esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
#### 2.2.1 Average Monthly Precipitation Data within the Basins  
The precipitation data within the basins were obtained from the total precipitation product of ERA5-Land. ERA5-Land is a reanalysis that provides a consistent evolution of land variables over several decades using a spatial resolution of 10 km. This reanalysis produces data that go back several decades in time, providing an accurate description of the climate of the past [48]. This last feature was particularly useful in our research, since  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dam**} & **Year Starting** & **Height** & **Reservoir Volume** & **Reservoir** & **Downstream** & **Rivers** & **Rivers** \\  & **Operation** & **(m)** & **(10\({}^{6}\) m\({}^{3}\))** & **Area (m\({}^{2}\))** & **Initial Channel** & **Upstream of** & **Downstream** \\  & & & & & & **Width (m)** & **Reservoir** & **of Reservoir** \\ \hline Prado & 1972 & 75 & 1010 & 125,400 & 302 & Prado, Cunday & Magdalena \\ Salvajina & 1985 & 148 & 764 & 203,100 & 84 & Cauca & Cauca \\ Betania & 1986 & 170 & 1971 & 700,000 & 236 & Magdalena, & Magdalena \\ Urra I & 2000 & 100 & 1740 & 740,000 & 112 & Sinu, Verde & Sinu \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the dams selected for the study.  
the precipitation station network was limited during the Betania project pre-dam period (1960-1984). Seeking coherence in the analysis, the same product was used for the Salvajina (pre-dam period 1976-1983) and Urr\(\acute{\rm a}\) i (pre-dam period 1991-1998) projects, even though for those dates, the precipitation station network had improved.  
Figure 1: Location of the selected dams in Colombia: (**a**) general location, (**b**) Urr\(\acute{\rm a}\) I project, (**c**) Salvajina project, and (**d**) Betania and Prado projects (see Table 2 for information about selected gauging stations).  
Using the monthly total precipitation product from ERA5-Land, the average precipitation for each month was calculated within the basins that supply the reservoirs. So, each basin had a monthly precipitation series between 1950 and 2020. For precipitation change analysis, the pre-dam and post-dam periods were forced to coincide with the selected periods in the discharge stations closest to the dams, since these are assumed to be the stations whose records are more affected (see Section 2.2.2 and Table 2).
# Deep Image Compression with Residual Learning
## 1 Introduction  
Image compression is a fundamental and well-studied problem in the data compression field. Typical conventional compression algorithms such as JPEG [1] and JPEG2000 [2] are based on transform coding theory [3]. JPEG adopts the discrete cosine transform (DCT) while JPEG2000 adopts the discrete wavelet transform (DWT) to convert an original image into a latent compression representation space.  
Recently, lossy image compression frameworks based on deep learning (DL) have raised interest in both deep learning and image processing communities [4; 5; 6; 7; 8; 9; 10]. Although these approaches are competitive with the existing modern engineered codecs such as JPEG [1], JPEG2000 [2], WebP [11], and BPG [12], several issues and challenges still need to be addressed. Firstly, existing learning methods commonly compress the full resolution image at one-shot regardless of the difference between high- and low-frequency information, which brings out some artifacts and block effects. Secondly, data compression is mainly reflected in the quantization stage, but it may introduce too many unnecessary errors. Based on previous works, a residual learning framework is proposed by introducing some novel technologies to properly improve the compression quality.  
In Section 1.1, DL-based image compression related works are reviewed and discussed. In Section 1.2, deep channel residual learning is introduced. In Section 1.3, global residual learning in full resolution is presented. In Section 1.4, the main contributions of this paper are summarized.
## 4 Ge Photodetector Light Coupling Schemes  
### Normal Incidence Photodetectors and the Bandwidth-Efficiency Tradeoff  
Normal incidence (NI) photodetectors are also known as vertical photodetectors or surface illuminated photodetectors. Normal incidence is the simplest light coupling scheme with incoming light illuminated on the top or bottom surface of the detector. Almost all the electrical structures, _i.e._, PIN, MSM and avalanche, can be fabricated in the fashion of NI photodetectors.  
Due to its low process complexity, NI photodetectors are widely used in communication technologies. However, they suffer from an inherent drawback due to the bandwidth-efficiency tradeoff. This tradeoff results from the opposite requirement of the thickness of the photoabsorption layer for high bandwidth and high efficiency [43]. The carrier-transit-time-limiting bandwidth \(f_{t}\) can be expressed as [44]:  
\[f_{t}\simeq 0.45\times\frac{\upsilon}{d} \tag{1}\]  
While the ideal efficiency \(\eta\) assuming zero reflection and full carrier collection is:  
\[\eta\simeq 1-e^{-\alpha\times d} \tag{2}\]  
Figure 3: A Calculated carrier-transit-time-limiting bandwidth and efficiencies of normal incidence PIN Ge photodetector.  
where \(v\) is carrier transit velocity, d is intrinsic region's thickness and \(a\) is material's absorption coefficient. Using \(v\) = 6 \(\times\) 10\({}^{6}\) cm/s for Ge and \(a\) = 4,000 cm\({}^{-1}\), the carrier-transit-time-limiting bandwidth and efficiencies _versus_ intrinsic region thickness can be plotted as Figure 3. As can be seen, for a Ge device with 3 dB bandwidth of 100 GHz, an intrinsic layer thinner than 0.27 \(\upmu\)m is required with a resulting efficiency of \(\sim\)10%.
## 1 Introduction
However, since the uncompensated delay jitter accumulates gradually when the oscillation number increases, the root-mean-square error (RMSE) will worsen proportionally and greatly limit the achievable resolution. Thus, measurement results will be meaningless when the RMSE is greater than one LSB. To avoid this effect, the key to improving this TDC structure's performance is to reduce the required oscillation number without changing the resolution goal. [PERSON] and [PERSON] [23] proposed a method of bidirectional operation on the Vernier delay lines, which could halve the oscillation number, but with double the resources consumed and a long dead time. In summary, the few tentative studies based on Vernier-type TDC did not thoroughly investigate methods to improve resolution, dead time, and resource consumption simultaneously, i.e., when the selected resolution is small, the resource consumption and dead time are large, and vice versa [21; 22; 23]. These are the cases that significantly limit the practical applications. This study proposes a fine-timestamp maker that uses a bi-time interpolation scheme (first presented in this paper), to alleviate resolution problems without adding extra resource consumption and dead time. Therefore, a relatively high-precision and high-throughput Vernier-type TDC would be achieved. Typically, the TDC's resolution is adjusted by the multiplexer or recompilation processes; instead of this approach, we design a linearly adjustable RO structure packaged as an IP core with less position dependence, to ensure the ideal resolution can be selected. Previous publications on FPGA-based Vernier TDC mainly adopt two ring oscillators working in Vernier mode [21; 22; 23], and its resolution is the period difference between two ring oscillators. Taking the system clock as the reference, this paper only needs one ring oscillator so that some factors incurring metastability risk can be avoided, which will also save the resource consumption of one RO. In conclusion, due to the resource-saving characteristics of the proposed method, the TDC design can attain an excellent multi-channel capability to realize more functions. Additionally, a resource-saving TDC structure with balanced performance can also achieve an ideal power consumption. The remaining structure of this paper is as follows. Section 2 describes the principle and structure of the fine-timestamp maker, including its composition, circuitry, and performance evaluation. Section 3 presents the design and time-interval experiments of the TDC. Section 4 discusses the experimental results and outlook on future research. Section 5 provides a summary and conclusions.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 2 Simulation Environments  
For evaluating the path planning algorithm, a mobile robot model, LiDAR sensor, visualization function, and maps provided by MATLAB 2022b's Mobile Robotics Simulation Toolbox are utilized. Figure 1a,b shows the directions of the linear velocity and angular velocity of the robot and the environment of the Mobile Robotics Simulation Toolbox, respectively. The forward/inverse geometry of the robot is provided, and the position, posture, movement path, sensor measurement distance(the dashed blue lines), etc. of the robot are shown through the visualization function, as in Figure 1b. For the simulation, the radius of the wheel of the mobile robot is set to 0.1 [m], and wheelbase, which is the distance from the left wheel to the right wheel, is set to 0.5 [m]. The local coordinate system of the robot is fixed to the center of the robot. In the simulation environment, the LiDAR sensor is configured in a radial form, with the center of the robot as the origin. According to the user settings, the robot obtains obstacle information of up to 4 m at 19 points through the LiDAR sensor. LiDAR sensors are widely used to create maps or model and track objects using numerous measurement points. However, in this paper, since large amounts of data in the form of a cloud are not required, the LiDAR sensor is simulated by a low-cost ultrasonic sensor widely used in mobile robots. In the reference paper [32], 5-7 robot skeleton points are set, and the distance from each point to the obstacle is calculated and simulated. Although this paper also uses the LiDAR sensor supported by MATLAB's Toolbox, it receives distance data by dividing 360 degrees into 19 points as if it were equipped with 19 ultrasonic sensors. Computation time can be saved by reducing the amount of processed data. The maximum measurement distance is set to 4 m. This study targets mobile robots that provide services in offices or commercial facilities. Accordingly, 4 m is considered the distance that people can feel as an open space in general indoor office environments. At too far a distance, the spacing between each sensor data increases, making it inappropriate to treat it as information about continuous obstacles. Reference [32] is referred to for the path planning and control process of the robot. All simulations are saved as figures, data files, and videos.
## 3 Methods
### Pre-Processing
#### 3.4.2 Spectral Preprocessing  
Though most of the factors influencing the spectral response of the sample are covered by ELM, yet there are some effects which ELM do not account for, like path length difference, scattering, and shading [36]. The shading effect is controlled by ensuring the flat surface of the sample. However, the path length difference and scattering can arise undesired responses which may also affect the reliability of the built model.  
The effects of these factors can be minimized by mathematical techniques proposed in literature [37], for example, Multiplicative Scatter Correction (MSC) [38], Standard Normal Variate (SNV) [39], and Savitzky-Golay filtering [40]. There is not any single criterion available, and the choice of pretreatment techniques solely depends on the calibration model and several iterations can be required for selection [41]. In this study, we have utilized SNV for standardizing the reflectance spectra and to remove the effect of particle size [39]. Mathematically, SNV calculation is made using the following equation,  
\[SNV(\lambda)=\frac{y(\lambda)-y^{-}}{\frac{\sqrt{\sum\{y(\lambda)-(y^{-}))^{2} }}}{n-1}} \tag{2}\]  
where SNV(\(\lambda\)) is the standard normal variation as a function of wavelength. \(y^{-}\) is the mean spectrum of the sample. Figure 9 shows the difference in calculated reflectance and SNV corrected reflectance.  
A Savitzky-Golay filter was used by the absorption spectroscopy community for smoothing and differentiation. [PERSON] and [PERSON] showed that this method can also be applied to the spectra acquired from the hyperspectral imaging sensor [42]. The Savitzky-Golay filter is a digital polynomial smoothing low-pass filter. It works by applying polynomial fitting on a set of input samples and evaluating the resulting polynomial at a single point within an interval. The main property of this filter is its work on the philosophy that preserving spectral features is more important than eliminating noise. Thus, it reduces noise in a spectral signal while preserving important peaks. For this study, Savitzky-Golay filtering with eleven points and 3 rd order polynomial has been applied to reduce the spectral variations in the acquired spectrum. Moreover, to remove the baseline effect, Savitzky-Golay's first-order derivative has also been calculated.  
Figure 9: SNV Corrected Spectra.
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.3 Other Transition Metals  
Ni and Co are the most investigated metals for their known and vastly reported catalytic activities towards DRM. However, recent studies have also investigated other transition metals and their role on the catalyst development process for a successful scaling up of DRM process. [PERSON] et al. [86] evaluated the effect of temperature on the catalytic activity of Mo-based catalysts doped with Ni on DRM. The catalysts were prepared by incipient wetness impregnation with a nominal load of 20 wt% Mo and a variable load (2-10 wt%) of Ni. The authors reported that the increase in Ni loading to 10 wt% was responsible for a higher Mo dispersion over the catalyst support (Al\({}_{2}\)O\({}_{3}\)), which positively affected CH\({}_{4}\) and CO\({}_{2}\) conversion. However, the authors highlighted that although the high Mo loading may have been responsible for a low conversion at T = 800 \({}^{\circ}\)C (\(\chi_{\text{CH}_{4}}\) = 30%, \(\chi_{\text{CO}_{2}}\) = 48%, H\({}_{2}\)/CO = 0.40), the Mo-based catalysts presented no coke deposition after the catalytic tests, which may lead to a higher catalyst stability.  
[PERSON] et al. [87] has recently reported the investigation on the effect of W loading on Ni/Al\({}_{2}\)O\({}_{3}\) catalysts for DRM. The catalysts prepared by wet co-impregnation had a W-loading ranging from 0 to 26.2 wt% of W and were tested for DRM at 700 \({}^{\circ}\)C. The highest concentrations of W on the catalysts (19.8 wt% and 26.2 wt%) inhibited DRM activity (\(\chi_{\text{CH}_{4}}\) < 20%) and the best results were achieved with intermediate W loadings (6.3 wt% and 11.9 wt%; T = 700 \({}^{\circ}\)C, \(\chi_{\text{CH}_{4}}\) = 55-60%, \(\chi_{\text{CO}_{2}}\) = 60-70%). These loadings, however, led to the production of syngas with a low H\({}_{2}\)/CO ratio of 0.5-0.8. Finally, the authors reported that the presence of W was responsible for suppressing coke formation due to a double mechanism: the restrained carbon diffusion on the lattice of Ni-W alloys, and the carbon formation-gasification mechanism that took place on the surface of the catalysts and yielded the formation of tungsten carbides.
# Service Interface Translation. An Interoperability Approach
## 2 Related Work
### Proposed Approach. Translators  
The work presented by [PERSON] et al. [20] opened a new viewpoint where the information interoperability between two systems can be addressed by means of translators. Their translator was focused exclusively on dynamic application protocol translation. In addition to protocol translators, semantic translators have been proposed to solve specific semantic interoperability issues. [PERSON] et al. [21] presented a translation approach to translate automotive interface description models from Franca to AUTOSAR frameworks.  
To fully close the mismatch gap between two systems, a complete service interface translation is needed. All the aspects defined in the service contract, both functional and nonfunctional requirements, have to be considered and addressed to overcome interoperability mismatches. Integration platforms have to provide mechanisms for dynamic and instant information translation across protocols, encodings, standards, ontologies, and semantics used by individual embedded systems.  
This paper proposes an approach to address interoperability between service oriented architecture-based heterogeneous systems. It is based on the autonomous generation of a suitable consumer interface that allows the consumption of the provided service despite mismatches between the systems. The proposed approach not only provides autonomous generation but also a deployment mechanism to permit a complete service interface translation. The approach is presented in detail in the following sections.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 5 Experimental Results
### Device Layer  
The device layer is mainly composed of MT3620 Azure Sphere Kit, ZigBee wireless sensor network devices and different types of sensors. First, data collected by ZigBee end device will be transmitted to ZigBee Coordinator via WSN. The data can be captured and parsed frame by frame with SmartRF Packet Sniffer, which is shown in Figure 9. It is clearly depicted that not only the original data, but also the protocol details of the frame are unveiled in detail. For example, the highlighted frame in Figure 9 indicates that it is a data request message from the ZigBee coordinator. The length of the frame, the details of the frame control field, the destination address, the source address, the link quality indication and the frame check sequence are also parsed.  
Figure 8: Editor view for Power BI reports.  
Figure 9: ZigBee data captured by Packet Sniffer.  
Figure 10 shows the captured waveforms for UART communication between the MT3620 gateway and CC2530 coordinator device, which are generated by Tektronix digital oscilloscope TBS1064. The sending and receiving UART waveforms are demonstrated in Figure 10a,b respectively. It is noted that the UART serial communication here is at TTL (Transistor-Transistor Logic) level, which means that the voltage will always remain between 0V and Vcc.
## 3 Methodology
### CovidDetNet Architecture Details  
The architecture of the proposed CovidDetNet model is depicted in Figure 2. As illustrated in Table 1, the CovidDetNet model has ten learnable layers: nine convolutional layers and one FC layer. The input layer in the proposed model is the initial layer, and it accepts 256 \(\times\) 256 input images for processing. The architecture has a total of thirty-one layers, including two cross-channel normalization layers, three maximum pooling layers (to reduce network size), four batch normalization layers, three clipped ReLu layers, five leaky ReLu layers, one global average pooling layer, one Softmax layer, and one classification layer. In the proposed CovidDetNet model, the leaky ReLu (nonlinear activation function) is utilized after the first five convolutional layers. In contrast, the ReLu activation is employed after the last four convolutional layers. The first two layers of architecture are followed by the cross-channel sectional normalization layer, whereas batch normalization layers follow the last four convolutional layers. Maximum pooling layers follow the first two and fifth convolutional layers. The output of the last FC layer is provided as an input to 3-way Softmax in the case of COVID-19 classification and detection (three-class classification).  
The first convolution layer extracts feature from the 256 \(\times\) 256 input chest radiograph image by using 128 filters of size 3 \(\times\) 3 with a 4-pixel shift (stride) at a time. After employing normalization and maximum pooling operations, the output feature of the initial convolutional layer is provided into the second convolutional layer. The next convolutional layer uses 512 filters of size 3 \(\times\) 3 having a stride value of 1-pixel at a time. The inputs are filtered by the third convolutional layer, employing 384 filters of size 3 \(\times\) 3 with a stride value of 1 pixel. Pooling layers do not follow the subsequent two convolutional layers, the fourth and fifth convolutional layers apply 256 filters of size 3 \(\times\) 3 to the input feature map with a default stride of 1 pixel. The sixth convolutional layer applies 960 filters of size 1 \(\times\) 1, whereas the seventh convolutional layer applies 960 filters of size 3 \(\times\) 3. The eighth convolutional layer applies 320 filters of size 1 \(\times\) 1, and the final (ninth) convolutional layer applies 1280 filters of size 3 \(\times\) 3.  
Figure 2: The architecture of the proposed CovidDetNet model.
## 3 Computational Results
### Zenith Angle Discretizations
As seen, a uniform angular distribution is not sufficient to resolve the fine structure of the angular distribution predicted by the Monte Carlo simulation (which is more obvious near 3\({}^{\circ}\)). In comparison, non-uniform scheme 2 successfully resolved the profile of the transmitted angular distribution. Panel (**b**) of Figure 9 plots the absolute relative error together with the phase function 3. It is evident that for angular discretization scheme 1, the profile of the absolute relative error resembled the profile of the phase function, indicating that the error was mainly caused by insufficient resolution with regards to the phase function. In contrast, the error curve from angular distribution discretization 2 eliminated such similarities, indicating that the error caused by angular discretization was less apparent. Finally, Figure 10 examines the absolute relative error of Markov chain approximations compared with Monte Carlo simulations with zenith angle discretization scheme 3. Phase function 3 was selected and the angular resolution was 0.25\({}^{\circ}\) for \(\theta<15^{\circ}\) and \(\theta>165^{\circ}\), and 2.5\({}^{\circ}\) for anywhere else. It can be  
Figure 8: Absolute relative error of Markov chain approximations compared to Monte Carlo simulations with zenith angle discretization scheme 2. Phase function 3 was selected. Panel (**a**) OD = 2, Panel (**b**) OD = 5. The angular resolution was 0.25\({}^{\circ}\) for \(\theta<30^{\circ}\) and 2.5\({}^{\circ}\) for \(\theta>30^{\circ}\). Figure 9: Solutions from Markov chain approximations compared to Monte Carlo simulations with zenith angle discretization scheme 2. Phase function 3 was selected and OD = 5.0. Panel (**a**). Transmitted angular distribution, Panel (**b**). Absolute relative error. seen in Figure 10 that the absolute relative error for both OD = 2 and OD = 5 cases was again reduced from 1% to 0.2% in the angular range of 0\({}^{\circ}\) to 15\({}^{\circ}\). However, the peak error near 180\({}^{\circ}\) was not reduced significantly. Two observations were made regarding this phenomenon: (1) The significant error of \(\sim\)1% near 180\({}^{\circ}\) only occured when phase function 3 was used, as can be seen in Figures 4-6, indicating this was an issue related to angular discretization; (2) The error near 0\({}^{\circ}\) was remarkably reduced by discretization scheme 3 whilst the error near 180\({}^{\circ}\) did not, which reflected that the error caused by coarse meshes in the range from 15\({}^{\circ}\) to 165\({}^{\circ}\) had been accumulated with multiple scattering effects.