esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
## 2 Luminescent Materials for VLC Systems
### Polymers  
After discovering luminescent properties in polymers, some investigations focused their efforts on improving the light output, lifespan, and power efficiency of polymers introduced in diode-based device structures [60; 61; 62]. This notion is the case for another type of OLED devices containing organic electroluminescent polymers, which are known as PLEDs, that were previously reported by [PERSON] et al. [60]. In this context, the principal difference between SMOLEDs and PLEDs is the processing method. PLEDs are more related to solution processing, whereas SMOLEDs are commonly based on thermal-vacuum evaporation. Thus, it would be desirable to use the PLED processing method because it is cheaper. Next, it is important to highlight that polymer optical fibers, such as PLEDs, present a set of advantages compared to copper cables, glass fibers, and wireless communication systems, since these materials grant excellent properties, such as high fracture toughness, elevated elastic strain limits, electromagnetic interference, etc. [45]. In this line, [PERSON] et al. [49] reported transmission speeds of 10 Mbps using a PLED based on luminescent poly[2-methoxy-5-(3',7'-dimethyloctyloxy)-1,4-phenylenevinylene] (MDMO-PPV), demonstrating the potential of polymers in the VLC area. On the other hand, a fast color converter was designed using a blend of conjugated polymers based on poly[2,5-bis(2',5'-bis(2''- ethylhexyloxy)phenyl)-p-phenylenevinylene] (BBEHP-PPV) and poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1,4-phenylenevinylene] (MEH-PPV), in which there was a color change from green to orange-red. This feature allowed us to reach a modulation bandwidth value of \(\sim\)200 MHz with a data rate of 350 Mbps [7]. More recently, a set of interesting PLEDs materials with potential applicability in VLC system were reported [63; 64; 22; 65]. Some examples of these types of materials are related to emissive \(\pi\)-expanded diketopyrrolopyrrole dyes, 1-mm core size polymethyl methacrylate (PMMA), P3 HT and PTB7 polymers, and polycarbonate multicore polymers, which were even studied in 5G VLC telecommunication systems. In general, advances in OLEDs devices are still under study in regard to their application in VLC, despite the disadvantages that organic active layers present. This point can be widely analyzed by observing the most prominent organic structures that have good electronic properties (i.e., prone to facilitate the carrier mobility) and produce efficient light conversion [23; 52].
# Chaotic Communication System with Symmetry-Based Modulation
## 1 Introduction
We will call this approach a _symmetry coefficient modulation_ (SCM). The proposed type of modulation assumes the benefits of the fully chaos-based communication following from the preservation of the Lyapunov spectrum during signal transmission in the medium. The key feature of the SCM technique is its good suitability for covert communication. The main contribution of the study is as follows:  
1. A novel modulation technique exploiting the geometric properties of the discrete chaotic models obtained by symmetric integration is presented. 2. The proposed approach allows avoiding changes in phase space and spectral characteristics of the carrier because chaotic oscillators do not bifurcate during SCM. 3. The possibility of data transfer by symmetry-based modulation is experimentally shown. The throughput of the communication channel in the absence of disturbances is estimated. The rest of the paper is organized as follows--in Section 2, the investigated chaotic system and the applied numerical method are described. Then, the architecture of SCM-based communication systems is proposed. Section 3 presents the results of numerical simulation and performance evaluation. Section 4 discusses some practical implementation issues, and Section 5 concludes the paper.
## 4 Results
### Simulation Results
The energy detector must have a high SNR (\(-\)3 dB) to detect PU signal with same parameters. Computational complexity is measured according to [36] by counting the number of real multiplications (RM), real additions (RA) and comparisons. The number of averaged values is noted as \(N_{AV}\), number of FFT points is noted as \(N_{FFT}\), the number of samples is noted as \(N_{S}\), the number of sub-channel is noted as \(N_{C}\) and the number of bins is noted as \(L_{B}\). The number of real operations \(C\) is described below:  
\[C=7N_{AV}N_{FFT}+5N_{FFT}\log_{2}N_{FFT}-\frac{N_{FFT}}{N_{C}}+7L_{B}N_{FFT}-1 \tag{9}\]  
According to this equation, the complexity indicator is medium at the same level that superimposed training (ST) [37] and energy detector in Frequency [36]. The simple energy detector [11] is classified as low and cyclostationary is high [36]. For the parameters in Table 1, \(C=1\times 10^{-5}\) real operations for the simple detector, \(C=3.85\times 10^{-5}\) real operations for the SCAR-detector [37] and \(C=43.5\times 10^{-5}\) real operations for the Renyi-based detector.
## 3 Targeting Bacterial Virulence  
### The ESX-1 Secretion System  
Lately, the interest in finding novel lead compounds, which prevent infection and dissemination by inhibiting bacterial virulence factors, has increased. These anti-virulence molecules target one or more proteins in the virulence machinery with one prominent example being the ESX-1 secretion system as ESX-1 deletion mutants show strongly attenuated phenotypes in vitro and in vivo [100]. ESX-1 is a type VII secretion system essential for host cell infection, bacterial spread and macrophage escape but not for bacterial growth in axenic cultures [101]. In a whole-cell-based phenotypic screening assay selecting for compounds that abrogate ESX-1 dependent host cell death, the two ESX-1 inhibitors BTP15 and BBH7 have been found and characterized [102]. BTP15 inhibits the histidine kinase MprB that regulates ESX-1 via the _espA-espC-espD_ operon. BBH7 on the other hand disturbs metal-ion homeostasis leading to zinc stress and thus hindering secretion of ESX-1 substrates such as EsxA and EsxB (Figure 1). These inhibitors can also be used to abrogate ESX-1 dependent activation of the cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS), a main driver of type I interferon (IFN) secretion, thus nicely linking anti-virulence drugs to modulation of the inflammatory response [103].
## 3 Research Model and Hypotheses
### Human Factors  
Personal innovativeness denotes the openness of an individual to new technology. Studies show that an individual's perceived innovativeness can influence how they respond to new technologies [9, 36]. Thus, personal innovativeness may predict whether a person intends to adopt an innovation earlier than others [5, 36]. Given the lag in adoption of cloud computing in the HPC context, we argue that innovative decision makers are more likely to adopt. Hence, we posit:  
Figure 1: Proposed framework.  
**Hypothesis 1 (H1).**_Personal innovativeness is positively related to the adoption of cloud computing for HPC._  
In order to enhance technological readiness, organizations are required to have specialized human resources (e.g., competent HPC staff and/or IT staff) who have the knowledge and skills to implement cloud computing for HPC. Thus, employees' technological competencies may influence cloud computing adoption for HPC workloads. Studies show that IT expertise is essential for organizations that intend to adopt cloud computing [5; 9; 29]. Whilst this expertise may vary depending on the type of cloud computing service, we posit that technical competence is relevant for HPC, as organizations using HPC often use proprietary, tightly configured, or specialist software requiring specialist expertise. Thus, we propose:  
**Hypothesis 2 (H2).**_IT/IS competence is positively related to the adoption of cloud computing for HPC._  
**Hypothesis 3 (H3).**_HPC competence is positively related to the adoption of cloud computing for HPC._
# Phoneme Recognition in Korean Singing Voices Using Self-Supervised English Speech Representations
## 2 Related Work
### Automatic Lyric Transcription  
In the context of singing, the linguistic elements are commonly known as lyrics. The process of automatically extracting these lyrics from a singing voice is referred to as Automatic Lyric Transcription [5]. There are two deep learning-based approaches applicable to ALT. The first approach involves the creation and utilization of large-scale finely annotated singing voice datasets for ALT model training, such as DALI [22], Sing! 300 \(\times\) 30 \(\times\) 2 [4], and DSing Corpus [23]. These datasets can be augmented or synthesized from existing speech datasets to enhance the musical relevance [24; 25]. The second approach is speech-singing adaptation [26], in which relatively well-designed ASR models are repurposed for ALT [27], in light of the similarity between ALT and ASR. Alternatively, well-trained ASR models can be fine-tuned to incorporate the necessary priors from large-scale speech datasets [28]. Finally, some have aimed to adapt the SSL models for ASR to the singing domain, such as [28], which proposed a transfer learning-based ALT method by adapting wav2 vec 2.0 [19], an SSL ASR model, to the singing domain. Additionally, unlike naively regarding ALT as ASR, other speech-singing adaptation methods, such as [14; 29], utilized the musical information to facilitate ALT.  
However, both SSL for audio processing and speech-singing adaptation prioritize English data. For ALT in Korean, annotated singing voice datasets are rare, and even the open-source speech datasets are not as comprehensive as their English counterparts, such as DALI. This is the reason why the approach in which a huge amount of Korean speech or singing data is required is difficult to adopt for ALT in Korean. Therefore, English-Korean adaptation leverages the strong phonetic priors obtained from large-scale English datasets. Musical information is then employed in the speech-singing adaptation.  
To further decouple from a specific language (English) and enable the efficient utilization of priors, our objective is to design a phoneme recognition system that recognizes phonemes (speech sounds) rather than words, which are the basic elements of language, when provided with audio consisting of the vocal portion of a Korean song. This system can serve as an initial step of ALT. We can then transcribe the lyrics consisting of words from the predicted phonemes by a separate language model in the post-processing.
## 3 Test Results and Discussion
### Crack Stress Thresholds
**} & \multirow{2}{*}{\(\sigma_{\text{c}}\)/MPa} & \multicolumn{3}{c}{**Crack Closure**} & \multicolumn{3}{c}{**Crack Initiation**} & \multicolumn{3}{c}{**Crack Damage**} \\ \cline{3-10}  & & \(\sigma_{\text{c}}\)/MPa & \(\sigma_{\text{c}}\)/\(\sigma_{\text{c}}\) & \(\sigma^{\prime}_{\text{ci}}\)/MPa & \(\sigma^{\prime}_{\text{ci}}\)/\(\sigma_{\text{c}}\) & \(\sigma_{\text{c}}\)/MPa & \(\sigma_{\text{c}}\)/\(\sigma_{\text{c}}\) & \(\sigma_{\text{c}}\)/MPa & \(\sigma_{\text{c}}\)/MPa & \(\sigma_{\text{c}}\)/\(\sigma_{\text{c}}\) \\ \hline Int-1 & 76.48 & 19.41 & 0.25 & – & – & 38.14 & 0.50 & 58.89 & 0.77 \\ Int-  
effect of 45\({}^{\circ}\) hole is greater than the 0\({}^{\circ}\) hole. On the other hand, the \(\sigma_{\rm ci}\) and \(\sigma_{\rm ci}\)/\(\sigma_{\rm c}\) of rock matrix in the pre-holed specimens increase with the increase of the mechanical properties of the filling material, and the filled specimens with \(\alpha=0^{\circ}\) is much larger than those with \(\alpha=45^{\circ}\). Among them, the \(\sigma_{\rm ci}\)/\(\sigma_{\rm c}\) of 0\({}^{\circ}\) specimens under Filling Modes A, B and C are 0.37, 0.62 and 0.69, while that of 45\({}^{\circ}\) specimens are 0.43, 0.54 and 0.58, respectively. All in all, the hole and inclusion in specimens with \(\alpha=0^{\circ}\) are easier to crack than that with \(\alpha=45^{\circ}\), but the rock matrix in filled specimens with \(\alpha=0^{\circ}\) is more difficult to crack than that with \(\alpha=45^{\circ}\). This indicates that the filling effect can significantly change the stress distribution around the hole and produce similar effect of crack arrest, so as to significantly increase the crack initiation stress of rock matrix, and the degree of increase is related to the hole angle. Figure 14c shows the crack damage stress \(\sigma_{\rm cd}\) and corresponding stress levels \(\sigma_{\rm cd}\)/\(\sigma_{\rm c}\) of each group of specimens. Among them, the \(\sigma_{\rm cd}\) of pre-holed specimens with \(\alpha=0^{\circ}\) and 45\({}^{\circ}\) are 40.45 MPa and 36.11 MPa, which are 29.75% and 37.29% lower than that of intact specimens, respectively. After filling, the change of \(\sigma_{\rm cd}\) with the filling mode is similar to that of UCS, which is basically linearly increasing with the change of Filling Mode from A to C, and the \(\sigma_{\rm cd}\) of specimens with \(\alpha=0^{\circ}\) is generally greater than that with \(\alpha=45^{\circ}\). However, the \(\sigma_{\rm cd}\)/\(\sigma_{\rm c}\) of specimens with \(\alpha=45^{\circ}\) is slightly higher than that with \(\alpha=0^{\circ}\), but there is no significant difference among each group. The damage stress levels of specimens with \(\alpha=0^{\circ}\) is 0.76 under different filling modes, while that of specimens with \(\alpha=45^{\circ}\) is 0.76-0.80. The above results show that both the hole angle and the filling mode have an effect on the crack damage stress of the pre-holed specimen, but have little effect on the corresponding stress level.
In recent years, great progress has been made in the field of SPP. Much SPP research has been conducted, especially for metamaterials and photonic crystals, as they can dramatically adjust the performance of SPPs [33]. The above literature review is explained through the explanation of metamaterial behavior with multiband characteristics. However, high EMR with multiband resonance exhibiting metamaterial structure design still needs to be improved. Since metamaterials consider their EMR to be inadequate in many of the applications discussed above, this research outlines a modified Coptic cross shaped split ring resonator with negative permittivity metamaterial for quad band satellite applications. This metamaterial is fabricated on low cost FR-4 substrate material showing resonances at 2.02, 6.985, 9.985, and 14.425 GHz that cover S-, C-, X- and Ku-bands with an EMR of 13.50. The negative permittivity was found to be 2.02-2.995 GHz, 6.985-7.945 GHz, 9.985-10.6 GHz, and 14.425-15.445 GHz, respectively. The novelty of this work is that the proposed design can provide a unique shape with miniaturized size showing a dimension of 0.074\(\lambda\)\(\times\)0.074\(\lambda\). The proposed metamaterial produced a quadruple resonance covering with four bands including S-, C-, X-, and Ku-band satellite applications. Moreover, it can provide wider bandwidth, i.e., 0.385 GHz (1.815-2.2 GHz), 0.92 GHz (6.41-7.33 GHz), 0.56 GHz (9.66-10.22), and 1.39 GHz (13.57-14.96 GHz). The developed modified Coptic cross shape exhibited a high effective medium ratio (EMR) of 13.50 that showed compactness, which is relatively high in this regard compared to the recently proposed metamaterials. The array results are almost like a unit cell due to low mutual coupling effect. A unique characteristic of this structure is that the resonance frequency can be tuned by modifying the sizes of the Coptic width. A widely used NRW method is used to achieve the effective medium properties. E-field, H-field, and surface current were also analyzed to understand the behavior of the metamaterial. In addition, this work describes the equivalent circuit modeling using ADS simulator and also analyzes the measurement results through the fabrication of unit cell and array of unit cells.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 4 Result and Discussion  
The results and discussion of the LSTM-RRM method are clearly described in this section. The implementation and simulation of the LSTM-RRM method was carried out in Network Simulator-3, which runs on a Windows 8 operating system with Intel core i3 processor and 4 GB RAM. In this LSTM-RRM method, losses in the 5G environment are minimized by using frequency interleaving and guard interval insertion. Subsequently, the LSTM-based RRM is accomplished to allocate adequate resources for the desired UEs. The height of the BS considered for this LSTM-RRM method is 10 m, and the system bandwidth is 100 MHz. The specifications considered for the LSTM-RRM method are shown in Table 2.
## 8 Discussion of Obtained Results
The EMG signal measured in all tested people has very similar values, oscillating around 2V, and the movement of muscles has a very characteristic effect on the measurement result; hence, the system detects this phenomenon very quickly. It is worth comparing the designed algorithm with other systems that can be found in the literature review. In [45], two data sets were assessed, i.e., the Open BCI data set and EEG signals obtained by Emotiv EPOC. The proposed system shows an overall accuracy of 93.05% and 85.00% for both datasets, respectively. In [46], the authors propose densely feature fusion convolutional neural networks (DFFN). The simulation results resulted in a 5% rating improvement in the BCI IV-2a dataset compared to regular CNN. Citing the above-mentioned studies only shows that the interest in classification and generally understood improvement of bioelectric signal processing is still popular and developed. The unquestionable advantage of the artificial neural network is its resistance to artifact interference, as the \(F3\) and \(F4\) channels are also one of the most important when detecting the mental arm movement command. Training of the feed-forward neural network was performed using 450 input and output training patterns, where the inputs were integrated EEG signals, and the outputs were a signal representing an image of movement, or lack thereof. Such a simple solution allowed us to make the system more reliable. The applied solutions turned out to be effective, as shown in Figures 6-15 and Section 4, presenting the results of signal pre-classification, as well as the characteristics Figures 18-27 in Section 7, which contain sample results of signal classification. Importantly, negative EMG verification successfully interrupted the LEGO robot's movement commands when muscle movements were detected (see Figure 29). In its absence, the device was controlled directly on the basis of the EEG waveforms classification. The discussed facts confirm the results obtained from the robot's encoders, which show the variable movement of its drives over time. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\boldsymbol{FT8/FT7}\) [\(\mu\)V] & \(\boldsymbol{FC6/FC5}\) [\(\mu\)V] & \(\boldsymbol{F4/F3}\) [\(\mu\)V] & \(\boldsymbol{F8/F7}\) [\(\mu\)V] \\ \hline _Person_ 1 & 1003 & 1275 & 978 & 728 \\ \hline _Person_ 2 & 1595 & 1582 & 1532 & 1383 \\ \hline _Person_ 3 & 1152 & 1253 & 1025 & 1002 \\ \hline _Person_ 4 & 1552 & 1453 & 1605 & 1211 \\ \hline _Person_ 5 & 729 & 797 & 737 & 756 \\ \hline _Person_ 6 & 828 & 979 & 798 & 703 \\ \hline _Person_ 7 & 1001 & 1023 & 1001 & 934 \\ \hline _Person_ 8 & 783 & 902 & 1012 & 765 \\ \hline _Person_ 9 & 821 & 989 & 1163 & 814 \\ \hline _Person_ 10 & 864 & 1174 & 986 & 527 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The value of the average amplitude of the integrated EEG signal for mental commands from individual electrodes.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 3 System Design
### Tag-Positioning Algorithm  
The multilateration algorithm is responsible for determining a stationary or moving device's position based on the gathered rangings to each available anchor. In the proposed UWB-based IPS, this algorithm is based on the Gauss-Newton optimization method, which uses a polynomial function to approximate a given function value until a certain precision is reached. This method is well-suited for solving (small residual) non-linear problems and is most commonly applied in positioning applications [62].  
Figure 3: Superframe structure (simplified version of the diagram published in [59]).  
Under the approach presented here, the multilateration algorithm is embedded in the tag, so that it can compute a new sample of its own position as long as two conditions are fulfilled:  
1. The number and position of the network anchors are well-known by the tag.
2. The tag has received one ranging value from at least three different well-known anchors during a TDMA slot.  
The first requirement is based on the dictionary of anchors' positions, which is hosted in the command and control center and downloaded by the tag once it is powered on or if any modification occurs. The second one is accomplished by the continuous exchange of UWB frames between the tag and the available anchors according to the SS-TWR localization technique.  
Lastly, all position samples computed over the \(T\) period with the Gauss-Newton optimization method are averaged to reduce the impact of outliers caused by the dynamism of the wireless channel, potential interference, and/or the movement of the tag itself. The resulting averaged position is sent to the command and control center.
# A Deep Learning-Based Algorithm for Ceramic Product Defect Detection
## 3 Experiments and Results
### Ceramic Product Crack Detection Algorithm Based on Adaptive Canny  
#### 3.2.1 Evaluation Metrics  
During the evaluation phase of the ceramic product crack detection algorithm, several key metrics, including ELE (Edge Localization Error), ECR (Edge Connectivity Rate), WER (Wrong Edge Rate), and FPS, were employed to assess the effectiveness of the experimental results. ELE (Edge Localization Error): This metric is used to quantify the degree of deviation between the edge positions detected by the algorithm and the actual true edge positions. The calculation method is as shown in Equation (11), where \(N\) represents the total number of true edge pixels; \(P_{i}^{detectd}\) represents the edge coordinates detected by the algorithm; and \(P_{i}^{true}\) denotes the true edge coordinates.  
\[ELE=\frac{1}{N}\sum_{i=1}^{N}||P_{i}^{detectd}-P_{i}^{true}|| \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **mAP@50 (\%)** & **F1-Score (\%)** & **Params (M)** & **GFLOPs (G)** & **Size (MB)** & **FPS (f/s)** \\ \hline YOLOv5s & 89.1 & 86.9 & 7.02 & 15.8 & 13.9 & 52 \\ YOLOv7 & 88.3 & 86.1 & 16.32 & 35.5 & 29.2 & 33 \\ YOLOv8s & 91.1 & 88.9 & 12.46 & 28.7 & 22.5 & 38 \\ YOLOv9s & 90.2 & 88.1 & 7.23 & 26.8 & 14.2 & 41 \\ YOLOv10s & 92.5 & 90.1 & 8.07 & 24 & 16.8 & 45 \\ Lightweight & & & & & & \\ YOLOv10s & 92.8 & 90.3 & 5.97 & 14.9 & 12.3 & 63 \\ (Ours) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of comparative tests.  
ECR is utilized to evaluate the algorithm's capability in maintaining structural continuity of the detected edges. It is calculated as the ratio of the number of matched continuous segments to the total number of actual continuous segments. WER focuses on measuring the algorithm's ability to detect edges in low-contrast regions. It is computed as the ratio of the number of detected weak edge pixels to the total number of actual weak edge pixels. FPS is employed to assess the algorithm's computational efficiency in real-time application scenarios. It is determined as the ratio of the total number of processed images to the total time consumed (in seconds).