esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Datasets and Methodology
### Datasets
#### 3.1.1 Earthquakes Dataset  
For the earthquakes dataset, we used the US Geological Survey (USGS) ([[https://earthquake.usgs.gov/](https://earthquake.usgs.gov/)]([https://earthquake.usgs.gov/](https://earthquake.usgs.gov/)), accessed on 20 June 2020) dataset, which provides the most rapid and accurate information regarding the location, size, and depth for all recorded earthquake events worldwide (Figure 1). The dataset also provides an extensive seismic data since 1850. Since we aimed to study how ML methods can be implemented with GPS ionospheric TEC estimations to predict earthquakes, we only examined the earthquakes that could be correlated with available GPS ionospheric TEC data between the years 1998 and 2021 (i.e., TEC data for the day of the earthquake and 48 consecutive hours before). Additionally, we only examined earthquake events where the number of sunspots on the day of the earthquake was at most 50, with no solar flare recorded on that day. After sorting through the entire database, we ended up with 106 earthquakes with magnitudes larger than Mw 6; thus, we applied our methodology and analysis only to the high magnitude earthquakes (i.e., larger than Mw 6).
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 5 Results
### Path Loss  
The fitting model is a powerful tool for estimating attenuation in a real environment. We employed the CI and FI models to analyze uplink data, comparing them with the Okumura-Hata model. These models and parameters enable the estimation of attenuation in environments similar to the one studied in this work.  
We will present our path loss values in two ways: first, by showing the path loss variation for the SF under study; then, by separating the modeling results for the three SFs.
## 1 Introduction
In [19], the authors studied the impact of HIs and fading on the spectrum sensing performance of an energy detector full-duplex wireless system. So far, the performance of satellite relay dual-hop networks with HIs has been investigated in [20]. The effects of HIs and co-channel interference on the STRN were first considered in [21]. In [22], the authors studied the individual impact of HIs on the CSTRN. However, it should be pointed out that most of the prior works considered the ideal CSI in the CSTRN. In practice, due to the high delay and fast fading natures, it is very hard to get the accurate CSI of the satellite links; thus, channel estimation is usually needed for the satellite links; however, channel estimation errors (CEEs) always exist when using the channel estimation. Besides, owing to the time-varying character of terrestrial links, the availability of the perfect CSI cannot be obtained in practice, either. Currently, the issue of CEEs for the CSTRN has been studied in several works [23; 24]. In [23], the impact of non-ideal CSI for both the harmful terrestrial interference link and desired satellite link on the power control scheme was investigated in the CSTRN. In [24], the authors analyzed the secrecy performance of the primary satellite system in the CSTRN with the underlay scheme and imperfect CSI. To the authors' best knowledge, the joint impact of CEEs and HIs on the performance of the CSTRN remains unreported, which leads to the contribution given in this paper. In this paper, we focus on the performance limitations in a CSTRN with both HIs and CEEs, where the primary terrestrial system shares the spectral resources with the secondary satellite system. Particularly, our main contributions can be outlined as follows:* We first establish a general and practical framework of a CSTRN with HIs and CEEs, where the cognitive satellite networks coexists with the multiple user primary terrestrial network with the interference temperature constraints. * After obtaining the end-to-end signal-to-noise-plus-distortion-and-error ratio (SNDER), novel closed-form OP and throughput expressions of the considered networks are obtained, which give a general and applicable method to characterize the key parameters on the considered network. * To gain more insights, the asymptotic analysis of OP and throughput at high signal-to-noise-ratios (SNRs) are provided, which enable a quantitative characterization of the impact of HI levels and CSI imperfections on the considered network. Notations: Bold uppercase letters denote matrices, and bold lowercase letters denote vectors; \(\left|\cdot\right|\) is the absolute value of a complex scalar; \(\exp\left(\cdot\right)\) is the exponential function, \(E\left[\cdot\right]\) the expectation operator, \(\mathcal{CN}\left(a,b\right)\) the complex Gaussian distribution, where **a** is the mean value of random complex vector and \(b\) the covariance matrix, and \(B\left(.,.\right)\) denotes the Beta function.
## 2 Parameter Optimization VMD-NLMS Principle
### NLMS Principal  
The normalized least mean square (NLMS) algorithm is a form of the principle of minimal disturbance [27]. The principle can be expressed as follows: from one adaptive loop to the next, the adaptive filter's weight vector should be changed minimally, subject to the constraints imposed by the output of the updated filter. It is called 'normalized', because it uses the squared Euclidean norm of the tap input vector to normalize the tap weight adjustment. The NLMS algorithm is described by the following formula:  
\[\overset{\rightarrow}{h}(n+1)=\overset{\rightarrow}{h}(n)+\mu(n)e(n) \overset{\rightarrow}{x}(n) \tag{10}\]  
where \(\overset{\rightarrow}{h}(n+1)\) is the weight vector of the \(n+1\) iterative filter; \(\overset{\rightarrow}{h}(n)\) represents the weight vector of the \(n\) iterative filter; \(e(n)\) is the error between the noisy signal and the original signal; and \(\overset{\rightarrow}{x}(n)\) is the input vector.  
The step size in the NLMS algorithm is a time-varying quantity and is defined as [30]:  
\[\mu(n)=\frac{\alpha}{\overset{\rightarrow}{x^{T}}(n)\overset{\rightarrow}{x }(n)}=\frac{\alpha}{\left\|\overset{\rightarrow}{x}(n)\right\|^{2}} \tag{11}\]  
where \(\overset{\rightarrow}{x^{T}}(n)\overset{\rightarrow}{x}(n)\) is the squared Euclidean norm of the input signal vector, \(\mu(n)\) is the step factor, and \(\alpha\) is a positive real scale factor. To make the algorithm converge, it should satisfy \(0<\alpha<2\).  
In practice, to avoid generating a large step size \(\left\|\overset{\rightarrow}{x}(n)\right\|^{2}\) when \(\mu(n)\) is too small, it can be further modified, as follows:  
\[\mu(n)=\frac{\alpha}{\left\|\overset{\rightarrow}{x}(n)\right\|^{2}+c} \tag{12}\]  
where \(c\) is a small positive constant added to avoid division by zero (regularization term).  
In summary, the proposed method combines VMD parameter optimization based on partial differential equations with the integration of NLMS adaptive filtering and envelope demodulation. It automatically optimizes VMD parameters to avoid over-decomposition and incorporates a multi-level design in parameter optimization, signal reconstruction, and noise suppression. Theoretically, this approach can effectively improve the accuracy and robustness of fault diagnosis.
###### Abstract  
Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signals with low optical signal-to-noise ratios (OSNRs) is critical for UWOC systems. Increasing the transmitting optical power and investigating more advanced signal processing technologies to recover transmitted symbols are two primary approaches to improve system tolerance in noisy UWOC signal channels. In this paper, we propose and demonstrate the application of deep echo state networks (DeepESNs) for channel equalization in high-speed UWOC systems to enhance system performance with both PAM and QPSK-OFDM modulations. Our experimental results demonstrate the effectiveness of DeepESNs in UWOC systems, achieving error-free underwater transmission over 40.5 m with data rates up to 167 Mbps. Moreover, we compare the performance of DeepESNs to conventional echo state networks and provide suggestions on the configuration of a DeepESN for UWOC signals.  
underwater wireless optical communication; signal modulation; digital signal processing; deep echo state network; machine learning
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 5 Applications of pXRF on Obsidian Artifacts from Central Mediterranean Sites
### Obsidian at Sites in Malta  
The Maltese Islands are about the same distance south of Sicily as Pantelleria, while they were occupied from the Neolithic, and, like elsewhere, were using obsidian from both Lipari and Pantelleria [3; 66]. Three different sites were excavated: Skorba, occupied throughout the Neolithic, and Tas-Silj, which is Bronze Age, both on Malta; and the Brochtorf Circle at Xaghra, on the smaller island of Gozo, which is Copper Age (Figure 14).  
Skorba has a combination of residential and ritual structures, with seven phases spanning from ca. 5500-2500 BCE (Ghar Dalam, Grey Skorba, Red Skorba, Zebbug, Ggantija, Saflieni, and Tarxien) and each with obsidian present [67]. Following the excavation, visual distinctions of the nearly 300 obsidian artifacts found were initially used to assign nearly 80% to Lipari (black-grey) and 20% to Pantelleria (dark green), and a selection of 25 were analyzed by IUAA to confirm this [7]. More recent analyses by pXRF confirmed the visual assignments while also indicating that those from Pantelleria mostly came from Balata dei Turchi with a small number from Lago di Venere, and that all of the Lipari obsidian came specifically from Gabellotto. The proportion of Lipari to Pantelleria for each time period was consistent, and quite similar to that for the sites on Utica.  
Much more recent excavations at the Brochtoff Circle at Xaghra revealed a large number of underground, individual chamber tombs dating to the 3 rd millennium BCE [68]. More than 100 obsidian artifacts were analyzed by pXRF, with the results being very different than at the site of Skorba, overall. Only 28% were from Lipari and 72% from Pantelleria, while at Skorba, none of the small number (8) of obsidian artifacts from the contemporary Tarxien phase came from Pantelleria. Again, all of the Lipari obsidian was from the Gabellotto subsource, while the Pantelleria artifacts came from multiple subsources on Pantelleria, mostly Balata dei Turchi. Since the many tomb chambers, representing about 700 individuals, were not of the same time period but spanned many  
Figure 13: Archaeological sites on Utica (north of Palermo, Sicily). The ellipse for Tramontana Alta is the sloped pathway along which surface artifacts were collected.  
families over at least a few hundred years, the high selection of Pantelleria obsidian was not a single incident, but an extended cultural burial practice.
## 3 Materials and Methods
### Preparation of Mg NP/GW Nanocomposite and Zero-Valent Mg NPs  
To prepare the Mg NP/GW nanocomposite, 10 g of GW was suspended in 200 mL of bi-distilled water. GW served as a support to prepare this two-phase nanocomposite. To prepare the second phase, (Mg NPs), magnesium chloride was used as a precursor for a typical precipitation process. Additionally, a separate 100 mL aqueous solution of MgCl\({}_{2}\cdot\)6H\({}_{2}\)O (0.05M) was prepared and added to the previous suspension in a 500 mL conical flask [78; 79; 80]. Gradually, 0.1 M aqueous NaOH solution was added drop by drop to adjust the pH to 6. This suspension was then stirred using a rotary shaker at 150 rpm at 25 \({}^{\circ}\)C overnight. Subsequently, centrifugation was employed to separate the produced nanocomposites, which were then subjected to repeated washing with distilled water and dried at 60 \({}^{\circ}\)C in an electric oven. The dried samples were finally stored for further analysis and application [81]. The process is shown in Scheme 1.  
Zero-valent Mg nanoparticles were prepared using a protocol similar to the one described [82]. Initially, an aqueous extract of GW was prepared by adding 10 g of GW to 200 mL of distilled water. This extract served as a precipitating and capping agent in the precipitation of the intended one-phase zero-valent Mg nanoparticles, prepared by a typical green synthesis process. The suspension was then shaken overnight using a rotary shaker at 150 rpm at 25 \({}^{\circ}\)C. Afterward, the suspension was filtered, and the filtrate was separated. Subsequently, a 100 mL aqueous solution of MgCl\({}_{2}\)-6H\({}_{2}\)O (0.05 M) was added to the filtrate. Dropwise addition of 0.1 M aqueous NaOH solution was carried out until the pH reached 6. Once the zero-valent magnesium nanoparticles were prepared, they were dried in a hot air oven at 60 \({}^{\circ}\)C and separated using centrifugation at 10,000 rpm [83]. The process is shown in Scheme 2.  
Figure 2: Schematic representation showing zero-valent Mg nanoparticle preparation.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 4 Discussion and Conclusions
Moreover, they represent a valuable tool for planning future investments in the port system, as the development of infrastructure expansion plans requires accurate and forward-looking forecasts of maritime traffic trends. Furthermore, these results could be generalized and applied to other maritime regions worldwide. Future research may extend this work by comparing port traffic forecasts using alternative modeling approaches, such as GARCH models in cases where stochastic nonlinear behavior is detected, or other nonlinear methods such as Support Vector Machines, as well as enhancements to the neural network model applied in this study. Additionally, the inclusion of high-impact exogenous variables--such as extreme weather events (e.g., hurricanes, storms) and global climate patterns like El Nino and La Nina--could be considered, as these phenomena may significantly alter the dynamics of maritime transport. Thiscomplexity demands the use of robust predictive models capable of integrating multiple sources of variability to improve forecast accuracy. Conceptualization, M.C.-M.; methodology, W.E. ; software, M.C.-M.; validation, A.R. and C.C. ; formal analysis, C.C. ; investigation, M.C.-M.; resources, M.C.-M.; data curation, W.E. ; writing--original draft preparation, C.C. ; writing--review and editing, A.R. ; visualization, W.E. and A.R. ; supervision, M.C.-M.; project administration, A.R. and C.C. ; funding acquisition, M.C.-M. All authors have read and agreed to the published version of the manuscript. This study was made possible thanks to the support of the Sistema Nacional de Investigacion (SNI) of the Secretaria Nacional de Ciencia, Tecnologia e Innovacion (Panama). Convocatoria Publica para el Ingreso de Nuevos Miembros al SIN de Panama 2020 (Grant Number: SIN-NM2020). Contrato de Estimulo Economico del SIN No. 29-2021. Not applicable. **Data Availability Statement**: Publicly accessible data were used, disclosed by Panamanian institutions such as the Panama Canal Commission, Panama Canal Authority, and National Institute of Statistics and Census of the Comptroller General of the Republic of Panama; additionally, data were obtained from the Presidente Roberto Chiari Library (Panama). The authors declare no conflicts of interest.
###### Abstract  
This paper presents an adaptive time-delayed control based on the sliding-mode (ATDC-SM). The proposed ATDC-SM provides a new adaptive law imposing time-varying boundedness that is developed to adjust the control gains appropriately while suppressing the negative impact generated by the robot manipulators. Moreover, the control gains are constructed as a continuous function with a fast adaptation rate and hence can remedy chattering and fluctuation inherent in the existing adaptive time-delayed control. These synergistic effects provide a fast convergence rate while producing stable control gains. Besides, the proposed ATDC-SM uses one-sample delayed estimation to cancel out complex nonlinear dynamics and unknown disturbances. Thus, it produces a simple structure but effective approach due to this estimation. From these benefits, the proposed ATDC-SM provides precise tracking performance without undesired side effects. It is shown that the tracking errors are uniformly ultimately bounded through a Lyapunov function. The effectiveness of the proposed ATDC-SM is illustrated through simulation with a one-link robot manipulator, which is compared to that of the existing control approaches.  
time-delayed control; sliding-mode control; adaptive control; fast adaptation; time-varying boundedness; robot manipulator +
Footnote †: journal: Applied
## Abbreviations  
The following abbreviations are used in this manuscript:  
ADC  
Analogue-to-Digital Converter  
ASIC  
Application-specific integrated circuit  
BLM  
Beam Loss Monitor  
CERN  
European Organization of Nuclear Research  
CNA  
Centro Nacional de Acceleradores  
DGRS  
Distributed Optical Fibre System  
FPGA  
Field Programmable Gate Array  
fToA  
fast Time-of-Arrival  
LET  
Linear Energy Transfer  
LHC  
Large Hadron Collider  
LVDS  
Low Voltage Differential Signaling  
MIP  
Minimum Ionizing Particle  
PCB  
Printed Circuit Board  
R2E  
Radiation to Electronics  
SEE  
Single Event Effect  
SFP  
Small Form-factor Pluggable  
ToA  
Time-of-Arrival  
ToT  
Time-over-Threshold
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 2 System Description and Performance Metrics
### Performance Metrics
#### 2.2.2 Secrecy Throughput  
The secrecy throughput represents the average secrecy rate when information is both secure and reliably transmitted. When \(p_{co}\) and \(p_{so}\) are independent of each other, the secrecy throughput is given by [19]  
\[\eta=\left(1-p_{co}\right)\left(1-p_{so}\right)R_{S}. \tag{4}\]
## 2 System and Channel Model
### Non-Zero Boresight Pointing Error Model
Specifically, when the boresight displacement \(s=\sqrt{\mu_{x}^{2}+\mu_{y}^{2}}\) is equal to zero, i.e., \(\mu_{x}=\mu_{y}=0\), and \(\sigma_{x}=\sigma_{y}\), the Beckmann model converges to the Rayleigh distribution, which is frequently used for pointing errors with zero boresight [18].
## 2 Nanostructure Materials for Arsenic Removal
### Application of Graphene Oxide-Hydrated Zirconium Oxide for As(V) and As(III) Removal  
Zirconium oxide is a generally applied inorganic substance that is chemically inert, non-toxic, and highly insoluble in water. It was stated that zirconium oxide demonstrated solid arsenic adsorption capacity, particularly for As(III) [37]. [PERSON] et al. (2013) [38] described the use of GO as substrate to support zirconium hydroxide (ZrO(OH)\({}_{2}\)) nanoparticles for the concurrent removal of As(III) and As(V) from aqueous solutions. The forming nanocomposites (GO-ZrO(OH)\({}_{2}\)) were prepared by the hydro-thermal co-precipitation reaction. It was observed that this material exhibited very high adsorption capacity for As(V) and As(III), i.e., 95 and 85 mg g\({}^{-1}\), respectively, reaching the equilibrium within 15 min. The obtained results showed that GO-ZrO(OH)\({}_{2}\) (ratio 1:100) is exceedingly effective for the concurrent elimination of As(III) and/or As(V) [38].
# Effect of Biochar Application Depth on Crop Productivity Under Tropical Rainfed Conditions
###### Abstract  
Although inherently fertile, tropical soils rapidly degrade soon after cultivation. The period of time for which crops, mulch, compost, and manure provide nutrients and maintain mineral fertilizers in the soil is relatively short. Biochar, on the other hand, has the potential to maintain soil fertility and sequester carbon for hundreds or even thousands of years. This study determined the effect of biochar application depth on the productivity of NERICA-4 upland rice cultivar under tropical rainfed conditions. A fixed biochar-soil ratio of 1:20 (5% biochar) was applied in three depths--10 cm (TA), 20 cm (TB), and 30 cm (TC) with a non-biochar treatment (CK) as the control. The study showed that while crop productivity increased, root penetration depth decreased with increasing biochar application depth. Soil moisture was highest under TA (probably due to water logging in sunken-bed plots that formed after treatment) and lowest under TC (due to runoff over the raised-bed plots that formed too). Grain yield for the biochar treatments was 391.01-570.45 kg/ha (average of 480.21 kg/ha), with the potential to reach 576.47-780.57 kg/ha (average of 695.73 kg/ha) if contingent field conditions including pest damage and runoff can be prevented. By quantifying the effect of externalities on the field experiment, the study showed that biochar can enhance crop productivity. This was good for sustainable food production and for taking hungry Africa off the donor-driven food ration the nation barely survives on.  
biochar; tropical rainfed condition; crop productivity; root-zone soil; application depth Article
## 4 Experimental Environment and Results
### Experimental Results
#### 4.2.3 Distance Estimation Based on Extended Kalman Filtering  
EKF has important theoretical significance and broad application prospects for the state estimation of nonlinear systems. Using the Extended Kalman Filter (EKF) method, a linearized standard Kalman filter model of the system can be established. The EKF algorithm has a simple structure and a certain accuracy, so it is widely used.  
At each reference point, the EKF is used to filter the obtained RSSI value and CSI effective value. The processing procedure (the reference point distance is 1 meter) is shown in Figure 15. Figure 15 shows the comparison of the distance value and the real value after the extended Kalman filter.  
Figure 14: 3D graph of CSI effective value attenuation with distance in IT-1 building.  
Figure 13: 3D graph of RSSI signal attenuation with distance in IT-1 building.  
To analyze the improvement of the distance estimation accuracy of the proposed method more intuitively and clearly, we compare the obtained estimated distance with the real distance. Figure 16 shows the accuracy of the proposed algorithm for distance estimation. We used the proposed algorithm to filter all reference points within 10m in two indoor environments. We use MATLAB to plot the estimated distance error. Figure 17 shows a comparison of estimated distance error values between 1 m and 10 m.  
Figure 16: The comparison of the distance value and the real value after the extended Kalman filter (EKF).  
Figure 15: The accuracy of distance estimation (the proposed algorithm).