esa-sceva/satcom-chunk-collection huggingface dataset:
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 2 Related Works
### Challenge-Response-Based Authentication Protocol  
Challenge-response protects data transmission between the client and server, thereby establishing a secure session key. [PERSON] et al. [12] proposed a capability-based, privacy-preserving switching authentication mechanism for 5G heterogeneous networks (HetNets), building on [PERSON]'s scheme [13] by incorporating user capability information with software-defined networking (SDN) technology. The scheme enables efficient authentication and key agreement between user equipment (UE) and base stations. However, it relies on fixed UE credentials and lacks a mechanism for credential updates, posing potential security risks. To strengthen resistance against physical capture attacks, [PERSON] et al. [14] designed a lightweight two-stage authentication and key agreement protocol for drones, integrating an embedded physical unclonable function (PUF) to ensure secure communication. [PERSON] et al. [15] also introduced a PUF-based access authentication scheme involving a dual authentication process between drones and ground terminals. Their approach supports mutual authentication, key agreement, and privacy protection, while eliminating the need for key storage and offering robustness against physical attacks. To address the issue of drones being vulnerable to capture during operation, [PERSON] et al. [16] proposed a secure and lightweight authentication and key agreement scheme based on hash functions and XOR operations. The scheme leverages the unique properties of PUFs to defend against various security threats, especially physical capture attacks. Aiming to overcome the limitations of many authentication schemes that do not adequately resist physical emulation and smart card loss attacks, [PERSON] et al. [17] presented a two-factor authentication protocol. This protocol employs PUF technology to prevent physical emulation and sensor node capture, while also improving the efficiency and security of session key management. While the challenge-response handover authentication protocol offers strong robustness, its high computational and storage demands make it impractical for resource-limited IoV environments.
## 4 Status Monitoring of Propeller Motor
### Experimental Data and Preprocessing  
For the prediction model, the input information has a crucial impact on prediction accuracy. Excessive input can only bring unnecessary noise to the prediction of the model.  
In this paper, the temperature of the pitch motor is selected as the main monitoring variable, and the ambient temperature (Env. Tem.), the torque, generator power, hub speed (generator speed), and wind speed of the pitch motor are taken as the input of the model. Table 1 illustrates some SCADA data of the No. 1 wind turbine in the wind farm. It is worth noting that the sampling frequency is 10 min. After a preliminary inspection of these observations, the range of each variable is as follows. The range of the ambient temperature is \([-273.17,\,45.43]\), and that of torque, power, hub speed, wind speed is \([0,1561.3]\), \([-10.69,1529.85]\), \([-598.63,\,1797.16]\), \([0,\,20.78]\), respectively. Additionally, the maximum and minimum values of the pitch motor temperature are 96.04 and \(-10.26\), respectively.  
Due to some fault conditions of the SCADA system, the collected data contains abnormal data. The abnormal data can be judged by the corresponding value range of attribute parameters. In the preprocessing of data, we deleted the record directly for the data whose attribute parameters exceed the reasonable value range. For example, when the ambient temperature is too low (\(<\)\(-60\)\({}^{\circ}\)C) or too high (\(>\)60\({}^{\circ}\)C), the records will be deleted. In addition, there are some missing values for some attributes. In this situation, we took the average data of 30 min before and after the operation to fill in the missing values. Because we only need the history of the normal state of the fan in the process of building the normal behavior model, we need to include the time period of fault records for filtering. Specifically, in the process of preparing training data and test data in each round, if there is a fault record in the initial training sample, the new training data and test data will be prepared from the first normal record after the fault. Moreover, the training and test windows will be continuously pushed forward to realize the learning and verification of the model.
# Sea Purslane as an Emerging Food Crop: Nutritional and Biological Studies
## 3 Results and Discussion
### Antioxidant and Enzymatic Activities  
The ethanolic extract of _H. portulacoides_ exhibits a higher DPPH activity (IC50 = 3.70 \(\pm\) 0.40 mg/mL) when compared to data obtained for extracts using other solvents (IC50 > 10 mg/mL) and analogous ABTS scavenging capacity as reported hexane, methanol, and water extracts [16]. When compared to the inhibitory potential of other halophytes, _H. portulacoides_ (IC50 = 0.15 mg/mL) shows higher activity than _Suada prunose, Suaeda maritima,_ and _Suaeda mollis_ (IC50 values of 0.54, 1.42, and 0.54 mg/mL, respectively) [62]. Nevertheless, it is still considerably less active than the antioxidant BHT (IC50 = 0.005 mg/mL) [31]. Regarding the TBARS assay, the _H. portulacoides_ extract did not present the capacity to inhibit lipid peroxidation in the range of concentrations tested.  
Antioxidants are able to chelate and reduce prooxidant metal ions responsible for the production of ROS, e.g., the ferrous ions that produce free radicals via the Fenton reaction [63]. The _H. portulacoides_ extract is more effective in reducing copper (CUPRAC--44.00 mg TE/g extract) than iron (FRAP--19.90 mg TE/g extract). This was already reported for water, methanol, and ethyl acetate extracts of _H. portulacoides_, with FRAP values ranging from 31.59 to 50.06 mg TE/g extract and CUPRAC from 51.83 to 71.21 mg TE/g extract [60].  
The acetylcholinesterase-inbibition was tested for the _H. portulacoides_ extract, though no significant activity was observed in the range of concentrations tested. Previous studies had already reported a very modest activity for water, methanol, and ethyl acetate extracts of _H. portulacoides_ against AChE [60]. Nevertheless, these results contrast with those reported for ethanolic extracts of the leaves of other halophytes, such as _Armeria pungens_, with a high activity against AChE (IC50 = 90.3 mg/mL) [64].  
The results for the \(\beta\)-carotene bleaching assay were obtained after 2 h of reaction. Values represent the mean \(\pm\) standard deviation of three independent experiments.
## 3 Full Description of the Approach
### Segmentation into Structural Elements
#### 3.2.2. Segmentations into Planes and Point Classification
Moreover, the last segmentation must have extracted at least one plane of 1800 points minimum. At the end of the successive wall planes segmentations, unsegmented points are stored in a remaining point cloud. Figure 6 presents the results of plane segmentations for several rooms of the 1 st floor of the INSA building and the associated numbers of segmentations and extracted planes. The number of segmentations depends on the size of the room and the complexity of the space. One segmentation is required for the segmentation of a small storeroom (Figure 6c) whereas 9 successive segmentations are required for a corridor of about 20 m composed by a plenty of planes. Figure 5: Point classification: (**a**) Perspective and top view of point cloud of a computer room without ceiling and floor. (**b**) Result of point classification. Finally, all extracted planes of a floor are grouped into walls by using two criteria that are the parallelism and distances between wall planes. Planes which are parallel considering a maximal angular value \(\alpha_{\beta\!\!\!/}\) of 5 degrees between them are selected. Then, they are grouped if they are close enough from each others. A maximal thickness \(th_{max}\) of 50 cm is defined for a wall. Figure 7 shows the result obtained for the first floor of INSA building. Each colour corresponds to one wall. Planes belonging to indoor parts of facade walls are identified by considering the borders of the floor. They are presented in grey in the figure. Figure 6: Plane segmentations of rooms (one colour per plane)—_Number of segmentations determined automatically and number of extracted planes._  
Thus, the first part of the developed approach allows identifying wall point clouds. Each wall is composed of 1 to \(p\) planes. At this stage, a floor map can be automatically generated by considering the intersection of the extracted planes with an horizontal plane. Moreover, ceiling heights can be deduced from the altitudes of planes corresponding to ceilings and floors.
# Beyond 5G Fronthaul Based on FSO Using Spread Spectrum Codes and Graphene Modulators
## 5 Numerical Results of the Network
Hence, the SINR follows a similar trend in the performance analysis, where the overall system performance can be impacted by the different amounts of graphene, while 25% shows the best performance. Lastly, one can further note from Figure 10 that the SINR degrades as the number of simultaneous antennas in the mobile cell increases, regardless of the modulator's graphene amount. This can happen due to the interference raise in the channel associated with the accommodation of more simultaneous antennas in the mobile cell.
## 3 Results
### Regression Model for 24-Hour Compressive Strength  
For the 24 h compressive strength (CS24h) measurements, regression analysis was performed in order to define the polynomial model. The reduced linear model (removing factor B) was the one that best presented satisfactory results. The B term was removed from the model due to its considerably high \(p\)-value (0.9491), which aimed at enhancing the model performance. Due to the linear nature of the model, the removal of the B term did not influence the other terms. Table 8 shows the model's F-value of 37.54 and \(p\)-values below 0.05, thereby providing evidence of the model's statistical significance after removing term B. The terms A and C were also deemed to be statistically significant, as was supported by their high sum of squares values and \(p\)-value less than 0.05 (0.001 for A and 0.0351 for C). The F-value was calculated to be 0.4778, thereby surpassing the significance level of 0.05 and indicating that the lack of fit was not statistically significant concerning pure error. The possibility of a misfit F-value occurring due to noise only stood at approximately 89.34%.  
The results of the regression model revealed a predicted R\({}^{2}\) value of 0.7607, which agreed well with an adjusted R\({}^{2}\) value of 0.7908. The difference between the predicted and adjusted R\({}^{2}\) values being less than 20% indicates the model's statistical significance. Moreover, the model demonstrated an adequate precision of 23.1254, thereby showcasing its ability to accurately describe the modeled data space. The standard deviation of the regression model was calculated to be 1.42, with a coefficient of variation (CV) of 2.43%.  
Figure 5: T-Funnel linear model: (**a**) Normal Plot of Residuals; (**b**) Residuals versus Run; (**c**) Box–Cox for Power Transforms; and (**d**) Predicted versus Actual.  
Table 9 presents the coefficients of the coded factors for a 95% confidence interval and are considered fits around the average response of all the runs. The standard errors for factors A, C, and D were 0.29, with VIFs equal to 1, thus confirming the orthogonality of the factors. For the VIF, values less than 10 were deemed acceptable.  
Figure 6 presents the normal plot of the studentized residuals (a) and the relationship between the predicted and actual values (b). Notably, the data points exhibited a close-to-linear dispersion. However, the adjusted coefficient of determination (R\({}^{2}\)) was determined to be 79.08%, and some exceptions can be seen in Figure 6b.
## 2 Method  
In this section, we will introduce the methodologies used for high-resolution reconstruction, focusing on the specific region of interest, which is the Birmingham area in the United Kingdom. The images for reconstruction are from the Sentinel-2 satellite, specifically the visible light data, and will be analyzed at different resolutions. In Section 2.1, we will present the network structure and training strategies for the super-resolution task, which aims to improve image quality while maintaining the fidelity of the original terrain features.
## 6 Conclusions
**Abbreviations**  
ADAM  
Adaptive Momentum Estimation  
ADN  
Application Dedicated Nodes  
AE  
Application Entity  
ANN  
Artificial Neural Network  
API  
Application Programming Interface  
ASN  
Application Service Node  
BiLSTM  
Bidirectional Long Short-Term Memory  
BPTT  
Backpropagation Through Time  
CMAQ  
Community Multiscale Air Quality  
CNN-LSTM  
Convolutional Neural Network--Long Short-Term Memory  
CONVLSTM2D  
Convolutional Long Short-Term Memory Two-Dimensional  
CPS  
Cyber-Physical System  
DL  
Deep Learning  
IN  
Infrastructure Node  
HVAC  
Heating Ventilation and Air Conditioning  
IoT  
Internet of Things  
JSON  
JavaScript Object Notation  
LSTM  
Long Short-Term Memory  
M2M  
Machine to machine  
MAE  
Mean Absolute Error  
MAPE  
Mean Absolute Percentage Error  
ML  
Machine Learning  
MN  
Middle Node  
MQTT  
Message Queue Telemetry Transport  
MSE  
Mean Square Error  
PM  
Particulate Matter  
PM0.3  
Particulate Matter of 0.3 \(\upmu\)m  
PM0.5  
Particulate Matter of 0.5 \(\upmu\)m  
PM1.0  
Particulate Matter of 1.0 \(\upmu\)m  
PM2.5  
Particulate Matter of 2.5 \(\upmu\)m  
PM5  
Particulate Matter of 5 \(\upmu\)m  
PM10  
RDBMS  
Relational Database Management System  
REST  
Representational State Transfer  
RNN  
Recurrent Neural Network  
RTU  
Remote Terminal Unit
## 4 Experiments  
We evaluate the pCR prediction performance of the proposed method through the experiments listed as follows. Specifically, we investigate the impact of input MRI sequences, analyze the pCR prediction performance of the proposed method and compare it with existing methods.  
1. Comparison of five types of input MRI sequences: 1.  
2. Comparison of the proposed method with its variants that differ in two factors, MRI feature vector extraction and pCR classification: * Three MRI feature vectors: radiomics features, MSFI embedding, and multi-parametric MRI embedding (ours). * Six classification models: logistic regression, xgboost, lightgbm, random forest (ours), MLP, and ensemble of the five classifiers.
3. Comparative evaluation of the proposed method with four competing baselines: 1.  
4. MLP classifier on radiomics features [25], and 3D-CNN classifier on MRI images [18].  
Figure 5: The overall pCR prediction method using both radiomics features and MSFI embedding, given three MRI sequences.  
To evaluate the overall pCR prediction performance, we use AUC (Area Under the ROC Curve), because it reflects the sensitivity and specificity of a classifier at the same time.  
\[AUC(f)=\frac{\sum_{q\in D_{0}}\sum_{t_{1}\in D_{1}}\mathbbm{1}[f(t_{0})<f(t_{1}) ]}{\left|\mathcal{D}_{0}\right|\cdot\left|\mathcal{D}_{1}\right|}. \tag{1}\]  
For a classifier \(f\), we estimate its AUC based on the Wilcoxon-Mann-Whitney statistic [47], as shown in Equation (1). \(\mathcal{D}_{0}\) and \(\mathcal{D}_{1}\) are the set of non-pCR patients and the set of pCR patients, respectively, and \(\mathbbm{1}[f(t_{0})<f(t_{1})]\) is an indicator function. We use an independent test cohort to measure the AUC of each classifier.
# Feasibility Study of Using Carbide Slag as In-Bed Desulfurizer in Circulating Fluidized Bed Boiler
Because the main components of carbide slag are different from limestone, and the particle size of carbide slag is smaller, the desulfurization activity is also different, which may affect its substitution for limestone as a desulfurizer. To our knowledge, most of the studies have been carried out on TGA, fixed bed, or laboratory-scale CFB. Due to the limitations of test conditions, it is not possible to simulate working conditions similar to the real CFB boiler on these devices. Thus these research results may not be applied to directly guide the application of carbide slag on the CFB boiler. So the aim of this work was to study the feasibility of taking the carbide slag as in-bed desulfurizer in large-scale CFB boilers. Calcination and sulfation characteristics of carbide slag were investigated through TGA. The effects of bed temperature especially the higher bed temperature, Ca/S mole ratio of carbide slag and limestone were explored in two different scale facilities, i.e., a 1 MW\({}_{\text{th}}\) pilot CFB boiler and a 690 t\(\cdot\)h\({}^{-1}\) CFB boiler
## 3 Results
### SEM Evaluation  
Different surface topographies were observed for the different polishing systems (Figure 3). O1 and HL showed surfaces with roundish depressions, typical of the finishing and polishing procedure performed with points, while HL showed the same round depressions but slightly better defined. A very similar surface was observed for SW, even if some superficial scratches were also present. SD showed some linear depressions instead. Since the result of SD in terms of roughness was quite favorable, it can be speculated that the linear depressions observed are of a low depth, scarcely influencing roughness measurement. When a laboratory F&P system is used, the result is similar but smoother than the clinical systems. The additional use of a finishing paste resulted in a noticeably smoother surface. The use of OG, a resin addressed to an in-office (chairside) glaze, made the surface quite smooth, but with some scratches that can still be identified even if they look partially filled by the resin. The nitrogen-glazed group showed the most homogeneous surface, with depressions or scratches that were almost not visible. The SEM observation of this group relates well to the findings of the study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{3}{c}{**Gloss (GU)**} \\ \cline{2-4}  & \multicolumn{3}{c}{**Crown Permanent**} \\ \cline{2-4}  & **Mean** & **SD** & **Sign.** \\ \hline NG & 90.7 & 1.3 & a \\ \hline SD & 51.7 & 9.7 & b \\ \hline LP & 38.1 & 10.0 & c \\ \hline OS & 35.3 & 7.7 & c \\ \hline OG & 30.5 & 0.8 & cd \\ \hline Ln & 27.7 & 0.9 & d \\ \hline HL & 18.5 & 6.0 & e \\ \hline SW & 12.9 & 2.6 & e \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of surface gloss (GU). Lowercase letters label statistically significant differences among the finishing systems (\(p<0.05\)).  
Figure 3: _Cont._
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 3 Applications of the Laser Feedback Interferometer
### Particle Sensing  
The self-mixing laser Doppler velocimeter is one of the promising technologies applied in the observation and detection of the particles flowing in liquid [64; 65], which has been recognized as the simplest, most cost-effective, and most self-aligned metrology. The system is shown in Figure 10 using a drained suspension including polystyrene latex standard spheres (PLS). The output power is modulated by the frequency-shifted scattered light generated by the motion of moving targets. When the moving target moves at a uniform velocity, the Gaussian power spectrum can be observed, and the peak frequency corresponds to the Doppler shift frequency \(f_{d}\).  
The angle of the incident laser light is set at \(\psi=16^{\circ}\) to the surface of the drained suspension. Here it is noted that the Gaussian spectrum is marked by the noise if the intensity of the light scattered from the moving particles is very weak. In this case, the motion can be revealed in its higher harmonics when the frequency of the relaxation oscillation of the laser output, \(f_{02}\), is made to coincide with \(f_{d}\) by tuning the pump current of the laser (\(i_{c}\)) at \(i_{c}=335\) mA, as shown in Figure 11b,c. Thus, the ultrahigh sensitivity measurement of extremely weak scattered light can be achieved. The spectral peak G in Figure 11 reflects the motion of the particles passing through the incident laser light. The frequency of the Gaussian is \(f_{d}=500\) kHz, which is related to the average velocity of the particles. The amplitude is associated with the intensity of the light scattered, which is proportional to the concentration of the particles.  
Figure 10: (**a**) Experimental setup. LD, laser diode; AP, anamorphic prism pair; OL, objective lens; SL, Nd:GdVO\({}_{4}\) solid-state laser; SG, slide glass; PD, photodiode receiver; PC, personal computer. (**b**) Configuration used to detect drained suspension. (Figure reproduced from Ref. [64]).
# Seven-Phase PMSM Drives Operation Post Two Types of Faults
## 2 Operation of the Seven-Phase Drive Post the Speed Sensor Failure  
### Seven-Phase PMSM Model  
The topology of the seven-phase PMSM drive is depicted in Figure 1. The exact model of the seven-phase PMSM is expressed on the Equations (1)-(3).  
\[[V_{s}]=r_{s}*[I_{s}]+\Delta[\varphi_{s}] \tag{1}\]  
where \([V_{s}]=[V_{sA},V_{sB},V_{sC},V_{sD},V_{sE},V_{sF},V_{sG}]^{T}\) are the inverter output voltages, \([I_{s}]=[i_{sA},i_{sB},i_{sC},i_{sD},i_{sE},i_{sF},i_{sG}]^{T}\) are the stator currents, \([\ \varphi_{s}]=[\varphi_{sA},\varphi_{sB},\varphi_{sC},\varphi_{sD},\varphi_{sE},\varphi_{sF},\varphi_{sG}]^{T}\) are the stator linkage fluxes, \(r_{s}\) is the stator resistance, and \(\Delta=\frac{d}{dt}\).  
The stator linkage fluxes can be expressed as:  
\[[\varphi_{s}]=L_{ms}*[I_{s}]+\varphi_{M} \tag{2}\]  
\[\varphi_{M}=\hat{\Delta}_{m}*\begin{bmatrix}\cos(\theta_{R})\\ \cos(\theta_{R}-2\pi/7)\\ \cos(\theta_{R}-4\pi/7)\\ \cos(\theta_{R}-6\pi/7)\\ \cos(\theta_{R}-8\pi/7)\\ \cos(\theta_{R}-10\pi/7)\\ \cos(\theta_{R}-12\pi/7)\end{bmatrix} \tag{3}\]  
where \(L_{ms}\) is the stator inductances matrix, \(\hat{\Delta}_{m}\) is the peak permanent magnet flux linkage, and \(\theta_{R}\) is the rotor position. The parameters of the PMSM motor that is used in this paper is given in the Appendix A.