esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis and Graphical Presentation  
The graphical representation of data and the nonlinear least-squares fitting procedure at 95.0% significance level (\(p\leq\) 0.05) were carried out in MATLAB 2019a (MathWorks Inc., Natick, MA, USA). The Levenberg-Marquardt algorithm was used for the fitting of experimental data in a series of iterative steps with a convergence criterion of 1.0 \(\times\) 10\({}^{-6}\). The coefficient of determination \(R^{2}\), root mean square error \(RMSE\) and mean absolute  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **Rotational Speed** & **Airflow Velocity** & **Mass Flow Rate** & **Volume Flow Rate** \\ \hline \(f_{s}\)**Hz** & \(\omega\)**, min\({}^{-1}\)** & \(v_{a}\)**, m\(\cdot\)s\({}^{-1}\)** & \(\dot{m}\)**, kg\(\cdot\)h\({}^{-1}\)** & **Q**, m\({}^{3}\cdot\)h\({}^{-1}\)** \\ \hline
10.0 & 578 & 0.011 \(\pm\) 0.000 & 8.57 \(\pm\) 0.36 & 7.00 \(\pm\) 0.29 \\
15.0 & 867 & 0.021 \(\pm\) 0.001 & 20.15 \(\pm\) 0.96 & 16.46 \(\pm\) 0.79 \\
20.0 & 1156 & 0.037 \(\pm\) 0.004 & 32.93 \(\pm\) 3.40 & 26.90 \(\pm\) 2.78 \\
25.0 & 1445 & 0.056 \(\pm\) 0.005 & 47.48 \(\pm\) 4.25 & 38.79 \(\pm\) 3.47 \\
30.0 & 1734 & 0.074 \(\pm\) 0.006 & 61.37 \(\pm\) 4.66 & 50.14 \(\pm\) 3.81 \\
35.0 & 2023 & 0.092 \(\pm\) 0.006 & 74.99 \(\pm\) 5.04 & 61.27 \(\pm\) 4.12 \\
40.0 & 2312 & 0.109 \(\pm\) 0.008 & 88.14 \(\pm\) 6.28 & 72.01 \(\pm\) 5.14 \\
45.0 & 2601 & 0.125 \(\pm\) 0.008 & 100.78 \(\pm\) 6.61 & 82.34 \(\pm\) 5.40 \\
50.0 & 2890 & 0.141 \(\pm\) 0.010 & 113.16 \(\pm\) 8.37 & 92.45 \(\pm\) 6.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operating settings utilized for the airflow resistance experiments.  
percentage error _MAPE_ were employed as statistical criteria to assess the goodness of fit, which were defined as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(\Delta P_{obs}-\Delta P_{pred}\right)^{2}}{ \sum_{i=1}^{n}\left(\Delta P_{obs}-\overline{\Delta P_{obs}}\right)^{2}} \tag{10}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(\Delta P_{obs}-\Delta P_{pred}\right)^{2} }{n}} \tag{11}\]  
\[MAPE=\frac{100}{n}\sum_{i=1}^{n}\left|\frac{\Delta P_{obs}-\Delta P_{pred}}{ \Delta P_{obs}}\right| \tag{12}\]  
where \(\Delta P_{pred}\) (Pa) is the predicted pressure drop, \(\Delta P_{obs}\) (Pa) is the observed pressure drop ascertained from experiments and \(n\) (-) is the number of observations. The sensitivity analysis using Monte Carlo simulations in MATLAB/Simulink 2019a (MathWorks Inc., Natick, MA, USA) performed to evaluate the influence of modeling parameters on pressure drop. Furthermore, the CAD design of the experimental test bench was carried out in SOLIDWORKS 2019 (Dassault Systemes, Waltham, MA, USA).
# Design of an Open Remote Electrocardiogram (ECG) Service
###### Abstract  
Currently, the deployment of services for real-time delivery of an electrocardiogram to a remote site has a cost that prevents its widespread diffusion, which would contribute to saving lives with prevention, assistance and rescue efficiency. To fill this gap, we introduce the design of a remote electrocardiogram service that privileges open, low-cost options. The architecture is based on the HyperText Transfer Protocol (HTTP) and uses commercial off-the-shelf devices to implement the sensor on the patient's side. The doctor uses a laptop browser to display the tracing, and a cloud computing instance connects the two using WebSockets. A prototype is built to evaluate overall performance, the power consumption of the patient's side device, and the quality of rendering on doctor's browser. The patient's sensor prototype device is portable, and its power consumption is below 1 Watt, thus allowing a daylong autonomy when operated on batteries. Its cost is below 50$, and the required hardware is commercially available. The whole design is ready for on-field evaluation, and it is available in a public repository.  
n ont-diagnostic ECG; WebSocket; Cloud PaaS; ECG acquisition; Arduino; ESP8266
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 4 IPN HandS Dataset  
The IPN Hand dataset [5] is a video-based benchmark for hand gesture recognition (HGR), consisting of continuous RGB recordings of 50 subjects performing a predefined set of gestures. It includes 13 gesture classes specifically designed for touchless screen interaction, covering both static poses and dynamic movements such as pointing, clicking, and zooming. Unlike isolated gesture datasets, IPN Hand features long, uninterrupted video sequences, each containing multiple gesture instances embedded in natural motion. Recordings were captured across approximately thirty diverse indoor scenes, exhibiting significant variation in lighting conditions, camera viewpoints, and background complexity, including both static and dynamic environments. The dataset was explicitly designed with two challenging real-world scenarios: one in which continuous gestures are executed without transition states between them, and another where subjects perform natural, non-gesture hand movements between gestures. These conditions make IPN Hand particularly demanding and realistic for continuous gesture recognition. In total, the dataset comprises 4218 labeled gesture instances and over 800,000 frames, with manually annotated temporal boundaries. Due to its scale and complexity, IPN Hand remains an active benchmark widely used in recent research on HGR [10; 11; 18; 19; 20].  
Building on the original IPN Hand dataset, we introduce IPN HandS, an extended version that incorporates frame-level hand skeleton annotations. These annotations were designed to support skeleton-based HGR by adding precise spatial representations that were not available in the original version. Each frame of gesture instances in the dataset now consistently includes one or two annotated hand skeletons, defined by 21 landmarks per hand, making it suitable for training sequence-based gesture recognition models. In addition to the spatial annotations, we implemented two key refinements to improve  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Tool** & **Total Time (min)** & **Per Video** & **Per Instance** & **Per Frame (s)** \\ \hline VIA-style Tool & 1054.2 & 70.3 & 2.8 & 1.1 \\ IPN HandS (ours) & 397.4 & 26.5 & 1.1 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 3: Average annotation time per video, gesture instance, and frame using a conventional tool (VIA) versus our IPN HandS tool.  
consistency and classification accuracy. These include a manual revision of the temporal boundaries across all gesture instances and restructuring of the gesture taxonomy to better distinguish overlapping motion patterns. IPN HandS thus enhances the temporal richness of the original dataset with dense spatial supervision, offering a comprehensive benchmark for evaluating gesture recognition under realistic and dynamic conditions.
# Review of Weed Detection Methods Based on Computer Vision
## 4 Weed Detection and Identification Methods Based on Deep Learning
### Weed Detection and Identification Methods Based on FCNs  
FCNs are algorithms that automatically learn features and implement forward and reverse processes in an end-to-end manner. In recent years, FCNs have made great achievements in computer vision [136] and remote sensing applications [137; 138]. [PERSON] et al. [139] proposed a method to detect weeds in color images automatically by using an FCN under severe occlusion. [PERSON] et al. [140] captured a high-resolution UAV image over a rice field and adopted an FCN for pixel-level classification. [PERSON] et al. [25] proposed a SegNet semantic segmentation method based on FCNs for the problem of weed detection in rice fields. Compared with the classic FCN model and U-Net model, the proposed method exhibited significantly higher accuracy and could effectively classify the pixels of rice seedlings, background, and weeds in rice field images. To control weeds in the early stages of growth, [PERSON] et al. [141] proposed a segmentation method based on FCNs for high-resolution remote sensing images. On the basis of the VGG16 CNN model, a pretrained FCN was used to fine-tune the object data. This method could effectively improve the segmentation effect. FCNs were used to solve semantic-level image segmentation and pixel-level classification of images, which further developed the problem of weed segmentation. However, this method only classified each pixel without considering the relationship among pixels.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Lack of Sink-to-Sink Cooperation  
Multiple DODAGs in the network are maintained by each single sink node. Nodes at the end of sub DODAG are called border nodes. These nodes receive DIO messages from their own DODAG and neighboring DODAG. In RPL, because of the neighboring sink high rank or larger distance, these DIO messages from neighboring DODAG are not forwarded by the border nodes in their own DODAG [56]. This creates a lack of coordination among sinks.
## 7 Conclusions and Future Works  
To solve the multiobjective DWTA problem effectively, a novel MOPSO algorithm (IMOPSO) is proposed in this paper. The algorithm constructs different learning strategies for dominated and non-dominated solutions in the current population, which makes the learning pertinence of particles clearer. IMOPSO adopts a search strategy based on SBX and PM. The exchange of information among particles is enhanced by the SBX operator of \(p_{best}\) in external archive, and the PM operation is performed on newly generated \(g_{best}\), thereby generating new global optimal particles. SBX and PM address the drawback that MOPSO algorithm is easy to fall into local optimal solution. The algorithm also uses a dynamic external archive maintenance strategy, which improves the diversity of non-dominated solutions. Two sets of experiments based on benchmark functions and multiobjective DWTA problem are used to verify the performance of IMOPSO. In order to illustrate the effectiveness of IMOPSO in solving MOPs, we compared three state-of-the-art multiobjective optimization algorithms: MOPSO, NSGA-II and MOEA/D. The experimental results show that the non-dominated solution obtained by the proposed IMOPSO algorithm is superior to other algorithms in convergence and distribution. Furthermore, IMOPSO has advantages in solving large-scale mixed integer programming problems.  
The benchmark functions and multiobjective DWTA model selected in this paper only have two objectives. In reality, there are often more optimization objectives in optimization problems. In the future, we will use more types of benchmark functions which include more objectives to verify the ability of the IMOPSO algorithm. In addition, IMOPSO will be used to solve more multiobjective optimization problem models. Moreover, we will compare IMOPSO with more intelligent algorithms. At present, there are many solvers for multiobjective optimization models, and we will use other solvers (e.g., Gurobi and CPLEX) to solve DWTA problems in future research.  
Conceptualization, L.K. and J.W.; methodology, L.K.; software, L.K.; validation, L.K.; formal analysis, L.K.; investigation, L.K. and P.Z.; resources, J.W.; data curation, L.K.; writing--original draft preparation, L.K.; writing--review and editing, L.K., J.W. and P.Z.; visualization, J.W.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
We thank the Defense Industrial Technology Development Program for their funding.  
The authors declare no conflict of interest.
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 2 Methodology
### Wavelet Phase Inversion Network  
Figure 4 shows the wavelet phase inversion network structure, which includes four convolution layers and two fully connected layers. For the first four convolution layers (indicated by a gray arrow and three orange arrows), the symbols below the colored blocks represent the length of the feature vectors extracted from the seismic data, where the first symbol \(L\) represents the length of the seismic trace. \(\lfloor\ \rfloor\) means rounding the length down to the nearest integer. The numbers above the colored blocks represent the number of channels of the feature vectors. For the last two fully connected layers, the numbers below the colored dots mean the number of hidden neurons. The input of the network consists of all traces of seismic data, which are first passed through a convolution layer with a kernel-size of \(29\times 1\) and a stride of 14 (indicated by the gray arrow). The subsequent convolution layers illustrated by the orange arrows in Figure 4 are characterized by a kernel size of \(4\times 1\) and a stride of 2. The purple arrow represents flattening the feature maps into a one-dimensional vector of length \(L_{\text{sequence}}\times n_{\text{channel}}\) (set as \(L/8\times 64\) in this paper). The subsequent fully connected layer further extracts this vector into a single value, which represents the phase of the wavelet \(\theta_{\text{pred}}\).
# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 4 Algorithm Design
### DGQL (Double-Greedy Reinforcement Learning) Algorithm Based on Information Representation
When the communication UAV chooses the action, a random number \(\mathit{md}^{\prime}\) within the range of [0, 1] is generated. Then it is compared with \(\xi_{1},\xi_{2}\) to determine the action within three action subsets. If \(0<\mathit{rnd}^{\prime}<\xi_{1}\), the UAV selects an action in the action subset \(a_{1}\) which contains one motion step. If \(\xi_{1}<\mathit{rnd}^{\prime}<\xi_{2}\), the UAV selects within the action subset \(a_{2}\) containing two motion steps. If \(\xi_{2}<\mathit{rnd}^{\prime}<1\), the UAV will select the action in action subset \(a_{3}\) of more than two motion steps. In this way, the probability of choosing one step, two steps and more than two steps for the communication UAV is \(\xi_{1}\), \(\xi_{2}-\xi_{1}\) and \(1-\xi_{2}\) respectively. The threshold values \(\xi_{1},\xi_{2}\) may be set according to the specific UAV setups and task requirements. When \(\xi_{2}\) approaches 1, the motion of the communication UAV is strictly limited to two steps or less. This process is shown in Figure 6 and the pseudo-code of the DGQL is shown in Algorithm 1. ```
Initialize\(r\) table set \(r(i,j)\) for\((i,j)\) in \(M\) radar policies and \(N\) jamming policies do  Generate \(\eta_{1}\), \(\eta_{2}\) via network topology if mode 1 then \(r(i,j)=\frac{\eta_{1}^{2}+\eta_{2}^{2}}{4}\) else if mode 2 then \(r(i,j)=\frac{4(\eta_{1}+\eta_{2})}{\theta_{0.5}^{2}}\) endif endfor  Set \(Q(s,a)\) arbitrarily for each episode do  Initialize\(s\) for each step of episode do  Generate random number \(rnd\) and \(rnd^{\prime}\) if\(rnd\)\(<\)\(\epsilon\)then  Extract action subset \(a_{1},a_{2},a_{3}\) from \(a\)  Generate random number \(rnd^{\prime}\) if\(0\leq rnd^{\prime}<\xi_{1}\)then  Choose \(a\) from \((s,a_{1})\) using policy derived from \(Q\) else if\(\xi_{1}<rnd^{\prime}<\xi_{2}\)then  Choose \(a\) from \((s,a_{2})\) using policy derived from \(Q\) else:  Choose \(a\) from \((s,a_{3})\) using policy derived from \(Q\) endif else  Choose \(a\) randomly from \(s\) endif  Take action \(a\), observe \(r\), \(s^{\prime}\) \(Q(s,a)\gets Q(s,a)+\alpha[r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{\prime}) -Q(s,a)]\) \(s\gets s^{\prime}\) until \(s\) is terminal endfor endfor
```  
**Algorithm 1**Double-Greedy Reinforcement Learning Algorithm
## 4 Systematic Review of the Research Pillars
### Sustainability in Urban Development
#### 4.3.3 Blockchain and Cybersecurity in Urban Digital Ecosystems  
By taking full advantage of its special features, like decentralization, data integrity, and safe transaction capabilities, blockchain technology significantly improves cybersecurity in urban digital ecosystems. These characteristics can successfully reduce the dangers of data breaches and illegal access, which are common in complex urban settings. The main features of how blockchain can improve cybersecurity are described in the sections that follow.  
The first feature is Data Integrity and Decentralization by using an open database, the blockchain reduces single points of failure and makes it more difficult for hackers to compromise the system [86]. The second is immutable records since sensitive data integrity is guaranteed by the blockchain's inability to change or remove data after it has been recorded there [86]. The third is improved identity by verification and authentication and secure identification management, especially in urban metaverse applications, blockchain can enhance user identification verification procedures, lowering the danger of identity theft and impersonation [87]. The fourth is smart contracts, where only authorized parties can access or alter data, which can automate and safeguard transactions [88]. The fifth feature is employing it in Intelligent Urban Areas, and Blockchain can improve the security of interconnected systems and data transfers by protecting IoT devices in smart cities [86]. The sixth is data privacy, in urban digital ecosystems, methods such as Decentralized Privacy-Preserving Machine Learning can safeguard user data while preserving functionality [87].
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 2 Operation Mechanism and Design Principles of Edge Couplers  
### General Operation Mechanism of Edge Couplers  
Fiber-to-chip edge couplers have been widely investigated by researchers for decades, and the inverse taper structure plays an indispensable role in edge coupling [33]. Corresponding to the direction of light propagation, inverse taper refers to a tapering waveguide with a gradual increase in width along the mode propagation direction, which means the narrow end of the taper is close to fiber, while the wide end is connected with photonic waveguides. Modal distribution is determined by both the mode order and the waveguide structure. For a specific mode, only waveguides with a certain cross-section area can support the entire mode [34]. The fundamental transverse electric (TE) mode is mostly applied in optical communication applications to transmit information, and most of the researches on silicon photonic edge couplers studied the transverse electric mode. The most commonly used size of silicon photonic waveguide that can support the fundamental TE mode propagation at negligible loss is about 200 nanometers high and about 500 nanometers wide [35]. A waveguide with a too small cross-section area cannot support an entire fundamental mode, and thus the mode will partially distribute in the outer region of the waveguide. In contrast, a waveguide with a too large cross-section area will easily excite undesirable higher-order modes [36; 37; 38]. The taper is just appropriate for mode conversion, since a gradually varying cross-section area supports mode transformation and mode size variation. On this basis, the narrow end of the inverse taper has a smaller cross-section area than the expected modal size, so it is unable to confine the incident mode completely, and a considerable percentage of the electromagnetic field distributes surrounding the taper tip. As the taper width becomes larger, it can support the entire mode and confine the electromagnetic field inside the taper integrally. Overall, an edge coupler based on an inverse taper whose narrow tip is aligned to the fiber core can convert a large mode incident from the optical fiber to the compressed guided mode in photonic waveguides.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 6 Conclusions  
This paper aims to monitor the verticality and analyze the horizontal motion of the W Tower in Guangzhou, China via GNSS technology, from which the following conclusions were drawn:  
* The data processing strategy for calculating the monitoring point's coordinate time series and deflections of the vertical are introduced. The 24-h observation result shows that the displacement variations of the W Tower are within \(\pm\)1.0 cm in the horizontal direction at most epochs, so it has less impact on the vertical reference transmission of the W Tower.
* Comparison shows that deflections of the vertical calculated by the Earth gravity field model (e.g., EGM2008) were also very close to that calculated by the GZGEOID model. The high-precision deflection of the vertical components play an important role in matching the results of the GNSS measurement and laser plumbing measurement. When considering such factors as the wind, the operation of a tower crane, and the installation of a curtain wall, one may conclude that the verticality of the W Tower is within the range of 2.0 cm after correction, which meets the design requirement of \(\pm\)3.0 cm according to the Technical Specification for Concrete Structures of Tall Buildings in China.  
[PERSON] and [PERSON] did the measurements, data processing, and contributed to the writing. [PERSON] and [PERSON] contributed to the revisions and discussions of the contents.  
This research was funded by National Natural Science Foundation of China grant number NSFC 41674006, 41731069.  
We acknowledge the valuable comments by Professor [PERSON] and three other anonymous reviewers.  
The authors declare no conflict of interest.