esa-sceva/satcom-chunk-collection huggingface dataset:
# Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification
## 3 Graphical Least Absolute Shrinking and Selection Operator (GLASSO)
### Glasso
Naturally, the performance of the likelihood ratio test depends on a good estimation of the PLDA model parameters, which depends on a good estimation of the within-class precision matrix. Therefore, it is important to reduce the estimation error in the empirical within-class precision matrix by finding the optimal value of \(\rho\). The optimal \(\rho\) can be considered as what can achieve the best performance on evaluation trials. In practice, it should be found with a validation dataset the domain of which is close to the target domain, because we do not know the target domain in advance. According to our experiments described in Section 5.3, the GLASSO-PLDA exhibits performance improvement in TD-SV only if a prerequisite is satisfied. The prerequisite is that the within-class covariance and accompanying precision matrix of embeddings should be close to diagonal. Unless the prerequisite is satisfied, the GLASSO converges at a point far from the optimal solution, does not converge, or even fails to estimate. The failure of the estimation is due to that \(\boldsymbol{\Phi}_{w}^{-1}\) is ill-conditioned, which means that \(\boldsymbol{\Phi}_{w}^{-1}\) has the value of infinity or is not a number. We describe the effect of the prerequisite on the performance in Section 5.3.
## 3 Results and Discussion
### Physiological Characterization
#### 3.3.3 Acid Compounds  
In this study, acid compounds played a crucial role in shaping the flavor profile of sea buckthorn wine through dynamic esterification and hydrolysis equilibrium. Studies have shown that short-chain fatty acids (e.g., hexanoic acid and heptanoic acid) serve as key precursors for ester formation, reacting with alcohols to generate aromatic esters that contribute fruity and floral notes to fruit wine. However, certain volatile acids (e.g., octanoic acid and decanoic acid) can lead to unpleasant, rancid, or irritating sensory attributes when their concentrations exceed specific thresholds, thereby disturbing the overall flavor balance of the wine [36]. A total of seven acid compounds were identified in this study. The analysis revealed that the total acid content in the JH group (mixed fermentation) was significantly lower than that in the JS group (single-fermentation control). Specifically, the concentration of hexanoic acid decreased from 351.11 \(\upmu\)g/L in the JS group to 334.03 \(\upmu\)g/L in the JH group, indicating that the mixed fermentation strategy effectively regulated acid accumulation, reducing excessive volatile acid content and improving the wine's mouthfeel complexity and sensory harmony. Notably, 3-methyl-1-butanoic acid exhibited a significant increase in the JH group, rising from 115.74 \(\upmu\)g/L to 226.18 \(\upmu\)g/L. Previous studies have indicated that certain non-_Saccharomyces_ yeasts can metabolize branched-chain amino acids (e.g., leucine) via the Ehrlich pathway, producing 3-methyl-1-butanoic acid as a metabolic byproduct [37]. Since _Pichia kudriavzevii_ is an active non-_Saccharomyces_ yeasts, it may possess a similar metabolic mechanism, promoting the accumulation of 3-methyl-1-butanoic acidduring fruit wine fermentation. Although fatty acids are often associated with undesirable odors, research suggests that they are perceptible only when their concentrations exceed 20 mg/L [38]. In this study, the concentrations of all detected acid compounds were significantly below their sensory thresholds, ensuring they would not adversely affect the wine's flavor. On the contrary, these acids, present in appropriate concentrations, may serve as important flavor precursors that react with alcohols to form aromatic esters, thereby enriching the overall aroma profile of the fruit wine.
## 5 Conclusions  
Efficient data representation with autoencoders, as has already been discussed in the literature [5; 17; 30; 31], can reduce the dimension of the learning space. In our experiments, we have shown the effectiveness of dimensionality reduction for RL of motor skills such as throwing.  
Figure 8: Image sequence of executing a learned throw at a target on with the TALOS robot.  
Figure 9: [REAL] Convergence of RL in AE latent spaces computed from either the simulated or the generalized dataset.  
It is clear that learning of autoencoders requires the acquisition of a sufficient amount of data. This is often challenging and time-consuming if data acquisition takes place in the real world. Simulation is the most obvious substitute for real-world experiments. Still, no matter how good, simulation can never fully replicate the physics of the real world [46]. In this paper, we propose an alternative approach, i.e., to acquire data for training autoencoders by generalizing from a small number of real robot performances of the task. Our experiments show that in the case of robotic throwing, the data obtained this way can better capture the physics of the real-world. Consequently, the Reinforcement Learning of throwing becomes faster.  
The task itself determines how many samples are required for training an autoencoder network for dimensionality reduction while still maintaining an accurate representation of the motor skill. We have shown that relatively few datapoints are needed to improve the Reinforcement Learning of throwing. Other tasks might require more example executions. The proposed methodology, while only shown on a relatively straightforward example, can be applied for more complex tasks, where otherwise thousands of real-world examples might be needed.  
While generating the database in simulation has been shown less effective than using a generalized database, we have shown elsewhere [18] that learning in simulation can also reduce the number of required real-world task executions. These two approaches are complementary and can benefit from each other.  
In the future, we plan to combine the proposed methodology with other methods that can transfer latent space actions from one robot to another and with methods that bridge the sim-to-real gap in motor learning. The proposed methodology is the first step in this direction.  
Conceptualization, methodology, investigation, and writing--original draft preparation: Z.L., R.P., and A.G.; writing--review and editing: A.U. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Horizon 2020 RIA ReconCycle, GA no. 871352, and program group Automation, robotics, and biocybernetics (P2-0076) supported by the Slovenian Research Agency.  
The authors declare no conflicts of interest.
## 4 Evaluation Proposal and Analysis
### Discussion  
As mentioned previously, to evaluate the different topologies, the comparison was applied using the paired observations method [21]. This process could help us to conclude which was the best metric in each scenario presented. In the six simulated scenarios of the tree topology, the results showed that the HOP metric was equivalent in three scenarios (10 nodes with 50/100 m and 70/90 m, 20 nodes with 50/100 m) and better in three scenarios (20 nodes with 70/90 m, 30 nodes with 50/100 m and 70/90 m). In the six simulated scenarios of the circular topology, the results showed that the HOP metric was equivalent in two scenarios (10 nodes with 70/90 m, 20 nodes with 50/100 m) and better in four scenarios (10 nodes with 50/100 m, 20 nodes with 70/90 m, 30 nodes with 50/100 m and 70/90 m). In the six simulated scenarios of the grid topology, the results showed that the HOP metric was equivalent in two scenarios (10 nodes with 50/100 m, 20 nodes with 70/90 m) and better in four scenarios (10 nodes with 70/90 m, 20 nodes with 50/100 m, 30 nodes with 50/100 m and 70/90 m). The benefits of knowing the topology of WSNs were clear, and it could help the administrator manage and design his/her network. Nevertheless, we know how hard it is to do this in the real world, where the nodes can be randomly arranged. However, if the topology could be considered for the administrator, as for the routing protocol, it could greatly help to increase the network lifetime. For example, a metric of the RPL could construct a virtual path considering the network topology observed. If the RPL used the distance perceived to build its DODAG, it could take advantage of the benefits shown in this work. Another relevant conclusion of our investigation was the correlation of the metrics ETX and HOP when we had more hops and possible routes. With the HOP metric, the most simple metric was applied, and the energy consumption was limited by it, considering only the route with fewer hops. However, the ETX metric was more sophisticated, considering not only the hops but also other parameters such as transmission packets, consequently consuming more processing to find the better path from the source to the destination. In this way, the metric HOP better fits a WSN with restrictions of the wireless signal coverage and energy consumption, besides behaving better in dense networks. Thus, after comparing the topologies separately, the tree and circular topologies were evaluated. In this case, comparisons were made only with the HOP metric, which was the result of being the best metric in the assessments made by each separate topology. The results were from the six simulated scenarios, and the circular topology was better in all scenarios. Then, the circular topology was
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Frequency-Based Haze and Rain Removal Network (FHRR-Net) Architecture
#### 2.3.2 Symmetric Skip Connections and Negative Mapping  
The encoder-decoder network with symmetric skip connections are suitable to restore degraded image [27]. The encoder of proposed network consists of convolutional layers, and the decoder consists of deconvolutional and convolutional layers. The encoder works to remove the major noise while preserving the important characteristics of the image. As the network goes deeper, the decoder works less because details along with the main noise can be removed. Symmetric skip connections have been proposed to solve this problem in deep networks which deliver the details of the image from the encoder to the decoder, increasing the image's restoring performance. To increase the training ability of the network, we also applied [PERSON] [10]'s negative mapping method which is one of the skip connections. The negative mapping technique is implemented in the network to help training by reducing the mapping range as shown in Figure 5. Using negative mapping significantly reduces the mapping range. Therefore, we suggested applying a negative mapping method to the encoder-decoder network to restore the detail of the image better.  
Figure 5: The overall architecture of FHRR-Net. The encoder decoder structure consists of a convolutional layer, followed by a batch normalization layer and a ReLu layer. The blue boxes indicate convolutional layers, and orange boxes indicate deconvolutional layers.
###### Abstract  
Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of \(\rho=0.998\) for parasites and \(\rho=0.987\) for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose.  
deep learning; thick blood smear; parasite count; white blood cell count; parasitemia determination; Faster R-CNN; SSD MobileNet; smartphone deployment +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://doi.org/10.3390/a14010017](https://doi.org/10.3390/a14010017)]([https://doi.org/10.3390/a14010017](https://doi.org/10.3390/a14010017))
## 3 Queuing Model and Dynamic Spectrum Algorithm
### Queuing Model with Two Queues and Multiple Servers  
In recent years, under the auspices of the IMO, the research on e-navigation is in ascendance [33; 34; 35]. E-navigation is a system concentrated on information transmission, providing a standard for the data package. Some data bits are used in the package to indicate the type of service, which is called the message ID. In the queuing model, a pre-classifier is used to analyze the type of service by the message ID in the data package. As shown in Figure 3, the SUs' communication requests are divided into queue \(SU_{q1}\) and queue \(SU_{q2}\) by the pre-classifier. The first arriving request will get the allocation of the CBS first. After assignment, the CBS will inform the SU of some related information. At the same time, the CBS also checks if a PU is using the required channel.  
As the arrival of each SU's communication request is a random event, the arrival of SUs' communication requests will follow the Poisson distribution [17; 36; 37; 38]. After the pre-classifier processing, the arrival rate \(\lambda_{q1}\) of the high-priority queue and the arrival rate \(\lambda_{q2}\) of the low-priority queue are also subject to the Poisson distribution.  
A large number of papers [17; 19; 36; 37; 39] use the exponential distribution as the distribution of the channel service time, which is the time during which SUs use the channel to communicate with each other. Assuming that the service time of \(SU_{q1}\) and \(SU_{q2}\) follow the exponential distribution with parameters \(\mu_{q1}\) and \(\mu_{q2}\), the average service rates of the two queues will equal \(\mu_{q1}\) and \(\mu_{q2}\), respectively.  
Since the use of the channel by a PU is also a random process of continuous time, its arrival rate follows the Poisson distribution with parameter \(\lambda_{p}\), and its service time follows the exponential distribution with a mean of \(\mu_{p}{}^{-1}\)[17; 19; 20].  
In order to ensure the communication efficiency of the high-priority queue, we introduce the adaptive priority parameter \(p\) in the DSA algorithm to dynamically adjust the weight of the two request queues.
###### Abstract  
Presently, multirotor unmanned aerial vehicles (UAV) are utilized in numerous applications. Their design governs the system's controllability and operation performance by influencing the achievable forces and moments produced. However, unexpected causalities, such as actuator failure, adversely affect their controllability, which raises safety concerns about their service. On the other hand, their design flexibility allows further design optimization for various performance requirements, including actuator failure tolerance. Thus, this study proposed an optimization framework that can be employed to design a novel actuator fault-tolerant multirotor UAV configuration. The approach used an attainable moment set (AMS) to evaluate the achievable moment from a multirotor configuration; similarly, standard deviation geometries (SDG) were employed to define performance requirements. Therefore, given a UAV configuration, actuator fault situation, and SDG derived from the designed mission requirement, the suggested optimization framework maximizes the scaling factor of SDG and fits it into the AMS by adjusting the design parameters up to a sufficient margin. The framework is implemented to optimize selected parameters of the Hexacopter-type of parcel delivery multirotor UAV developed by the PNU drone, and a simulation was conducted. The result showed that the optimized configuration of the UAV achieved actuator fault tolerance and operation-performing capability in the presence of a failed actuator.  
Keywords:fault-tolerant configuration; multirotor UAV; attainable moment set; required moment +
Footnote †: journal: Applied
## 3 IVE-KF Algorithm  
The metal target parameters in the process of detection can be considered equivalent to a state vector in a dynamic system. Since the noise can be approximated by a Gaussian distribution during the detection process [8], the KF method can be used to predict the state information of the metal target. However, since the optimized model still has three nonlinear parameters related to the position, the EKF must be used to solve the nonlinear parameters in the model. The EKF uses the first derivative in the Taylor series to approximate the nonlinear function of the model. Therefore, in the detection process, the linear and nonlinear parameters in the target state vector are estimated by KF and EKF, respectively. However, the performance of the KF algorithm is sensitive to its initial value. In the case of a large initial value error, the filtering accuracy is low, and the results are sometimes divergent. In order to overcome this problem, it is necessary to provide an initial value \(\hat{\mathbf{x}}_{0}\) that is relatively accurate for the filter algorithm. \(\hat{\mathbf{x}}_{0}\) contains the position estimation and magnetic polarizability tensor estimation of the metal target.
# Observing Material Properties in Composite Structures from Actual Rotations
## 3 Results
### Example 2: Cantilever Beam with Two-Iteration Processes  
Consider a cantilever beam with 3-m length, the same cross-section, and the same properties as in Example 3.1 modeled with one beam element and two nodes as presented in Figure 5. The boundary conditions of the structure are the total horizontal and vertical displacements and rotation due to bending restricted in node 1 (this is to say \(\mathrm{u_{1}}=\mathrm{v_{1}}=\mathrm{w_{b1}}=0\)). The only external force applied is assumed as a concentrated vertical force in node 3 of 100 kN (\(\mathrm{V_{3}}=100\) kN). As in the previous example, the length of the elements L, the Poisson's ratio, and the Young's modulus E are assumed as known in the inverse analysis, while the inertia I, and the shear area \(\mathrm{A_{v}}\) for elements 1 and 2 are assumed as unknown. To obtain these four unknown parameters, the measurement set should include at least four rotations. Nevertheless, the analysis of the traditional OM proves that no set of four rotations in the COM enables the proper identification of the unknown parameters. Therefore, a set of vertical deflection and rotation should be measured together in this example. To perform the COM2, a measurement set that consists of two rotations and two vertical deflections (\(\mathrm{w_{2}}\), \(\mathrm{w_{3}}\) and \(\mathrm{v_{2}}\), \(\mathrm{v_{3}}\)) is employed. The rotations and the vertical deformations including the shear effects of this structure are presented in Figure 5.  
Figure 5: Example 2. (**a**) FEM for a cantilever beam. (**b**) Deformed shape with the value of bending vertical deformation and with bending and shear vertical deformation at node 2. (**c**) Deformed shape with bending rotation and with bending and shear rotation at node 2.  
To check the robustness of the method, a set of random values is chosen for the inertia and shear area to be used to calculate the hypothetical site measurements. A total of 50 different random sets of the initial value coefficients for the inertia and the shear area were randomly chosen between 0.8 and 1.2 of the theoretical values. Also, no matter what set of the initial value is chosen, in all circumstances, the obtained values of the two-iteration process have proper convergence to the real values of the mechanical properties. The normalized average, standard deviation, and coefficient of variation of the remaining results as well as the mode are presented in Table 3.  
These results prove the adequate convergence of the proposed methodology in cantilever beams for random initial values of the unknown parameters. These results show the applicability of the proposed methodology in cantilever structures. It is important to highlight that the obtained results of the original COM process for this example lack physical meaning, as ratios of 0.562 and 3.000 were obtained for the inertia in I\({}_{1}\) and I\({}_{2}\), respectively.
# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 2 Drone vs. Bird Detection Challenge 2020
### Evaluation Protocol  
The Drone vs. Bird Detection Challenge 2020 allowed up to three submissions for each participating team. Each submission had to include a result file for each sequence similar to the provided annotation files. The particular result files had to provide the frame numbers and the predicted bounding boxes ([top_x, top_y, width, height]) and corresponding confidence scores for each frame. If the frame number was not listed, no detection was assumed. To rank the submitted results, the popular Average Precision (AP) was employed as evaluation metric. AP is based on the Intersection over Union (IoU) criterion for matching ground truth and detected object boxes. The IoU is the ratio of the area of overlap between the estimated bounding box and the ground truth bounding box to the total area of union:  
\[IoU=\frac{\text{detection}\cap\text{ground truth}}{\text{detection}\cup \text{ground truth}}.\]  
Figure 4: Examples with birds that are the main disturbing objects.  
Figure 3: Distribution of drone sizes across the ground truth annotations in the train data.  
Detections are counted as true positive detections, if an IoU with a ground truth annotation is above 0.5. Otherwise detections are counted as false positives. Ground truth annotations that are not assigned to a detection, are counted as false negatives, i.e., missed detections. As the AP is calculated by taking the area under the precision-recall curve, it summarizes the whole precision-recall curve into a single metric and thus, encompasses the various precision-recall trade-offs of a detector. Note that the test sequences have been made publicly available one week before the submission deadline. Notice also that the rules of the challenge do not restrict the training data used by the algorithms to the Drone vs. Bird training set only.