esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 1 Introduction
In 2019, [PERSON] [21] proposed a deep learning method, constructed an underwater acoustic signal feature extraction model based on a generative confrontation network, combined with a deep neural network classifier for modulation recognition, and could effectively extract classification features from underwater acoustic signals. Deep learning has a wide range of application prospects in the field of underwater acoustic targets [22]. In this paper, we propose a DCNN-KFAT method for underwater acoustic moving target tracking. After the target is detected in the early stage of tracking, we calculate the target azimuth and distance, and then use beamforming to perform target azimuth-related weighting on the echo signal, convert the obtained target frequency domain data into time domain data, generate a data set with the target time-frequency image as a sample, and finally use DCNN to train the data set to generate a model to identify the target. In the follow-up process of tracking the target, we use Kalman filter to predict the target azimuth and distance, weight the received echo signal, generate all possible azimuth-related signal time-frequency images, and use the trained model for recognition. We determine the target category according to the output of the model, and finally get the target azimuth and distance. According to the current target recognition result, it is associated with the existing target, and the target tracking information is updated. The rest of the article is as follows: Section 2 describes the active tracking method proposed in this article, Section 3 describes the simulation test, Section 4 describes the discussion, and Section 5 gives the conclusion.
## 3 Methods
### The Stereo Matching Algorithm in this Paper  
The stereo matching algorithm in this project is a new stereo matching algorithm based on the region stereo matching algorithm. The matching cost calculation method combines SAD [25] and census non-parametric transformation. The aggregation of matching costs is based on the idea of adaptive weight, which changes the weight of each pixel according to the local distribution of pixels in the support window. Finally, the dense disparity image is obtained by left-right consistency detection and post-processing (interpolation, median filtering, etc.).  
The matching cost aggregation based on adaptive weight first assigns weight to each pixel in the support window based on the gray scale and distance similarity between each pixel in the support window and the central pixel, that is, the closer the color is and the smaller the distance is, the greater the weight will be. Then, the aggregation matching cost of all pixels in the support window can be obtained according to the assigned weight and corresponding matching cost. This method can effectively avoid the mismatching phenomenon caused by depth discontinuity and color difference. The corresponding gray scale and distance similarity in the adaptive weight refers to the absolute gray scale difference between two pixels and the geometric distance between pixel coordinates, respectively; that is, the weight is assigned according to the absolute gray scale difference between each pixel in the support window and the central pixel and the geometric distance between pixel coordinates. The distribution rule is shown in Equation(2).  
\[\left\{\begin{array}{c}w(p,q)=\exp(-\frac{D_{c}(q)}{\lambda_{c}}-D_{d}(q)/ \lambda_{d})\\ D_{c}(q)=\mid I(q)-I(p)\mid\\ D_{d}(q)=\sqrt{\left(u_{q}-u_{p}\right)^{2}+\left(v_{q}-v_{p}\right)^{2}}\end{array} \right.,q\in N(p) \tag{2}\]  
where \(p\) is the center pixel of the supporting window, \(N(p)\) is the set of all pixel points in the window, \(w(p,q)\) is the weight of pixels in the support window, \((u_{q},v_{q})\), \((u_{p},v_{p})\) are pixel coordinates of point \(p\) and point \(q\), respectively. \(D_{c}(q)\), \(D_{d}(q)\) are the absolute value of gray difference and pixel coordinate geometric distance between point \(p\) and point \(q\), respectively. \(\lambda_{c}\), \(\lambda_{d}\) are gray and distance factors, respectively, which are used to adjust the influence of the absolute value of gray difference and the geometric distance of pixel coordinates on the weight.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Input Features  
#### 3.1.1 Audio Features  
We use 90-D log-mel filterbank features. Each feature vector is obtained from 25-ms-long Hamming-windowed input speech at every 10 ms.
## 4 Numerical Examples
### Comparison of Computing Efficiency between AIFEM and MCM  
To illustrate the computational efficiency of the AIFEM and the MCM, Table 5 lists the time consumed by each method in the two examples. The calculations were implemented by MATLAB R2023a on an Intel Core CPU i5-12500H 2.5 GHz. According to Table 5, the AIFEM consumes much less time than the MCM; therefore, the computational efficiency of the AIFEM is much higher than the MCM. The AIFEM reflects more efficient calculation efficiency across different examples. Based on computational efficiency, the AIFEM is a reasonable method to calculate the transient temperature response of TPSs with uncertainties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Uncertain Level (\%)** & **Time (s)** & **Bound** & **AIFEM (\({}^{\circ}\)C)** & **MCM (\({}^{\circ}\)C)** & **Error (\%)** \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{300} & UB & 263.38 & 257.72 & 2.20 \\  & & LB & 205.23 & 209.73 & 2.15 \\  & & UB & 378.41 & 368.87 & 2.59 \\  & & LB & 308.91 & 317.68 & 2.76 \\  & & UB & 292.03 & 283.80 & 2.90 \\  & & LB & 181.18 & 186.48 & 2.84 \\
10 & & UB & 406.84 & 394.97 & 3.01 \\  & & LB & 283.73 & 292.75 & 3.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Temperature response bounds and relative error at Node 2 of TPS model.  
Figure 7: TPS temperature response at uncertainty level 10%: (**a**) Node 1; (**b**) Node 2; (**c**) Node 3.
## 4 Slotted Waveguide Injection Simulations  
The previously studied slotted waveguides have been attached to the two plasma cavities in order to obtain the new microwave launch geometry. In the case of the \"plasma  
Figure 6: \(|S_{11}|\) versus **(a)**\(w_{slot}\), **(b)**\(\mathsf{l}_{slot}\) and **(c)**\(\mathsf{d}_{short}\). For each parameter variation, the remaining ones have been fixed to the nominal design values reported in Table 1.  
Figure 7: **(a)** Slotted-twisted waveguide antenna and **(b)** computed \(|S_{11}|\). The antenna retains the design values reported in Table 1 except for \(\mathsf{d}_{short}\), now equal to 7.954 mm.  
shaped\" cavity, the slotted waveguide has also been twisted in order to follow the outer wall shape. The two geometries (vacuum volumes) are visible in Figure 8.  
The \(|S_{11}|\) curves have been calculated for both cavities with the new slotted waveguide microwave injection system, as shown in Figure 9. From the plot it can be observed that, using the new microwave injection system, more modes are coupled inside both cavities with respect to the standard axial one. This could be advantageous when one applies the frequency tuning technique in order to improve the ion source performances.  
The electric field plot, at the frequency of 14.466 GHz, inside the \"plasma-shaped\" cavity employing the side-coupled slotted waveguide is shown in Figure 10b. It can be seen that the field presents a maximum around the cavity center and its profile results now symmetric along the cavity axis with respect to the configuration shown in Figure 4 relative of the axial injection system. It is worth noting that, with the same side-coupled slotted waveguide, the cylindrical cavity still presents a predominance of modes with off-axis electric field maxima, as seen in Figure 10a.  
Figure 8: Slotted waveguide injection system coupled to: (**a**) standard cylindrical and (**b**) “plasma-shaped” cavities. Simulations have been made by using vacuum volumes.  
Figure 9: \(|S_{11}|\) curve, for the standard cylindrical cavity and the “plasma-shaped” one with the slotted waveguide microwave injection system, inside the operational bandwidth of 14-14.5 GHz.  
The obtained field symmetry leads to a homogeneous power distribution inside the cavity from the multiple radiating slots, with a potential advantage in terms of power absorbed from the plasma. Furthermore, the use of the slotted waveguide could in principle lead to an increase of the total employable power by creating a splitted-injection system composed of multiple slotted waveguides placed radially with respect to the cavity [20].
## 3 Target Tracking Method  
The target tracking method includes mutual observation information based on a cooperative platform, the spatial registration method, and the maritime target tracking method.
# YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation
## 1 Introduction
With the advancement of AI, deep learning has shown promise in addressing complex computer vision tasks in agriculture, with several deep learning algorithms, such as convolutional neural networks (CNNs), being successfully applied to recognition activities [13]. Integrating computer vision and artificial intelligence (AI), particularly with deep learning techniques, has revolutionized agricultural practices. Object detection models, such as convolutional neural networks (CNNs), have revealed substantial progress in automating tasks previously performed manually, enhancing speed and accuracy [12]. The YOLO (you only look once) model family stands out due to its real-time detection capabilities. However, image segmentation accuracy remains a critical factor for computer vision applications, as it directly impacts the performance of identification models. Recent studies emphasize the importance of striking a balance between computational efficiency and segmentation fidelity, thereby ensuring robust results without compromising system performance [14]. Current research indicates that YOLO-based models are highly effective for detecting and classifying Capsicum (pepper) fruits, including tasks such as growth stage classification, segmentation, and real-time identification [10; 15]. While YOLO and data augmentation are well established for general _Capsicum_ detection and classification tasks [15; 16], their application to efficiently and accurately identify pepper varieties remains underexplored. The YOLOv8m (medium) model offers a favorable trade-off between detection accuracy and computational efficiency. It outperforms the smaller variants (nano and small) in precision while requiring significantly fewer resources than the larger models (large and extra-large), as previously verified in the current literature [16]. This outcome makes YOLOv8m a robust and efficient choice for deployment on hardware-constrained devices. The authors emphasize that the YOLO architecture was explicitly designed to be scalable, allowing researchers and practitioners to adjust model complexity according to the specific requirements of both the application and the available hardware. Moreover, YOLO-based models have been increasingly applied in the agriculture sector for tasks such as crop and pest detection, contributing to advancements in precision agriculture and process automation. To address these challenges, the present study develops an automated method for identifying pepper varieties post-harvest using the YOLOv8m architecture and data augmentation. The main contributions include the construction of an annotated image dataset, the application of advanced deep-learning techniques, and a comprehensive evaluation of detection performance. The paper is structured as follows: the subsequent sections present the theoretical background, describe the materials and methods, report the results, and discuss the findings and implications for automated quality control in the post-harvest scenario.
# Fluorescence Correlation Spectroscopy Measurement Based on Fiber Optics for Biological Materials
## 3 Results and Discussion
### Robustness  
To validate the robustness of the FB-FCS system, the same sample was measured on different days for two weeks without any mechanical adjustment, as shown in Figure 4. The sample consisted of green fluorescent beads with a diameter of 0.04 \(\upmu\)m (Polystyrene Fluorescent Microspheres, Dragon Green, FSDG001, Cosmo Bio, Tokyo, Japan). The beads were diluted to a concentration of 0.41 \(\upmu\)M using pure water. The 10-s measurement was repeated 15 times, and the averaged ACF was analyzed each day. There was almost no difference in the shape of the normalized ACFs for two weeks, as shown in Figure 4a. In addition, neither the diffusion time nor the CPM showed a large difference, as shown in Figure 4b,c. The mean diffusion time in a day was 1154.1 \(\pm\) 54.3 ms (mean \(\pm\) standard deviation), and its coefficient of variation was 4.7%. The mean CPM in the days was 572.4 \(\pm\) 24.5 kHz (mean \(\pm\) standard deviation), and its coefficient of variation was 4.3%. These results indicate that reliable and reproducible FCS data can be obtained without any adjustment of the developed system.
# Update on Endemic Fluorosis in a Region of Europe (Tenerife, Canary Islands)
###### Abstract  
Fluoride is an anion that is widely distributed in nature whose main source is volcanic emissions. This anion reaches humans mainly through water and can produce beneficial effects at certain doses, with toxic effects occurring at high doses. There are regions of the world that stand out for their high concentrations of fluoride, as it has produced endemic fluorosis in their population. This is the case for Tenerife, one of the islands of the Canary archipelago of volcanic origin. The aims of this study are to perform a historical review of fluoride concentrations in this region and to assess the current levels. The determination of fluoride will be carried out by potentiometry using an ion-selective electrode. The high concentration of fluoride in the water supply continues to be a problem, since about 55% of the studied municipalities on the island of Tenerife exceed the parametric value for the concentration of fluoride in the water (1.5 mg/L). The municipalities with the highest fluoride concentrations in Tenerife are El Tanque (10.61 mg/L), Icod de los Vinos (5.28 mg/L), Tegueste (4.37 mg/L) and La Victoria de Acentejo (3.63 mg/L). The child population aged from 1 to 3 years with a daily water consumption of 1.5 L exceeds the ULs in 21 of the 31 studied municipalities of the island. Meanwhile, the child population aged from 4 to 8 years (daily water consumption of 1.5 L) greatly exceeds the ULs in 13 of 31 of these municipalities. In the case of the adult population (consumption of 2 L/day), it exceeds the ULs in 3 of the 31 studied localities. In addition, since only exposure induced from one type of food has been studied, and not in regard to overall diet, it can be concluded that there may be a risk of overexposure to fluoride for the population aged 1 to 3 years in all the municipalities studied, as well as for the population aged 4 to 14 years and the adult population in most of them. Thus, it can be determined that, currently, with the recommended water consumption, the population could be exposed to a toxicological risk depending on the municipality and age.  
endemic fluorosis; water supply; risk assessment; Tenerife +
Footnote †: journal: Applied sciences  
1 Area of Toxicology, Universidad de La Laguna, Canary Islands, 38071 La Laguna, Tenerife, Spain; [EMAIL_ADDRESS] (R.J.-M.); agiguuthal.edu.es (A.G.F.); [EMAIL_ADDRESS] (C.R.); [EMAIL_ADDRESS] (A.H.); [EMAIL_ADDRESS] (S.A.-V.)
## 1 Introduction
Joint angle data from different walking subjects have been recorded using an IMU sensor system and compared with the joint angles obtained by means of the proposed model, which was adapted to each subject using available anthropometric knowledge and relying on the estimated parameters. Furthermore, to validate the use of the proposed approach for diagnostic purposes, the same procedure has been repeated with reference to a cohort of subjects with Parkinson's disease (PD) [18; 19; 20], and the estimated stiffness parameters (meaningfully concerning both the shoulder and the elbow for a better movement characterization) have been used as objective quantitative indices of muscle stiffness. Indeed, the results of this paper, as for [21], show how the same dynamic model might capture the main features of the upper limb movement of walking subjects, whereas its parameters characterize the progress of the (PD) pathology. In this regard, PD is a neurodegenerative disorder characterized by motor and non-motor symptoms [18],The cardinal motor symptoms are rest tremors of limb extremities, muscle rigidity [19], bradykinesia, i.e., slowness in movements, and gait instability. It is well known that PD strongly affects gait, causing instability and an altered range of motion [20]. This specific neurologic condition has been chosen because muscle stiffness is a cardinal symptom of the disease, and one of the challenges in this field of research is to find a method for measuring it since, to date, there is no validated method--beyond the (almost subjective) Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) [22]--that is capable of quantifying this symptom over a compact subset of the set of real numbers [23; 24; 25]. In this light, the paper moves, though within a different context, along the direction of [26]. It aims to complement the latest contribution in the literature that provides estimates of physiological parameters of a dynamic model for the elbow time profile during walking while linking them to the action of the [PERSON]'s disease.
# Detecting and Mitigating Attacks on GPS Devices
## 5 Detection Techniques and Their Comparison
### GPS jamming Detection
#### 5.1.2 Antenna Array-Based Methods for Jamming Detection  
These approaches generally rely on using some statistical property of the received signal combined with spatial processing to detect the presence of interference. The authors of [71] proposed an antenna array approach based on measuring the carrier phase differences of incoming signals. Specifically, the double difference of the carrier phases was used to detect the presence of a jammer. The double difference in the carrier phases of these two signals will be extremely small if a jammer generates two PN code signals because they were generated from the same platform and arrived at the receive antenna from the same direction. Two PN code signals generated by actual GPS satellites will arrive from different directions and have a much larger double difference. This procedure can be simply implemented by measuring the carrier phase of the incoming signal and comparing it with a value that would be representative of a jammer-generated signal. The authors of [71] reported good detection performance; however, this type of approach would require an antenna array, which is more complex than the typical patch antennas found in most commercial GPS receivers. It is unclear if this approach would be feasible for smaller, unmanned platforms.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 8 Discussion
### Exfiltration Countermeasures  
#### 8.1.1 Detection  
Security software is utilized to identify running programs on a computer and uncover the process of establishing air-gap channels. In this approach, security solutions such as antivirus (AV) strive to detect malicious operations by recording and monitoring the logs of software processes interacting with the CPU. When it comes to power line air-gap channels, abnormal workloads are imposed on the CPU, enabling security software to detect them through this characteristic. However, due to the diverse types of software employed in computers and considerable variability in user behavior, both software and user actions can result in unexpected fluctuations in the CPU workload. Consequently, this detection method may yield a significant number of false positives or false negatives.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
###### Abstract
The multi-scale nature of remote sensing also provides insight into the often emergent spatial and temporal patterns and properties of ecosystems that may not be fully identified nor understood when approached from the perspective of a local region. Earth parameter data records derived from optical-infrared (optical-IR) and microwave satellite observations spanning multiple decades are particularly valuable in distinguishing large characteristic natural climate variability from more subtle environmental trends in cold regions [14; 19; 34; 35] and for the detection of local to regional disturbances [36; 37]. Finer spatial-resolution optical-IR and active microwave sensors are essential for distinguishing the complex spatial heterogeneity in permafrost landscapes [38; 39; 40] and for near-real time applications such as monitoring river ice jams [41] and glacial lake outburst flooding [42]. This paper provides an overview of recent progress and prospects in remote sensing of cold regions by first reviewing general principles and methods in measuring a selection of key environmental variables, including glacier ice; snow; surface water bodies; permafrost and surface deformation; vegetation and terrestrial carbon process. We then summarize recent environmental changes documented by the remote sensing data record. Finally, we explore opportunities for leveraging available and future satellite missions, and integrating emerging remote sensing and big data techniques to establish a next-generation monitoring system for cold regions.
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 3 Materials and Methods
### BLE/PDR Integrated System Based on EKF
In the EKF, the process of prior estimation is expressed as follows:  
\[X_{k}=A_{k}^{-}\cdot\hat{X}_{k-1} \tag{27}\]  
\[P_{k}^{-}=A_{k}\cdot P_{k-1}\cdot A_{k}^{T}+Q_{k} \tag{28}\]  
The Gain matrix is expressed as (29):  
\[G_{k}=P_{k}^{-}\cdot H_{k}^{T}\cdot\left(H_{k}\cdot P_{k}^{-}\cdot H_{k}^{T}+ R_{k}\right) \tag{29}\]Then, the state vector and the covariance matrix are updated according to the observations. The update process is written as follows:  
\[\hat{X}_{k}=X_{k}+G_{k}\cdot(Z_{k}-H_{k}\cdot X_{k}) \tag{30}\]  
\[P_{k}=P_{k}^{-}-G_{k}\cdot H_{k}\cdot P_{k}^{-} \tag{31}\]  
where \(X_{k}\) and \(\hat{X}_{k}\) are the prior and posterior state estimate vector, \(G_{k}\) is the gain matrix of EKF, \(P_{k}^{-}\) and \(P_{k}\) are the prior and posterior system covariance matrix, \(Q_{k}\) is the covariance matrix of the process noise, \(R_{k}\) is the covariance matrix of the observational noise vector. When the integrated system runs, the prior position, step length, and heading angle are obtained by the PDR method. The state vector composed of previous elements and the other observation vector obtained by the BLE position method is input into the fusion method. When the fusion method is cyclically executed, the corrected position results will be obtained.