esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
###### Abstract  
Acoustic source localization in the spherical harmonic domain with reverberation has hitherto not been extensively investigated. Moreover, deep learning frameworks have been utilized to estimate the direction-of-arrival (DOA) with spherical microphone arrays under environments with reverberation and noise for low computational complexity and high accuracy. This paper proposes three different covariance matrices as the input features and two different learning strategies for the DOA task. There is a progressive relationship among the three covariance matrices. The second matrix can be obtained by processing the first matrix and it effectively filters out the effects of the microphone array and mode strength to some extent. The third matrix can be obtained by processing the second matrix and it further efficiently removes information irrelevant to location information. In terms of the strategies, the first strategy is a regular learning strategy, while the second strategy is to split the task into three parts to be performed in parallel. Experiments were conducted both on the simulated and real datasets to show that the proposed method has higher accuracy than the conventional methods and lower computational complexity. Thus, the proposed method can effectively resist reverberation and noise.  
direction-of-arrival; spherical microphone array; covariance matrix; convolutional neural network Article
## 5 Discussion  
### Result Interpretation  
In the calculation of the objective weight of each evaluation index, it was found that indicators \(\mathrm{B_{1}}\) and \(\mathrm{B_{4}}\) have the highest weight, reaching \(\mathrm{B_{1}}=0.5057\), \(\mathrm{B_{4}}=0.4425\), indicating that \(\mathrm{B_{1}}\)(_the interface provides clear information_) and \(\mathrm{B_{4}}\)(_The information organization structure on the interface is clear_) are the most significant indicators to the in-vehicle HUD interface. Combined with the design schemes, the background shapes of the warning signs for each scheme are rectangle, circle and triangle. Under the condition of equal length and width, the rectangular area is the largest, which can attract the attention of the driver to the greatest extent, occupy the largest visual area, rapidly improve the driver's situational awareness level and enable him to quickly detect the danger and take action [43].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & \(\mathbf{S_{i}}\) & \(R_{i}\) & \(\mathbf{Q_{i}}\) \\ \hline
1 & 0.141 & 0.119 & 0.000 \\
2 & 0.541 & 0.377 & 0.628 \\
3 & 0.932 & 0.463 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(S_{i},R_{i}\) and \(Q_{i}\) of each scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & \(\mathbf{w_{j}}\) & **Indicator** & \(\mathbf{w_{j}}\) \\ \hline A\({}_{1}\) & 0.000 & \(\mathrm{B_{2}}\) & 0.0057 \\ A\({}_{2}\) & 0.000 & \(\mathrm{B_{3}}\) & 0.0057 \\ A\({}_{3}\) & 0.0057 & \(\mathrm{B_{4}}\) & 0.4425 \\ A\({}_{4}\) & 0.000 & \(\mathrm{C_{1}}\) & 0.000 \\ A\({}_{5}\) & 0.0057 & \(\mathrm{C_{2}}\) & 0.0057 \\ A\({}_{6}\) & 0.0057 & \(\mathrm{C_{3}}\) & 0.000 \\ A\({}_{7}\) & 0.000 & \(\mathrm{C_{4}}\) & 0.0057 \\ B\({}_{1}\) & 0.5057 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation of each indicator weight \(w_{j}\).  
Furthermore, by retrospect of participants' oral description after the experiment, several insights were found that could be beneficial during the design process. For example, concerning the navigation signs, the majority of participants denoted that the navigation arrows would block or interfere with the pointing arrows on the road in some cases, or be excessively analogous to the painted arrows on the road, resulting in cognitive confusion and thus reducing the performance of driving main tasks. This resembled the conclusion reached in the previous literature [44], so the participants were more drawn to the boomerang-type navigation sign, as illustrated in scheme 1.
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 5 Challenges in UAV Routing
### Air Traffic Control  
Many rules and regulations govern the movement of objects in the airspace. These lead to blocking and opening of flying zones [7]. Even though air traffic control has a significant impact on designing and implementing routes for UAVs, none of the current VRP approaches to solve UAV routing has considered this due to its complex nature. Countries have established systems for notifying blocks of airspace where particular limitations are placed on the flight of all types of UAVs. Such areas are typically either: Prohibited Areas, Restricted Areas, or Danger Areas such as military ranges. Other airspaces may have temporary restrictions imposed at specific times, either as a result of a longer-term pre-planned event or in reaction to a short notice occurrence, such as an emergency incident. Permanent Prohibited, Restricted, or Danger areas are marked on aviation 'Visual Flight Rules' (VFR) flight charts (maps), which are readily available for purchase online and proprietary VFR flight-planning and navigation software and apps contain such information in their mapping databases. UAVs around  
Figure 8: Factors that affect energy consumption of UAVs [14; 139].  
airfields or airports that are designated as 'protected aerodromes' are tightly restricted. UAVs of any size must not be flown within the Flight Restriction Zone (FRZ) of a protected aerodrome, without appropriate permission. This information about the restricted or controlled air space is taken as input data and used as constraints in UAV route planning. [PERSON] et al. (2016) [PERSON] et al. (2016) uses the Time-dependent TSP in Controlled Airspace (TDTSPPCA), which consists of finding the best order of visit of a UAV over a set of waypoints, aiming at minimizing the operational cost and, at the same time, ensuring a minimum separation with respect to the planned air traffic. However, the technology already exists to build UAVs that can see electronically and avoid other aircraft [PERSON] et al. (2016).
# A Diagnosability-Integrated Design Approach Based on Graph Theory
## 3 Diagnosability Integrated Design Method Details
### Measurement Point Determination  
#### 3.1.1 Diagnosis Plot of Measurement Point Determination  
The column vectors of Equations (1) and (2) indicate the difficulty of the fault or fault pair to be detected or isolated by the tests in the system. The tests were grouped according to the contents of Equations (1) and (2) and using the K-means clustering algorithm. Use \(test(f_{i})\) to denote the set of tests that can detect faults \(f_{i}\) at a smaller cost, and use \(test\big{(}\big{(}f_{i},f_{j}\big{)}\big{)}\) to denote the set of tests that can distinguish fault pair \(\big{(}f_{i},f_{j}\big{)}\) at a smaller cost.  
According to the results of \(test(f_{i})\) and \(test\big{(}\big{(}f_{i},f_{j}\big{)}\big{)}\), the detection and isolation capability of the measurement point \(t_{i}\) for different faults and fault pairs can be further revealed. In order to more graphically represent the diagnosis capability of measurement points for different faults, the diagnosis plot of measurement point was introduced in this paper.  
The following algorithm was used to draw the diagnosis plot of measurement point \(\mathcal{G}_{t_{i}}\).  
Based on Algorithm 1 and Definition 1, it can be inferred that  
**Definition 1**.: _Diagnosis plot of measurement point. The diagnosis plot of measurement point \(\mathcal{G}_{t_{i}}\) is used to qualitatively represent the diagnosis capability of the signal from measurement point \(t_{i}\) for different faults, denoted as \(\mathcal{G}_{t_{i}}=\left(V_{t_{i}},E_{t_{i}}\right)\), where \(V_{t_{i}}\) denotes the set of vertices of the graph \(V_{t_{i}}\subseteq\mathcal{F}\), indicating that the faults corresponding to the vertices can be detected by test \(t_{i}\). The set of edges of the graph is denoted as \(E_{t_{i}}=\left\{\left\{f_{i},f_{j}\right\}\middle|f_{i},f_{j}\in V_{t_{i}}\right\}\), \(\left\{f_{i},f_{j}\right\}\) indicates that the fault pair \(\left(f_{i},f_{j}\right)\) can be isolated by test \(t_{i}\)._  
**Corollary 1**.: _The diagnosis plot of measurement point \(\mathcal{G}_{t_{i}}=\left(V_{t_{i}},E_{t_{i}}\right)\) for any measurement point \(t_{i}\)_  
\(f_{0}\in V_{t_{i}}\)_,_  
\(\forall f_{i}\in V_{t_{i}}\)_,_ \(\exists\left\{f_{i},f_{0}\right\}\in E_{t_{i}}\)__
## 2 Materials and Methods
### Characterization
#### 2.5.7. Evaluation of Blood Compatibility  
A haemolytic activity assay was performed with 4% red blood cell suspension, which was purchased from Henan Yuechi Biotechnology Co., Ltd., Zhengzhou, China. First, rH/CS/PCL MPs were dispersed in PBS solution with concentrations of 0.0625, 0.125, 0.25, 0.5, 1, 2, and 4 mg mL\({}^{-1}\). Subsequently, 100 \(\mu\)L 4% red blood cell suspension was added into 1 mL microparticle/PBS solution at each concentration. The positive and negative controls were the distilled water containing 4% red blood cell suspension and PBS, respectively. All samples were incubated at 37 \({}^{\circ}\)C for 1 h and then centrifuged at 2000 rpm min\({}^{-1}\) for 3 min to obtain supernatants. The absorbance of the supernatants was measured at 545 nm (JK-UVS-752N, SHANGHAI JINGKE, China), and haemolysis percentages of bowl-shaped particles were calculated by using the following Formula (1) [35].  
\[\text{Hemolysis rate}(\%)=\frac{A_{x}-A_{-}}{A_{+}-A_{-}}\times 100\% \tag{1}\]  
where \(A_{x}\) is the OD\({}_{545}\) of the test sample, \(A_{-}\) is the OD\({}_{545}\) of the negative control group, and \(A_{+}\) is the OD\({}_{545}\) of the positive control group.
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 2 Related Works  
Recently, there have been many works focused on reducing the energy consumption of task scheduling in cloud computing. [PERSON] et al. [12], as pioneers, designed an energy-efficient cloud computing architecture model based on CPU and proposed an energy-efficient resource allocation algorithm, but did not consider hardware resources other than CPU. [PERSON] et al. [13] considered multiple resources; they first designed a mathematical model to measure the energy consumption in the process of task execution, and then proposed a scheduling algorithm that can minimize the average energy consumption of each task. [PERSON] et al. [14] classified tasks and VMs, and scheduled tasks to corresponding VMs based on the classification results. This scheduling algorithm can both improve resource utilization and reduce energy consumption. [PERSON] et al. [15] weighed the energy consumption and performance of the cloud computing system. They abstracted the problem into a double-objective integer programming problem and designed a two-stage heuristic algorithm. [PERSON] et al. [16] designed a dynamic hybrid resource deployment algorithm to minimize energy consumption of data centers by improving the average utilization rate of physical machines. The algorithm is based on the K-means clustering algorithm in unsupervised learning and the KNN classification algorithm in supervised learning. Note that the previous works are mostly based on approximation algorithms. The solution results often lack theoretical support and can only be evaluated by numerical experiments, thus lacking in promotion.  
In terms of the algorithm research in BPPC, [PERSON] et al. [17] studied the lower bound of the problem and designed various heuristic algorithms. [PERSON] et al. [18] proposed an iterated local search algorithm. This metaheuristic algorithm can enhance the search procedure with a variety of local and large neighborhoods. [PERSON] et al. [19] proposed an exact algorithm based on branch-and-price. They utilized the structure of conflict constraints to design an efficient branching strategy. This strategy can preserve the model of subproblems after branching, thus greatly improving the efficiency of the algorithm. [PERSON] et al. [20] designed a new label-setting scheme that can effectively solve the pricing problem and accelerate algorithm execution speed. [PERSON] [21] considered a variant model of BPPC, which allowed fragments of items. He proved that the proposed new model was still NP-hard and designed a heuristic algorithm based on a lower bounding scheme. It is noteworthy that the existing studies focus on the algorithm design for single-dimension resource requirements.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 2 Related Work
### Coughvid  
The COUGHVID dataset [27] is a large publicly available cough sound dataset. The dataset contains over 20,000 recordings and contains the labels COVID-19, symptomatic, and healthy. Data collection was conducted using a web application from 1 April 2020 to 10 September 2020. After 10 s of cough sound recording for each person, participants were asked to fill out and submit simple information, including age, gender, and current condition. To remove non-cough recordings from the database, a cough detection model was applied and the cough sound scores of all recordings were analyzed. The scores were indicated by \"cough_detected\" in the metadata. Audio data were in the WEBM or OGG format, and the sampling frequency was 48 kHz.  
[PERSON] et al. [22] proposed a multi-branch deep learning network using the COUGHVID dataset as a model for diagnosing COVID-19. Only data with a cough detection score of 0.9 or higher were used, and the filtered data were 4446 recordings from healthy people, 923 recordings from symptomatic people, and 380 recordings from people who tested positive for COVID-19, for a total of 5749 audio files. In order to rectify the imbalance in the number of data, they increased the number of COVID-19-positive recordings to 750 by adding Gaussian noise, pitch shifting, and shifting or increasing the time signal. By selecting an equal number of recordings from symptomatic and healthy people, a total of 2250 audio samples were used. MFCCs and the mel spectrogram, which are commonly used in audio classification and speech recognition, were used as acoustic features for the network training. In addition, clinical features such as respiratory status or fever were used as a one-dimensional vector of binary numbers. The MFCCs were input to a dense layer of 256 and 64 nodes, and the mel spectrograms were input to a ResNet-50 that was pre-trained on the ImageNet dataset. Clinical features were input to a dense layer of 8 nodes. The output values of all results were connected to form a combined multi-branch. The sensitivity and specificity of this model were 0.85 and 0.99, respectively.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 4 Implementation  
Here we consider AR systems that rescuers are likely to use in indoor disaster situations. The AR system aims to provide preliminary information necessary for rescuers to perform the rescue mission, as well as enhanced functionality for navigating the disaster area and making decisions regarding the mission.  
The algorithm in this paper is part of the AR system under study and was developed to generate a topology map using field images. It calculates the shortest distance to the destination.  
When implementing the pathfinding application, we assumed that the image is processed in a form that can be used in the system and that the user's location is trackable. Microsoft's HoloLens 2 was used to run this application, and the Unity Engine and MRTK libraries were used as development tools.  
Due to the nature of Unity Engine, the application was converted into C++ when compiling the code, and C# was used for implementation. The detailed system environment is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parts** & **Specifications** \\ \hline
**CPU** & Intel i7-1260P \\ \hline
**RAM** & DDR4 3200 mHz 16 GB (8 \(\times\) 2) \\ \hline
**GPU** & Intel Iris Xe (96 eu) \\ \hline
**OS** & Windows 11 21H2 \\ \hline
**IDE** & Unity 2020.3.25f1 \\ \hline
**Language** & C\# \\ \hline
**AR Device** & Microsoft HoloLens 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System environment.  
Figure 9: Avoiding the creation of duplicate paths. (**a**) Scenarios that allow duplicate paths between nodes on a straight line; (**b**) detection of duplicate paths during path creation.  
Figure 10 is a photograph of a user selecting a destination location. The red circle indicates the user's location, and the green box indicates the destination. The destination can be selected through the user interface (UI) at the bottom. Clicking the Start button generates a path to the selected destination.  
The generated path is displayed on top of the mini-map, and the route is guided through HoloLens 2. Figure 11 shows the virtual object for navigating superimposed on top of the real world viewed through HoloLens 2.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Analytical Methods
#### 4.2.2 Mass Spectrometry
However, TOF mass spectrometers usually tend to be less sensitive than other mass spectrometers of the same generation, and these methods are less commonly used for routine sample screening and quantitation than LC-MS/MS methods. However, TOF- and HRMS-based instruments can be used to qualitatively identify toxins that lack analytical standards, unknown toxins, and/or their metabolites or quantify using standards of other structurally similar toxins [82; 83; 84]. LC-MS/MS is the most commonly used chromatographic method for cyanobacteria detection in general, but it is limited by the need for analytical standard material to be available. For example, LC-MS/MS methods for the measurement of pyrmnesins and other toxins produced by _P. parvum_ have not been commonly reported in the literature; these represent an emerging class of contaminants for which there is presently little information. The key issue preventing their measurement is the absence of a pure analytical reference standard for these toxins, which could be used for calibration. The ability to incorporate an internal standard would be ideal for highest precision for an analytical method, ideally a stable, isotopically labeled form of the analyte, but along with the native (unlabeled) pyrmnesins, these are not available. To date, only a few studies have reported isolating potential toxins, including pyrmnesin molecules, from _P. parvum_, [85; 86; 87; 88; 89], and replication of this work is yet to be reported in the peer-reviewed literature. Analytical standards for the pyrmnesins, either normal or isotopically labeled, along with any other structurally similar molecule are not currently available. A few studies have used UTEX strain 2797 as the source material for the preparative isolation of toxin material for analytical method development [46; 63; 89; 90; 91; 92], but these preparations are not quantitatively exact enough to allow for use as true analytical standards. As a result, there are currently no published validated methods for the quantitative analysis of pyrmnesins [93].
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Mobility Models
#### 3.1.5 The Enhanced Gauss-Markov Model  
The EGM mobility model is proposed in [6]. It is based on the GM mobility model, but the direction is computed slightly differently; most importantly, a progressive mechanism for boundary avoidance is implemented. The key mechanisms of the mobility model are as follows. When the node is far enough from the boundaries, the next speed and the next direction deviation are calculated normally (very similarly to GM) using certain equations. When the node gets close to a boundary (within a distance of 250 m from it), a turning toward the center region is initiated. This is in order to smoothly/gradually avoid hitting the boundary. If, despite the boundary-avoidance scheme, it is still determined that the next position of the node falls outside the region, then \"it is brought back in\" a little more forcefully. However, the situations where more \"force\" is used are expected to be rare.  
Figure 5 shows the trajectory of a UAV when the EGM mobility is used in a simulation. As we can easily observe, the trajectory is noticeably smoother than the one generated with RWP (Figure 1). There are virtually no sharp turns, and the node does manage to avoid the boundaries in a very smooth and progressive way. This behavior that we observe at the boundaries is an advantage that EGM clearly has over the other mobility models where we observe sharp turns at the boundaries.
## 4 Application of the SLaTCoW Method: Estimation of Ultrasonic Attenuation
First, the method in [24] provides the local attenuation at the position of a point on the sample, while the method proposed in this work provides the global attenuation over a given distance on the sample, here 4 mm. The value of \(\alpha\) (estimated by the proposed method) can be then considered as the average of \(\alpha_{loc}\) (estimated by the method in [24]) measured at each local point along the given distance on the sample. Second, the value of \(\alpha\) is estimated over the range of frequency (\(f\in[40.5,41.0]\) MHz) and that of \(\alpha_{loc}\) is estimated at the ZGV resonance frequency (\(f_{S_{1}S_{2}}=40.28\) MHz). Before the conclusion of this work, we propose hereby another application of the SLaT-CoW method for future work: the monitoring of cumulative damage [20; 21] in materials through the estimation of the ultrasonic attenuation from experimental measurements done at different stages of the fatigue process. Indeed, when the material fatigues, an increase in the ultrasonic attenuation is expected. Its monitoring should hence be insightful and could give information about the fatigue progress and stage. Recently, in [21], we observed the dramatic drop of the experimental quality factor in the early fatigue stage in aluminum, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attenuation** & **Value (dB/cm/MHz)** & \\ \hline \(\alpha_{2}\) & 0.40 \(\pm\) 0.01 & Exp. \\ \(\alpha_{1}\) & 0.54 \(\pm\) 0.01 & Exp. \\ \(\alpha\) & 0.47 \(\pm\) 0.01 & Exp. \\ \(\alpha_{lit}\)[100] & 0.26 \(\pm\) 0.04 & Ref. [50] \\ \(\alpha_{lit}\)[110] & 0.30 \(\pm\) 0.01 & Ref. [50] \\ \(\alpha_{lit}\)[111] & 0.230 \(\pm\) 0.006 & Ref. [50] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimated ultrasonic attenuation in aluminum. compared to the numerically calculated values by a theoretical modeling considering only a thickness variation of the fatigued plate, which required the improvement of the conducted modeling by accounting for the change of material attenuation. The proposed method can thus be used for a better understanding of ultrasonic attenuation at different fatigue stages through its global estimation along the damaged region, which could help improving/completing the empirically based theoretical modeling proposed in [21].