esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Active Systems
### Bems  
An advanced Building Energy Management System is proposed for the full automatic control and operation of the introduced active systems. The main tasks will be:  
* the measuring of all the electrical power consumptions in the Stadium;
* the control and the inspection of the active energy production systems operation.  
Specifically, the proposed BEMS should execute the following tasks:  
* inspection and control of the hot water production and swimming pools heating systems, by gathering temperature signals from the solar collectors primary loop, the swimming pools and the thermal storage tanks, and by sending commands towards the involved circulators and motor-valves in the hydraulic networks;
* inspection and control of the GHP system for the indoor space conditioning, by gathering temperature signals from the indoor conditioned space and the source and load heat tanks, and by sending orders to the heat pumps, the circulators and the motor-valves in the hydraulic network and the air fans and the dampers in the air ducts network;
* the outdoor and indoor space lighting system, by gathering data regarding the occupants' presence and the natural available light in the indoor space, the outdoor sports facilities operation schedule, the security lighting operation schedule, etc.  
Due to the BEMS introduction, 20% additional annual electricity savings is estimated, expected to occur mainly from the more efficient operation of the circulators of the hot water hydraulic network, the air fans of the air ducts network and the new circulators of the GHP system. The BEMS contribution to the annual electricity saving is not possible to be precisely calculated with a computational or empirical method, due to the too many involved parameters and the considerable amount of different operation states of the controlled systems, which make the operation simulation practically impossible. For this reason, the annual electricity saving percentage is adopted at 20% [36], based on the technical data provided by BEMSs' developers and manufacturers.  
The BEMS contribution to the annual electricity saving should be calculated on the new expecting annual electricity consumption, after the realization of the proposed passive and active systems. By applying the above presented percentage, the total annual electricity savings due to the introduction of the BEMS is calculated at 181,696 kWh.
## 3 Materials and Methods
### Proposed Architectures
#### 3.1.4 Architecture with Domain-Specific Attention (Approach 2)  
This domain-specific architecture incorporated a new branch focused on the feature domain that provided attention weights for each feature through vector \(\boldsymbol{\alpha}_{f}\), in parallel to those weights for the time domain, \(\boldsymbol{\alpha}_{T}\). Figure 2 shows the set of input feature processing methods that were compared, and a subsection is devoted in Supplementary Materials for each.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 4 Experiments  
### Datasets  
The experiment was conducted on the publicly available brain tumor segmentation dataset BraTS2018 [38]. Multimodal brain tumor segmentation has the longest history of all MICCAI competitions and has the largest number of participants in almost all competitions each year. It is a good platform for understanding the cutting edge of segmentation methods. The dataset has a training set of 285 cases, using T1, T2, T1 CE and flair modes, respectively, and needs to segment the whole tumor (WT), tumor core (TC) and enhanced tumor (ET). The size of each MR image is \(240\times 240\times 155\).
## 4 Optimization of Experimental Parameters
### Electromagnetically-induced Transparency (EIT)-assisted DROP Spectra  
We compared the two transmission methods in the CP and CTP configurations. Two different scanning methods were used to obtain the spectral signals, as shown in Figure 5; keeping the 698 nm coupling laser in free running mode, we obtained the two-photon transition spectra with the Doppler background shown in Figure 5a,c by scanning the 852 nm probe laser. Compared with the spectra obtained by scanning the 698 nm coupling laser, the spectra had a large Doppler background. Furthermore, when we locked the weak 852 nm probe laser to the \(5p^{6}6s\ ^{2}S_{1/2}\) (\(F=4\))-\(5p^{6}6p\ ^{2}P_{3/2}\) (\(F^{\prime}=5\))  
Figure 4: The two-photon transition spectra with no Doppler background were measured with two different 852 nm probe lasers: (**a**) an 852 nm distributed Bragg reflector (DBR)-type diode laser with a typical linewidth of \(\sim\)2 MHz; (**b**) an 852 nm external-cavity diode laser (ECDL) with a typical linewidth of \(\sim\)200 kHz.  
Figure 3: Comparison of optical-optical double-resonance (OODR) and double-resonance optical pumping (DROP) spectra for the Cs \(5p^{6}6p\ ^{2}P_{3/2}\) (\(F=5\))-\(5p^{6}7d\ ^{2}D_{5/2}\) (\(F^{\prime}=4\), 5, 6) transitions. (**a**) OODR spectra; (**b**) DROP spectra.  
hyperfine transition, we scanned the 698 nm laser over the 5\(p^{6}6p\)\({}^{2}\)\(P_{3/2}\) (\(F^{\prime}\) = 5)-5\(p^{6}7d\)\({}^{2}\)\(D_{5/2}\) transition to obtain the spectra without a Doppler background, as shown in Figure 5b,d.  
Figure 5 shows that the linewidth in the CP configuration was wider than that in the CTP configuration. There was an atomic coherence effect, e.g., EIT, and incoherence effects, e.g., spontaneous emission and DROP. The narrow part of the spectra due to the coherent process of atoms in the CTP configuration was caused by the EIT. The wider part was a result of the DROP process. In the CP configuration, DROP, accompanied by spontaneous emission, was the two-photon optical pumping process caused by the incoherence effect [33].
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 2 Literature Review for Wind Power Forecasts Using NWP Data  
### Overview of Wind Power Forecasting Methods  
Wind power forecasts are classified by various methods according to time horizons or methodologies. The classification based on time horizon is different in various research studies. However, it can be divided mainly into four main categories:  
* Very short-term forecasting: from a few seconds to 30 min ahead; it is for real-time system operations.
* Short-term forecasting: from 30 min to several hours ahead; it is for economic dispatch and load management.
* Medium-term forecasting: from several hours to one day ahead; it is for unit commitment and electricity market operation.
* Long-term forecasting: more than one day ahead; it is for power system risk assessment and energy planning.  
In terms of forecasting methodologies, most of the wind power forecasting approaches are divided into persistence methods, Numerical Weather Prediction (NWP) methods, statistical methods, artificial intelligence methods, spatial correlation methods, and hybrid methods [19].
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
###### Abstract
To demonstrate the advantage of the ID system for acceleration, the authentication speed and authentication accuracy are evaluated for the ID-based authentication system. We overview the technical choices for side-channel authentication schemes and compare their effectiveness based on both theoretical analysis and experiments based on field-programmable gate array (FPGA). 2. This paper evaluates the error-rate of ID-based side channel authentication in a laboratory environment. The authentication accuracy is quantitatively estimated as the false acceptance rate and the false rejection rate. First, a quantitative discussion of the side-channel information is performed according to the number of distinguishable provers. The side-channel information of the provers is experimentally obtained from AES implementations on FPGA. The histograms for rejection and acceptance trials are both approximated to a normal distribution. Based on the principle that the false rejection rate and false acceptance rate are set to be equal, the parameters in the authentication can be determined. As a result, the authentication accuracy can be determined. This part of the contribution has been partially discussed by us in [4]. 3. In our evaluation, both a non-profiling leakage model and a profiling leakage model are considered for different scenarios. Similar to side-channel attacks, the leakage model describes the relations between the side-channel leakage and the processed data. Generally speaking, one can expect side-channel attacks to have a reduced data complexity with a more accurate leakage model. Specifically, we use a Hamming distance (HD) model as the non-profiling leakage model and the XOR (exclusive-or) model proposed in [5] as the representative of the profiling leakage model. It is expected that the profiling model will improve the authentication accuracy of the system. The experiments show that the XOR model leads to a larger mean and smaller variance for the histogram of the correlation coefficients compared to that of the HD model. The authentication accuracy and the authentication time are compared between the HD model and the XOR model. The rest of the paper is organized as follows. Section 2 reviews the previously proposed scheme for side-channel authentication. Section 3 presents the idea of an ID-based authentication system for side-channel authentication. Section 4 explains the setup for the evaluation of ID-based side-channel authentication. In Sections 5 and 6, the evaluation of the ID-based side-channel authentication system with regard to the authentication speed and the authentication accuracy is presented. Section 7 concludes this paper.
# Formation Mechanism of Metro Rail Corrugation Based on Wheel-Rail Stick-Slip Behaviors
## 4 Analysis of Wheel-Rail Stick-Slip Characteristics
### Wheel-Rail Stick-Slip Characteristics of Small Radius Curve
Therefore, the lateral torsional stick-slip vibration intensity of the outer rail-outer wheel of the driven wheelset is relatively weak, that is, the outer rail corrugation of a small radius curve is relatively slight or not easy to occur. If the rail surface irregularity is considered, the strong lateral torsional stick-slip vibration may happen between the outer rail and outer wheel of the driven wheelset, which may lead to obvious corrugation on the outer rail. The above analysis shows that on the small radius curve, the lateral torsional stick-slip vibration of inner rail-inner wheel of the guiding wheelset leads to the generation of the inner rail corrugation, and the lateral torsional stick-slip vibration of outer rail-outer wheel of the driven wheelset may also occur, but the intensity is weak and the probability is low. Therefore, the inner rail corrugation on the small radius curve is more serious, and the outer rail corrugation is less or does not exist, which is consistent with the field measurement. Figure 8: Wheel–rail stick–slip relation curves of guiding wheelset of small radius curve. (**a**) Longitudinal stick–slip curve of the outer wheel-outer rail, (**b**) Lateral stick–slip curve of the outer wheel-outer rail, (**c**) Longitudinal stick–slip curve of the inner wheel-inner rail, (**d**) Lateral stick–slip curve of the inner wheel-inner rail.
## 5 Real-Time Experiments
### Experiment on Industrial Autoclave  
The new tuning method was also tested on an industrial autoclave, as shown in Figure 23. An autoclave is used in the production of plastics. Process input is the electric power on heaters (from 0 to 100%), and process output is inner temperature (in \({}^{\circ}\)C). The process was supervised by a SCADA system connected to the underlying PLC controllers with a sample time of 1 s.  
The process was self-regulatory with a main time constant of more than 1 day. However, the desired closed-loop time constant was less than 30 min. On the other hand, in the first few minutes,  
Figure 23: Industrial autoclave.  
Figure 22: Process input (voltage on pump in %) and output (water level in mm multiplied by a factor of 2) signals during closed-loop experiment on hydraulic process.  
the process open-loop step-response was similar to the IP with delay (see Figure 24). Therefore, for the calculation of the controller parameters, only the first part of the process open-loop response was used.  
First, a step-change at the process input (change of electric power from 0% to 2%) was applied. Figure 24 shows the process response (temperature). From the measured open-loop responses, using Expressions (17)-(19), the following characteristic areas were calculated:  
\[A_{0}=0.0044,\ A_{1}=0.3651,\ A_{2}=17.86. \tag{47}\]  
Using Expressions (12) and (14), the PI controller parameters were calculated:  
\[K_{I}=0.052,\ K_{P}=1.55, \tag{48}\]  
where factor \(b=0\) was chosen in order to improve tracking performance.  
Figure 25 shows the closed-loop responses on a reference change. From the obtained closed-loop responses, it is evident that the proposed method results in quite efficient control of the autoclave temperature.  
Figure 24: Process input (electric power in %) and output (temperature in \({}^{\circ}\)C) signals during manual experiment on autoclave within first 3 min.
## 2 Methods  
### Ensemble Empirical Mode Decomposition (EEMD)  
EEMD is an adaptive signal processing method improved by EMD [34]. It inherits the advantage that EMD can realize the corresponding time-frequency decomposition according to the local characteristics of the signal and effectively solve the phenomenon of mode aliasing, so that the decomposed intrinsic mode function (IMF) components have more concentrated frequency information, especially for the research of nonlinear and nonstationary signals. The core of the EEMD algorithm is to make use of the statistical characteristics of the zero mean of Gauss white noise [35]. The specific steps of the algorithm are as follows:  
(1) Assuming that \(x(t)\) is the signal to be analyzed, a Gaussian white noise with an amplitude coefficient of \(\varepsilon\) is added to it, and the number of iterations is set to \(N_{0}\) times, that is  
\[x_{j}(t)=x(t)+\varepsilon\omega_{j}(t)\ \ j=1,2,\cdots,N_{0} \tag{1}\]  
where \(\omega_{j}(t)\) is the white noise sequence added for the \(j\)th time, and \(x_{j}(t)\) is the noise-contaminated signal.  
(2) Decomposing \(x_{j}(t)\) using EMD to obtain IMF components.  
(3) Repeat steps (1) and (2) for \(N_{0}\) times, using a different white noise sequence for each time.  
(4) Compute the average of all IMF components. Namely  
\[\overline{IMF}_{i}=\frac{1}{N_{0}}\sum_{j=1}^{N_{0}}IMF_{i}^{j} \tag{2}\]  
where \(IMF_{i}^{j}\) is the \(i\)-layer IMF component obtained by the \(j\)th decomposition.  
(5) The decomposition results of EEMD are as follows:  
\[x(t)=\sum_{i=1}^{n}IMF+\overline{r} \tag{3}\]  
where \(\overline{r}\) is the mean of the \(N_{0}\) decomposition trend term.
# Geometrical Parametric Study on Steel Beams Exposed to Solar Radiation
## 1 Introduction
The available literature on the influence of the daily and seasonally variation of atmospheric thermal loads on steel sections and girders is limited. A few experimental studies [33; 34; 35; 36; 37] have tried to give clearer picture about the behavior of steel sections under direct environmental exposure, while some field works on steel sections and girders were also published during the last years [38; 39; 40]. However, most of these studies did not pay attention to the effect of sectional properties. [PERSON] et al. [41] showed that steel truss girders exhibited larger transverse temperature gradients compared to vertical ones. [PERSON] et al. [42] instrumented three steel sections of different sectional shapes with thermocouples and left them exposed to solar radiation changes. Rectangular box-section, circular tube section and I-section forms were adopted in their study. Their records showed that there are almost no temperature changes across the sectional plate thickness and along the span of the beams, which a typical result due to the high thermal conductivity and thin walls of steel sections. The authors also concluded that section size is one of the most influential factors affecting the non-uniformity of temperature gradients in such sections, where larger steel sections were predicted to suffer higher temperature changes. [PERSON] et al. [43] showed numerically for steel I-sections that the sectional strength against buckling can be improved by using a larger flange width and thickness. A very recent study [44] on I-steel beams was directed experimentally and numerically to evaluate the influence of the several sectional aspects under environment exposure. The investigated parameters were the section's shape and its depth, the thickness of section member's (flange and web), the width of the flanges in addition to size comparisons. The author concluded that larger beams suffer higher temperature gradients by approximately 8 \({}^{\circ}\)C. Similarly, for sections with equal flange width and flange thickness, temperature gradients in deep sections are higher than in shallow ones by approximately 7 \({}^{\circ}\)C. The author also concluded that the thickness of the flange is an influential factor on the vertical temperature variation.
## Abbreviations  
ACCESS Australian Community Climate and Earth-System Simulator  
AE Automatic Encoder  
AI Artificial Intelligence  
AMV Atmospheric Motion Vector  
ANFIS Adaptive Neuro Fuzzy Inference System  
ANN Artificial Neural Network  
AOD Aerosol Optical Depth  
ARIMA Autoregressive Integrated Moving Average  
ARMA Autoregressive Moving Average with Exogenous Variables  
ARX Autoregressive with exogenous inputs  
BPNN Back Propagation Neural Network  
BRT Boosted Regression Trees  
CARDS Coupled Autoregressive and Dynamical System  
CCM Cross-Correlation Method  
CLSTM Convolutional Long Short-Term Memory  
CMD Cloud Motion Displacement  
CNN Convolutional Neural Networks  
CSL Clear Sky Library  
CSM Clear Sky Model  
DBN Deep Belief Network  
DCNN Deep Convolutional Neural Networks  
DHI Diffuse Horizontal Irradiance  
DL Deep Learning  
DNI Direct Normal Irradiance  
DNN Deep Neural Network  
ECMWF European Centre for Medium-Range Weather Forecast  
ELM Extreme Learning Machine  
ERA5 ECMWF Reanalysis v5  
FTM Fixed Threshold Method  
GB Gradient Boosting  
GPS Global Forecast System  
GHI Global Horizontal IrradianceHCF  
Hraze Correction Factor  
HRRR  
High-Resolution Rapid Refresh  
HSR  
Hourly Solar Radiation  
HYTA  
Hybrid Thresholding Algorithm  
ISR  
Incident Solar Radiation  
JMA  
Japan Meteorological Agency  
k-NN  
k-Nearest Neighbors  
LM  
Levenberg-Marquardt  
LST  
Land Surface Temperature  
LSTM  
Long Short-Term Memory  
MAE  
Mean Absolute Error  
MAPE  
Mean Absolute Percentage Error  
MBE  
Mean Bias Error  
MCE  
Minimum Cross Entropy  
MLP  
Multi-Layer Perceptron  
MM5  
Mesoscale Model version 5  
MOS  
Model Output Statistics  
MP  
Megapixels  
MSG  
Meteostat Second Generation  
MTF  
Multi Transform Fusion  
MVIRI  
Meteosat Visible and InfraRed Imager  
NAM  
North American Mesoscale  
NARMAX  
Non-linear Autoregressive Models with Moving Average and Exogenous Input  
NDFD  
National Digital Forecast Database  
NWP  
Numerical Weather Prediction  
PCA  
Principal Component Analysis  
PSM  
Physical Solar Model Version 3  
PSO  
Particle Swarm Optimization  
PV  
Photovoltaic  
RBM  
Restricted Boltzmann Machine  
RBR  
Red-Blue Ratio  
RF  
Random Forest  
RGB
Red Green Blue  
RMSE  
Root Mean Square Error  
SAE  
Stacker Automatic Encoder  
SARIMA  
Seasonal Autoregressive Moving Average  
SDE  
Standard Deviation  
SOM  
Self-Organizing Maps  
SREF  
Short Range Ensemble Forecast  
SS  
Skill Score  
SVC  
Support Vector Classifier  
SVM  
Support Vector Machines  
SZA  
Solar Zenith Angle  
VARX  
Vector Autoregressive with Exogenous Input  
WC  
Water Content  
WNN  
Wavelet Neural Network  
WP  
Water Path  
WRF  
Weather Research and Forecasting  
WT  
Wavelet Transform  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Reference** & **Aim** & **Methods** & **Forecast** & **Data Period** & **Location** & **Input** & **Comparison** & **Performance Metrics** & **Results and Observations** \\ \hline \hline \multicolumn{1}{c}{Physics-based endogenous} & \multic
## 3 Sink-Based Centralized Transmission Scheduling
### Dealing with Frame Loss
#### 3.4.1 Consecutive WuC Transmission  
If a sensor node misses a WuC, the node cannot wake up to transmit its data frame. The function of consecutive WuC transmissions reduces the probability that a sensor node will fail to wake up.  
WuC retransmission timing is determined by considering the WuC detection time. In order to wake up both the sender and the receiver at the same time, we add a sequence number to each WuC. For example, in the case of three transmissions, the sequence number is changed in the following order: 2, 1, 0. Both nodes check when the retransmission of the WuC will end (the sequence number becomes 0) by referring to the sequence number and decide the wake-up timing.  
The number of WuC transmissions is determined by the sink node according to the following algorithm.  
1. Calculate the average Signal-to-Noise Ratio (SNR) based on the distance between the sink node and the sender node in this hop.
2. Determine the WuC error rate (WER) from the average SNR using the SNR-BER (Bit Error Rate) table and the property of channel fading.
3. Find \(n\), the minimal number of WuC transmissions that satisfies the following condition: \(\text{WER}^{n}\ <0.0025\), i.e., the target WER under retransmissions is no more than 0.25%.
## 3 Dataset Overview
### Optimal Modeling Approach  
Binary cross-entropy loss is grounded in information theory. Based on [PERSON]'s entropy concept [39], the formulation also draws from [PERSON]'s minimum cross-entropy principle [40] and [PERSON] maximum entropy framework [41], which allow probabilistic modeling of uncertainty under incomplete knowledge.  
Wildfire prediction constitutes a probabilistic classification task, necessitating a binary cross-entropy loss function.  
\[\mathcal{L}_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(\hat{y}_{i})+(1-y_{i} )\log(1-\hat{y}_{i})] \tag{1}\]  
where \(y_{i}\) represents the true label, and \(\hat{y}_{i}\) is the predicted probability. This approach enabled the model to assign confidence scores to predictions, rather than make rigid binary decisions. Class-weighted loss functions were also implemented to address the remaining imbalance, with weights inversely proportional to class frequencies. Combining dataset balancing techniques and appropriate loss functions yielded improved generalization capabilities, enhancing the wildfire risk assessment and mitigation strategies.
## 3 Background
### Cryptographic Hash Functions  
A hash function can take an amount of data as input and outputs a string with a specified number of bits. The input data are a message, and the output text is called a hash [57]. Hash functions are used in various information security mechanisms, including digital signatures and password protection [58]. A hash function can have a variety of characteristics, including the following:  
1. Deterministic: Given an input M, the hash function H returns a unique output H(M) = x [57].
2. One-way: Given an input M, computing H(M) = x is simple. However, other than brute force, it is impossible to extract M from H(M) = x given x [56].
3. Collision-free: It is infeasible to find two values for M and M' such that M6 = M0 and H(M) = H(M0) [56]. A hash function must satisfy all three properties listed above to be considered usable in cryptography [59].