esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel ICT Framework for Sustainable Development Goals
## 5 The ICT Framework Description  
This section describes our approach to an ICT framework for sustainable development goals. We follow [PERSON] in using the terms \"framework\", \"theory\", and \"model\" [109; 110]. Frameworks are meta-theoretical concepts/devices for providing a general language for describing relationships at multiple levels and scales. Theories, which are analytical tools for understanding, explaining, and making predictions, can be generated (translated) from frameworks by making assumptions about variables/attributes and their relationships for a particular framework of a socio-ecological system. Models are very specific working examples of a theory. Therefore, the ICT framework aims to provide the basic lexicon of concepts and terms [110] to be used in building ICT platforms for addressing SDGs.  
The ICT framework consists of three modules, Figure 4: data module, sustainability module, and governance module. It envisions governance systems and procedures that address SDGs on the local level by assessing/modeling various social-ecological systems, which are connected in a network of local platforms, ensuring global challenges to be addressed holistically. The methods from network governance together with mixed qualitative and social network analysis can be used (1) to ascertain the link between network governance properties, leadership positions and decision-making outcomes, (2) to assess network management strategies and their relation to outcomes, and (3) to analyze relations across multiple levels of networks in order to improve understanding of processes and outcomes. Moreover, the platform links social networks, behavioral sciences, data science, governance science,and digital technologies to effectively address SDGs by creating intelligent governance models and decision-making tools for citizens and governing bodies.  
The network of local platforms ensures that the concepts of tele-coupling and polycentric governance are implemented. Tele-coupling, a novel interdisciplinary umbrella concept, refers to those interactions between coupled human/natural systems that take place over distances [105]. Moreover, interactions within a system are called intra-coupling interactions, while peri-coupling refers to environmental and socio/economic interactions between adjacent systems [178]. [PERSON] [164] has developed the concept of \"polycentric governance\" aiming to understand complex interconnected governance systems by considering various decision-making actors that operate with some level of self-governance, but are connected through processes of collaboration, coordination, and/or conflict [179; 180].
## 2 Data and Methods
### TL-LUE Model
\(\mathrm{C}\) is multiple scattered radiation; \(PAR_{dir}\), \(PAR_{diff}\) are the incoming direct and diffuse photosynthetically active radiation. \(PAR_{diff,u}\) represents the diffuse \(PAR\) under the canopy ([PERSON] et al., 2016); \(LAI_{su}\) and \(LAI_{sh}\) are the \(LAI\) of sunlit and shaded leaves. \(R\) represents the sky clearness, which is calculated as S/(1367\(cos\theta\)); \(cos\) is the representative zenith angle for diffuse radiation transmission. \(T_{s}\) and \(W_{s}\) represent the effects of air temperature and water stress on vegetation photosynthesis ([PERSON] et al., 2016; [PERSON] et al., 2016). \[T_{s}=\frac{(T-T_{min})\times(T-T_{max})}{(T-T_{min})\times(T-T_{max})-\left( T-T_{opt}\right)\times\left(T-T_{opt}\right)} \tag{13}\]  
\[W_{s}=\frac{VPD_{max}-VPD}{VPD_{max}-VPD_{min}} \tag{14}\]  
\(T\) represents the daily temperature of the sites, which is derived from FLUXNET. \(T_{max}\), \(T_{min}\), \(T_{opt}\) denote maximum, minimum, and the most opt temperature for the vegetation growth. \(VPD_{max}\), \(VPD_{min}\) represent \(VPD\) when \(GPP\) reaches its maximum and minimum values, respectively. For sites with different ecosystem types, the values of these parameters are also referred to in [PERSON] et al. (2016). TL-LUE introduces \(\mathrm{C}\) as CO\({}_{2}\) stress ([PERSON] et al., 2016). \[C_{s}=\frac{C_{i}-\Gamma^{\star}}{C_{i}+2\Gamma^{\star}} \tag{15}\]  
\[\Gamma^{\star}=4.22\times\mathrm{e}^{\frac{37830(T-298.15)}{28.136\Gamma}} \tag{16}\]  
\[C_{i}=C_{a}\times\chi \tag{17}\]  
\[\chi=\frac{\xi}{\xi+\sqrt{VPD}} \tag{18}\]  
\[\xi=\sqrt{\frac{356.51\mathrm{\ K}}{1.6\eta^{\star}}} \tag{19}\]  
\[PAR_{diff,u}=PAR_{diff}\times e^{-\frac{0 SAI\times\Omega}{cos\theta}} \tag{11}\]0  
\[PAR_{diff,u}=PAR_{diff}\times e^{-\frac{0 SAI\times\Omega}{cos\theta}} \tag{11}\]1  
\(C_{i}\) is the intercellular concentration of CO\({}_{2}\); \(C_{a}\) is the atmospheric CO\({}_{2}\) concentration (using the FLUXNET CO\({}_{2}\) data). \(\chi\) is the ratio of leaf-internal to ambient CO\({}_{2}\), \(\mathrm{K}\) is the Michaelis-Menten coefficient; \(\eta^{\star}\) is the viscosity of water under 25 \({}^{\circ}\)C, which is set at 0.8903; \(\mathrm{K_{c}}\) and \(\mathrm{K_{o}}\) are the Michaelis-Menten coefficients of Rubisco for CO\({}_{2}\) and O\({}_{2}\), respectively; \(\mathrm{P_{o}}\) is the partial pressure of O\({}_{2}\); and \(\mathrm{R}\) is the molar gas constant (8.314 Jmol\({}^{-1}\) K\({}^{-1}\)).
## 3 Model and Methodology
### Path Presentation  
A UAV path can be denoted as a sequence of waypoints, which is shown in Figure 3. In this figure, there is a possible path between the starting point \(S\) and destination point \(D\), which can be expressed as a waypoint set \(\mathbf{WP}=\{\textit{WP}_{1},\textit{WP}_{2},\textit{WP}_{3},\textit{WP}_{4}, \textit{WP}_{5},\textit{WP}_{6},\textit{WP}_{7}\}\). The size of \(\mathbf{WP}\) is seven, as shown in Figure 3, which is a variable based on different flying requirements. In order to obtain the accurate coordinates of all the seven waypoints as well as to simplify the calculation, \(N\) vertical lines are made to \(x\)-Axis passing each waypoint \(WP_{i}\) (\(i=1,2,\ldots,7\)), all of which are parallel to \(y\)-Axis and are denoted as a vertical line set \(\mathbf{L}=\{L_{1},L_{2},L_{3},L_{4},L_{5},L_{6},L_{7}\}\). Both the waypoint set \(\mathbf{WP}\) and the vertical line set \(\mathbf{L}\) share the same size \(N\) (\(N=7\)). It is obvious that the size \(N\) can be quite different based on different scenarios. Without loss of generality, the distance \(d\) between every two neighboring lines in the vertical line set \(\mathbf{L}\) will be kept the same. It is assumed that the distance between vertical line \(L_{5}\) and \(L_{1}\) and the distance between vertical line \(L_{7}\) and \(L_{D}\) are also set as the same value \(d\). As a result, this accurate value of the distance \(d\) can be calculated easily under the condition that the size of the two used sets and the coordinates of the starting point \(S\) and destination point \(D\) are provided.  
It is supposed that the coordinate of a waypoint \(WP_{i}\) can be expressed as \((x_{i},y_{i})\) and \(i=1,2,\ldots,N\). The specified positions of the starting and destination points \(S\) and \(D\) can be given as \((x_{S},y_{S})\) and \((x_{D},y_{D})\), respectively. In this way, the abscissa of any waypoint \(i\) in the waypoint set \(\mathbf{WP}\) can be determined by using Equation (1). \(N\) is the total number of waypoints on the path.  
\[x_{i}=x_{S}+i\cdot\frac{x_{D}-x_{S}}{N+1} \tag{1}\]  
Figure 3: Path presentation.  
Figure 2: Overall framework of the proposed method.  
Once the abscissae of all the waypoints in the set _WP_ are determined by using Equation (1), calculating the global optimum vertical coordinates of all the \(N\) waypoints becomes the objective of a path-planning algorithm.
## 3 Discussion of Results
### Effect of Glass on Workability of Mortar and Concrete  
The consistency of mortar increases linearly with the increase in the glass-cement replacement level, as shown in Figure 5a,b. The irregular shape (Figure 3) of glass does not make a significant negative contribution to the flowability of the fresh mix due to the low intra-particle absorption of glass [13]. By replacing cement with 5, 10, 15 and 20 wt.%, the flowability of mortar increased by 4.5%, 17.1%, 24.0% and 30%, respectively. This implies that glass inclusion in mortar can facilitate the reduction of the water-cement ratio [8]. Despite the flowability or consistency of the mixture, no bleeding was observed in both mortar and concrete samples. From Figure 5b, the consistency of the fresh concrete is linearly increasing with the glass content as the slump values increase by 17.3%, 33.3% and 46.7% when the glass content was 10, 20 and 30 wt.%, respectively.  
Figure 4: Effect of glass on the setting time of mortar.  
Figure 5: Cont.
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 2 Related Works
### 3D Object Detection Based on the Fusion of Camera Images and LiDAR Point Clouds  
To exploit the advantages of the camera and LiDAR sensors, various camera and LiDAR fusion methods [25; 26; 27; 30; 31; 32] have been proposed for 3D object detection. The approaches proposed in [13] detected the objects in two sequential steps, where the region proposals were generated in the first step based on the camera image, and then the LiDAR points in the region of interest were processed in the second step to detect the objects. However, the performance of these methods is limited by the accuracy of the camera-based detectors. In [31], [PERSON] et al. proposed a two-stage Multi-View 3D object detector (MV3D), where LiDAR point clouds are projected to Bird Eye View (BEV), accurate multi-view 3D proposals are generated, and the region-wise features are extracted by using a multi-view fusion network. [PERSON] et al. [26] proposed Aggregate View Object Detection (AVOD)to integrate LiDAR BEV and camera front-view features for Region Proposal Networks (RPNs) and a second stage detection network. However, projection methods [26; 33] may miss spatial structure information. [PERSON] et al. [27] proposed a two-stream network architecture EPNet and designed a LiDAR-Image Fusion module to generate the point-wise correspondence. [PERSON] et al. [24] presented a Multi-task Multi-sensor Fusion (MMF) detector, where 4 tasks including 2D and 3D object detection, ground estimation, and depth completion. [PERSON] et al. developed Frustum Point-Net [30] that first used a mature 2D detector to generate candidate boxes on images and then extracted the point clouds from the 3D bounding frustums for box refinement. [PERSON] et al. [32] presented an end-to-end detector without requiring 2D image labels to associate LiDAR points with image pixels. Most of these methods [24; 26; 31; 32] project the objects detected in 2D images to 3D space and cannot fully utilize the semantic features from images. Our method can utilize more detailed localization information from camera sensors.
## 3 Methodology
### Data Preprocessing  
#### 3.1.1 Categorical Variable Conversion  
The features affecting the service states of tunnels in the database can be classified into two types: categorical variables (e.g., functional classification, tunnel shape, and ground condition) and numerical variables (e.g., longitude, latitude, and average daily traffic). The NTI dataset represents categorical variables using integer codes. For instance, geologic conditions are coded as 1 for soil, 2 for rock, and 3 for mixed face. Assigning the integer values to categorical variables facilitates the training and prediction of machine learning models. In this study, the integer codes were processed using one-hot encoding, which transforms discrete categorical variables into multidimensional binary forms. However, categorical variable conversion is not needed for the LGBM model. For datasets with high-dimensional features, using LGBM is advantageous for the optimal handling of categorical features because one-hot encoding may introduce an excess of new features.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
###### Abstract
Furthermore, EKF was used in [12] to compute the joint UE position and base station orientation. Only the multi-station scenario, with four base stations, was studied in [12]. Another downlink 5G positioning method with joint ToA and AoA measurements was studied in [13] for a single base station. The positioning in [13] relied on a maximum likelihood estimator and on CRLBs instead of actual AoA and ToA estimates. AoA estimation through an ESPRIT algorithm was recently addressed in [14]. A generic mmWave multiple-input multiple-output (MIMO) signal was considered in [14], and the focus was only on azimuth angle estimation, not a joint azimuth-elevation estimation. Another work focusing on angle estimation in 5G mmWave systems was [15]. The focus there was also on azimuth elevation, and estimation errors below 2.8\({}^{\circ}\) were achieved. The focus of this work is the single-station 3D positioning of the UE in the sub-6 GHz band by using the radio signal propagation properties. The subspace-based algorithm and statistics-based algorithm that are able to jointly estimate the azimuth, elevation angles, and distance for the 3D positioning of the SRS transmitter (UE) are described in Section 3. The merits of the this work are summarized as follows:  
* Minimum additional signaling and infrastructure: In this work, the positioning information is extracted by the propagation process estimation of the SRS on the receiver side. This means that the method can be applied to existing systems without additional signaling, protocol, or hardware/infrastructure modifications. * Free from the multiple sites synchronization: Our work is different from TDoA-based multilateration or hyperbolic approaches that require costly timing synchronization between distributed sites and the positioning function. The joint azimuth, elevation, and delay estimation methods are used in this work. * High capacity: Our work benefits from the orthogonality in the time and frequency domains of SRSs from the different UEs. The algorithms can be applied to each individual UE for positioning estimation without interference from other UEs. Theoretically, the positioning capacity equals the number of Zadoff-Chu sequences used for UEs. * Flexibility: The position estimation algorithms of this work can easily adapt to the different subcarrier spacing and channel bandwidth combinations in the 5G NR for diverse positioning accuracy levels required by different application scenarios. The rest of this paper is organized as follows: In Section 2, the system and signal models are describe to facilitate the discussion of the 3D positioning. Then, the subspace-based joint angle-delay estimation and EM-based signal clustering are introduced in Section 3. Section 4 shows the performances of both algorithms in different scenarios. The conclusion is given in Section 5.
## 3 Results and Discussion
### Fluorescence Quenching Mechanism  
Fluorescence spectroscopy is a valuable tool to study protein-ligand interactions. Of the three aromatic amino acid residues, tryptophan (Trp) contributes most to intrinsic protein fluorescence [28]. The effect of PDQL on the fluorescence emission spectra of HSA was first tested (Figure 2). The strong fluorescence peak of HSA at approximately 340 nm decreased in the absence of PDQL (\(\lambda_{\text{ex}}\) = 280 nm, 298 K). The results indicated that the binding of PDQL to HSA quenched the intrinsic fluorescence of the tryptophan residue in HSA. Figure 2: Fluorescence quenching spectra of HSA in the presence of different concentrations of PDQL at 298 K. \(\lambda_{\text{ex}}\) = 280 nm; the HSA concentration was 1.0 \(\times\) 10\({}^{-6}\) M; (a-i) PDQL concentrations (\(\times\)10\({}^{-6}\) M) were 0.000, 1.333, 2.667, 4.000, 5.333, 6.667, 8.000, 9.333 and 10.667, respectively. (x) 1.0 \(\times\) 10\({}^{-6}\) M PDQL only. Fluorescence quenching could proceed via dynamic or static quenching or a combination of both [29; 30]. Dynamic quenching involves the collisional encounters between the quencher and the fluorophore during the lifetime of the excited state, whereas static quenching involves the formation of a ground state complex between the fluorophore and the quencher [31]. To determine the mechanism for the quenching of the intrinsic HSA fluorescence by PDQL, the experimental data were analyzed using the Stern-Volmer Equation (1) [32]. \[F_{0}/F=1+K_{\text{SV}}\left[\text{Q}\right]=1+K\text{q}\tau_{0}\left[\text{ Q}\right] \tag{1}\]  
where \(F_{0}\) and \(F\) are the steady-state fluorescence intensities of HSA in the absence and presence of the quencher, respectively. \(K_{\text{SV}}\) is the Stern-Volmer quenching constant; Kq is the bimolecular quenching constant; \(\tau_{0}\) is the average lifetime of the biomolecule without the quencher (\(\tau_{0}\) = 10\({}^{-8}\) s\({}^{-1}\)) [33]; and [Q] is the concentration of the quencher. Figure 3 shows that the Stern-Volmer plots at different temperatures and different concentrations of a quencher. The observed linear dependence between \(F_{0}/F\) and the molar concentration of PDQL indicates a single quenching mechanism, either static or dynamic. The calculated values of \(K\)q for all PDQL-HSA reactions in Table 1 fell in the range of 2.77 \(\times\) 10\({}^{12}\) and 5.21 \(\times\) 10\({}^{12}\) mol\(\cdot\)L\({}^{-1}\). These values are two orders of magnitude greater than the maximum collisional quenching constant of 2.0 \(\times\) 10\({}^{10}\) mol\(\cdot\)L\({}^{-1}\)[18; 34; 35], so static quenching presumably occurred via the formation of a complex.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 4 Simulation and Results  
### Experimental Setting  
To evaluate the visual performance, the simulation compares the results of the existing low-light image enhancement technique and the low-light image processed by the proposed algorithm. The image used in the evaluation is a \(600\times 400\)-sized image from the LOL dataset, which is widely used in low-light image enhancement research [3]. To analyze the influence of the parameters of the proposed method on the resulting images, experiments are conducted under different conditions.  
First, \(\tau\), which is used in the low-light image enhancement process, is set to the range of \([0,1]\), and the closer the value is to \(0\), the brighter the illumination enhancement image is. Conversely, the closer \(\tau\) is to \(1\), the more contrasting the colors in the resulting image. The proposed technique performs reasonably well for \(\tau=0.45\) when the value of \(\tau\) is varied in \(0.05\) increments from \(0\) to \(1\) in the simulation.  
The color correction weight \(\alpha\) used for color correction is the color correction weight, and the higher the value, the stronger the color correction effect. If the color correction weight is \(0\), an uncorrected image is output, and if it exceeds \(\alpha=2\), the color distortion becomes severe. When the proposed algorithm is simulated by varying the value of the color correction weight \(\alpha\) in increments of \(0.1\), the most natural color correction is achieved when the value is \(\alpha=1\).  
Since the steering kernel used in the denoising process is used to remove noise amplified during the enhancement of low-light images, it does not require strong denoising performance, so the small size of \(15\times 15\) is sufficient. Also, due to the importance of preserving details such as edges and text, the best performance is achieved using a small window of \(3\times 3\) for the similarity comparison.  
For the other parameters, we select the values that show the best overall performance when running experiments under various conditions, and the results are shown in Table 1.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 2 Materials and Methods
### \(\mu\)-Synthesis Control of a Three-Story Building  
#### 2.6.1 Properties and Parameter Uncertainties  
Through an application consisting of a three-story shear building [34], we aimed to demonstrate the efficiency of the robust \(\mu\)-synthesis method presented in our study. We obtained the nominal model of this structure based on known geometry, material properties, and boundary conditions.  
Therefore, as described in the previous section, the structure is a rectangular building with 3-DOF. Each floor measures 4.5 m by 3 m, resulting in three floors measuring 9 m in height.  
The floor's mass is identical from bottom to the top, equaling 1000 kg per floor. The structure's damping coefficients remained constant across all the floors, at 1407 N.m/s, respectively. In terms of stiffness coefficients, these parameters had a significant impact on the structure's rigidity. To make the structure more flexible, we set each floor's stiffness in our study 10 times lower than in [24], specifically 98 kN/m instead of 980 kN/m. However, the three natural frequencies of the structure were 1.19 Hz, 3.58 Hz, and 5.97 Hz, respectively. However, we used the design parameters of the AMD as proposed by [PERSON] et al. 1997 [35]. These parameters encompass the mass ratio (\(\mu\)), frequency ratio (\(\gamma\)), and the state parameters of the AMD (\(m_{a}\), \(c_{a}\), and \(k_{a}\)) as follows:  
\[\mu=\frac{m_{a}}{{M_{t}}^{\prime}},f=\frac{\omega_{a}}{\omega_{s}}=\frac{1}{1+ \mu}\bigg{[}1-\beta.\sqrt{\frac{\mu}{1+\mu}}\bigg{]};\ \xi=\frac{\beta}{1+\mu}+\sqrt{\frac{\mu}{1+\mu}} \tag{34}\]  
\[m_{a}=\mu.M_{t};c_{a}=2.\gamma.m_{a}.\omega_{a};\ k_{a}=\omega_{a}^{2}.m_{a} \tag{35}\]  
where  
\(\beta\) represents the structure's damping, which is equal to 0.02;  
\(\mu\) represents the mass ratio, which is equal to 0.05;  
\(M_{t}\) represents the total mass of the structure, which is equal to 3000 kg;  
\(f\) represents the tuning ratio;  
\(\omega_{a}\) represents the AMD's natural frequency;  
\(\omega_{s}\) represents the natural frequency of the structure's first mode.  
Therefore, the design parameters of the AMD used in this study are as follows:  
\[m_{a}=150\ \text{kg},\ c_{a}=1185.948\ \text{N.m/s},\ \text{and}\ k_{a}=2608.33\ \text{ kN/m}.\]  
When we placed the AMD actuator on the last floor, it generated the necessary active force to counter seismic loads, whereas we placed the ABS actuator on the first floor, which exerted a force on the entire structure through the other floors. If we neglected to factor in potential uncertainties in the structure's parameters, such as mass, stiffness, and damping, the controller's control force might not have been effective in its actual implementation conditions. Indeed, any uncertainties in these parameters would induce a less accurate control. That is why, to ensure robust seismic protection with the \(\mu\)-controller, we considered that there were 30% of uncertainties in the stiffness of the three floors of the structure.