esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 4 Analysis and Discussion of Experimental Results
### Analysis of Fitting Results  
#### 4.3.1 Visual Results Comparison  
Through the aforementioned evaluation method, we conducted tests on point clusters under special circumstances. Whether the segmented region is oversized, incomplete, or even when the clustering results deviate, the VLFGM method consistently outperforms the LS, demonstrating more pronounced optimization effects on vertex coordinates. The comparative results are presented in Figure 13. By employing an improved approach to fit corner points, feature points from various segmented images can be accurately extracted. This technique also corrects deviations in vertex positions and compensates for the impact caused by incomplete image segmentation. The utilization of improved method enables more effective extraction and utilization of corner information in images, thereby enhancing accuracy and reliability in various application scenarios.  
For images with better imaging quality, such as those in groups 1, 5, and 6 of Figure 12, fitting their key points for localization is not challenging. To clearly compare the fitting performance of different methods on the images with poor imaging performance, this study presents 5 groups of different result images, as shown in Figure 14. When the image segmentation results are relatively normal, the fitting results of Hough line detection, LSD, LS, and VLFGM are nearly identical and realistic, as depicted in Figure 14a,b. In this scenario, VLFGM degenerates into LS for obtaining higher scores in line fitting, considering the weighted votes of preferred points against other points. However, in special cases such as Figure 14c-e, there are some differences in the effects of different methods. Hough and LSD are feature-based methods relying on edge points or corner features, which are sensitive to significant gradient changes in pixels but cannot differentiate interest points, resulting in more noisy lines in the detection results and thus appearing more chaotic. Furthermore, LS cannot discard outliers in the points, leading to deviations in the fitted line slope as it directly regresses on the sample points. Consequently, LS fitting results exhibit larger errors in the vertexes of the enclosed shapes. In contrast, the proposed method can assign weights, extracting the principal component information from the sample data and outputting a line most related to the majority of points. This method demonstrates more stable fitting performance and, visually, can still achieve satisfactory results even in poorly performing images.
## 3 UWB Signal Propagation Properties
### Temporal and Spatial Propagation of UWB Signals  
In general, the performance of a wireless communication system depends on the characteristics of the channel. The UWB communication channel is only made between the signal sender and receiver. During the signal propagation, the direction of signal propagation will be shifted with an offset due to the obstacles. The signals at the receiver are the superposition of multiple signals, and the time of signal arrival and propagation path are different, which will also lead to changes in the phase and amplitude of the signals at the receiver. Therefore, it is necessary to establish a reliable UWB channel model. In fact, the UWB channel can be regarded as an open loop control system, as shown in Figure 2. The given transmitted signal is the input, and the received signal is the output [41].  
UWB channels are divided into four types according to four typical channel conditions, CM1, CM2, CM3, and CM4, as shown in Table 2. Figure 3 shows the UWB transmitting signal, and Figures 4-7 show receiving signal among CM1-CM4 channels, respectively, in which the Y-axis represents the amplitude of the normalized UWB signal. The amplitude gain \(Gain_{Amp}\) can be calculated as  
\[Gain_{Amp}=\frac{Amp-Amp_{\min}}{Amp_{\max}-Amp_{\min}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Common Channel** & **Distance** & **Path Characteristics** \\ \hline CM1 & 0–4 m & LOS \\ CM2 & 0–4 m & NLOS \\ CM3 & 4–10 m & NLOS \\ CM4 & Poor NLOS multipath channel \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four typical channel conditions.  
Figure 2: Signal transmission block diagram. Scope1: signal by the sender, Scope: received signal by the receiver.  
Figure 4: UWB signals received after indoor multipath CM1 fading.  
Figure 5: UWB signals received after indoor multipath CM2 fading.  
Figure 3: UWB Transmit signals. LOS means line-of-sight, and NLOS means non-line-of-sight.  
Figure 6: UWB signals received after indoor multipath CM3 fading.
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods  
The optimization of energy production in PV systems is critical for enhancing system efficiency and minimizing energy losses. In this context, MPPT algorithms are developed to ensure that PV panels operate at their MPP. This section provides a detailed analysis of the three MPPT methods used in the designed system: ANN-based, RL-based, and conventional MPPT. A summary of the inputs and layers used for MPPT systems is presented in Table 6.  
The three MPPT methods used in the MATLAB-designed system have distinct characteristics and advantages:  
* ANN-based MPPT method: Provides high accuracy and adaptability but requires a time-intensive training process.
* RL-based MPPT method: Continuously learns and adapts to environmental changes but has a slower initial learning phase.
* Conventional MPPT method: Simple and cost-effective but less effective under rapidly changing conditions. It does not have any adaptation ability.  
Each method is tailored to optimize the energy generation of PV systems under different scenarios, contributing to the overall efficiency and reliability of the system. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Inputs** & **Hidden Layers** & **Output** \\ \hline ANN-based (MLP) & Solar irradiance, temperature, load power & 1 hidden layer, 10 neurons, sigmoid & MPPT efficiency \\ RL-based & Solar irradiance, temperature & Q-table for state-action pairs & Optimal action (voltage/current adjustment) \\ (Q-Learning) & Conventional & Voltage, current & None & Adjusted voltage/current \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of inputs and layers.  
characteristics, algorithms, working principles, and design details of each method are explained below.
## 3 Simulated Phased Array with P-CRLH Phase Shifters
### Main Beam at Broadside  
For the main beam at the broadside, the four patch antenna elements in Figure 8 are individually excited with the unit cell P-CRLH phase shifters in Section 2.1. This will make the feeding of antenna elements with the same phases (\(-46.7^{\circ}\)) at the design frequency  
Figure 8: A 1 \(\times\) 4 linear phased array with: (**a**) directly excited phases and (**b**) P-CRLH-excited phases.  
Figure 9: Simulated mutual coupling between adjacent antenna elements with P-CRLH-excited and without P-CRLH-excited (directly excited) phase shifters.  
of 5.5 GHz and will result in a constructive radiation pattern at the broadside direction. For comparison, the patch antenna elements in the array are directly excited with phases equal to \(-45^{\circ}\). The simulated far-field radiation patterns with directly excited and P-CRLH-excited phases are shown in Figure 10. Overall, there is a good agreement between the two results with a 0.7 dB difference in the broadside gain and a 1.1 dB difference in the first sidelobe level.
# Online SOPDT Model Identification Method Using a Relay
## 3 Simulation Results and Discussion
### Comparision with Other Methods  
[PERSON] and [PERSON] [12] applied the method formulated by [PERSON] and [PERSON] [25] to an asymmetrical relay test to identify four parameters of an SOPDT model. They tested its performance on a plant like the one described in example 2. The models obtained as a result of this test and those presented by [PERSON] et al. [17], [PERSON] et al. [9], and [PERSON] et al. [26] were compared with the model determined above, as shown in Table 1, in which the last column gives the integral quality indicator defined as  
\[I=\frac{1}{T}\int_{0}^{T}\bigl{|}y_{p}(t)-y_{m}(t)\bigr{|}\ dt \tag{35}\]  
where \(y_{p}\) and \(y_{m}\) are the output of the process and model under the step change, respectively, and \(T\) is the integration time, where \(T=10(T_{1}+T_{2})\).  
Figure 7 shows a Nyquist diagram of the plant and selected models.  
RC--[PERSON] and [PERSON]; SWY--[PERSON], [PERSON], and [PERSON]; KK--model obtained using the presented method.  
Figure 6: Step responses of the plant from example 4 and its model.  
The possibilities offered by this method were also compared with the results obtained in [18] in which [PERSON] and coworkers considered a plant as follows:  
\[G_{3}(s)=\frac{2\,e^{-s}}{(10s+1)(5s+1)} \tag{36}\]  
They used a discrete wavelet transform for the analysis of the noisy limit cycle and the unknown parameters were calculated numerically, allowing them to produce a highly accurate model: \(\text{Gm(s)}=1.986\text{-}\exp(-0.999\text{s})/(9.8479s+1)(5.045s+1)\). Using the method described with the input data of \(K=1.996\), \(\theta=1.01\), \(\omega_{1}=0.539\) rad/s, and \(\omega_{2}=0.1242\) rad/s, the resulting model had a transfer function \(\text{Gm(s)}=1.991\text{-}\exp(-1.01\text{s})/(10.23s+1)(4.84s+1)\) and was therefore equally accurate and determined much more simply.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings
### Performance Evaluation Histogram of Contrast-Enhanced Images  
In this section, the results of the contrast enhancement images with their histograms produced by the proposed art and other developed histogram methods are shown using low contrast and bright images. Figure 7a-f illustrates the visual results of the before and after enhancement images. It can be observed from Figure 7a-f that the obtained results by HE, AHE, CLAHE, and ESIHE were found to be less satisfactory because of visual perception difference in color and brightness. In addition, fundus anatomic features (disc, cup, and retinal vessels) and DR-based lesions were reported to be less visible, which leads to poor performance of the segmentation approaches. Similarly, the output produced by the proposed method was noticed to be reasonable in image color, textural, and brightness information. Figure 8a-f shows the histogram variance performance of all applied arts with reference to an original image histogram. Since the good histogram is one, it has a flat and wide range of gray information [12]. As can be noticed in Figure 8a-f, the visual appearance and interpretation of the proposed art histogram was comprehensively better than HE, AHE, and CLAHE. This is because the histograms of HE, AHE, and CLAHE did not have a wide range of flattened gray level, and thus caused false positives of dark regions. Meanwhile, the ESIHE histogram was found to be nearly flattened, but did not have a wide gray range, which leads to brightening the dark region of the image. Thus, the presented image quality enhancement art could be an efficient strategy in improving the sharpness of fundus features and in preserving the fine details of an image. Moreover, it could improve the performance of the computational segmentation and classification procedures in the localization of abnormal features in fundus images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **No. of Images** & **Field of View** & **Resolution in (Pixels)** \\ \hline DRIVE [30] & 40 lossless fundus images & 45\({}^{\circ}\) & 768 \(\times\) 584 \\ MESSIDOR [31] & 1200 lossless fundus images & 45\({}^{\circ}\) & 1440 \(\times\) 960; 2240 \(\times\) 1488; \\ \multicolumn{3}{c}{images} & 2304 \(\times\) 1536 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the fundus image benchmarks used in the experiments.  
Figure 8: _Cont._  
Figure 7: An enhancement comparison of fundus images: (**a**) Input image, (**b**) proposed enhanced image, (**c**) histogram equalization (HE), (**d**) adaptive histogram equalization (AHE), (**e**) contrast-limited adaptive histogram equalization (CLAHE), and (**f**) exposure-based sub-image histogram equalization (ESIHE).
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 3 Key Supporting Technologies for the WMSNs
### Routing Protocols
ReInForM, which provides desired reliability in data delivery based on the priorities of packets, uses redundant copies of a packet to increase its end-to-end probability of data delivery, but it only focuses on the reliability issue of QoS domain and does not take energy consumption and timeliness into account. As we can see, although the typical routing protocols for WMSNs involve QoS issues, there are still defects in them, affecting their adaptability to multimedia data transmission in WMSNs. For the scenario of underground coalmines, link costs and network topology can vary over time. Therefore, the network should be aware of these changes and handle them quickly. Besides, WMSNs for coalmine will both act as a data collector and an emergency rescuer, and this may require the network to be suitable for time-sensitive applications while remaining power efficient. Thus, the designing of routing protocols for underground coalmine WMSNs should be based on the characteristics of network structure, the channel allocation scheme for MB-OFDM-UWB and the features of service for underground coalmine WMSNs. The protocols should be with QoS guarantee and good scalability, enabling them to adapt to the requirements of being real-time and reliable for the wireless video surveillance, wireless voice communication and underground environment monitoring.
## 3 Results
### Keywords Clustering  
The co-occurrence network of keywords shown in Figure 2 was acquired using VOSviewer version 1.6.18. The examination of the keywords included in the articles that were retrieved provides useful assistance, as does its visual depiction as a co-occurrence network (Figure 2). From the 136 scientific publications available in the Scopus databases, a total of 1364 keywords were obtained. These keywords were then sorted and categorized according to how frequently they appeared in the red fruits and edible coating topic. Figure 2, which shows the network of terms with at least five occurrences, provides important information. There were seventy-one keywords in all, arranged into four clusters. The first group was symbolized by red color and was primarily concerned with health-promoting qualities. It included terms such as bioactive compounds, antioxidants, functional foods, carotenoids, health benefits, fruits, vegetables, and food waste. This cluster depicts the variability and diversity of red fruits studied by scientists and nutritionists to better understand their influence on human health. The green color helped us to identify the second cluster, which is primarily concerned with food preservation and shelf life, using terms such as food packaging, active packaging, packaging materials, food manufacturing, food preservation, nanotechnology, and their influence on food safety and quality. This demonstrates that one of the primary goals of scientific study was to link edible coatings to shelf life and bioactive content preservation. The inclusion of the term \"nutraceuticalals\" is of major value in characterizing the health-promoting features of this cluster, which is closely associated with red fruits. The blue cluster focuses on the production and application of edible coatings, with keywords including chemistry, biochemistry, spray drying, electrospinning, and encapsulation. The smallest cluster, in yellow, comprised terms related to natural edible coating and sustainability, including proteins, plant extracts, biological characteristics, metabolism, sustainable development, chitosan, biopolymers, and biodegradability. This cluster serves as a junction/continuity point for the red, green, and blue clusters, all strongly connected to subjects relating to sustainable food processing and nutrition.  
Figure 2: Network of keywords frequently co-occurring in Scopus databases regarding red fruits and edible coating.
## 1 Introduction  
Reliable communication in doubly dispersive channels, where high delay and Doppler spreads due to multipath and mobility are experienced, is becoming an increasingly prominent research topic toward the development of next-generation wireless networks [1, 2]. The widely adopted OFDM (orthogonal frequency division multiplexing) is suitable for static frequency-selective multipath channels but experiences drastic performance degradation in high-mobility scenarios due to the loss of orthogonality between the subcarriers, as analyzed in [3], inducing intercarrier interference (ICI). To cater to high-mobility scenarios, several novel waveforms have been introduced in literature. An overview of some of the existing modulation schemes for high-mobility wireless channels is reported in [4]. Orthogonal time-frequency space (OTFS) modulation was proposed in [5, 6] and is garnering a lot of attention due to its robustness against the Doppler effect. OTFS is a two-dimensional modulation scheme that multiplexes information symbols in the delay-Doppler domain, where the channel representation becomes sparse and the channel can be considered almost time-invariant [5, 6, 7]. In contrast, OFDM multiplexes information symbols in the time-frequency (TF) domain, where mobility generates a frequency response of the time-varying channel.
## 3 Methodology
### Conversion Model of Position of Anchor-Dropping  
The anchor-dropping position is the location where the anchor was dropped when the ship is in the operation of anchoring. As shown in Figure 6a, the position of dropping  
Figure 5: Schematic diagram of the safety distance model of anchoring ships. **(a)** The two anchoring ships with synchronous motion. **(b)** The two anchoring ships without synchronous motion. **(c)** The safety margin of the two anchoring ships with synchronous motion. **(d)** The safety margin of the two anchoring ships without synchronous motion.  
anchor position is \(A_{1}\), and the point recorded is actually \(A_{1}^{\prime}\), due to the position sensor antennas usually being installed far from the anchoring equipment. In Figure 6, \(a\) and \(b\) represent the distance from the GPS/ GNSS/ BDS sensors to the bow and stern ends of the ship, respectively; \(c\) and \(d\) represent the distance from the GPS/ GNSS/ BDS sensors to both sides, respectively. The values of \(a\), \(b\), \(c\) and \(d\) are obtained from the ship's drawings or from GPS/ GNSS/ BDS sensor parameters. Additionally, the distance between the anchor chain hole and the bow is \(e\), and the distance between the anchor chain hole and the windlass is \(k\).  
In Figure 6, a coordinate system with the same ship position as the origin is established. The \(X\)-axis represents the fore-aft axis and the \(Y\)-axis represents the athwartship axis. \(\theta\) represents the angle between the line connecting the anchor chain hole and the ship position and \(Y\)-axis. \(\theta^{\prime}\) represents the angle between the line connecting the anchor position with the ship position and \(Y\)-axis. The coordinates of the position of anchor-dropping \(A_{1}(\varphi_{1},\lambda_{1})\) can be calculated with Equation (9) and the ship position coordinates \(A_{1}^{\prime}(\varphi_{1}^{\prime},\lambda_{1}^{\prime})\) were recorded at the time of anchoring.  
\[\left\{\begin{array}{l}\varphi_{1}^{\prime}\approx\varphi_{1}+\frac{Z}{18 \beta 2}\times\cos\theta\\ \lambda_{1}^{\prime}\approx\lambda_{1}+\frac{Z}{18\beta 2}\times\sin\theta\end{array}\right. \tag{9}\]  
where, \(Z\) is the distance between the anchor-dropping position of the ship and the position actually recorded by the ship during anchor dropping, and \(\theta\) is the included angle between the connecting line from the anchor chain hole and the ship position in the coordinate system and the abscissa.
# Review of Automatic Estimation of Emotions in Speech
## 4 Models to Use Measures of Analysis for Speech Emotion Classification
### Other Approaches  
Earlier ways to do SER include kernel regression, maximum likelihood Bayes classification, least-squares regression, and k-nearest neighborhood methods [49, 61]. In recent years, several feature sets have been developed that combine low-level acoustic features. GeMAPS [54, 62] is a set of combined spectral and intonational features often used for SER. The feature extraction tool openSMILE is popular software to find both long-term and short-term features, often called Low-Level Descriptors [63]. Proficient choice of such features can help SER accuracy [64]. These hand-crafted features were popular for SER in the 2010s, but have mostly been replaced now by self-supervised learning (SSL) features (Section 7.3).
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### Gns3  
GNS3 [53] is an open-source software to emulate, configure, test, and troubleshoot virtual and real networks. It allows running small and complex topologies on a single commodity host. GNS3 supports both emulated and simulated devices. For having a full emulation, an image must be provided (e.g., Cisco IOS), and GNS3 will emulate the hardware for the selected device. On the other hand, it can simulate the features and functionalities of a set of devices (e.g., layer 2 switch).  
GNS3 consists of two software components: the GNS3-all-in-one software (GUI) and the GNS3 virtual machine (VM). The former is the client part, and it can run over the main Operating Systems. This component allows for creating topologies, but the devices created need to be hosted and run by a server process. GNS3 permits to allocate the server part (i) locally on the same PC, (ii) locally in a Virtual Machine (VM), or (iii) remotely in a VM.  
Initially, GNS3 could only emulate Cisco devices using a software called Dynamips, but, nowadays, it supports many devices from multiple network vendors, including Cisco virtual switches, Brocade vRouters, Cumulus Linux switches, Docker instances, multiple Linux appliances, and many others.  
As a graphical network emulation, orchestration is done entirely in the provided GUI. This feature highlights usability but, on the other hand, prevents programmability.