esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Work  
In this work, an analytic LTE-based uplink NB-IoT baseband model is derived. We proposed a low complexity receiver framework based on the MAP turbo decoding scheme and FDTE equalizer algorithms. Furthermore, the analysis and mathematical formulation of our proposed scheme are presented, including the detection sequence analyzed using one-tap equalization and frequency domain operations. Simulation of BER performance shows that our scheme is fairly robust over conventional MMSE FDTE, which can enhance transmission reliability of UE data in NPUSCH and hence reduce computation complexity suitable for LPWA NB-IoT systems.  
Future study will look into the modeling of the NB-IoT network PHY layer, the proposals of energy efficiency technology for MCS selection, the improvement of data rate and the management of network scalability, which can be a framework based on the modified MAP decoding and MMSE equalization to improve the transmission reliability of uplink user data and achieve wide-area coverage.  
The proposed schemes can also be extended to perform NDMRS channel estimation for carrier frequency offset (CFO) compensation to estimate the frequency error with self-interference cancellation (SIC) on the received NPUSCH information sequence, while canceling interference at the receiver will also be a topic of interest. Additionally, this algorithm can also be extended to HARQ for the NPUSCH uplink control channel as well as being energy-efficient to optimize the ACK/NACK feedback signaling. In this framework, signaling only needs to indicate through the DCI, which are lost during the uplink transmission, and hence allows saving power consumption of the NB-IoT network.  
Conceptualization, formal analysis, investigation, methodology, software, writing--original draft, M.J.A.; supervision, analysis, writing--review and editing, L.Q.; analysis, writing--review and editing, R.S.Z.; analysis, writing--review and editing, C.O.N.; analysis, writing--review and editing, H.B.K.; analysis, writing--review and editing, A.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors declare no conflict of interest.
## 2 Structural Assessment. Limit States Calculation Methods, Design Situation, and Loads
### GEO Ultimate Limit States
#### 2.2.1 GEO Limit State: Bearing Resistance  
Table 1 contains the approaches of EC7-1 and CTE DB SE-C for the calculation of the design bearing resistance for drained conditions. As shown in Equations (8)-(35), the expressions are quite similar in both codes. In order to determine this resistance it is necessary to apply a set of correction coefficients: bearing capacity factors (\(N\) values), the inclination of the base (\(b\) values), the depth of the base (\(d\) values), the shape of the footing (\(s\) values), foundation on top of a slope (\(t\) values) and the inclination of the loads (\(i\) values).
# Research Progress and Applications of 2D Antimonene
## 2 Theoretical Research and Experimental Methods
The bent and folded antimonene nanoribbons treated with edge hydrogenation have a large semiconductor band gap, and the current is almost zero at the low bias pressure of 0-0.8 V. The conductance of antimonene nanoribbons treated with edge oxidation was significantly increased, and the adjustable negative differential resistance behavior was detected. As early as the 1980s, antimony has been experimentally studied on conductor substrates [35; 36; 37] through the evaporation of antimony by filament or liquid pool, and the growth of antimony films were mainly on GaAs (110), and the study of its electronic properties (band gap) was carried out using STM and Raman spectroscopy. Due to the influence of surface layer coupling, the research has been hindered. From 2016, the experimental preparation of antimonene has been using mechanical exfoliation, liquid phase exfoliation and molecular beam epitaxy.
## 3 Collaboration Performance Evaluation Based on Combination of AHP and EW
### Determining the Comprehensive Weight Based on Combination of AHP and EW  
Analytic hierarchy process (AHP) is a subjective weighting method which can better reflect the subjective feelings and experience of experts, but its results are greatly influenced by human subjective factors. The entropy weight method is a method based on objective facts, which only considers the fluctuation of data and ignores the engineering experience of experts. Therefore, neither can accurately reflect the importance of collaboration performance indicators.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**First-Level Indicators** & **Second-Level Indicators** & **Third-Level Indicators** \\ \hline \multirow{5}{*}{Enterprise finance} & \multicolumn{2}{c}{Input–output ratio (\(G_{1}\))} \\ \cline{3-4}  & & Timely settlement rate (\(G_{2}\)) \\ \cline{3-4}  & & Cost saving rate (\(G_{3}\)) \\ \cline{3-4}  & & Cash conversion rate (\(G_{4}\)) \\ \hline \multirow{5}{*}{Collaboration performance} & \multicolumn{2}{c}{Proportion of joint purchases to total purchases (\(G_{5}\))} \\ \cline{3-4}  & & Procurement timeliness (\(G_{6}\)) \\ \cline{3-4}  & & Inventory turnover (\(G_{7}\)) \\ \cline{3-4}  & & Synchronization of production plan (\(G_{8}\)) \\ \cline{3-4}  & & Material supply flexibility (\(G_{9}\)) \\ \cline{3-4}  & & On-time delivery (\(G_{10}\)) \\ \cline{3-4}  & & Project construction flexibility (\(G_{11}\)) \\ \cline{3-4}  & & Comprehensive project quality qualification rate(\(G_{12}\)) \\ \cline{3-4}  & & The stability of supply and demand (\(G_{13}\)) \\ \cline{3-4}  & & Output value of jointly developed products (\(G_{14}\)) \\ \cline{3-4}  & & Timeliness of information delivery (\(G_{15}\)) \\ \cline{3-4}  & & Degree of information sharing (\(G_{16}\)) \\ \hline \multirow{5}{*}{Enterprise strategy} & \multicolumn{2}{c}{Goal congruence (\(G_{17}\))} \\ \cline{3-4}  & & Corporate culture compatibility (\(G_{18}\)) \\ \cline{3-4}  & & Degree of strategic goal matching (\(G_{19}\)) \\ \cline{3-4}  & & Historical cooperation times (\(G_{20}\)) \\ \cline{3-4}  & & Degree of corporate trust (\(G_{21}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Enterprise collaboration performance evaluation index system.  
In this paper, a combined weight model based on minimum deviation is proposed, which abstracts the comprehensive weight determination into a multi-attribute decision-making problem. This will effectively reduce the \"polarization\" effect of the subjective and objective weighting methods, and achieve dynamic optimality among the two weighting methods. It not only takes into account the subjective experience of decision makers, but also takes into account the objective facts, so that the evaluation results are more realistic. The specific process is as follows:
## 2 Mathematical Models
### Parametric Optimization  
Parametric optimization involves finding the solution to a standard optimization problem, but this time, the solution is expressed as a function of certain parameters. These parameters are variables that are important for making decisions but cannot be directly controlled by the designer. Within the context of systems design, examples of parameters might include design specifications that have not been finalized, environmental factors, or characteristics of other interconnected parts of the system. Conceptually, solving a parametric search problem can be understood as applying standard optimization techniques for every possible combination of the parameter value(s). Let \(\Lambda\subseteq\mathbb{R}^{p}\) represent the set of all feasible parameter vectors, \(\lambda^{*}\). We can extend Equation (3) to handle this parametric scenario, as follows:  
\[\begin{array}{l}\eta(\lambda^{*})=\min_{\xi}f(\xi,\lambda^{*})\\ s.t\quad(\xi,\lambda^{*})\leq 0,\\ h(\xi,\lambda^{*})=0,\\ \lambda^{*}\in\Lambda,\\ \xi\in\chi.\end{array}\]  
When dealing with parametric optimization, the objective function is optimized considering the parameter vector as an input. Specifically, \(\eta(\lambda^{*})\) represents the relationship between the parameter vector \(\lambda^{*}\) and the minimum value of the objective function \(f(\xi,\lambda^{*})\) over all feasible \(\xi\in X\). Consequently, the solution is not a single point but rather a set (which could be infinite). The constraints \(g\) and \(h\) are those that include both the design variables and the parameters.  
The idea of parametric optimization has been explored in fields like economics by [PERSON] and [PERSON] (2007), [PERSON] and [PERSON] (2007), often with the goal of examining how solutions change in response to variations in one or a small number of parameter variables. This kind of analysis is valuable for understanding how a system behaves when faced with uncertain parameters. Examples include changes in market demands and prices in economics, or variations in boundary conditions and system properties in engineered systems. When these kinds of fluctuating conditions are incorporated into an optimization problem, a parametric optimization approach can be employed to analyze their impact on the resulting solution.
## 1 Introduction  
### Background and Context  
Health data administration is becoming more complex, and robust approaches are being sought to protect privacy. Health professionals can produce comprehensive patient records with easier-to-track patient health, offer real-time data access, and may help save money [1]. With the growing popularity of electronic medical records and the continual development of healthcare technology, protecting critical patient information has emerged as an essential issue. The broad consequences of data breaches in the healthcare sector include compromised trust in the entire system and individuals' confidentiality [2, 3]. Developing and enhancing privacy-preserving tools remain crucial as we navigate this challenging environment [4]. This study explores the world of health data management with a particular focus on privacy, demonstrating the latest advances in ShinyAnonymizer, a tool designed to tackle the complex task of integrating data availability with high privacy rules. Robust privacy-preserving methods are even more critical when considering the strict requirements for data protection established by law, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) [5, 6]. It may be challenging in current technologies to find the right balance between the value of data and privacy. Data can become less useful for research and analysis if anonymization methods that rely on the past are applied [7]. Although advanced methods, including hashing and encryption, provide more and more extraordinary privacy promises, their execution can be physically difficult and time-consuming. The development and enhancement of methods that guarantee privacy remain vital as we traverse this complex scenario with a focus on privacy; this study dives beyond the topic of health data management and presents the latest current advances in ShinyAnonymizer, a tool developed to facilitate the complex task of integrating rigorous privacy laws with data availability. To offer various methods for anonymization simply accessible to users, ShinyAnonymizer was initially created to anonymize health data. Also, by merging data anonymization, hashing, and encryption with the analysis of data, visualization, and privacy-focused statistically significant paradigms, the new version presented in this research produces significant productivity improvements. This allows data analysts and researchers to use an extensive selection of effective data management solutions.
###### Abstract  
Lowland river basins are characterised by complex hydrologic and hydraulic interactions between the different subsystems (aerated zone, groundwater, surface water), which may require physically-based dynamically-coupled surface water and groundwater hydrological models to reliably describe these processes. Exemplarily, for a typical north-eastern Germany lowland catchment (Tollense river with about 400 km\({}^{2}\)), an integrated hydrological model, MIKE SHE, coupled with a hydrodynamic model, MIKE 11, was developed and assessed. Hydrological and hydraulic processes were simulated from 2010 to 2018, covering strongly varying meteorological conditions. To achieve a highly reliable model, the calibration was performed in parallel for groundwater levels and river flows at the available monitoring sites in the defined catchment. Based on sensitivity analysis, saturated hydraulic conductivity, leakage coefficients, Manning's roughness, and boundary conditions (BCs) were used as main calibration parameters. Despite the extreme soil heterogeneity of the glacial terrain, the model performance was quite reasonable in the different sub-catchments with an error of less than 2% for water balance estimation. The resulted water balance showed a strong dependency on land use intensity and meteorological conditions. During relatively dry hydrological years, actual evapotranspiration (ETa) becomes the main water loss component, with an average of 60%-65% of total precipitation and decreases to 55%-60% during comparatively wet hydrological years during the simulation period. Base flow via subsurface and drainage flow accounts for an approximate average of 30%-35% during wet years and rises up to 35%-45% of the total water budget during the dry hydrological years. This means, groundwater is in lowland river systems the decisive compensator of varying meteorological conditions. The coupled hydrologic and hydraulic model is valuable for detailed water balance estimation and seasonal dynamics of groundwater levels and surface water discharges, and, due to its physical foundation, can be extrapolated to analyse meteorological and land use scenarios. Future work will focus on coupling with nutrient transport and river water quality models.  
**Keywords:** groundwater-surface water interaction; integrated hydrological-hydraulic modelling; lowland hydrology; [PERSON]; MIKE 11; Tollense river; water management +
Footnote †: journal: Applied Soft. 2020, 10, 1281; doi:10.3390/app10041281
# Erythermal Solar Irradiance, UVER, and UV Index from Ground-Based Data in Central Spain
## 3 Results  
### UVER Solar Irradiation  
#### 3.1.1 Daily Values  
Figure 2 shows the yearly evolution of the daily (green) and monthly average (red) UVER irradiation values (kJm\({}^{-2}\)) in Valladolid, Spain, for the period 2013-2019. The curves of the daily and monthly UVER values show a sinusoidal evolution, with minimum values in January and December that range between 0.43 kJ/m\({}^{2}\) in January 2018 and 0.47 kJ/m\({}^{2}\) in December 2014. Similar results have been obtained by other authors [23].  
Monthly maximum values range between 4.61 kJ/m\({}^{2}\) in July 2018 and 5.18 kJ/m\({}^{2}\) in July 2016. The values obtained in 2018 are slightly lower than those obtained for previous years, due to the importance of the precipitation effect. Maximum daily UVER values range between 5.76 kJ/m\({}^{2}\) on 19 th June and 6.12 kJ/m\({}^{2}\) on 22 nd June 2016.  
Figure 1: Normalized erythema action spectrum, (red), spectral irradiance on the surface (blue), and spectral ultraviolet erythemal radiation (UVER) irradiance (purple).  
Minimum daily UVER values range between 0.06 kJ/m\({}^{2}\) on 31 st December 2015 and 0.14 kJ/m\({}^{2}\) on 4 th January 2016. In addition, there are some strong fluctuations of daily UVER values in winter months that could be explained by the different meteorological events influencing the region.  
The situation makes cloud coverage more abundant in winter months (January and February) than in the rest of the year [23], which could be explained by the Atlantic low-pressure systems usually associated with warm air, high winds, and atmospheric lifting.
## 4 Results and Discussion
### Result Analysis
#### 4.1.1 Experiment 1 Results  
Experiment 1 assessed the feasibility of using brain morphological features for classifying ASD and TD individuals. Two sub-experiments were conducted using 80% of the ABIDE dataset, employing the 10-fold CV technique with or without random search for hyperparameter tuning. The experimental results, presented in Figure 9, enable a comparison of the performance of the tested models based on the extracted descriptors.  
The accuracy of ASD classification varied across different algorithms and features sets. However, upon analyzing the above figure, it is clear that the baseline models did not consistently perform well across all feature sets. The mean accuracy values ranged from 51.4% to 65.3%, depending on the feature set. The DT model in the F3 group had the lowest mean accuracy, while the RF model in the F6 group, which incorporated 342 biomarkers/features from all brain regions, achieved the highest mean accuracy.  
Figure 9: Mean ASD classification accuracy of the seven models on features sets in Experiment 1: (**a**) without random search; (**b**) with random search. Within each subplot, callouts are used to highlight the models with the lowest accuracy (indicated by a red frame), highest accuracy (indicated by a green frame), and models that achieved an accuracy of 65% or higher (indicated by a yellow frame).  
Table 5 presents the performance results of the best baseline model in terms of average CV accuracy among the seven models studied for classifying ASD based on different brain feature sets.  
Based on the results presented in Table 5, it is evident that the CatBoost model consistently outperforms the other models across most feature sets. The SVM and RF models also demonstrate good performance.  
The random search process for hyperparameter tuning was limited to 20 iterations, as further improvements were not expected beyond that point. In each iteration, hyperparameters were tuned, and performance analysis was conducted to identify the parameters with the best performance.  
Figure 9 indicates improved performance for most models after hyperparameter tuning. The RF model with F3 achieved the lowest accuracy (52.55%), while CatBoost with F6 achieved the highest accuracy (66.28%). The best hyperparameters were a learning rate of 0.02, 60 iterations, and a depth of 6.  
Table 6 presents the results of the best model for each feature set. CatBoost demonstrates excellence once again, and XGB also exhibits good performance.
# TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction
## 4 Dataset and Experimental Set-Up
### TRAP-2017 (Traffic Mining Applied to Police Activities)  
TRAP-2017 was released by the Italian National Police [30]. It was acquired using Number Plate Reading Systems in 2016, from 1 January to 31 December, on 27 gates distributed over the Italian highway. The dataset is composed of 365 commas separated values (CSV) files containing the following data: plate number, gate, lane, timestamp and nationality (of the plate). The total number of rows is 111089717. Each gate represents a point of the highway network on which the traffic flow prediction can be performed. In this study, the prediction has been done on gate 1 considering only the timestamp field which uniquely represents the transit of a vehicle.  
Data have been aggregated over a 5-min time window enumerating the number of vehicles that have transited under the gate and successively normalized with min-max rule. The \(\Delta\) lag has been set to 5 min for three reasons:  
1. To be consistent with other authors implementations (comparison aims);
2. \(\Delta\) = 5 min produce the best accuracy for all models with respect to other solutions (i.e., 15, 30 and 45 min);
3. \(\Delta\) = 5 min implies a near real-time prediction, therefore it allows to promptly implement strategies of traffic control. The dataset has been preprocessed as follow:  
1. Data have been aggregated over 5-min time window: the number of vehicles which have transited under the gate 1 every 5 min are reported along with the time stamp captured of the last vehicle belonging to the 5 min time window.
2. Time windows with 0 transited cars, have been reported as 0.
3. Data have been separated in months and days.
4. Data has been normalized with min-max rule within [0.1-1.0].
5. The sliding window approach has been then used on the normalized data (lag = 5).
6. Monday has been selected as the day of forecasting.
7. The preprocessed data is then fed to the various neural network architectures.  
The input to the various architectures are the following 5 min lag datapoints: T\({}_{5}\), T\({}_{10}\), T\({}_{15}\), T\({}_{20}\), T\({}_{25}\). These are used to do forecast T\({}_{30}\) datapoint. The days used for training were 60 days for PeMS dataset and 44 days for TRAP-2017.
# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 2 The Related Literature
### Occlusion and False Positive Error  
Researchers have recently focused on overcoming the challenges in pedestrian target detection. [PERSON] et al. proposed MGAN [37] to address the occlusion problem. [PERSON] et al. [38] used strength, depth, and motion characteristics to construct a component-based classification model. [PERSON] et al. [39] proposed an occlusion-processing method that combines single-stage detection models to reduce false positive errors. An occlusion-sensitive detection score is obtained by updating the output tensor to include the component confidence score. [PERSON] et al. proposed an occlusion-aware R-CNN framework [40] by introducing aggregation loss to address the occlusion problem. [PERSON] et al. proposed repulsion loss [41], which includes three parts: the loss value between the predicted box and the target ground-truth box; the loss value between the predicted box and the adjacent target ground-truth box; and the loss between the predicted and adjacent predicted boxes that are not predicting the same real target.  
We introduce additional attitude information to describe a partially occluded pedestrian object, which produces the following benefits:  
1. Pedestrian pose information has a strong robustness to occlusion and the changes in appearance caused by variations in clothing and lighting. Additionally, pose estimation algorithms [42; 43; 44; 45; 46; 47; 48] have considerably progressed in recent years.
2. The attention mechanism enables the model to focus on the most crucial regions in an image to improve the representation of target pedestrian features and its detection performance in occlusion scenarios. The attention mechanism can also reduce irrelevant information interference and increase the robustness and detection accuracy of pedestrian targets.
# Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks
## 3 Cooperative Game in Ultra-Dense Wireless Cellular Networks
### Utility Design and Bargaining Cooperative Game Formulation
If \(\alpha_{m}\) = 1 and \(\beta_{m}\) = 0, MeNB\({}_{m}\) only considers the EE, and if \(\alpha_{m}\) = 0 and \(\beta_{m}\) = 1, MeNB\({}_{m}\) only considers the DE. If \(\alpha_{m}\) = 0 and \(\beta_{m}\) = 0, MeNB\({}_{m}\) only considers the spectrum efficiency._  
Therefore, the unique Nash bargaining game equilibrium can be achieved by solving the following Nash optimization problem. By adopting the objective utility function in Lemma 1, the optimization problem can be rewritten as:  
\[\textbf{P1}:\max_{0\leq p_{m}\leq p_{m}^{MAX},0\leq p_{n}\leq p_{n}^{MAX}} \prod\limits_{m=1}^{M}{(u_{m}-u_{m}^{\min})\prod\limits_{n=1}^{N}{(u_{n}-u_{n }^{\min})}} \tag{16}\]  
\[\text{subject to }\ C1:EE_{m}-EE_{m}^{0}\geq 0,m\in\mathfrak{M},\]  
\[\ C2:\ DE_{m}-DE_{m}^{0}\geq 0,m\in\mathfrak{M},\]  
\[\ C3:\ SE_{m}-SE_{m}^{0}\geq 0,m\in\mathfrak{M},\]  
\[\ C4:EE_{n}-EE_{n}^{0}\geq 0,n\in\mathrm{N},\]  
\[\ C5:DE_{n}-DE_{n}^{0}\geq 0,n\in\mathrm{N},\]  
\[\ C6:SE_{n}-SE_{n}^{0}\geq 0,n\in\mathrm{N} \tag{17}\]