esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nanocomposite films are environmentally friendly polymers which become a part of very essential techniques in various research fields such as for preserving the shelf life of foods in recent years due to imparting new chemical, mechanical, and physical properties [1; 2; 3; 4]. Nanomaterials such as nano/ZnO, nano/TiO\({}_{2}\), nano/SiO\({}_{2}\), and nano/clay are well known in academia and industry for their excellent characteristics such as high rigidity, strength, and slightly toxic [5; 6; 7; 8; 9; 10]. Silicon dioxide (SiO\({}_{2}\)) is known as silica which is found in nature as silica sand, quartz, crystal, vapor-silica, colloidal silica, silica gels, and aerogels. Nano/SiO\({}_{2}\) films are widely used in many fields such as antireflection coating [11] and packaging industry against the permeation of gases which prevent fruits,vegetables, and drinks to be rotten quickly, inhibit the volatile compounds, and maintain the sensorial and physiological qualities. Nano/SiO\({}_{2}\) films are considered to have excellent ecological performance due to the uniform emulsion particle size distributions, anti-wear, low-water-absorption, chemically-inert, biocompatible, and friction-reduction properties [12; 13]. Furthermore, nano-SiO\({}_{2}\) can prevent bacterial attacks invivo and invitro [14]. There are several techniques for producing nano/SiO\({}_{2}\) films that allow better control of the thickness such as sol-gel, chemical and physical vapor depositions [15], etc. The agglomeration occurs between particles when the particle size is less than 25 nm with a reduction in the electrostatic repulsion between particles [16; 17]. Many studies aimed to improve the nano/SiO\({}_{2}\) film properties by new techniques and by modifying particles that are highly cost and not environmentally friendly [18]. Nano/SiO\({}_{2}\) has been approved for safety rating that will not be digestible by humans [19]. Though, several studies focused on the effect on preservation properties. [PERSON] et al. [20] and [PERSON] et al. [21] used nano/SiO\({}_{2}\)/chitosan on jujube and longan fruits preservations.Rarely reported studies on the modified characteristics of nano/SiO\({}_{2}\) films after the addition of chitosan and nisin which could exert certain effects on microorganisms.  
In the current research, novel films prepared by chitosan, nano/SiO\({}_{2}\), and nisin were characterized by evaluating the antimicrobial effect on cantaloupe fruit shelf-life extension. Besides, the researchers wish the application may make a reference to a large-scale packaging industry in the future.
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 1 Introduction
Due to the limitations of past research work, the authors felt the need for a re-evaluation of the deep learning models on a larger dataset of COVID-19 chest X-ray images and the current study was implemented due to the availability of a database containing 3616 COVID-19 chest X-ray images, the highest ever worked upon, to the best of the authors' knowledge. The main aim of the subject research work is the performance evaluation of deep learning models on a dataset with the largest number of COVID-19 images ever used, to the best of the authors' knowledge, to see the improvements in the performance metrics. The subject study addresses a major gap in research on COVID-19 detection and, in doing so, makes the following contributions:To the best of our knowledge, this study is the first that benchmarks deep learning models on the largest COVID-19 dataset. * In this study, we developed a custom CNN model for benchmarking on the subject dataset. * The custom CNN model outperformed MobileNetV2 in terms of precision, which is of high scientific value. * The current study includes a detailed and comprehensive evaluation of the performance involving several performance metrics to observe the impact of increasing the COVID-19 dataset on the research work. The subsequent sections include related work in Section 2, while Section 3, Methodology, describes the dataset and deep learning models utilized for the purpose of evaluation, as well as the experimental setup and evaluation metrics for benchmarking. Next, in Section 4, results are described with graphical elaborations. Finally, Sections 5 and 6 include discussion, conclusion, and limitations and future work.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 4 Speech Enhancement Experiment  
### Corpus  
For the objective experiments, 30 phonetically balanced utterances belonging to six speakers (three male and three female) were taken from the NOIZEUS corpus ([1], Chapter 12). The clean speech recordings had lengths of two sec to four sec depending on utterances ([1], Chapter 12). We generated a noisy speech data set by mixing the clean speech with real-world non-stationary (_bubble_, _street_) and colored (_factory2_ and _f16_) noise recordings at multiple SNR levels (from \(-5\) dB to \(+15\) dB, in 5 dB increments). This provided 30 examples per condition with 20 total conditions. The _street_ noise recording was taken from [27] and the rest of the noise recordings were taken from [24]. All clean speech and noise recordings in the noisy speech data set are single channel with a sampling frequency of 8 kHz.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 2 Latency Definition and the Ideal Case Study  
Ideal case latency is studied initially, evaluating the effects of TX tuning--MCS selection, payload length, and PPDU aggregation--on time delays. However, the latency performance metric is defined first.
# Forward Link Optimization for the Design of VHTS Satellite Networks
## 5 Case Studies
### Scenario 1
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{\(G/T\) [**dB/K**]} & **Optimal** & \multirow{2}{*}{\begin{tabular}{c} **CINR\({}_{b}\)** \\ **[dB]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Gbps Per** \\ **[dB]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ideal** \\ **Seatellite** \\ **(Ideal)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Satellite** \\ **Satellite** \\ **(Ideal)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **[Kg]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Total** \\ **[Kg]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Cost Per** \\ **Gbps in** \\ **(Crobit [Mc]** \\ \end{tabular} } \\  & 159 & 14.4485 & 1.2126 & 192.8105 & 6.5135e3 & 8 & 498.7832 & 2.5869 \\
20 & 125 & 15.7243 & 1.3154 & 164.4252 & 6.3954e3 & 7 & 433.5103 & 2.6365 \\
23 & 89 & 17.0365 & 1.4219 & 126.5506 & 6.2381e3 & 5 & 360.5645 & 2.8492 \\
25 & 64 & 17.9914 & 1.4998 & 95.9895 & 6.0952e3 & 4 & 311.4916 & 3.2451 \\
27 & 37 & 19.0556 & 1.5870 & 58.7190 & 5.8777e3 & 2 & 254.7458 & 4.3383 \\ \hline \end{tabular}
\end{table} 
Table 2: Analysis in clear sky conditions for Scenario 1. Figure 7: Variation of the cost per Gbps in orbit for different optimal numbers of beams depending on the user \(G/T\). Scenario 1. Figure 9 shows the distribution of ModCods in the service area with VCM for scenario 1. Five different ModCods were used: 64 APSK 32/45-L, 16 APSK 7/9, 16 APSK 3/5, 16 APSK \(\frac{1}{2}\)-L, and QPSK 11/20. It can be seen that the ModCod assignment in the service area varies according to the link condition of each site. In terms of satellite capacity, 94.2 Gbps is obtained using CCM and 129.75 Gbps with VCM for 99.9% availability.
## 5 Cavity Analysis in CW Operation
### Stability Ranges and Limits  
When solving the stability requirements of the cavity illustrated in Figure 1, one finds that the laser is stable for two bands of \(\delta\) values bounded between four stability limits, \(\delta_{0}<\delta<\delta_{1}\) and \(\delta_{2}<\delta<\delta_{3}\), given by:  
\[\delta_{0}=0,\qquad\qquad\delta_{1}=f_{2}^{2}/(L_{2}-f_{2}),\qquad\qquad\delta_{ 2}=f_{1}^{2}/(L_{1}-f_{1}),\qquad\qquad\delta_{3}=\delta_{1}+\delta_{2} \tag{5}\]  
Note that for simplification, Equation (5) was calculated without the Ti:sapphire crystal between \(M1\) and \(M2\). The addition of Ti:sapphire crystal (or any material with thickness \(L\) and refractive index \(n\)) will shift all the stability limits, \(\delta_{i}\), by a constant factor of \(L(1-1/n)\). However, Equation (5) can still be used without any change by redefining the distance between \(M1\) and \(M2\) in Figure 1 to be: \(f_{1}+f_{2}+L(1-1/n)+\delta\).  
Near the stability limits given in Equation (5), the stable cavity mode can be intuitively visualized using geometrical optics, as illustrated in Figure 2. The four stability limits can be named according to the mode size behavior on the cavity end mirrors, as follows: (1) plane-plane limit (Figure 2a); the curved mirrors (illustrated as lenses) are separated by \(f_{1}+f_{2}\), forming a perfect telescope, which produces a collimated beam in both arms; (2) plane-point limit (Figure 2b); the curved mirrors are separated by \(f_{1}+f_{2}+\delta_{1}\), such that the focal point between the curved mirrors is imaged on \(EM2\); (3) point-plane limit (Figure 2c); the curved mirrors are separated by \(f_{1}+f_{2}+\delta_{2}\), and the focal point between the curved mirrors is imaged on \(EM1\); (4) point-point limit (Figure 2d); the curved mirrors are separated by \(f_{1}+f_{2}+\delta_{3}\), and the focus point between the curved mirrors is imaged on both the end mirrors. Kerr lens mode-locking usually occurs near one of the stability limits, as explained in Section 7.  
Figure 1: Standard (**a**) X-shaped and (**b**) Z-shaped configurations of a linear Ti:sapphire cavity with a Brewster-cut crystal. Both configurations work equally well. The folding angles, \(\theta_{1}\) and \(\theta_{2}\), allow for the compensation of the astigmatism from the Brewster crystal, as discussed in Section 5.4.
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 3 Similarity-Driven Edge Bundling (SDEB)
### Backbone Construction
This process creates a hierarchical structure in which in which the deeper the cluster (from the root to the leaves), the more similar the objects are. As a result, the most similar objects are closely placed on the _backbone_, and the path between them is reduced (design principle **(1)**). Also, the vertices are linked through unique paths (design principle **(2)**) and the path between two data vertices only contains intermediate vertices (design principle **(3)**).
## 3 Experimental Setup and FEM Theory
### Graphite Rod Sample  
In this paper, the sample was cut from graphite electrodes used in metallurgy. The raw materials of graphite electrodes mainly include solid carbon raw materials and binders. Solid carbon raw materials usually contain 50% anthracite, 30% petroleum coke, and 20% crushed graphite. The binder is coal pitch and coal tar. The manufacturing process of graphite electrodes can be divided into five steps: raw material calcination, crushing, mixing, pressure forming, and baking [30]. Raw material calcination is performed by putting solid carbon raw materials into a furnace isolated from the air for calcination. Crushing is carried out to process the calcined electrode material into fine powder. The mixing step comprises mixing the electrode raw materials and the binder to produce a paste-like electrode raw material. Pressure forming involves putting the mixed electrode raw materials into extrusion molding equipment and processing them into a predetermined shape. Baking involves putting the formed electrode into a baking furnace with a protective medium for heating and baking according to the baking process. According to the manufacturing process, the graphite rod is a carbon material with a disordered orientation texture. Therefore, the graphite rod is homogeneous and isotropic.  
The theoretical density of the graphite electrode sample is 1870 kg/m\({}^{3}\), the Young's modulus is 8.5 GPa, and the void volume fraction is 17%. The sample length is 200 mm and 400 mm, and the diameter is 50 mm. To ensure better coupling between the transducer and the test piece, the two ends of the test piece were turned flat by a lathe. We placed the sample between the transmitting and receiving transducer to measure the ultrasonic velocity.
### Composition of Room Impulse Responses  
In an enclosed space a proportion of the radiated sound source will be reflected off the room boundaries and eventually decay due to absorption by the room surfaces or air. Any impulsive stimulus will rapidly change the nature of the soundfield from being coherent to partially coherent to non-coherent. Thus, this linear time invariant system is characterised by a Room Impulse Response, consisting of the following components (shown in Figure 1):  
* The Direct Sound (DS): The DS reaches a listeners' ears directly from the source before being reflected from the boundaries of the enclosure [8]. Its amplitude is large with less energy loss relative to the reflections because of the shorter propagation path. Its function is to transmit sound information and provide the direction of source.
* The Early Reflections (ERs): These are the sound waves that arrive in a temporal order after being reflected from at least one boundary of the enclosure [8]. They arrive within typically 10 to 80 ms after the direct sound, and typically constitute up to fourth order reflections before the soundfield becomes stochastic. Their energy is reduced by absorption or scattering. The early reflections can increase perceived overall sound pressure level and sound clarity.
* The Late Reverberation (LR): is a chaotic sound field that consists of diffuse reflections [8]. It is an exponentially attenuated dense collection of echoes diffusing in all directions. The echo density is proportional to the square of time. An appropriate amount of late reverberation can contribute to a sense of spatialisation and fullness, although too much can destroy the clarity of the sound.
* The Initial Time Delay Gap (ITDG): This is the time period between the direct sound and the first arriving reflection. ITDG is the main contributor towards the perception of 'presence' [9], an attribute that is recognised as the perceptual sense of feeling boundaries of an enclosed space [10]. It is the hearing-equivalent of'seeing' the walls of a room [11].  
If a binaural dummy head is used to record the impulse, then the resultant IR is known as a Binaural Room Impulse Response (BRIR) and contains both the room characteristics and spectral and temporal binaural cues. Binaural auralisation therefore uses two channels to simulate the binaural listening experience typically over a pair of headphones but also over loudspeakers using cross-talk cancellation methods [12].
# Bias Current of Semiconductor Laser: An Unsafe Key for Secure Chaos Communication
## 1 Introduction  
The secure optical chaos communication process has received considerable attention due to its excellent features, such as hardware encryption, high transmission rate, long transmission distance, and compatibility with the existing fiber networks. The first field experiment of optical chaos communication was demonstrated in the commercial optical networks of Athens, which achieved a rate of 1 Gb/s with a transmission distance over 120 km [1]. Considering the robustness and cost, external-cavity semiconductor laser (ECL) is a promising chaotic transceiver, due to its simple structure, which is capable of integration. Photonics integration of chaotic ECL has become a research focus and some integrated chaotic semiconductor lasers have recently been reported [2; 3; 4; 5].  
Chaos-based communication can be realized only when the parameters of chaotic transceivers are matched. A parameter match means that the parameter values of a chaotic transmitter and receiver can ensure synchronization, and realize message encoding and decoding [6; 7; 8]. Thus, the parameters of chaotic lasers are generally considered to be key in optical chaos communication [9]. Multi-user communication is the trend of secure chaos communication. Current semiconductor integration technology can manufacture massive lasers with matched internal parameters, which means that the laser internal parameters are public. Therefore, for ECLs like chaotic transceivers, the controllable external parameters, including bias current, external-cavity length, and feedback strength should be selected as the keys, to ensure security. For example, [PERSON] et al. proposed the external-cavity length as a key [10]. However, this is unsafe because the laser chaotic oscillation contains external-cavityresonances, leading to signature of feedback time delay, which exposes the external cavity length [11; 12]. Many efforts have been made to suppress or eliminate the time delay signature, to enhance security by increasing the complexity of feedback cavity, such as double-mirror feedback [13], polarization-rotated feedback [14], fiber Bragg grating (FBG) feedback [15], chirped fiber Bragg grating (CFBG) feedback [16], random grating feedback [17], and feedback phase modulation [18]. Nevertheless, from the viewpoint of integration, the external cavity length is fixed, which is then also unsuitable for acting as a key, once the ECL is integrated. By comparison, the laser bias current is easy to adjust. However, for a solitary laser, the bias current is related to the relaxation oscillation frequency (\(f_{\mathrm{RO}}\)). Therefore, the safety of using a bias current as a key, is worthy of a detailed investigations.  
In this study, we numerically analyzed the relaxation oscillation signature (ROS) in a chaotic laser, as a function of the bias current, and then used it to crack the optical chaos communication, based on external-cavity lasers. The risk of a bias current in chaos communication was also analyzed.
## 2 Theoretical Background
### Industry 4.0  
Industry 4.0 (I4.0) refers to the digital transformation of manufacturing [20]. Popularized in Germany in the early 2010s [20, 21], this concept brings together a set of emerging technologies aimed at making production systems more intelligent, flexible, and autonomous [22].  
The main technological building blocks of I4.0 include the Internet of Things (IoT), which facilitates real-time data acquisition via connected sensors [23, 24]; artificial intelligence (AI), used for predictive analysis and automatic anomaly detection [25, 26]; Cyber-Physical Systems (CPSs), which ensure the interconnection between the physical and digital worlds [22]; or cloud computing, facilitating the storage and distributed exploitation of data [20, 25]. In addition, there are tools such as digital twins, which allow virtual simulation of the behavior of machines [27, 28], or augmented reality, used for training or assistance with interventions [29, 30].  
These technologies are profoundly transforming traditional industrial models. They enable the transition from static and hierarchical management to a connected, responsive, data-driven organization. Maintenance, in particular, becomes a particular area of application of these innovations [25, 31, 32].
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
###### Abstract  
Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults occur. In the recent literature, several data-driven methods for RUL prediction have been proposed. However, most of them are based on traditional (connectivity) neural networks, such as convolutional neural networks, and alternative mechanisms have barely been explored. Here, we tackle the RUL prediction problem for the first time by using a membrane computing paradigm, namely that of Spiking Neural P (in short, SN P) systems. First, we show how SN P systems can be adapted to handle the RUL prediction problem. Then, we propose the use of a neuro-evolutionary algorithm to optimize the structure and parameters of the SN P systems. Our results on two datasets, namely the CMAPSS and new CMAPSS benchmarks from NASA, are fairly comparable with those obtained by much more complex deep networks, showing a reasonable compromise between performance and number of trainable parameters, which in turn correlates with memory consumption and computing time.  
Spiking Neural P Systems; NEAT; Remaining Useful Life; predictive maintenance; CMAPSS +
Footnote †: journal: 2022, 15, 98. [[https://doi.org/10.3390/a15030098](https://doi.org/10.3390/a15030098)]([https://doi.org/10.3390/a15030098](https://doi.org/10.3390/a15030098))
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 5 Conclusions  
This study focused on predictive maps created by applying RF, OK, and a combined RF-OK interpolation model based on soil contamination data. The performance of each model was compared after configuration using a diverse set of options and parameters. Optimization was performed using the GridSearchCV hyperparameter-tuning technique. Predictive maps were generated based on the optimized RF, OK, and RF-OK models, and the interpolation performance was evaluated using the RMSE and R2 metrics. The findings demonstrated that the combined RF-OK model (RMSE = 52.884 mg/kg, R2 = 0.915) outperformed the individual techniques (RF: RMSE = 66.214 mg/kg, R2 = 0.867; OK: RMSE = 65.101 mg/kg, R2 = 0.871).  
Combining a machine learning model with a geostatistical technique can produce more reliable predictive maps than individual interpolation models. Therefore, the objective results obtained through hyperparameter tuning of the options and parameter settings of each technique are expected to be useful for mapping more reliable predictions.  
H.H.: methodology, analysis, validation, visualization, and writing the original draft; J.S.: conceptualization, project administration, supervision, and reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Evaluation Index**} \\ \cline{3-4}  & & **RMSE** & **R2** \\ \hline [PERSON] et al. [22] & OK & 9.81 (g\(\cdot\)kg\({}^{-1}\)) & 0.32 \\  & ANNK & 8.89 (g\(\cdot\)kg\({}^{-1}\)) & 0.60 \\ [PERSON] et al. [23] & RF & 31.21 (t\(\cdot\)ha\({}^{-1}\)) & 0.49 \\  & RFOK & 29.97 (t\(\cdot\)ha\({}^{-1}\)) & 0.53 \\ [PERSON] et al. [24] & OK & 2.07 (g\(\cdot\)kg\({}^{-1}\)) & 0.36 \\  & ELMOK & 1.40 (g\(\cdot\)kg\({}^{-1}\)) & 0.67 \\  & RF & 66.21 (mg/kg) & 0.87 \\ This study & OK & 65.10 (mg/kg) & 0.87 \\  & RFOK & 52.88 (mg/kg) & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the prediction performance between existing studies and this study.  
**Funding:** This work was supported by the Energy & Mineral Resources Development Association of Korea (EMRD) grant funded by the Korean government (MOTIE) (2021060001, Data science-based oil/gas exploration consortium).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The author declares no conflicts of interest.