esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Frequency Instability  
In order to evaluate the long-term characteristic of the frequency transfer system, we should usually measure the relative fractional frequency stability of the frequency transmission link. In this experiment, we had measured the timing fluctuation of the frequency transfer system shown above, therefore, the Allan deviation of the frequency transfer link can also be calculated as the relative fractional frequency stability. In the same way as in the previous timing fluctuation measurement, the stability measurements have three scenarios, which are the transfers without phase compensation, with phase compensation, and with shorted fiber optic links. Here, Figure 5 shows the stability results of the RF transmission link for three different scenarios. Curve (i) is the stability result without the phase-compensation, showing an Allan deviation of 4.6 \(\times\) 10\({}^{-14}\) at 1 s and 6.3 \(\times\) 10\({}^{-15}\) at 2000 s, respectively. Curve (ii) is the stability result with the phase-compensation, showing an Allan deviation of 2.2 \(\times\) 10\({}^{-14}\) at 1 s, and 8.5 \(\times\) 10\({}^{-17}\) at 2000 s, respectively. Curve (iii) is the Allan deviation of the shorted fiber link, which can be regarded as the measured floor of our system, which is the lower boundary of the stability of our frequency transmission link. This floor is limited only by the noise of the optical and electronic components in the system. Curve (iv) is the frequency stability of a commercial H-master clock (Microsemi, MHM-2010). Comparing curves (i) and (ii), we can find that with the joint phase compensation technique, the long-term stability of the frequency transmission link is much improved by about two orders of magnitude at 2000 s. At the same time, we also found that the frequency stability shown in curve (ii) is superior to H-master at all measurement times from 1 s to 2000 s, which indicates that the optical-electronic joint phase compensation method proposed in this paper can realize the transfer of the atomic clock (Cs clock and H-master) without stability loss in a long-distance fiber link.  
Figure 4: Timing fluctuation results. (**a**) Without phase compensation. (**b**) With phase compensation. (**c**) The measurement floor attributed by the short link. The sample rate is 1 point/second for all curves.
## 3 Model and Methods
### Sensitivity Analysis by SIMLAB  
In the LUE models, the accuracy of GPP estimation is affected by the structure of the canopy. The CI is a key parameter for the description of the canopy structure. With the availability of improved global long-term CI products, it is feasible to incorporate the temporal variation in the CI in remote sensing and land surface modeling research in the future [60]. Hence, the seasonal variation in the CI would lead to changes in GPP to some extent. The accuracy of GPP estimation partly depends on the CI, which is why it is important to classify the canopy of vegetation into sunlit and shaded leaves in the TL-LUE model. In this study, due to the mutual influence between various input parameters, the total sensitivity of GPP to the CI was evaluated by the extended Fourier amplitude sensitivity test (EFAST) [61] formed by combining Fourier amplitude sensitivity analysis (FAST) [62] and Sobol [63], and the sensitivity was measured by the variance of GPP outputted from the TL-LUE model caused by the changes in the CI. This sensitivity analysis can be implemented by SIMLab 4.0 software [64]. SIMLAB is a comprehensive stand-alone software for performing the total sensitivity analysis based on Monte Carlo and sampling-based methods. The Joint Research Centre (JRC) of the European Commission has provided financial assistance for the design and enhancements to SIMLAB since its first version was released in 1999. SIMLAB is available for any individual or organization from the website: [[https://ec.europa.eu/jrc/en/samo/simlab](https://ec.europa.eu/jrc/en/samo/simlab)]([https://ec.europa.eu/jrc/en/samo/simlab](https://ec.europa.eu/jrc/en/samo/simlab)), accessed on 28 May 2023. The sensitivity of the anisotropic flattening index to vegetation parameters in the double canopy reflectance model is analyzed by the EFAST method [65].
## 6 V2X Testbed Setup and Algorithm Implementation  
### Hardware Setup and Network Diagram  
In this section, we elaborate on how we implement the proposed V2X technology selection algorithm in our V2X testbed. For this, we created a small-scale V2X testbed setup, as shown in Figure 6. We have integrated the following technologies such as DSRC, C-V2X PC5 and 5G NR Uu in the testbed.  
Figure 5: A small part of the detailed decision tree showing selection between the three technologies.  
Figure 6: Network diagram of the experimental V2X setup.  
The hardware setup, as shown in Figures 7-10, consists of two V2X nodes, both having Intel NUCs as the main processing units. An in-house developed vehiCu1 Ar coMmunIcation maNagement frameWrCk (CAMINO) framework [16] is setup on top of them for managing the different V2X communication technologies and the services running on top of them. The NUCs are connected via ethernet cable with the Cohda's MK6c [20] and MK6 [21] modules. MK6c module provides C-V2X PC5 functionality whereas MK6 is used in DSRC mode. For precise positioning information, each device (Cohda MK6c, MK6 and NUCs) has its own GPS receiver. A portable Open RAN-based 5G standalone (SA) setup (Figure 7) is used for long-range V2X communication. It consists of a general high-end processing unit, a power unit, a Benetel 5G outdoor Radio Unit RAN650 [22], a 4G modem (for remote access and management), and a USRP. The Open RAN-based SA 5G solution is hosted on the processing unit along with the Open5 GS Core, an open source 5G network core compatible with 3 GPP Release 16. Two 5G Quected UEs are connected to the NUCs via USB cables, and both of them connect using the Uu link with the Benetel 5G radio unit (Figure 7). A total of two V2X nodes, referred to as OBUs have been set up and named as OBU1 and OBU2. A Message Queuing Telemetry Transport (MQTT) server deployed on the 5G in-a-box setup is used for the long-range C-ITS message exchange between OBUs. For experimentation with mobility, OBU2 is placed inside a vehicle, whereas OBU1 is kept stationary. The stationary unit (OBU1) and the mobile unit (OBU2) are shown in Figure 8 and Figure 9, respectively. Figure 10 shows the Tx/Rx and GPS antennas on the roof of the vehicle.
## 5 Experimental Results
### DSP Implementation Comparison  
This section compares the controllers used across the experimental implementation regarding computation burdens. The two following criteria are used: execution time and program memory.  
Table 8 collects the obtained results. From Section 3 it should be noted that the FPR controller approximation for \(\alpha=0.75\) uses an order approximation of 2, while when \(\alpha\) is set to 1.5, it uses order 3.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Controller** & \(\mathbf{V_{150 Hz}}\) **(V/A)** & \(\mathbf{V_{250 Hz}}\) **(V/A)** & \(\mathbf{V_{350 Hz}}\) **(V/A)** & \(\mathbf{V_{450 Hz}}\) **(V/A)** & \(\mathbf{V_{550 Hz}}\) **(V/A)** & **THD (\%)** \\ \hline PR & 0.035 & 0.0127 & 0.290 & 0.433 & 0.402 & 0.95 \\ PRHC & **0** & **0** & **0.092** & **0.265** & 0.443 & **0.71** \\ \(\alpha=0.75\) & 0.010 & 0.052 & 0.133 & 0.280 & **0.372** & **0.71** \\ \(\alpha=1.5\) & 0.0130 & 0.125 & 0.294 & 0.456 & 0.424 & 0.97 \\ \hline \hline \end{tabular} Bold values indicate the best option.  
\end{table}
Table 7: Multi-harmonic disturbance response and total voltage THD content for all four controllers.  
Figure 17: Disturbance rejection time response. The orange line in the right figure depicts the disturbance on the tracking error plot.  
From Table 8, it can be seen that the FPR arises as one outstanding controller for covering a wide bandwidth of controlled frequencies and improving the rejection capability over other classical options such as PR or PRHC. Therefore, any advanced low-order controller, like the proposed FPR controller for voltage-controlled applications, can reduce execution time, allowing the saved time to be allocated to other critical tasks, such as generating real-time alarms.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology
### Convolutional Neural Networks  
The convolutional neural network (CNN) was introduced by [31] in the late 1980s and contains three main key layers: convolution, pooling, and fully connected. Convolutional layers are used to extract features from input data by placing the kernel and the input image through a convolutional process to extract features. The feature map is created by sliding a small 5 \(\times\) 5 \(\times\) 3 rectangular matrices over the image, which is known as the kernel. The pooling layer is used to condense the feature size, which is divided into average, maximum, and minimum pooling. In the field of computer vision (CV), CNN has recently achieved  
Figure 1: The high-level diagram of the proposed work for LAC.  
higher performance in comparison to conventional machine learning methods. Several CNN-based architectures are developed for image classification, and each has its own types of pros and cons. For instance, some of these models are effective but computationally expensive, while some are lightweight and resource friendly. After a thorough analysis of recent models, we choose Inception-v3 and DenseNet121 for LAC. These models have a balance between effectiveness and computational efficiency, which makes them suitable for accurate and concise land-cover classification. The proposed diagram of fully connected layers is given in Figure 3.
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
## 4 Discussion
### Red-NIR versus Full NIR  
Most dual-wavelength pulse-oximeters are equipped with a red, typically 660 nm, and a NIR LED in the range 900-940 nm. These wavelengths are selected based on the SpO\({}_{2}\) contrast, i.e., the absorption difference between Hb and HbO\({}_{2}\), and the low absorbance of water and other species in blood and tissue. The illumination is shielded from the environment in transmissive and reflective pulse-oximeters. For camera-based pulse-oximetry this is not the case, which has consequences for applications where visible light is not tolerated, e.g., for sleep monitoring. Using solely wavelengths in near-infrared (NIR) which are (almost) invisible to the human eye, the SpO\({}_{2}\) contrast reduces by a factor of 2 as can be observed from the estimated update vectors \(P_{by}^{u}\). A decreased SpO\({}_{2}\) contrast will increase the noise of the measurement. However, the strength of the PPG signal is also larger in full NIR, which could positively impact the performance because of the very small signals of the camera which are up to 100 times smaller than those of conventional contact probes [9]. In order to  
Figure 10: An overview of the evaluation metrics (pulse) quality, root-mean-square error (RMSE) and bias for all recordings in dataset using the model-based and empirically-derived calibration models. The results are calculated for two dual-wavelength and three-wavelengths combinations, and the derived four-wavelengths model.  
investigate how both factors should be balanced, we evaluated the performance for both red-NIR and full NIR wavelengths.  
The comparative results show that red-NIR yields a slightly better performance than the full-NIR wavelength combinations. Both, however, satisfy the ISO standard of an RMSE \(<\)4%, which is very important as it greatly widens the scope of possible applications for camera-based pulse-oximetry, e.g., for the detection of sleep apnea. It should, however, be noted that the genericity of this observation should be verified on a population, rather than one subject. These results confirm our earlier observations [8], although with slightly different wavelengths in NIR.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 3 Results
### Path Selection  
Figure 6 shows the categorical distribution achieved by the RL agent for the transmission with satellite 1 (\(sat_{1}\)), satellite 2 (\(sat_{2}\)) and both satellites (\(sat_{1,2}\)) in the different contexts, corresponding to the probabilities \(P(1)\), \(P(2)\) and \(P(1,2)\), respectively. These are the path selection probabilities achieved by both the AC agent and the optimal policy after convergence.
## 3 Methodology
### Feature Extraction
#### 3.3.1 Speeded Up Robust Features (SURF)  
The SURF feature descriptor is a robust and fast algorithm for comparison, local, and similarity invariant representation of the images [35]. The SURF feature descriptor completely relies on its faster computation of operators utilizing box filters. While the Hessian matrix determinant is maximum, the SURF feature descriptor detects the blob-like  
Figure 3: Detected portion of cutting edges: (**a**) serviceable edges and (**b**) edges with high wear.  
Figure 2: Acquired sample images from cutting edge dataset.  
structure. Let us consider the point \(x=(x,y)\) in the acquired image \(I\); the Hessian matrix \(H(x,\sigma)\) with scale \(\sigma\) is determined at \(x\) using Equation (1):  
\[H(x,\sigma)=\begin{bmatrix}L_{xx}(x,\sigma)&L_{xy}(x,\sigma)\\ L_{xy}(x,\sigma)&L_{yy}(x,\sigma)\end{bmatrix} \tag{1}\]  
where \(\frac{\partial^{2}}{\partial x^{2}}\) indicates Gaussian second-order derivation, \(L_{xx}(x,\sigma),L_{yy}(x,\sigma),\text{and}L_{xy}(x,\sigma)\) states convolution of \(\frac{\partial^{2}}{\partial x^{2}}\) at point. In the SURF feature descriptor, the scale spaces are categorized into octaves to recognize the interest points at different scales, where each octave includes a series of intervals. The convolutional window scale with the parameters octave (\(o\)) and interval (\(i\)), is specified in Equation (2), and the relationship between scale \(\sigma\) and window size is mathematically stated in Equation (3):  
\[L=3\times(i\times 2^{o}+1) \tag{2}\]  
\[L=\sigma\times 9/1.2 \tag{3}\]  
Furthermore, the SURF key points are calculated utilizing Equation (4):  
\[DoH(x,L)=max\big{(}\sum_{k_{i}=i-1}^{i+1}\sum_{k_{x}=x-2^{o}}^{x+2^{o}}\sum_{k _{y}=y-2^{o}}^{y+2^{o}}DoH\big{(}k_{x},k_{y},o,k_{i}\big{)}\big{)}\geq\lambda \tag{4}\]  
where \(DoH\) indicates determinant of the Hessian matrix and \(\lambda\) denotes positive threshold value. If the Hessian matrix trace value is higher than zero, a bright blob with scale \(L=3\times(i\times 2^{o}+1)\) is detected at \((x,y)\). Around 1288 feature vectors are extracted from the acquired images using the SURF feature descriptor.
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 3 Fuzzy Approaches for Energy Performance of Buildings Forecasting
### Fuzzy Models Identification
#### 3.3.2 FIR Models  
As in the case of ANFIS, the first step in order to obtain the FIR models is to discretize the data by converting quantitative values into fuzzy data. For this reason, it is necessary to specify the two parameters described in the previous section, granularity and shape of the membership functions,as well as a third parameter that refers to the discretization algorithm. Depending on the algorithm chosen, the distribution of the membership functions in the variable space may vary, which has a direct impact to the reasoning process and, therefore, to the model predictions. Contrary to FIR, ANFIS does not have this discretization parameter since ANFIS distributes uniformly all the membership functions that describe a specific variable and then performs an optimization process. Figure 7 shows an example of uniform (upper plot) and non-uniform (lower plot) distribution of the membership functions of a variable.  
In this research, based on the observation of the data distribution, the equal with partition (EWP) algorithm has been used for the discretization of the RA and OH variables, and the equal frequency partition (EFP) algorithm is used for the discretization of the rest of the variables. EWP performs a uniform distribution of the membership functions. EFP distributes the membership functions of a variable in such a way that all the classes contain the same number of data points. Visual-FIR allows the modeller to choose between 15 discretization algorithms, some of them belonging to the hierarchical family and others to the fuzzy family [26].  
Once the data has been discretized, FIR methodology performs a feature selection process where the most relevant causal relations between the input variables and the output variable are identified. To this end, the model structure identification process of FIR is used, which performs a feature selection based on the entropy reduction measure, as described in Section 2.1.  
FIR bounds as optimal HL and CL masks the one shown in Figure 8. That means that for both outputs (HL and CL), the features that have higher relevant causal relation with the output are RC (relative compactness) and GA (glazing area). The use of the other variables does not improve the predictive power of FIR models. Therefore, these two variables represent the minimum subset of variables needed to accurately estimate the heating and cooling loads.
###### Abstract  
Biochar has attracted considerable attention in numerous industrial and environmental applications because of its advantageous properties. Pyrolysis, a cost-effective and eco-friendly engineering technique for improving biochar's physicochemical and adsorption properties, is important in a variety of environmental applications. The effect of pyrolysis temperature and ball-milling time on the physicochemical properties of biochar derived from rice bran was investigated in this study, and its effectiveness in the aqueous removal of rhodamine B (RhB) dye was evaluated. The biochar was prepared by pyrolyzing rice bran at various temperatures, i.e., 400, 500, 600, and 700 \({}^{\circ}\)C (RB 400, RB 500, RB 600, and RB 700, respectively). In addition, in order to investigate the effect of the ball-milling time on the RB 600 biochar, it was milled for 30, 60, 120, 180, and 240 min. The surfaces of the raw material and biochar exhibited honeycomb-like pores and a layered structure. The biochar structure shrank, became fragile, and cracked as the pyrolysis temperature increased. After ball milling, the honeycomb-like pores and layered structure of the pristine biochar were transformed into irregular particles. The particle size decreased as milling time increased. Furthermore, the physicochemical properties of ball-milled biochar were superior to those of pristine biochar. According to the Raman spectral analysis, the \(I_{\mathrm{D}}/I_{\mathrm{G}}\) ratio decreased as the pyrolysis temperature and the milling time increased, indicating a decreased disorder and an increased graphitization in the biochar. The efficiency of RhB removal increased as the pyrolysis temperature and ball-milling time increased, and up to 82% of RhB was removed from 50 mg of biochar milled for 180 min at 600 \({}^{\circ}\)C.  
biochar; high-energy ball-milling: pyrolysis; adsorption; Raman spectroscopy Article  
Effects of Pyrolysis and Ball-Milling on the Physicochemical and Rhodamine B Removal Characteristics of Rice-Bran-Derived Biochar. _Appl. Sci._**2023**, 13, 4288.  
[[https://doi.org/10.3309/appl3074288](https://doi.org/10.3309/appl3074288)]([https://doi.org/10.3309/appl3074288](https://doi.org/10.3309/appl3074288))
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 3 Techniques and Methods
### Trust Based Secure and QoS Routing Scheme
#### 3.3.1 Trust Recommendation Using HELLO Messages  
Figure 2 is the flowchart for the exchange of HELLO messages which are mostly used in the ad-hoc on demand distance vector routing protocol (AODV) to determine link connectivity. If each and every node keeps information about its neighboring nodes, it would help any node to make a better decision on routing. Since our routing scheme also uses HELLO messages to exchange QoS trust recommendations and to discover neighbors, we modify the HELLO packets to include some extra fields i.e., Residual Energy, Link Quality and Channel Quality as these parameters are the major reasons for unintentional node failure in mobile Ad hoc network affecting the QoS provided by MANET. Thus, QoS trust values are propagated through the HELLO packets. Each and every node periodically sends HELLO packets which incorporate the QoS parameters, allowing each node to obtain information about QoS trust values of its neighbors. Then, a node is trusted according to its QoS parameters values.  
If a node that sends hello message is a trusted node, then, the intimacy with the sending node is calculated and the result is saved in a neighbor table to be used to build a suitable route for routing. Intimacy is an aggregation of direct interaction experience that determines the level of interaction with a sending node.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 1 Introduction  
The Andes in Patagonia form a major obstacle for the southern hemisphere Westerlies. Moist air masses from the Pacific Ocean nourish the glaciers and icefields of the Patagonian Andes [1]. The largest of these ice bodies is the Southern Patagonia Icefield (SPI) covering \(\sim\)13,500 km\({}^{2}\) in 1944/1945 [2], measured to a reduced \(\sim\)13,000 km\({}^{2}\) in a 1986 Landsat TM mosaic [3], used for the first glacier inventory (1992) [4, 5]. Several studies have reported a continuous retreat and thinning of itsoutlet glaciers causing one of the largest contributions to sea level rise outside the large ice sheets [6; 7; 8]. [PERSON] and [PERSON] [9] showed that mass loss rates of Patagonian glaciers in the 21 st century are about a magnitude larger when compared to those of the period since the Little Ice Age to today. Still, there are considerable uncertainties about the absolute mass change rates, the processes driving those changes and how they vary over time. Additionally, there is only limited knowledge about the regional climate conditions and their changes. Only very few surface mass balance measurements exist (e.g., [10; 11; 12]) and modeling attempts are also very limited (e.g., [13; 14]).  
Modern remote sensing technologies offer the potential to cover large areas and to provide glacier mass balance estimates for entire regions. Time series from the Gravity Recovery and Climate Experiment satellite (GRACE) revealed mass changes between 23 and 29 Gt\(\cdot\)a\({}^{-1}\) for the southern Andes. However, the measured gravity signal is also strongly influenced by the mass changes due to glacio-isostatic adjustments. For Patagonia, rates of up to 41 mm\(\cdot\)a\({}^{-1}\) have been reported close to the SPI [15; 16]. Moreover, the integrated GRACE signal cannot resolve individual glacier catchments. Satellite radar and laser altimetry techniques are not optimal for Patagonia due to the large footprint (radar) or frequent cloud coverage (laser). Similar difficulties exist for optical satellite imagery and photogrammetric approaches. Synthetic aperture radar (SAR) imagery is not affected by clouds and interferometric techniques allow the repeated derivation of surface elevation. In February 2000, the SRTM acquired a global baseline digital elevation model (DEM) by bi-static SAR interferometry between 56\({}^{\circ}\) S and 60\({}^{\circ}\) N, to which subsequent measurements can be compared.  
In this study, we aim at (1) measuring elevation and mass changes of the entire SPI between 2000 and 2015/16 using DEMs from the TanDEM-X and SRTM missions and (2) investigating possible seasonal or annual changes of the elevation for the southern tip of the SPI using repeat TanDEM-X acquisitions between 2012 and 2016.