esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 4 Fido2 cap: FIDO2 Captive-Portal Authentication Protocol
### Threat Model and Security Assumptions  
In FIDO2 CAP, the architecture of a captive portal is adapted to use FIDO2 authentication. It is important to highlight that we do not introduce any new security element not present in a captive portal or a FIDO2 authentication service. The main components  
Figure 4: Registration message flow in FIDO2 CAP. The Registrar has the required permissions to access the Registration Portal UI. After specifying the username, FIDO2 registration starts. Finally, the FIDO2 credentials (referred as Authenticator) are registered and linked with the user account.  
of the authentication system are the User Equipment, the User Portal and the FIDO2 Authenticator. Therefore, the security assumptions and threat analysis are the same as in FIDO2, described in the FIDO Security Reference [22]. Further, in FIDO2 CAP, we assume that the communication between the different captive-portal elements of the architecture is trustworthy, such as between the User Portal and the Enforcement Device, or between the User Portal and the User Database.  
In this paper, we focus on preventing attacks during authentication by adapting captive portals to use FIDO2 authentication. Attacker capabilities include (1) impersonating the Access Point (AP) of the network, (2) listening to the channel between the client and the User Portal, and (3) performing online brute-forcing attacks towards the User Portal. A captive-portal authentication system relying on a password is vulnerable to all these attacks. With FIDO2 CAP, the authentication relies in asymmetric cryptography to proof the possession of the FIDO2 credentials, without sending them. Also, in FIDO2, the web browser includes security checks on the authenticity of the User Portal during authentication. FIDO2 credentials only work with the WAWA server in which they have been registered. Finally, online brute-forcing attacks towards public-key cryptography authentication currently not proved to be a threat.  
In this threat model, we should take into account other types of attacks [18]. First, other network-based attacks may take advantage of vulnerabilities of the Enforcement Device. These may differ depending on the nature of the network in which FIDO2 CAP is implemented. For instance, an attacker could impersonate authenticated User Equipment to bypass the Enforcement Device by a spoofing attack. On the other hand, an attacker may gain access to the Registration Portal, allowing them to register their own FIDO2 Authenticator within the network. Therefore, registration should be protected by temporal authorisation tokens or by administrator credentials.
# Inductive Thermography as Non-Destructive Testing for Railway Rails
## 6 Measurements of Head Checks from Rails in Service  
### The Inspected Specimens  
Several different rail pieces with head checks have been tested and three representative ones were selected to be presented here. The pieces vary in material as well as how the head checks formed:  
* RP02: This specimen is a 25 cm long rail head with a R350 HT rail grade (see Figure 12). The rail piece was cut out of a demounted rail where head checks occurred during service. Three different micrographs were created from RP02: a cross section of the side view, a vertical cut of the frontal view and a 45\({}^{\circ}\) cut. These micrographs revealed head checks with a maximum length of 1803 \(\upmu\)m and a maximum penetration depth of 640 \(\upmu\)m. Figure 12b shows the side view micrograph image. The travel direction on this rail piece was from left to the right, therefore the head checks incline in a north-west to south-east (NW-SE) direction and the angle \(\alpha\) is denoted as a negative angle.
* RP03: This specimen of rail grade R260 is 45 cm long and it is also a cut-out of a demounted rail. It shows long head checks at the gauge corner and the travel direction was also from left to the right, causing a NW-SE head check orientation (see Figure 13).
* RP04: Specimen RP04 is a cut-out piece of a rail with a rail grade R350 HT, see Figure 14a. It was tested on the rail-wheel test rig at voestalpine Rail Technology GmbH [2]. The travelling direction was from the right to left, therefore the head checks have an orientation of NE-SW and the inclination angle \(\alpha\) has a positive sign. A micrograph of the side view shows head checks with a maximum length of 1675 \(\upmu\)m and a maximum penetration depth of 1050 \(\upmu\)m, see Figure 14b.  
Figure 12: (**a**) A photo of the head checks on RP02; (**b**) the head checks in cross section side view micrograph of RP02.  
Figure 13: (**a**) A close up of the head checks on RP03; (**b**) a photo of RP03.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 1 Introduction
For example, some scholars propose to directly add frequency hopping to improve the reliability of STBC-DCSK [23]. Although these models can make use of the sensitivity of DCSK to initial values, noise-like characteristics and the simple scrambling method to improve the security of MIMO communication system to a certain extent. However, since these schemes need to transmit chaotic reference sequences directly during the communication process, the security of MIMO communication system is still threatened. Inspired by [18; 21; 22], in this paper, we propose the scheme which uses the FH-OFDM-DCSK to improve the security of MIMO communication system which combines STBC and DCSK and needs CSI. In this paper, we propose a MIMO communication system assisted by FH-OFDM-DCSK (MIMO-FH-OFDM-DCSK). In this design, we use DCSK and FH modules to improve the security of MIMO communication system which is encoded by STBC [24]. On the basis of using DCSK and FH modules, the proposed scheme hides the user data in the chaotic sequences. At the same time, the proposed scheme inherits the advantage of FH-OFDM-DCSK scheme in eliminating the delay line circuit. We then derive the theoretical bit error rate (BER) and secrecy capacity expressions for the proposed system. The simulation results over both additive white Gaussian noise (AWGN) channel and Rayleigh fading channel are also provided to validate the effectiveness of the proposed scheme and the superior performance to benchmark scheme. Briefly, the major contributions include:  
1. We propose to use FH-OFDM-DCSK to improve the security of the MIMO communication system. The initial values of chaotic sequences and the method of generating FH module are shared only between legitimate users. Thus, eavesdroppers or malicious users can not recover the received unknown information. Thus, the scheme proposed in this paper can improve the security of the MIMO communication system. 2. We analyze the theoretical BER and secrecy capacity for the proposed scheme. Then, simulation results are provided to verify the effectiveness of the scheme proposed in this paper and that the proposed MIMO communication system can achieve higher secrecy capacity. The rest of this paper is organized as follows: In Section 2, the principle of the MIMO-FH-OFDM-DCSK system is introduced. Then, theoretical performances are analyzed in Section 3. Section 4 shows simulation results to provide the BER of legitimate users and eavesdroppers over the AWGN channel and Rayleigh fading channel as well as the secrecy capacity of the proposed scheme and the benchmark scheme, and Section 5 presents our conclusion.
# Effective Use of Flax Biomass in Biorefining Processes
## 3 Results and Discussion
### Biocomposite Production Process
#### 3.3.2 Mechanical Properties of Biocomposites  
The effects of the selected flax varieties biomass on both the tensile and flexural properties of PLA composites are shown in Table 9.  
The use of the PLA composites of flax fillers in the amount of 20% resulted in a decrease in tensile strength in the range of 15-19%. Compared to PLA, the flexural strength of the composites decreased in the range of 13-19%. On the other hand, depending on the variety of linen, an increase in modulus was noted during the elongation (54-60%) and bending (35-64%) of the compositions in relation to PLA.  
Natural raw materials containing cellulose, hemicellulose, lignins and pectins are active hydrophilic fillers [45]. The decrease in tensile and flexural strength indicates that despite the use of hydrophilic flax biomass in combination with a hydrophilic biodegradable polymer (PLA), the structure of the biocomposite does not show full adhesion at the interface of individual components [46]. The reason for this may be the different content of fiber and shives in the biomass unit or the different content of cellulose in the composition for different varieties of flax, which is responsible for the effectiveness of adhesion with the biodegradable polymer matrix. The filler/matrix adhesion is an important factor affecting the final mechanical properties of composites. Good interface adhesion ensures effective stress transfer between the matrix and natural filler [27; 47].
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 2 Related Works
### Convolutional Neural Network Model  
As shown in Figure 3, the typical convolutional neural network is generally composed of input (Input), convolutional layers (Conv), pooling layers (Pool), fully-connected layer (FC) and output (Output) layer [19].  
Several different convolution kernels are usually used in order to extract different features called feature maps of the input data. The convolution process can be described as follows:  
\[\mathbf{x}_{j}^{l}=f(\sum_{i\in M_{j}}x_{i}^{l-1}*W_{ij}^{l}+b_{j}^{l}) \tag{5}\]  
where \(\mathbf{x}_{j}^{l}\) and \(\mathbf{x}_{i}^{l-1}\) represent the feature map of the output of the \(j\)-th channel of the convolution layer \(l\) and the output of the previous layer respectively; \(W_{ij}^{l}\) is the convolution kernel connecting the \(j\)-th feature map of the convolution layers \(l\) and \(l-1\), \(\mathbf{b}_{j}^{l}\) is the bias of the \(j\)-th feature map of the layer \(l\), \(M_{j}\) represents the subset of any current input feature map, '\(*\)' refers to the convolution operation and \(f\) indicates an activation function.  
To reduce the dimension of the feature maps and keep the characteristic scale invariant, the pool layer follows a down-sampling rule for the feature maps. After the alternate transfer of multiple  
Figure 2: HSI color model.  
convolution layers and pooling layers, the output is the features for image recognition through the fully-connected layer.
## 2 End-to-End QoS in WSNs  
While the traditional reliability, latency, and throughput QoS parameters also apply to the WSNs, their importance differs from the legacy communication networks. In WSNs, sensing applications can tolerate high latency and low throughput. However, the reliability in WSNs is particularly significant. In the traditional computer networks, the data is routed via highly reliable wired links, while only the end links may be wireless, for example by utilizing cellular connections or Wireless LAN (WLAN). In WSNs, packets are forwarded via multiple wireless hops. On each wireless link, the packet error rates (PER) of 10%-30% are common [6; 7], which significantly decreases the end-to-end reliability. For example, assuming 30% PER and 3 transmission attempts on each link, 97% of packets are received over one hop based on a simple probability calculation. After 10 hops, the success probability is only 76%.  
There are other aspects in QoS, which are outside the scope of this paper. However, as an introduction we can identify data accuracy, security, mobility, and energy efficiency parameters. These constitute a profile that describes the type of QoS in a network as shown in Figure 2. The data accuracy and security parameters emphasize the data centric nature of the WSNs. The data accuracy describes the accuracy of sensor measurements, whereas the security ensures that unauthorized parties do not gain access or tamper with the sensed data. The mobility is important in tracking WSNs as a node may be attached to moving objects. Due to the significance of the network lifetime, energy efficiency is considered as a QoS parameter. Usually, the other parameters have a trade-off with the energy efficiency. For example, throughput can be increased with a higher duty cycle thus reducing sleeping while increasing energy usage.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### Fault Analysis of the DFIG-Based Wind Farm with LVRT Capability
#### 2.3.1 The Single-Phase Ground Fault Case  
The 35 kV system of wind farm is not grounded. The zero-sequence impedance of the wind farm only contains the zero-sequence impedance of transformer grounded and transmission line when a fault occurs on the transmission line. Therefore, the zero-sequence current of the wind farm is relatively large and is not influenced by the DFIG. Due to the weak power supply of the wind farm, the positive and negative sequence currents are small. The zero-sequence current is the main component of the wind farm side current when a ground fault occurs. For the real DFIG-based wind farm shown in Figure 4, the DFIG is equipped with LVRT technology afterwards. A single-phase ground fault occurs on Line 1 and the real fault waveform records are shown in Figure 5.  
For the DFIG-based wind farm power system shown in Figure 4, the fault k occurs at 0.04 s and the fault duration is 40-60 ms; then, the differential protection initiates and the circuit breaker trips. The measured fault currents at the two terminals of the Line 1 are shown in Figure 5a,b separately. Obviously, the phase B current of the grid side is much larger than that of the wind farm side during the fault. Since wind farm is a weak power supply system, the short circuit current of the wind farm is  
Figure 5: The real fault waveform records when a phase B ground fault occurs on the transmission line connected to a DFIG-based wind farm with LVRT capability: (**a**) currents of the grid side; (**b**) currents of the wind farm side; (**c**) currents of the transformer T\({}_{2}\); and (**d**) Root-Mean-Square (RMS) current values of the transformer T\({}_{2}\).  
small, hence the differences of the three-phase currents is not obvious. In order to observe the fault currents provided by the wind farm in detail, the measured currents on the high-voltage side of the transformer T\({}_{2}\) are shown in Figure 5c. The RMS values of the positive, negative and zero-sequence currents are shown in Figure 5d. The three-phase short circuit currents are essentially coincident and the zero sequence current is much larger than the positive and negative sequence currents. In summary, for ground faults, the fault current of the wind farm side is dominated by the zero sequence current, the frequency of which is 50 Hz. The frequency deviation is not obvious.
###### Abstract  
The ability of Fourier transform infrared (FTIR) spectroscopy in analyzing cells at a molecular level was exploited for investigating the biochemical changes induced in protein, nucleic acid, lipid, and carbohydrate content of cells after irradiation by graded X-ray doses. Infrared spectra from in vitro SH-SYSY neuroblastoma cells following exposure to X-rays (0, 2, 4, 6, 8, 10 Gy) were analyzed using a ratiometric approach by evaluating the ratios between the absorbance of significant peaks. The spectroscopic investigation was performed on cells fixed immediately (t\({}_{0}\) cells) and 24 h (t\({}_{24}\) cells) after irradiation to study both the initial radiation-induced damage and the effect of the ensuing cellular repair processes. The analysis of infrared spectra allowed us to detect changes in proteins, lipids, and nucleic acids attributable to X-ray exposure. The ratiometric analysis was able to quantify changes for the protein, lipid, and DNA components and to suggest the occurrence of apoptosis processes. The ratiometric study of Amide I band indicated also that the secondary structure of proteins was significantly modified. The comparison between the results from t\({}_{0}\) and t\({}_{24}\) cells indicated the occurrence of cellular recovery processes. The adopted approach can provide a very direct way to monitor changes for specific cellular components and can represent a valuable tool for developing innovative strategies to monitor cancer radiotherapy outcome.  
neuroblastoma cells; Fourier transform infrared microspectroscopy; X-ray radiation; radiation dose effects 2020 10.2974 10.3390/app10082974
## 2 Materials and Methods
### System Structure
#### 2.1.2 Algorithm Process  
In Figure 3, an anti-spoofing system consisting of four low-cost commercial board components is shown. On the one hand, the broadcast ephemeris information is basically impossible to be forged, and the expected DOAs can be obtained from the prior position of the antenna and the broadcast ephemeris information. On the other hand, using the geometric constraint information of the four antennas and the space information formed between the antennas and the satellite, the method of calculating the integer and fractional parts of the carrier single difference is adopted separately. The phase error caused by power-on clock error, antenna phase center error, and multipath effect is compensated. Then, the calculated DOAs can be quickly calculated. According to the data distribution characteristics of the residual error between the calculated DOAs and the expected DOAs, SSE can be used to construct a binary hypothesis testing model to achieve spoofing detection. It can output the spoofing state of the receiver in real-time, identify the spoofing signal, and find the direction information of the spoofer.
## 2 Sampling  
Eleven marble fragments were collected from different points at the two study sites; four from the complex of _San Domenico Maggiore_, and seven from the cloister of _San Marcellino e Festo_ (Figure 2). Suitable stainless-steel tools, such as lancets and small chisels, were used to sample representative but noninvasive portions of material affected by the presence of black crusts.  
With respect to the complex of _San Domenico Maggiore_ (Figure 2a-c), samples were retrieved from the facade of the church, specifically from the marble portal located on the south-east side, alongside the apse of the church, overlooking _San Domenico_ square.  
From first macroscopic observation, the black crusts (SD series) looked rather homogeneous, compact, with a smooth surface and thin thickness. They also showed good adhesion to the underlying stone substrate, which was rather degraded, with evidence of swelling, poor compactness, and sometimes powdery appearance.  
With respect to the cloister of _San Marcellino e Festo_ (Figure 2d-f), the samples were taken from the large square plan monumental cloister, enriched with a garden adorned with marble fountains, statues, and various artifacts. The seven samples were taken from three different sculptures: one sample from  
Figure 1: Complex of _San Domenico Maggiore_ (40\({}^{\circ}\)50\({}^{\prime}\)55\({}^{\prime\prime}\)N; 14\({}^{\circ}\)15\({}^{\prime}\)16\({}^{\prime\prime}\)E) and cloister of _San Marcellino e Festo_ (Naples, Italy) (40\({}^{\circ}\)50\({}^{\prime}\)49\({}^{\prime\prime}\)N; 14\({}^{\circ}\)15\({}^{\prime}\)28\({}^{\prime\prime}\)E): (**a,c**) Aerial view of the two monumental complexes by Google Earth; (**b**) view of the _San Domenico Maggiore_ church with the apse facing the eponymous square; (**d**) General view of the _Clositer_ and _San Marcellino e Festo_ church with evidence of the tiled dome.  
a marble well (SM-P series) (Figure 2f), three from a marble structure with arches and pillars (SM-A series) (Figure 2e), and three from a female marble bust (SM-S series) (Figure 2d). Additionally, in this case, all black crusts showed a homogeneous and compact morphology, a very thin thickness and good adhesion to the underlying stone substrate. As for the stone substrates, they looked fairly cohesive and slightly altered.  
Samples with location description and heights are reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Complexs of San Domenico Maggiore (SD Series)**} \\ \hline
**Sample ID** & **Sampling Location** & **Sampling Heights** & **Exposition to Washout** \\ \hline \multirow{3}{*}{SD1} & \multicolumn{2}{c}{Facade of the San Domenico church, main portal.} & \multirow{3}{*}{SD1} & \multirow{3}{*}{Sampling on one of the left pillars (looking towards the portal), in a} & \multirow{3}{*}{2.00 m} & \multirow{3}{*}{WELL} \\  & slightly curved area and on a vertical and external surface. &
## 3 Methodology  
### Exploratory Data Analysis  
#### 3.1.1 Data Integrity and Visualization  
The dataset aggregates data from diverse load types in Australia, covering dates ranging from 1 January 2006 to 1 January 2011. It comprises six distinct features, namely electrical load consumption ('SYSLoad'), the dry bulb temperature ('DryBulb'), the dew point temperature ('DewPnt'), the dry bulb temperature ('WetBulb'), humidity, and electricity price ('ElecPrice'). The target variable is the electrical load consumption values, represented by 'SYSLoad'. All variables, except for 'ElecPrice' depict the climatic information. 'ElecPrice' can be obtained from the Australian Electricity Market Operator (AEMO).  
Before performing a time series analysis, it is necessary to evaluate whether the data are stationary or not. The fact that the data is not stationary indicates that the prediction model cannot adequately detect the basic points in the data and that the results obtained are likely to have an error rate. For this reason, the stationarity of the used dataset was tested using the Augmented Dickey-Fuller test statistical method. The statistical values obtained as a result of this test are shown in Figure 4, Table 1. If the \(p\)-value is less than 0.05, it means that the null hypothesis is rejected. This proves that the dataset used is stationary.  
Since the goal of the study was to estimate the medium-term (one month ahead) load, the half-hourly load data were first converted to daily data. At this stage, the sum of all values of each characteristic for a day was resampled and 1827 days of load observation data were obtained. In order to obtain a more stable prediction result, the data to be processed by the model must have integrity and be normalized. For this purpose, the data used was first checked for null fields and it was found that there were no missing data. To visualize the dataset, Figures 4 and 5 show the change in electricity consumption over the years based