esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
The experimental setup is schematically illustrated in Figure 1. At the TX side, EDB, PAM-2, or PAM-4 optical modulation was generated using PRBS random sequences at bit rate \(R_{b}=25\) Gbps by an offline digital TX and digital signal processing (DSP) block, which is described in detail in [10]. Here we briefly mention that for PAM-2 and PAM-4 options were generated in a standard way, while for EDB the following setup was used: a 2-level pre-coded signal was generated at TX. It was precoded by using a standard XOR-and-delay-based block. At RX, the received signal was detected by using a duobinary equalizer-based receiver. A 3-level signal was used as a training sequence for adaptive equalization (AEQ).  
The resulting offline TX block generated signal was converted into an analog electrical signal i(t) by a Keysight (Santa Rosa, CA, USA) 92 GSa/s arbitrary waveform generator (AWG). We could change the DML's resulting output ER by changing the peak-to-peak voltage, which is a parameter could be set in the AWG. After the amplification, a DC-bias was added to the signal by a Bias-T. The bias current of the laser \(i_{b}\) was varied to optimize the performance. Then, the experimental setup drove a 1550 nm C-band 10G-class Gooch and Housego (DML (Ilminster, UK). The transmitted optical power was set as \(P_{TX}=8.5\) dBm. The resulting modulated optical signal was propagated over a conventional SMF with length \(L=20\) km or 25 km. The received optical power (ROP) was measured by a power meter after a variable optical attenuator (VOA). At the RX side, the signal was received by a 10G-class avalanche photodiode (APD) followed by an electrical amplifier and a 100 GSa/s sampling rate Tektronix (Beaverton, OR, USA) real time oscilloscope (RTO). In the offline Digital RX + DSP block, we proposed two solutions: (a) an FFE with a number of taps \(N_{taps}=20\) (we termed this as \"FFE\") and (b) an FFE with \(N_{taps}=20\) followed by a DFE with \(N_{taps}=5\) (we termed this as \"FFE + DFE\") to partially compensate for linear and nonlinear impairments.  
As performance metrics, we used the required ROP (RROP) at bit error rate (BER) target of \(BER_{T}=10^{-2}\); the resulting ODN loss, defined as the difference in dB between the transmitted optical power \(P_{TX}\) and the received one (i.e., _ODN loss_, dB \(=P_{TX}-\) ROP, both in dBm); and the _maximum ODN loss_ (_max. ODN loss_, dB \(=P_{TX}-\) RROP, both in dBm). This is the typical metric used in the PON environment, since PON standards are very demanding in terms of ODN loss.
## 3 Materials and Methods
### MODIS NLST Analysis and Magma Chamber Identification  
This study employs MODIS nighttime LST data to investigate the spatial distribution and activity characteristics of potential magma chambers in the study area. To minimize the impact of random temperature fluctuations, long-term monthly average MODIS LST data from January 2003 to December 2023 (252 months) were used. These data were used to generate isotherm maps and detect geothermal anomalies through spatial analysis and density-based segmentation, thereby inferring the potential distribution of subsurface magma chambers [55]. MODIS data were reprojected and georeferenced using the Google Earth Engine (GEE) platform to ensure consistency with the study area's coordinate system and maintain spatial alignment.  
To further investigate the activity characteristics of potential magma chambers, this study analyzes annual and monthly variations in the multi-year average land surface temperature (LST) of geothermal anomaly zones from 2003 to 2023, aiming to reveal temporal patterns in geothermal dynamics. Monthly average air temperature (AT) data were also obtained from the National Earth System Science Data Center [56] and compared with LST data to analyze the causes of geothermal anomalies and further examine the temporal evolution and activity of subsurface magma chambers.  
The raw MODIS nighttime LST data are provided in brightness temperature, and the conversion to Celsius is performed using the following formula [57]:  
\[\text{LST($\lx@math@degree$C)}=\text{DN}\times 0.02-273.15 \tag{14}\]  
Here, LST refers to the nighttime LST (\(\lx@math@degree\)C), DN is the pixel value, and 0.02 is the scaling factor used to convert DN to Kelvin. The constant 273.15 is subtracted to convert the result from Kelvin to Celsius.
## 2 Materials and Methods
### Statistical Analysis  
IBM SPSS Statistics version 22.0 (IBM Corp., Armonk, NY, USA) was used for the statistical analysis of the findings obtained in the study. The suitability of the parameters for normal distribution was evaluated using Kolmogorov-Smirnov and Shapiro-Wilk tests, which determined that the parameters were suitable for normal distribution. To evaluate the effects of the resin composites, detox drinks, and time factors on roughness and translucency, three-way repeated measures ANOVA, post hoc Bonferroni, and Tukey tests were employed. To evaluate \(\Delta\)E according to resin composites and detox drinks, a two-way ANOVA and a post hoc Tukey test were employed. [PERSON]'s correlation analysis was used to assess the correlation between surface roughness, colour change, and translucency. Significance was assessed at the \(p<0.05\) level.  
Except for the T0 measurements of the orange detox drink, there was a statistically significant difference between the mean surface roughness values of the universal resin composites in all other groups (\(p<0.05\)).  
When the effect of different detox drinks on the surface roughness of each universal resin composite tested was examined, it was found that the mean surface roughness values with OM and OS on T1, T2, and T3 did not show a statistically significant difference between the detox drinks tested (\(p>0.05\)). The highest mean surface roughness values with OM were obtained from the green detox drink on T1 and from the orange detox drink on T2 and T3. The highest mean surface roughness values with OS were obtained with the orange detox drink on T1 to T2 and with the red detox drink on T3. The mean surface roughness values with FU on T1 showed a statistically significant difference between the detox drinks (\(p<0.05\)). The highest mean surface roughness values with FU at all time points were obtained with the orange detox drink. The mean surface roughness values with EU on T1 were not significantly different between the detox drinks (\(p>0.05\)), whereas there was a significant difference in the mean surface roughness values between the detox drinks on T2 and T3 (\(p<0.05\)). The highest mean surface roughness values with EU were obtained from the orange detox drink on T1 and T3 and the red detox drink on T2.  
It was found that the mean surface roughness values of the universal resin composites tested increased over time as a result of exposure to the different detox drinks, with the highest mean surface roughness values at the T3 (except for the OM and EU groups and the red detox drink).
## 4 Simulation Design and Analysis  
To validate the proposed AADRC method, simulations are conducted using MATLAB/SIMULINK 2021. The computer configuration which is utilized for the simulation is presented as follows: i5-2500 CPU @ 3.30 GHz and 4 GB of RAM. The model parameters of the SBW system (1) are detailed in Table 1. Moreover, to demonstrate the superiority of the proposed AFSEFBC with AESO, a comparative analysis, which is conducted between the proposed approach and the benchmark ADRC method [36], is presented in this section. To ensure a fair comparison, both methods utilize an identical sampling time of \(T_{s}=4\times 10^{-3}\) s. Furthermore, Table 2 provides the tuned controller and observer gains for both the proposed control scheme and the ADRC. Afterward, we detail the simulation outcomes and correlative examinations across two diverse scenarios. In these two cases, the angle of the front wheels is controlled to track a sinusoidal trajectory. It is important to note that, for the sake of comparing the robustness of various controllers, the controller and observer gains are held constant across the two cases. Moreover, both the Coulomb friction torque as expressed in \(\tau_{c}\) in (1) and the self-aligning torque \(\tau_{sel}\) as given in (3) are taken into account in these two cases. To account for the various road conditions throughout the simulation, \(\rho_{\tau}\) is utilized to determine \(\tau_{sel}\) as outlined below:  
\[\rho_{\tau}=\begin{cases}155,&0<t\leq 20\text{ s},\quad\text{Road with snow}\\ 585,&20<t\leq 40\text{ s},\quad\text{Road with wet asphalt}\\ 960,&40<t\leq 60\text{ s},\quad\text{Road with dry asphalt}.\end{cases} \tag{51}\]
# A Survey on Cyber Risk Management for the Internet of Things
## 6 Recommendations
### Recommendations for IoT Cyber Risk Calculation
_How can future works use IoT threat knowledge bases to effectively calculate IoT risk?_ An IoT threat knowledge base is a great method to store threat data in a structured way, with simple risk calculations using likelihood and impact to define the risk of a threat. Future works need to consider the lack of existing knowledge bases for IoT, meaning that a threat knowledge base will need to be created. Despite this, another issue is the potential to neglect critical threat events and the difficulty in estimating likelihood and impact. Forming a knowledge base takes time, but threat models, existing papers, and reports of cyber incidents can justify the values used. For example, within the healthcare-focused work of [PERSON] et al. [43], the literature is used to curate a base of threat knowledge. Another example from [PERSON] and [PERSON] [76] is to use existing bases, like CVE, that contain vulnerability records and scores. However, these are not always IoT specific, which requires researchers to filter out non-IoT records. One pitfall with the surveyed papers, is that we did not see an example of a threat base that contains values that could be used in other instances. Overcoming these issues requires threat, vulnerability, likelihood, and impact identification processes to be streamlined. In turn, the current uses of IoT threat knowledge bases do not necessarily consider human vulnerabilities, which should not be overlooked. In turn, researchers may benefit from using risk matrices or graph modelling alongside a threat knowledge base. _How can future works use risk weighting to effectively calculate IoT risk?_ The use of weighted risk calculations focuses on the importance of various IoT risk parameters and how IoT critical components can be prioritised, which could also be implemented into graph models and risk matrices. IoT weighting is especially useful for prioritiising crucial impact types, such as physical harm, where the potential consequences of an attack need to be eradicated. In our review, many impact types are suggested, and all IoT domains likely contain at least one critical consequence that needs to be focused on, for example, the potential threat to life within healthcare domains. However, risk weighting can also be applied to assets and users that could be affected by such an impact to further strengthen the prioritisation. For example, if within a healthcare setting, there is a potential threat to life, the patients and implanted medical devices that could cause or be deeply affected by such threats hold increased significance when assessing risk. It is notable that IoT user interaction and behaviours could also be assigned weights to signify critical cyber hygiene practices and the lack thereof.
# The Influence of Arabinoxylan on the Properties of Sourdough Wheat Bread
## 3 Results and Discussion
In general, the 1% addition of AX_HYD slightly reduced the increase in the bread crumb hardness, while the 1% addition of AX_NM and AX_CR had no significant effect (\(p<0.05\)) on the reduction in the increase in the crumb hardness of the sourdough wheat bread after 1 and 3 days of storage compared to the hardness of the bread without AXs (Table 5). This may have been due to insufficient AXs in the bread to effectively affect the retrogradation of starch, which is responsible for the staling of the bread [15]. Tables 6 and 7 show the results of the moisture loss of the crumb during the storage of the sourdough breads without and with the AX preparations. On the first day of storage, the addition of 1% and 2% of the AX_NM and AX_CR preparations had an influence on reducing the moisture loss of the crumb in the central part of the sourdough bread compared to the breads with the AX preparations on the day of baking (\(p<0.05\)), which was not observed for the bread with AX_HYD and the bread without AXs (Table 6). On the third day of storage, a significant loss of moisture in the central part of the bread crumb was observed in all the baked breads. In the peripheral part of the breads (crust), the moisture loss was observed on the first as well as the third day of storage in all the variants of the breads (Table 7). The crust drying occurred with crumb moisture retention in the central part of the AX sourdough bread. The water migration from the crumb to the crust did not occur or was delayed. In general, the addition of all the preparations at both levels resulted in a higher moisture content of the crumb in the central (Table 6) and peripheral (Table 7) parts of the bread on the first and third days of storage compared to the bread without AXs. The most effective in reducing the crumb moisture loss was the addition of 2% of the cross-linked AX preparation, and, to a lower extent, the addition of 2% of AX_NM. The application of 1% AX_HYD resulted in a higher crumb moisture content in the central part of the bread after the first day of storage compared to the bread without AXs. However, after three days of storage, the moisture content of the crumb in the central part of bread with 1% AX_HYD was the same as the bread without AXs. The unmodified and cross-linked AXs had the strongest influence on reducing the moisture loss of the crumb in the central and peripheral parts of the bread, which was probably due to the high molar mass of AX (446,330-574,710 g/mol). In other studies, data have been reported on the influence of sourdough on reducing moisture loss in the crumb of wheat bread during storage [13; 14]. However, the influence of hydrocolloids on reducing moisture loss in the crumb of sourdough wheat bread has not been studied to date.
# Control Strategies for Piston Trajectory in Ionic Compressors for Hydrogen Storage
## 3 Results and Discussions
### Hydrogen Discharge Mass and Specific Energy Consumption Results for Different Control Methods  
The hydrogen mass and specific energy consumption delivered by the ionic compressor in a single cycle are important indicators for evaluating its performance. Figure 12 shows the hydrogen mass delivered and the specific energy consumption after a cyclic operation for the different control methods. The maximum mass delivered in a cyclic operation was 1.14 g, obtained using the Position-S control method, while the lowest observed hydrogen mass was 1.11 g when operating under the Dual-PS control method, resulting in a maximum difference of 0.03 g between these two methods. Furthermore, the smallest specific energy consumption per unit mass of hydrogen discharged from the compressor  
Figure 11: Energy consumption and isothermal efficiency of the compressor system with different control methods.  
Figure 10: Comparison of \(p\)-\(V\) diagram among different control methods.  
was found to be 2389.38 J/g in the system utilizing the dual-PS control method, while the largest specific energy consumption of the compressor was observed to be 2410.92 J/g when utilizing the Position-P control method. The maximum difference in the specific energy consumption between these two cases was found to be 21.54 J/g. The hydrogen mass delivered by the ionic compressor for one cycle operation with the Position-P control method was found to be 1.13 g. The optimal control method was then identified as the Position-S strategy, which demonstrated the highest mass of hydrogen product, relatively low specific energy consumption, and relatively high control accuracy.
###### Abstract  
In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on \"where and when\" crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are grown in a given area by exploiting an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation. The method also has the ambition to provide information, at pixel level, regarding the period in which a given crop is cultivated during the season. To this end, we propose a solution called Class Activation Interval (CAI) which allows us to interpret, for each pixel, the reasoning made by CNN in the classification determining in which time interval, of the input time series, the class is likely to be present or not. Our experiments, using a public domain dataset, show that the approach is able to accurately detect crop classes with an overall accuracy of about 93% and that the network can detect discriminatory time intervals in which crop is cultivated. These results have twofold importance: (i) demonstrate the ability of the network to correctly interpret the investigated physical process (i.e., bare soil condition, plant growth, senescence and harvesting according to specific cultivated variety) and (ii) provide further information to the end-user (e.g., the presence of crops and its temporal dynamics).  
cap =1  
13 May 2021  
1
## 2 Methods
In the following, given Pt1000 values represent the mean value of these Pt1000 sensors. Two different types of measurements were conducted: (i) \"static\" measurements, where the gas cell with loaded sample gas was closed and the gas temperature was regulated by the thermostat, and (ii) \"dynamic\" measurements, where the samples gas continuously flowed through the gas cell. For the latter, heating of the gas was achieved by an upstream metal foam electrical resistance heater and the gas flow was controlled by a computer programmed breath simulator (ASL 5000 TM, Ingmar Medical Ltd., Pittsburgh, PA, USA). For both types of measurements, CO\({}_{2}\):N\({}_{2}\) gas mixtures with CO\({}_{2}\) mole fractions of 1.5, 3 or 5% (uncertainty: 5% relative) and pure N\({}_{2}\) were used as sample and reference gas, respectively. In order to record spectra, the thermal time-dependent frequency down-chirp of the QCL laser pulse is used (intrapulse mode, see, e.g., Refs. [14, 16]). Unless noted otherwise, measurements were performed with a pulse width of 500 ns and 20 kHz pulse repetition rate. The detector signal was captured by a digital oscilloscope (Agilent InfiniVision DSO-X 3104A, bandwidth 1 GHz) and uploaded to the computer for synchronous data processing. As the data transfer rate to the computer was too low to capture the detector signal of each single laser pulse event, we have proceeded to an average of 512 sweeps for each recorded spectrum. In this way, spectral scans and, in turn, temperature measurements are obtained  
Figure 2: Scheme of the experimental setup. A detailed description can be found in the text. with a sampling rate of about 5-8 Hz. Measurements with shorter integration times will be briefly discussed at the end of this work.
## 3 Spatial Phase Coding
### Destructive Spatial Phase Coding  
This section describes the destructive SPC scheme. In the destructive SPC scheme, the two different channels are destructively superimposed. So, the magnitude of superimposed channel coefficient is decreased. The destructive SPC modifies the phase of the channel coefficient by using the pre-coding vector different from pre-coding vector of the constructive SPC. The pre-coding vectors \(C_{1}^{d},\,C_{2}^{d}\) of the destructive 1-bit and 2-bit feedback SPC are as follows,  
\[C_{1}^{d}=\left\{\begin{array}{ccc}e^{-j\pi},&0<|\alpha|\leq\pi/2&(\text{ State 1})\\ 1,&\pi/2<|\alpha|\leq\pi&(\text{State 2})\end{array}\right\}, \tag{6}\]  
\[C_{2}^{d}=\left\{\begin{array}{ccc}e^{-j\pi},&\left(\begin{array}{c}0<| \alpha|\leq\pi/4\\ 7\pi/4<|\alpha|\leq 2\pi\end{array}\right)&(\text{State 1})\\ e^{j\pi/2},&\pi/4<|\alpha|\leq 3\pi/4&(\text{State 2})\\ 1,&3\pi/4<|\alpha|\leq 5\pi/4&(\text{State 3})\\ e^{-j\pi/2},&5\pi/4<|\alpha|\leq 7\pi/4&(\text{State 4})\end{array}\right\}. \tag{7}\]  
Figure 2: Constructive 1-bit and 2-bit feedback SPC.  
Figure 3 shows the destructive 1-bit and 2-bit SPCs. The transmitter multiplies the transmission signal by the pre-coding vectors \(C_{1}^{d}\), \(C_{2}^{d}\), and then transmits the signal. When the signals are transmitted by multiplying the pre-coding vectors \(C_{1}^{d}\), \(C_{2}^{d}\), the SNR of the received signal can be decreased because the amplitude of the superimposed channel coefficient can be decreased [18; 19]. Therefore, the destructive SPC scheme can reduce the magnitude of the superimposed channel coefficient and the SNR of the interference signals. The MRT scheme using full feedback information can remove the phase of the channel experienced by the interference signal. Therefore, the distortion of the received signal due to the interference signal can be significantly reduced. As described above, the MRT scheme uses the full feedback information, and the implementation complexity is very high. This paper uses the destructive SPC scheme which uses a small number of feedback information and can efficiently mitigate the interference signal. Additionally, the destructive 2-bit feedback SPC can reduce the superimposed channel coefficient \(H_{s}^{d}\) more efficiently than the destructive 1-bit feedback SPC [18; 19]. Therefore, this paper focuses upon the destructive 2-bit feedback SPC scheme.
## 2 Principle of PSIFTIS and the Information Reconstruction Method
### Principle of Polarization Pattern Decoupling and Information Reconstruction in PSIFTIS
\(S_{0}\) represents the total intensity of light, \(S_{1}\) represents the difference between \(0^{\circ}\) and \(90^{\circ}\) linearly polarized light components, \(S_{2}\) represents the difference between \(45^{\circ}\) and \(135^{\circ}\) linearly polarized light components, and \(S_{3}\) represents the difference between right-handed and left-handed circularly polarized light components. Since the amount of \(S_{3}\) component in the reflected light of most objects in nature is very small, it is taken as approximately zero. The corresponding spectra of each Stokes component are:  
\[\left\{ \begin{array}{l}B_{S_{0}}(\
u)=B_{I_{0}}(\
u)+B_{I_{90}}(\
u)\\ B_{S_{1}}(\
u)=B_{I_{0}}(\
u)-B_{I_{90}}(\
u)\\ B_{S_{2}}(\
u)=B_{I_{45}}(\
u)-B_{I_{135}}(\
u)\end{array} \right. \tag{4}\]  
The second route preprocesses the polarization interference image array to obtain four-channel infrared polarization images, and the Stokes parametric method is used to quantitatively describe the polarization state of the object. According to Equations (5) and (6), the degree of polarization (_DoP_) and angle of polarization (_AoP_) images can be calculated [20]:  
\[DoP=\frac{\sqrt{{S_{1}}^{2}+{S_{2}}^{2}}}{S_{0}} \tag{5}\]  
\[AoP=\frac{1}{2}\text{arctan}(\frac{S_{2}}{S_{1}}) \tag{6}\]  
After obtaining the polarization intensity image, polarization degree image, and polarization angle image, the polarization degree image and polarization angle image are fused to obtain the polarization feature image, and then the polarization intensity image and the polarization feature image are decomposed into high- and low-frequency components by wavelet transform to obtain the polarization fusion image.