esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 4 Improved Detections
### The Iterative-FNFT (IFNT) Detection  
The incremental FNFT (I-FNFT) detection--sketched in Figure 1 in green--uses the NFT causality property to reduce the noise entering the standard FNFT detection, applying an iterative detection [24]. When detecting the \(k\)-th symbol, the I-FNFT strategy processes the signal samples up to the time instant \(t_{k}\), such that the noise located after \(t_{k}\) does not enter the FNFT, resulting in a reduction of noise in the nonlinear frequency domain, where symbols are detected. The pseudocode for the I-FNFT detection is shown in Algorithm 1.  
```
1:Data: Samples of the received signal \(\bar{\mathbf{r}}=(\bar{\mathbf{r}}_{1},\ldots,\bar{\mathbf{r}}_{N_{b}})\)
2:Result: The detected symbols \(\bar{\mathbf{x}}_{1},\ldots,\bar{\mathbf{x}}_{N_{b}}\)
3:for\(k=1,\ldots,N_{b}\)do
4: Apply the FNFT to \(\bar{\mathbf{r}}_{1},\ldots,\bar{\mathbf{r}}_{k}\)
5: Obtain a noisy version of the nonlinear spectrum
6: Apply standard detection (matched filtering and sampling)
7: Obtain \(\hat{x}_{k}\)
8:endfor
```  
**Algorithm 1** I-FNFT detection  
The computational cost of the I-FNFT is that of a single FNFT, since the nonlinear spectrum is evaluated independently on each nonlinear frequency, by using, for example, the Boffetta-Osborne method [28].
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 2 Preliminaries
### Energy Efficiency in Different IR-HARQ Protocols  
The application scenario shown in Figure 1 can be simplified into an end-to-end transmission block diagram as shown in Figure 3. \(N_{b}\) information bits \((b_{1},b_{2},\ldots,b_{N_{b}})\) are encoded into \(LN_{s}\) symbols. Each encoded sequence \(\mathbf{c}_{i}\) contains \(N_{s}\) symbols, so the transmission rate is \(R=\frac{N_{b}}{N_{b}}\) in each HARQ round. The sender transmits the coded symbol sequence \(\mathbf{c}_{i}\) with power \(P_{i}\) in the \(i\) th \((1\leq i\leq L)\) round. The receiver combines \((\mathbf{c}_{1},\mathbf{c}_{2},\ldots,\mathbf{c}_{i})\) to decode the information bits. The transmit power \(P_{i}\) can either be constant or change with the HARQ round \(i\). When the packet is decoded successfully, an acknowledgment (ACK) signal will be fed back to the sender, and the sender starts to transmit next packet. Otherwise, an negative acknowledgment (NACK) signal will be fed back and the sender will transmit the coded symbol sequence \(\mathbf{c}_{i+1}\) with the power \(P_{i+1}\) until the packet is decoded or the maximum number of transmissions \(L\) is reached.  
Similar to [36], we define the energy efficiency \(\eta_{EE}\) as the ratio of the average number of correctly decoded information bits \(\mathcal{K}\) to the average total consumed energy \(\mathcal{E}\), i.e.,  
\[\eta_{EE}=\frac{\mathcal{K}}{\mathcal{E}}(\text{bits}/\text{J}). \tag{11}\]  
Subsequently, we discuss the energy efficiency of two different IR-HARQ transmission schemes.
## 4 Beam Capacity Estimates
### Flexural Capacities  
Table 5 provides the specimen's analytical flexural strength, curvature, and rotation capacity at first yield (M\({}_{y,0}\), \(\phi_{y,0}\), \(\theta_{y,0}\)), effective yield (M\({}_{y,\text{eff}}\), \(\phi_{y,\text{eff}}\), \(\theta_{y,\text{eff}}\)), and at ultimate point (M\({}_{u}\), \(\phi_{u}\), \(\theta_{u}\)) in both loading directions. First yield and ultimate expected flexural capacities were obtained via moment-curvature (M-\(\Phi\)) analysis using the software XTRACT v3.08 [37] with as-tested concrete compressive strengths and rebar yield and ultimate stresses per mill certificates. Confinement provided by the transverse reinforcement was accounted for in terms of increased strength and ductility of the core concrete. No additional confinement provided by the FRP was considered. The material model proposed by [PERSON] et al. [38] was used for both the unconfined and the confined concrete materials. Since the fibers in the FRP laminates were oriented perpendicular to the beam's axis, it was assumed that the FRP did not provide any additional flexural capacity. The adopted strain values for the unconfined concrete associated with compression yield, peak strength, crushing, and spalling were 0.0014, 0.002, 0.004, and 0.006, respectively. The tensile strength of concrete was estimated as the modulus of rupture, in accordance with Equation 19.2.3.1 in ACI 318-14. A parabolic strain-hardening model was used for the reinforcing steel, assuming a hardening onset strain value of 0.012 and a failure strain equal to the corresponding experimental percent elongation at failure. The effective yield point was determined as the intersection of the secant line to the first yield point with the regression line fitted to the set of M-\(\Phi\) values within the inelastic hardening range of the curve. Rotation at yield (for both, first, and effective yield points) was derived from the corresponding curvature value at yield as: \(\theta_{y}=\phi_{y}\) L\({}_{s}\)/2 (with L\({}_{s}\) the shear span). Rotation at ultimate capacity was determined as: \(\theta_{u}=\theta_{y,0}+(\phi_{u}-\phi_{y,0})\)L\({}_{\text{P}}\), assuming a plastic hinge length (L\({}_{\text{P}}\)) equal to the effective depth of the beam's section (L\({}_{\text{P}}=\) d). To note, the calculated M-\(\Phi\) relationships represent flexural strength and deformation capacities under monotonic loading, ignoring strength degradation due to cyclic loading and additional flexibility due to shear deformations and/or strain penetration at the support.  
Figure 9: Instrumentation layout: (**a**) specimen S1B; (**b**) specimen S5.
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 2 Deep Small-World Neural Network (DSWNN)  
A deep auto-encoder network is a deep learning network with multiple hidden layers between the input and out layers, which can model complex non-linear relationships among multiple types of variables. The parameters of this network are initialized by unsupervised learning for input data layer by layer, and then supervised learning is used for fine-tuning. In this network framework, deep learning models lead to more complex features at higher output layers, and the learned complex features will be invariant with the change of input data [22; 23]. In this paper, the proposed DSWNN model is an improved deep auto-encoder network that has small-world characteristics. Such improvement adds additional neuron connections between non-adjacent hidden layers on the basis of the original network structure. In particular, the DSWNN model is composed of multiple RBM stacks, and Figure 1 gives an example of the DSWNN structure with four hidden layers. The training process of the DSWNN model includes three phases, pre-training, small-world transformation and fine-tuning, which are applied to obtain the model parameters.  
Figure 1: Structure of the deep small-world neural network (DSWNN) model.
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### Comparing Scheduling Efficiency
#### 4.4.1 Average Slowdown Comparison  
First, the final policy quality of each scheduler on the training set was verified. In DeepRM and RLSchert, the job_rate was set to 0.7. The comparison result of the slowdown of each scheduler on the training set is shown in Table 6. From the results, we can observe that, regardless of whether the kill policy is used, RLSchert performs better than other schedulers. Besides, the kill policy used here is the PPO-based policy. If the rule-based kill policy is used, the average slowdown is 8.93 (not shown in the table), which is bigger than 8.83. This also illustrates that the dynamic kill policy enhanced by PPO is better than the rule-based policy.  
The average slowdown comparison result of each scheduler on the test set is shown in Table 7, which is consistent with the result on the training set. RLSchert with the PPO-based kill policy is the best. Besides, the result of RLSchert with the rule-based kill policy is 8.69. At the same time, as a static scheduling policy, the SAF is superior to other heuristic scheduling algorithms in the slowdown metrics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Method & Tetris & Random & SJF & Tetris* & SAF & DeepRM & **RLSchert** \\ \hline \hline \multicolumn{8}{c}{_Scheduling **without** kill policy_} \\ \hline AveSlowdown & 15.31 & 12.81 & 11.63 & 11.62 & 11.44 & 10.6 & **9.7** \\ \hline \hline \multicolumn{8}{c}{_Scheduling **with** kill policy_} \\ \hline AveSlowdown & 14.82 & 12.65 & 11.81 & 12.64 & 11.88 & - & **8.83** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The policy quality of the average slowdown on the training set.  
Figure 9: The convergence behavior of imitation and reinforcement learning with the kill policy.
# Hamming Code Strategy for Medical Image Sharing  
[PERSON]  
1 Institute of Computer Graphics and Image Processing, Hangzhou Dianzi University, Hangzhou 310018, China 11 Institute of Computer Graphics and Image Processing, Hangzhou  
In the recent decades, some researches related to protecting privacy information for mobile services on different techniques, such as the redesign of the architecture of network [6], the non-interactive privacy-preserving protocol for image similarity computation [7], the data sharing protocol by using a new cryptographic primitive named online/offline attribute-based proxy re-encryption, and the transform key technique [8], have been developed. Also, some encryption methods based on cryptography, such as homomorphic encryption [9], elliptic curve cryptography based encryption [10] and chaotic oscillation theory-based encryption [11; 12], were designed and applied to provide the confidentiality of patients' health information.  
On the other hand, one characteristic of medical images is their high level of accuracy and detail. If a medical image is made blurred and loses its details, it will be useless. Therefore, instead of employing costly encryptions, we study how to make stored medical images less meaningful to the hackers who, somehow, successfully break into a database full of medical images.  
In this paper, we propose a scheme, which creates shadow images (referred to as shadows) from medical images and stores them in different databases. The original images are only retrievable if all the shadows are collected. Individually, a shadow would not visually reveal any information regarding a patient's condition. This would satisfy the requirement for medical images. The proposed scheme is based on Hamming code, which has been used extensively in image processing [13; 14; 15]. Its encoding and decoding processes are very efficient, thus, it can help our scheme achieve low computational costs and guarantee a fast response when deployed on mobile devices.
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling
### Software and Data Formats  
#### 4.3.1 Software  
The BIM software that is currently available can be categorized into two main groups: 3D modeling and 3D visualization. Three-dimensional modeling software features parametric design tools primarily focused on modeling new constructions but can also be employed to create digital representations of existing buildings [47]. Most modeling software, in any case, facilitates the creation of standard elements typical of contemporary construction, requiring more manual modeling work for buildings with unique geometries, such as heritage buildings. On the other hand, 3D visualization software is designed, as its name suggests, solely for viewing the geometry of the BIM model and, in most cases, for consulting the associated metadata. Minor operations, such as taking measurements or creating sections, are typically available, but they do not allow for editing the model.  
The current BIM market offers a wide range of 3D modeling and visualization software, providing diverse options tailored to various needs in heritage and construction modeling. Commonly used 3D modeling software includes Autodesk Revit, ArchiCAD, Nemetschek Allplan, Bentley OpenBuildings Designer, Edificius, SketchUp, Tekla Structures, Vectorworks, and 3 DE experience. Popular choices for 3D visualization include Autodesk Viewer, SketchUp Viewer, Tekla BIMsight, and Navisworks Freedom. While not exhaustive, this list offers a snapshot of the most frequently adopted tools, reflecting the broader range of BIM software available for modeling and visualizing both contemporary and historic structures.  
Autodesk Revit is by far the most used BIM software. In fact, according to the European Architectural Barometer [64], 45% of architects in Europe use Autodesk Revit as BIM software, followed by Graphisoff's ArchiCAD, which is used by about 33% of professionals. However, there are differences between countries; for instance, in Spain, up to 74% of BIM users use Revit and only 4% use ArchiCAD [65], while in Germany, the main BIM software is Nemetschek's Allplan, chosen by 31% of architects for BIM purposes.  
Although BIM software can be affordable for large companies and public institutions, it is typically very expensive, making accessibility a challenge for small businesses and private users. Among the most commonly used modeling software in the BIM sector, only Edificius is completely open access. Visualization software is generally designed and developed by BIM software suppliers. Tekla and Autodesk, for example, have created BIM software in response to the need to adapt 2D and 3D drawing tools to the current trend of using BIM technology as the primary working method in the construction and structural design of new buildings. Accessibility to BIM tools presents a challenge as the methodology expands, especially since some countries have mandated the use of BIM technology for project presentations of newly constructed buildings to ensure their safety and accuracy.
# Numerical Laplace Inversion Method for Through-Silicon Via (TSV) Noise Coupling in 3D-IC Design
###### Abstract  
Typical 3D integrated circuit structures based on through-silicon vias (TSVs) are complicated to study and analyze. Therefore, it seems important to find some methods to investigate them. In this paper, a method is proposed to model and compute the time-domain coupling noise in 3D Integrated Circuit (3D-IC) based on TSVs. It is based on the numerical inversion Laplace transform (NILT) method and the chain matrices. The method is validated using some experimental results and the Pspice and Matlab tools. The results confirm the effectiveness of the proposed technique and the noise is analyzed in several cases. It is found that TSV noise coupling is affected by different factors such as source characteristics, horizontal interconnections, and the type of Inputs and Outputs (I/O) drivers.  
3D-IC design; NILT; TSV noise coupling; RDL; chain matrix; interconnect line Article
## 1 Introduction
* A realistic experimental validation is carried out to demonstrate the feasibility and performance of this solution in real life. * We provide a in-depth description of the architecture, scenarios, testing platform and methodology, illustrated with meaningful pictures. * We discuss the impact of using SCHC, both on energy consumption and latency, under different conditions (radio conditions, payload size, transport mode) and give potential future research directions on the benefits of using SCHC over NB-IoT. The rest of the paper is structured as follows. A review of the relevant literature is presented in Section 2, along with a summary table of the existing work on applying SCHC to LPWA networks. Section 3 provides some technical background for readers not familiar with SCHC and cellular IoT networks. A detailed description of the scenarios and the architecture is given in Section 4, followed by a description of the testing environment and configuration in Section 5. Section 6 discusses the results, and finally, Section 7 concludes the study, highlights the main findings and provides potential future research directions. In order to make it easier to read, Table 12 in the Appendix A summarizes the abbreviations used in this paper.
## 6 Nanofertilizers
### Zeolite-Based Nanofertilizer System for Sustainable Agriculture  
Zeolites are mineral materials that are safe to the soil and function well to conserve available soil nutrients, for efficient crop production and reduction in environmental hazards associated with agricultural activities [68]. Natural zeolite, which comprises over fifty mineral types of basically alkali and alkaline earth aluminosilicates, has been developed into nanofertilizer in recent times. This is mainly due to its availability and inexpensive nature, and it has been applied in the cultivation of maize [69; 70]. Apart from providing some minerals for the plants, it is a smart carrier and regulator of major mineral fertilizers. Zeolite acts as a carrier of nitrogen and potassium fertilizers, leading to reduced application quantities with enhanced productivity [71]. Zeolite compound was found to mix with humus materials and enhance the productivity of crops. Zeolite nanocomposites of nitrogen, phosphorus and potassium (NPK), in addition to other nutrients and micronutrients of amino acids, mannose, Ca, Fe, and Zn, have been reported, which have aided crop growth and are absorbed in those crops, as explained in Figure 1[6]. One disadvantage of zeolite is its inability to absorb and adsorb anions, thus it is enhanced in this perspective with biopolymers [68]. The three-dimensional crystal structure of zeolite allows high cationic exchange, and, coupled with its excellent porosity and extensive surface area, it is capable of retaining positive and negative nutrient ions for a long time. These exposed surfaces have excellent interactions with cations and polar molecules due to the effectiveness of the hydrated negative inorganic ions contained therein. All these properties have positioned zeolite as one of the most sought materials to be used in fertilizers. It also aids in the gradual release of the nutrients into the soil and absorption by the plants, which will avoid the case of nutrient loss as in conventional systems [72]. As compounds of interest to researchers in different fields, the International Agency for Research on Cancer (IARC) and Food and
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 5 Experimental Results
### Cross-Subject Model Experiment  
In the second part, the aim was to create a cross-subject and a cross-session models. Creating a model using the EEG signals from the multiple subjects is often very challenging. Revealing brain activity that shows the same signals for several people encounters a problem caused by subjective perception. Nevertheless, it can be assumed that if human speech is created and controlled in the same areas of the brain, it may also show similar brain waves, and these similarities can be revealed by machine learning.  
In this part we selected data sets from combination of 3 subjects which were divided into 80% training set and 20% validation set. The created model was tested on the data set from the 4 th subject.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject** & **Precision** & **Recall** & **Accuracy** & **F1 Score** \\
**Number** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline
1 & 87.61 & 89.11 & 86.65 & 88.36 \\ \hline
2 & 73.76 & 85.16 & 74.72 & 79.05 \\ \hline
3 & 89.84 & 90.68 & 88.75 & 90.26 \\ \hline
4 & 88.64 & 90.43 & 88.35 & 89.53 \\ \hline Average & 84.96 & 88.85 & 84.62 & 86.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results for test set single-subject model.  
Figure 3: Graphical representation of speech labels (a blue shape) compared with predicted labels (a red crosses) within the range 0 to 1.
## 3 Results and Discussion  
### Atomization of Cellulose Suspension  
The morphology of the suspended cellulose before atomization was observed using by AFM. This technique consists of a mechano-optical instrument that is capable of resolving the topography of a material by means of a nanometer-scale probe at high magnification. Figure 2 shows two AFM images that were obtained from the dilute resuspension of the as-received sample in water for the scanned areas of 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m (Figure 2a,b) and 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m (Figure 2c,d). It can be observed that samples are formed by a complex entangled network of micro- and nanofibers with mean diameter dimensions ranging from 10 nm, for individual nanofibrils, to 146.2 nm for bundles of these nanofibrils. In Figure 2e,f, corresponding to the 3D phase representation of each scanned area, one can see that the highest topography of these figures corresponds to the bundles of nanofibrils, whereas the lowest valleys are related to the smaller nanofibril bundles or individual ones.  
The morphology of the resultant atomized cellulose powder obtained from the diluted cellulose suspension in water was observed by FESEM and its micrographs, at different magnifications, are shown in Figure 3. As it can be seen in Figure 3a, atomization gave rise to micrometer aggregates of cellulose sizing approximately 3 \(\pm\) 1 \(\upmu\)m. The FESEM image that was taken at higher magnification, shown in Figure 3b, evidences the formation of microparticles with a rod-like morphology and an aspect ratio of nearly four. Figure 3c, showing the magnification of the microaggregate surface, reveals that these structures were formed by the so-called MFC, originally present in the suspended cellulose with fibers with thicknesses in the nano-range. Therefore, atomization was able to successfully yield microparticles of cellulose, in the powder form and with a low aspect ratio, being constituted by MFC. Similar results have been recently obtained by [PERSON] et al. [38] during the atomization of 0.5-1 wt % CNC suspensions by spraying techniques at high pressure (244.8 kPa). The authors reported that the morphology of the nanocrystals was maintained, although they also tended to form micrometric agglomerates due to the high attractive forces and their high aspect ratio.  
Figure 2: Atomic force microscopy (AFM) image of microfibrillated cellulose (MFC) in tapping mode for a scanned 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m area in height (**a**) and amplitude (**b**) and for a scanned 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m area at height (**c**) and amplitude (**d**). Three-dimensional phase representation of the scanned 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m area (**e**) and 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m area (**f**).