esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Channel State Information  
Practically speaking, channel state information (CSI) significantly influences the NOMA system's performance, and several efforts have been made to implement channel estimation using NOMA scenarios [57]. In [58], a new linear estimator was developed to maximize the average effective signal-to-interference noise ratio (SINR) of the strong user, with a finite SINR required for the weak user to identify the CSI. Meanwhile, several researchers are looking at NOMA-based solutions in various CSI circumstances because the CSI is difficult to collect using conventional approaches. Two power allocation techniques and the performance of NOMA in an incomplete CSI environment were reported [59]. Furthermore, using uplink NOMA systems, the researchers demonstrated that insufficient CSI causes improper decoding and additional interference with the intended signal [60]. Consequently, how to efficiently collect flawless CSI is a crucial challenge in NOMA-aided approaches, and new techniques must be used to address this issue. Although numerous recent research contributions have developed various reliability and sum data rate optimization methods, these techniques demand high computational complexity because of the nonlinear optimization. They are unable to produce an associated power allocation mechanism against a given CSI. In particular, virtually all of the key benefits of NOMA techniques rely mainly on CSI; as a result, several strategies have been presented in previous studies to further enhance the effectiveness of channel estimates [61]. Conventional approaches, however, are unable to trace the alteration in the channel state in real time due to the complexity of the channel conditions in multiple-user systems [62]. Usually, the drastically fluctuating channel characteristics cause CSI acquisition to be disrupted and the NOMA system efficiency to suffer.
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 3 Case Study
### Model of the Three-Wheeled Omnidirectional Mobile Robot  
The state space representation of a three-wheeled omnidirectional mobile robot in global coordinates is taken from [23]; then, the motors' dynamics are added such that the following state space model is derived:  
Figure 3: Three-wheeled omnidirectional mobile robot.  
\[\dot{x}_{1} =x_{4} \tag{19}\] \[\dot{x}_{2} =x_{5}\] \[\dot{x}_{3} =x_{6}\] \[\dot{x}_{4} =a_{1}x_{4}-a_{2}x_{5}x_{6}-b_{1}\cos(x_{3})v_{1}-\sqrt{3}b_{1}\sin (x_{3})v_{2}\] \[\dot{x}_{5} =a_{2}x_{4}x_{6}+a_{1}x_{5}-b_{1}\sin(x_{3})v_{1}+\sqrt{3}b_{1} \cos(x_{3})v_{2}\] \[\dot{x}_{6} =a_{3}x_{6}+b_{2}v_{3}\] \[\dot{x}_{7} =-\lambda_{1}(x_{7}-18000u_{1}-\text{Ysign}(\Omega_{1}))\] \[\dot{x}_{8} =-\lambda_{2}(x_{8}-18000u_{2}-\text{Ysign}(\Omega_{2}))\] \[\dot{x}_{9} =-\lambda_{3}(x_{9}-18000u_{3}-\text{Ysign}(\Omega_{3}))\]  
where  
\[v_{1}= \frac{x_{7}-\text{Ysign}(\Omega_{1})}{18000}+\frac{x_{8}-\text{ Ysign}(\Omega_{2})}{18000}-2\frac{x_{9}-\text{Ysign}(\Omega_{3})}{18000}\] \[v_{2}= \frac{x_{7}-\text{Ysign}(\Omega_{1})}{18000}-\frac{x_{8}-\text{ Ysign}(\Omega_{2})}{18000}\] \[v_{3}= \frac{x_{7}-\text{Ysign}(\Omega_{1})}{18000}+\frac{x_{8}-\text{ Ysign}(\Omega_{2})}{18000}+\frac{x_{9}-\text{Ysign}(\Omega_{3})}{18000}\]  
the signals \(u_{i}\) are the inputs to the system, \(\Omega_{i}\) represent the shaft velocity of the \(i\)-th motor, \(i=1,2,3\); the states \(x_{1}\), \(x_{2}\), and \(x_{3}\) are the positions at the coordinates (x, y), and the orientation \(\varphi\) of the mobile robot, respectively. While \(x_{4}\), \(x_{5}\) are the mobile robot linear velocities \(v_{x}\), \(v_{y}\), respectively, and \(x_{6}\) is the angular velocity \(\dot{\varphi}\) of the vehicle about its center of mass. The states \(x_{7}\), \(x_{8}\), and \(x_{9}\) represent the motor velocities (RPM). Since real motors have fast dynamics, the parameters \(\lambda_{1}=100\), \(\lambda_{2}=100\), and \(\lambda_{3}=100\) are chosen such that a fast convergence is achieved, whilst the parameter \(\text{Y}=1000\) (RPM) is selected according to the datasheet of the motors. The other system parameters are identified by using the least squares approach in [24]. The derived parameters are reported in Table 1.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 6 Discussion  
This study demonstrates the significant potential of neural networks in predicting shot accuracy in badminton using Quiet Eye (QE) and biomechanical data. Our model, achieving 85% accuracy on the test set, not only validates the critical role of QE in badminton performance but also establishes a novel approach to integrating visual attention strategies with machine learning techniques in sports science.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments
### Ablation Studies
#### 4.5.1 The Model Components  
Table 3 shows the effect of each architectural component. Starting from a base model without any attention modules or bi-directional refinement (Table 3 (a)), we first add the geometric keypoint attention at the coarsest level (Table 3 (b)). This yields a minor improvement, suggesting that global alignment provides more stable initialization. In contrast, adding the flow attention module alone (Table 3 (c)) results in a more substantial performance gain, highlighting the importance of context-aware fine-level refinements. When both attention modules are employed jointly (Table 3 (d)), we observe further improvements across all metrics, indicating that the two modules complement each other by handling large and small deformations at different scales. Finally, incorporating both bi-directional refinement and attention modules yields the best performance, with a mean TRE of 2.24 mm and the lowest errors across all evaluated percentiles (25%, 50%, and 75%).
## 4 Numerical Results
### Performance of SBL Blind Channel Estimation  
The performance of blind channel estimation of SBL is verified by simulation, the multipath length of three outputs is assumed to be 900 sampling points. We use normalized mean square error (NMSE) to evaluate the performance of SBL channel estimation.  
Figure 2 shows the estimated CIR of the three channels. Figure 3 shows the difference between the original CIR and the CIR estimated by SBL. The first 900 sampling points correspond to channel 1, the 901-1800 sampling points correspond to channel 2, and the 1801-2700 sampling points correspond to channel 3. Figure 4 shows the NMSE of three channels. Refer to Figures 2-4, by using SBL, we can achieve the estimation of underwater acoustic sparse channel accurately with the increase of SNR. In addition, we also simulate the performance of channel estimation under time-varying channel. We set the simulation SNR to  
In this section, we verify the performance of the SBL-based blind channel estimation method. Refer to Figures 2-5, we found that the SBL-based channel estimation method can show good performance. After we obtained the estimated CIR, the channel equalization can be used to eliminate channel interference. However, even the channel interference is eliminated, the likelihood-based method cannot effectively identify MPSK signals in the non-cooperative condition.  
Figure 4: Comparison of normalized mean square error (NMSE) in the three channels.  
Figure 3: Comparison of CIR in estimated CIR and original CIR.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 6 Architecture of WSN vs. Architecture of IDS
### Naive or Flat-Based WSN Architecture for Stand-Alone IDS  
The opposite philosophy to centralized IDS is stand-alone IDS, which is a node-centric architecture. Each node individually uses an IDS detection model to detect any possible attack locally without needing to exchange any information with the adjacent nodes or a central BS unit. This approach does not exhibit latency when detecting node attacks or introducing communication overhead; however, energy consumption at the node level is higher than in a centralized IDS, and the nodes have lower battery life.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 4 Experimental Setup and Performance
### Dataset Descriptions
#### 4.1.3 The Geintra Overhead ToF People Detection dataset (GOTPD)  
The GOTPD is a multimodel dataset containing both depth and infra-red video recordings which were captured using Kinect 2 camera. The camera was positioned above the heads for people detection. The dataset is composed of 48 video sequences each having different illumination conditions, various complex environments, various objects like hats, chairs, caps etc. The dataset frames consist of both single and multiple persons. Figure 14 shows some examples of the GOTPD dataset [59,60].
# Ibuprofen Removal by Aluminum-Modified Activated Carbon (AC@Al) Derived from Coconut Shells
## 1 Introduction
The novelty of this study is the development of a sustainable and low-cost approach for the production of activated carbon derived from waste biomass, such as that of coconut shells, as a competitive adsorbent for the adsorption of Ibuprofen. To the best of our knowledge, there have been no recent studies in the literature using an aluminum-modified activated carbon of similar origin for the removal of this drug. However, a study conducted using bamboo-based activated carbons containing dispersed aluminum as an adsorbent for the removal of ciprofloxacin hydrochloride yielded positive results [14]. Similarly, in another study, the application of activated carbon commercially modified with aluminum chloride to clean anionic dye in wastewater was effective [15]. Adsorbent dosage, pH, initial IBU concentration, contact time and temperature were some of the factors affecting adsorption that were investigated in this study. Moreover, data were evaluated and modelled using kinetic and isothermal adsorption equations. Thermodynamics and adsorption-desorption research was also carried out.
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 2 Materials and Methods
### Immunofluorescence and Images Acquisition  
IF on cell samples was performed following the protocol previously reported [12]. Briefly, SH-SYSY cells were fixed in paraformaldehydethe (PFA) (252549, Merck Life Science, Milan, Italy) 4% in PBS and probed overnight at 4 \({}^{\circ}\)C with anti-Nrf2 (Santa Cruz Biotechnology, Dallas, TX, USA, sc-365949, dilution 1:100). Fibroblasts fixated in PFA were incubated in HCl 2 M for 20 min, then probed overnight with anti-8-OHdG (Santa Cruz Biotechnology, Dallas, TX, USA, sc-66036, dilution 1:100). Subsequently, cells were incubated for 1 h with goat anti-mouse secondary antibody Alexa Fluor 555 (ThermoFisher Scientific, Waltham, MA, USA, A28180). DAPI (D9542, Merck Life Science, Milan, Italy) staining was used to visualise nuclei. Hippocampal slices from male Wistar rats were derived from experiments obtained in our previous work [13]. Information regarding the maintenance, housing and treatment of animal colonies, as well as animal care are already reported in [PERSON] et al., 2014 [13]. Sections were deparaffinized using xylene and subsequently rehydrated at decreasing concentrations of ethanol. The slides were then boiled in 10 mM sodium citrate buffer (Carlo Erba Reagents S.r.l, Milan, Italy, 479487), pH 6.1, and processed for the antigen-retrieval procedure, using a microwave oven at 360 W for 6 min. After cooling, the blocking of non-specific bonds was performed for 1 h in PBS containing 3% BSA and 0.5% Triton-X 100 (Merck Life Science, Milan, Italy, X100). Subsequently, samples were incubated overnight with anti-P-CREB antibody (Santa Cruz Biotechnology, Dallas, TX, USA, sc-7978, 1:50 dilution) diluted in PBS containing 1.5% BSA and 0.25% Triton-X 100. After 24 h, samples were incubated for 1 h with goat anti-rabbit secondary antibody Alexa Fluor 546 (ThermoFisher Scientific, Waltham, MA, USA, A-11035). Finally, coverslips were mounted with Fluoroshield mounting medium (F6182, Merck Life Science, Milan, Italy).  
The preparations were analysed using the Leica TCS SP8 confocal microscope at 40\(\times\) magnification, and the images were acquired with LAS X software (version 3.5.5) (Leica Camera, Wetzlar, Germany) for Windows 10. All acquisition parameters, such as the laser intensity, the gain of the photomultiplier, and the image magnification, were always kept unchanged between the experimental groups considered in this study. Specifically, for 8-OHdG, a wavelength of 562-725 nm was selected, with a gain of 613 and laser excitation at 8%. The same wavelength and laser excitation were set for Nrf and P-CREB, with the gain set to 744 and 648, respectively.
## 2 Related Work
### Agricultural Price-Prediction Methods
[PERSON] et al. [37] used a combined model based on Prophet and SVM to achieve price prediction for ginger. They first used Prophet to decompose the ginger price series. Then, trend and period terms were predicted by Prophet, and the random terms were predicted by SVM. Finally, the ginger price prediction values were recombined by obtained prediction series, and the study showed that the combined model could effectively improve the prediction performance. [PERSON] et al. [38] proposed the use of extreme learning machine (ELM)-based seasonal trend decomposition (STL) to predict the prices of Chinese cabbage, pepper, cucumber, kidney bean, and tomato, and successfully predicted the prices of vegetables with high seasonality. [PERSON] et al. [18] proposed a prediction method for hog prices based on similar sub-series search and support vector regression. They predicted the cyclical component of the hog price series by using the most similar sub-series search method and predicted the trend component by using support vector regression, and finally recombined the predicted series. They succeeded in addressing the pseudo-cycle problem caused by the varying cycle length. [PERSON] et al. [39] proposed a combined model based on STL, an attention mechanism, and LSTM, and verified the superiority of the combined model by predicting five vegetables: cabbage, radish, onion, pepper, and garlic.
## 4 Experimental Results
### Detection Results  
The detection results of the test data are displayed in Figure 8. The figure shows the original image, the GT annotation and the predicted image. The predicted image's similarity with the GT images can be observed. The proposed model detected images with acceptable accuracy and performed quite well. For scratch detection, the model was able to detect weak scratches. Although the GT mask did not provide the weak scratch information, the proposed model can still predict it, and the result is better than the GT mask image.  
Figure 9 shows the incorrectly detected images of all the defects. Various reasons are to blame for false detection, firstly the inter-class similarity and the intra-class diversity of the dataset. Secondly, for some images, annotation files are not apt. The limited number of anchor boxes in RetinaNet is not provided, especially for very small or very long defects. In some images, it is difficult to distinguish between the foreground and the background parts of the image, which made the model confused during detection. One of the main reasons is the less availability of datasets for training. In segmentation detection, the boundary of the scratch defect is not clear or distinct, which leads to unacceptable segmentation. The faint boundaries of the defects cause the detection of the defect incorrectly.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Defects** & **SSD [20]** & **Faster-RCNN [20]** & **YOLO-V2 [20]** & **YOLO-V3 [20]** & **EDDN [20]** & **Xception [21]** & **Proposed Method** \\ \hline Pitted Surface & 0.839 & 0.815 & 0.454 & 0.239 & 0.851 & 0.75 & 0.8504 \\ Inclusion & 0.796 & 0.794 & 0.592 & 0.580 & 0.763 & 0.50 & 0.7117 \\ Patches & 0.839 & 0.853 & 0.774 & 0.772 & 0.863 & 0.67 & 0.8987 \\ Rolled-in Scale & 0.621 & 0.545 & 0.246 & 0.335 & 0.581 & N/A & 0.6794 \\ Crazing & 0.411 & 0.374 & 0.211 & 0.221 & 0.417 & N/A & 0.6928 \\ Scratches & 0.836 & 0.882 & 0.739 & 0.570 & 0.856 & 0.93 & 0.7942 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of average precision between deep learning models and the proposed method.  
Figure 7: Comparison of mean average precision of deep learning models with ours.  
Figure 8: Correct detection of (**a**) crazing (**b**) inclusion (**c**) patches (**d**) pitted surface (**e**) rolled-in scale (**f**) scratches defects, including test image, GT image and predicted image.
## 3 Experiment Details
### Specim Preparation  
#### 3.2.1 Mix Proportions  
Standardized mix design procedures do not, as yet, exist for PC systems produced with binders prepared with aggregates and polymer resins [24]. Hence, in this study, the mix proportions were determined based on a flow of \(110\pm 5\%\), as specified in ASTM C109/C109M-02: testing method for Compressive Strength of Hydraulic Cement Mortar [25]. The optimum polymer content satisfying all  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Filler**} & \multirow{2}{*}{
\begin{tabular}{c} **Density** \\ **(g/cm\({}^{3}\))** \\ \end{tabular} } & \multicolumn{4}{c}{**Particle Size (\(\mu\)m)**} & \multicolumn{4}{c}{**Chemical Composition (\%)**} \\ \cline{3-10}  & & **Mean** & **Median** & **Mode** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **Fe\({}_{2}\)O\({}_{3}\)** & **SO\({}_{3}\)** \\ \hline Fly ash (FA) & 2.20 & 22.29 & 10.75 & 7.77 & 49.1 & 22.7 & 7.15 & 1.33 & 8.31 & 0.99 \\ Blast furnace slag (BFS) & 2.91 & 13.41 & 10.10 & 16.40 & 29.3 & 13.3 & 48.5 & 2.72 & 0.53 & 2.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical and chemical properties of fillers from waste materials.  
Figure 1: Particle size distribution of fillers from virgin materials: (**a**) Ground calcium carbonate and (**b**) silica flour.  
Figure 2: Particle size distribution of fillers from waste materials: (**a**) Fly ash and (**b**) blast furnace slag.  
of the mechanical properties for the PC was between 12.4% and 15.6% [26]. In this study, 13% was applied. The mix proportions, as determined by trial and error, are shown in Table 7.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 3 The Definition and Scope of Space Debris Remediation
### The Deficiencies of the Legal Framework Related to Space Debris Remediation (SDR)
* Another relevant concern of launching states and entities with regard to ADR and OOS missions is security, especially for military satellites. As satellite infrastructure is a strategic asset, it is questionable whether state registries which do not possess enough financial and technological capabilities to remove their objects by themselves would give consent to third parties to undertake SDR. Furthermore, ADR systems entail a capability which is not restricted only to space debris and they could be used, if such an intent is given, for the removal or diversion also of assets. This dual characteristic, both civil and military, makes ADR a sensitive capability and presents a hurdle to reaching agreement between states for its implementation in practice and to raise funding in cooperation for the development of ADR techniques.
## 2 Model Description, Datasets, and Methods  
### Model Description  
The CAS-ESM2 is an Earth system model that was developed by the CAS. The model consists of several components, such as the Atmospheric General Circulation Model (IAP AGCM version 5) [22], the Ocean Model (LICOM version 2) [23], the Common Land Model (CoLM) [24], and the Dynamic Global Vegetation Model (IAP-DGVM) [25, 26]. The CAS-ESM2 was built to simulate complex interactions between physical climate systems and the carbon cycle. Detailed information and the performance of the CAS-ESM2 in CMIP6 standard experiments can be found in [21].  
NPP in CoLM is calculated as the difference between the simulated GPP and Ra. The calculation occurs in each time step at the level of the plant functional types. An increase in CO\({}_{2}\) concentration can stimulate vegetation photosynthesis by elevating the internal leaf CO\({}_{2}\) concentration, and can also enhance water use efficiency by changing leaf stomata. Thus, NPP increases with CO\({}_{2}\) concentration, and is also influenced by climatic conditions such as temperature, water availability, and photosynthetically active radiation (PAR) [24]. The absorbed PAR is calculated following the method of dividing canopy into sunlit and shaded leaves. Both the sunlit and shaded leaf area indices are associated with leaf area index (LAI), which can be treated in two ways in CoLM. One is to update LAI at each daily time step via the phenology module when dynamic global vegetation model is active. In this way, the climate affects LAI, and changes in LAI in turn regulate the climate. The other treatment of LAI is to read it as an input dataset when the dynamic global vegetation model is inactive. The dataset is based on climatological observations at a monthly resolution. In this way, seasonal variations in LAI can regulate the climate, but there is no response of LAI to climate changes. The two experiments (abrupt-4 \(\times\) CO\({}_{2}\) and piControl) of the CAS-ESM2 used in this study treated the LAI following the second way.