esa-sceva/satcom-chunk-collection huggingface dataset:
# DanceCaps: Pseudo-Captioning for Dance Videos Using Large Language Models
## 3 Pseudo-Captioning Based Dance Caption Generation
### Tags
#### 3.1.2 Dance Tags  
Dance tags describe the characteristics of choreography in the video, independent of other elements such as body movements, dancers, location, or music. Rather than focusing on detailed movements, these tags represent broader and more general features like the type and genre of dance, providing a summary of the choreography's key characteristics. In the proposed method, two tags are extracted and used: choreography steps and dance genre.  
Choreography steps are fundamental building blocks in dance composition, representing specific sequences of movements that form the core of a dance routine. These steps encompass a wide range of elements, including spatial patterns, body shapes, timing, and energy dynamics, which are carefully combined to convey emotional or narrative content within the dance. Examples of common choreography steps include \"walking out step\" and \"Brooklyn step\". To extract these choreography steps, the metadata of the choreography video is utilized. For in-the-wild choreography videos, metadata typically includes the video title, description, and hashtags, which often provide information about the choreography or music featured in the video. However, the metadata does not always include choreography steps in sufficient detail to build a classifier. When choreography steps are present in the metadata, they are extracted and used, but if they are not, the choreography steps are not extracted separately.  
For the dance genre, the method uses the genres from the AIST++ dataset, which include Break, Pop, Lock, Waack, Hip-hop, House, Krump, Street Jazz, and Ballet. K-pop, unlike traditional dance genres, is characterized by its fusion of multiple genres, and therefore is treated as a separate genre, bringing the total number of genres to 10. Similar to choreography steps, if the genre is tagged in the video's metadata, it is extracted and used as a tag. If the metadata does not contain genre information, a classifier is trained to identify the dance genre from the movements displayed in the video. Since the previously defined temporal motion tag already describes the detailed choreography movements over time, a model is trained to classify dance genres based on the temporal motion tag. To do this, dance videos with genre information in the metadata are collected, and a model, as illustrated in Figure 5, is trained for genre classification. A pretrained BERT encoder is used to encode the temporal motion tags, which is then connected to a dense layer to classify the genre. During training, the BERT encoder is frozen, and only the dense layer is updated. The trained model is then used to extract the genre tag from the temporal motion tag in a sequential process.  
Figure 5: Overall flow of extracting the genre tag from the dance video. If genre is contained in the metadata, it will be directly exploited as genre tag. If not, genre is estimated from the temporal motion tag using BERT encoder and trained genre classifier.
## 2 Literature Review
### IoT Benefits for the MS  
The MS involves various departments with discrete system groups designed to support manufacturing operations, maintenance processes, involved businesses, and training functions. IoT technology is aimed at digitizing all manufacturing activities and connecting them into one platform, enabling interconnections among objects and exchanging real-time data with business analytics systems and machine learning [6; 24]. The IoT-generated data are high-quality and high-accuracy, which renders them distinctive, valuable, and useful for providing context awareness and decision support, such as accurate market demand forecasts, resource management, and reduced inventory costs [1; 39].  
Furthermore, sensor and data storage integration with the business operation systems improves facilities and assets management [41], process automation [1; 42], manufactory floor remote monitoring [17; 38], enhances predictive and preventive production equipment maintenance [18; 19; 43], and reduces unplanned downtime and costs [3; 4; 27; 37; 44; 45]. In addition, IoT sensors can monitor and detect equipment that does not have tasks and turn it off automatically for energy- and fuel-savings purposes [19; 37; 46; 47].  
IoT technology can help to improve workplace safety conditions by providing safe and practical solutions [28; 37; 48]. IoT capability enables the collaboration between human workers and robots, facilitates remote operations [4; 37], provides wearable equipment to monitor employees' health [39], and captures accurate data from low-safety environments where employees' lives are in danger, such as from falls, collisions, or burns [49]; moreover, IoT technology can collect real-time data from supply-chain activities and integrate it with business actions. These seamless integration capabilities provide unparalleled visibility for tracking export and import activities, scheduling, and planning production activities [28; 33; 34], which bring transparency to the processes [9] and response to market demands [50]. The MS can gain the aforementioned benefits directly from IoT implementation and many other benefits can be observed over time [23].  
Regarding the indirect benefits, the IoT can collect valued information during the product lifecycle, analyze customer behavior changes, and improve services and performance based on customers' expectations and experiences [37]; thus, the IoT can produce higher customer satisfaction and loyalty levels with time [11; 49; 51]. Moreover, customer data collection and market status analysis enables the business to gain valuable insights to predict future products and services [4]. IoT prediction methods can help organizations become proactive by shifting from being \"reactive-oriented\" in response to events to being proactive in anticipation of them [52]; thus, recognizing and taking advantage of future opportunities [52]. This distinctiveness would give the organization a tactical benefit over competitors, creating new revenue opportunities and economic sustainability [28; 53; 54].
# A Novel Cloud Approach for Connected Vehicles
## 5 Evaluation
### Simulation Results
#### 5.1.4 Discussion  
This analytic evaluation highlights the efficiency of our approaches. VCDNoCAM provides the same service as already existing ones (NDN and RENE) for freshness, but with a lower cost on the network overhead. The NDN method gives disastrous results in an environment where the data are numerous and often change; resulting in the need to send interests very often for all data to ensure to keep them updated. NDN has been thought to make the network traffic lighter without taking into account the link to the client. Yet in vehicles, the G5 channel has a reduced bandwidth, and we have to save it as much as possible. So, if want to guarantee the authenticity and integrity of every message, it quickly becomes cost ineffective. We saw that we managed to save this network overhead related to the security layer by grouping a set of data in one set of metadata, which is heavier, but makes it possible to reduce the number of security data to only one occurrence. We also added a sequence number mechanism, which avoids sending the data to someone who already has them. Despite these two mechanisms, still, the results are not satisfying (VCDN). The RENE method is more adapted to such an environment because the data are only pushed when it changes. However, as with NDN, the final link towards the client is not taken into account and every vehicle in the same RSU's area is going to individually receive the information. By exploiting the existing technologies, we manage to widely reduce the cost of the data related to the ICN exchanges (VCDNoCAM), by integrating these exchanges in the standardized regularly broadcasted messages (CAMs). Thus, we use the CAM's security layer. In addition, the data are sent to every vehicle in range with only one message. The results highlight the gain in network overhead with this method. Finally, we also use CAMs to send the data farther than only RSU's range (GeoVCDN). This additional functionality improves the quality of service but has a cost on the network overhead. However, it stays lower than the existing ICN methods, and, as we saw, it is more scalable.  
Figure 28: Update Data Frequency.
## 4 Wheel Flat Identification Based on Acceleration of Axlebox  
### VMD and IMFs Sifting  
The simulated axlebox acceleration is analyzed by the VMD-ES method. This section only lists the process of the VMD and IMFs sifting with the length of 20 mm wheel flat at 350 km/h. Figure 6 shows the result of VMD. According to the principle mentioned in Section 3, the value of number of modal components K is 10.  
Figure 6 shows that there is almost no impact components in IMF1 and IMF2. Meanwhile, the impact position and frequency \(\Delta f\) are consistent in IMF3-IMF10, which are generated by the wheel flat. Therefore, VMD can be used to decompose the axlebox acceleration to extract the impact characteristics of the wheel flat from the signals with interference. Then, sift IMFs with the kurtosis principle mentioned in Section 3. The results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Components** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **IMF9** & **IMF10** \\ \hline Kurtosis & 2.82 & 2.99 & 7.39 & 8.54 & 11.67 & 11.74 & 13.61 & 12.76 & 14.93 & 15.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kurtosis value of IMFs.  
Figure 6: The result of VMD.  
Kurtosis is a measurement of the outlier tendency in a distribution, which is sensitive to shocks. Table 2 shows that the kurtosis value of IMF1 and IMF2 is much lower than that of IMF3-IMF10. In fact, outliers are more likely to appear when kurtosis is more than 3, otherwise outliers are less likely to occur. Thus, IMF3-IMF10 have more tendency to generate outliers which are generated by the wheel flat. This also verifies that VMD can be used to extract the impact features of the wheel flat. Therefore, IMF3-IMF10 need to be reconstructed.
## 3 Dataset and Cluster Analysis
### Clustering Technique  
A crucial step of cluster analysis is selecting a similarity metric. A similarity metric determines how the available data are evaluated and placed accordingly in a given coordinate and/or scored for clustering. Clearly, the performance of clustering highly depends on the quality of the metric. Since general-purpose metrics such as the Euclidean distance often fail to capture the intrinsic yet possibly not obvious features of the data of interest, an appropriate metric needs to be selected with care [24]. For selecting a similarity metric, which is closely related to distance metric learning, the task at hand needs to be taken into account. Considering the interests of this study, the k-nearest neighbors (kNN) algorithm [25] is selected as a classifier since it is non-parametric and does not require any training, making its results directly reflect the cluster performance. Among the distance metric learning methods specifically targeted for kNN classification, Neighborhood Components Analysis (NCA) [26] is selected for this study as it allows the reduction of the data dimension [24]. Dimension reduction is useful for our research, where the input data size is much larger compared to the number of classes and possibly the size of the relevant feature domain. In essence, NCA is a supervised Mahalonobis distance learning, which linearly transforms the input data \(\mathbf{x}\in\mathbb{R}^{N}\) into a suitable domain with the output \(\mathbf{y}\in\mathbb{R}^{M}\). NCA is also a non-parametric method and thus does not make any assumption on data statistics or shapes, which is also favorable for this study.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 1D-Based Architectures
Because the related frequency gap between the carrier and the signal in the \(IQ+\)offset \(C\) system is eliminated in the \(IQ+C\) scheme, the CW tone is transmitted in proximity to the spectrum edge. Furthermore, the tone can be appended at the transmitter either electrically (Figure 10c(i)) or optically (Figure 10c(ii)). In the former approach, suitable sinusoids can be added to the modulating RF signals from the DACs that drive the \(IQ\) modulator (\(IQM\)). However, some RF swing is sacrificed in this approach for tone generation. In the latter scheme, a power split portion of the transmit laser is frequency shifted for the tone generation. Nevertheless, this approach demands a second modulator that can be used for frequency shifting. At the receiver, to recover the field information of the carrier-signal beating, the SSBN can be removed through DSP using either interference cancellation (IC) or any  
Figure 10: The 1D transceiver architectures based on (**a**) the IM with direct detection Rxs (i) DSP-free and (ii) with DSP and (**b**) \(IQ+\)offset \(C\)/DD and (**c**) \(IQ+C\)/DD-KK/or DD-IC. IM: intensity modulator; DAC: digital-to-analog converter; AMP: amplifier; C: carrier; DSP: digital signal processing; RF: radio frequency; ADC: analog-to-digital converter; PD: photodetector; CDR: clock and data recovery; \(IQM\): \(IQ\) modulator. **Note**: Spectra allocations of the schemes are illustrated between the transmitters and the receivers. The colors in the spectra represent independent modulation data carried by the sidebands. This convention applies to all the figures concerning modulation sidebands in this tutorial (adapted from [12; 14; 34; 37]). SSBN mitigation scheme, as discussed in Section 4.2. Furthermore, the minimum electrical bandwidth of the receiver is \(R\). Digital chromatic dispersion compensation is supported at the receiver [14; 18; 29; 119].
# A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs
## 2 Methodology
### Matrix-Based Kinetics Equations
These are considered in the present work and brief descriptions on their implementation are described below. To handle three-body reactions, the quantity \(\log(\mathbf{M})\) is added to \(\log(\mathbf{K}_{f})\), where \(\mathbf{M}\in\mathbb{R}^{N_{C}\times N_{R}}\) is a matrix of third-body concentrations for each reaction (\(\mathbf{M}_{ij}\) is 1 if reaction \(j\) does not include a third body). The entries in \(\mathbf{M}\) can be obtained through the matrix multiplication \(\mathbf{M}=\mathbf{C}\mathbf{E}\), where \(\mathbf{E}\in\mathbb{R}^{N_{S}\times N_{R}}\) is a matrix of third-body efficiency factors. In practice, for computational savings, the third body concentration matrix equation is evaluated only for the subset of total reactions \(N_{R}\), since third body concentration terms are generally not present in every single reaction. Additionally, falloff and pressure-log reactions, which constitute two of the reaction types discussed further below in Section 2.2, are treated separately from standard Arrhenius reactions as defined in Equation (6). In summary, they modify the standard matrix-based formulation of the forward rate constant logarithm by incorporating much more complex and arithmetically intensive expressions. As such, pressure-log and falloff formulations such as that of Lindemann, Troe, or SRI are handled on the GPU in a custom non-matrix fashion. More information on the mathematical implementation expressions for these reactions can be found in the Cantera and Chemkin documentation [26; 27]. Lastly, it is necessary to acknowledge that the treatment of kinetics in the logarithm space introduces scenarios in which logarithms of zero concentration are required. There are several ways to treat this with effectively zero propagating error--some options are listed below. 1. Let the GPU handle the zero concentrations natively. For example, CUDA supports the operation of exp(log(0))=0 through the Inf floating point placeholder. That is, CUDA ensures that the operation log(0)=-Inf, and that exp(-Inf)=0. 2. Replace either the zero concentrations or mass fractions with an extremely small positive number and then proceed with the computations. This number should be small enough (i.e., 1.0e-300) to effectively produce a huge negative number upon taking the logarithm. 3. Replace the logarithm of the zero concentrations directly with a huge negative number, e.g., through a re-definition as log(0)=-1.0e300. It should be noted that errors are indeed introduced from all of the above treatments of the zero logarithm, as it is mathematically undefined and the numerical treatments incur some truncation penalties. However, these errors are microscopic relative to other error sources that are encountered in a reacting flow simulation and are unnoticeable in practice. The reader is referred to Appendix B for additional verification, which provides sample results using the second option.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works
### Imaging Photoplethysmography
#### 2.1.3 Signal Processing  
Independent component analysis, a blind source separation technique initially employed by [PERSON] et al. [44], aims to remove artifacts and noise by separating the fluctuations caused by the pulse from raw PPG signals. [PERSON] et al. developed different color transformations to improve pulse extraction: blood volume pulse signature (PBV), chrominance signal combination (CHROM), spatial subspace rotation (S2R), and plane orthogonal to skin (POS) [49].  
Different band-pass filtering techniques have been previously employed to remove artifacts and noise from raw PPG signals. [PERSON] et al. used detrending operations to refine the pulse signal by removing irrelevant trends [50] and thus improve beat detection. The PPG signal can be filtered from its Fourier transform representation [2] or wavelet transform representation [45]. The latter allows filtering of artifacts and noise without any drastic impact on the pulse amplitude [16]. Ultimately, biomedical parameters like pulse rate, vasomotor activity (pulse amplitude), breathing rate, oxygen saturation, and pulse transit time can be computed from filtered PPG signals [2; 6; 21]. Stress level can also been assessed from some of these parameters [51; 52].
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 4 Asymptotic Performance Analysis for Long-Distance Short-Packet Transmission
### Analysis of Relay Gains
\[\epsilon\approx\frac{L^{\frac{3}{2}}\sqrt{\frac{E^{th}(L)}{N^{th}(L)}\left( \frac{E^{th}(L)}{N^{th}(L)}+2\right)}\exp\left(-\frac{N^{th}(L)\left(\ln\left( 1+\frac{E^{th}(L)}{N^{th}(L)}\right)-\frac{L\ln 2}{N^{th}(L)}\right)^{2} \left(1+\frac{E^{th}(L)}{N^{th}(L)}\right)^{2}}{2L\frac{E^{th}(L)}{N^{th}(L)} \left(\frac{E^{th}(L)}{N^{th}(L)}+2\right)}\right)}{\sqrt{2\pi N^{th}(L)}\left( \ln\left(1+\frac{E^{th}(L)}{N^{th}(L)}\right)-\frac{L\ln 2}{N^{th}(L)} \right)\left(1+\frac{E^{th}(L)}{N^{th}(L)}\right)}. \tag{51}\]
## 4 Discussion
### Contribution to Research  
Before starting a new technology development, monitoring and analyzing the competitors' related patents is usually recommended. To discover the development status of competitors, however, there is little previous literature to give any information about doing a patent analysis effectively. Herein, this paper provided a set of patent indicators to analyze the specific technology field, and these indicators could help people skilled in the art of exploring the development status clearly and easily. This contribution is most important for managing the technology development. Especially, new combination indicators, RGR and RDGR and RPP and RPA, are introduced to discover their R&D strategies and technology advantage. Furthermore, the technology-function matrix and citation indicator adopt different analytical principles and may offer different aspects in the exploring development trend. Finally, people can use an MDS analysis to find out the technology similarity among assignees of interest. All these results can be integrated into the patent landscape analysis and to avoid patent infringement. Based on this information, R&D resources, strategies, planning, and administration can be managed more effectively.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### The Criteria to Evaluate the Performance of the ROE Distributions  
Providing ROEs with a temporally and spatially uniform distribution is the critical issue for the design of LEO constellations in a GNSS RO mission. The performance of the temporal and spatial distribution of the ROEs is evaluated by the fitness function. Since the fitness function evaluates the optimality of the distribution of ROEs based on the spatial-temporal grids, it is not able to provide any insight into the point-to-point uniformity of ROEs in each grid and over the globe. Therefore, the uniformity in the spatial and temporal distribution of ROEs observed by the optimal LEO constellations are further assessed with the coefficient of variation (COV), which is defined to represent the uniformity degree of the point distribution [46] and is expressed as follows [47]:  
\[COV=\frac{1}{\overline{\gamma}}(\frac{1}{M}{\sum_{i=1}^{M}{(\gamma_{i}- \overline{\gamma})}^{2}})^{\frac{1}{2}} \tag{7}\]  
\[\overline{\gamma}=\frac{1}{M}{\sum_{i=1}^{M}{\gamma_{i}}} \tag{8}\]  
where \(M\) denotes the total number of ROEs, \(i\) denotes the \(i\)th ROE, \(\gamma_{i}\) is the minimal distance between the \(i\)th ROE and its nearest neighbor and \(\overline{\gamma}\) is the mean of all the values of \(\gamma_{i}(i=1,2,\dots\dots\,M)\). The smaller the COV, the smaller the variation of the minimal distance to its nearest neighbor, and the  
Figure 2: The optimization algorithm flowchart of the LEO constellation with certain number of satellites and certain constellation pattern.  
more uniform distribution of the points. A low value of COV corresponds to an ROE distribution close to a regular grid, so an optimized GNSS RO mission should be characterized by a low value of COV.
## 3 Evaluation Antennas  
Considering the spatial restrictions of a cellular device, as well as the main manufacturing processes (e.g., printed circuit boards (PCBs) and low-temperature co-fired ceramic (LTCC)), the potential options for 5G phased arrays are mostly reduced to 2D planar or quasi-planar configurations. The following eight AUTs were selected that correspond to the antennas developed to validate the performance of measurement systems [32], plus a planar array with different beam steering and polarization configurations (see Figure 1):  
1. Horn antennas loaded with a slot array, \(y\) linear polarization. One at \(10\,\)GHz, \(30\,\)GHz and \(90\,\)GHz. Short descriptions: _SlottedHorn10G_, _SlottedHorn30G_, and _SlottedHorn90G_.
2. A cavity-fed dipole array, \(y\) linear polarization. One at \(10\,\)GHz, \(30\,\)GHz and \(60\,\)GHz. Short descriptions: _DipoleArr10G_, _DipoleArr30G_, and _DipoleArr60G_.
3. A planar \(3\times 3\) array of half-wavelength square patches at \(60\,\)GHz. The antennas were excited independently for each \(x/y\) polarization port. The following two configurations were produced: (i) circular polarization (short description: _PatchArray60G_circular_) and (ii) two simultaneous beams pointing at \(\theta=30^{\circ},\phi=0^{\circ}\), and \(\theta=10^{\circ},\phi=-90^{\circ}\), for \(x\)- and \(y\)-linear polarizations, respectively (short description: _PatchArray60G_L30 xL10y_).  
Finite-difference time-domain (FDTD) simulations were created with Sim4 Life V5.0 [33]/SEMCAD X V18.0 [34] with high resolution to generate reliable reference fields for validation. In this study, we have selected a range of \(2\)-\(150\,\)mm for the validation and testing of the developed method. For example, the range at \(30\,\)GHz corresponds to \(\lambda/5-15\lambda\), which covers both near-field and far-field regions and the distances that are important for the compliance testing of millimeter-wave devices.