esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of the Experimental Results  
Figure 3 shows the stress-strain curves of the specimens under uniaxial loading. The uniaxial compression process of the single-crack concrete specimen can be divided into four stages: (1) The nonlinear compaction stage where mostly internal micro-cracks are present in the specimen. The strain is small, and the stress remains unchanged, resulting in a relatively smooth curve. (2) The linear elastic deformation stage, with no obvious crack development. (3) The stable crack expansion stage. The slope of the curve gradually decreases as wing cracks form and develop rapidly with increasing loading. When the slope of the curve is close to zero, secondary cracks often occur in the periphery of the wing cracks. (4) The unstable crack expansion stage. At this stage, the stress reaches its limit and the material loses its stability; the wing cracks expand rapidly, reaching the specimen surface, then the curve falls suddenly, indicating quasi-brittle failure.  
Figure 3: Stress–strain curves of concrete specimens with different crack angles. (**a**) Crack width = 0.5 mm; (**b**) Crack width = 3.0 mm.  
It can be seen in Table 3 and Figure 3 that the stress-strain curves and the peak strength under uniaxial compression vary with the pre-load crack angle \(\beta\). The presence of cracks weakens the peak strength of the samples from 10.19% (case 1) to 41.09% (case 7). The fracture angle and width play a significant role in the mechanical properties of the concrete samples. As the crack angle increases, the strength of the specimen first decreases and then increases. Under the same single fracture angle, the strength of the fracture specimens including 0.5 mm wide crack is higher than that of the specimens with 3.0 mm wide crack.  
Figure 4 depicts the failure modes of the crack tip for the specimens with different pre-load crack angles. The fracture angle of the single non-closed crack for the specimens decreases and then increases gradually under normal conditions, for example, the rupture angle is smaller when \(\beta\) = 15\({}^{\circ}\) and 45\({}^{\circ}\).
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment
### Experimental Setup  
#### 3.2.1 Data Analysis  
As shown in Figure 5, the length of each sample of raw data was 64, and the raw data produced irregular waveform diagrams. The difference in the amplitude value was readily apparent. After FFT, the difference between health and faults at the peak value was also obvious.
## 3 Results and Analysis
### Psychaocustic Parameters  
Taking into account the results and code obtained by [PERSON] et al. in [44], the audio signal generated after the auralization was processed to compute the psychoacoustic parameters (that is, loudness, sharpness, roughness, fluctuation strength, and tonality--together with the Zwicker's psychoacoustic annoyance) with a specific software programmed in Matlab [45]. Table 8 summarizes the computed values for the average value and standard deviation for the different parameters.  
These values can be related to the subjective response evaluated with the public survey based on the Swedish Soundscape Quality Protocol. In our case, a total of 30 surveys were carried out, 61.5% of which were conducted by men and 38.5% by women. In addition, the information on the age ranges of the respondents corresponded to those under 30 years of age (7.7%), between 30 and 50 years of age (34.6%), and over 50 years of age (57.7%). The statistical mode value of the description of the global soundscape is good (2), with average equal to 2.3 and standard deviation equal to 1.2, while the statistical mode value in the evaluation of the appropriateness of the global soundscape to the perceived environment is very appropriate (4), with average value equal to 3.4 and standard deviation equal to 1.1.  
Figure 14 shows the overall averages of the subjective ratings (on a scale of 1 to 5, where 1 is \"strongly agree\" and 5 is \"strongly disagree\") for each of the respondents' perceived soundscape ratings. This figure shows that, in general, although listeners perceive the soundscape as little annoying (4.03/1.37), little chaotic (4.19/1.05), and slightly exciting (3.42/1.27) due to the perceived reverberation, they interpret it as monotonous (2.26/1.25) and little active (2.46/1.48) as the auralized event just presents one main sound source, which is the bailiff, but with the interpolated reverberation in each position. Therefore, the environment is perceived as somehow pleasant (2.46/1.24) due to the ambient reverberation that does not allow the source to be perceived properly at some points.  
Figure 14: Survey with average response of perceived soundscape of the Water Tribunal.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Loudness** & **Sharpness** & **Roughness** & **Fl. Strength** & **Tonality** & **Psychoac.** \\  & **(Sone)** & **(Accum.)** & **(Asper)** & **(Vacil)** & **(-)** & **Annoy.** \\ \hline
**Avg** & 8.99 & 1.31 & 0.06 & 0.44 & 0.13 & 11.17 \\
**Std** & 7.46 & 0.15 & 0.06 & 0.57 & 0.15 & 8.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average and standard deviation values of the computed psychoacoustic parameters for the auralization of the bailiff sound source in the environment of the Water Tribunal.  
According to the average value of the psychoacoustic annoyance obtained in Table 8 compared with the results shown in [44,45], we found that the results in the survey are in good accordance with the subjective perception, qualified as \"little annoying\".
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 4 Erase Algorithm  
Unlike the program and read operations described above, the erase operation is difficult to disturb because block erase operation is performed. Also, since the operation time is very long compared to the program operation, the performance criterion is also relatively less intensive than that of the program/read operation. Therefore, most of the erase operation algorithms are focused on improving cell reliability or cell variation rather than improving the performance.
# Diagnostic Performance of Serological Assays in the Detection of SARS-CoV-2: A Review
## 3 Results  
### Study Selection and Characteristics  
A total of 212 articles were identified after the electronic research in four different databases (PubMed, Scopus, Science Direct, Cochrane Library). Ten studies were excluded based on duplication removal and 202 items were assessed for eligibility. Overall, 192 articles were not included with reasons and only 10 articles were included in this review: 83 studies were excluded after examining the title, 47 after analyzing the abstract (absence of quantitative data about IgM and IgG titer), 41 after reading the full text (diagnosis confirmation not specified or absence of both IgM and IgG titer analysis), and 21 because of their study design (reviews or case reports). The main characteristics of each selected article are summarized in Table 2, which reports the number of enrolled patients and blood samples and their source, the diagnostic tool used to ascertain the positivity of SARS-CoV-2 (real-time RT-PCR), and the type of serological assay whose diagnostic capability was evaluated. By adding the participants of each study, a total of 1362 subjects were included in this review, of which 945 were cases (SARS-CoV-2-positive patients, whose diagnosis was confirmed by real-time RT-PCR), 310 were negative controls, and 107 were subjects with suspected infection (SARS-CoV-2 clinical diagnosis, real-time RT-PCR negative patients). Eight of the included articles were conducted in China, with the remainder in Italy. Data about IgM and IgG titer, sensitivity, specificity, and the positive and negative predictive value provided by each serological test were recorded in this review. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Studies** & \begin{tabular}{c} **Definition** \\ **of Cases** \\ \end{tabular} & \begin{tabular}{c} **Representativeness** \\ **of Cases** \\ \end{tabular} & \begin{tabular}{c} **Selection of** \\ **Controls** \\ \end{tabular} & \
\begin{tabular}{c} **Definition of** \\ **Controls** \\ \end{tabular} &
\begin{tabular}{c} **Comparability** \\ **Exposure** \\ \end{tabular} & **Total** \\ \hline [PERSON] et al. 2020 & + & + & + & + & +\(-\) & +\(-\) & 7 \\ [PERSON] et al. 2020 & + & + & + & + & +\(-\) & +\(-\) & 6 \\ [PERSON] et al. 2020 & + & + & + & + & +\(-\) & ++\(-\) & 7 \\ [PERSON] et al. 2020 & + & + & + & + & +\(-\) & ++\(-\) & 7 \\ [PERSON] et al. 2020 & + & + & + & + & +\(-\) & ++\(-\) & 7 \\ [PERSON] et al. 2020 & + & + & - & \(-\) & +\(-\) & ++\(-\) & 5 \\ [PERSON] et al. 2020 & + & + & + & + & +\(-\) & ++\(-\) & 7 \\ [PERSON] et al. 2020 & + & + & - & - & +\(-\) & ++\(-\) & 5 \\ [PERSON] et al. 2020 & + & + & - & - & +\(-\) & ++\(-\) & 5 \\ [PERSON] et al. 2020 & + & + & - & - & +\(-\) & ++\(-\) & 5 \\ \hline \hline \end{tabular} \(+=\) star assigned; \(-=\) star not assigned  
\end{table} 
Table 1: Quality evaluation of the included articles. Figure 1: Flow chart of the item assessments.
# Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors
## 3 IIoT Requirement Analysis
### Application Sector Specific Requirements
#### 3.2.2 Agriculture  
In the agriculture sector, specific requirements have been identified, including the need to monitor agricultural production and the measurement of factors that influence crop and livestock growth [47]. Additionally, ensuring reliable access to the web is essential in this context [44]. The monitoring of the food supply chain and the ability to track wasted resources and use data for improvement purposes are also critical aspects of agriculture-sector requirements [47].
## 2 Notation  
Let \(K\in\mathbb{N}\) and \(\mathbf{n}=(n_{1},\ldots,n_{k})\in\mathbb{N}^{K}\). We set \([K]\coloneqq\{1,\cdots,K\}\) and consider \(K\) finite sets  
\[\Omega^{k}=\left\{\mathbf{x}_{i_{k}}^{k}:i_{k}\in[n_{k}]\right\}\subset\mathbb{R} ^{d},\quad k\in[K],\]  
consisting of points \(\mathbf{x}_{i_{k}}^{k}\in\mathbb{R}^{d}\), which are called _atoms_. We set \(\mathbf{\Omega}\coloneqq\Omega^{1}\times\cdots\times\Omega^{K}\). Additionally, we define the index set  
\[\mathbf{I}\coloneqq\left\{\mathbf{i}=(i_{1},\ldots,i_{K}):i_{k}\in[n_{k}],k\in[K]\right\}\]  
and the set of \(K\)-dimensional matrices (tensors)  
\[\mathbb{R}^{i}\coloneqq\mathbb{R}^{i_{1}}\times\cdots\times\mathbb{R}^{i_{K} },\quad\mathbf{i}\in\mathbf{I}.\]  
Let \(\mathbb{P}(\Omega^{k})\) denote the set of probability measures on \(\Omega^{k}\). In this paper, we consider \(K\) discrete probability measures, also called _marginal measures_, \(\mu^{k}\in\mathbb{P}(\Omega^{k})\), given by  
\[\mu^{k}=\sum_{i_{k}=1}^{n_{k}}\mu^{k}_{i_{k}}\delta_{\mathbf{x}_{i_{k}}^{k}},\quad k \in[K],\]  
where the probabilities satisfy  
\[\mu^{k}_{i_{k}}\geq 0,\quad\sum_{i_{k}=1}^{n_{k}}\mu^{k}_{i_{k}}=1\quad\text{ for all }i_{k}\in[n_{k}],\,k\in[K],\]  
and, for all \(A\subset\Omega^{k}\), the Dirac measure is given by  
\[\delta_{\mathbf{x}_{i_{k}}^{k}}\left(A\right)\coloneqq\begin{cases}1&\text{if }x_{i_{k}}^{k}\in A,\\ 0&\text{otherwise}.\end{cases}\]  
For \(\mathbf{G},\mathbf{H}\in\mathbb{R}^{\mathbf{n}}\), we denote their component-wise (Hadamard) product by  
\[\mathbf{G}\odot\mathbf{H}\coloneqq\left(G_{i}H_{i}\right)_{i\in\mathbf{I}}\in\mathbb{R}^{ \mathbf{n}},\]  
and similarly, their component-wise division by \(\oslash\), as well as the Frobenius inner product  
\[\left\langle\mathbf{G},\mathbf{H}\right\rangle_{\mathbb{F}}\coloneqq\sum_{\mathbf{i}\in \mathbf{I}}G_{i}H_{i}\in\mathbb{R}.\]  
The tensor product (Kronecker product) of \(\mathbf{u},\mathbf{v}\in\mathbb{R}^{m}\) is denoted by \(\mathbf{u}\otimes\mathbf{v}\in\mathbb{R}^{m\times m}\). Analogues can be defined for tensors of different size.
## 4 Experiments and Results
### Contrasting Models and Ablation Experiments  
LSTM has been widely applied in prediction scenarios, primarily for capturing temporal dependencies. In time series forecasting tasks, it can effectively explore the time-varying features of data. For example, [PERSON] et al. [32] used LSTM to develop a voltage stability prediction model for power systems, while [PERSON] et al. [33] proposed an LSTM-based ship fuel consumption prediction model using a self-attention mechanism. To capture both temporal and spatial dependencies simultaneously, CNN is used to capture spatial relationships. The combination of CNN and LSTM has become a common temporal model for traffic flow prediction [26; 27; 28; 29; 30]. To evaluate the impact of the frequency domain features proposed in this paper on prediction results, the following models were compared:  
1. LSTM-CNN: LSTM is a special kind of RNN that learns long-term dependent information and is widely used in sequential data processing. CNN is a class of deep learning models specialized for processing data with grid structure. Temporal deep learning + Convolutional Neural Network approach to capture both temporal and spatial correlations;
2. Bi-LSTM-GAT: Bi-LSTM is a bi-directional LSTM model that achieves bi-directional reads by adding an inverse layer. GAT [17] utilizes an attention mechanism to adaptively learn the connectivity between nodes to achieve weighted aggregation of neighbors and achieve the ability to capture spatial correlations for spatial correlations with complex connectivity. This experiment captures both temporal and spatial correlations;
3. LSTM-CNN-TF: Transformer [18] captures the long-range dependencies in the frequency domain through a self-attention mechanism and combines it with the classical time-domain temporal deep learning + convolutional neural network approach to realize the combined time-domain and frequency-domain prediction proposed in this paper.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 4 Evaluation Results
### Comparisons with Other Models  
To enable a comprehensive comparison with other networks, we applied Conv-TasNet (C-TasNet) [22], DCCRN [8], DPCRN [27], MNTFA [28], and CRN [29] on the proposed dataset:  
1. Conv-TasNet shares structural similarities with the second part of the NABFCN network. In this experiment, we employed one channel of the proposed noisy dataset for its training and testing.
2. Similarly to C-TasNet, DCCRN is a single-channel model [8] and was the winner of the Interspeech2020 DNS challenge, where complex rules were implemented based on CNNs and RNNs. It was also trained and tested on one channel of the proposed noisy datasets.
3. Unlike C-TasNet and DCCRN, CRN is a dual-channel network [29]. For CRN-i, the parameters were selected based on [29], while for CRN-ii, we fine-tuned the parameters based on the dataset, with reference to [4].
4. The dual-path convolutional recurrent network (DPCRN) [27] combines DPRNN and CRN, making it possible to obtain a well-behaved model.
5. The multi-loss convolutional network with time-frequency attention (MNTFA) [28] for speech enhancement is an attention-based speech enhancement model where axial self-attention (ASA) is developed to model long-time dependency.  
Table 7 presents the experimental results, where the best outcomes are highlighted in bold. Notably, the proposed method achieved a significant improvement over the single-channel models of C-TasNet, DCCRN, DPCRN, and MNTFA, showing the benefits over the usage of a single channel. Furthermore, compared with the dual-channel CRN-ii model, the proposed method achieved superior performance, regardless of the size of the CRN-ii model.
## 2 Lens Antenna: Reference and Reduced Height Designs
### Reduced Height Lens Design with a Single 3D Printing Material (Model 3)
As shown in Figure 5b, the measured material properties are found to be \(\epsilon_{r}\) = 2.32 and \(tan\delta\) = 0.0804 for the reference, and \(\epsilon_{r}\) = 2.15 and \(tan\delta\) = 0.0686 for the proposed perforated block. Both are measured at 5.8 GHz. They deviated slightly from the simulation setup of 2.5 but are within the acceptable measurement error range. The part with a dielectric constant of 2 in the model 2 lens design can be replaced by this configuration, made of a single resin (\(\epsilon_{r}\) = 2.5). While maintaining the size of the model 2 lens design, we replace sections #1 and #2 in Figure 2b,c with perforated structures, as shown in Figure 6a,b. Note again that all the to-be-printed blocks of the lens in the proposed design in Figure 6 require only the same material as the resin. This enables the 3D printing process to be carried out using a single material, and the effectiveness of 3D printing technology for lens design can be maximized through one-take printing. The electrical properties of the lens antennas, including the reference, model 2, and the proposed model 3, are compared in Figures 6 and 7. As shown in Figure 6c,d, the three designs of the lens antenna operate at the same target frequency and show very similar radiation patterns. The directivity of the proposed lens antenna model 3 is 14.6 dBi, and its simulated gain is 9.82 dBi with a radiation efficiency of 31%. The low radiation efficiency is mainly due to the poor loss characteristic of the resin used. However, the gain is still acceptable compared to the electrical size of the lens, as shall be seen from Table 1. The operating bandwidth is 2.4% with a -10 dB criterion. Figure 7 shows the electric field distribution at 5.8 GHz, and Figure 8 shows Poynting's vector distribution at 5.8 GHz. In Figures 7a and 8a, for the reference design, the radiated spherical wave is manipulated into the plane wave by the lens structure. Consequently, we see that such a transformation is observed in the reduced propagation path in Figures 7b and 8b for model 2, and a similar distribution is observed in Figures 7c and 8c for model 3. Thus, it is proved that a similar radiation performance can be maintained at the reduced lens height, and such reduced height can be realized using a single printing material. Figure 4: Ring resonator simulation. (**a**) Setup with a sample of \(\varepsilon_{r}\) = 2.5. (**b**) Setup with a sample of \(\varepsilon_{r}\) = 2. (**c**) Setup with a sample of \(\varepsilon_{r}\) = 2.5 with holes. (**d**) Transmission coefficient according to \(R\). (**e**) Transmission coefficient according to the number of holes. Figure 5: Dielectric properties of the printed resin. (**a**) Printed reference and perforated cylinders with resin material. (**b**) Measured dielectric properties of the samples. Figure 6: Proposed design (model 3). (**a**) Simulation model. (**b**) Perspective view of the proposed design. (**c**) Reflection coefficient. (**d**) \(\theta\)-sweep radiation pattern (\(\phi=0^{\circ}\)).
## 4 Results
### Assessment: Wrist Strength  
Mean isometric wrist strength for the trained limb significantly increased from pre (T0) to post-intervention (T1) for flexion (\(p\) = 0.04) and radial and ulnar deviation (\(p\) = 0.03 for both). Mean isometric wrist strength significantly increased by 2.77 kg, 1.08 kg and 2.51 kg for flexion, radial and ulnar deviation, respectively. There was significant improvement in the control limb from T0-T1 in radial deviation, increasing by 3.84 kg (\(p\) = 0.03; 9.14 \(\pm\) 3.96 kg at T0, 12.98 \(\pm\) 2.95 kg at T1). Although not significant (flexion: \(p\) = 0.13, extension: \(p\) = 0.13, ulnar: \(p\) = 0.06), the control limb showed improvements in wrist strength for flexion, extension, and ulnar deviation, increasing on average by 2.15 kg, 1.89 kg and 3.04 kg, respectively. No significant differences were found between the trained and control  
Figure 4: Figural error displayed for S06 at T0 (**A**) compared to T1 (**B**) for the trained limb. Significant reduction in figural error from pre-post intervention (\(p\) = 0.03). Blue line represents target trajectory, and the grey line represents the participant’s trace attempt for each of the 6 laps.  
Figure 3: Median tracking error (degrees) comparing the trained (black/grey) and control (colors) limbs at T0 and T1. The amount of tracking error significantly decreased from T0 to T1 (\(p\) = 0.03).  
limbs at T1 as both limbs showed almost equal improvements (flexion: \(p\) = 0.14, extension: \(p\) = 0.94, radial: \(p\) = 0.44, ulnar: \(p\) = 0.23).  
There were no significant differences for maximum grip force in the trained limb (\(p\) = 0.93; 29.81 \(\pm\) 10.23 kg at T0, 29.46 \(\pm\) 9.58 kg at T1) or control limb (\(p\) = 0.25; 31.67 \(\pm\) 11.16 kg at T0, 32.49 \(\pm\) 11.11 kg at T1), indicative that grip strength did not increase post-intervention. There were no significant differences for the submaximal grip force endurance test for the trained limbs (\(p\) = 0.13). However, group averages showed a 60.12% and 78.0% increase from baseline after the 4-week intervention for the trained (\(p\) = 0.13; 34.28 \(\pm\) 43.86 s at T0, 54.89 \(\pm\) 40.08 s at T1) and control limbs (\(p\) = 0.06; 32.86 \(\pm\) 11.16 s at T0, 58.50 \(\pm\) 45.79 s at T1), respectively. No significant differences were found when comparing the trained limb to the control limb at T1 (\(p\) = 0.29).