esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Retinal Implant  
A PIFA antenna with a MedRadio (401-406 MHz) band is presented by Orfeas and Nikita in Ref. [103] for a retinal implant. The antenna diameter is approximately 12 mm with a thickness of 1.8 mm in PEEK encapsulation. In this work, Rogers 3210 is selected as the substrate with \(\varepsilon_{r}\) = 10.2 and tan \(\delta\) = 0.003. This antenna achieves a peak gain of \(-\)36.82 dBi with a bandwidth of 3.4% in PEEK encapsulation. It can achieve a SAR of 2 W/kg for 10 g of tissue when the input power is 21 mW in PEEK encapsulation.
## 5 Conclusions  
On the basis of in situ monitoring data, the development of concrete temperature at an early age was studied and a simplified method for real-time prediction of early-age temperature was obtained. The relevant conclusions are summarized as follows:  
(1) At an early age, the external environment and old concrete blocks demonstrated no obvious influences on the internal temperature of new concrete. The heat released by cement hydration and a postcooling system were found to be the main factors that control the internal temperature development of concrete. For rapid prediction of early-age temperature in mass concrete, it is feasible to only consider the contribution of hydration heat and the cooling effect of cooling pipes on the development of concrete temperature.  
(2) Under the same construction background, a linear relationship was observed between PTR (the difference between the placement temperature and the pouring temperature) and DAP (the difference between the air temperature and the pouring temperature) during concrete pouring, and the rate of change of PTR according to DAP is mainly affected by the effect of cooling measures, such as a spray cooling system. The cement hydration rate was found to be quite slow, with few contributions to temperature variation during this stage.  
(3) A simple empirical formula for calculating the cooling effect of the cooling pipes was formed, which well reflected the influence of the cooling pipes at the measuring point of the point-thermometer.  
Figure 11: Monthly averages of the related data: (**a**) Baihetan dam; (**b**) Wudongde dam.  
However, other layouts of cooling pipes were not considered; further research is needed to improve this formula.  
(4) Owing to the Baihetan arch dam being thicker than the Wudongde arch dam, successful application of the forecasting method in the two dams and different sized concrete blocks between the two dams indicated that the process of this predictive formula could be used for other, similar projects. This method may be more suitable for larger concrete blocks because the interior of the block is closer to the adiabatic environment. Although it was proven to be a convenient method to achieve real-time temperature warning system predictions, this method still needs to be applied and improved in further practice.  
Conceptualization, Y.H.; formal analysis, G.A., Q.L., Y.H., and H.Y.; funding acquisition, Q.L. and Y.H.; investigation, G.A.; methodology, G.A.; resources, N.Y.; supervision, N.Y., Q.L., and Y.H.; validation, G.A., N.Y., Q.L., and Y.H.; visualization, G.A.; writing--original draft, G.A.; writing--review and editing, N.Y., Q.L., Y.H., and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China, grant numbers 51979145 and 51839007.  
The authors declare no conflict of interest.
## 5 Methods
### Independent Component Analysis for Artifact Removal
Another known limitation is that ICA performs poorly when attempting to separate components that follow similar Gaussian distributions. Although this is a theoretical constraint, in practice many neural and artifact-related sources are sufficiently non-Gaussian, allowing ICA to perform well in real-world EEG applications. Finally, even when ICA correctly isolates artifact components, distinguishing them from brain-related activity can be difficult--especially when their features are subtle or overlap in spectral content or scalp distribution. In such cases, expert review or dedicated algorithms are required to guide the removal process. Incorrectly discarding components may result in either insufficient artifact removal or the loss of neural signals. As demonstrated in [24], rank deficiency can negatively affect the performance of ICA. Therefore, prior to ICA decomposition, the rank of the preprocessed data from each session was verified to ensure that full rank was preserved. No rank deficiency was observed, and ICA was applied without compromising data rank. In this study, the well-known and widely used FastICA [25] algorithm was employed, with the number of components set to match the number of channels in the data. Default function parameters were used. The hyperbolic log function (logcosh) was chosen to measure departures from normality, offering a suitable balance of performance and speed in standard applications. The algorithm was run for a maximum of 200 iterations, providing sufficient steps to ensure convergence, with a tolerance of 0.0001 to achieve accurate results without excessive computation time. The initial matrix for separating signals was generated randomly, allowing the algorithm to start with neutral assumptions. Artifact components were discarded by means of visual inspection, involving the manual evaluation of the component contents and their impact on the signal. This procedure was performed over the entire duration of the recorded signal.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 5 Application and Evaluation
### Implementation  
In the following, we introduce the technologies we used for the proof-of-concept implementation of the presented approach. The prototypical implementation will serve as the basis for the following application and evaluation. The full implementation has been published and can be used for research purposes under the CC BY 4.0 license (DOI: 10.5281/zenodo.7323786).  
General: The prototype is mainly implemented in Java. The TSA Negotiator and the Data Recorder were implemented analogously to the outlined processes in Figures 4 and 5. For the handling of any cryptographic operations and security-related protocols, such as the local creation of a timestamp request, the library Bouncy Castle was used. Bouncy Castle is a collection of open-source cryptographic programming interfaces [43]. It provides many methods for handling, e.g., X.509 certificates, key pairs, and timestamping authorities.  
Timestamping Authority: For the certification of the data, an open timestamping authority of the \"German Research Network (DFN)\" is used. For research purposes, the TSA can be used free of charge. The exchange of data complies with RFC 3161. The TSA used can easily be replaced by any other RFC 3161-compliant TSA via the adjustment of a single parameter in the implementation [44].  
Data Recording Database: For the persistence of the recorded data and the TSA signatures, a MongoDB instance is utilized. MongoDB is a No-SQL database that is well suited for storing semi-structured and unstructured data. Therefore, the database is ideally situated to persist the original records data, which can occur in a wide variety of different file formats, along with the associated signature of the TSA [45].  
Communication: For communication between the participants, a simple peer-2-peer was implemented using the client-server framework Netty. The entire TSA negotiation is performed over the P2P network. The data that needs to be transmitted is exchanged with each other via TCP/IP.  
Deployment: For a user-friendly deployment on multiple machines, the implementation was fully containerized using Docker [46]. For this purpose, two Docker containers were created containing all components, methods, and interfaces needed.
## 2 Materials and Methods
### Laser-Induced Breakdown Spectroscopy (LIBS)  
Laser-induced breakdown spectroscopy was applied for the detection and analysis of the lithium element on the graphite anodes. The laser source was focused on the graphite electrode surface and electrode materials were ablated and excited. During the ablation process, plasma was generated and emitted the element-specific spectrum. In this work, a LIBS system (type: FiberLIBS, SN013 Scepta GmbH, Teltow, Germany), a nanosecond Nd:YAG laser (laser wavelength 1064 nm, laser pulse duration 1.5 ns, repetition rate 100 Hz), a Czerny-Turner spectrometer, and two charge-coupled device (CCD) detectors were applied for element mapping and depth profiling. For generating laser-induced plasma, a laser focus diameter of about 100 \(\upmu\)m was generated by applying a focusing lens with a focal length of 75 mm [12]. The detectors are enabled to cover spectra in ranges of 229-498 nm and 569-792 nm. For the quantitative detection of lithium, a calibration procedure had to be performed which is described in more detail in [13]. Due to the spot size of the laser beam, the horizontal resolution of element mapping is limited to 100 \(\upmu\)m.
# Enhancing Methyl Orange Degradation with Laser-Generated ZnO and Ce-Doped ZnO Nanoparticles
###### Abstract  
The presence of Methyl Orange, a hazardous organic compound typically found in industrial wastewater, presents a significant environmental challenge, necessitating the development of effective strategies for its removal to mitigate adverse impacts on aquatic ecosystems and human health. In this work, we utilized 60-watt continuous-wave (CW) and high-power CO\({}_{2}\) laser with 10.6 \(\upmu\)m wavelength to synthesize Zinc Oxide nanoparticles (ZnO NPs) doped with 1% Cerium (Ce) as photocatalyst at standard laboratory conditions through Laser-Assisted Chemical Bath Synthesis (LACBS) technique. The synthesized nanostructures were thoroughly characterized using UV-vis spectroscopy, field-emission scanning electron microscopy (FESEM), and X-ray diffraction (XRD) techniques. Subsequently, their efficiency in degrading Methyl Orange (MO) dye was evaluated under UV and sunlight irradiation. The findings revealed that the 1% Ce-doped ZnO sample exhibited enhanced photocatalytic efficiency under both UV and sunlight irradiation, as well as active adsorption capabilities in the absence of light. Notably, the most effective photodegradation performance was achieved under solar radiation, with an impressive 95% of MO dye degradation observed after just 90 min of exposure.  
Ce-doped ZnO; CO\({}_{2}\) laser; photodegradation; photocatalyst; methyl orange Article
## 3 Reference Context
### Reconfiguration Model  
The reconfiguration model serves to evaluate the status of the manufacturing system and facilitate decision making regarding reconfiguration activities, adhering to two distinct policies: periodic review and continuous review. In order to dynamically support reconfiguration, the assessment prioritizes the direct workload of jobs for each manufacturing operation, ensuring adaptability to changing demands and operational needs. When scheduling identifies the next manufacturing operation (k), its direct workload increases, considering the processing time of the operation:  
\[WL_{k}=WL_{k}+PT_{ik} \tag{1}\]  
Then, the average direct workload over the manufacturing operations is computed:  
\[WLav=\frac{\sum_{k=1}^{K}WL_{k}}{K} \tag{2}\]  
The machines' reconfiguration activities can be initiated under two conditions: periodic review and continuous review.  
Following the periodic review approach, at each period Tp, the reconfigurations of the machines are evaluated. First, it is determined if WLav is greater than a threshold Thav. If this condition is true, then the potential machines to reconfigure can be evaluated. Then, the operation k1 with higher direct workload and the operation k2 with lower direct workload are determined. To start the reconfiguration activities, the direct workload k1 has to be greater than WLav \(\times\) (1 + Th), and k2 has to be lower than WLav \(\times\) (1 - Th). If the above conditions are true, then the reconfiguration model determines the machine to reconfigure. The first machine detected that performs the operation k2 with a lower workload compared with the average workload is reconfigured to perform operation k1, which is characterized by a critical level of workload.  
In the case of a continuous review, the reconfiguration of the machine is evaluated after every change in workload computation. When the next operation of the job is determined, and the relative WLk is updated (Equation (1)), if WLk is greater than WLav \(\times\) (1 + Th), then it is verified if the operation k2 with the lower workload WLk2 is lower than WLav \(\times\) (1 - Th).  
If the above conditions are true, then the reconfiguration model determines the machines to reconfigure.  
The first machine detected that performs the operation k2 with a lower workload compared with the average workload is reconfigured to perform operation k1, characterized by a critical level for the workload.
# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 3 Materials and Methods  
We have designed a model that aims to classify tumor grades correctly. The workflow consists of 5 steps. These are tumor segmentation, feature extraction, statistical analysis, feature selection and classification. The brain tumor regions were evaluated for the Region Of Interest (ROI) interactively. The size of the ROI should be large enough to accurately capture the tissue information, thus revealing statistical significance. It should also be noted that the size of the ROI may depend on the MRI acquisition parameters. Using a 200 \(\times\) 200 pixel ROI in an image with a resolution of 2.5 \(\times\) 2.5 mm\({}^{2}\) is not the same as for an image of 0.7 \(\times\) 0.7 mm\({}^{2}\). Generally, the tumor region found in brain MRI images has a very small proportion in the general image. This situation makes the detection of the tumor difficult. For this purpose, probable tumor region was taken into a convex area manually with ROI. In applications, the multi-gene region that is not more concave than rectangular is determined manually. As a result of this process, the part outside the ROI region will be ignored, so higher success will be achieved. For texture analysis, regions of interest (ROIs) for each MR imaging set of each patient were manually drawn slice-by-slice in the axial plane for each of the available sequences.  
In next step, ROIs were segmented with the GrowCut segmentation algorithm [25]. Later, 3D wavelet transform filters were used to detect radiomic features of tissue properties from multispectral layers. Quantitative parameters of textural features and first-order features were obtained using the radiomic feature extraction method. The statistical significance of the data was tested with the Mann-Whitney U non-parametric test. Radiomic features resulting from the radiomic feature extraction process were classified by the DNN. Figure 1 shows the block diagram of the proposed system.  
Figure 1: The block diagram of the proposed system.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 4 Algorithm Introduction and Simulation Analysis
### Simulation Analysis
Finally, we incrementally increased ISL bandwidth while holding the other variables constant to investigate how the transmission capacity of ISL affects the system cost. Figure 9 shows how effectively the ISL bandwidth affected the system cost. The increased bandwidth enabled faster offloading of subtasks among satellite nodes. It allowed for the system cost to be gradually reduced as well, and the GABPSO algorithm still performed the best. The effect of the same growth on system cost was more noticeable when the ISL bandwidth  
Figure 6: System cost vs. high-load satellite rate. Figure 7: System cost vs. subtasks original data quantity. was minimal. When the ISL bandwidth is large enough, the inter-satellite transmission delay and energy consumption will be negligible. In that case, the system cost will almost equal the delay and energy consumption required for the computation. It is anticipated that inter-satellite task scheduling will be able to be finished in a relatively short time in the future if satellite transmission performance is markedly enhanced. In the above simulation scenarios, the GABPSO algorithm was always able to achieve the best performance because it took into account the fast converge capability of the BPSO algorithm and the variational properties of the GA. Due to its cross-mutation property, the GA algorithm was also better able to look for better solutions. The MPSO and BPSO algorithms had the worst overall performances because they tended to fall into local optima. The simulation results are consistent with the analysis in the convergence curve.
## 5 Case Analysis
### The Effect Factor Analysis
#### 5.3.5 Block Geometric Position  
Keeping other parameters invariant, we change \(\alpha_{1}\) and analyze the effect of the block geometry position on the strength and equilibrium slip coefficient. \(\alpha_{1}\) ranges from \(0^{\circ}\) to \(180^{\circ}\).  
From Figure 15a, we can see that with the increase of \(\alpha_{1}\), the strength slip coefficient and the equilibrium slip coefficient of the quadrilateral block are approximately sinusoidal changes. When \(\alpha_{1}\) changes from \(30^{\circ}\) to \(120^{\circ}\), the quadrilateral block starts falling off. From Figure 15b, we can also observe that with the increase of \(\alpha_{1}\), the equilibrium slip coefficient of the triquetrous block is approximately sinusoidal, but the strength slip coefficient is undulating. When \(\alpha_{1}\) changes from \(-20^{\circ}\) to \(10^{\circ}\), and from \(60^{\circ}\) to \(120^{\circ}\), the triquetrous block starts falling off. The change of \(\alpha_{1}\) causes the change of the cleat angle around the block, which causes the change in the induced stress field and at last causes a fluctuation in the strength and equilibrium slip coefficient.  
Figure 14: Influence of the cleat angle on the slip coefficient distribution. (**a**) The quadrilateral block. (**b**) The triquetrous block.
## 3 Results
### Performance of Manual and Semi-Automated RFF Estimation Methods
Post hoc analyses showed a large effect for both voicing offset (\(p<0.001\), \(V=0.59\)) and onset (\(p<0.001\), \(V=0.62\)), such that aRFF-AP was more likely to correctly identify the boundary cycle. Finally, the boundary cycle classification accuracy was significantly different between semi-automated RFF algorithms (aRFF-AP, aRFF-APH) for both voicing offset and onset (\(p<0.001\)); yet, the size of this effect was negligible for offset (\(V=0.08\)) and small for onset (\(V=0.15\)). Figure 5: Boundary cycle identification of each RFF estimation method (manual, aRFF-AP, aRFF-APH). For (**a**) voicing offset and (**b**) voicing onset. Results for manual RFF estimation are shown in light orange, aRFF-AP in dark orange, and aRFF-APH in teal.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
###### Abstract  
In cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). However, traditional reconstruction algorithms exhibit suboptimal performance, and conventional WSS methods fail to fully capture the inherent structural information of wideband spectrum signals. Moreover, most existing deep learning-based approaches fail to effectively exploit the sparse structures of wideband spectrum signals, resulting in limited reconstruction performance. To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing compressed wideband signals. BEISTA-Net integrates the iterative shrinkage-thresholding algorithm (ISTA) with deep learning, thereby extracting and enhancing the block sparsity features of wideband spectrum signals, which significantly improves reconstruction accuracy. Next, we propose BSWSS-Net, a lightweight network that efficiently leverages the sparse features of the reconstructed signal to enhance WSS performance. By jointly employing BEISTA-Net and BSWSS-Net, the challenges in CSS are effectively addressed. Extensive numerical experiments demonstrate that our proposed CSS method achieves state-of-the-art performance across both low and high signal-to-noise ratio scenarios.  
Keywords:compressive spectrum sensing; deep learning; wideband spectrum signal reconstruction; wideband spectrum sensing; block sparsity +
Footnote †: journal:
## 3 Empirical Results
### Malmquist Productivity Index  
Malmquist productivity index (MPI) was used to evaluate the productivity change of a company between different periods. The index is defined as the combination of Catch-up representing the change in efficiency and Frontier-shift indicating the technological changes. In order to evaluate the change of the productivity, efficiency, and technology of 24 companies from 2013 to 2022, this research used DEA-Solver Pro V13.2 to analyze.  
Firstly, the efficiency change is examined with results shown in Table 6. It is observed that each DMU experienced the change in efficiency year by year with the great fluctuation of average catch-up scores in the early period, and then slightly increased in later years. As observed, all the DMUs experience both improvements and regress in efficiency with the corresponding score higher and lower than 1, respectively.  
In 2014-2015, MPI hit the highest score and it means that, in 2015, the ICT industry of Vietnam has the best productivity, besides that it is the only one year that both Catch-up and Frontier scores above 1, which means that this year is the best productivity improvement scenario because the productivity change is associated with a positive technology shift and an improvement in technical efficiency. As we discussed before, from 2017 to 2022 the index of Catch-up scores is under 1 and Frontier-shift scores are more than 1. As defined, MPI is mixed scores from Catch-up and Frontier, so, in this case, it points out that the only reason for productivity gain is due to average technology progress. However, in this case, the technology moves from a positive shift facet towards a negative shift facet, indicating an unfavorable strategy change. Figure 1, below, shows the average mean of MIP change.
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 3 Electrical Modeling of CNT Networks
### Layered 2D Models for CNT Network Simulations  
Applications involving thicker layers of CNT-filled material require modeling the CNT network in a way that accounts for the location of the nanotubes across the thickness of the conductive layer. One possibility is to iterate the generation of the 2D network and consider the material as composed of different layers, each represented by a 2D network. This approach is proposed in [46; 47] based on the assumption that CNTs tend to align mostly parallel to the substrate, and therefore connections along the vertical direction are negligible. To validate this approach, the authors validate the model by comparing the simulated and measured resistivity of a CNT-based conductive layer of different widths and lengths. The experiments were matched by means of two fitting parameters: the nanotube density and the ratio between the junction resistance and the theoretical limit contact resistance. Nevertheless, the model fails to catch the resistivity decrease observed for widths greater than 2 \(\upmu\)m.