esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
## 1 Introduction  
Generative Adversarial Networks (GANs) have been one of the many breakthroughs in Deep Learning methods in recent years. Several different variations of the model have been introduced since the method was first introduced in Reference [1]. One of the most popular variations of this work is that of the Conditional Generative Adversarial Network (CGAN) [2] in which the generator and discriminator (we briefly review the GAN process in Section 2) are both conditioned on some observed information. In the application of time series forecasting, future values are conditioned on information observed from the past, either from the time series itself, some set of associated exogenous data, or a combination of the two. This conditioning addition to the GAN formalism makes the CGAN approach particularly useful as a foundational model for time series prediction. Most applications of (C)GANs, however, have been within computer vision and, to a lesser extent, in natural language processing and simulation models [3; 4; 5].  
The literature on the application of any form of GAN model to problems associated with time series is, to date, limited. However, some recent literature shows the potential usefulness of the method. For example, the work reported in Reference [6] applies a recurrent GAN to generate realistic, synthetic, medical data series, and, in Reference [7], a (standard) GAN model is used to generate realistic financial asset prices and analyze their distributions.  
In Reference [8], a GAN is used to forecast high-frequency stock data, and, in Reference [9], GANs are used to generate missing values in incomplete time series. We note that the GAN models used in all these applications make point estimates for the forecast. Although a perfectly valid approach, and one that has a long history in time series forecasting, we argue that probabilistic forecasts are a prerequisite in many application domains, in which knowledge of the predictive uncertainty is as vital as the prediction itself. In this work, we expand on the CGAN algorithm to allow a full predictive probabilitydistribution, rather than a point value. To obtain richer predictive densities, we model the posterior distribution using a finite Gaussian Mixture model (GMM). Although we find only occasional evidence that such non-Gaussian predictions offer significant benefits, we note that producing them is not much more costly than single Gaussian predictive distributions, and so present our approach as a more general multi-component model.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 5 Evaluation
### Discussion  
According to the obtained results and graphs from the previous section, clearly we can say that our proposed framework/solution (CACOIOV) outperforms the other two algorithms in terms of the mentioned network metrics and is more optimized. The following performance metrics that include packet loss, delay, and formation of cluster numbers have significantly been reduced in the IoV network compared to the ACO and AODV algorithms.  
Satiability of cluster and network can be obtained by having a minimum number of clusters. This factor not only has a direct impact on reducing the resource requirements but also decreases the routing cost of whole network. In addition to this, our results also illustrate that throughput and \(DPDR\) have increased as an outcome of the CACOIOV algorithm. Evidently, CACOIOV has the highest performance compared to others. This achievement is attained in light of three novel and effective ideas. Introducing the selection of first node intelligently in the procedure of CH selection was an efficient step toward the improvement in quality of the ant tour. Proposing the dynamic evaporation idea can be another reason the CACOIOV scheme performs significantly better. This factor balances the convergence process by fastening the convergence speed at the start of route discovery and reaching zero dynamically. Developing this mechanism improved the global search ability remarkably when discovering the best tour/solution that leads to optimized clustering in the network. Finally, the third effective idea for choosing an appropriate vehicle such as a CH is proposing DA-TRLD. This algorithm has a direct impact on the enhancement of cluster stability. All these experimental methods make CACOIOV perform better in all scenarios. Generally, applying DA-TRLD to CACOIOV, ACO, and AODV does not mean that they can have similar performance in all comparison parameters. CACOIOV and ACO have a close result in terms of cluster formation but they differ in other comparison parameters. On the other hand, obtained results of AODV reveal the worst performance compared to others in all cases. Generally, AODV uses the algorithm of route discovery for data transmission among nodes. AODV intention achieves better DPDR and latency prevention. This reactive algorithm propagates a message path failure as Route Error Message (RERR) to all nodes in the network for achieving its goal. As revealed, this algorithm was not successful in providing a better \(DPDR\) result than the ACO and CACOIOV. In addition, to explain the reason for high end-to-end delay of AODV compared to CACOIOV, we can mention a considerable delay in the process of route discovery of this reactive protocol.
## 3 Results
### Hydrological Control
#### 3.2.2 Hand-Pumped Borehole Flow Tests  
Possible insight into the aquifer flow field was obtained from hand-pumped borehole flow test data on all mapped rural water points in the study area via the Malawi Standard hand-pumped test to fill a 20 litre bucket (to check compliance with the 0.25 L/s Malawi Standard for supply). Although unlikely to give maximum yields, especially for alluvial aquifer boreholes, the data exist for every water point and is broadly indicative (recognising that ideal aquifer parameter pumping test data are rarely obtained in Malawi). A histogram of mWater flow data from 4436 boreholes in the Basin displays a mean of \(0.295\pm 0.071\) L/s (Figure 5b). The spatial distribution confirms that higher end flow rates more consistently occur in the south west Basin, around the Phalombe River catchment and towards the west side of the Phalombe District (Figure 5c). The distribution is consistent with the expected area of deeply-cut paleo-river system, where thicker deeper sediments of increasingly fluvial rather than lacustrine origin are expected [5; 49], including basal gravels prevalent near the Phalombe mouth from material torrentially swept into the down-warped basin [31].  
Comparison with the potentiometric surface (Figure 5a and Figure 5d) suggests that the opening out of gradients (albeit sparse data) and flow towards the Phalombe River catchment may be assisted by the more permeable deposits there. Higher borehole flow rates in central Phalombe District nearing the interface of the high-level or terrace 5 deposits where the gradients are steepest may again be indicative of permeable (e.g., colluvium, basement) units feeding these wells and, or restricted flows ahead in the (semi-)confined area driving borehole and spring/baseflow emergence. The handpump flow test data hence reinforce our conceptualisation.
## 2 New EMI Shielding Materials: Biochars, Scaffolds, Rare Earth, and Ferrite-Based Materials
### Rare-Earth-Based Materials as EMI Shielding Material  
Next-generation microwave-absorbing compounds and hybrid materials could potentially outperform traditional materials in terms of their ability to absorb microwaves, their persistence, their stability at high temperatures, weight reduction, their corrosion resistance, and the need to expand the frequency spectrum over which materials can efficiently absorb microwaves. Research and development in the last few years have promoted magnetic metal oxide composites or perovskite oxides doped with REEs as innovative materials for good electromagnetic wave absorbance due to their specific structures, interfacial polarization, and multiple reflections, showing excellent conductive properties and magnetic losses [60]. By doping rare earth elements, the dielectric loss and dipole polarization can be increased, and the wave absorption performance of ferrite can be further improved. [PERSON] et al. [61] prepared a Nd\({}^{3+}\)-substituted Gd-Co ferrite composite and found this to be a promising EW absorption material with a wide effective absorption bandwidth in the X-band. On the other hand, it was observed that appropriate La doping enhances the dielectric and conductive mismatch levels of the material. This enhancement leads to a stronger interface polarization capability [62]. Different microstructures and morphologies manifest different EMI shielding properties. Addressing the challenge of creating absorption-dominant EMI shielding requires significant attention to detailed structural design [63]. This strategy aims to produce structures that minimize the reflection of undesirable electromagnetic noise, capturing electromagnetic waves (EMWs) through various attenuation mechanisms. The rare earths (REs) for electromagnetic wave absorption applications can be classified into several groups: RE-doped ferrites (RE-Fs), RE-transition metal intermetallics (RETMIs), RE oxides (REOs), and other categories [64]. Graphene-based materials, metal-organic frameworks (MOFs), ferrites, molybdenum disulfide, MoS\({}_{2}\), and other innovative materials can significantly enhance EMW absorption properties by doping or substitution with REs due to the magnetic moment originating from their unpaired electrons [65]. The high-frequency permittivity of rare-earth Er-doped MoS\({}_{2}\) films was studied [66]. The size of nanocrystals depends on the reaction time; the reactant concentration and composi
## 5 Experimental Results
### Average Results across All Sectors in DFSS  
We develop effective feature selection algorithms for the stock market by analyzing the average results across all sectors. Figure 2 and Table 2 illustrate visual representations and tabulations of these average outcomes for all sectors using the Dynamic Feature Selection System (DFSS). These averages are computed from 600 results obtained by varying feature selection percentiles, look-back sizes, and window sizes for each sector, reflecting the performance and stability of the algorithms.  
The performance comparison of these algorithms was conducted against a benchmark of random feature sampling (No Feature Selection, NOFS). An algorithm is deemed superior to NOFS in terms of average performance and stability if its average result is positioned in the upper right corner relative to the NOFS result. The findings indicate that, on average, wrapper-based algorithms demonstrate high performance and stability in forecasting stock prices in domestic sectors. In contrast, filter methods show markedly lower performance and stability. This can be attributed to their limitation of considering only univariate correlations, as opposed to multivariate ones. Ensemble and embedded methods exhibit marginally lower performance. Based on the high performance of the predictive model, the wrapper model, as known, demonstrated the highest performance. Ultimately, by balancing both stability and performance, the wrapper-based SHAP algorithm is identified as the optimal feature selection method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Method** & **Rank Mean** & **Rank STD** \\ \hline SHAP & Wrapper & 7.222 & 4.3 \\ BorutaSHAP & Wrapper & 7.453 & 4.483 \\ RFE & Wrapper & 7.468 & 4.407 \\ NOFS & Random Selection & 7.608 & 4.678 \\ Embedded Random Forest & Embedded & 7.628 & 4.341 \\ Borda Count & Ensemble & 8.117 & 4.406 \\ Embedded LightGBM & Embedded & 8.137 & 4.591 \\ Mutual Information & Filter & 8.368 & 4.913 \\ Variance Threshold & Filter & 8.687 & 4.68 \\ Permutation Importance & Wrapper & 8.768 & 4.618 \\ ANOVA F-value & Filter & 8.878 & 4.654 \\ Reciprocal Rank & Ensemble & 9.163 & 4.618 \\ MultiSURF & Filter & 9.185 & 4.744 \\ Fisher Score & Filter & 9.580 & 4.610 \\ Embedded Lasso & Embedded & 9.683 & 4.622 \\ Chi2 & Filter & 11.058 & 4.849 \\ Boruta & Wrapper & 14.650 & 3.543 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average results in rank mean’s descending order across all sectors in DFSS.  
Figure 2: Average results across all sectors in DFSS (red box is a feature selection algorithm with a high friendman rank compared to a low standard deviation).
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
###### Abstract  
To further enhance fairness in the allocation of en-route space-time resources in the collaborative trajectory selection program, a study on the plan preferences between air traffic control (ATC) and airlines in the selection process of the final plan is conducted based on the initial resource allocation plans, considering the roles of airlines in resources allocation decisions. By using Stackelberg game theory, a game model is established for the roles played by ATC and airlines in the process of selecting plans. Then, combining the overall consideration of ATC for all affected flights, the preferences of airlines for initial allocation plans are obtained, and the option range of selectable plans is narrowed down to determine the optimal allocation plan. The results of the example analysis show that the proposed model and method can effectively select the optimal allocation plan from the six initial allocation plans, select the trajectories and entry slots in the congestion areas for airlines that better meet the operation demand, and provide the decision basis with more preferences for ATC to select the final allocation plan. When ATC prefers the lowest overall delay cost, the delay cost of the selected optimal allocation plan is 267.7 min, which is 23.84% lower than the traditional RBS algorithm; when considering the preferences of the main base airline in East China, the delay cost of the selected optimal allocation plan is 287.7 min, which is 18.15% lower than the traditional RBS algorithm.  
air traffic management; en-route network; Stackelberg game model; resource allocation; selection preference; fairness Article
## 2 Data and Methods
### Models and Calibration  
#### 2.2.1 Wrf  
The Weather Research and Forecasting (WRF) model, which based on the mesoscale atmospheric model (MM5), was designed as a new generation of numerical weather prediction (NWP) to be applied for a wide range of research and operational prediction problems. [30; 31; 32; 33]. In this study, three nesting domain are set up for WRF (Figure 1): the innermost Dom3 has 1 km pixel size, the second Dom2 has 3 km pixel size, and the outer Dom1 has 9 km pixel size. The driving data were acquired from the Final Operational Global Analysis (FNL) data of the National Centers for Environmental Prediction (NCEP) to provide WRF with initial and boundary fields. The different options for physical parameterization schemes need attention in different practical applications. In this study, based on the climatic characteristics and the underlying conditions of the two study catchments, the Yonsei University of microphysical processes scheme [34], the Kain-Fritsch of boundary layer scheme [35] and the Purdue-Lin of cumulus convection scheme [36] were screened. It should be noted that the cumulus convection scheme was not used in the innermost Dom3 where the convective rainfall generation is assumed to be explicitly resolved [37]. Table 2 lists the experimental details and physics schemes of the WRF related configurations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Event ID** & **Catchment** & **Rainfall Window** & \
\begin{tabular}{c} **Accumulated** \\ **24 h Rainfall (mm)** \\ \end{tabular} & **Spatial Cv** & **Temporal Cv** &
\begin{tabular}{c} **Type** \\ **Label** \\ \end{tabular} \\ \hline
1 & Fuping & 07/29/2007 20:00 to & 63.4 & 0.400 & 0.601 & 1 \\  & & 07/30/2007 20:00 to & 50.5 & 0.193 & 1.082 & 2 \\
2 & Fuping & 07/31/2012 10:00 & 30.9 & 0.740 & 2.393 & \\
3 & Fuping & 08/12/2013 07:00 to & 45.5 & 0.459 & 1.378 & 3 \\  & & 07/21/2012 04:00 to & 172.2 & 0.610 & 1.887 & \\
6 & Zijingguan & 06/06/2013 22:00 to & 52.1 & 0.426 & 1.887 & \\  & & 06/07/2013 22:00 & 52.1 & 0.426 & 1.887 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Storm events selected from the two study catchments and their spatiotemporal rainfall evenness characterized by \(Cv\).
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 3 Methodology  
A standard FL system comprises a central server and multiple individual clients with their own raw datasets that are stored locally. The central server is responsible for coordinating the training by selecting a random set of clients, randomly initializing the model's global weights, broadcasting it to the clients, collecting and aggregating the updated weights received from the clients after training the model (local weights), and finally, sending the updated global model back to the clients for the next round of training. This process will be repeated until the model meets the criteria defined by the system owner. From the client viewpoint, they receive the global model broadcast by the server and train this model over several epochs using locally stored data, then send the updated model back to the server for aggregation. Clients then wait to receive the updated global model for the next round of training. It is worth emphasizing that the clients only share the updated model weights with the server and that the server has no access to the client's private data. Figure 3 shows a concise illustration of an FL system. To implement an FL system, several algorithms and open-source frameworks have been proposed [30; 31; 32; 33; 34; 35; 36; 37]. In this study, we use the Federated Averaging (FedAvg) algorithm and FedML framework to implement our FL system. We also considered other aggregation algorithms. Most of the models we considered are based on FedAvg with slight modifications that help address particular data problems, such as unfavorable convergence behavior or objective inconsistency. Some models required actual distributed learning and so could not be tested in our simulation environment. We had a good convergence with FedAvg, so we did not expect the modified models to improve our performance. We did try FedProx as an aggregation algorithm, but for our data, it did not perform as well as FedAvg, and because FedAvg is a well-known and accepted model, we decided not to proceed further with other models.  
MobileNetV3 is used for the classification of the _clean_ and _contamination_ frames and DeepLabv3+ for the precise segmentation of contaminated areas in each video frame.  
Figure 3: A concise illustration of an FL system.
# Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set
## 1 Introduction
Because the raw MSTAR images have high dimensionality and speckle noise, thus making the classification more complicated and influencing the recognition performance worse, the sizes of the raw MSTAR images are not unified, and a preprocessing of MSTAR images is recommended in this paper to maintain the useful information, suppress the speckle noise and reduce the dimensionality. Meanwhile, conventional and unconventional feature extraction methods are utilized to extract features and reduce the dimensionality further. Then, the processed images are inputted into the framework, and the sparse coefficient of the test sample is worked out. So the identity of the test sample is inferred by the reconstructive errors calculated through the sparse coefficient. The comparisons between SRC and SVM are carried out to verify the excellent performance of SRC. Besides the average recognition accuracy, the recognition rate of each target is discussed. The organization of the paper is as follows: we give a brief review of the sparse representation-based classification in Section 2. Section 3 describes the preprocessing of the MSTAR images before putting them into the classifier. The results compared with SVM will be presented in Section 4. Finally, the paper is concluded in the Section 5.
## 2 Materials and Methods
### Cone Beam Computed Tomography Measurements  
A cone beam computed tomography (CBCT) scan was performed using buccal retractors to isolate the soft tissue contours, with a field of view (FOV) of 7 \(\times\) 15 cm, a 0.3 mm voxel size, and a resolution of 0.5 mm (scan time: 20 s, 14 bits grayscale) using an i-CAT Classic (Imaging Sciences International, Inc., Hatfield, PA, USA). The CBCTs were performed before tooth extraction, after the implant surgery (T1) and 4 months (T2) post-implant placement. To align the original DICOM data from the T1 and T2 CBCTs scans, a superimposition was performed following the methodology previously described in the literature [21], by the OnDemand3D software (Version 1.0.9.3223, CyberMed Inc., Seoul, Republic of Korea) and transformed into the (.gipl) format using the ITK-SNAP software (version 3.6.0, www.itksnap.org, accessed on 30 September 2020) to enable proper superimposition in the Slicer software (version 5.0.2, www.slicer.org, 30 September 2020). The linear measurements were made using the same reference points, in which the vertical and horizontal measurements followed the parallelism of a perpendicular line drawn on the implant. A horizontal reference line (RL) was determined at the implant platform with the smooth/rough surface junction [3]. A single-blinded and calibrated examiner performed all the measurements (Figure 1). Horizontal width hard tissue alteration changes between the T1 and T2 of the lingual/palatal and buccal bone wall (HBT) at five levels were measured: the RL and at 0 mm, \(-\)1 mm, and \(-\)2 mm above the RL; and 2 mm below the RL. The buccal and lingual bone level changes were determined by variation on vertical bone height between baseline (T1) and 4 months (T2) after implant placement (\(\Delta\)VBH T1-T2). The buccal vertical soft tissue thickness was measured prior to tooth extraction using a CBCT scan. One of the major advantages of using a 3D Slicer is the ability to achieve transparency between T1 and T2 slices, allowing the same reference marking, traced only once, to be reproduced consistently across both analysis time points.