esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 4 Wireless Communication System Using Chaotic Filter
### Transmitter and Receiver  
In modern digital communication systems, binary information symbols are usually mapped into a predefined constellation using constellation mapping, such as multi-phase shift keying (MPSK) and multi-stage quadrature amplitude modulation (MQAM). In this  
Figure 5: Lyapunov exponents of dynamical system Equations (10) and (11).  
paper, binary-phase shift keying (BPSK) is taken as an example, and binary information is mapped to obtain bipolar symbols \(s_{m}\in\{+1,-1\}\). After being oversampled, mapped symbols are then encoded by an RRC-based chaotic pulse-shaping filter \(p(t)\) to generate the baseband waveform \(x(t)\). Different roll-off factors of the shaping filter can be selected according to different needs. The encoding chaotic baseband wireless waveform at the transmitter with \(\beta=0.5\) and the corresponding bit information are shown in Figure 6 with a blue solid line and red dotted line, respectively.  
The received signal passing through the wireless channel can be written as:  
\[r(t)=h(t)*x(t)+w(t), \tag{14}\]  
where '\(*\)' denotes convolution, \(w(t)\) is AWGN with \(w\sim\mathcal{CN}\left(0,\sigma^{2}\right)\), and \(\sigma^{2}=N_{0}\). \(h(t)\) is the impulse response of the wireless channel, which can be modeled as:  
\[h(t)=\sum_{l=0}^{L-1}\alpha_{l}\delta(t-\tau_{l}), \tag{15}\]  
where \(\alpha_{l}\) and \(\tau_{l}\) are the attenuation and propagation delay corresponding to path \(l\), respectively, and \(\delta(\cdot)\) is the Dirac delta function. Assume that delay \((l=0,1,\ldots,L-1)\) satisfies \(0=\tau_{0}<\tau_{1}<\ldots<\tau_{L-1}\), and channel fading \(\alpha_{l}\) is modeled as a negative exponential decay \(\alpha_{l}=e^{-\gamma\tau_{l}}\), where \(\gamma\) is the damping coefficient. Equation (15) is a statistical average channel model [25] for a practical wireless communication channel, and it is considered for the theoretical performance analysis.  
At the receiver, the matched filter \(g(t)\) expressed in Equation (9) is used to maximize SNR. The filter output \(y(t)\) is:  
\[\begin{split} y(t)&=r(t)*g(t)\\ &=s(t)*p(t)*h(t)*g(t)+W(t),\end{split} \tag{16}\]  
where \(W(t)=w(t)*g(t)\) is the filtered noise. If \(w(t)\) is AWGN with a zero mean, then \(W(t)\) is also Gaussian noise with a zero mean [26].  
After sampling \(y(t)\) by \(n=\lfloor t\rfloor\), a symbol decision algorithm is implemented as:  
\[s(n)=\left\{\begin{array}{ll}1,&\text{if }(y(n)\geq\theta(n)),\\ -1,&\text{if }(y(n)<\theta(n)),\end{array}\right. \tag{17}\]  
where \(\theta(n)\) is the threshold to carry out the symbol decision process. The threshold calculation algorithm for the proposed method is described as follows.  
Figure 6: Encoding chaotic waveform and corresponding bit information.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Networking Protocol and Hardware Selection
#### 2.1.2 Communication Protocol  
With multiple devices sampling data and streaming it back to a computer, there is an unavoidable bottleneck at the receiving port, and a protocol that minimises the effect of the bottleneck is required. The user datagram protocol (UDP) and transmission control protocol (TCP) are the most commonly used protocols with many other protocols being based on them [17]. Table 2 comparing the two protocols shows that the UDP packet header is less than half the size of TCP's, which means that UDP packets can be transmitted with less overhead. Both UDP and TCP perform error checking that allows them to detect and discard damaged packets, but UDP generally has higher speed. If a packet is dropped, TCP will attempt to recover the packet by requesting that the original sender attempts  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Max Nodes** & **Data Rate (Mb/s)** & **Range (m)** \\ \hline Requirements & \(\geq\)10 & \(\geq\)4.064 & \(>\)10 \\ \hline Wi-Fi & 255 & 6 & 100 \\ \hline Zigbee & 255 & 0.244 & 50 \\ \hline Bluetooth & 7 & 1 & 10 \\ \hline BLE & 7 & 1 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of wireless networking protocols [14; 16]; data rates are theoretical maximums.  
to send the packet again. This can significantly slow down data transfer via TCP, and hence TCP is marked as low speed in Table 2. UDP does not attempt recovery, the receiving device is unaware of any unreceived packets. In this application large amounts of data are being collected and the occasional loss of a single data point has no significance, UDP is found to be more suitable, especially because slowing down of TCP due to attempting to recover lost packets becomes worse as the number of the sensor nodes in the network increases.
# Systematic Review on the Use of CCPM in Project Management: Empirical Applications and Trends
## 5 Final Considerations  
This study aimed to critically analyze the theoretical and practical contributions of the recent literature on the Critical Chain Project Management (CCPM) method in multi-project environments through a systematic review. Specifically, it sought to identify methodological trends, application gaps, and potential directions for future research. Based on the results obtained, it can be stated that all proposed objectives were fully achieved.  
The analysis of 62 publications revealed significant advances in CCPM research over the past decade, particularly in terms of methodological refinement and interdisciplinary integration. Modeling and simulation-based approaches were predominant, reflecting a strong technical orientation in recent studies. However, the scarcity of empirical investigations and the lack of detailed contextual data--such as organizational characteristics and project typologies--represent important gaps that limit the generalization and practical applicability of the proposed models.  
The study also identified a growing trend toward integrating CCPM with emerging methodologies and technologies, such as BIM, Lean Construction, agile frameworks, predictive tools, and real-time digital monitoring platforms. These hybrid approaches demonstrate the method's adaptability and reinforce its relevance in contexts marked by complexity, resource constraints, and high uncertainty.  
Another important finding concerns the low representation of studies applied to Engineer-to-Order (ETO) systems and service-oriented organizations. These contexts--characterized by high product and process customization, variable demand, and constant project initiation--are particularly well-suited to CCPM principles. However, the lack of empirical evidence in these environments points to a promising agenda for future research.  
Finally, this study contributes not only by systematizing and critically evaluating the existing body of knowledge but also by proposing a research agenda focused on empirical validation, sectoral diversification, and methodological integration. The results reinforce both the theoretical robustness and the practical potential of CCPM, while also highlighting the need for deeper, context-sensitive investigations that promote its adoption in real-world multi-project management environments.  
Conceptualization, A.d.O.M. and F.E.V.d.A.; methodology, F.E.V.d.A., V.G.B., and A.d.O.M.; formal analysis, C.J.R.A., D.O.d.S., and A.d.O.M.; writing--original draft preparation, V.G.B., C.J.R.A., and D.O.d.S.; writing--review and editing, F.E.V.d.A., V.G.B., and A.d.O.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
## 2 Related Works
### Throughput Optimization Strategy Based on New Fog Architecture
The Lagrange relaxation algorithm was utilized to obtain the optimal solution of spectrum resource allocation for enhancing the delay, throughput, and packet loss ratio performances.
## 4 Discussion  
In reality, population density is mostly available in large areas such as counties in China (2017) and suburbs in Australia. On the other hand, imperviousness can be accurately derived at the corresponding scale with population density (large area) by several advanced sub-pixel classification techniques such as linear spectral mixture analysis (LSMA) or neural network ([PERSON] et al., 2016; Landsat, 2017) from high to medium resolution satellite images, e.g., Landsat (2017). However, this imperviousness represents total imperviousness involving residential imperviousness and non-residential imperviousness. Residential imperviousness simply refers to residential buildings where people live. Non-residential imperviousness encompasses industrial areas, commercial buildings, or transportation infrastructures. Both residential and non-residential impervious areas have different characteristics in terms of their relationship to population size. For example, for a high non-residential suburb (e.g., industrial suburb), where population density is often low, total imperviousness estimation is seriously underestimated based on population density alone and vice versa.  
This indicates the importance of estimating residential and non-residential imperviousness separately by taking land use types into consideration. However, satellite-derived imperviousness cannot directly be separated without incorporating land use data ([PERSON] et al., 2017). Due to the lack of land use data, underestimation and overestimation of total impervious  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PDA**} & **Population Density** & **Non-Residential** & **Residential Imperviousness** & **Total Imperviousness** \\  & **(Persons/km\({}^{2}\))** & **Imperviousness (\%)** & **(\%) and 95\% Prediction** & **Intervals** & **(\%) (Method 2)** \\ \hline Ripley Valley & 2609 & 3.1 & 28.3 (\(\pm\)3.7) & 31.4 (27.7–35.1) \\ Greater Flagstone & 1667 & 2 & 18.2 (\(\pm\)3.6) & 20.2 (16.6–23.8) \\ Yarrabilba & 2273 & 2.7 & 24.7 (\(\pm\)3.6) & 27.4 (23.8–31) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predicted total, residential, and non-residential imperviousness for PDAs using the method recommended by this study.  
ness is common for non-residential regions where high total imperviousness exists and for residential areas where low total imperviousness exists [52]. Therefore, considering land use data is of great importance to develop separate residential and non-residential imperviousness estimation models.  
In an urbanized region, another important factor resulting in different patterns of total imperviousness is building height. For highly populated areas, suburbs with multi-story (tall) buildings, overestimation of population density is common [52; 53]. To address this problem, Lidar data which provide data on height of buildings (structure of buildings) could be used in the future studies to test whether or not relationships between population density and residential imperviousness could be improved at suburb scale.
# Design and Optimization of Origami-Inspired Orthopyramid-Like Core Panel for Load Damping
## 4 Mechanical Testing Procedure  
### Compression Test  
In order to estimate the fabricated panel performance under compressive loads, the compression test is conducted here. A compression-testing general machine WDW-100 (Bairoe, Shanghai, China) was employed to test the origami-inspired panel samples. For the load to be uniformly applied to the core panel structure, two thick steel plates were placed on both sides of the panel. A 1.5 mm/min stroke was used in the test, consistent with ASTM (American Society of Testing Materials) D1621/10 standards. Five specimens were tested for each origami structure.
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 2 Cell Manipulation Based on Holographic Optical Tweezers
### Dynamic Model
The cell with a larger radius will have a larger value of \(r_{0}\). According to the ratio of \(r_{0}\) to the cell radius \(r\), the value of \(r_{0}\) can be roughly obtained. This practice will cause a small deviation in the value of \(r_{0}\). However, because the slight deviation from \(r_{0}\) will only change the magnitude of the optical trap force slightly, it will not cause the cell to escape. Therefore, the system remains stable. The viscous resistance can be calculated by [PERSON] formula [44]:  
\[\textbf{F}_{d}=6\pi\eta r\textbf{X} \tag{4}\]  
where \(\eta\) represents the viscosity of the mucus and \(r\) represents the radius of the trapped cell. It is very important to ensure that cells do not escape the control of holographic optical tweezers. Therefore, it is necessary to constrain the spatial position of cells and optical traps. The general practice is to limit the distance between them to \(r_{0}\). The constraint is \(||\mathbf{C}-\mathbf{X}||\leq r_{0}\). According to (2)-(4), and the constraints, the dynamic model of cell movement manipulated by holographic optical tweezers can be obtained:  
\[\frac{\beta}{k}\dot{\mathbf{X}}=\mathbf{C}-\mathbf{X},||\mathbf{C}-\mathbf{X}||\leq r_{0} \tag{5}\]  
where \(\beta=6\pi\eta r\) represents the viscous resistance coefficient. \(||\mathbf{C}-\mathbf{X}||\) represents the distance between the optical trap center and the cell center. \(r_{0}\). represents the distance between the optical trap center and the cell center when the optical trap force \(\mathbf{F_{t}}\) reaches its maximum.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 2 Foundations and Evolution
### Progression of Artificial Intelligence (AI) in Robotic Applications  
Artificial Intelligence (AI) has expanded the functional capabilities of robotic systems from scripted task execution to dynamic, goal-driven behaviors [7; 26]. Early AI integration involved rule-based systems and expert-driven programming. The introduction of machine learning (ML), particularly supervised and reinforcement learning approaches, enabled robots to generalize from data rather than relying solely on pre-specified rules [27; 28]. More recently, deep learning (DL) architectures, such as recurrent neural networks (RNNs) and transformers, have empowered robots to perform complex perception and decision-making tasks in highly dynamic environments [29; 30; 31; 32]. Within collaborative robotics, AI facilitates context-aware action planning, predictive human intent recognition, and proactive collaboration strategies [7; 24; 33; 34]. As robots increasingly learn from interaction histories, multimodal sensory data, and demonstration-based training, the boundaries of adaptive autonomy continue to expand.
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### 4.1.1. Peatland Mapping  
Satellite-based remote sensing has significantly advanced peatland mapping [11]. Several studies have developed methodologies to classify wetlands from multiple satellite remote-sensing data sources using advanced classification algorithms [39; 65; 66]. These methodologies effectively discriminate between different wetland classes, such as bog, fen, marsh, swamp, and shallow water [39; 66], demonstrating the capability of high-resolution satellite imagery to identify and delineate different peatland types.  
Multispectral sensors have allowed for detecting subtle variations in vegetation and soil properties [40; 67]. However, despite these advancements, challenges exist, particularly in areas with dense vegetation cover or mixed land use, such as peatlands [68]. Additionally, the accuracy of peatland classification algorithms can be affected by the presence of shadows, cloud cover, and atmospheric interference in the case of utilising passive-based remotely sensed data [65]; thus, utilising Synthetic Aperture Radar (SAR) data is helpful for overcoming the challenges of cloud cover and shadows. Various SAR sensors are operational, offering multiple options between and within a single sensor. When selecting a SAR sensor for peatland mapping, considerations such as wavelength (e.g., L-band, X-band, and C-band), polarization options (with fully polarimetric sensors providing more information than dual- or single- polarisation sensors), and spatial resolution are crucial. L-band has demonstrated the ability to provide distinctive information about peatland surfaces and is relatively insensitive to vegetation variability [69]. Nonetheless, given the intricate nature of peatland ecosystems, acquiring sub-metre resolution data is imperative for accurately mapping the sporadic patterns of peatland vegetation. It is important to note that such high-resolution data acquisition is associated with associated costs [28].
## 4 Discussion and Analysis  
### Mechanical Analysis  
Based on previous research and the experiments conducted in this study, the formation mechanism of a complex fracture network in HDR during cyclic hydraulic fracturing can be summarized below (Figure 13).  
Thermal stress-induced fracture formation is shown in Figure 13a. HDR is subjected to thermal stress, leading to the volume expansion of internal mineral particles. This expansion generates additional stress, damages the internal structure, and induces the formation of thermal stress cracks. With the \"injection-stop\" or \"high-low-displacement cyclic pumping\" of water, there will be cyclic stress on the rock. Coupled thermal and cyclic stress induces crack network formation (Figure 13b). The stress generated under thermal-mechanical coupling exceeds that from thermal or mechanical stress alone, making the rock more susceptible to fatigue damage. As a result, cracks near the wellbore gradually turn, branch, and eventually evolve into a fracture network.  
Cyclic hydraulic fracturing primarily relies on thermal-stress coupling to induce fatigue damage in reservoir rock. This process initiates, redirects, and bifurcates fractures, ultimately forming a connected fracture network around the wellbore that enables efficient  
Figure 13: Schematic diagram illustrating the formation mechanism of a complex fracture network in HDR using cyclic hydraulic fracturing: (**a**) Thermal stress-induced fracture formation. (**b**) Coupled thermal and cyclic stress induces crack network formation.  
heat extraction. Overall, damage from cyclic loading and unloading tests is more complex than that from uniaxial compression tests, suggesting that cyclic hydraulic fracturing is more effective in fracture generation than conventional methods and offers broader application potential.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Comparison with State of the Art
#### 4.3.1 Experiments on VeRi-UAV  
On the VeRi-UAV dataset, the methods of comparison include [1; 6; 22; 30; 31; 32; 37; 45; 46; 47; 48]. Table 2 compares our proposed DMANet to other methods in the VeRi-UAV dataset. View decision-based compound match learning (VDCML) [37] is a method for vehicle ReID that learns the similarity between different views. Compared with DMANet, where Rank-1 and mAP are 29.7% and 32.4% higher than VDCML (ResNet50), although VDCML can extract effective features for vehicle ReID to a certain extent, it is more dependent on pre-defined rules or weight allocation, lacking automatic learning and adaptability. In contrast, the attention mechanism introduced by DMANet is more adaptive and expressive. DMANet achieves better results than VDCML in vehicle ReID tasks. To assess the importance of a feature based on all its components, contrastive attention net (CANet) [1] practices cooperation among the part features obtained by reweighting the part feature. Compared with DMANet, mAP is 9.1% higher than CAM. The reason is that DMANet can pay attention to the feature information of different scales to express the appearance characteristics of the vehicle more comprehensively and can capture a richer feature representation. In contrast, CAM is more dependent on the attention of limited-scale features and cannot fully use multi-scale feature information. Table 2 shows the comparison results with the methods mentioned above in detail. DMANet achieved excellent results. Compared with the baseline, DMANet shows an improvement of 7.45%, 1.17%, 1.52%, and 0.53% for different metrics of mAP, Rank-1, Rank-5, and Rank-10. In the end, we can come to the conclusion that DMANet comprehensively utilizes features of different levels and granularity to capture richer feature representations, achieving higher accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline Siamese-Visual [39] & 25.98 & 41.98 & 50.61 \\ VGG CNN M [40] & 28.34 & 39.27 & 43.48 \\ SCAN [35] & 40.49 & 53.74 & 60.55 \\ GoogleLeNet [41] & 45.23 & 64.88 & 70.38 \\ RAM [42] & 45.26 & 59.35 & 64.07 \\ CN-Nets [43] & 55.91 & 76.54 & 82.46 \\ TCRL [22] & 56.44 & 77.21 & 82.98 \\ EMRN [32] & 63.47 & 79.84 & 84.66 \\ CANet [1] & 63.68 & 80.73 & 85.40 \\ HPGN [30] & 64.18 & 82.19 & 85.88 \\ HSGNet [6] & 64.22 & 85.31 & 86.36 \\ AM + WTL [44] & 69.11 & 87.23 & 91.64 \\ GiT [31] & 72.48 & 85.83 & 89.61 \\ \hline Baseline & 70.94 & 84.56 & 88.22 \\
**Ours** & **76.63** & **88.54** & **91.75** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different methods on UAV-VeID (%).
## 5 Experimental Results
### RQ4: How Does the Proposed Technique Perform in Open Source Software (OSS)?  
In order to reinforce the rationale for our statement in RQ1, experiments were performed on 6 OSS datasets. We conducted additional experiments on the performance of our approach in open source projects with different characteristics and class imbalance ratios compared to the maritime domain. Table 9 shows the results of the experiment. The results are compared to the PD performed by [PERSON] et al. [7], and show that all PDs and balances were improved in all datasets with significant difference beyond large effect size. Note that we considered balance, increasing PD without worsening PF, in JIT-SDP. The best performance values are indicated in bold face and significant differences beyond medium size were underlined in the table.  
Figure 8: Correlation between class_weight and balance.  
Figure 9: Correlation between min_impurity_decrease and balance.
## 4 Case Study
### Results and Discussion
#### 4.5.3 Comparison with Other Benchmark Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Arima & 0.5955 & 0.4320 & 0.6259 \\ PACF-ELM & 0.5630 & 0.4085 & 0.6888 \\ HST-ELM & 0.4400 & 0.2862 & 0.8047 \\ EMD-PACF-ELM & 0.3341 & 0.2686 & 0.8398 \\ VMD-SE-PACF-ELM & 0.2996 & 0.2343 & 0.8722 \\ VMD-SE-ER-PACF-ELM & 0.2755 & 0.2087 & 0.8912 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation indicators of different prediction models. Figure 17: Comparison of prediction results via different models. based on the decomposition algorithm is generally better than that of the model not optimized by this method, which indicates that the use of the decomposition algorithm to pre-process original sequences is an efficient method to optimize the prediction of DD. Among the different decomposition algorithms, VMD outperforms EMD and the consideration of the ER enables prediction results closer to the true values, which are consistent with the conclusions in Figure 17 and Table 6.
## 3 Experiment Results  
The time-domain characteristics of the 167 Mbps PAM signal, both with and without DeepESN processing, are depicted in Figure 2a,b. These eye diagrams illustrate the temporal properties of the PAM signal and effectively highlight the improvements brought about by DeepESN processing. Additionally, Figure 2c presents the received waveform of the 100 Mbps OFDM signal. The labels \"RAW\", \"DESN\", and \"Tx\" correspond to the raw signal (no signal processing), signal post DeepESN processing, and transmitted signal (reference signal), respectively.  
Figure 2: \(Cont.\)