization. Figure 9 illustrates the defuzzification process, ultimately producing a crisp output value of 3.75 from the aggregated fuzzy set.  
The defuzzification in Figure 9 is performed using the Centroid Method, which calculates the center of mass of the fuzzy set to obtain a crisp value. The output, 3.75, represents the solar power performance level derived from the aggregated fuzzy rules based on input conditions such as solar irradiance or utilization rate.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 2 Design Principle of Spatial Multiplexing Hologram
### Spatial Multiplexing Hologram for Multi-User Power Coverage  
Given the compact apertures of receivers in VLC systems, which are optimized for a high speed and sensitivity, signal transmission often involves long distances and substantial optical losses. These factors can lead to substantial pointing errors, potentially jeopardizing the success of communications. To mitigate these challenges, VLC systems must be capable of customizing the coverage area of beams at the receivers' plates, thereby augmenting communication robustness [15].  
Figure 3 illustrates the methodology for adjusting beam coverage. In the preprocessing stage, location correction is performed as described in Section 2.1. Moreover, the size of the beam's coverage area must also be predetermined. Utilizing the previously determined magnification factors, \(\alpha_{x}\) and \(\alpha_{y}\), the desired radius \(r\) of the coverage area at a distance \(z\) from the source plate is adjusted to \(r_{0}=r/\sqrt{\alpha_{x}^{2}+\alpha_{y}^{2}}\).  
As described in Section 2.1, users' information is translated into images. Following Equations (2)-(6), the SMH is synthesized. It is important to note that this section primarily concentrates on ensuring adequate power coverage on the received plate, thus obviating the need for weight optimization specific to power allocation. Additionally, larger power coverage is not always better. While setting a larger light spot at the receiver plane can ensure power coverage, power dispersion remains a concern. This dispersion can lead to a lower data rate or even reduce the power below the receiver's sensitivity threshold.
## 6 Discussion and Future Directions
### Performance Evaluation Metrics
#### 6.4.1 Cache Hit Ratio  
The cache hit ratio is an important indicator in privacy-preserving applications [163]. Higher values of the cache hit ratio indicate less possibility of privacy leakage.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
###### Abstract  
Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this paper, we propose a novel Constrained Spatial Smoothing (CSS) approach for the problem of spatial data reconstruction. We observe that local continuity exists in many types of spatial data. Based on this observation, our approach performs sparse recovery via a finite element method, while in the meantime enforcing the aggregated observation constraints through an innovative use of the Alternating Direction Method of Multipliers (ADMM) algorithm framework. Furthermore, our approach is able to incorporate external information as a regression add-on to further enhance recovery performance. To evaluate our approach, we study the problem of reconstructing the spatial distribution of cellphone traffic volumes based on aggregate volumes recorded at sparsely scattered base stations. We perform extensive experiments based on a large dataset of Call Detail Records and a geographical and demographical attribute dataset from the city of Milan, and compare our approach with other methods such as Spatial Spline Regression. The evaluation results show that our approach significantly outperforms various baseline approaches. This proves that jointly modeling the underlying spatial continuity and the local features that characterize the heterogeneity of different locations can help improve the performance of spatial recovery.  
Keywords:spatial sparse recovery; constrained spatial smoothing; spatial spline regression; alternating direction method of multipliers +
Footnote †: journal: Applied Sciences
## 1 Introduction
The results show that the latency of the proposed protocol adaptation scheme is smaller than that of the protocol transparent transmission without resolution and conversion. For the same length of data, the latency and reliability for Modbus TCP and 5G are smaller than those for Modbus RTU and 5G. The main contributions of this paper are as follows:  
* We focus on the Modbus protocol, which is the most widely used in industry, and propose a protocol adaptation scheme with protocol resolution and conversion for both Modbus RTU and Modbus TCP. Meanwhile, we compare the proposed protocol adaptation scheme with the transparent transmission scheme. * We develop a wireless PLC prototype system based on commercial 5G for the end-to-end transmission of Modbus RTU or Modbus TCP, where the network address translation is realized for the automatic matching of 5G. * We perform extensive experiments to evaluate the effectiveness and superiority of the proposed scheme and developed prototype system. The remaining of this paper is given as follows. In Section 2, we briefly introduce the relevant research on the multi-protocol fusion, the combination of industrial control protocols and wireless technology, and the 5G experimental platforms. In Section 3, we analyze the frame structure of the 5G and Modbus protocol. In Section 4, we propose the protocol adaptation scheme between 5G and Modbus RTU/Modbus TCP in detail. In Section 5, we develop the wireless PLC prototype system based on the commercial 5G. Furthermore, the experimental evaluations for a 5G-based industrial wireless controller are given in Section 6. Finally, we summarize the full text and expound with regard to further research in Section 7.
## 4 Experiments
### Implementation Details and Metrics  
MDNet and all comparison models were implemented in the PyTorch(2.1.0) framework based on an NVIDIA GeForce RTX 4080 GPU. The parameter configurations of all comparison algorithms strictly follow the settings in the original literature. MDNet uses ImageNet-pre-trained ResNets as the CNN backbone. Without losing generality, we used the Transformer module of AMTNet [51] in the experiments. During the training phase, the input image size was set to 256 \(\times\) 256 pixels. The model was trained for 200 epochs using an AdamW optimizer with a batch size of eight, an initial learning rate of 1 \(\times\) 10\({}^{-4}\), and weight decay of 0.01. The backbone network was ResNet-50. During the testing phase, only \(P_{3}\) was used to generate the final result as the predicted change map.  
To analyze the performance of MDNet and the comparison algorithms, we used the four most commonly used metrics for change detection tasks, including precision (Pre), recall (Rec), F1-score (F1), and Intersection over Union (IoU).
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 3 Materials and Methods  
### Problem Setting  
To simulate the realistic scenario of few-shot learning, we propose a problem setting:  
The paper partitions the dataset into a base class \(\mathbf{C_{b}}\) and a new class \(\mathbf{C_{n}}\), where the intersection of \(\mathbf{C_{b}}\) and \(\mathbf{C_{n}}\) is \(\phi\). For each query image in \(\mathbf{C_{b}}\), a set of support images along with their corresponding true density maps is provided. However, fewer support images are available for the query images in \(\mathbf{C_{n}}\). The paper randomly selects \(k\) images from the dataset, from which one object is randomly selected as a support image. In fact, in this paper, we record the coordinates of the selected support image within the query image and then crop out the corresponding part. This cropped part is passed through the backbone (ResNet) to obtain the support features.  
In this paper, \(f_{S}\in\mathbf{R}^{K\times C\times H_{S}\times W_{S}}\) and \(f_{Q}\in\mathbf{R}^{C\times H_{Q}\times W_{Q}}\) are designated to represent the feature maps of the support images and query images, respectively, which are obtained from the ResNet network. Here, \(K\) signifies the total number of support images employed in the process, \(C\) represents the number of channels, and \(H_{S}\), \(W_{S}\), \(H_{Q}\), and \(W_{Q}\) stand for the heights and widths of the features of the support image and the query image, respectively.
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
###### Abstract  
In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts' knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.  
ontology; agriculture; insect pests +
Footnote †: journal: Applied Sciences
# A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages
## 1 Introduction
The remainder of this paper is structured as follows: In Section 2, we describe the theory of the INS/GNSS integrated navigation system in detail, including its mathematical model [17; 33; 34]. Section 3 introduces the design of the proposed FECAM-iTransformer and the INS/FECAM-iTransformer integrated navigation system. Section 4 presents the experiments conducted, along with a comprehensive comparative analysis. Section 5 discusses the results and significance of this research. Lastly, Section 6 makes a conclusion.
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 3 Proposed Method
### Color Structured Light Stripe Edge Detection Method  
As analyzed above, 3D imaging systems using color stripe patterns have many outstanding advantages. However, the influence of noise and the characteristic of the object surface make accurate color stripe edge detection difficult in many practical applications. In some situations, the edges are detected with a very large distance between themselves and the ones in the ground-truth images. Moreover, many wrong detections may occur because of the colored textures on the object's surface. These issues surely reduce the quality of 3D reconstruction. In this paper, the GAN-based method for color structured light stripe edge detection (Figure 7) includes the following steps:  
1. The GAN-based model for color structured light stripe edge detection is trained with the prepared training set and pre-set parameters. With the properties of the GANs, this model can significantly reduce the effect of noise and characteristics, even colored textures, of the object surface;
2. Test images are preprocessed with a bilateral filter to reduce noise and preserve edges in the images. Similar to the images in the training set, these images are sequentially cut into patches of 128 \(\times\) 128 pixels. These patches are then fed into the trained model for prediction;
3. The predicted image patches consist mainly of two components: stripes (bright regions) and background (dark regions). They are sequentially merged together to obtain large images of the same size as the original test image;
4. With such clean resulting images, a morphological skeletonization algorithm is applied to detect the centerlines in the images. The positions of these lines are equivalent to the positions of the left edges of the color stripes in the deformed pattern images.  
Figure 6: Illustration of input samples in the training set. (**a**) Labeled images; (**b**) Image patches.  
Traditional edge detection methods for color stripe pattern images usually rely on some threshold to determine the existence of an edge in the images. However, the influence of noise and the complex characteristics of the objects' surface make many edges undetected or wrongly detected. To overcome these challenges, the color structured light stripe edge detection method proposed in this paper has the following improvements: (1) The specific dataset is designed with high quality; (2) the deeper network structure based on GANs with attention gates helps the model to learn more high-level features. Thus, the trained model can effectively reduce the effect of noise in the image and produce high-quality images, such as ground-truth images. As a result, the centerlines in the predicted images, corresponding to the edges in the color stripe pattern images, are accurately detected. In other words, with the proposed network, our method converts the task of detecting edges in color stripe pattern images into detecting centerlines in curved line images.
## 5 Results and Discussion
### Overall Performance Evaluation  
This section evaluates the overall effectiveness of the proposed cluster-head rotation and balanced inter-cluster communication methods. Performance is assessed in terms of residual energy, network lifetime, and throughput delivered to the base station with state-of-the-art methods, i.e., RLEACH [55], CRPPCM [56; 57], and EERPMS [57].
# Influence of TTS Systems Performance on Reaction Times in People with Aphasia
## 2 Materials and Methods
### Synthetic Speech Assessment
#### 2.3.3 Jitter and Shimmer Parameters  
Jitter and shimmer are two acoustic measurements widely used as metrics for pathological voice detection and acoustic analysis in general [20]. Jitter and shimmer are measurements of the fundamental frequency disturbance, and they have been proven to be useful in describing vocal characteristics [21]. Jitter is defined as the parameter of the fundamental frequency variation of successive periods, while shimmer refers to the amplitude variation of the sound wave [22]. Once the onset time of the glottal pulse is recognised, jitter can be determined using different measures. The current study considered jitter local and jitter relative average perturbation (RAP). The former represents the average absolute difference between two consecutive periods, divided by the average period; its threshold limit to identify pathologies is 1.04%, and it is expressed as a percentage [20]:  
\[\text{Jitter (local)}=\frac{\frac{1}{N-1}\ \sum_{i=1}^{N-1}\left|T_{i}-T_{i+1} \right|}{\frac{1}{N}\sum_{i=1}^{N}T_{i}}\]  
The latter is the relative average perturbation, meaning the average absolute difference between a period and the average of it and its two neighbours, divided by the average period; its threshold value for detecting pathologies is 0.68% [23], and it is expressed as:  
\[\text{Jitter RAP}=\frac{(1/N-2)\ \sum_{i=2}^{N-1}\left|T_{i}-((T_{i}+T_{i-1}+T_{i+1 })/3)\right|}{\frac{1}{N}\sum_{i=1}^{N}T_{i}}\]  
Shimmer refers to irregular period-to-period variation in the peak amplitude of the signal. In the current study, shimmer local was taken into consideration. Shimmer local is defined as the average absolute difference between the amplitudes of two consecutive periods, divided by the average amplitude. The limit for detecting human voice pathologies is 3.81% [22], and it is expressed as a percentage:  
\[\text{Shimmer (local)}=\frac{\frac{1}{N-1}\ \sum_{i=1}^{N-1}\left|A_{i}-A_{i+1 }\right|}{\frac{1}{N}\sum_{i=1}^{N}A_{i}}\]  
The parameters described here are commonly measured for long sustained vowels. However, in our case, the samples are short sentences. For this reason, the interpretation of the results was made keeping in mind this aspect. Another aspect to consider was that, according to [24], significant differences could occur in jitter and shimmer measurements between different speaking styles, especially concerning the shimmer measure. Praat software was used to extract these measurements from all of our test samples [25].
# LaRoAwan and Urban Waste Management--A Trial
## 2 LoRa Technology--Long Range
### LoRaWAN  
The LoRaWAN protocol is a link layer and network layer protocol, including Medium Access Control (MAC) layer protocol, considering the OSI model, allowing sensors with a LoRa radio interface to communicate with applications connected to the Internet. It is promoted by the LoRa Alliance and free to use.  
A LoRaWAN network is based on four components, as shown in Figure 1:  
1. The sensor device, usually with energy and computational limitations;
2. The gateway, a network element that receives and transmits data from and to devices;
3. The network server, which forwards messages received by a set of gateways to the applications and vice versa;
4. The application, somewhere on the Internet, that receives and sends data to the sensors through the network server.  
LoRaWAN defines three device classes. The first, the most used, is Class A, which features devices that are mostly asleep, battery powered, and transmit information (uplink) only when necessary. It is possible to return some information to these devices (downlink) using reception windows that are located after the transmissions from the devices. Class B devices are devices that regularly receive information and as such combine with the network the times when they will wake up to receive that information. Finally, Class C devices are always active and can always receive information when needed.  
In terms of security, LoRaWAN specifies two levels of security, the first between the device and the network, the second between the device and the application. This ensures that only the application can interpret the data sent by a device.  
The LoRaWAN protocol also introduces extra features, such as the Adaptive Data Rate, which allows the network to negotiate with the devices the parameterization of the physical LoRa transmission, optimizing consumption and spectral efficiency. Figure 2 presents a layer diagram for LoRa and LoRaWAN.