esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Setup and Results  
### Experimental Evaluation  
In order to better understand the kit operation, we have conducted a set of experiments. In particular, the setup shown in Figure 4 was used to conduct different experiments. The Powercast transmitter was connected to a voltage supply and placed on a table near its edge in a room facing the harvester. We have considered different distances (in meters) in order to carry out the experiment. Initially, we fixed the harvester at the other end of the setup table. After every test, the receiver was moved to the next distance (towards the  
Figure 3: P2110 harvestor circuit block diagram [35].  
Figure 2: Powercast Kit components consist of: (**a**) TX91501-3W Transmitter; (**b**) access point WSN-AP-01; (**c**) harvester P2110-EVB; (**d**) wireless sensor board WSN-EVAL-01; (**e**) pickit-3; (**f**) patch antenna and (**g**) dipole antenna.  
transmitter) to redo the test and the process was repeated for different distances as will be depicted later in the paper.  
The energy received by the antenna was observed at the beginning on the frequency analyzer MS2720T, which showed two expected peaks, one at 915 MHz, which was used to transmit the RF wave, and the other one at 2.4 GHz, which was used to transmit the data. Furthermore, in order to fully understand the energy charging and harvesting process, several tests with different settings as depicted on Figure 5 were conducted. We observed that on some settings, e.g., Figure 5a,d, the output voltage reached the maximum voltage of 3.3 V, since the wireless charging process was able to harvest enough power required by the booster circuit to generate the maximum possible output pulse signal. Furthermore, it was noticed that this maximum voltage was not obtained on other settings (Figure 5b,c). Therefore, it is important to choose the suitable capacitor and load resistor values to be able to generate the required output voltage signal.  
Figure 4: Experiment setup.  
Figure 5: (**a**) Testing without a load and capacitor; (**b**) Testing without a capacitor and with a 660 \(\Omega\) load; (**c**) Testing with the on board capacitor C3 capacitor and a 660 \(\Omega\) load; (**d**) Testing with the on board C5 capacitor and with a 660-\(\Omega\) load.
## 2 Related Work
### Adaptive Bin Estimation  
Early CNN-based depth estimation approaches can be viewed as a per-pixel regression task. Some approaches employ an autoencoder to extract features for making extensive per-pixel regression to construct the depth maps of rational naturalness. Compared to CNN-based depth methods based on one-step regression without the alignment of depth anchors, AdaBins can provide the individual alignments of estimated depth centers for different real-word perspective ranges. The common sense of AdaBins is to split the depth range into several subintervals so that the division of bins is estimated adaptively per image. Thus, the probabilities of per-pixel depth falling into these dynamic bins can be measured. Finally, the depth of each pixel can be predicted by linear combination of each bin center and their corresponding probabilities. [PERSON] et al. [12] show that monocular depthestimation can be treated as a pixel-based classification task by utilizing an AdaBins module. AdaBins modules can be useful and appropriate tools to offer high-precision depth estimation for remote sensing and aerial images [24; 25]. There are several AdaBins structures suggested to further promote depth estimation performance with semantic, mask transformer, multi-scale and knowledge distillation techniques [25; 26; 27; 28] to alleviate computation. Hence, the AdaBins module can support the high practicality of z monocular depth estimation network in real-distance detection tasks. The ZoeDepth network [29] exploits a hierarchical-level bin fused decoder and dataset-dependent multiple configurations for pre-training and metric fine-tuning to obtain high-quality and high-resolution depth estimation. It belongs to computation-heavy depth estimators. On the contrary, our proposed models are cost-effective approaches.
## 5 Use Case: IWSN Resilience Using Maintenance 5.0  
The contributions of the use case in this work which validate the proposed model are the following: first, a brief jamming mitigation simulation of Q-learning algorithm using python language, second, a comparison of mitigation times with and without Worker 5.0; third, the previous experiment [107] has been carried out again but now measuring the detection and mitigation times, in order to calculate resilience; fourth, resilience has been  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal State** & **Performance** & **Resilience Metrics** & **Technologies Used** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Normal operation** \\ \((t_{0}-t_{R})\) \\ \end{tabular} } & \multirow{2}{*}{P\({}_{o}\) (t)} & Security, access control, segmentation, risk level, resourcefulness & IWSN, IBAN (wearables), IIoE, edge computing, 5G, 6G, blockchain, holography \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Symptoms** appear \((t_{1}-t_{0})\) \\ \end{tabular} } & \multirow{2}{*}{Decrease from P\({}_{o}\)(t) to P\({}_{1}\)(t)} & \multirow{2}{*}{MTTR, redundancy} & Cloud and green computing, Advanced Simulations, Big Data Analytics \\ \hline \multirow{6}{*}{\begin{tabular}{c} **Detection** \((t_{2}-t_{1})\) if failure is simple, complex, complicated, or catastrophic \\ \end{tabular} } & \multirow{6}{*}{\begin{tabular}{c} \(P_{2}(t)_{B}\) \\ (Complex) \\ \end{tabular} } & \multirow{6}{*}{\
\begin{tabular}{c} \(P_{2}(t)_{B}\) \\ traffic, network topology, resourcefulness, latency, quality of service \\ \end{tabular} } & Cognitive and prescriptive analytics performed by H-CPS, AI, digital twins, softbot \\ \cline{2-4}  & & & Prescriptive and predictive analytics performed by H-CPS, AI, digital twins, softbot \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \cline{2-4}  & & & & \\ \hline \multirow{6}{*}{
\begin{tabular}{c} **Mitigation** procedure \\ \((t_{3}-t_{2})\) \\ \end{tabular} } & \multirow{6}{*}{Increase from the \(P_{2}(t)_{n}\) to \(P_{3}(t)\)} & \multirow{6}{*}{Reliability, MTBF, availability, MTFE, restoration delay} & Additive manufacturing, cobots, renewable resources, fintech, bionics, Immersive technologies (XR: AR, VR, drones, exoskeletons, the digital assistant \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance analysis.  
calculated using the maintenance 5.0 framework demonstrating that a human-in-the-loop maintenance framework could improve physical assets' resilience. A deeper validation of the jamming mitigation will be performed in future work. The role played by worker 5.0 in jamming detection and mitigation is shown in Figure 14.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Current Colombian Agricultural Outlook  
In Colombia, several advanced digital agriculture projects are already underway, offering fertile ground for the integration of 6G technologies. These initiatives are primarily led by AGROSAVIA and the Ministry of Science, Technology and Innovation, and target different components of the agricultural production chain. To illustrate the potential of 6G more concretely, we describe below four key agricultural domains that could benefit significantly from its deployment.
## 3 Results and Discussion
### Fiber Morphology--Scanning Electron Microscopy (SEM)  
The SEM images indicated no crystallized ITZ on the surface of the microfibers, suggesting that the drug was in a non-crystalline state after electrospinning [36]. N1 and N3 samples prepared at a 1 mL/h flow rate compared to N2 and N4 at a 2 mL/h flow rate led to smaller-diameter fibers. The needle-to-collector distance of 10 cm resulted in a lower fiber diameter compared to 15 cm, specifically for samples N1 (887 \(\pm\) 238 nm) and N2 (907 \(\pm\) 206 nm) compared to N3 (1101 \(\pm\) 292 nm) and N4 (1175 \(\pm\) 226 nm).  
All fiber histograms were unimodal, N1 appeared to be symmetric, N2, N3, N5, N6, and N7 were right-skewed, and N4 was left-skewed (with the highest values for fiber diameter, flow rate, and needle-collector distance). The lowest fiber diameter of 887 \(\pm\) 238 nm was associated with the lowest flow rate and needle-to-collector distance. The needle-to-collector distance influenced the diameter of the fibers in a statistically significant manner (\(p\) < 0.001, when comparing N1 to N3 and N2 to N4).
## 4 Materials and Methods
### Site-Directed Mutagenesis of hNudt16  
Human Nudt16 amino acid-substituted mutants (E76Q, double mutations of glutamic acid at positions 79 and 80; EE-QQ; and EE-KK) were obtained via site-directed mutagenesis using the QuikChange Site-Directed Mutagenesis Kit (Stratagene California, La Jolla, CA, USA). The DNA primers used for mutagenesis are listed in Table S2. The starting DNA template pBMH-Nudt16 plasmid was used (where the _hNudt16_ coding sequence, with a sequence encoding four additional histidines introduced just after the two C-terminalhistidines of hNudt16, was cloned between NcoI and BamHI restriction sites) [12]. Mutagenesis reactions were performed in a 50-\(\upmu\)L final volume containing 1\(\times\) Pfu polymerase reaction buffer (10 mM KCl, 10 mM (NH4)2 SO4, 20 mM Tris-HCl (pH 8.8), 2 mM MgSO4, 0.1% Triton X-100, and 0.1 mg/mL BSA), 50 ng template plasmid DNA, 125 ng of forward and reverse primers, 1 \(\upmu\)L of 10 mM dNTP, and 1 \(\upmu\)L (2.5 U) of Pfu DNA polymerase. The PCR cycling conditions were as follows: 95 \({}^{\circ}\)C for 2 min, 12 cycles of 95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 1 min, and 68 \({}^{\circ}\)C for 7 min and 15 s, and a final incubation at 4 \({}^{\circ}\)C in the case of single mutation E76Q. In the case of double mutations (EE-QQ and EE-KK), the number of cycles was increased to 18 (instead of 12). Next, methylated plasmid DNA was digested for 1 h at 37 \({}^{\circ}\)C with 1 \(\upmu\)L of DpnI restriction enzyme, and then 10 \(\upmu\)L of DpnI-treated mix was transformed to chemicompetent _E. coli_ XL1-Blue cells. Plasmid DNA was isolated from selected colonies obtained after transformation using QIAprep Spin Miniprep Kit (QIAGEN GmbH, Hilden, Germany) and analyzed by DNA sequencing for desired mutations. Finally, the _hNudt16_ coding sequences with confirmed mutations were re-cloned into the pET16b expression vector as NcoI-BamHI restriction DNA fragments, and verified by DNA sequencing.
## 3 System Model  
The NS resource allocation can be defined as a feasible path that slice traffic should follow in infrastructure networks with adequate resource availability. In order to introduce the NS resource allocation model, we first introduce the VNF resource allocation process and then describe the substrate network and slice logical network with analyzing topological and structural characteristics. We then introduce the substrate node importance metric and construct the mapping relationship with a pair-decision variable to deploy communication service chains onto substrate network resources in a coordinated way. With the consideration of network capacity and link bandwidth, we finally define a cost function for capturing the latency.  
In the resource allocation model, the same type of NSIs refer to a set of slices with similar latency threshold and traffic which is needed to be forwarded when mapping VNUs into substrate DCs. We define that each slice is a specific E2E traffic flow, consisting of a set of communication service instances steered through an ordered set of VNUs. That is to say, the VNU which carries dedicated VNFs can be instantiated as AMFs (5G Core Access and Mobility Management Functions), firewalls, service gateways, and so on. Each communication service instance is an atomic entity, which can be viewed as a service chain with function processing logic.
## 3 Results and Discussion
### Best Model (YOLOv8) Details  
In summary, the YOLOv8 architecture proved to be the most suitable for the task of document layout analysis due to its balanced performance in all criteria, especially in recall and mAP50, which suggests its greater robustness for this application.  
To obtain a more detailed view of YOLOv8 performance, Table 5 shows the metrics obtained by class, which had its test set randomly generated without the Table class. In this case, a mAP of 0.995 was obtained for the Image class, 0.911 for the Text class and 0.768 for the Graphic class, ordered from the highest result to the lowest. Most of the instances are from the Text class, which makes it easier to learn this class. The regions of the Image class, although not as numerous, could be recognized by the model. Unlike the Graphic class, which has more complex patterns and one of the fewest instances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Recall** & **Precision** & **mAP50** \\ \hline Text & 0.825 & 0.888 & 0.911 \\ Image & 1.000 & 0.759 & 0.995 \\ Graphic & 0.602 & 0.887 & 0.768 \\ \hline
**all** & **0.809** & **0.845** & **0.891** \\ \hline \hline \end{tabular}
\end{table}
Table 5: YOLOv8 performance by class.  
Thus, from an individual point of view, the highest accuracies were obtained for the Text and Image classes, with values above 90%. The algorithm was particularly excellent at detecting regions in the Image class, with an mAP of almost 100%. Finally, YOLOv8 achieved a global mAP of 0.891.  
Figure 6 shows the confusion matrix obtained by YOLOv8 on the test set, where the rows indicate the class that was predicted and the columns indicate the true class corresponding to the object detected by the model.  
For the Graphic class, 26 true positives, 13 false positives, and 7 false negatives were obtained. For the Image class, the two existing instances were correctly identified. For the Table class, there are no instances in the test set. For the Text class, 2294 true positives, 252 false positives, and 615 false negatives were found. Note the presence of 611 false negatives from the Text class that were detected as background, as well as 252 background regions that were incorrectly classified as text. This error may have been caused by noise in the document or scanning artifacts. However, the confusion matrix reinforces YOLOv8s ability to detect text, as it had a high number of true positives for this class.
## 3 Characteristics of the Actual Laser Imaging Waveform  
The quality of imaging waveform affects the result of the centroid calculation, thus the sensor developed, in this paper, adjusted the peak intensity of the imaging waveform by controlling the CMOS exposure time. In order to improve the efficiency of the adjustment algorithm and the adaptability to different objects, it was necessary to further analyze the characteristics of the imaging waveform.
###### Abstract  
Superconducting magnets' coils need rods for suspension in vacuum dewars to minimize heat conduction. Previously, rod sets had to be custom-matched to specific magnet models, hindering interchangeability. However, designing or repairing magnets required new rod manufacturing, which was costly and time-consuming, especially with low-conductivity composite materials. In this study, a design of a multi-branch support structure with various adjustment functions is evaluated, which is applied to a customized MCZ superconducting magnet and tested for a long period. Those obtained results show that a new multi-branch rod design with adjustable functions developed for a custom MCZ magnet could effectively improve material strength utilization to 85.14% and reduce the cross-sectional area by 16.22%. Then, the heat leakage is cut significantly. The 50 K cold shield opening was also reduced by 5196 mm\({}^{2}\), lowering heat radiation by 45%. The assembly time period was shortened to 47 min. The innovation of this study proved that the novel pulling rod structure addresses the issues of traditional rods.  
superconducting magnets; cold mass suspension rods; flexible and adjustable rod structure; high material strength utilization rate; structural design optimization Article
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 5 Simulation Results
### Algorithm Throughput Analysis  
The number of packets that are received by the sink of DBR, LEACH, UCUBG and this algorithm is shown in Figure 7. It can be seen from it that the number of packets sent to the sink by DBR, LEACH, UCUBG and this algorithm is the same before the death of nodes. However, the number of packets received by the sink of this algorithm is larger than that received by the sink of DBR, LEACH and UCUBG. This is because this algorithm is superior to the other three in the aspect of balanced energy consumption. Nodes in this algorithm start to die later than those in the other three. In addition, this algorithm has a longer network life cycle than the other three.  
Figure 6: Variation in network coverage: (**a**) with the number of rounds; (**b**) with the number of dead nodes.
## 4 Applications for IoT Technology in Smart Buildings
### Structural Health Monitoring  
Structural Health Monitoring (SHM) is an advanced system that employs sensor networks, data gathering technologies, and complex algorithms to evaluate the integrity and performance of structures in real time. These systems use several sensors, including strain gauges, accelerometers, and fiber optic sensors, to assess essential factors such as strain, displacement, vibrations, and temperature [113]. The data is handled by data acquisition systems (DAS), which convert sensor inputs into digital format and synchronize the information for thorough structural evaluation [113]. Vibration-based damage detection is an essential method used in structural health monitoring (SHM), whereby alterations in modal characteristics, including natural frequencies and damping ratios, are examined to identify possible damage [114]. Modal analysis methods, such as Fast Fourier Transform (FFT), are used to transform time-domain vibration signals into frequency-domain data, therefore revealing structural alterations that may signify stiffness degradation or damage. Moreover, SHM systems often use nondestructive testing (NDT) techniques, including ultrasonic testing and acoustic emission monitoring, to identify interior flaws without inflicting harm on the structure. Machine learning technologies, such as Principal Component Analysis(PCA) and Artificial Neural Networks (ANN), augment structural health monitoring (SHM) by detecting patterns and anomalies indicative of structural problems, hence facilitating predictive maintenance measures [115]. SHM systems use real-time monitoring via IoT integration, whereby wireless sensors relay continuous data to cloud platforms or edge devices, enabling remote oversight and immediate alarms when structural parameters surpass established limits. Finite element models (FEM) are often used with structural health monitoring (SHM) data to simulate and analyze structural responses to diverse loads, facilitating damage diagnosis and localization. The integration of these technologies enables structural health monitoring (SHM) to facilitate early identification of structural problems, enhance safety, optimize maintenance timelines, and diminish the need for expensive emergency repairs, making it a vital element of contemporary infrastructure management.
# Optimization Method for Improving Efficiency of Thermal Field Reconstruction in Concrete Dam
## 2 Materials and Methods
### Distributed Temperature Sensing (DTS)
The photoelectric signal converter and amplifier include a photodetector and an amplifier. The photodetector's role is to convert the optical signal into an electrical signal, which is subsequently amplified by the amplifier. Finally, the data acquisition and processing device comprises a data acquisition card, a personal computer (PC), a mobile client, and a cloud platform, facilitating the collection, analysis, and storage of data. The optical fibers are usually buried in the concrete according to certain rules. This process must consider the ease of construction, cost control, and construction quality. Additionally, it is crucial to ensure that the data collected can support the subsequent reconstruction of the thermal field.
# A Digital Currency Architecture for Privacy and Owner-Custodianship
## 1 Introduction  
As a form of money, cash offers many benefits to its owners. Cash is possessed directly by its owners, allowing them to transact privately, without fear of being profiled, discriminated against, or blocked, while knowing that their money is as good as everyone else's. Cash is an obligation of the central bank, and there is no intermediary that can default or abuse its trusted position as custodian. In contrast, bank deposits are intrinsically custodial and require their owners to trade the advantages of cash for a more limited set of rights. In a rush to eliminate cash, societies risk imposing this choice upon everyone. In this article, we ask whether digital currencies can offer the benefits of cash in the age of electronic payments.  
[PERSON] and his co-authors argue that anonymity is a salient feature of cash, that privacy of transactions is essential, and that the specific design features of central bank digital currency (CBDC) could have a significant impact on financial integrity [1]. Our proposal provides a solution with the flexibility to accommodate the widely-acknowledged requirements and goals of CBDC and which is more akin to cash. Specifically, it delivers a measure of control by restricting peer-to-peer transactions. However, it does not offer the near-total degree of control that seems to be taken as a requirement in some designs [2], and instead its retail applications are exposed to a corresponding limitation to their scalability,but not one that cannot be overcome by introducing additional control, in limited contexts, outside the operating plane of the ledger.  
We propose an approach that allows regulators to finely tune their choice of trade-offs in a trilemma of scalability, control, and privacy. For example, it might require that certain (or all) businesses cannot accept payments larger than a certain size without collecting or reporting additional information that limits privacy, or it might require that some individuals or non-financial businesses have a larger or smaller cap on the volume of their withdrawals into non-custodial wallets. To draw an analogy, it operates like an automated conveyor belt holding keys that are trying to meet a lock, and if they are the right fit, as determined either at large or on a case-by-case basis, then the transactions take place in an automated way. For the avoidance of doubt, such automation can include so-called \"embedded transactions\" wherein payments can be seamlessly integrated into the transaction without independent mechanisms or reconciliation.  
The rest of this article is organized as follows. The next section provides essential background on digital currencies, distributed ledger systems, and privacy-enhancing technologies. Section 3 introduces our approach to digital currency that offers strong privacy and owner-custodianship while also facilitating essential regulatory oversight. Section 4 provides an analysis of the salient features of our design. The final two sections offer our recommendations and conclusion, respectively.