esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 3 System Implementation
### Process Level
#### 3.2.2 FGS-PID Controller  
The FGS-PID controller design uses the methodology presented in [13]. The inputs to the control system correspond to the error variables (\(e(t)\)) and the first derivative of the error (\(\dot{e}(t)\)), while the outputs are the variables \({K_{p}}^{\prime},{K_{d}}^{\prime}\) and \(\alpha\), which determine the parameters of the PID controller by  
\[\begin{split} K_{p}&=K_{p_{\min}}+(K_{p_{\max}}-K_{p _{\min}}){K_{p}}^{\prime}\\ K_{d}&=K_{d_{\min}}+(K_{d_{\max}}-K_{d_{\min}}){K_{d }}^{\prime}\\ K_{i}&=\frac{K_{p}^{2}}{\alpha K_{d}}\end{split} \tag{4}\]  
For the linguistic variables (\(e(k)\)) and (\(\dot{e}(k)\)), the following membership functions are implemented: big negative values (NB), medium negative (NM), negative small (NS), zero (ZO), small positive (PS), medium positive (PM) and big positive (PB) values. Figure 4 shows the error membership functions. Figure 5 shows the membership functions of the error derivative.  
The output variables \({K_{p}}^{\prime}\) and \({K_{d}}^{\prime}\) have two membership functions, small (S) and big (B), which are of the Gaussian type with standard deviation 0.4247 and center at 0 and 1, respectively. These membership functions are shown in Figure 6. The output variable \(\alpha\) has four membership
# Blockchain Technology for Intelligent Environments
## 2 Blockchains
### Consensus Protocols
#### 2.3.1 Proof-of-Work (PoW)  
Proof-of-Work (PoW) [16], or _Nakamoto consensus_, is the first and most popular consensus mechanism for blockchains to date, primarily known through its use in Bitcoin. It is based on the combination of (i) the _longest chain wins_ principle, (ii) a computationally hard problem to build (or _mine_) the next block, and (iii) a reward for mining a new block into the chain. The most often used computationally hard problem is _reverse hashing_, which is finding a value whose cryptographic hash satisfies some criteria. As cryptographic hash functions are non-reversible, the only way to find a solution is by employing brute-force techniques. Unfortunately, reverse hashing has no real-world scientific or societal causes or benefits beyond just achieving the consensus per se. Its sole purpose is to incentivize participating nodes (known as _miners_) to devote their resources to the chain that has grown to be the longest, i.e., the winning chain, thereby effectively achieving a universal, decentralized consensus on which a specific chain version is unanimously agreed upon.  
Unless a single entity controls more than 50% of the world's mining capacity, it is in each miner's interest to abide by the rules. Unfortunately, with the increasing popularity of massive mining pools, the scenario of aggregating more than half of the world's hash power under a single administrative entity is no longer unlikely. Thus, the danger of what is known as the _51% attack_ cannot be entirely ruled out.  
PoW's main drawback is, however, the exorbitant amount of energy it requires. For example, Bitcoin's mining power is expected to surpass the entire power consumption of countries like Ireland or Denmark by 2020 [24]. This puts the long term viability of PoW-based consensus at stake, notably in terms of their carbon footprint and their effect on global ecology. It has thus fueled strong research efforts in devising alternative consensus mechanisms, some of which are described below.
# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
## 3 Experiments, Results and Discussion
### Setting Labels for Different or Normal Faults  
We used the fault detection time in the data record to determine the time that the fault occurred. The data of 8-24 h before and after each fault record of the steam turbine were intercepted for analysis. First, the t-SNE algorithm described in the previous section was adopted to map the 34-dimensional data to a two-dimensional space. Then, the K-means clustering method was used to separate the fault data from the normal data. The processing results of the algorithm are visualized in Figure 2. Green data points represent normal data, and other colors represent different fault data points.  
The time-series data after clustering was compared to fault records to distinguish fault data from normal data. As shown in Figure 3, each figure is a data graph of different faults arranged by time. In the figure, the time of the red line is the actual time recorded for the five types of faults.  
Table 4 shows the information of failure data for five types. Compared to the time of fault records, it can be seen that this method can distinguish fault data and normal data of steam turbines, and it has a certain predictive ability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Sample Size** & **Time Range** \\ \hline Steam turbine & 340,468 & January to August in 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical information of the data set on steam turbines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Fault Discovery Time** \\ \hline
1 & 3 Feb 2018 2:07 \\
2 & 11 Feb 2018 6:19 \\
3 & 13 Mar 2018 7:28 \\
4 & 10 Jun 2018 7:44 \\
5 & 7 Aug 2018 23:17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five types of faults in the fault record.
## 2 Results and Discussion
### Organic Volatiles Isolated by HS-SPME  
In the recent past, instrumental analysis of the honeys has become dominated by headspace analysis, particularly the rapid, solvent-less, and easy-to-use HS-SPME extraction technique [12,13]. Several fiber coatings with different polarity and extraction mechanism have been used for the extraction of honey headspace compounds [14,15]. In this research, polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibre was selected.  
Terpenes were the most abundant headspace organic volatiles in the samples (Table 1): linalool (4.0%-6.6%), three isomers of lilac aldehyde (1.0%-11.7%), three isomers of lilac alcohol (0.1%-1.2%), _cis_- and _trans_-linalool oxide (0.6%-3.1%). Linalool, _cis_- and _trans_-linalool oxides are common terpenes of honey volatiles [15]. The second group of abundant volatiles were norisoprenoids: C\({}_{9}\)-norisoprenoids [such as 4-ketoisophorone (5.0%-7.4%), _a_-isophorone (2.1%-6.5%), 2-hydroxyisophorone (0.0%-0.9%) or 2-hydroxy-4-ketoisophorone (0.0%-0.6%)] and C\({}_{10}\)-norisoprenoid safranal (0.0%-1.0%).  
\begin{table}
\begin{tabular}{c l c c c c} \hline
**No.** & **Compound** & **RI** & **Min.** & **Max.** & **Av.** & **SD.** \\ \hline
1. & Dimethyl sulfide & \(<\)900 & 0.0 & 0.9 & 0.43 & 0.45 \\
2. & 2-Methylbutanal & \(<\)900 & 0.0 & 0.9 & 0.37 & 0.47 \\
3. & 2,5-Dimethylfuran & \(<\)900 & 0.0 & 2.9 & 1.30 & 1.47 \\
4. & 3-Methylpentan-3-one & \(<\)900 & 2.0 & 2.4 & 2.20 & 0.20 \\
5. & Octane & \(<\)900 & 0.0 & 1.7 & 0.83 & 0.85 \\
6. & Furfural & \(<\)900 & 0.0 & 0.4 & 0.23 & 0.21 \\
7. & 3-Methylpentan-1-ol & \(<\)900 & 0.0 & 0.4 & 0.20 & 0.20 \\
8. & 3-Methylpentanoic acid & 941 & 1.0 & 1.3 & 1.13 & 0.15 \\
9. & Benzaldehyde & 965 & 2.0 & 4.5 & 3.37 & 1.27 \\
10. & Phenylacetaldehyde & 1048 & 0.5 & 2.8 & 1.43 & 1.21 \\
11. & _trans_-Linalool oxide (furan & 1076 & 2.0 & 3.1 & 2.53 & 0.55 \\ type) & & 1091 & 0.6 & 1.0 & 0.80 & 0.20 \\
12. & _cis_-Linalool oxide (furan & 1101 & 4.0 & 6.6 & 5.13 & 1.33 \\
13. & Linalool & 1102 & 0.0 & 0.8 & 0.37 & 0.40 \\
14. & Nonanal & 1106 & 0.5 & 1.9 & 1.10 & 0.72 \\
15. & Hotrienol & 1106 & 0.5 & 1.9 & 1.10 & 0.72 \\
16. & \(\alpha\)-Isophorone & 1124 & 2.1 & 6.5 & 4.10 & 2.23 \\
17. & Lilac aldehyde (isomer I) & 1151 & 1.0 & 4.9 & 3.30 & 2.04 \\
18. & 4-Ketoisophorone & 1152 & 5.0 & 7.4 & 6.17 & 1.20 \\
19. & Lilac aldehyde (isomer II) & 1159 & 6.1 & 11.7 & 9.00 & 2.81 \\ \hline \end{tabular}
\end{table}
Table 1: _Prunus mahaleb_ L. honey volatiles composition obtained by HS-SPME followed by GC and GC-MS analysis.  
Identified headspace phenyllpropane derivatives were: coumarin (0.9%-1.8%), benzaldehyde (2.0%-4.5%), phenylacetaldehyde (0.5%-2.8%), benzoic acid (0.2%-1.4%), 4-methoxybenzaldehyde (1.4%-10.9%) and 3,4,5-trimethylphenol (1.1%-3.6%). Benzaldehyde, phenylacetaldehyde, 4-methoxybenzaldehyde, 3,4,5-trimethylphenol and coumarin were found among _P. mahaleb_ volatiles [6] isolated by hydrodistillation.
## 2 Materials and Methods
### Visco-Thermal Losses Model  
The visco-thermal losses in the system are considered both in the resonators and in the slits by using its effective complex and frequency dependent parameters [25]. On the one hand, the effective parameters in the slits are given by  
\[\rho_{s}=\rho_{0}\left[1-\frac{\tanh\left(\frac{h}{2}G_{\rho}\right)}{\frac{h }{2}G_{\rho}}\right]^{-1},\quad\kappa_{s}=\kappa_{0}\left[1+(\gamma-1)\frac{ \tanh\left(\frac{h}{2}G_{\kappa}\right)}{\frac{h}{2}G_{\kappa}}\right]^{-1}, \tag{6}\]  
with \(G_{\rho}=\sqrt{i\omega\rho_{0}/\eta}\) and \(G_{\kappa}=\sqrt{i\omega\text{Pr}\rho_{0}/\eta}\), and where \(\gamma\) is the specific heat ratio of air, \(P_{0}\) is the atmospheric pressure, Pr is the Prandtl number, \(\eta\) the dynamic viscosity, \(\rho_{0}\) the air density, and \(\kappa_{0}=\gamma P_{0}\) the air bulk modulus. On the other hand, the complex and frequency-dependent density and bulk modulus in a rectangular cross-section tube are given by [25]:  
\[\rho_{\mathit{eff}} =-\frac{\rho_{0}a^{2}b^{2}}{4G_{\rho}^{2}\sum\limits_{k\in\mathbb{ N}}\sum\limits_{m\in\mathbb{N}}\left[a_{k}^{2}\beta_{m}^{2}\left(a_{k}^{2}+ \beta_{m}^{2}-G_{\rho}^{2}\right)\right]^{-1}}\,, \tag{7}\] \[\kappa_{\mathit{eff}} =\frac{\kappa_{0}}{\gamma+\frac{4(\gamma-1)G_{\kappa}^{2}}{a^{2} b^{2}}\sum\limits_{k\in\mathbb{N}}\sum\limits_{m\in\mathbb{N}}\left[a_{k}^{2}\beta_{m} ^{2}\left(a_{k}^{2}+\beta_{m}^{2}-G_{\kappa}^{2}\right)\right]^{-1}}\,, \tag{8}\]  
with the constants \(\alpha_{k}=2(k+1/2)\pi/a\) and \(\beta_{m}=2(m+1/2)\pi/b\), and the dimensions of the duct \(a\) and \(b\) being either the neck, \(a=b=w_{n}^{(n)}\), or the cavity, \(a=b=w_{c}^{(n)}\) of the \(n\)-th Helmholtz resonator. Using the effective parameters for the neck and cavity elements given by Equations (7) and (8), the impedance of a Helmholtz resonator including the thermoviscous losses and the \"end\" correction due to the radiation can be obtained [26].
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Transport Model
By summing up these interfering signals, we can obtain the total energy of the interference signals. The traditional solution to reducing interference is keeping sufficient distance be-tween service beams to reduce interfering antenna gain and deploying spot beams. \(SNR_{k}\):  
\[SNR_{k}=\frac{\left|h_{k,n}\right|\cdot P_{n}}{\sigma^{2}} \tag{4}\]  
Shannon formula:  
\[R_{k}^{t}=B_{k}^{t}\times log_{2}(1+SINR_{k}^{t}) \tag{5}\]  
\(B_{k}^{t}\) represents the bandwidth allocated to \(user_{k}\) by the satellite at time \(t\). \(R_{k}^{t}\) is the information rate of \(user_{k}\) at time \(t\). Offered throughput in \(cell_{i}\):  
\[SNR_{k}=\frac{\left|h_{k,n}\right|\cdot P_{n}}{\sigma^{2}} \tag{6}\]
## 4 Performance Analysis
### Comparison of the Power Patterns for All Beamformers
#### 4.3.1. Comparison of the Power Pattern for the FDL-Based Beamformer  
In Figure 4a, the output power of the LCCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)9.78 dB, \(-\)7.08 dB, and \(-\)18.95 dB, respectively. For 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, the output power of LCAA is \(-\)8.16 dB, \(-\)4.69 dB, and \(-\)17.07 dB, respectively. Then, the output power of the LCPAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)9.92 dB, \(-\)7.33 dB, and \(-\)19.10 dB, respectively. The output power of the LRNAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)9.83 dB, \(-\)7.16 dB, and \(-\)18.99 dB, respectively. The output power of LCPAA is higher than that of the other beamformers, as seen in the figure. So, the LCPAA beamformer performed better than the other beamformers.
### Advantages of Using Cerberis3 in a Hub-and-Spoke Network Topology
#### 1.3.2 Key Performance Metrics  
1. Quantum bit error rate (QBER): The QBER measures the errors in the quantum channel, which must be kept low to ensure secure key distribution.
2. Secret key rate: The secret key rate indicates the speed at which secure keys are generated and distributed between the two QKD nodes.
3. Link visibility: The link visibility measures the signal-to-noise ratio in the quantum channel, which impacts the maximum distance and key rate.
# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 4 Results  
### Surface Settlement Prediction Using ANN  
The dataset from Attiko Metro included 61 records detailing TBM operational characteristics, geotechnical conditions, and tunnel geometric details. While we acknowledge that 61 records are not the best-case scenario for comprehensive analysis, they provided sufficient data for applying the O'Reilly and New theory [24]. The Peck constant (K) was calculated at 0.50 based on retrospective interval analysis and empirical data. To enhance measurement accuracy, parametric assessments were conducted for volume loss values ranging from 0.1% to 0.8%, aligning predictions with actual measurements in uncertain ar  
Figure 4: Geotechnical parameters and a section of the geological background of the interval (Attiko Metro, 2007 [23]).  
Figure 3: The EPB machine used during the excavation of interval 5.  
Geotechnical parameters are crucial for assessing soil properties during TBM tunneling, directly influencing performance, safety, and settlement predictions. Key parameters include the following:  
* Cohesion (c) in face area (kN): Measures soil strength at the tunnel face. Higher cohesion enhances stability, reducing collapse risk and aiding safe excavation.
* Cohesion (c) in overburden (kN): Refers to soil strength above the tunnel. Increased cohesion minimizes deformation and settlement risks.
* Angle of friction (\(\varphi\)) in face area: Represents soil resistance to movement at the tunnel face. A higher angle improves stability and reduces collapse risk.
* Angle of friction (\(\varphi\)) in overburden: Indicates soil resistance above the tunnel. A higher angle enhances overall stability and reduces settlement likelihood.  
Geometrical parameters are critical for designing and executing TBM tunneling projects. They influence construction methods, safety, and logistical planning to align with site conditions. Key parameters include the following:  
* Overburden (m): Depth of soil or rock above the tunnel. It determines the load and pressure on the tunnel, influencing stability and design.
* Distance to water table (m): Vertical distance from the tunnel invert to the water table. A shallow water table increases seepage risks, while a deeper one may require enhanced waterproofing.
* Distance from shaft (m): Distance from the tunnel to the access point. A shorter distance can affect TBM launching and cause ground disturbance, while greater distances may lead to ground stress and surface settlements.  
To optimize neural network input processing, the data were normalized using the MinMaxScaler library. This common data science technique scales features to a range of 0 to 1, preserving measurement relationships without unit-related issues.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 3 Related Work
The authors consider scenarios of downlink and uplink transmissions in saturated and non-saturated conditions and determine the gain from OFDMA in throughput and latency, respectively. In this work, only static OFDMA configurations are considered, but still the obtained results show that with OFDMA, the median latency can be decreased from 5 ms to less than 1 ms. The authors also remark that uplink OFDMAhas potential for significant performance improvement. However, in this study, the authors consider an idealistic scenario when all STAs can transmit with the fastest MCS and also do not consider frequency selectivity of the channel. In [29], the authors develop a recursive channel splitting and resource scheduling algorithm for 802.11 ax downlink OFDMA transmissions. The developed algorithm implements the divide-and-conquer approach, descending from wide RUs to the smaller ones and splitting the resource allocation problem into sub-problems for smaller RUs. It provides a suboptimal solution because when the problem is divided between RUs, the sets of STAs that are considered are divided, too. The proposed solution is compared with an optimal solution found by exhaustive search, with a suboptimal \"user-pairing resource allocation\" algorithm designed for LTE uplink [30], and with a greedy algorithm that splits the channel into equal-size RUs, the number of which equals to the number of users. In the studied scenario, the recursive algorithm outperforms the other considered suboptimal solutions and does not lose to the exhaustive search. Although the authors consider that the same-size RUs can have different conditions, e.g., caused by frequency-selective fading, they consider only downlink transmissions and thus leave the transmission power and MCS control out of consideration, and while solving the scheduling task, assume that for each STA and RU, the STA uses the best MCS allowed by the signal-to-noise ratio. By contrast, we focus on the power control and MCS selection problem while scheduling OFDMA resources. We show that it is essential to consider the dynamics of the channel while scheduling resources. To sum up, many studies of OFDMA in 802.11 ax focus on OFDMA random access and do not consider the problem of scheduled transmissions or consider it in a very simplified form, while the existing research on schedulers for 802.11 ax disregards the frequency selective and non-stationary properties of the channel, assumes ideal knowledge of CSI at the AP, and omits the power control aspect of uplink OFDMA transmissions. In the present work, we fill this gap, designing a scheduler that can distinguish between the channel conditions in RUs located in different positions in the frequency band and developing a solution to control the transmission power of STAs to let them compensate possible changes in channel attenuation. In our study, we take into account the fact that the AP does not always have actual information about the CSI and buffer status for all the STAs.
## 2 Data and Methods
### Inversion and Modeling Data  
#### 2.2.1 FMS Data  
With the inversion of the TSF before the Yangbi earthquake, 40 FMSs with Mw > 4.8 from 1 January 1976 to 30 April 2021 were collected for the surrounding area (98\({}^{\circ}\) E\(-\)102\({}^{\circ}\) E, 23.7\({}^{\circ}\) N\(-\)27.2\({}^{\circ}\) N). These data were from the Global Centroid Moment Tensor Catalog (GCMT) of Harvard University. The focal mechanisms of these earthquakes show that the focal types in the study area are mainly strike-slip [37; 38].  
We used 13 earthquake events (Ms > 4.0) within 15 km of the epicentral area before and after the Yangbi earthquake to study the changes in the regional TSF. FMSs are shown in Table 1[35].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Earthquake Time**} & \multicolumn{2}{c}{**Epicenter Location**} & \multicolumn{2}{c}{**Magnitude**} & \multicolumn{2}{c}{**Fault-Plane I**} & \multicolumn{3}{c}{**Fault-Plane II**} \\ \cline{2-10}  & **Longitude (\({}^{\circ}\))** & **Latitude (\({}^{\circ}\))** & **Ms** & **Strike** & **Dip** & **Rake** & **Strike** & **Dip** & **Rake** \\ \hline
2021.5.18 21:39 & 99.93 & 25.65 & 4.2 & 41 & 78 & 8 & 309 & 82 & 168 \\
2021.5.19 20:05 & 99.91 & 25.65 & 4.4 & 229 &
## 3 Results and Discussion
### Radiometric Sensitivity of EAP Size-Based PFT Detection--Magnitude of \(\delta R_{rs}\phi\)  
Having established that at low biomass the PFT signal in the blue is easily overwhelmed by the effects of \(a_{gd}(\lambda)\) and \(b_{bmap}(\lambda)\), and that pigment effects are generally secondary to those of \(\delta D_{eff}\), the PFT signal due to phytoplankton scattering in the 500 to 600 nm region can be evaluated for sensitivity in terms of changes in \(D_{eff}\) and biomass. To this end, the EAP model is again coupled with Hydrolight to simulate expected variability in \(R_{rs}\) due to changes in \(D_{eff}\) with the aim of evaluating the sensitivity of the model.  
Figure 10A demonstrates how the combined effects of biomass and \(D_{eff}\) interact to form the maximum available \(\delta R_{rs}\phi\) signal at low biomass and small size ranges. The figure shows that this maximum lies between 520 and 570 nm--the exact wavelength varies with both size difference and biomass. The shifting position of maximum \(\delta R_{rs}\phi\) is shown in Figure 10B. Increasing biomass improves the ability to trace the size-related effects, and a \(D_{eff}\) change from 2 um up to at least 8 um is not detectable at the threshold in oceanic conditions with Chl \(a<1\) mg/m\({}^{3}\).  
Figure 9: \(\delta R_{rs}\) sensitivity to \(a_{gd}\) and \(b_{bmap}\) at 570 nm, for a high biomass _Myrionocta rubra_-dominated assemblage, to a high biomass peridinin (carotenoid)-containing dinoflagellate-dominated assemblage.  
Using \(1\times 10^{-3}\) sr\({}^{-1}\) as a threshold for detection by satellite, it can be seen that an ecologically significant shift in \(D_{eff}\) from 2 or 3 to 6 \(\upmu\)m, such as at the onset of an oceanic bloom, looks potentially detectable from about 2 mg/m\({}^{3}\). By 10 mg/m\({}^{3}\), even a small change in \(D_{eff}\) results in a detectable change in \(R_{rs}\phi\), but, as biomass falls below this, the change in \(D_{eff}\) must be increasingly large to be detected. This is consistent with inversion studies of EAP sensitivity [37]. Note that this experiment addresses only the phytoplankton-related signal, and that when attempting to identify these signals in the bulk \(R_{rs}\), it is necessary to consider the sensitivity of the phytoplankton signal to the optical effects of the non-algal constituents. These results can be considered to show the minimum threshold for potential detection i.e., the signal is further ambiguated by non-algal optics in a real-world context.  
The spectrally shifting nature of the \(\delta R_{rs}\phi\) signal for oceanic PFT applications provides a strong case for hyperspectral sensors in the 520 to 570 nm wavelength region. The extent to which the \(\delta R_{rs}\phi\) signal persists in fixed waveband ratios is investigated in the next section on shape sensitivity.