esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Search on Speech Evaluation
### Databases
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**File ID** & **Data** & **\#occ.** & **dur. (min)** & **\#spk.** & **Ave. MOS** \\ \hline EC-20170520 & test & 3266 & 21.25 & N/A & 3.38 \\ \hline EC-20170527 & test & 2602 & 17.87 & N/A & 3.42 \\ \hline EC-20170603 & test & 3527 & 23.87 & N/A & 3.90 \\ \hline EC-20170610 & test & 3846 & 24.22 & N/A & 3.31 \\ \hline EC-20170617 & test & 3368 & 21.55 & N/A & 3.36 \\ \hline EC-20170624 & test & 3286 & 22.60 & N/A & 3.65 \\ \hline EC-20170701 & test & 2893 & 22.52 & N/A & 3.47 \\ \hline EC-20170708 & test & 3425 & 23.15 & N/A & 3.58 \\ \hline EC-20170715 & test & 3316 & 22.55 & N/A & 3.82 \\ \hline EC-20170722 & test & 3929 & 27.40 & N/A & 3.88 \\ \hline EC-20170729 & test & 4126 & 27.45 & N/A & 3.61 \\ \hline EC-20170909 & test & 3063 & 21.05 & N/A & 3.64 \\ \hline EC-20170916 & test & 3422 & 24.60 & N/A & 3.40 \\ \hline EC-20170923 & test & 3331 & 22.02 & N/A & 3.24 \\ \hline EC-20180113 & test & 2742 & 19.02 & N/A & 3.80 \\ \hline EC-20180120 & test & 3466 & 21.97 & N/A & 3.28 \\ \hline EC-20180127 & test & 3488 & 22.52 & N/A & 3.56 \\ \hline EC-20180203 & test & 3016 & 21.60 & N/A & 3.90 \\ \hline EC-20180210 & test & 3214 & 23.20 & N/A & 3.71 \\ \hline EC-20180217 & test & 3094 & 20.33 & N/A & 3.57 \\ \hline EC-20180224 & test & 3140 & 20.78 & N/A & 3.56 \\ \hline millennium-20170703 & test & 8714 & 55.78 & N/A & 1.10 \\ \hline millennium-20171030 & test & 8182 & 57.05 & N/A & 3.44 \\ \hline ALL & train & 3,729,924 & 27729 & N/A & 3.04 \\ \hline ALL & dev1 & 545,952 & 3742.88 & N/A & 2.90 \\ \hline ALL & dev2 & 149,255 & 930.64 & N/A & 3.25 \\ \hline ALL & test & 90,021 & 605.48 & N/A & 3.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**File ID** & **\#occ.** & **dur. (min)** & **\#spk.** & **Ave. MOS** \\ \hline
13\_000500\_03\_1\_19421\_642906 & 875 & 5.55 & 2 (1 ma. 1 fe.) & 3.11 \\ \hline
13\_000400\_007\_0\_19432\_643097 & 563 & 3.53 & 2 (1 ma. 1 fe.) & 3.47 \\ \hline
13\_000400\_005\_0\_19422\_642932 & 718 & 3.57 & 2 (1 ma. 1 fe.) & 2.92 \\ \hline
13\_000400\_005\_0\_19422\_642923 & 1898 & 11.62 & 1 (1 fe.) & 3.27 \\ \hline
13\_000400\_005\_0\_19422\_642922 & 1733 & 11.67 & 1 (1 fe.) & 3.19 \\ \hline
13\_000400\_04\_0\_19388\_642448 & 1107 & 7.43 & 1 (1 ma.) & 2.53 \\ \hline
13\_000400\_003\_0\_19381\_642399 & 1403 & 8.13 & 3 (2 ma. 1 fe.) & 2.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the SPARL20 database, used as test data in the evaluation: number of word occurrences (#occ. ), duration (dur.) in minutes (min), number of speakers (#spk.) and average MOS (Ave. MOS). These characteristics are displayed for training (train), development (dev) and testing (test) datasets.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 3 Trajectory Planning
### Coordinate Space Trajectory Planning  
According to the requirement of the actual task, the motion trajectory of the PM was designed. A door-shaped trajectory was introduced to meet the needs of picking and placing operations in the industry.  
\(P_{0}\) is the picking point and \(P_{5}\) is the placing point (see Figure 3). The trajectory was composed of three straight lines and two arcs. The transition arcs at both ends could avoid sudden changes in speeds and acceleration.  
Figure 3: Door-shaped trajectory.  
Assuming that the door trajectory is in the XOY plane, and \(P_{0}(x_{0},0,z_{0})\) is the initial position, the straight line and circular arc in Figure 3 were interpolated, and the door-shaped trajectory was obtained, as shown in Equation (4):  
\[\left\{\begin{aligned} (x,y,z)&=(x_{0},0,z_{0}-s(t_{i}))& t_{0}<t_{i}\leq t_{1}\\ (x,y,z)&=\big{(}x_{0}+r_{p}-r_{p}cos\varphi_{1},0,z_{0 }-h-r_{p}sin\varphi_{1}\big{)}& t_{1}<t_{i}\leq t_{2}\\ (x,y,z)&=\big{(}x_{0}+r_{p}+s(t_{i})-0.5\pi r_{p}-h,0,z_{ 0}-h-r_{p})& t_{2}<t_{i}\leq t_{3}\\ (x,y,z)&=\big{(}x_{0}+r_{p}+l+r_{p}sin\varphi_{2},0,z_{ 0}-h-r_{p}cos\varphi_{2}\big{)}& t_{3}<t_{i}\leq t_{4}\\ (x,y,z)&=\big{(}x_{0}+2r_{p}+l,0,z_{0}-s_{f}+s(t_{i}) \big{)}& t_{4}<t_{i}\leq t_{5}\end{aligned}\right. \tag{4}\]  
\(\varphi_{1}=(s(t_{i})-h)/r\) and \(\varphi_{2}=\big{(}s(t_{i})-h-(1/2)\pi r_{p}-l\big{)}/r_{p}t_{1}\ldots t_{5}\) correspond to the arrival time at \(P_{1}\ldots P_{5}\), respectively. \(t_{i}\) is any time in the cycle.
## 5 Discussion  
Burn permits and satellite products are the main resources for fire activity information necessary for prescribed burning emissions estimation. Burn permits provide prescribed burning records but are only available in some states. Satellite products detect worldwide wildland fires but detecting small fires can be particularly challenging. Additionally, satellite products do not differentiate the burn types of fires. Some studies [20; 21] assume fires detected by satellite products are all prescribed burnings in the southeastern United States because prescribed burning is more common than the other types of fires. The method for burn-type differentiation is still essential for prescribed burning emissions estimation from satellite products since wildfires and prescribed burning have different fire behaviors and emission patterns [7]. Prescribed burning emissions estimation also relies on the burned area and emission factors. So, we derived an adjustment factor for the FINN burned area and evaluated the uncertainty of the emissions across different frameworks (FINN-provided emissions and BlueSky).
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 3 The LoRa Physical Layer
### Performance Evaluation
#### 3.4.2 Network Coverage  
This experiment aims at testing the network coverage of LoRa. Tests were conducted in a suburb of Paris, with mainly low-rise residential dwellings. The temperature was 15 \({}^{\circ}\)C, and the ambient humidity was 55%. The gateway was located on the second floor of a house, outside the window. Five different test points were chosen, with the distance to the gateway as shown in Figure 6. The end-device was in a car during the tests.  
The transmission power of the end-device was set to 14 dBm, which is the default value as specified by [23]. To test the performance of different spreading factors, the packet acknowledgment and retransmission was turned off. The link check was also disabled so that the spreading factor will not change even if there is packet loss; by default, LoRa will adapt the spreading factor according to the link quality. Spreading factors of 7, 9 and 12 were chosen for the tests.  
Figure 7 shows the packet delivery ratio of different spreading factors with various distances. About 100 packets are transmitted to the network server in each test with a sequence number. The higher spreading factors have better coverage, as discussed in Section 3.2: for a spreading factor of 12, more than 80% of packets were received at Point D (2800 m), while no packet was received when using a spreading factor of seven. It is worth noting that the gateway was located in the second floor, which was about 5 m above the ground (normally, such a base station would be located at a higher altitude to achieve better coverage), and the test Point D was right behind a building of seven floors. The high delivery ratio using the high spreading factor has the cost of a much lower bit rate, as shown in Equation (2). On the other hand, the network coverage with low spreading factors is much lower.  
Figure 5: Minimal observed RSSIs with different spreading factors.  
It is important to note that the purpose of the tests above is to test the coverage of the LoRa physical layer using different spreading factors. In a real LoRa network with the LoRaWAN protocol, the end-devices are able to automatically increase the spreading factor if the transmission with the lower spreading factor fails. Furthermore, retransmission is also used if necessary. Therefore, in a network with LoRaWAN, a higher delivery ratio can be achieved.
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 2 Wideband Array Signal Model
Despite the fixed angular information of the sources, the covariance matrix at each frequency differs due to frequency-dependent characteristics. This leads to a distinct mapping between the covariance matrix \(\mathbf{R}_{j}\) and the incidence angle \(\theta\) for each sub-frequency band. General processing techniques, such as ISM and CSM, are commonly paired with traditional algorithms such as MUSIC or SR to perform DOA estimation. However, these model-driven strategies face significant limitations when applied to complex environments. Machine learning methods establish a mapping relationship between the input and output through automatic data learning, which can overcome the limitations of traditional array models, and they can accurately estimate the DOAs of signal sources without requiring a complex mathematical model. For wideband signals, it is necessary to establish the mapping relationship between the array covariance matrix at each frequency point and the incident angle, i.e., \(\mathbf{F}=\left\{\mathbf{R}_{1},\mathbf{R}_{2},\cdots,\mathbf{R}_{j}\right\} \rightarrow\left\{\theta_{k}\right\}_{k=1}^{K},\) in order to obtain the angular information of the source. Although a mapping model has been given in [38], the dimensionality of the parallel input data grows with the number of sub-bands, resulting in a significant increase in the overall network training time.
## 8 Simulation and Results
### Simulation Setup
To investigate the influence of the growing traffic on the efficiency of the developed microservice-based VANET, we examined four different values for the total number of deployed vehicles. In addition, we analyzed the impact of varying levels of TD by assessing three distinct traffic density scenarios. The three TD scenarios provide additional insights into the impact of alterations in vehicle distribution on the VANET. These scenarios involved varying levels of TD, ranging from low to high, with each level correlating to different distances between vehicles and frequencies of interaction. The vehicles were placed along the route based on the traffic densities stated in Table 3.  
Three main performance metrics were considered for evaluating the proposed micro service-based VANET: reliability, availability, and latency. The reliability of the proposed network was assessed by measuring the successfully received packets. The packet delivery ratio (PDR) was calculated for the proposed system and compared with other existing solutions. The system availability was assessed by measuring the number of blocked tasks (BT) among the assigned tasks to vehicles. The results were compared with other existing systems to indicate the performance improvement of the developed microservice-based VANET. The latency performance of the proposed network was assessed by calculating the performance improvement in latency of handling assigned computing tasks. The following equation was used to calculate the percentage improvement of latency compared with the traditional MEC VANETs. \[Latency\;improvement\;(\%)=\frac{Average\;latency_{Traditional}-Average\;latency _{System(i)}}{Average\;latency_{Traditional}}\;\;\;\forall\;i\in\mathbb{R} \tag{1}\]
## 4 Natural Antioxidants and Their Role in Cosmetics
### Vitamins
#### 4.1.3 Vitamin A (Retinol)
Additionally, retinoids stimulate fibrillin synthesis, a critical component of the extracellular matrix, which supports skin elasticity and structural integrity [31; 45; 46]. Vitamin A is essential for maintaining skin and hair health. The primary bioactive metabolites of vitamin A are retinoic acid (RA) and retinal, which play critical regulatory roles in various biological processes. Retinoic acid modulates hair follicle stem cells in a dose-dependent manner, affecting the hair cycle, wound repair, and melanocyte stem cell activity. Additionally, RA influences melanocyte differentiation and proliferation, with effects that vary depending on concentration and timing [47]. Exposure to ultraviolet (UV) radiation reduces retinoid levels in the skin, affecting their physiological functions. Retinal, a key metabolite, is essential for the phototransduction cascade, which triggers melanogenesis. In epidermal phototransduction, retinal plays a pivotal role in melanin synthesis induced by UV radiation. However, UV exposure simultaneously depletes retinoids and their nuclear receptors [47]. Retinol and retinyl palmitate, an ester of retinol and palmitic acid, are used in hair, facial makeup, and skincare products, generally at concentrations between 0.1% and 1.0% [32]. Currently, synthetic retinol dominates the cosmetics industry due to its stability, high purity, and controlled efficacy. Retinol is commonly synthesized in laboratories by processing carotenoids such as \(\beta\)-carotene. Numerous processes have been developed for the synthesis of retinol and its esters. Some plant oils, such as rosehip oil, sea buckthorn oil, and carrot oil, contain provitamin A (\(\beta\)-carotene), which the body can convert into retinol. These oils are used in natural cosmetics, although they contain milder forms of retinoids compared to pure retinol [32].
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### Introducing Twisting: Lay Length Definition  
In practical cable designs, the five segments often twist around the central axis to improve mechanical stability and reduce certain electromagnetic imbalances. Twisting is typically parameterized by a _lay length_\(\lambda\), defined as the longitudinal distance required for one full 360\({}^{\circ}\) twist [16; 17]. A smaller \(\lambda\) implies a tighter pitch (more twists per meter), while a larger \(\lambda\) indicates a gentler helix, approaching a parallel arrangement as \(\lambda\to\infty\).
## 5 Materials and Methods
### Data Preprocessing
#### 5.2.3 Feature Engineering  
Temporal Features: Hour of the day (0-23) and day of the week (0-6) were extracted from the pickup timestamp.  
Categorical Encoding: Categorical variables such as the day of the week and weather conditions were transformed into numerical values for model compatibility.  
Feature Normalization: Min-Max Scaling was applied to numerical attributes (e.g., trip distance, coordinates) to standardize values within the range [0, 1].  
The preprocessing stage ensured that the dataset was structured, cleaned, and optimized for predictive modeling. The final dataset consisted of 30,000 enriched taxi trip records, incorporating essential spatial and temporal attributes. This structured dataset was used to train the LSTM model for travel time prediction and to analyze the impact of different genetic algorithm variations on route optimization.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 3 Method  
### Overview  
This study aims to enhance the quality of low-resolution (LR) images by reference images (Ref) and proposes a new reference-based super-resolution (RefSR) method based on a generative adversarial network (GAN) named STGAN. The overall workflow of the method is shown in Figure 1, which mainly includes a generator and two discriminators. The objective of the generator is to reconstruct the fine texture in the LR image using the reference image to produce a high-resolution (HR) image. To obtain clear and visually satisfactory super-resolution results, we introduce discriminators D\({}_{1}\) and D\({}_{2}\) in the image and gradient domains, respectively, where the gradient discriminator aims to focus on the local structure of the image to optimize sharpness and detail reconstruction by analyzing the image gradient information. This strategy inspired by RRSGAN [43] helps the model to accurately infer local intensity changes and better preserve geometric details and texture information. The generator learns finer appearance features and avoids distortion of geometric details by receiving feedback from both discriminators simultaneously. In this way, STGAN not only improves the image resolution, but also ensures the naturalness and accuracy of the reconstructed image, thus meeting the demand for high-quality image reconstruction.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 3 Convolutional Neural Network Architecture
### CNN Model Salient Features
#### 3.1.1 Optimizer  
The Adam algorithm amalgamates the Momentum and RMS functions, which means that it keeps an exponentially decaying average of prior squared gradients as well as an exponentially decaying average of previous gradients. Due to the component of speed and the ability to adjust gradients, Adam is an effective optimizer. This is also why it is the most commonly utilised optimizer. ADAM has outperformed the competition, particularly in terms of convergence speed, which is why it is the most widely used and efficient optimizer.
## 3 Method
### Loss Function  
In this method, the loss function involves three components. They are classification loss, confidence loss, and location loss. For both classification loss and confidence loss, the _BCE_ (Binary Cross-Entropy) loss is used. Equation (9) of _BCE_ loss is as follows:  
\[L_{BCE}(p(y),\ y)=-\omega*(\ y\ast\ln(p(y))+(1-\ y)\ast\ln(1-p(y))) \tag{9}\]  
where \(y\) denotes the predicted class, \(p(y)\) denotes the probability of the class y, and \(\omega\) denotes the weight value.  
In addition, _CloU_ loss is used for the localization loss. Equations (10)-(12) of _CloU_ loss are as follows:  
\[L_{CloU}=1-IoU+\frac{\rho^{2}\left(b,b^{st}\right)}{c^{2}}+\alpha v \tag{10}\]  
\[v=\frac{4}{\pi^{2}}\bigg{(}\arctan\frac{w^{st}}{h^{st}}-\arctan\frac{w}{h} \bigg{)}^{2} \tag{11}\]  
\[\alpha=\frac{v}{1-IoU+v} \tag{12}\]  
where \(b\), \(b^{st}\) represent the centroids of the anchor and object boxes, and \(\rho\) represents the Euclidean distance between the two centroids. \(c\) represents the diagonal distance \(\alpha\) of the smallest rectangle that can cover both the anchor and object boxes and is the parameter used to balance the proportions. \(v\) is used to measure the consistency of the proportions between the anchor and object boxes. \(w\) and \(h\) represent the width and height of the boxes.  
In summary, the total loss function used in this paper is (13)  
\[L_{\text{Total}}=1.5L_{BCE}(p(y),\ y)+0.05L_{CloU} \tag{13}\]
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
###### Abstract  
Hydrogen produced from renewable sources has the potential to tackle various energy challenges, from allowing cost-effective transportation of renewable energy from production to consumption regions to decarbonizing intensive energy consumption industries. Due to its application versatility and non-greenhouse gaseous emissions characteristics, it is expected that hydrogen will play an important role in the decarbonization strategies set out for 2050. Currently, there are some barriers and challenges that need to be addressed to fully take advantage of the opportunities associated with hydrogen. The present work aims to characterize the state of the art of different hydrogen production, storage, transport, and distribution technologies, which compose the hydrogen value chain. Based on the information collected it was possible to conclude the following: (i) Electrolysis is the frontrunner to produce green hydrogen at a large scale (efficiency up to 80%) since some of the production technologies under this category have already achieved a commercially available state; (ii) in the storage phase, various technologies may be suitable based on specific conditions and purposes. Technologies of the physical-based type are the ones mostly used in real applications; (iii) transportation and distribution options should be viewed as complementary rather than competitive, as the most suitable option varies based on transportation distance and hydrogen quantity; and (iv) a single value chain configuration cannot be universally applied. Therefore, each case requires a comprehensive analysis of the entire value chain. Methodologies, like life cycle assessment, should be utilized to support the decision-making process.  
hydrogen economy; hydrogen production; hydrogen storage; hydrogen transportation and distribution; hydrogen utilisation
## Article
It combines overall searching of the feed point index value with hierarchical optimization, greatly reducing the size scale of the problem. According to their expected performance characteristics, all feasible restoration paths are ranked in [50], using two different forms of restoration performance parameters. In case these remedial actions do not prevent the blackout event, each TSO must have a restoration plan to supply the power consumers quickly and safely. Therefore, since there is always a great possibility for a blackout to occur, this work focuses on the development of a new restoration strategy, implementing the efficient A* Algorithm. The restoration procedure varies for transmission lines and non-black start (NBS) units. There have been numerous local and international studies conducted, and every Transmission System Operator (TSO) employs a unique restoration process [51; 52]. In Australia, the goal of the Australian Energy Market Operator (AEMO) is to restart major generators within 90 min after a total blackout and to reach 40% of the system's peak load within four hours [53; 54; 55]. The Pennsylvania New Jersey Maryland (PJM) TSO in the USA plans to undertake the system restoration with a priority on system stability and to restore 80% of the total system load in 16 h [56; 57]. This study begins with a brief presentation of ENTSO-E and the advantages it provides to all participating countries to ensure the European Union's energy policy objectives of affordability, sustainability, and security of supply. Moreover, it describes the safety criteria of the European Transmission System, the defense plans, and the remedial actions that every TSO must have prepared to prevent a major blackout. A blackout analysis follows, and the phase-by-phase progression of a blackout is presented. Then, a careful examination of the event on 8 January 2021, when the European electrical system split into two separate electrical systems [26], is given. Different initial power system states and how the European system would be affected are investigated. This work tries to find similarities between past blackouts and the state of the European power grid in 2022 and to present possible scenarios that could lead to a blackout in a wide area in Europe (e.g., one or more countries) or the whole Europe. Finally, a system restoration methodology is presented. It considers the minimization of the restoration time, using both black start (BS) units and tie lines from neighboring TSOs, creating separate islands which will finally be synchronized. A three-stage restoration strategy is proposed to manage this process: start-up of main units (withpriority in the BS units), restoration of interconnection transmission lines, and restoration of important loads (less important load will be restored afterwards). The proposed algorithm is fast enough, and it succeeds in using all available sources to start the NBS units, supplying the first critical loads and then the rest of the power that consumers need.