esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Challenges and Limitations
### Applicability and Interpretability in Critical Communications  
The applicability of AI models is especially significant in critical communications, where detecting anomalies and managing traffic can have far-reaching implications. While AI models such as deep learning and reinforcement learning excel in accuracy, they often lack interpretability, which poses challenges in real-world deployment.  
The lack of interpretability can have a paradoxical impact on security. On the one hand, interpretable models like decision trees and rule-based systems allow operators to understand the reasoning behind an AI decision, making them reliable in critical situations. On the other hand, less interpretable models like deep learning can improve anomaly detection rates, potentially identifying complex patterns that human operators might overlook. The trade-off lies in balancing interpretability with performance, as outlined in Table 14.  
The findings indicate that hybrid approaches combining interpretable methods with high-performing models can offer a middle ground. For instance, deploying decision trees to provide explanations alongside deep learning for anomaly detection enhances both security and usability. Future research should focus on explainable AI (XAI) techniques tailored for critical communications, where decision traceability is paramount.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 4 Our Proposal: Difference DV_Distance Using Correction Coefficients of Unknown Nodes
### Weighted Processing for Correction Coefficients of All Direction Anchor Nodes  
The correction coefficients for direction anchor nodes reflect their influence on the distance difference from unknown node to target anchor node. However, the correction coefficient only from a single direction anchor node cannot be sufficient to reflect the actual properties of the whole network. In addition, the influence of other direction anchor nodes are not fully utilized, which cause the phenomenon that the actual localization accuracy is not very high.  
The correction coefficients for all direction anchor nodes are considered in this paper, and the average weighted processing is employed to assign different weights to every correction coefficient respectively, and the final correction coefficient of the unknown node should be consisted of all the correction coefficients of direction anchor nodes, and it will be closer to the actual correction value in the network. The detailed equations for the average weighted processing are expressed as follows:  
\[w_{p,i}^{k}=\frac{h_{p,i}}{h_{p,k}} \tag{18}\]  
\[\overline{\omega}_{p,i}^{k}=\frac{w_{p,i}^{k}}{\sum\limits_{k}w_{p,i}^{k}} \tag{19}\]  
\[C_{p,i}=\sum\limits_{k}\overline{\omega}_{p,i}^{k}C_{p,i}^{k} \tag{20}\]  
where \(w_{p,i}^{k}\), the ratio of \(h_{p,i}\) and \(h_{p,k}\), represents the weight parameters for direction anchor node \(k\), reflecting the distance relationship. If the ratio is relatively larger, it shows that the direction anchor node \(k\) is closer to the unknown node \(p\), and then its weight parameter is greater. \(\overline{\omega}_{p,i}^{k}\) is the normalized weight parameter for direction anchor node \(k\), and the sum of all the normalized weight parameters for direction anchor nodes is ensured to be 1 through normalization. At last, \(C_{p,i}\) represents the final correction coefficient from the unknown node \(p\) to the target anchor node \(i\) after weight processing.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods  
### Study Sites  
Figure 1 illustrates the spatial distribution of the two study sites. The first was located at the National Station for Precision Agriculture, Xiaotangshan (XTS), Beijing, China (40\({}^{\circ}\)10\({}^{\prime}\)48\({}^{\prime\prime}\)N, 116\({}^{\circ}\)26\({}^{\prime}\)24\({}^{\prime\prime}\)E). In 2002 and 2004, the biophysical parameters for winter wheat were determined through field and laboratory measurements: in the 2002 campaign, 48 plots 32.4 m \(\times\) 30 m in size were used, along with three winter wheat varieties, while in 2004, 42 plots 32.4 m \(\times\) 30 m in size were used and 21 winter wheat varieties were planted [30; 42]. The other two study areas (US-Ne2 and US-Ne3) were situated at the University of Nebraska-Lincoln, NE, USA (41\({}^{\circ}\)9\({}^{\prime}\)54\({}^{\prime\prime}\)N, 96\({}^{\circ}\)28\({}^{\prime}\)12\({}^{\prime\prime}\)W; 41\({}^{\circ}\)10\({}^{\prime}\)47\({}^{\prime\prime}\)N, 96\({}^{\circ}\)26\({}^{\prime}\)23\({}^{\prime\prime}\)W). The US-Ne2 and US-Ne3 sites covered an area of about 65 ha, and featured a soybean (even years) and maize (odd years) crop rotation. The two sites had different levels of water stress that affected the crops (more details about these sites can be found in [43]). The biophysical parameters for the soybean crops were measured from June to September in 2002 and 2004 at the US-Ne2 site, and from June to September in 2002 at the US-Ne3 site. Table 1 provides additional information for the three sites.  
Figure 1: Location of the winter wheat study sites in Beijing, China and the soybean study sites in Nebraska, USA.
## 4 Multi-Objective Area Coverage Optimization Problem
### Multi-Objective Area Coverage Optimization Model  
Based on coverage situation verification for grid points, we set up a multi-objective area coverage optimization model in order to improve coverage quality. The relative fitness function considers the following two factors.  
(1) Missed ratio  
After grid partition is conducted in the area, coverage ratio is approximately equal to the ratio of the sum of covered grid points to the sum of points in the monitoring area. Coverage ratio is an important evaluating factor for monitoring quality, and it is important to reduce the missed coverage ratio. One of the objective function is as follows.  
\[f_{1}=1-\frac{\sum_{x=0}^{m_{1}}\sum_{y=0}^{m_{2}}C\left[x\right]\left[y\right]} {\left(m_{1}+1\right)\left(m_{2}+1\right)} \tag{10}\]  
where \(\sum_{x=0}^{m_{1}}\sum_{y=0}^{m_{2}}C\left[x\right]\left[y\right]\) is the sum of the points covered by one directional sensor node, and \(\left(m_{1}+1\right)\left(m_{2}+1\right)\) is the sum of grid points in monitoring area. The objective is to minimize the objective function value.  
(2) Redundancy ratio  
It is defined as the ratio of the sum of grid points covered by at least two sensor nodes to the sum of points in the monitoring area. Considering the deployment cost, it is wise to reduce redundancy ratio to avoid the waste of resources. The other objective function is defined as follows.  
\[f_{2}=\frac{\sum_{x=0}^{m_{1}}\sum_{y=0}^{m_{2}}R\left[x\right]\left[y\right]} {\left(m_{1}+1\right)\times\left(m_{2}+1\right)} \tag{11}\]where \(\sum_{x=0}^{m_{1}}\sum_{y=0}^{m_{2}}R\left[x\right]\left[y\right]\) is the sum of the points covered by at least two directional sensor nodes, and \(\left(m_{1}+1\right)\times\left(m_{2}+1\right)\) is the sum of grid points in monitoring area. The objective is to minimize the objective function value.  
In our approach, we choose to minimize the linear combination of the above two objective functions instead of minimizing them individually because the two objectives conflict with each other. To optimize the two objectives at the same time, the coverage optimization fitness function is defined as follows.  
\[Fitness1=\alpha\times f_{1}+\left(1-\alpha\right)\times f_{2},0<\alpha<1 \tag{12}\]  
where the \(\alpha\) is a constant defined by weighing the importance of the sub-objective, and its value is between 0 and 1. Our objective is to minimize the fitness value in order to achieve better area coverage optimization effect.  
The multi-objective area coverage problem can be summarized as searching for a sensing direction group of sensor nodes \(\left(\overrightarrow{D_{1}},\overrightarrow{D_{2}},\overrightarrow{D_{3}}, \overrightarrow{D_{4}},...,\overrightarrow{D_{n}}\right)\). When all sensor nodes in network rotate their sensing directions according to this group, we can improve coverage ratio and reduce redundancy ratio.
# Thermoelectricity Modeling with Cold Dipole Atoms in Aubry Phase of Optical Lattice
## 3 Results
### Seebeck Coefficient  
The dependencies of \(S\) on potential amplitude and temperature are shown in Figure 4 for two Fibonacci-like values of density \(\
u\approx 1.618\) and \(\
u\approx 2.618\). The results clearly show that in the KAM phase \(K<K_{c}\) we have only rather moderate values of \(S\sim 1\) being close to those value of noninteracting particles (a similar result was obtained in [9; 15]). In the Aubry phase at \(K>K_{c}\), we have an increase of \(S\) with the increase of \(K/K_{c}\), and a decrease of \(S\) with the increase of \(T/K_{c}\). This is rather natural since at \(T\gg K_{c}\) the lattice pinning effect disappears due to the fact that the atom energies become significantly larger than the barrier height, and we approach to the case without potential corresponding to the KAM phase. The maximal obtained values are as high as \(S\approx 10\)-\(15\) being still smaller those obtained for ion chains [9; 15]. Nevertheless, as shown below, we find very large figure of merit \(ZT\) values in this regime.  
Figure 3: Dependence of the Aubry transition critical threshold \(K_{c}\) on the atomic density \(\
u\) shown for different chain lengths \(L=34\) (blue curve), \(55\) (green curve) and \(89\) (black curve). The red full curve shows the theoretical dependence (3), the dashed cyan curve shows the fit with \(K_{c}=0.0137(\
u/\
u_{g})^{\alpha}\), \(\
u_{g}=1.618...\) and \(\alpha=4.82\pm 0.08\).
###### Abstract  
Reliably monitoring deformation associated with linear infrastructures, such as long-span bridges, is vitally important to assess their structural health. In this paper, we attempt to employ satellite interferometric synthetic aperture radar (InSAR) to map the deformation of Donghai Bridge over a half of an annual cycle. The bridge, as the fourth longest cross-sea bridge in the world, located in the north of Hangzhou Bay, East China Sea where the featureless sea surface largely occupied the radar image raises challenges to accurately co-register the coherent points along the bridge. To tackle the issues due to co-registration and the limited number of synthetic aperture radar (SAR) images, we adopt the termed temporarily-coherent point (TCP) InSAR (TCPInSAR) technique to process the radar images. TCPs that are not necessarily coherent during the whole observation period can be identified within every two SAR acquisitions during the co-registration procedure based on the statistics of azimuth and range offsets. In the process, co-registration is performed only using the offsets of these TCPs, leading to improved interferometric phases and the local Delaunay triangulation is used to construct point pairs to reduce the atmospheric artifacts along the bridge. With the TCPInSAR method the deformation rate along the bridge is estimated with no need of phase unwrapping. The achieved result reveals that the Donghai Bridge suffered a line-of-sight (LOS) deformation rate up to \(-2.3\) cm/year from January 2009 to July 2009 at the cable-stayed part, which is likely due to the thermal expansion of cables.  
InSAR; coherent point; co-registration; offsets; Donghai Bridge; deformation monitoring 2018
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 3 Results  
From a total of 38,500 patients admitted to the emergency department over 18 months, we extracted 640 patients according to Angus and AHRQ sepsis criteria. Finally, 425 individuals were included in the research after a physician reviewed the extracted information to control for sepsis and removed 215 patients who were not verified as sepsis cases. Figure 1 illustrates the study flow chart. There were 249 males (58.6%) and 176 females (41.4%) in study population. The median age was 77 (IQR 69-83, range 25-97). Hypertension was the most common comorbidity (173 patients, 40.7%), followed by diabetes (105 patients, 24.7%) and cancer (98 patients, 23.1%). Baseline and demographic data are reported in Table 1.  
Most patients came to ED from home (333 patients, 78.4%), 9.2% (20 patients) from long-term care, and 12.5% (53 patients) from ambulatory clinics or within four weeks from hospital discharge. Mortality was strongly related to patient provenience, with patients from long-term care bearing the highest risk for mortality (48.7%), compared to readmission after hospital discharge (20.7%) or admission from home (10.5%). Overall mortality was 15.3% (65 patients). The most represented sites of infection were the abdomen (33.2%) and lungs (30.8%). Pneumonia was the most prevalent sepsis site (30.8%) in non-survivors (Table 1). A total of 52 patients (12.2%) presented with septic shock, resulting in higher mortality than sepsis patients (26.2% vs. 9.7%, \(p\) = 0.001). Heart rate, mean arterial pressure, and respiratory rate were significantly different between survivors and non-survivors (\(p\) = 0.001). Laboratory values and clinical scores are presented in Table 2.  
C-reactive protein was higher in non-survivors than survivors (17 [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20], \(p\) = 0.008), while serum lactate levels were similar. The median APACHE II score was higher in non-survivors than in survivors (15 [11; 12; 13; 14; 15; 16; 17] vs. 12 [8; 9; 10; 11; 12; 13; 14; 15; 16], \(p\) < 0.001). A positive SOFA was more frequent in non-survivors than in survivors (37.5% vs. 12.9%, \(p\) = 0.001). We found no association between the type of pathogen identified by specimen cultures and mortality. The most frequent bacterium identified through blood culture was _E. coli_ (38.1% of cases), followed by _E. faecalis_ (6.2%) and _S. aureus_ (5.1%). Negative blood cultures were present in about 40% of cases (Table 3).
# Multiuser Transmit Precoding Design for Dimming Compatible Visible Light Communications
## 2 System Model and Optical Power Constraints
### Dimming Control  
In VLC, the main functionalities of LEDs are to provide first indoor illumination and then communications. Since people are insensitive to fluctuations of light signals as long as the frequency of modulation is above 100 Hz, but only perceive the average optical intensity of LEDs, the average optical power should remain constant over time to provide stable brightness. Given the linear relationship between the radiated optical power and drive current, the constraint imposed on the signal \(x_{i}\) of the \(i^{\text{th}}\) LED to achieve the stable brightness is given by:  
\[\begin{split}\text{E}\{x_{i}\}&=\text{E}\left\{\sum_{j=1}^{N _{r}}p_{ij}s_{j}+d_{i}\right\}=\sum_{j=1}^{N_{r}}p_{ij}\text{E}\left\{s_{j} \right\}+d_{i}\\ &=\sum_{j=1}^{N_{r}}p_{ij}b_{j}+d_{i}=d_{t,i},\;i=1,2,\cdots,N_{t}, \end{split} \tag{14}\]  
where \(d_{t,i}\) is the average amplitude of the drive current required for the target dimming level. Equivalently, the illumination requirement of (14) for all \(N_{t}\) LEDs takes the concentrated form of:  
\[\text{E}\{\textbf{x}\}=\textbf{P}\textbf{b}+\textbf{d}=\textbf{d}_{t}, \tag{15}\]where \(\mathbf{b}=[b_{1},\cdots,b_{N_{t}}]^{T}\) and \(\mathbf{d}_{t}=[d_{t,1},\cdots,d_{t,N_{t}}]^{T}\). From (15), one can observe that both \(\mathbf{P}\) and \(\mathbf{d}\) need to be designed to achieve the target dimming level. Since the dimming level of the \(i^{\text{th}}\) LED is defined as \(\eta=(d_{t,i}-d_{L,i})/(d_{H,i}-d_{L,i})\), the average amplitude of the drive current for a dimming level \(\eta_{t}\) can be set to:  
\[d_{t,i}=d_{L,i}+(d_{H,i}-d_{L,i})\eta. \tag{16}\]  
where \(\eta\) is in general between zero and one.
# Characterization and Crystallinity of Two Bioactive Sealers: Qualitative and Quantitative Analysis
## 3 Results
### XRD Results
#### 3.4.2 Quantitative Analysis of XRD  
Figure 6B illustrates the data of the crystal size of each sealer before and after immersion in different solutions. In the set dry samples, Endosequence exhibited a significantly smaller size (0.47 \(\pm\) 0.18 nm), while AH-26 exhibited the significantly largest crystal size (0.78 \(\pm\) 0.12 nm) (\(p<\) 0.001). During the immersion period, the crystal size insignificantly decreased for Cerafil, while it increased for Endosequence, with no significant difference between them (\(p>\) 0.05).  
Cerafil revealed the greatest significant crystalline index (CI\({}_{\rm XRD}\)) compared to the other two sealers in all environments (\(p<\) 0.001), particularly after 30 days of aging in PBS (Figure 6C).  
Figure 5: XRD phase composition of Cerafil (**A**) and Endosequence (**B**) root canal sealers presenting the spectra of a raw material (**a**) and after a 30-day immersion period in water (**b**) and PBS (**c**).
## 1 Introduction
In this way, the system trajectory is mapped incrementally, which, however, necessarily leads to a loss of accuracy with increasing distance [30]. The resulting accuracy of the model is, therefore, determined not only by the accuracy of the sensors themselves but also by the quality of the algorithm calculating the trajectory [31]. One important advantage of this system when comparing it to static scanners lies in the fact that the gradual movement while scanning enables the elimination of shadowing/obstruction, which is particularly beneficial for complex or rugged objects [32]. This paper aims to test the performance of the SLAM technology in creating a digital model of a bridge. In view of the complexity of the used bridge structure, the emphasis will be put on the accuracy of the acquired cloud (especially in terms of the spatial relationships of the individual parts of the bridge) along with the assessment of the local quality of the cloud (especially noise). The accuracies of the positions of the piers and of horizontal parts of the bridge will be also compared. Furthermore, the characteristics of the acquired data (noise) will be investigated, and the use of a smoothing (denoising) procedure for improving the digital model quality will be proposed and tested.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 1 Introduction
However, it can be argued that this is a necessity for reliable spoofing detection. DOA based detection requires the array to be calibrated, which further increases the cost and complexity of the system. The focus of this research was to develop and assess blind detection methods which do not require DOA, thereby reducing the receiver complexity, cost, and the need for array calibration. A background on snapshot receivers, server-based processing, and the implementation of beamforming to improve performance is provided in Section 2. The spoofing detection algorithm for a single pair of spoofed satellites is presented in Section 3, with additional theoretical analysis to support performance expectations. An expansion to system-level detection methods is done in Section 4. The experimental setup is introduced in Section 5, results are presented in Section 6, and discussed in Section 7. Finally, conclusions are drawn in Section 8.
# Experimental Study of a Novel Prototype of Dual-Cycle Two-Phase Cooling System for a Data Cabinet
## 3 Results and Discussion
### Cooling System Energy Consumption Analysis  
#### 3.3.1 Energy Consumption Analysis of the PCTCS  
Figure 10 presents the energy consumption profile of a chip cooling system. Specifically, Figure 10a illustrates the power consumption of an outdoor condenser cooling fan under varying ambient temperatures. The reservoir temperature (T\({}_{203}\)) is set to one of four target values: 30 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, or 45 \({}^{\circ}\)C. However, the fan's overall operational trend remains consistent--low power at low ambient temperatures and increased power at higher ambient temperatures.  
When the ambient temperature is relatively low, the fan's power consumption remains minimal with only minor fluctuations. As the ambient temperature gradually increases, the temperature difference for heat exchange diminishes. To maintain the chosen target temperature, the fan speed must rise, leading to higher power consumption. Eventually, the fan reaches its maximum capacity at sufficiently elevated ambient temperatures, and  
Figure 10: The pump-driven chip-level two-phase cooling system energy consumption of: (**a**) power consumption of condenser fan at different outdoor ambient temperatures; (**b**) average annual COP under different operating modes.  
Figure 9: Effect of pump control strategy on cooling system performance [16].  
it can no longer keep the set temperature stable. Once the fan is at maximum power, any further increase in ambient temperature has little effect on power consumption.  
Based on the energy consumption curve of the fan, it is more rational to adopt different target control temperatures under varying outdoor temperatures. This operational mode is termed the \"ideal mode\". The \"ideal mode\" refers to a dynamic control strategy that adjusts cooling setpoints in real-time in response to changes in outdoor temperature. This approach ensures that the chip temperature remains relatively low for most of the year while maintaining high overall system performance of the cooling system.  
Figure 10b demonstrates the annual average COP of the chip cooling system under different operational modes. As the control temperature T\({}_{203}\) decreases, the performance of the cooling system gradually declines. However, even when T\({}_{203}\) is set to 30 \({}^{\circ}\)C, the annual average COP still reaches 32.7. In the ideal mode, the annual average COP reaches an impressive 44.0, ensuring both a relatively low chip temperature and efficient operation of the cooling system.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods  
### Experiment Protocol  
In this section, we present the experiment protocol used to calibrate and evaluate the cognitive-driven gain controller system. The experiment protocol consists of a calibration phase, an open-loop AAD phase, and a closed-loop AAD phase (see Figure 1).
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### The Optimistic Consensus Algorithm  
The optimistic consensus algorithm enhances the consistency of consensus protocols; nevertheless, its practicality is constrained. It can effectively reach consensus solely when implemented on particular edge device nodes. Moreover, incorporating a reputation system to validate the main node seems rather hopeful. Even with reputation-driven reward and punishment systems in place, the main node continues to encounter threats from malicious actions or potential failures.  
In the optimization of optimistic consensus algorithms, enhancing the reputation evaluation mechanism is crucial. In the future, adopting a time-weighted reputation model can more accurately assess the long-term behavior of nodes and prevent reputation fraud. Additionally, integrating secret sharing mechanisms or multi-signature techniques can ensure system continuity and security, even in the event of primary node failures.  
Looking ahead, blockchain is expected to play an increasingly vital role in smart manufacturing. With the advancement of AI, 5G communication, and digital twin technology, blockchain can serve as a trusted data infrastructure, facilitating cross-enterprise and cross-regional collaboration in intelligent production. Moreover, its decentralized data management capabilities can support the development of distributed smart manufacturing platforms, enabling seamless data sharing and optimized resource allocation among different manufacturing enterprises. In the future, blockchain is poised to drive mechanical manufacturing toward a higher degree of automation, intelligence, and traceability, providing strong support for the digital transformation of the global manufacturing industry.