esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Motion Simulation of an Underwater Glider in the Vertical Plane
## 2 UG Design
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Term** & **Value** & **Description** \\ \hline \(M_{B}\) & 79.99 & Maximum buoyancy of glider (kg) \\ \hline \(m\) & 78.24 & Mass of glider (kg) \\ \hline \(\overline{m}\) & 9.48 & Mass of movable mass (kg) \\ \hline \(\mathbf{r}_{\overline{m}\overline{i}}\) & 0.5463 & Initial position of movable mass along \(x\)-axis (equilibrium) (m) \\ \hline \(\mathbf{r}_{\overline{m}3}\) & 0.0397 & Initial position of movable mass along \(z\)-axis (equilibrium) (m) \\ \hline \(m_{p}\) & 1.4840 & Mass of piston assembly (kg) \\ \hline \(\mathbf{r}_{p\overline{i}1}\) & 0.1607 & Initial position of piston along \(x\)-axis (equilibrium) (m) \\ \hline \(\mathbf{r}_{r\overline{i}3}\) & 0.0287 & Initial position of piston along \(z\)-axis (equilibrium) (m) \\ \hline \(m_{s}\) & 67.27 & Mass of static equilibrium (kg) \\ \hline \(\mathbf{r}_{s\overline{i}1}\) & \(-0.0805\) & Initial position of static mass along \(x\)-axis (equilibrium) (m) \\ \hline \(\mathbf{r}_{s\overline{i}3}\) & 0.0053 & Initial position of static mass along \(z\)-axis (equilibrium) (m) \\ \hline \(\mathbf{m}_{b\overline{i}}\) & 1.75 & Initial mass of ballast in neutral state (kg) \\ \hline \(\mathbf{r}_{b\overline{i}1}\) & \(-0.0019\) & Initial position of ballast water along \(x\)-axis (equilibrium) (m) \\ \hline \(\mathbf{r}_{b\overline{i}3}\) & 0.0287 & Initial position of ballast mass along \(z\)-axis (equilibrium) (m) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the glider (fresh water). \[\mathbf{d}_{b}=\frac{1}{2}\mathbf{d}_{p} \tag{17}\]  
When the piston moves, the mass of ballast water in the buoyant cylinder after the suspension state changes accordingly, as follows:  
\[m_{b}=m_{bi}+m_{0} \tag{18}\]  
Subsequently, Equation (13) can be rewritten as  
\[\mathbf{r}_{CG}=\left(\overline{m}\mathbf{d}_{\overline{m}}+\ \mathbf{m}_{p} \mathbf{d}_{p}+\ \mathbf{m}_{s}\mathbf{r}_{s}\right)/m \tag{19}\]  
The relative position of the center of gravity to the center of buoyancy of the glider in different states can be deduced as follows:  
\[\mathbf{r}_{r}=\mathbf{r}_{CG}-\mathbf{r}_{CB} \tag{20}\]  
Because only the motion of the glider in the vertical plane is analyzed in this study, the recovery moment of the glider is expressed as  
\[\tau_{hs2}=mg(\mathbf{r}_{rx}cos\theta+\mathbf{r}_{rz}sin\theta) \tag{21}\]  
Here, the \(\mathbf{r}_{rx}\) and \(\mathbf{r}_{rz}\) present the position of center of gravity in \(x\)- and \(z\)-axes in the body coordinate, respectively.
## 7 Discussion and Limitation
### Limitations  
Along with the positive aspects and relation among the constructs, there are some limitations in the existing study, which are supportive for the expected effort. Firstly, the data was only collected from India. Secondly, the focus of this study is only Internet games as the study recommends consideration of online mobile games, which gives more insight to the game developer for more innovation. Thirdly, in this research, the data was collected from a group of 16- to 35-year-old individuals, although the study suggests considering data of those above 35 years old because, in many regions, online games are very popular in the beyond 35 years old age group. However, the findings might not be possible to generalize, but it would be interesting to expand this research work to a global scale, as well as even explore other social media platforms to find symmetric relations between different considered factors.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 5 Modification Processes of Kaolin: Enhancing Industrial Applicability
### Mechanochemical Activation  
Mechanochemical activation is a modification technique that involves the reduction of particle size, leading to the increased pore volume and surface area of kaolinite materials [43]. This process enhances the surface reactivity of kaolinite and improves its ion exchange capacity. Mechanochemical activation can be achieved through specific micromization processes (dry pulverization of kaolin raw materials, vibratory mills, jet mills, cyclone autogenous mills, high-speed mechanical impact, ultrafine pulverizes, etc.) or industrial grinding techniques (wet grinding, extrusion, chemical immersion, and dry superfine grinding) [44]. During mechanochemical activation, kaolinite particles undergo delamination, resulting in the formation of large spheroidal agglomerates from the delaminated particles. The delaminated product is a wet xerogel with a randomly structured network, in which hydroxyl units interact to form coordinated water molecules. This interaction is a result of proton migration between hydroxyl units, a process known as prototropy (Equations (4) and (5)).  
\[\text{OH}^{-}\Leftrightarrow\text{H}^{+}+\text{O}^{2-} \tag{4}\]  
\[\text{H}^{+}+\text{OH}^{-}\Leftrightarrow\text{H}_{2}\text{O} \tag{5}\]  
The grinding process leads to a decrease in the structural order of kaolinite, which coexists with disordered materials. The degree of structural order in the delaminated precursor significantly influences the physicochemical properties of the mechanochemically activated kaolinite. Specifically, kaolinite with high structural order experiences a more pronounced effect from mechanochemical activation compared to kaolinite with lower structural order [45]. Excessive grinding, however, can negatively impact the kaolinite crystal structure, transforming it into an amorphous material with reduced surface area and pore volume. This alteration affects the overall performance and application potential of the kaolinite material in various industrial processes.
# An Audio Distortion Dynamic Range Index for Evaluating the Performance of Audio Systems
## 2 Definition of the New Index  
The unwanted signals produced by a DUT will inevitably introduce distortion when a pure input signal goes through. Here the unwanted signals include the harmonic, spurious and noise components. The proposed ADDR index comprehensively depicts the process of how the dynamic range performance of the DUT is affected by these distorted signals, and the definition is as follows.  
Audio Distortion dynamic range (ADDR): For a full-scale pure sine-wave input of a specified frequency, the ratio of the power of the DUT's output averaged spectral component at the input frequency, \(f_{i}\), to the power summation of distorted spectral components omitting those components above a threshold. The distorted spectral components are observed over a specified frequency band, and the range of the threshold is from the maximum of the magnitude of the distorted spectral components to the maximum of the magnitude of the noise spectral components:  
\[\small\begin{split}\Delta\mathbf{IDR}(\mathrm{dB})=20\mathrm{log}_{ 10}\left(\frac{|X_{a}(f_{i})|}{|X_{a}(f_{h\prime})|+|X_{a}(f_{\vartheta})|+|X_{ a}(f_{\eta\prime})|}\right),\left(\begin{array}{c}f_{h\prime}=f_{h}\, \left(|X_{a}(f_{h})|\leq X_{ref}\right),\\ f_{\vartheta}=f_{s}\,\left(|X_{a}(f_{s})|\leq X_{ref}\right),\\ f_{\eta\prime}=f_{n}\,\left(|X_{a}(f_{n})|\leq X_{ref}\right).\end{array}\right) \end{split} \tag{1}\]  
where  
\(X_{a}\) is the averaged spectrum of the DUT output;  
\(f_{i}\) is the input signal frequency;  
\(f_{h\prime}f_{s},f_{\eta}\) are the frequencies of the set of harmonic, spurious and noise spectral components, respectively. They are all within the specified frequency band;  
\(X_{ref}\) is the threshold, whose range is from the maximum of \(\{|X_{a}(f_{h})|,|X_{a}(f_{s})|,|X_{a}(f_{n})|\}\) to the maximum of \(\{|X_{a}(f_{n})|\}\);  
\(f_{h\prime},f_{\vartheta\prime},f_{\eta\prime}\) are the frequencies of the set of harmonic, spurious and noise spectral components that equal to or lower than the threshold \(X_{ref}\), respectively.  
The definition method between the existing indexes and the ADDR index is different. The SNR, SINAD, THD, and spurious-free dynamic range (SFDR) indexes are based on the ratio of the power of the fundamental signal to different categories of distorted spectral components. However, the ADDR index does not classify the distorted spectral components by treating them all as the unwanted signals. The definition of the ADDR index replaces classification with a variable threshold. The relative size of the threshold represents the distance from the magnitude of the fundamental signal. The ratio of the power of the fundamental signal to the distorted components at a fixed threshold describes the corresponding degree of signal headroom. The ADDR results corresponding to all the thresholds moving from the maximum to the minimum (ADDR plot) describes the changing process of the headroom degree from the relative worst to the relative best.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 4 Investigated Methods to Support RTA
### Smart Preliminary Channel Access (Smart PCA)  
This paper proposes an improved PCA which we call Smart PCA and which works as follows. If the number \(M\) of RTA STAs in the network is one or the maximum channel reservation time is less than the duration of one RTA transmission \(T_{s}\):  
\[\begin{bmatrix}M=1,\\ T_{\text{PCA}}-(T_{RTS}+SIFS+T_{CTS})<T_{s};\end{bmatrix} \tag{1}\]  
then RTA STAs access the channel in the same way as in the PCA. Otherwise, the modification of the PCA is as follows.  
The RTA STA starts PCA by generating an RTS frame \(T_{\text{SmartPCA}}\) before the beginning of \(T_{exp}\) interval. SIFS after receiving the CTS frame from the AP, the RTA STA transmits a special frame, which we call SPCA. It has almost the same structure as the RTS frame but has additional fields containing information about the beginning and the duration of the \(T_{exp}\) interval, see Figure 2. After receiving the SPCA frame, other RTA STAs with data can access the channel, but only if their transmission ends before the start of the \(T_{exp}\) interval. If the transmission fails, the RTA STAs can retry, but before each channel access, they will ensure that their transmissions end before the \(T_{exp}\) interval.  
Compared to the PCA, in this method, the RTA STA starts channel access earlier due to the transmission of an additional SPCA frame:  
\[T_{\text{SmartPCA}}=T_{\text{PCA}}+SIFS+T_{SPCA}.\]  
The SPCA frame consumes additional channel time, and therefore it is useless if condition (1) holds because there are either no other RTA STAs, or they are unable to finish their transmission before \(T_{exp}\) interval. Thus, the Smart PCA guarantees that, in case of timely RTS transmission, RTA STA can transmit data frames as soon as they appear within the \(T_{exp}\) interval. At the same time, if condition (1) is not met, the improved method allows RTA STAs to reduce delays by transmitting data in the channel reserved by another RTA STA.  
Note that with both PCA and Smart PCA, RTA transmissions, including the PCA procedure, consume more channel time than short RTA transmissions in the Tuned EDCA.
# Null Subcarrier Index Modulation in OFDM Systems for 6G and Beyond
## 4 Proposed NSC-OFDM-IM
In the case of employing all carrier positions, regardless of whether they are active or inactive subcarriers, they all carry the information by index and complex modulations. The number of \(K\) complex subgroups is given by:  
\[K=\frac{N_{FFT}}{\hat{M}} \tag{15}\]  
If the number of used frequency locations N\({}_{used}\) is less than the OFDM spectrum, this means the system is padded with zeros in the frequency domain, and as in the classical zero-padded OFDM (ZP-OFDM) symbol, the proposed scheme here can be interpreted as the null subcarrier index modulation for the zero-padded OFDM system (NSC-ZP-OFDM-IM). Depending on the decimal value of the \(p_{0}\) bits, the selection of the inactive carrier index within the complex sub-band group occurs randomly, and hence, the active carriers in each sub-band in the frequency domain can be interpreted as sparsely distributed active subcarriers. The remaining part of the NSC-OFDM-IM scheme at the transmitter is designed as a classical OFDM system. The complex modulation and guard interval addition are applied accordingly. The signal is then given to the wireless multipath channel.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 6 Conclusions  
This paper focuses on multi-object trajectory association in occluded scenarios. It uses instance segmentation to optimize trajectory association methods under occlusion conditions. We propose a mask-integrated multi-hypothesis tracking algorithm that achieves trajectory association through short tracklet pairs. The framework incorporates a mask-conditioned feature fusion network and an occlusion-aware mask propagation network that enable better trajectory association in occluded scenarios and enhance the robustness of the MOTS framework. The proposed mask-based framework is tested on public datasets, and the effectiveness is proved scientifically.  
Conceptualization, H.S., D.Z., D.Y. ([PERSON]) and D.Y. ([PERSON]); methodology, H.S., D.Z., D.Y. ([PERSON]) and D.Y. ([PERSON]); software, H.S., D.Z., D.Y. ([PERSON]) and D.Y. ([PERSON]); formal analysis, H.S., D.Z., D.Y. ([PERSON]) and D.Y. ([PERSON]); investigation, D.Z.; resources, H.S. and D.Y. ([PERSON]); data curation, H.S., D.Z. and D.Y. ([PERSON]); writing--original draft preparation, H.S., D.Z. and D.Y. ([PERSON]); writing--review and editing, H.S., D.Z. and D.Y. ([PERSON]); visualization, H.S., D.Z. and D.Y. ([PERSON]); supervision, H.S., D.Y. ([PERSON]), X.L. and W.K.; project administration, H.S., D.Y. ([PERSON]), X.L. and W.K.; funding acquisition, H.S., D.Y. ([PERSON]), X.L. and W.K. All authors have read and agreed to the published version of the manuscript.  
This study is partially supported by the National Natural Science Foundation of China (No. 62372023), the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2023 ZX-11), the Research Start-up Funds of Hangzhou International Innovation Institute of Beihang University under Grant No. 2024 KQ087, and the Zhejiang Provincial Natural Science Foundation of China under Grant No. LQN25F020028 and supported by the Open Funding of the State Key Laboratory for Intelligent Coal Mining and Strata Control (Grant No. SKLIS202405).  
Ethical review and approval were waived for this study, because it solely utilized the publicly available MOTS dataset [1], which contains anonymized data and does not involve direct interaction with human subjects or collection of new personal data. No new human or animal subjects were involved in our work.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. The data used in this study were sourced from the publicly available MOTS dataset, for which consent was obtained by the original data collectors.  
Data Availability Statement: The data presented in this study are openly available in the MOTS dataset at [[https://motchallenge.net/data/MOTS/](https://motchallenge.net/data/MOTS/)]([https://motchallenge.net/data/MOTS/](https://motchallenge.net/data/MOTS/)) (accessed on 29 May 2025).  
Conflicts of Interest: The authors declare no conflicts of interest.
## 2 Methods
### Data Collection
#### 2.2.3 Aerial Hyperspectral Data Collection  
We used a hyperspectral dataset collected in July of 2014 at the Bonanza Creek Long Term Ecological Research Station, where the APEX field site is located. (Figure 2, right panel). The aerial dataset included the APEX study site and surrounding peatland ecosystems (Figure 4). The aerial image encompassed a variety of peatland cover types including black spruce and tamarack bogs, sedge meadows, tussock grasslands, and birch and willow shrub communities. Airborne hyperspectral data were collected by the remote sensing company SpecTir. The instrument used in the data collection was a ProSpecTir VNIR SWIR dual sensor push broom imaging spectrometer that collected data at 360 channels from approximately 400 to 2450 nm at a spectral resolution of approximately 2.9 nm in the visible and near-infrared, and 8.5 nm in the short-wave infrared portions of the electromagnetic spectrum. The imagery was collected approximately 200 m above ground surface, resulting in a spatial resolution of 1 m\({}^{2}\)[PERSON] et al. (2018). The study area is approximately 2 km\({}^{2}\) in size. Data were processed using the Spectral Angle Mapper algorithm in the Python-based Spectral Classification Plug-in ([PERSON] et al., 2017). Ground control was performed by SpecTir and participants in this study. More detailed information on the geolocation and post-processing of the aerial hyperspectral data is provided in [PERSON] et al. (in review) [PERSON] et al. (in review).  
Figure 3: Bands included in SPRUCE and APEX spectral sampling. Dashed line indicates SPRUCE data and dotted lines indicate APEX data. Data represent sample spectral scans collected at each site.
# Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images
## 3 Experimental Results
### Matching Performance Analysis
#### 3.3.2 Feature Match Performance Analysis  
Precision, recall, and theF1 score are used to evaluate the feature matching performance of DDCE and the feature match methods listed in Tables 2 and 4. Single-modality and multi-modality image matching methods are adopted for the performance evaluation of the feature matching method.  
The quantitative comparison of the feature matching accuracy of each method is shown in Table 7. It can be found that multi-modality image matching methods achieve better performance than single-modality image matching methods. Due to the difference of pixel intensities and gradients between infrared and visible images, it is difficult for single-modality image matching methods to match infrared and visible images. To match infrared and visible images, hand craft methods need to be modified according to characteristics of infrared and visible images. Deep learning methods need to be trained by massive training data.  
Among multi-modality image matching methods, edge-based methods achieve better performance than gradient-based methods. DDCE leverages consistent edge structures to establish feature descriptions and achieves the second highest F1 score among these methods. Although the similarity of image structures decreases as the spectral difference increases, image structures still maintain better consistency than image pixel intensities and gradients.  
Precision and recall of multi-modality image matching methods on infrared and visible images are lower than those of single-modality image matching methods on visible images. Due to the large difference between infrared and visible images, the similarity of descriptions of common feature points is low. Even though common feature points can be detected, the number of common feature points that can be correctly matched is still small. As a result, the match recall of feature points is low. Due to the existence of non-common feature points and the low similarity of common feature points, the match precision of feature points is low as well.
## 2 The European ENTSO-E RG-CE System
### Advantages of the Interconnected ENTSO-E System
#### 2.1.3 Environmental Advantages  
Global awareness of the environment is increasingly influencing decisions about renewable energy sources (RES), the locations of new power stations, and transportation systems. With the interconnection and cooperation of the systems, requirements in installed capacity (reduction in requirement for spinning reserve of each system) can be reduced overall, while priority can be given to the operation of environmentally friendly power stations and the usage minimization of polluting stations.
## 4 Methodology  
### Data Preparation  
To realize the proposed machine learning models, we used data provided by Tupras. The data included temperature and pressure sensor data, and 263 laboratory measurements (167 measurements from the debutanizer _Unit A_, and 96 from the debutanizer _Unit B_), all sampled simultaneously at irregular day intervals. We consider that the irregular sampled data should not affect the machine-learning model training since temperature and pressure sensor inputs are used to estimate LPG C5 content. As described in Section 3, experts informed us that light components, such as C5, do not vary much between feedstocks. The debutanizer follows a previous distillation phase, where LPG is separated from the rest of crude oil derivatives. Therefore, the debutanizer's operational pressure and temperature are used to influence the observed LPG C5 concentration.  
We found that sensor data were available at different frequencies when creating the dataset for training machine-learning models. Although some sensors provided readings every five seconds, others provided data at a minute level. We thus resampled all sensor data at a minute level and set all values to second zero. When resampling, we considered the last sensor reading. We chose to impute values using forward filling for missing values, considering that missing sensor readings are most likely to have a value similar to the last one observed. Since we had no information regarding set-point configurations on past operations, we ran a change level detection algorithm on sensor reading time series. The algorithm computed the mean of the past sensor readings and compared the value to the new sensor reading. If the new reading was above a certain threshold, we considered a new set-point was set. We empirically tried different threshold values and obtained the best results, corroborated with plots manual inspection, by setting it to 4%. In Figure 2A, we provide an example of three time series of sensor data, enclosing some level changes within dashed squares. In Figure 2B, we show two plots that illustrate how the change level detector works. The plot at the top shows average signal values, while the plot at the bottom shows the residual, computed as the difference between the new sensor reading and the average signal value.  
Experts instructed us that the timestamps from laboratory samples did not match sensor data timestamps. To match them, timestamps from laboratory samples had to be transposed four to five hours earlier. Since accurate data regarding time transposition was missing, we decided to consider sensor values measured in 15 minutes slots for a time range of an hour and a half (see Figure 3). Since operational conditions change when a new set-point is given, we computed the median sensor value since the last change level detected and the upper bound time considered to match the laboratory reading.