esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 4 Simulation and Performance
### Performance Metrics
#### 4.1.3 Acquisition Time  
One of the performance metrics on this research is to use a generalization of [PERSON] method [39], where the time from when the receiver is turned on to when the user solution is obtained, or the first position fix (TTFF) metric, is subdivided into different contributions, distinguishing among three scenarios: cold, warm, and hot start. TTFF depends on the status of the receiver, the availability and validity of the data required to compute the navigation solution, the carrier to noise ratio \(C/N_{0}\), the number of visible satellites, the receiver method of processing on all the signals from the visible satellites [40], and the influence of the ionosphere, tropospheric refraction, multipath and many other sources of error. This metric is a crucial factor in GNSS receiver design because it is perceived as the primary performance characteristic in the mass-market for receivers:  
\[TTFF_{cold}=T_{warm\:up}+T_{acq}+T_{track}+T_{CED}+T_{GST}+T_{PVT}\]  
where \(T_{warm\:up}\) is the receiver warm-up time; \(T_{acq}\) is the acquisition time; \(T_{track}\) is the settling time for code and carrier tracking; \(T_{CED}\) is the navigation data read time (clock correction and ephemeris data); \(T_{GST}\) is the GNSS system time; and \(T_{PVT}\) is the time to compute the navigation solution.  
By focusing on reducing the acquisition time \(T_{acq}\), the total TTFF can be decreased by using new algorithms and technology. The proposed algorithm will have an impact on \(T_{acq}\) by reducing the number of samples.
## 5 Discussion
### Limitations  
The use of proprietary databases makes it difficult to carry out external validations and requires more time to understand the infrastructure and structure of the records. Furthermore, the schemes of the health record databases in a Colombian context focus the most on administrative topics and usually lack schemes for research. For that reason, we needed a long time to understand the native structure and extract, transform, and load the data required for this study. This could be more efficient with the standardization of conditions and the analysis of data through common data models such as OMOP.  
There was a high frequency of typos and misspellings in the narratives that were treated compared with the vocabulary of a corpus not specialized in the biomedical or clinical domain. This required a better approach to treat these special tokens and be careful with the impact that removing words could have, or changing the meaning of the sentence [43].  
The choice of the ICD-10 code can vary in different institutions, is influenced by the school where the doctors were trained, and it depends on their level of knowledge and other multiple elements, as described in [44]. In addition to these factors, the different electronic medical record software can influence the quality of classification depending on the usability and functionality [45]. For this reason, using a supervised validation system for the medical records labeling, the quality of the data can be increased and, therefore, the performance of the model.  
Concerning pretrained deep learning models, we applied identical preprocessing steps as those used with the word-embedding schemes, including the removal of stop words. Although eliminating stop words could potentially result in the loss of specific contextual nuances, we expect that, due to the nature of the classification task, the impact on outcomes will not be substantial. However, future works can address this concern by exploring various preprocessing pipelines.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 2 Theoretical Background
### Information Systems and Learning Software  
The implementation of Industry 4.0 uses the concept of an automation pyramid in connection with information technology. The pyramid is closely related to the vertical integration of information systems in an enterprise. Typical solutions and technologies in this vertical integration include data acquisition sensors: programmable logic controllers (PLC) that control production processes and take control levels, supervisory control and data acquisition (SCADA), which allows managing different levels of support processes and supervision, manufacturing execution systems (MES) controlling production processes, and intelligent enterprise resource planning (ERP) management for the enterprise level, the highest level in this hierarchical image [41; 42; 43]. ERP supports enterprise-wide planning such as business planning, supply chain management, sales and distribution, accounting, human resources management, and the like. These are usually commercially available solutions.  
German SAP SE (Systems, Aplications & Product in Data Processing Service) is currently the leading SAP solution. In traditional ERP tools, the decision-making process is centralized at the highest level; most available ERP solutions do not support rapid adaptation in production planning due to unplanned events. MES supports reporting, scheduling, dispatching, product tracking, maintenance, performance analysis, workforce tracking, resource allocation, and more [44]. Most systems work with their own way of storing data and, often, with their own data format. The use of a production system (MES) as a central database is a solution to such an issue [45]. In the future, a decentralized IT solution in smart factories might be used. In order to give the employees the right data in time, a support system is needed [46]. As [PERSON] [47] noted, it is the right time to check the readiness of ERP systems to meet the demands of the factories of the future. SAP developed its predictive maintenance module, based on firm integration of robots, machines (to be maintained), and ERP. Predictive maintenance is based both on the integration of data from ERP data sensors and the predictive algorithms.  
Learning software includes pattern and machine learning (ML), which embodies some of the aspects of the human mind that allow us to deal with an extremely complex solution to the problem with the speed overcoming even the fastest computers [48]. [PERSON] et al. [49] reported the most commonly used techniques: case-based reasoning (CBR) [50], artificial neural networks (ANN) decision trees (DT) [51], Bayesian networks (BN) [52], support vector regression (SVR) [53], genetic algorithms (GA) [54], genetic programming (GP) [55; 56], association rules (AR) [57], rule induction (RI) [58], and fuzzy algorithms [59].
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 5 Simulation Results
### Packet Dropped  
When the number of transmissions and receptions increases, the packet drop ratio also increases due to collisions. In the simulation, we used the random uniform model [24], where the packet drop is related to the bad link status through which it is propagated. If the link status is good, the sink successfully receives a data packet; on the other hand, if the link status is bad, the packet is dropped. In our simulations, we set a 30% probability of a bad link status, which means \(p_{bad}=0.3\) and \(p_{good}=0.7\). CNs gather data and deliver them to the MS; when these have more data to deliver in a limited time, then the packet drop ratio increases. Dense networks have higher packet drop ratios as compared to sparse ones. In Figure 10, the packet drop ratio of 3D-SM is greater than DDRP because it has more packets to transmit to the sink, so the probability of packet dropping also increases.  
In AUV-PN, there is a greater probability of packet drops, as this protocol engages two-hop transmission of the MN's data through the CH to the MS. Furthermore, it depends on the number of packets sent to the sink; DDRP's throughput is less in comparison with 3D-SM.  
Figure 10: Dropped packets. (**a**) when all nodes die; (**b**) when first node dies.  
Figure 9: Network throughput. (**a**) when all nodes die; (**b**) when first node dies.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 5 Discussion
### Economic Viability  
The economic feasibility of DTs significantly influences their agricultural implementation. Their adoption faces substantial financial barriers, particularly for small-scale operations. Initial investments in sensor networks, drones, and data infrastructure create notable entry barriers, compounded by ongoing maintenance and technical support costs.  
Aquaponic systems illustrate these challenges well, as their high equipment costs and labor expenditures, frequently reaching 50% of operational budgets, limit adoption. However, the study by [PERSON] et al. [100] demonstrates that DT automation can reduce labor costs by up to 70%, with IoT integration showing significant operational savings. Irrigation systems similarly achieve improved cost-effectiveness through DT approaches.  
The technology's long-term economic potential is clear, but widespread adoption requires overcoming initial cost barriers and demonstrating tangible financial benefits across different farm scales. This necessitates strategic implementation planning and scalable solutions tailored to varying operational capacities.
## 5 Methodology
### Federated Learning Process  
Edge-FLGuard uses federated learning (FL) to collaboratively train a global model across distributed nodes while keeping raw data local, thereby preserving privacy and scalability.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Specification** \\ \hline Edge Devices & Raspberry Pi 4, Jetson Nano \\ FL Aggregator & Ubuntu 22.04, 16 GB RAM, 8 vCPUs \\ ML Libraries & TensorFlow 2.13, PyTorch 1.13 \\ FL Framework & Flower (FLwr) + custom client-server pipeline \\ Simulation Tools & Mininet, Scapy, Wireshark, Docker \\ Protocols & MQTT (Mosquito), gRFC with TLS 1.3 \\ Reproducibility & 5 runs with fixed random seeds \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specification of the hardware, software, and simulation tools used in the experimental testbed for benchmarking Edge-FLGuard under real-world conditions.
# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Distributed Ensemble Learning (DEL)
#### 6.1.3 Isolated G0 vs. DEL  
In this section, we quantify the statistically significance of the results using the [PERSON] post hoc test. Since \(DEL\) looks beneficial for the scenarios that are presented in Figures 8a,b and 9a, we deep-dive into those scenarios in particular. We perform the statistical test on conditions where the G0 weight yields the highest \(DEL\) F1-score accuracy.  
**Content-based split scenarios:** For the scenario with the _meanBitrate_ in the feature set, when the split index was set to 2, the \(p\)-value was 0.000027 at \(W=0.6\), which is the weight that \(DEL\) benefits G0 statistical significantly 6%. When the split index was set to 4, the \(p\)-value was 0.00001 at \(W=0.5\) again benefiting G0 in a statistically significant manner. The improvement of G0 accuracy was 9%. When the split index was set to 6, the \(p\)-value was 0.00002 at \(W=0.1\) again benefiting G0 in a statistically significant manner. The improvement of G0 accuracy was also 9%.  
For the scenario without the _meanBitrate_ in the feature set, we apply the statistical significance test on split index set to 2, where the best weight is set to \(W_{0}=0.6\). The \(p\)-value was 0.05 indicating \(DEL\) marginally benefits G0. For the remaining scenarios, \(DEL\) does not help G0.  
**Random-split scenarios:** There were two scenarios where \(DEL\) benefited G0 in a statistically significant manner. First scenario was when _meanBitrate_ feature is included, and the split index was 6. In that case, the \(p\)-value is calculated as 0.03 at weight \(W_{0}=0.4\) with a F1-score improvement of 9%. The second scenario was when _meanBitrate_ was excluded and where the split index is set to 2, \(p\)-value is calculated as 0.007 at weight \(W_{0}=0.2\) which yielded a 12% F1-score improvement.
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of Noise Power
#### 4.3.2 Performance of Metric of Double Measurements  
Figures 11 and 12 show the Fisher information provided by using the metrics of AOA/RSS, TOF/AOA and RSS/TOF and the corresponding error bounds under different deviations of the metrics of RSS, AOA and TOF. From these figures, we can find that the Fisher information and error bounds are highly affected by AOA deviation, while the impact of RSS and TOF deviation on the Fisher information and error bounds is slight.  
Figure 11: Sum of the diagonal elements in the FIM by using the hybrid (**a**) AOA/RSS, (**b**) TOF/AOA and (**c**) RSS/TOF.  
Figure 10: Error bounds under different locations of the fourth AP by the JMART.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR
### Case Studies: Non-Optimal Results
#### 3.1.6 Georgia Power (GP)  
By reducing the voltage drop from the substation to the end of line customer meter, GP reduced transmission and distribution system losses and gained additional margin to implement voltage reduction to reduce peak demand. On most of its circuits, GP typically holds service voltage at 123 V from the substation through the end of a given feeder line, as opposed to previously operating its substations at 126 V. GP claims that the resulting efficiencies reduce peak load by 264 MW system-wide, saving $4.6 million in avoided peak power purchases during implementation in the summers of 1999 and 2000 [24].
## 5 Performance Evaluations  
In order to evaluate the performance of global and local updates described above, we used the open source network simulator NS-2 [35] version 2.33 as it is widely used for research in wireless sensor networks. We have added a new routing agent (_i.e._, GeoCHT) into NS-2 over the currently implemented network stack.  
In the following, we provide first a description of the metrics for evaluating the performance of the proposed mobility handling methods and for evaluating the routing accuracy and networking performance of the convex hull based geocasting. Next, we compare our convex hull based geocasting (_i.e._, GeoCHT) with another geocasting protocol, GEAR, in terms of routing accuracy and networking performance. After that, we present the scenarios characteristics for evaluation of the performance of the proposed mobility handling method. Finally, we analyze the obtained simulation results.
## 2 Results and Discussion
### Fine Frequency Shift/Continuous Frequency Tuning  
For a fine frequency shift or continuous frequency tuning, we replace the normal passive low dielectric buffer layer underneath the metal antenna with a ferroelectric material, whose permittivity (**e**) changes when a different electric field is applied. In this case, based on our previous measurement from 0.2 THz to 1.2 THz, the permittivity of PDLC in that frequency band of operation was calculated using the modified Nicolson-Ross-Weir (MNRW) extraction technique. The unit cell antenna based on single segment Hilbert fractal is shown in Figure 5a. As it is different from the fractal pattern where the frequency shift is more discrete and highly depended on its fractal pattern, the voltage induced dielectric constant change in the antenna can create much more continuous and infinitesimal frequency change. An example of this frequency shift due to the permittivity change using PDLC substrate on a Hilbert fractal antenna is shown in Figure 5b. One can clearly observe that the frequency changes proportionally with permittivity. However, the frequency shift (0.504 to 0.540 THz) is due to \(\varepsilon\) change from 2.93 to 2.51 @ 0.5 THz from extraordinary to ordinary polarization in the PDLC (Figure 5b), which is about the same as the reconfigurable fractal antenna when changes one segment to two segments, as shown earlier in the simulation (Figure 4). Eventually, several larger dielectric constant change ferroelectric or magneto-electric materials (currently developing in the lab) will be developed for the proposed reconfigurable frequency tunable antenna design. The main challenge with integrating the antenna eventually with the oscillator, such as RTD, is the impedance matching. Care must be taken when designing the tunable antenna so an additional tunable impedance matching circuit might be needed to ensure a good energy coupling between the antenna and the rest of the RF components or circuits.  
Figure 5: (**a**) One segment unit cell antenna and (**b**) return loss of the proposed antenna from extraordinary to ordinary polarization in the PDLC. Continuous frequency tuning using dielectric changed using ferroelectric material (PDLC). Here, frequency shifts are due to liquid crystal changes from the ordinary to extraordinary state. (**c**) Side view and (**d**) 3D perspective view of the unit cell antenna. The thickness of the PDLC is \(\text{tp}_{\text{PLC}}\) = 50 \(\mu\)m, with a liquid crystal size of 10 nm. The top Hilbert fractal (au) and the bottom metal (Au) thickness is 200 nm.  
Due to the substrate material chosen currently, the return loss is slightly lower than expected. However, for immediate THz near field application, it will be sufficient. The cross-sectional and 3D perspective view are illustrated in Figure 5c,d, respectively, for the clear observation of PDLC.
## 4 Experiments
### Implementation Details  
Following experimental conventions [30; 41], this paper trains and evaluates the proposed model on the DTU dataset. To verify the model's generalization ability, the model trained on DTU is directly tested on the Tanks&Temples dataset without any modifications. The depth sampling numbers \(\{D_{k}\}_{k=0,1,2}\) at different stages are set to 16, 8, and 4, with depth sampling range (\(d_{min}\) and \(d_{max}\)) configured as 425 mm and 935 mm. The temperature parameter (\(t_{c}\)) in the polarcross attention mechanism is set to 2. We train this paper's model for 14 epoches. The Adam optimizer [42] with \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\) is employed to optimize the model. The experiment is conducted on one NVIDIA RTX3090 GPU with a batch size of 2. The initial learning rate is set to 0.001 and is reduced by a factor of 2 after 8, 10, and 12 epoches. For DTU dataset training, input image resolution is 1600 \(\times\) 1200 with \(N\) (number of input images) set to 5. On the Tanks&Temples dataset, \(N\) is set to 7, and the input image resolution is 1080 \(\times\) 2048.
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 2 Overview of the Experiment  
### Specim Design  
Three cylindrical piers were created according to the geometric similarity ratio of 1/3, and the geometric dimensions and reinforcement situations of the three specimens were exactly the same. The effective height of the pier was 1400 mm (clear height 1200 mm), and the size of the base was 1000 mm (length) \(\times\) 1000 mm (width) \(\times\) 400 mm (height). The diameter of the pier was 300 mm, and the height of the SFRC in the plastic hinge region was 300 mm. The strength grade of the concrete was C40, and the type of steel bar was HRB400. The diameter of the longitudinal steel bar was 16 mm, and the diameter of the spiral stirrup was 8 mm. In the SFRC region at the bottom of the pier, the volume ratio of steel fibers was 1%. The axial compression ratio is an important parameter in quasi-static tests. According to the relevant specifications and literature [3; 19; 20], the axial compression ratio of the three specimens was set to 0.2. The torsion-bending ratios were 0, 0.17, and 0.36, respectively, labeled as SFRC-1, SFRC-2, and SFRC-3. The specific dimensions and reinforcement details of the SFRC bridge piers are shown in Figure 1, and the testing parameters of the three specimens are listed in Table 1.
## 3 Experiment Results and Analysis  
### Specim FW-1  
The schematic diagram of the tested phenomena in the FW-1 group is shown in Figure 12. At the early stage of specimen loading, when the beam end was loaded at 1/600 (\(\Delta\) = 5 mm), there was no noticeable change in deformation and strain of the specimen, and the panels did not produce cracks. When the displacement angle reached 1/500 (\(\Delta\) = 6 mm), cracks appeared at the bottom of the mortar of panel joints of No. 1 and No. 2 (Figure 12a). When the displacement angle reached 1/400 (\(\Delta\) = 7.5 mm), the sound of the panel corner rubbing and the crack of inter-slab splicing mortar continued to increase (Figure 12b). When the displacement angle reached 1/350 (\(\Delta\) = 8.6 mm), the sound of mutual extrusion between panels could be clearly heard, and vertical cracks were produced in the splicing of the No. 1 and No. 2 panels (Figure 12c). Some small pieces of ALC fell off during the loading of 1/300 (\(\Delta\) = 10 mm) and 1/250 (\(\Delta\) = 12 mm) (Figure 12d). When the displacement angle reached 1/200 (\(\Delta\) = 15 mm), there was evident slippage between the enclosure reinforcement and the end panel, and there was a noticeable misalignment between the panel where the splices fell off. When the displacement angle reached 1/50 (\(\Delta\) = 60 mm), the crack tended to gradually become wider. At displacement angle 1/40 (\(\Delta\) = 75 mm), the angle weld at the upper part of panel No. 2 broke off, and a crack appeared at the hook headbolt hole at the lower part of panel No. 3. When the displacement angle reached 3/10 (\(\Delta\) = 90 mm), there was serious breakage at the welded joint of the left upper beam connector, and the cracks of the panels continued to expand (Figure 12e). At displacement angle 7/20 (\(\Delta\) = 105 mm), each panel corner was damaged on a large scale, and the No. 3 panel presented oblique cracks (Figure 12f).