esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Performance Measures  
Mean absolute error (MAE) and root mean square error (RMSE) were applied to evaluate the effectiveness of the flight delay as performance measures. These two indexes can reflect the difference between predicted and observed values, and have frequently been used as performance measures in previous studies related to flight delays:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert\hat{y}_{i}-y_{i}\rvert \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}} \tag{8}\]  
where \(\hat{y}_{i}\) and \(y_{i}\) are the predicted and observed delay of the flight \(i\), respectively, and \(N\) is the number of flights.
###### Abstract  
Terahertz (THz) imaging and spectroscopy set-ups require fine optical alignment or precise control of spatial mode profile. We demonstrate universal, convenient and easy-to-use imaging--resonant and broadband antenna coupled ultrasensitive titanium-based--dedicated to accurately adjust and control spatial mode profiles without additional focusing optical components of weak power THz sources. Versatile operation of the devices is shown using different kinds of THz--electronic multiplier sources, optical THz mixer-based frequency domain and femtosecond optoelectronic THz time-domain spectrometers as well as optically pumped molecular THz laser. Features of the microbolometers within 0.15-0.6 THz range are exposed and discussed, their ability to detect spatial mode profiles beyond the antennas resonances, up to 2.52 THz, are explored. Polarization-sensitive mode control possibilities are examined in details. The suitability of the resonant antenna-coupled microbolometers to resolve low-absorbing objects at 0.3 THz is revealed via direct, dark field and phase contrast imaging techniques as well.  
microbolometer; terahertz sensing; terahertz imaging systems; dark field method; phase contrast method +
Footnote †: journal: Applied  
_Article_  
Tianium-Based Micropolometers: Control of Spatial Profile of Terahertz Emission in Weak Power Sources
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 4 Problem Formulation and Solution
### Problem Solution  
If the number of users and cells is limited, the optimum solution can be achieved with an exhaustive search (ES).  
Figure 2: Example of user-cell association based on different metrics.  
However, its complexity significantly increases with \(\mathcal{N}_{\mathcal{C}}\) that in a UDN can be very high; thus, we propose an heuristic based on an iterative procedure. We suppose to start with all the cells in idle state, then successively the procedure turns on cells until an increase in the utility function is achieved. More in detail, at the \(i\)-th iteration, successively, one idle cell (the \(c\)-th) at a time is activated and the utility function is calculated \(EE^{[\text{sec}]}(c,i)\). The cell, \(\hat{c}^{i}\), that provides the highest utility is selected, \(EE^{[\text{sec}]}(\hat{c}^{i},i)\). If the new utility value is higher than that at previous iteration (i.e., \(EE^{[\text{sec}]}(\hat{c}^{i},i)\)\(\geq\)\(EE^{[\text{sec}]}(\hat{c}^{(i-1)},(i-1))\)), the selected cell is activated. The iteration stops when the utility derived at iteration \(i\) is lower than that at previous iteration or when all the cells have been activated (i.e., \(i=\mathcal{N}_{c}\)). The procedure is described in Algorithm 1.  
```
Initialization \(\mathbf{\Phi}^{0}=[0,\cdots,0]\) \(i=1\) Iterations while\(i<=\mathcal{N}_{\mathcal{C}}\)do for\(c=1:\mathcal{N}_{\mathcal{C}}\)do \(\mathbf{\Phi}^{i}_{imp}=\mathbf{\Phi}^{(i-1)}\) if\(\mathbf{\Phi}^{i}_{imp}(c)==0\)then \(\mathbf{\Phi}^{i}_{temp}(c)==1\)  Calculate the allocation matrix that maximizes for each UE the secure EE  Calculate the Utility function (8) \(EE^{[\text{sec}]}(c,i)\) endif endfor  find \(\hat{c}^{i}\) s.t. \(EE^{[\text{sec}]}(\hat{c}^{i},i)=max_{c}\{EE^{[\text{sec}]}(c,i)\}\) if\(EE^{[\text{sec}]}(\hat{c}^{i},i)>EE^{[\text{sec}]}(\hat{c}^{(i-1)},(i-1))\)then \(i=i+1\) else \(i=C\) endif endwhile
```  
**Algorithm 1** Iterative Algorithm of the heuristic procedure
## 1 Introduction
To achieve precise vibration suppression control, it is essential to accurately model the nonlinear dynamic disturbance components within the system. However, since the BLS involves a large number of randomly initialized parameters-such as the number of feature nodes, weight initialization ranges, and the configuration of enhancement layers-these hyperparameters significantly affect the model's convergence speed and prediction accuracy during practical implementation. Under constrained excitation trajectories, it becomes increasingly difficult to fully characterize nonlinear disturbances using only the structural regression matrix. Therefore, incorporating evolutionary algorithms to optimize the model has become an important means of enhancing identification performance. Similar \"nonstructural disturbance + parameter optimization\" frameworks have been shown to improve torque estimation accuracy, particularly in low-speed regimes [15]. In summary, although existing methods have made certain progress in parameter identification for vibration control, several limitations remain. These include a heavy reliance on physical modeling [6], high computational cost associated with neural network training [7], lack of controller-compatible interfaces in some optimization approaches, and inadequate handling of multimodal nonlinear behaviors [16]. To address these challenges, this paper proposes a dynamic identification method that integrates the BLS with PSO. The proposed approach takes joint state variables as inputs and employs the BLS to model nonlinear friction, thereby constructing a nonstructural mapping from joint states to frictional torque and overall torque responses. This framework enablesautomatic learning of the frictional dynamic response without relying on explicit model assumptions. Meanwhile, PSO is used to optimize the structural and hyperparameters of the BLS network, enhancing the global accuracy of the identification model. In addition, by combining trajectory excitation design with the efficient structure of BLS, the method reduces the dependency on large-scale training datasets, thereby improving data efficiency and adaptability in real-world robotic applications. The remainder of this paper is organized as follows: Section 2 presents the dynamic modeling and excitation trajectory design of the flexible-joint manipulator. Section 3 introduces the BLS-PSO-based identification method and its implementation. Section 4 discusses simulation results and analyzes the effectiveness of the proposed method. Finally, conclusions and future work are given in Section 5.
## 2 Materials and Methods
### Methods
#### 2.2.2 Ensemble Kalman Filter  
The Kalman filter (KF) is an algorithm that minimizes variance and updates the state estimate when measurements are obtainable [38]. The cost function of KF can be expressed as follows [39]:  
\[J=\left(X_{k}^{a}-X_{k}^{f}\right)^{T}P^{-1}\Big{(}X_{k}^{a}-X_{k}^{f}\Big{)}+ \left(Y_{k}-H\Big{(}X_{k}^{f}\Big{)}\right)^{T}R^{-1}\Big{(}Y_{k}-H\Big{(}X_{ k}^{f}\Big{)}\Big{)} \tag{2}\]  
Figure 1: Flow chart of model development. For a description of the abbreviations and variables, please refer to Appendix A (Table 1).  
where \(X_{k}^{a}\) and \(X_{k}^{f}\) denote the analyzed and forecast estimates, respectively, at time instant \(k\), \(Y_{k}\) denotes the vector of measurements, \(H\) denotes the measurement operator that maps the model state \(X_{k}-Y_{k}\), \(P\) denotes the error covariance of the predicted model state, and \(R\) denotes the measurement error covariance matrix. The KF update equation can be obtained by minimizing with respect to \(X_{k}^{a}\)[37]:  
\[X_{k}^{a}=X_{k}^{f}+K\Big{(}Y_{k}-H\Big{(}X_{k}^{f}\Big{)}\Big{)} \tag{3}\]  
\[K=P_{k}^{f}H^{T}\Big{(}HH_{k}^{f}H^{T}+R_{k}\Big{)}^{-1} \tag{4}\]  
where \(P_{k}^{f}H^{T}\) denotes the cross covariance between a specific state and the prediction, \(H\Big{(}X_{k}^{f}\Big{)}\), for an ensemble Kalman filter (EnKF).  
\[P_{k}^{f}H^{T}=\frac{X_{k}^{f}-\bar{X}_{k}^{f}}{u-1}q_{k}^{T} \tag{5}\]  
\[q_{k}^{d}=H(X_{k}^{f}-\bar{X}_{k}^{f})=(y_{k}^{f}-\bar{y}_{k}^{f}) \tag{6}\]  
where \(y\) is individual ensemble member of the prediction and \(HP_{k}^{f}H^{T}\) denotes the error covariance matrix of the prediction.  
\[P_{k}^{f}=\frac{(X_{k}^{f}-\bar{X}_{k}^{f})(X_{k}^{f}-\bar{X}_{k}^{f})^{T}}{u -1} \tag{7}\]  
\[HP_{k}^{f}H^{T}=\frac{q_{k}q_{k}^{T}}{u-1} \tag{8}\]  
where \(u\) denotes the ensemble number.  
We apply the EnKF to obtain the daily LUE through assimilating observations of GPP measured at flux sites. The estimated relative errors for observations are set to 15% according to the method described in a previous study [39]. The ensemble size is set to 200 [40]. The initial value of LUE is set to 0.075 gC J\({}^{-1}\) (d/s)\({}^{-1}\) (\(\approx\)0.87 gC MJ\({}^{-1}\)). The lower limit of LUE is set to 0.01 gC J\({}^{-1}\) (d/s)\({}^{-1}\) (\(\approx\)0.12 gC MJ\({}^{-1}\)), and the upper limit is set to 0.60 gC J\({}^{-1}\) (d/s)\({}^{-1}\) (\(\approx\)6.94 gC MJ\({}^{-1}\)) [41].
## 3 Results
### Absence of Outliers, Leverage, or Influential Points That May Bias Model Coefficients  
Analysis of influential points was carried out by computing [PERSON]'s distance and DFBETA measures. Although they are computationally very similar, there is a difference between them. [PERSON]'s distance simultaneously measures the influence of points over all parameter estimates; DFBETA measures the same influence for each observation on each explanatory variable at a time. Both of them compute the regression coefficients for the regression model with a particular case excluded, and then recompute the model with the same case included.  
[PERSON]'s distance is computed for each observed value as the difference between the observed value and the same value observed by cutting off each case from the model input. DFBETA is computed for each explanatory variable, and it is the difference between the observed value and the same value observed by cutting off each case from the model input.  
Both analyses showed that the dataset contained neither outliers nor influential points that may have hindered the model. Maximal [PERSON]'s distance was \(<\)1 [53]. The five DFBETAs computed on each variable were all below the cut-off value of 0.08, according to formula \(2/\sqrt{n}\), where \(n\) is the number of observations [54].  
Figure 8: Histogram of standardized residuals (above) and P–P plot of standardized residuals along normal line (below).
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method
### Construction of RSPT  
RSPT is composed of the shortest paths from other nodes in the K-hop subgraph to DR. DR can efficiently evaluate the load status of the downlink multicast path using RSPT, even in cases of routing asymmetry. DR can further select the MJN that minimizes the cost of the multicast path to join the multicast tree, reducing network load congestion and improving multicast scalability. Therefore, RSPT is a key component of our proposed method.  
The construction of RSPT consists of three main steps: firstly, the construction of a K-hop subgraph, secondly, the collection of information using probe messages, and lastly, the generation of RSPT.
## 4 Conclusions  
This study proposes three future charging strategies based on predicted load using a hybrid ML technique. To evaluate its effectiveness, the proposed approach was compared with predicted results hourly over three days ahead in both spring and winter. The performance of the charging strategies was assessed in terms of peak shaving, valley filling, PAR optimization, and cost reduction.  
The main conclusions of this study are summarized as follows:  
* The EV charging strategy using the ML load forecasting approach for residential buildings developed in the present investigation is a significant step forward in enhancing energy efficiency and improving grid stability.
* Overnight charging reduces home charging during peak periods, reducing the PAR value by 27% and 28% for spring and winter, respectively. Similarly, Workplace/Other Charging Sites contribute to reducing peak home charging demand, resulting in a 25% and 22% drop in PAR value for spring and winter, respectively. The results demonstrate the effectiveness of the proposed strategy in significantly reducing electricity costs for the fleet of EVs while managing the peak demand for grid operators. The result indicates that the proposed approach can improve the PAR by up to 50% and cost up to 54% in spring and 49% and 56% in winter compared to the predicted load.
* Based on a real-life charging load dataset collected from 1000 EVs in nine provinces in Canada, the proposed approach is able to provide a reliable charging strategy for residential buildings under a wide range of overnights and workplaces.
* The current real-life dataset indicates a maximum charging power of 7.7 kW in residential buildings. Comparing these results with the recent literature reveals that introducing electric heat pumps, increasing EV battery capacities, and adopting higher charging power in residential settings lead to higher peak demand and shorter charging times. Consequently, effective charging strategies have become more crucial than ever. Without them, these trends may result in power quality issues, including voltage fluctuations and increased harmonic distortion in distribution networks.
## 4 Results  
Once the methodology has been applied to the wastewater treatment plant, data are available to clearly observe all the lack of effective sources in the plant. More precisely, it presents important results regarding the performance and design of the plant, where more than 60% of the production loss is explained by only four elements, which share high EOI. Furthermore, it is possible to establish that bottlenecks are caused by elements with an EOI of over 90%. Therefore, it might be favorable to consider redundancies or overcapacity configurations on these elements (such as the cyclone separator) to reduce the expected loss of production. Moreover, since the expected availability of the system is 87.78%, decision-makers can develop production plans according to more accurate capacity forecasts.  
It is also very advised to make use of data from reliable information systems in order to make decisions or data analysis [27; 28], between the most recommended information systems and methods is possible to identify online data capture in real-time, recording  
Figure 5: Criticality scatter plot.  
data from production, as well as from maintenance activities or related events, such as stoppages and equipment parameters, as it is the case for a constructed wetland for the treatment of winery wastewater in Italy [29]. In fact, by combining the use of networked equipment with advanced data analytics, wastewater treatment plants can improve their operations by performing in a more synergistically, efficient, and resilient manner. The latter is a concept widely known as Industry 4.0, which is defined by Cyber-physical Systems (CPS) as transformative technologies mainly focused on the management of linked and dependent systems of physical assets with the plant's computational capabilities [30]. The scope of this concept comprehends more than just maintenance, but production, logistics and other services managed by an organization. It aims to strongly improve the economic potential of today's factories [31].  
The most powerful opportunities of the proposed methodology aim to improve the availability of components with high P-OEI, reducing the impact brought by the unavailability of certain equipment, pushing reliable and sustainable actions at the same time as intelligent investments are placed on the plant by including redundancies that will strategically reduce risks and costs.
## 4 Theoretical Results
### Transfer Functions of the Vehicle-Track Interaction  
First, the most important vehicle-track-soil transfer function \(F_{S}/s\) = \(H_{T}\)\(H_{V}\) is presented for the variation of some vehicle and track parameters. The vehicle-track-soil transfer function is strongly influenced by the unsprung mass of the vehicle, the wheelset mass (Figure 4a). The dynamic axle loads are proportional to the wheelset mass for frequencies up to 64 Hz. For this frequency range, the transfer function increases strongly with \(F_{S}/s\sim\)\(f^{2}\). If soft under-sleeper pads are added to the track (Figure 4b), resonances can be seen at frequencies between 16 and 64 Hz. Above these resonance frequencies, the dynamic axle-loads can be reduced to less than one-tenth of the values of a standard ballast track. Vehicle-track resonances, however, do not occur for the standard ballast track in the frequency range of interest.
# Seismic Ground Response Prediction Based on Multilayer Perceptron
## 2 Analytical Methods and Dataset
### Conventional Model  
Seismic ground response analysis is used to predict the free-field movement on the ground surface during an earthquake [26]. The flow of the seismic ground response analysis is shown in Figure 4. It requires the following input data: the shear moduli of soils at low level of strain, the normalized shear modulus, damping curves as a function of strain, and the earthquake motions at the bedrock or at a certain depth.  
We used a computer program SHAKE2000, a conventional physical model for seismic ground response analysis. SHAKE2000 computes the response in a system of homogeneous, viscous-elastic layers of infinite horizontal extent subjected to vertically traveling shear waves. It is based on the continuous solution to the wave-equation adapted for use with transient motions through the Fast Fourier Transform algorithm. The nonlinearity of the shear modulus and damping is taken into consideration for using equivalent linear soil properties with an iterative procedure to obtain values for modulus and damping compatible with the effective strains in each layer [27].  
KiK-net provides the profile information of the ground with the types of strata, classified as top soil, sandy gravel, clayey gravel, and granite. The nonlinear dynamic properties of each soil are employed from the proposals in previous literature as shown in Table 2. For the top soil, the mean values of normalized shear modulus and damping ratio curves proposed for sands by [PERSON] and [PERSON] [28] were taken. For both sandy and clayey gravels, the mean values proposed for gravel by [PERSON] et al. [29] were implemented. The mean values of rocks by [PERSON] [30] were used for granite. The shear modulus reduction and damping curves employed are shown in Figure 5.  
Figure 4: Flow of seismic ground response analysis.  
Figure 3: Schematic of MLP process for the seismic ground response analysis.
# NextGen ADS-B Software-Defined Reception with Enhanced Techniques
## 3 Methodology and Solutions
### ADS_B in Message Decoder  
Upon receiving the ADS-B message, two subfunctions of the decoder and message assembly need to be executed to perform all control, decoding, and formatting tasks required to transform the received information into properly structured reports for usage by operator programs. Either the ADS-B receiver element itself or an additional decoding unit with an interface to the receiver may carry out these subfunctions. In this research, Universal Glass Cockpit (UGC) software, which is User Datagram Protocol (UDP) based connection software for displaying information, has been used for ADS-B in the user interface.  
The components of an ADS-B burst data are immediately classified by their Type Code value. The key attributes downlinked by every Type Code are call sign, latitude, longitude,  
Figure 5: Message Error Correction Algorithm.  
altitude, speed, tracking angle, and vertical rate. Within the same group, Type Code values also have their individual indicators. For instance, a TC of 1 to 4 in the identification group denotes various aircraft categories as described by the Mode-S standard in accordance with [1]. Therefore, the message decoding is performed based on Type Code (5 bits), ICAO aircraft address (24 bits), and Data (56 bits). It is highly important to link data with the same ICAO in order to have a consistent track of aircraft. All mentioned activities have been done and validated with the open-source code of the pyModeS decoder [27]. Figure 6 shows a sample outcome of the UGC application for ADS-B In during the flight test. It displays each flight ID information including position and velocity information, and distance from the airplane in the table format.
## 4 System Model, Algorithms, and Security Model
### Algorithms  
The SP-MAACS scheme uses the following four algorithms in its construction.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 2 Existing Studies and Literature Review
### Communication Protocols and Network Architectures  
Network optimization remains critical for reliable deployment in agricultural settings. [PERSON] and [PERSON] (2019) developed a hybrid architecture combining ZigBee mesh networks with LoRaWAN gateways, achieving 99.7% transmission reliability while reducing power requirements by 43% compared to cellular alternatives [19]. For remote monitoring applications, [PERSON] et al. (2022) implemented Sigfox-based networks achieving 40 km ranges with minimal power consumption (50 mA during transmission, 2 uA in sleep mode), enabling 5-year battery lifespans in infrastructure-limited regions [23].  
Data transmission efficiency was significantly enhanced by [PERSON] and [PERSON] (2022), who implemented specialized compression algorithms to achieve 76% payload reduction, enabling transmission of 24-h environmental data within Sigfox's 12-byte limitations [22].