esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of the Pore-Structure Characteristics of the Filter Layer
### Ideal Spherical-Particle Filter Layer  
Firstly, it was assumed that the filter layer consists of spherical particles. The plane projections for a loose arrangement and a tight arrangement are shown in Figures 1 and 2, respectively.  
Assuming that the radius of the uniform spherical particles is \(R\) and that the effective pore radii of the filter are \(r_{1}\) and \(r_{2}\) for the loose and close-packed arrangements, respectively, the values can be calculated from Figures 1 and 2, as presented in Equations (1) and (2).  
\[r_{1}=\sqrt{2}R-R=0.414R \tag{1}\]  
\[r_{2}=2(\frac{\sqrt{3}}{3}R-\frac{R}{2})=0.155R \tag{2}\]  
Thus, for ideal uniform spherical particles, the effective pore radius of the filter ranges from 0.155 R to 0.414 \(R\).
## 2 Reliability Analysis Method
### ISSA-Kriging-MCS Model
#### 2.2.3 MCS Method  
In research on engineering structural reliability, the calculation of reliability indexes is one of the key steps. The MCS method is a powerful tool for evaluating the reliability of complex structures. The method has wide applicability and a simple and reliable principle, and it is not limited by traditional analytical methods. Many structural reliability analysis methods can be used to compare and verify the accuracy of the MCS method. The basic theory is as follows:  
\[P_{f}=\int_{-\infty}^{+\infty}I[g_{X}(x)]f_{X}(x)dx=E\{I[g_{X}(x)]\} \tag{12}\]  
where the joint probability density function is \(f_{X}(x)\); \(I(x)\) is the characteristic function of \(x\).  
\[\lim_{N\rightarrow\infty}(P\bigg{|}\frac{n_{f}}{N}-P_{f}\bigg{|}<\varepsilon)=1 \tag{13}\]  
When the frequency \(\frac{n_{f}}{N}\) of \(N\) random events with \(Z\leq 0\) converges to \(P_{f}\) with a probability of 1, then the estimate of \(P_{f}\) is  
\[\widehat{P_{f}}=\frac{n_{f}}{N}=\frac{1}{N}\sum_{k=1}^{N}I[g_{X}(x_{k})] \tag{14}\]  
The corresponding reliability indexes are as follows:  
\[\beta=-\varnothing^{-1}\Big{(}P_{f}\Big{)} \tag{15}\]
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion
### Adaptive Modeling of BCI and Months  
To enhance the applicability of the BCI across different months, it is essential to adjust its threshold monthly. To achieve this, we used GEE to plot the average reflectance spectra  
Figure 12: Results of different indices calculated for four regions with poor natural background conditions. The red and black regions represent the extent of bare coal, while the blue areas highlight the sections of focused contrast.  
for a 100% bare coal fraction for each month from March to October (no images were available for January, February, November, and December) in the three study areas. As shown in Figure 13, the reflectance curves of different bands in the bare coal region from March to October exhibit periodic changes, which can be approximated by the equation: \(R=y_{0}+A\sin(\pi(x-x_{c})/4)\). Since the periods of different bands are the same, this model is also applicable to the BCI. Therefore, the mathematical relationship between the BCI and the months can be expressed as: \(BCI_{min}=0.8+0.2\sin(\pi(x+1)/4)\). Based on this model, we calculated the optimal thresholds for detecting bare coal (BC fraction \(\geq 90\%\)) using the BCI for each month and subsequently evaluated the accuracy of bare coal identification across different months.  
As shown in Figure 14, the distribution of exposed coal in the Shengli coalfield test area demonstrates an overall identification accuracy exceeding 90%. The kappa coefficient ranges from 0.82 to 0.90, the commission rate is between 0.026 and 0.188, and the omission rate is between 0.114 and 0.24. This indicates that the threshold model performs well in practical applications, achieving the goals of year-round image availability and continuous monitoring. Although this model ensures good overall accuracy for BC detection, for non-long-term bare coal areas monitoring, we recommend using data from July to September if sufficient high-quality images are available. As can be seen from Figure 14, BC identified using July to September imagery produces more reliable results in terms of overall accuracy, kappa coefficient, commission rate, and omission rate. This approach ensures high accuracy while maximizing the detection of bare coal areas.  
Figure 13: BC reflectance and BCI in relation to different months.
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 3 Results  
### Volume Ratio of the Metal Powder  
In this study, our goal was to realize a high conductivity by using only 3D-printed metal powder without post-processing such as a deposition process. As it was impossible to add an additional conductive material other than the printed metal, the content of the metal composite resin was very important. In general, the higher the metal content, the more advantageous it was to realize conductivity during the laser sintering; however, the light transmittance and photo-polymerization were difficult, so the strength of the structure was significantly reduced. Therefore, it was necessary to increase the metal content as much as possible at the level where PP 3D printing was still possible. In general, with the powder injection molding (PIM) process, it is known that the powder content can be up to 60 vol%. Accordingly, in this study, as shown in Figure 2, the rheological properties of the composite resin were visually observed for an aluminum content of 50 to 60 vol%. In the case of 60 vol%, it was difficult to even mix the composite resin due to the high powder content and blading was almost impossible due to the agglomeration of the powders. In the case of 55 vol%, the viscosity was lowered and it was possible to mix the composite resin, but during the actual blading, when pressure was applied, the material was agglomerated by the region and was not evenly spread. On the other hand, in the case of 50 vol%, which is widely used in existing composite resin 3D printing, a smooth flattening process was possible with an appropriate viscosity and flowability and it was confirmed that curing was also possible. Experiments were carried out by fixing the content of the aluminum powder to 50 vol%, which was the maximum content possible for a smooth printing process.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the PHY Layer
#### 4.1.3 Duration of Symbols and Guard Intervals in Wi-Fi 6  
Due to shorter subcarrier spacing, the symbol duration in Wi-Fi 6 has been updated with different Guard Intervals (GIs) (Figure 3). A longer GI increases the tolerance to jitter across users and leads to higher efficiency and throughput in the uplink MU OFDMA  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RU** & **20 MHz** & **40 MHz** & **80 MHz** & **160 MHz** \\  & & & & **(or 2 \(\cdot\) 80 MHz)** \\ \hline
26-tone & 9 & 18 & 37 & 74 \\
52-tone & 4 & 8 & 16 & 32 \\
106-tone & 2 & 4 & 8 & 16 \\
242-tone & 1 & 2 & 4 & 8 \\
484-tone & - & 1 & 2 & 4 \\
2 \(\times\) 996-tone & - & - & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RU sizes and their maximum numbers supported by the different channel widths.  
Figure 7: RU sizes in a 20 MHz channel.  
compared to Wi-Fi 5. Thus, the bandwidth wasted by the GI is reduced. The larger GI values are applied to combat the signal delay spread in outdoor environments [29].
## 4 Research Methodology
### Sensor-Based Approaches
#### 4.5.2 Environmental Sensors  
[PERSON] et al. proposed a fog-assisted health monitoring system for instantaneous applications, and they demonstrated the system's high performance and security using a hospital [60]. However, a major obstacle in this investigation was the interoperability of diverse devices. [PERSON] et al. prototyped a non-invasive, low-power, and economically viable sensor system to detect intravenous fluid bag levels instantaneously for use in e-healthcare applications [61]. Carers may utilize this tool to monitor intravenous fluid bag status online, and estimate when bags will run out of fluid.
## 3 Case Study  
### Data Source and Experimental Setup  
The historical irradiance data applied in the above proposed solar irradiance forecasting models is based on the dataset of Elizabeth City State University and Desert Rock Station. The first irradiance dataset in our simulation is downloaded from the National Renewable Energy Laboratory (NREL),which is measured by the Elizabeth City State University at Elizabeth City from 2008 to 2012 [55]. There are 1817 days of solar irradiance data available with 5 min time resolution. The second irradiance dataset in our simulation is downloaded from the National Oceanic & Atmospheric Administration (NOAA) Earth System Research Laboratory website, which is measured by the Surface Radiation station at Desert Rock from 2014 to 2017 [56]. There are 1196 days of solar irradiance data available with 1 min time resolution.  
To meet the international standard of short-period solar irradiance forecasting, the irradiance data should be further transformed to be the data with 15 min time resolution by taking the average of irradiance points data in the span of every 15 min. Therefore, there are total 96 irradiance data points in one day. Considering the earliest sunrise time and the latest sunset time in three years, we only use daily data points that range from 18 th to 78 th. As for the forecast periodicity, we use the historical irradiance data from the previous three days to predict the irradiance value for the next day. Therefore, in the solar irradiance forecasting model, the input variable is the historical irradiance data from the previous three days and the output variable is the predicted irradiance value for the next day.  
All experimental platforms are built on high-performance Lenovo desktop computer equipped with the Win10 operating system, Intel(R) Core(TM) i5-6300 HQ [EMAIL_ADDRESS] GHz, 8.00 GB RAM, and NVIDIA GeForce GTX 960M GPU. We use Python 3.6.1 with Keras [57] and Scikit-learn [58] to establish the DWT-CNN-LSTM forecasting models for day-ahead solar irradiance.
# Learning Deep CNN Denoiser Priors for Depth Image Inpainting
## 4 Experiments and Results  
It is well known that CNNs benefit from the availability of large training data. To learn the CNN denoiser, we followed Reference [23] in using 2000 images from ImageNet [29] for training. The noisy images were obtained by adding Gaussian noise for noise levels in the range (0,30) in increments of two. The hardware simulation platform was a Lenovo R720 computer (Lenovo company, Haidian district, Beijin, China) with an Nvidia GPU. The software platform used for simulation was MATLAB R2017a with MatConvNet. It takes about one day to train the CNN denoiser. We empirically found that using a larger training database can slightly improve the effect and greatly increase time.  
Under the same settings, the inpainting effect should be evaluated in terms of both subjective visual quality and objective metrics. As depth image inpainting is not yet a mature area of research, there are no public datasets or algorithms available for comparison; therefore, we introduced three sets of depth images for comparative experiments: the Mask depth image set, the Teddy depth image set and the Art depth image set. These image sets include both natural areas and artificial areas that need to be repaired. For comparison with our method, we used the total variation (TV) [30], group-based sparse representation (GSR) [9] and low rank with L0 gradient regularization (LRL0) [8] algorithms.  
The objective metrics can be assessed on the basis of two objective metrics: the peak signal-to-noise ratio (PSNR) and the feature similarity (FSIM) index [31].  
In Experiment (1), we obtained degraded depth images directly from a Kinect sensor, as shown in Figure 2.  
In Experiment (2), we obtained degraded depth images by adding artificial text, as shown in Figure 3. The original images were the images repaired with the GSR algorithm in Experiment (1).  
Figure 4: Visual contrasting of the inpainting results (1): (**a**)–**(d)** represent the inpainting effects of the four algorithms (total variation (TV), group-based sparse representation (GSR), low rank with L0 gradient regularization (LRL0) and the proposed algorithm) for the mask depth image; (**e**)–**(h)** represent the inpainting effects of the four algorithms (TV, GSR, LRL0 and the proposed algorithm) for the toy depth image; and (**i**)–**(l)** represent the inpainting effects of the four algorithms (TV, GSR, LRL0 and the proposed algorithm) for the Art depth image.  
Figure 3: Experimental test images (2): (**a**) represents the Mask depth image; (**b**) represents the Teddy depth image; (**c**) represents the Art depth image.  
Figure 2: Experimental test images (1): (**a**) represents the Mask depth image; (**b**) represents the Teddy depth image; (**c**) represents the Art depth image.  
In summary, the proposed algorithm has advantages in terms of its inpainting effect. Hence, the proposed algorithm is an effective inpainting method for single depth images.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology
### Data  
The landslide data used in this study were sourced from the Italian Rainfall-Triggered Landslide Inventory, compiled by [PERSON] et al. [27]. This catalog contains 6312 records of rainfall-induced landslides that occurred across Italy between January 1996 and December 2021. With relatively precise landslide locations and occurrence dates, this dataset provides a robust foundation for reconstructing rainfall-triggering events. Based on previous studies [28; 29; 30], we incorporated key static environmental factors (Figure 2) that influence landslide initiation, including elevation, slope, lithology, soil type, land use, aspect, plan curvature, profile curvature, distance to the roads and distance to the rivers. The data sources and spatial resolutions of these factors are detailed in Table 1, and the code definitions for categorical factors with their corresponding full names are provided in Table 2. These static parameters play a crucial role in determining the rainfall thresholds required to trigger landslides. These spatial variables, which contribute to determining the rainfall thresholds required to trigger landslides [7; 31; 32; 33], have been extensively validated for their effectiveness in enhancing the accuracy and reliability of landslide prediction models [34; 35]. This study employed the Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) [36] as the rainfall dataset. CHIRPS is a globally available raster data product with a spatial resolution of 0.05\({}^{\circ}\), providing daily precipitation records from 1981 onward with continuous updates. Renowned for its high accuracy and reliability, CHIRPS is widely utilized in precipitation monitoring and the analysis of antecedent rainfall events in landslide studies. As a key indicator of soil hydrological dynamics, soil moisture plays a crucial role in modulating the cumulative effects of rainfall events. It is essential for establishing precipitation thresholds that trigger landslides. Integrating soil moisture data enhances the predictive accuracy of landslide models and provides a robust foundation for defining rainfall thresholds [37; 38]. In this study, we incorporated the European high-resolution soil moisture dataset (SoMo.ml-EU), developed by [PERSON] et al. [39]. This dataset provides daily soil moisture records spanning 2003 to 2020 at a spatial resolution of 0.1\({}^{\circ}\), covering three soil layers (0-10 cm, 10-30 cm, and 30-50 cm).
# On the Energy Performance of Iridium Satellite IoT Technology
## 7 Conclusions  
To our best knowledge, in this work, we provided the first energy performance study of a satellite IoT technology. We first performed a current consumption characterization of an Iridium device, based on real hardware. On that basis, we derived a current consumption model and evaluated the energy performance of an Iridium device in terms of average current consumption, device lifetime, and energy cost of data delivery. We also compared the energy performance of Iridium technology for IoT use cases with that of well-established long range IoT technologies such as LoRaWAN and Sigfox.  
Energy-wise, Iridium is a suitable technology for relatively infrequent message transmission. The asymptotic lifetime of the battery-operated Iridium IoT device considered, for a battery capacity of 2400 mAh, is 3.55 years. For very low message rates (e.g., once per day), it performed similarly to LoRaWAN or Sigfox. However, Iridium energy underperformance increases with message rate, compared to the latter technologies. However, Iridium still offers nearly a one-year lifetime for a rate of one message every 100 min.  
Figure 11: Lifetime of a device running on a 2400 mAh battery for Iridium, Sigfox. and LoRaWAN, as a function of \(T\) and for \(p=0\).  
This work focused on the use case of a battery-operated IoT device using Iridium to send messages periodically such as a sensor. However, the work also allows us to conclude that a battery-operated actuator using Iridium might only be feasible for latency-tolerant applications. Since delivering a message (e.g., a command) to such an Iridium device requires the device itself to previously send a data message, and the time between uplink message transmissions needs to be relatively long for a practical battery lifetime, sending a command to an actuator may involve a significant delay (e.g., in the order of hours, or even greater).  
Conceptualization, C.G.; Methodology, C.G., S.M.D. and H.N.; Validation, C.G., S.M.D. and J.P.; Formal analysis, C.G.; Writing and review, C.G., S.M.D. and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Spanish Government through project PID2019-106808 RA-I00, AEI/FEDER, EU, and by Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya through project 2017 SGR 376.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Conclusions  
We reviewed our recent progress on arrayed waveguide gratings and receiver integration with throughput of 100-Gb/s and beyond. These WDM applications are targeted for emerging data-centric communications and computing. The low-loss flattop MM-AWG is an essential component in all the proposed photoreceivers. The assembly techniques for hybrid integration are also critical for precise, compact, and cost-effective solutions, with appropriate optical coupling with higher-mode beams from the MM-AWG output. Wavelength scalability, higher bit rates, and more sophisticated modulation will be exploited for future functional multi-wavelength links.  
All the studies are summarized in Table 1. The 10-Gb/s receiver was fabricated by mechanical alignment with eight-channel WDM by 1.25 Gb/s on a 20-nm wavelength grid in the 1.5-\(\upmu\)m band. The 40-Gb/s receiver was fabricated by visual alignment with four-channel WDM by 10 Gb/s on a 24.5-nm wavelength grid in the 1.3-\(\upmu\)m band. The third 100-Gb/s receiver was assembled by active alignment with four-channel WDM by 25 Gb/s on an 800-GHz grid in the 1.3-\(\upmu\)m band, and this is a basic configuration enabling us to further ramp up total throughput to meet the growing demands on data-centric communications and computing.  
**Author Contributions:** Conceptualization, Y.D.; investigation, Y.D., T.Y., Y.N., S.T., M.N. and S.K.; supervision, K.S. and Y.I. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **10-Gb/s Photorecceiver** & **40-Gb/s Photorecceiver** & **100-Gb/s Photorecceiver** \\ \hline Total bit rate (Gb/s) & 10 & 40 & 100 \\ WDM (channels) & 8 & 4 & 4 \\ Baud rate (Gb/s) & 1.25 & 10 & 25 \\ Wavelength band (\(\upmu\)m) & 1.5 (C-band) & 1.3 (O-band) & 1.3 (O-band) \\ Channel spacing (nm) & 20 (CWDM) & 24.5 & 4.5 (LAN-WDM) \\ Standard & - & VSR-5 & IEEE 802.3 ba \\ Alignment technique & Mechanical & Visual & Active \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of integrated wavelength division multiplexing (WDM) photoreceivers with MM-AWG.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Human-Assisted Within-Show Diarization
### Performance of Within-Show Human Assisted Diarization  
Figure 3 illustrates the evolution of DER for an audio file for both stopping criteria. As expected, _All_ leads to more questions and achieve a better, lower, final DER than the _2c_ criterion. This example shows the necessity of taking into account the cost of human interaction to fairly compare HASD systems.  
A first set of experiments is performed to compare the Longest and Cluster center _sample_ selection methods for both stopping criteria. Results are presented in Tables 5 and 6. The two sample selection methods are comparable in terms of number of questions asked per hour of audio and CQR. This is visible on the penalized DER which preserves the conclusions drawn by observing the DER.  
Comparing the two stopping criteria, we observe that the _All_ criterion succeeds to improve the DER more than the _2c criterion_ for _SincDS_ and _ResDS_ models on the Development and Evaluation sets (see Tables 5 and 6). However, by taking into account the cost of question generation (for a \(t_{pen}\) empirically set to 6s), it can be concluded that the _2c_ criterion presents a better compromise between DER improvement and human interaction cost, except when the longest (or resp. cluster center) segments are the representative of clusters and _ResDS_ model is used on the Evaluation (resp. Development) set.  
Figure 3: Tracking of the DER corresponding to a single show file (with duration of 42 min) by applying question-correction with different methods. Points in each question indicate that it has resulted in a correction.