esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The paper introduces a new approach to the external calibration of multi-channel SAR sensors. A survey of results based on the analysis of multi-channel SAR data acquired by DLR's airborne F-SAR and DBFSAR sensors suggest that the approach can be used to effectively estimate calibration corrections to counteract propagation direction dependent effects, thereby significantly improving the radiometric and interferometric data calibration quality.  
The calibration approach yields antenna phase center position and interferometric baseline corrections in three dimensions as well as three dimensional antenna pointing estimates. The results reported show that the antenna phase center refinement leads to improved and more consistent geometric accuracy in focused SAR imagery and also improves the fidelity of interferometric and polarimetric SAR phase measurements. The antenna pointing estimates, which are provided separately for transmit and receive where the multi-channel scenario demands it, are seen to improve the radiometric accuracy. Pointing corrections also compensate for systematic, residual amplitude variations in the Doppler spectrum after SAR processing.  
Unlike current state of the art techniques, the method is based entirely on the analysis of range compressed raw data and does not require azimuth compression. It remains applicable in cases where azimuth compression presupposes accurate calibration and, in particular, can be applied to data that is irregularly or under-sampled in azimuth. This is especially relevant in view of the digital beam-forming techniques envisaged for future HRWS missions, such as the Tandem-L formation [33].
## 4 Seismic Excitation
### The Role of the Restitution Coefficient and the Mass of the VI-NES  
This section analyzes the influence of the restitution coefficient \(r\) first and then the mass \(M\) of the VI-NES. Given that this paper is an initial study aimed at assessing the effectiveness of the proposed external device when equipped with VI-NES in reducing seismic effects in frame structures, the restitution coefficient is treated as a variable parameter, thus following the approach outlined in [27]. The results related to \(r\) displayed in Figure 4 refer to Frame 1 (see Table 2), the constant \(M\) (\(\eta=0.1\)), and the excitation provided by the Kobe earthquake record. The maps in the top row of Figure 4 were obtained for \(r=0.9\), whereas those in the bottom row are for \(r=0.5\).  
The reduction of the restitution coefficient, corresponding to an increase in the energy dissipation of the VI-NES, significantly affected the gain maps. The primary impact of a decrease in \(r\) was observed in the extent of the advantage regions (light gray areas) rather than the values of the gain coefficients. Specifically, for \(r=0.5\), the maps revealed a larger advantage region that encompassed almost the entire parameter plane. This suggested a greater number of combinations of parameter values for which the protection device proved effective. However, while the positions of the relative minima of the gain coefficients were influenced by \(r\), a decrease in \(r\) did not result in significant reductions in the minimum values of \(\alpha_{1}\) and \(\alpha_{2}\). Upon examining the graphs in the first column, the minimum values of \(\alpha_{1}\) were 0.65 for \(r=0.9\) and 0.56 for \(r=0.5\). Similarly, the minimum value of \(\alpha_{2}\) changes from 0.7 for \(r=0.9\) and 0.66 for \(r=0.5\).  
Figure 5 investigates the influence of the mass of the VI-NES \(M\) through a variation of \(\eta\). The top row of Figure 5 includes the maps obtained for \(\eta=0.05\), and the bottom row includes those for \(\eta=0.20\). The comparison among the respective maps shows that the increase of \(M\) was more beneficial for the superstructure because it mostly results in a wider advantage region in the \(\alpha_{2}\) map obtained for \(\eta=0.20\). The comparison of the values of the  
Figure 3: Gain maps of the coupled frame structure without and with a VI-NES excited by the Kobe earthquake (Frame 1, \(\eta=0.1\), \(r=0.7\)).  
gain coefficients between maps shows that the increase of \(M\) leads to larger values of \(\alpha_{1}\), which correspond to larger displacements of the connection story (the first story of the frame structure). The drift of the superstructure, conversely, benefits from an increase in \(M\), as the values of \(\alpha_{2}\) obtained for \(\eta=0.20\) are generally smaller than those obtained for \(\eta=0.05\).
## 4 Case Study
### Debutanizer Column  
#### 4.2.1 Process Description  
In naphtha, propane and butane need to be removed from the process, in which the debutanizer column is an important part of the desulfurization and naphtha separation unit in the petroleum refinery production process. Referring to reference [48], a brief schematic diagram of the process is shown in Figure 6.  
The goal of the debutanizer column is to minimize the concentration of butane at the bottom of the column while maximizing the yield of purified gasoline. Therefore, accurate estimation of the butane content is important to improve the effectiveness of the debutanizer column. This industrial process includes 7 process variables, and the specific meanings of the variables are shown in Table 4.  
In this study, 2285 data points were collected in the process: 1142 random sample points in the dataset were divided into training set samples, accounting for 50% of the total data volume, for model training; 343 random sample points were used as validation set samples, accounting for 10% of the total data volume, for the optimization and integration pruning of model parameters; and 800 sample points were used as test set samples, accounting for 40% of the total data volume.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Variables** & **Variable Description** \\ \hline u1 & Top temperature \\ u2 & Top pressure \\ u3 & Reflux flow \\ u4 & Flow to next process \\ u5 & Sixth tray temperature \\ u6 & Bottom temperature \\ u7 & Bottom pressure \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input variables in the debutanizer column.  
Figure 6: Flowchart of the debutanizer column [49].  
Similar to the penicillin fermentation process in Section 4.1, the GWO algorithm was used to optimize the kernel function parameters and the number of hidden variables adaptively in the base model construction process with the goal of minimizing the prediction error. According to the data characteristics of the debutanizer column, the selected kernel functions include the Gaussian kernel function, the Cauchy kernel function, and the Laplace kernel function, and the search range of the corresponding kernel functions is set as \(\sigma\in[0.03,0.05,0.07,0.09,0.1,0.2]\) for the Gaussian kernel function, \(\delta\in[0.01,0.02,0.03,0.05,0.07,0.09,0.1,0.2]\) for the Cauchy kernel function, and \(\gamma\in[0.07,0.1,0.2,0.3,0.4,0.5,0.7]\) for the Laplace kernel function.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 4 Performance Evaluation
### Comparative Evaluation of UDP in Terms of Offloading Percentage
Obviously, due to the operation of the UDP and KDP methods that place the UAV-BSs in actual UE concentrations, the maximum offloading rate is achieved, subject to the capacity constraints regarding the maximum number of UEs that each UAV-BS can serve simultaneously. Concerning the examined techniques that utilize ML methods, from Figure 3, it is observed that UDP provides significant offloading gains as compared to KDP for all configurations. This happens due to the use of the k-medoid algorithm for the UDP method, in contrast to the KDP that utilizes the k-means algorithm to form the candidate clusters. As opposed to k-means, k-medoid is not deceived from the outliers of the cluster and thus is more robust to the extreme positions of the UEs in the hotspots. It should be noted that for 150 UEs and 10 available UAV-BSs, where each UAV-BS can serve 10 UEs  
Figure 2: Percentage of UEs offloaded from the MBSs utilizing the UDP method, for different number of UAV-BSs and weighting factor \(a\). simultaneously, the maximum offloading that can be achieved is 66.7%. In this case, the UDP method achieves 59% offloading, which is equal to 88.5% of the maximum possible offloading percentage. Meanwhile, KDP reaches 73.5% of the maximum possible offloading percentage, which is 15% below the UDP.
## 3 Analyses Used in the Methodology  
The FEMA P695 methodology involves two fundamental types of analysis. The first is nonlinear static pushover analysis, which consists of applying incrementally increasing lateral loads following a predefined distribution pattern, typically one that resembles the first-mode lateral displacement shape. The second is incremental dynamic analysis (IDA) [64], which requires the selection of a suite of ground motion records consistent with the seismogenic source that most significantly affects the archetypes. These records are applied incrementally until the maximum structural response reaches a level that enables the identification of collapse.  
To perform these analyses, nonlinear models must be developed. For this purpose, the finite element software SeismoStruct V2024 [65] was used. The nonlinear models required both the geometric and constitutive data derived from the design and details described in  
Figure 8: Moment-Curvature diagram and curvature demand in the Y direction, the force orientation represented by the red arrow, T-shaped wall.  
Figure 7: Moment-Curvature diagram and curvature demand in the X direction, the force orientation represented by the red arrow, T-shaped wall.  
the previous section. The floor slabs were modeled as rigid diaphragms, an assumption justified by the adequate distribution of shear walls in both orthogonal directions of the structure. The structural members were modeled using fiber-based elements, and the material behavior was represented using the constitutive models of [PERSON] et al. [66] for concrete and [PERSON] [67] for reinforcing steel. The parameters of these constitutive models are presented in Tables 5 and 6.  
The fiber section model, during the analysis, determined the element state by integrating the response of the different fibers composing the wall cross-section. In comparison with other models, the fiber section approach has the advantage of not requiring empirical calibration, which considers the wall's behavior under the expected load condition, necessary for plastic hinge models. In addition, by considering the stress-strain curves and the integrated fiber response, this approach inherently captures the element's axial and flexural behavior, updating the stiffness at each analysis step. Despite its benefits, this model has the following limitations:  
* Reinforcement buckling and the bond-slip effects are not considered.
* It does not integrate the axial and flexural loads, with the effect of the shear forces (P-M-V interactions).  
The previous phenomena can be implemented but require a highly complex modeling approach, beyond the scope of this study. However, the modeling definition and approach can adequately represent the response of the wall when compared with its tested specimens under cyclic loads [68; 69].
# Towards Client Selection in Satellite Federated Learning
## 4 Algorithm
### Client Selection
#### 4.2.1 Delay  
We set \(c_{k}\) as the computation capability of satellite \(k\)[29]. If the client is selected, it will obtain the global model parameter \(\omega_{t}\) from the PS. We denote \(W\) as the data size of the parameter that client \(k\) downloads from the PS, and \(Q_{k}\) as the size of the updated parameter uploaded to the PS. After finishing the local computing, the new local model parameter \(\omega^{k}\) is sent to the server. The time cost of this process \(T_{k}^{S}\) consists of the propagation delay, the transmission delay, and the computing delay, which can be calculated by  
\[T_{k}^{S}=\frac{s_{k}}{c}+\frac{W}{R_{k}}+\frac{X_{k}}{c_{k}} \tag{13}\]  
where \(s_{k}\) is the distance from the client to the server, and \(c=3.0\times 10^{8}\) m/s is the speed of light; \(X_{k}\) is the required CPU cycles of one round of local training.  
If the PS is the GS, \(s_{k}\) can be calculated by  
\[s_{k}=\sqrt{R_{e}^{2}+(R_{e}+h)^{2}-2R_{e}(R_{e}+h)\cos\gamma} \tag{14}\]  
or if the PS is the satellite, \(s_{k}\) is \(|\overrightarrow{AB}|\), which can be calculated by Equations (4) and (5).  
Hence, the time cost of a round training process in FL, that consists of a \(K\) client and one server, can be expressed as:  
\[T_{agg}=\max_{k\in\mathcal{K}}\Bigl{[}T_{k}^{S}\Bigr{]}+\sum_{k=0}^{K}Q_{k}D_{agg }/c_{s} \tag{15}\]  
where \(c_{s}\) is the computation ability of the PS, and \(D_{agg}\) is the number of CPU cycles required for the average size of each parameter datum to aggregate.
# Target Selection Strategies for Demucs-Based Speech Enhancement
###### Abstract  
The Demucs-Denoiser model has been recently shown to achieve a high level of performance for online speech enhancement, but assumes that only one speech source is present in the fed mixture. In real-life multiple-speech-source scenarios, it is not certain which speech source will be enhanced. To correct this issue, two target selection strategies for the Demucs-Denoiser model are proposed and evaluated: (1) an embedding-based strategy, using a codified sample of the target speech, and (2) a location-based strategy, using a beamforming-based prefilter to select the target that is in front of a two-microphone array. In this work, it is shown that while both strategies improve the performance of the Demucs-Denoiser model when one or more speech interferences are present, they both have their pros and cons. Specifically, the beamforming-based strategy achieves overall a better performance (increasing the output SIR between 5 and 10 dB) compared to the embedding-based strategy (which only increases the output SIR by 2 dB and only in low-input-SIR scenarios). However, the beamforming-based strategy is sensitive against the location variation of the target speech source (decreasing the output SIR by 10 dB if the target speech source is located only 0.1 m from its expected position), which the embedding-based strategy does not suffers from.  
D 2023  
23 June 2023  
3 July 2023
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CBR Performance
#### 5.1.2 Packet Delivery Ratio  
Next, we study the packet delivery ratio by varying the vehicular moving speeds. As shown in Figure 9, the CBR has a higher success rate for delivering packets to destinations at different speeds. In a high speed environment, network topology is rapidly changed making the delivery ratio decrease, especially for RIVER. In Figure 9b, RIVER has a higher performance than GyTAR when the moving speed is lower. Nonetheless, secondhand information used in RIVER becomes much more unreliable as the moving speed rises. Eventually, GyTAR overtakes RIVER in a highly dynamic environment. The CBR also suffers from the rapidly changed topology in a high moving speed. As the vehicular moving speeds are increased, the speed of the CRT Map update may not catch up with the speed of the network change (This effect will be further discussed later). As a result, the success rate of CBR becomes lower. However, the CBR considers consecutive road segments and recalculates weights at intersections based on the last updated information making the proposed CBR outperform the other two schemes. The packet delivery ratio with all the vehicular densities at an average moving speed of 80 km/h is shown in Figure 10. High vehicular density can increase the success rate since the network becomes more connected. The proposed CBR can outperform in all cases.  
Figure 8: End-to-end delay vs. vehicular density.  
Figure 9: Delivery ratio vs. moving speed: (**a**) 100 vehicles; and (**b**) 300 vehicles.
## 3 MTR Algorithm Based on Multi-Layer Sparse Structure
### Robust MTR by Alleviating Noises  
Since the learning of relationships between output targets in MTR models is affected by some potential structured noises, when there is a strong correlation between different targets, the structured noise affects the modelling of the relationship between multiple output variables. Although these noises do not interfere with the correlation modelling between feature inputs and different output targets, they can interfere with the mining of correlation information between targets and lead to some bias in the correlation learning between target outputs. Therefore, considering the undesirable effects of structured noises, the sensitivity of the regression model is reduced by learning the structured noises in order to explore the correlation knowledge more accurately. First, an auxiliary matrix is introduced in the latent variable space to explicitly encode the potential structured noises, which results in the following output structure:  
\[\mathbf{Y}=\mathbf{S}(\mathbf{A}\mathbf{X}+\mathbf{F}) \tag{8}\]  
where \(\mathbf{F}\) is an unobserved latent feature matrix used to capture the structured noises in the latent space. By imposing the corresponding sparse regularization constraint on \(\mathbf{F}\) and kernelizing the structure of Equation (8) into Equation (7), we obtain the final multi-layer MTR model considering structured noise as follows:  
\[\min_{\mathbf{A},\mathbf{F}}\frac{1}{N}\|\mathbf{Y}-\mathbf{S}(\mathbf{A} \mathbf{K}+\mathbf{F})\|_{F}^{2}+\lambda_{1}\|\mathbf{A}\Phi(\mathbf{X})^{ \top}\|_{F}^{2}+\lambda_{2}\|\mathbf{S}^{\top}\|_{2,1}+\lambda_{3}\|\mathbf{ F}\|_{F}^{2}. \tag{9}\]  
where, \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are the non-negative regularization parameters corresponding to the different regularization constraints. By introducing the learning of structured noises to improve the robustness of the model and weaken the adverse effects of structured noises on the algorithm, to explore the complex correlations more accurately among the multi-target outputs. The final objective function of MTR algorithm based on multi-layer sparse structure (MTR-MLS) is shown in Figure 2.  
Figure 2: Structure of MTR-MLS model.
## 4 Dataset Annotation
### Manual Annotation and Consistency Evaluation  
The dataset was manually annotated by the linguists in two passes. At the end of each annotation pass, inter-annotator agreement was calculated to evaluate the annotation consistency. Inter-annotator agreement threshold was set at \(\geq\)0.6 (e.g., a coefficient 0 or less = poor, 0.01-0.2 = slight, 0.21-0.4 = fair, 0.41-0.60 = moderate, 0.61-0.80 = substantial and 0.81-1.0 = perfect agreement), to finalize the annotation.  
In the first pass, a sample of queries was extracted to test the annotation rules and to bring both annotators to the same level of labelling agreement. Due to the large diversity in the dataset, random sampling could have resulted in a lack of topical coverage in the drawn sample. To address this, coverage clustering techniques were useful to collate the textually similar queries. Therefore, _K_-means clustering with clusters = 80 (evaluated using the elbow method) was used [36] and a random sample of 20 queries from each cluster, resulting in 1600 queries, was extracted for the Annotation Pass I. The two linguists annotated the queries and an inter-annotator agreement using [PERSON]'s kappa coefficient [34] was calculated. The agreement value was 0.7213, indicating substantial agreement between the annotators.  
The disagreements were analyzed and categorized into two groups: informational-transactional (INFO-TRAN) and informational-navigational (INFO-NAV). In the INFO-TRAN level disagreement, the queries were inherently ambiguous, e.g., _Uchu_ (privers in train) is subject to interpretation of the annotator. It could be informational in the context of finding facts or procedures related to the query or it could be transactional in the context of finding or downloading a resource related to the query, e.g., a book or video. In the INFO-NAV level disagreements, e.g., _Uchu_ (Olympics sports), and _Uchu_ (Rail Madrid), labeling the queries as either informational or navigational were both valid, unless more contextual information was available for disambiguation. This analysis signifies the complex structure of queries and the requisite annotation challenges. The remaining corpus was annotated in the Annotation Pass II by dividing the data in four batches.
## Nomenclature  
\begin{tabular}{l l} Symbol & Description \\ \(x,y,z\) & Position coordinates respect to World frame \\ \(\dot{x},\dot{y},\dot{z}\) & Linear velocity respect to World frame \\ \(\ddot{x},\ddot{y},\ddot{z}\) & Linear acceleration respect to World frame \\ \(\phi,\theta,\psi\) & Attitude coordinates respect to World frame \\ \(\dot{\phi},\dot{\theta},\dot{\psi}\) & Angular velocity respect to World frame \\ \(\dot{\phi},\ddot{\theta},\ddot{\psi}\) & Angular acceleration respect to World frame \\ \(W_{R_{B}}\) & Rotation matrix for Body frame to World frame \\ \(p,q,r\) & Angular velocity respect in Body frame \\ \(\dot{p},q,t^{\dagger}\) & Angular acceleration respect in World frame \\ \(g\) & Gravity \\ \(m\) & Mass of QUAV \\ \end{tabular}
## 2 Experimental Setup and Detection Method
### Data Collection Process
#### 2.2.3 Processing the Dataset  
To properly implement the dataset created using Labelbox, another online platform was used initially to process the dataset. This platform was called Roboflow [32]. Roboflow was chosen as it was a compatible platform for Labelbox and was also capable of converting the JSON file from Labelbox into a darknet compatible dataset. This darknet dataset consisted of a.TXT annotation file as well as the photos that correspond to each annotation. The dataset was split into training, validation and test with 1042 images going to training,  
Figure 6: Training dataset with updated tree bounding box.  
Figure 7: Final iteration of the training dataset with each flower cluster identified (blue boxes), and each corresponding apple tree identified (pink boxes).  
298 into validation and the remaining 149 into testing. Finally, all images were resized for images of different sizes. This dataset would then be used for the YOLOv4 algorithm for training and testing the system to observe the performance. As well as to determine if any adjustments or improvements are needed.  
Following the first iteration of the data annotation and processing, as stated previously, a different setup and data was needed to produce a more robust and stronger model. This also resulted in the implementation of a conversion script for the Labelbox JSON files as Roboflow only allowed limited usage on guest accounts. This script would read the JSON file and output the compatible. TXT file which had bounding boxes for each image. Additionally, it was later discovered that YOLOv4 did not need to have the input images resized before training as the algorithm would have a default image resolution that would automatically apply zero padding to images to fit the scale of the model, this can be seen in Figure 8.
# 4D Building Reconstruction with Machine Learning and Historical Maps
## 5 Results
### Inferring Building Heights from a Historical Map--Bologna Case Study  
The implemented method was also tested on two historical versions of the Bologna city centre (1884 and 1945). Among the transformations of the city in this time frame, the almost complete destruction of the medieval defensive walls and the heavy damages of war bombardments are the most relevant.  
Starting from the city's actual topographic database and following the regressors evaluation (Section 5.1), Random Forest and Catboost methods were used to predict building heights in the historical maps. The predicted heights evaluation was performed, considering only some unchanged buildings as a test set (Tables 14 and 15). Figure 15 presents some general and detailed views of the 3D buildings reconstruction for the Bologna city centre.