esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 6 Data Sources and Availability of Data
### Data in the Literature
#### 6.1.1 Weather  
Weather data is relatively easy to come by, as there are several meteorological measurement and re-analysis campaigns that provide data to the public. One of the data sources is the European Centre for Medium-Range Weather Forecasts (ECMWF) [105], who provide real-time and re-analysis data for, among others, ocean waves and wind speeds. The data sets vary in length and resolution, depending on the kind of data (ERA-interim re-analysis data for ocean waves is e.g., available from 1979, with a spatial resolution of 80 km). The three measurement campaigns FINO 1,2,3 from the \"Bundesministerium fur Wirtschaft und Energie\" and \"Projekttrager Julich\" provided online from the Bundesamt fur Seeschiffrahrt und Hydrographie (BSH) [106] supply measurements of wave heights and wind speed in the offshore environment among their data. The records vary in lengths, with FINO 1 measuring data since 2003 and providing wind speed measurements in 10-min aggregated means. [PERSON] et al. [4] provide monthly wind speed data for a UK offshore wind farm and annual average wind speed for four UK offshore wind farms. Climate data from an operating offshore wind farm NoordzeeWind is available for three years [107; 108; 109]. The WaveNet database from the Centre for Environment, Fisheries and Aquaculture Science (CEFAS) [110] provides real-time wave data from a network of buoys. The British Oceanographic Data Centre (BODC) [111] provide instrumentally recorded ocean current, tide, and wave data. Another source of weather data is the Norwegian Meteorological Institute [112], providing wind speed and direction as well as temperature and precipitation, with varying time-resolution dependent on the type and location of the measurement. [PERSON] [113] presented Wavewatch III, a wave model that can provide wave spectra at a selected location. Some publications use data from unpublished sources, such as from operating wind farms, airports, and an oil platform. Which weather data is used in which publication can be seen in Table 5.
## 4 Characterization of Real-Life Signals  
A set of experiments is performed to explore how RSSI can be used to characterize signals from real-life technologies. First, the selected technology types and the motivations are introduced  
Figure 4: The RSSI of OFDM signals generated by GNU Radio, with a variable amount of subcarriers in the last symbol of a packet (indicated in the title of the graphs); the first row displays RSSI measurements over time, while the corresponding histograms are displayed in the second row.  
Figure 5: The categories of signals and the corresponding PDF.  
in Section 4.1. Then, the RSSI measurements obtained by both a high-end spectrum analyzer and a small-scale SDR device are observed in Sections 4.2.1 and 4.2.2, respectively. Finally, the appropriate features are extracted in Section 4.3 for technology recognition.
# Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
## 4 Results and Analyses
### Comparative Analyses  
#### 4.3.1 Performance Benchmarking Against Previous Studies  
Recent studies have demonstrated various approaches to speech-based age prediction, with promising results. [PERSON] and [PERSON] [10] focused on three broad age groups, achieving accuracies of 78.6% for men and 71.9% for women using traditional speech processing techniques. [PERSON] and [PERSON] [3] explored different datasets and classification granularities, reporting 72% accuracy on the Common Voice dataset for six age groups (teens through sixties) and notably higher accuracy (96%) on a Korean speech recognition dataset when classifying just three broad categories (children, adults, elderly). [PERSON], [PERSON] [43] achieved a remarkable performance, with 97% accuracy, by combining CNN-based Mel-Spectrogram analysis with comprehensive acoustic feature extraction (including MFCCs, spectral contrast, roll-off, and bandwidth) for six age groups. [PERSON], [PERSON] [44] demonstrated robust performance across different demographic categories, reporting accuracy exceeding 85% across various accent groups and approximately 88% for gender-based classifications.  
While our achieved accuracies of 88.16% for males and 81.95% for females might appear lower than some previous studies, our research makes significant contributions by addressing more challenging and practical aspects of age prediction. Specifically, our approach successfully distinguishes between ten distinct five-year intervals, representing a considerably more complex task than the broader categorizations of three to six groups used in previous studies. This fine-grained classification capability using MFCCs + AMFCCs with 1D-CNN demonstrates the robustness of our approach in handling the nuanced characteristics of Korean speech, while maintaining reasonable accuracy levels. Our results establish an important foundation for applications requiring detailed age prediction, such as personalized service delivery and age-sensitive content adaptation, particularly  
Figure 11: Confusion matrix for female age prediction using 1D-CNN with MFCCs + AMFCCs.  
within Korean language contexts. This work not only extends the boundaries of previous research but also addresses the growing demand for more precise age classification in real-world applications.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 4 System Control  
### Hybrid Impedance Control of Hyper-Redundant Space Manipulators  
When a hyper-redundant space manipulator is tasked with wrapping around an external target, it encounters multiple collisions with the surrounding environment at various points. In such scenarios, relying solely on a position controller or a force/position hybrid controller may compromise the manipulator's motion accuracy, as these methods are heavily dependent on precise trajectory planning and controller performance. Moreover, the manipulator must also stabilize the forces arising from the mass of the object itself during collisions, which can further complicate control. As discussed in previous work [5], employing a multi-point contact, segmental force/position hybrid control approach often results in repeated contacts and separations with the target, generating significant instantaneous forces. These forces, especially in the case of frequent collisions or interactions with stiff materials, can not only reduce the operational accuracy but also cause structural damage. Therefore, there is a pressing need to develop an active compliance control strategy that can effectively manage the additional forces from both the object's mass and the external collisions, thereby enhancing the safety and reliability of the manipulator's operations.  
The concept involves integrating impedance control mechanisms into the joints that come into contact with external objects.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 5 P4-Based Implementation of ABC
### Ingress Control Flow  
The ingress control flow of the ABC prototype comprises activity metering, determination of the egress port, and acceptance decision through activity AQM. It leverages two match action tables.  
The first table holds rules for each aggregate with exact match on IP source address as this defines the aggregates in our experiments. The rules contain configuration parameters for activity metering and register numbers related to state variables. The associated logic calls an extern for activity metering and stores the resulting activity value in user-defined metadata that is carried with the packet. Activity metering is implemented as extern because our rate measurement requires an exponential function and floating point division, and the activity calculation further requires a logarithm (see Equation (1)). The extern leverages the packet's arrival date and size which are available as standard metadata, the configuration parameters passed as table entries, and three state variables for aggregate-specific rate measurement.  
The second table determines the egress port and performs the acceptance decision. Determination of the egress port works like IP forwarding described above. Then, the egress port specific queue length is read and an extern is called that performs activity AQM including activity averaging. Besides the egress port, the rules contain configuration parameters for activity averaging and activity AQM, and register numbers related to state variables for the purpose of activity averaging. The extern accepts or denies the packet. In case of acceptance, the register for the egress port specific queue length is incremented and the packet is forwarded to the egress control flow. Otherwise, the packet is dropped.
## 4 Discussion
This may be due to the lighting conditions inside the culture flask since high biomass concentrations and the characteristic color of the leachate (present in dilutions of 5% \(v/v\)) may reduce the ability of light to penetrate the flask and reduce photosynthetic capacity. Different researchers have documented the efficiency of adding inorganic carbonaceous salts to algal cultures. [PERSON] et al. [33] reported how the addition of NaHCO3 significantly increased biomass and pigment concentrations. Other authors such as [PERSON] et al. [49] found that 0.6 g\(\cdot\)dm\({}^{-3}\) of NaHCO3 boosts up to a 23% higher concentration on a strain of _Scenedesmus_ sp. This specific result is similar to the final concentration achieved in this research. Na2 CO3 has also been highlighted as having its positive effects in terms of high biomass production and lipid increase. [PERSON] et al. [50] reported that 20 mg\(\cdot\)dm\({}^{-3}\) of Na2 CO3 increased two-fold the final concentration of _S. obliquus_. Finally, a study found that the controlled addition of Na2 CO3 coupled with NO3 in a specific C/N ratio can dramatically increase the concentration of biomass and hydrocarbons in _B. braunii_[40]. When considering algal production in landfill leachates, the first metabolites that come to mind are lipids and carbohydrates. Both metabolites are important for their application as raw material to produce biodiesel and bioethanol [5]. Since most of the studied wastewaters cannot be employed to produce food or feed products, the energetic focus is the next step towards the sustainability of the algal production system. Therefore, most studies focus heavily on the effect of the leachate concentration on the synthesis of lipids and carbohydrates [51; 52; 53; 54; 55; 56]. The work of [PERSON] et al. [49] proved that nitrate depletion together with stepwise addition of NaHCO3 enhances the synthesis of lipids. These results are in accordance with the work of [PERSON] et al. [57], who found that a high concentration of NaHCO3 in _Chlorella_ sp. for example, 160 Mm (13.33 g\(\cdot\)dm\({}^{-3}\)), stimulated lipid accumulation, although it inhibits cell growth. According to [PERSON] et al. [58; 59], the type and concentration of the carbon source are particular to every single strain. Some are adapted to high carbonaceous environments, while others do not tolerate high levels of this ion, which in turn may synthesize more or carbohydrates, lipids, or even proteins. In a recent study, [PERSON] et al. [60] found that the addition of NaHCO3 substantially increased the protein content in _S. obtusus_. Since the first two are the goal for many studies, the last may hinder the thermal efficiency of the biomass produced [61; 62; 63; 64; 65]. Carotenoids are considered high-value metabolites of great interest as nutraceuticals [60] which may not have value since the biomass was produced on leachate. Therefore, these low-cost carotenoids can be exploited as cheap colorants or dyes for nonhuman products such as plastics or fabrics, and the colorant-free biomass can be used for biofuels or even bioplastics production.
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 2 Related Works  
### Generative Adversarial Networks  
A generative adversarial network (GAN) [9] is a machine learning framework for data generation, which attempts to generate new data that follow similar distribution as those in the training sets. A GAN includes two adversarial networks, the generator network and the discriminator network. The generator strives to generate a plausible output to fool the discriminator while the discriminator tries to classify generated outputs correctly, keeping from being fooled. Let \(x\) denote a source vector from the true data source \(X\), \(G(x)\) denote the generator's output, and \(D(x)\) be the probability of \(x\) coming from the true data source rather than the generated output. [PERSON] et al. [9] formulated the adversarial loss \(V(G,D)\) of GANs as,  
\[V(G,D)=\ \mathrm{E}_{x\sim p_{\mathrm{data}}}[\log D(x)]+\ \mathrm{E}_{x\sim p_{g}}[ \log(1-D(x)] \tag{1}\]  
Figure 1: The proposed framework, in which the framework derives the interpolated frame \(y_{n}\) from two given ones. Two given input frames \(x_{n-1}\) and \(x_{n+1}\) are fed into the generator network \(G\) for an interpolated frame. The interpolated frame \(y_{n}\) can go through the second generator with the refinement network, denoted by \(G\_RN\), for further improvement. \(D\) and \(D\_RN\) represent the discriminators of the original network and refinement network, respectively.  
The training of GANs is a minimax problem that finds \(D\) and \(G\) such that  
\[\begin{array}{cc}\max&\min&V(G,D)\\ D&G\end{array} \tag{2}\]  
That is, the generator \(G\) tries to produce samples that minimize \(\log(1-D(x))\), and simultaneously the discriminator \(D\) aims to maximize the probability of assigning the correct label (to both true samples and generated outputs of \(G\)). Following that strategy iteratively, both networks enhance their performance in the training process and produce more and more desirable output. Since 2014, the GAN has been used to solve various problems in image and video applications including image deblurring [10], realistic visual manipulation [11] and video frame generation [12; 13; 14].  
Various improvements and extensions have been made on GANs. One notable direction is the introduction of nonrandom input models such as conditional GAN [15], StyleGAN [16], and SRGAN [17]. In contrast to the models with random inputs [4; 18; 19], nonrandom input models use labeled data or real images instead of noise as in the input, which allows GANs to generate outputs of specific features.
## 5 Limitations, and the Future Studies  
We must acknowledge that there are several limitations in this study. Firstly, only nine kinds of typical materials of medical products were considered when selecting the experimental samples, and other materials, such as more sustainable or eco-friendly materials, were not selected. It is important for us to compare these novel materials with typical materials in future studies. Secondly, no material sample was color-processed. In future studies, the visual and tactile perceptual experiences of different-colored materials on children will be considered to help designers choose the best design. Thirdly, only Chinese children aged 10 to 16 were chosen as test subjects. In future studies, we will choose more test subjects from different cultures and linguistic backgrounds and enlarge the sample size to improve the reliability and applicability of the study. Next, this study only examined children's transient perceptual experiences for different material surfaces, and we will have deeper insights into whether perceptions change with repeated use or as children grow older in future research. This study was based on _kunsei_ engineering theory, which is subjective in the perceptual evaluation of each material. In future studies, it will be combined with multi-domain methods, such as EEG and eye tracker experimentation, to ultimately obtain user perceptions. Finally, we only focused on insulin syringes and investigated the relationship between children's perception and surface materials. In future studies, we will experiment with other kinds of children's household medical products for broader applicability that may benefit manufacturers in the product design process.
## 3 Simulation Performance Analysis  
### Modal Analysis  
The theory governing the SAW resonator postulates that the SAW induced by the IDT can be comprehended as a combination of multiple pairs of IDT excitation signals. Consequently, the link between the inherent frequency of the SAW and \(\lambda\) may be ascertained [27]:  
\[f_{0}=\frac{v_{p}}{\lambda} \tag{1}\]  
where \(f_{0}\) is the center frequency of the SAW device and \(v_{\text{p}}\) is the propagation speed of the sound wave. The propagation velocity \(v_{\text{p}}\) of the SAW can be found by using the following formula:  
\[v_{p}=\frac{\lambda(f_{\text{sc}+}+f_{\text{sc}-})}{2} \tag{2}\]  
where \(f\)sc+ and \(f\)sc\(-\) are the symmetric and antisymmetric mode frequencies of the SAW, respectively [28].  
The \(k^{2}\) value for the device's electromechanical coupling can be computed by using the following equation [29]:  
\[k^{2}=\frac{\pi^{2}(}{4}\frac{f_{\text{sc}+}-f_{\text{sc}-})}{f_{\text{sc}+}} \tag{3}\]  
The \(TCF\) can be computed by substituting the parameters \(T=0\)\({}^{\circ}\)C and \(T_{0}=25\)\({}^{\circ}\)C into the given equation and calculating the resulting \(v_{\text{p}}\)[30]:  
\[TCF=\frac{1}{T-T_{0}}\frac{v(T)-v(T_{0})}{v(T_{0})} \tag{4}\]  
Due to the higher sound velocity of the diamond substrate compared to that of the ZnO piezoelectric film, the Rayleigh and Sezawa modes coexist in a single multilayer structure [17]. Firstly, a ZnO/SiO\({}_{2}\)/diamond model is developed to characterize the propagation of acoustic waves in this layered structure. The effectiveness of SAW devices featuring multilayer film designs depends on multiple factors, including the thickness of the piezoelectric film and IDT, the construction of the transducer, and the characteristics of the piezoelectric material. In a layered system in which the SAW is a dispersive wave, the ratio of the acoustic wave's wavelength to the thickness of the various films, including the piezoelectric film, determines the acoustic parameters' dispersive characterization. As a result, optimizing the geometric parameters can significantly enhance the SAW device's performance. Following that, by using the method of controlling variables, specifically when the thickness of the Al electrode can be considered negligible, this study investigates the influence of the normalized thickness variations (h1/\(\lambda\), h2/\(\lambda\)) of the ZnO film and SiO\({}_{2}\) layer on the \(v_{\text{p}}\) and \(k^{2}\) of the Rayleigh and Sezawa modes in this structure. Next, the effect of IDT thickness on the acoustic performance of the Rayleigh and Sezawa modes when h1/\(\lambda=0.4\) and h2/\(\lambda=0.2\) is investigated, and the modal analyses of the Rayleigh and Sezawa modes are conducted after determining the parameters, as seen in Figure 2, which shows the resonance modes of the Rayleigh and Sezawa waves.
## 3 Results
### Objective III: Significance Testing
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**RH** & **MC** & **UV** & **BWD** & **P** & _UA_ & _UE_ \\ \hline MC & 0.3198 & 0.1032 & 0.0052 & 42.8\% & 74.7\% & 58.7\% \\ UV & 0.0567 & 0.3008 & 0.0044 & 36.2\% & 83.1\% & 71.2\% \\ BWD & 0.0101 & 0.0101 & 0.1897 & 21\% & 90.4\% & 88\% \\
**P** & 38.7\% & 41.4\% & 19.9\% & **1** & & \\ _PA_ & 82.7\% & 72.6\% & 95.2\% & & **OA = 81\%** \\ _PE_ & 71.8\% & 53.3\% & 94\% & & **MICE = 70.3\%** \\ \hline
**MH** & MC & UV & BWD & **P** & _UA_ & _UE_ \\ MC & 0.463 & 0.0361 & 0 & 49.9\% & 92.8\% & 85.4\% \\ UV & 0.0428 & 0.3083 & 0.0043 & 35.5\% & 86.7\% & 79.6\% \\ BWD & 0 & 0.0053 & 0.1402 & 14.5\% & 96.4\% & 95.8\% \\
**P** & 50.6\% & 35\% & 14.5\% & **1** & & \\ _PA_ & 91.5\% & 88.2\% & 97\% & & **OA = 91.2\%** \\ _PE_ & 82.9\% & 81.8\% & 96.5\% & & **MICE = 85.3\%** \\ \hline
**PH** & MC & UV & BWD & **P** & _UA_ & _UE_ \\ MC & 0.4951 & 0.0462 & 0.0066 & 54.8\% & 90.4\% & 80.1\% \\ UV & 0.0202 & 0.2496 & 0.0101 & 28.0\% & 89.2\% & 84.4\% \\ BWD & 0 & 0.0083 & 0.1639 & 17.2\% & 95.2\% & 94.1\% \\
**P** & 51.5\% & 30.4\% & 18.1\% & **1** & & \\ _PA_ & 96.1\% & 82.1\% & 90.7\% & & **OA = 90.9\%** \\ _PE_ & 91.9\% & 74.2\% & 88.7\% & & **MICE = 85\%** \\ \hline
**PH2** & MC & UV & BWD & **P** & _UA_ & _UE_ \\ MC & 0.3865 & 0.1104 & 0.0123 & 50.9\% & 75.9\% & 57.3\% \\ UV & 0.0438 & 0.2096 & 0.0063 & 26.0\% & 80.7\% & 71.1\% \\ BWD & 0.0056 & 0.0139 & 0.2116 & 23.1\% & 91.6\% & 89\% \\
**P** & 43.6\% & 33.4\% & 23\% & **1** & & \\ _PA_ & 88.7\% & 62.8\% & 92\% & & **OA = 80.8\%** \\ _PE_ & 79.9\% & 44.1\% & 89.5\% & & **MICE = 70.2\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Volz timber harvest population matrices. NOTE: Dataset is denoted in top-left corner of each sub-table, where **RH** = pre-harvest, **MH** = mid-harvest, **PH** = post-harvest, etc. Classes are labeled as follows: MC = mature canopy, UV = understory vegetation, BWD = bare ground/Woody debris. **P** = proportion of total, _UA_ = user’s accuracy, _UE_ = UA-based efficacy, _PA_ = producer’s accuracy, and _PE_ = PA-based efficacy. **OA** = overall accuracy and **MICE** = map-level image classification efficacy.
## 3 Our Implementation
### Candidate Generator  
In the first step of NBV, candidates for camera poses that could potentially scan new information about the environment are generated. Our proposed method is based on finding edges between known and unknown environment and utilizing the potential field [18]. There are numerous methods of edge detection in a grid map, in an image, or in a point cloud, summarized in [19]. For the sake of simplicity, the edges are detected by sequentially searching all cells of the map and verifying the states of adjacent cells. A cell is considered to be on an edge if it meets a required ratio of unknown to known neighboring cells. Its position is then referred to as \(P_{c}\). This ratio will be adjustable as a parameter of the algorithm. It would not be efficient to search all cells of the map in each iteration; therefore, the edges are only searched in the new altered states. The candidate's \(P_{c}\) direction is determined by the potential field, in which the known space is the repulsive force \(F_{r}\) and the unknown space acts as an attractive force \(F_{a}\). The closer the cell is to the cell lying on the detected edge, the more impact it has. Therefore, the elements of vectors \(F_{r}\) and \(F_{a}\) have the following values:  
\[x=sgn(x_{r})(r-|(x_{r})|) \tag{1}\]  
\[y=sgn(y_{r})(r-|(y_{r})|) \tag{2}\]  
\[z=sgn(z_{r})(r-|(z_{r})|) \tag{3}\]  
where \(x_{r}\), \(y_{r}\), and \(z_{r}\) are the coordinates of the adjacent cell relative to the cell lying at the detected edge and r is the maximum effective distance between the cells. From the equations, we can conclude that the closer cell has a bigger effect than the distant cell. The resultant force is then sum of all the forces from all the adjacent cells (Equation (4)), where m is the number of unknown cells that act by an attractive force and n is the number of known cells that act by a repulsive force.  
\[\overrightarrow{F}=\sum_{i=0}^{n}\overrightarrow{F_{a}}-\sum_{i=0}^{m} \overrightarrow{F_{r}}. \tag{4}\]  
A graphical demonstration of determining the candidate's direction is shown in Figure 2, where the black cell represents the cell lying on the detected edge between the known and unknown environment. White cells represent the known environment and act with a repulsive force, while gray cells represent the unknown environment and act with an attractive force. The maximum effective distance is set to the value of two adjacent cells.  
The position of the candidate \(P_{c}\) is determined by the cell lying on the detected edge between the environments, but due to the minimum scanning distance of the camera, the position of the candidate must be shifted by a constant value in the z-axis so that the camera can also capture this edge.