esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Satellite Platform Vibration Spectrum Model
### Maximum Likelihood Estimation (MLE) of Vibration Error  
The onboard accelerometer can measure vibration amplitude samples for the pointing error in both the horizontal (\(x\)) and vertical (\(y\)) directions [48; 52]. It is assumed that these \(x\) and \(y\) samples are independent and identically distributed (i.i.d.) zero-mean Gaussian random variables with the same variance. Then, the radial pointing error \(R\) can be computed as follows:  
\[R=\sqrt{X^{2}+Y^{2}}. \tag{66}\]  
Since \(R\) represents the magnitude of a two-dimensional vector formed by independent Gaussian-distributed variables, it follows a Rayleigh distribution. The PDF of the Rayleigh distribution for the random variable \(R\) is given by  
\[f_{R}(r)=\frac{r}{\sigma_{\mathrm{vib}}}\exp\left(-\frac{r^{2}}{2\sigma_{ \mathrm{vib}}^{2}}\right), \tag{67}\]  
where \(\sigma_{\mathrm{vib}}\) is the standard deviation of the optical beam's displacement due to platform vibration. To estimate the standard deviation \(\sigma_{\mathrm{vib}}\) of the optical beam's displacement due to platform vibration from the observed data \(R_{i}\), the MLE method can be employed. MLE is a statistical method used to estimate the parameters of a probability distribution based on observed data. The principle behind MLE is to find the parameter values that maximize the likelihood function, which represents the probability of observing the given data under different parameter values.  
The MLE for the standard deviation \(\sigma_{\mathrm{vib}}\) of a Rayleigh distribution can be derived by maximizing the likelihood function, and the expression for the estimation of \(\sigma_{\mathrm{vib}}\) can be written as follows [53; 54]:  
\[\hat{\sigma}_{\mathrm{vib}}=\sqrt{\frac{1}{2m}\sum_{i=1}^{m}R_{i}^{2}}, \tag{68}\]  
where \(R_{i}=\sqrt{X_{i}^{2}+Y_{i}^{2}}\) are the radial pointing error values calculated for each sample in a given window, and \(m\) is the number of samples in the window.  
Due to the dynamic nature of the satellite platform's vibration environment, a sliding window approach can be applied to estimate the standard deviation \(\sigma_{\mathrm{vib}}\) to suit the dynamic nature of the laser link communication environment. This allows the estimator to adapt to changes in the platform's vibrational characteristics over time. The sliding window size can be varied, and the standard deviation \(\sigma_{\mathrm{vib}}\) can be estimated using the MLE method described above.  
Figure 7: (**a**) Satellite platform vibration PSD. (**b**) Time-domain pointing error signal.  
In this method, the basic steps are as follows. For each sliding window of size \(w\), the radial errors \(R_{i}\) can be calculated for the corresponding \(X_{i}\) and \(Y_{i}\) values. The MLE for \(\sigma_{\text{vib}}\) can then be computed and updated for each new window position as the window moves through the dataset. This approach provides a dynamic estimation of the standard deviation of the radial pointing error.
# Performance-Determining Variables of a Simulated Skimo
## 2 Methods
### Experimental Design  
Conducted over three days, the study involved a series of tests evaluating both upper and lower body strength, along with various endurance parameters. These tests included double poling on an ergometer, running on a track, and uphill skiing on a treadmill. The assessment protocol comprised submaximal stages, incremental tests to exhaustion, and a maximal sprint to determine the participants' maximal glycolytic capacity.  
One week after these laboratory tests, the skiers participated in a simulated on-snow skimo sprint competition (see Figure 1). The researchers analyzed overall performance and measured the time spent in seven specific sections of the competition. During these assessments, continuous monitoring was conducted using a heart rate (HR) monitor and a global navigation satellite system (GNSS). The combination of these tests aimed to provide comprehensive insights into the skiers' physiological capacities and performance characteristics. For the simulated sprint competition, participants used their personal skiing gear, including poles, boots, skis, and climbing skins. Standardized skins were provided during all laboratory skiing examinations. All participants were familiar with the testing protocols.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training** & \\ \hline  & **Hours** & **\%** \\ \hline Total & 278:58 \(\pm\) 82:37 (202:33-355:23) & \\ Zone 1 & 193:01 \(\pm\) 68:16 (129:53–256:10) & 68.69 \(\pm\) 11.75 (57.83–79.56) \\ Zone 2 & 67:15 \(\pm\) 30:29 (39:03–95:26) & 24.17 \(\pm\) 9.39 (15.49–32.85) \\ Zone 3 & 11:20 \(\pm\) 05:15 (06:28–16:11) & 4.41 \(\pm\) 2.81 (1.81–7.00) \\ Zone 4 & 05:23 \(\pm\) 01:24 (04:05–06:41) & 1.97 \(\pm\) 0.26 (1.73–2.20) \\ Zone 5 & 01:37 \(\pm\) 01:04 (00:37–02:36) & 0.65 \(\pm\) 0.49 (0.20–1.09) \\ \hline Zone 1, 55–72\% of HRmax; Zone 2, 72–82\% of HRmax; Zone 3, 8–87\% of HRmax; Zone 4, 87–92\% of HRmax; Zone 5, 92–100\% if HRmax. Based on the 5-Zone Intensity Scale used by the Norwegian Olympic Federation [22]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive data of 6-month training volume and training intensity distribution in a group of male and female skimo athletes. Data are presented as mean \(\pm\) SD (CI).  
Figure 1: A schematic overview of the experimental protocol. Abbreviations: VLmax, maximal glycolytic capacity test; SJ and CMJ, squat jump and counter movement jump tests; 1 RM, one-repetition maximum tests; GElower, gross efficiency test during treadmill skiing; VO2 max, maximal oxygen uptake test during treadmill skiing; GEupper, gross efficiency during upper body ergometry; VO2 peak, peak oxygen uptake during 3 min test on ski ergometer.
## 2 Materials and Methods
### Short-Time Fourier Transform  
Ultrasonic wave signals can be analyzed in the time and frequency domains. Time-of-flight and amplitude can be determined in time-domain analysis, and the components of frequencies and their magnitudes can be found from the frequency-domain analysis. The frequency spectrum of the original signal (\(f(t)\)) can be obtained by a Fourier transform as defined by [33]  
\[F(\omega)=\int_{-\infty}^{+\infty}f(t)e^{-i\omega t}dt \tag{1}\]  
where \(F(\omega)\) is the amplitude of the component of frequency, \(\omega\) is the angular frequency, and t is time. The original signal, \(f(t)\), can be reconstructed by the summation of all frequency components. One of the drawbacks of the Fourier transform is that it does not offer any information about the original signal; hence, it is not useful for analyzing time-variant and non-stationary signals. From the Fourier transform of a continuous signal, only the frequency information at several time intervals can be acquired. In this study, to overcome this limitation, the STFT was applied to the received ultrasonic GW signals propagated through specimens. The STFT for a window function (\(h\)) is defined [33] as:  
\[STFT(\omega,\tau)=\int_{-\infty}^{+\infty}f(t)h(\tau-t)e^{-i\omega t}dt \tag{2}\]  
where the term, \(f(t)h(\tau-t)\), has some information about the original signal \(f(t)\) at time \(\tau\). The result of \(STFT(\omega,\tau)\) presents information about the original signal \(f(t)\) at time \(\tau\) and frequency \(\omega\). Furthermore, the original signal \(f(t)\) can be reconstructed from a combination of localized information in the time and frequency domains [33], as indicated below.  
\[f(\tau)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}STFT( \omega,\tau)\psi(\tau-u)e^{-i\omega t}d\omega t \tag{3}\]  
The resulting image of the STFT of the received ultrasonic GW signal propagated through the 304 SS plate represents the relationship between time and frequency; it is possible to express the time information according to frequency. Furthermore, dispersion characteristics can be seen clearly in each wave propagation mode. The dispersion rate depends on the propagation distance of the ultrasonic GW, but the dispersion trend is preserved. Hence, it is possible to classify the defects by analyzing the mode changes in the resulting image of the STFT of the ultrasonic GW signals from specimens with and without defects.  
By analyzing the result in the time-frequency domain, it can be depicted as an image diagram to confirm the mode conversion difference. In a time-frequency image map, the color of each pixel represents an amplitude value for that time and frequency. Therefore, by comparing the difference in the number of pixels in the respective image diagrams, it can be confirmed that the color of the pixels in the image diagram varies according to the difference in the presence or absence of defects. Through this process, samples can be classified according to the presence or absence of defects from the STFT result of the received guided ultrasound.
# HIEN: A Hybrid Interaction Enhanced Network for Horse Iris Super-Resolution
## 2 Materials and Methods
### Shallow Feature-Extraction Module
#### Image-Reconstruction Module  
The image-reconstruction module aims to enlarge the feature map from a smaller size to the ground truth size. It consists of a pixelshuffle convolution layer and a \(3\times 3\) convolution layer. The pixelshuffle convolution effectively redistributes pixel values in the spatial domain, reducing checkerboard artifacts, while the \(3\times 3\) convolution layer maps the feature space to the color space [20]. Finally, the image-reconstruction module generates high-quality images \(I_{SR}\in\mathbb{R}^{(h\times s)\times(w\times s)\times 3}\), where \(s\) represents the upsampling factor. This process can be represented as:  
\[I_{SR}=\text{Conv}_{3\times 3}(H_{\text{up}}(F_{d})) \tag{5}\]  
where \(H_{\text{up}}(\cdot)\) denotes the subpixel convolution operation, and \(I_{SR}\) represents the super-resolved image.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Stage** & **Module / Key Operation** & **Output Tensor Shape** \\ \hline Input & Low-Resolution Image & \(3\times 64\times 64\) \\
1. Shallow Feature Extraction & \(3\times 3\) Convolution & \(180\times 64\times 64\) \\
2. Deep Feature Extraction & & \\
2a. Local Path & LFEM with ERDBs & \(180\times 64\times 64\) \\
2b. Global Path & GFEM with PATBs & \(180\times 64\times 64\) \\
2c. Feature Fusion & Addition + Convolution & \(180\times 64\times 64\) \\
3. Image Reconstruction & PixelShuffle + Convolution & \(3\times 256\times 256\) \\ Output & Super-Resolved Image & \(3\times 256\times 256\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data flow and tensor shape transformation within HIEN. The shapes are presented in the format (Channels \(\times\) Height \(\times\) Width).
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Process Logs and Models
#### 2.5.3 Dependency Graphs  
**Definition 6**.: _A dependency graph is a directed graph \(\mathcal{G}=(V,E)\) over the set of activities \(\mathcal{A}\), where \(V\subseteq\mathcal{A}\) and \(E\subseteq V\times V\) are edges that encode causal relationships between the activities. The set \(V\) includes two distinguished activities: the initial activity \(a_{{}_{\top}}\), and the final activity \(a_{{}_{\top}}\), such that every other activity in \(V\) is reachable from \(a_{{}_{\bot}}\), and \(a_{{}_{\top}}\) is reachable from every other activity. Each activity \(a\in V\setminus\{a_{{}_{\bot}},a_{{}_{\top}}\}\) must occur along some path from \(a_{{}_{\bot}}\) to \(a_{{}_{\top}}\). Additionally, for all edges \((a_{x},a_{y})\in E\) it must hold that \(a_{x}\
eq a_{{}_{\top}}\) and \(a_{y}\
eq a_{{}_{\bot}}\)._2.5.4. Process Discovery and Background Knowledge  
Because many dependency graphs can in principle be associated with a given log by process mining algorithms, expert background knowledge plays an essential role in identifying the particular graph among those graphs modeling the data that best fits the needs and specifications of the process. Given the focus of this paper, a hybrid approach to process mining is particularly useful when the log in question is incomplete, i.e., when not all possible behaviors of a process have been recorded. In such cases, expert background knowledge can compensate for a lack of data during mining activities. This knowledge can be expressed in the form of precedence constraints over the graph topology. Precedence constraints define the order in which activities should occur in a process, and can be either positive or negative. Positive constraints take one of two forms: an arc constraint \(S\to a\), indicating that a set of activities \(S\) must directly precede activity \(a\), or a path constraint \(S\leadsto a\), indicating that a set of activities \(S\) must precede activity \(a\), though not necessarily directly. A negative constraint is the negation of a positive constraint, meaning that certain activities or paths must not precede others.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 2 Background and Related Works
\begin{table}
\begin{tabular}{p{12.3 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & **Optimisation Problem** & **Algorithm** & **Gap** \\ \hline
[29] & Power Control for Downlink SINR & Deep Reinforcement Learning & It only considers the interference problem with terrestrial UEs and does not consider UAVs. \\ \hline
[30] & Power control for uplink power and sum rate & deep neural networks & It only considers the interference problem with terrestrial UEs and does not consider UAVs. \\ \hline
[36] & beamforming and power control for uplink sum-rate & convex optimisation & To ensure the optimal performance of the power domain concept at the receiver, it is imperative that the channel gain difference between users is appropriate. This requirement limits the effective number of user pairs that can be served by clusters. \\ \hline
[37] & beamforming for SINR & Q-learning & Q-learning becomes inefficient when the state–action space becomes excessively large \\ \hline
[38] & SINR & Mean-Field Game Theory & When the number of users in game theory increases, it is challenging to implement \\ \hline
[39] & Power control for SINR and spectral efficiency & CNN & It deals with energy harvesting networks, which is similar to a UAV but still does not take into account parameters like altitude \\ \hline Proposed & Power control for SINR & Deep Q-learning & The proposed algorithm solves the problem of interference mitigation for UAVs owing to the higher altitude and favourable LoS characteristics \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature. increasing the complexity. Another method employed within the literature is path planning for UAVs, as discussed in [21; 22]. This optimises the trajectory of the UAV so it can take the path with the least interference possible. One clear disadvantage of this would relate to not knowing the path of the UAV beforehand. In addition, dynamic routes could also render this method ineffective. Even though current AI/ML approaches such as [8; 27; 28; 29; 30] have been effective at mitigating interference, these methods have been deployed for UAVs in general or for users in 5G networks but not both, which is the unique problem that the proposed method in this paper solves. In Table 1, the literature is summarised and shows that the current solutions for mitigating interference are not sufficient. To address this issue, this paper proposes an interference mitigation approach building upon the work presented in [12].
## 4 Conclusions  
1. The heterojunction CdS/Pb\({}_{5}\)(CO\({}_{3}\))\({}_{3}\)O(OH)\({}_{2}\) was deposited on glass and glass/ITO substrates via CBD, to which an ion exchange process with selenium ions was applied to obtain the heterojunction CdS/PbSe.
2. The films evolved were uniform and had a good adhesion on the heterostructure ITO/CdS/PbSe, which was characterized by I vs. V, obtaining an ohmic response.
3. Thermal annealing was necessary to improve the diffusion between the layers of the ITO/CdS/PbSe heterostructure and to obtain well-interlaced interfaces, changing from an ohmic to a diode response in the I vs. V characterization.
4. The turn-on voltage and ideality factor under the darkness condition of the diode after thermal annealing were 0.8 V and 2.4, respectively. The ideality factor value may be due to diffusion between ITO/CdS caused by the annealing treatment, which behaved as a MOS hybrid layer, suggesting a Schottky diode response.
5. An ITO/CdS/PbSe device made by aqueous solution methods represents a good, scalable, and promising alternative to produce cost-effective devices.
# Quantum Computing Approaches for Mission Covering Optimization
## 4 Algorithm Implementation
### Scenario 2 (Two Constraints)  
#### 4.2.1 QA and QAOA  
In this scenario, the objective function measures just the mission cost, as opposed to the previous scenario that also measures precedence cost. Therefore, the objective function is the sum of the mission cost and the constraint function:  
\[f_{obj}(x)=\sum_{m\in\mathbf{M}}\mathsf{MC}(x,m)+\lambda\cdot\mathsf{CONSTR}( x). \tag{21}\]  
Two different constraints are embedded in the objective function using Lagrange multipliers. The first one was discussed in the previous scenario and assures that resources are allocated to no more than one mission. It is formulated in a similar way as before:  
\[\mathsf{CONSTR}_{1}(x,r)=\Big{(}\sum_{m\in\mathbf{M}}x_{m,r}-1\Big{)}^{2}. \tag{22}\]  
The additional constraint (buddy constraint) requires that the amount of resources allocated from set \(\mathbf{R}_{1}\) must be the same as the number of resources allocated from set \(\mathbf{R}_{2}\). This hard equality is formulated as:  
\[\mathsf{CONSTR}_{2}(x,m)=\Bigg{(}\sum_{r\in\mathbf{R}_{1}}x_{m,r}-\sum_{r\in \mathbf{R}_{2}}x_{m,r}\Bigg{)}^{2}. \tag{23}\]  
The total constraint function expressed in Equation (21) can be expressed as the sum of both of these constraints:  
\[\mathsf{CONSTR}(x)=\sum_{r\in\mathbf{R}}\mathsf{CONSTR}_{1}(x,r)\] \[+\sum_{\begin{subarray}{c}m\in\mathbf{M}\\ m\
eq l\end{subarray}}\mathsf{CONSTR}_{2}(x,m)\quad. \tag{24}\]  
For QAOA, the starting state and the mixing Hamiltonian are the same as defined in Equations (12) and (14).
## 2 Materials and Methods
### Feature Extraction and Classification Using Conventional Models  
To feed the data to SVM classifiers, features were extracted from T1, FLAIR, and T1-FLAIR images by using handcrafted and transfer learning approaches. The T1-FLAIR image was obtained after subtracting FLAIR image from the T1-weighted image.  
**Handcrafted features:** An area of \(5\times 5\times 5\) was selected around each image voxel and 11 features including (i) intensity of images (1 feature); (ii) statistical measures such as mean, median, standard deviation, skewness, and kurtosis (5 features); (iii) gray-level co-occurrence matrix based features, including contrast, correlation, energy, and homogeneity [27] (4 features); and (iv) distance of each voxel from the segmented ventricles (1 feature) were extracted. Ventricles were segmented using [PERSON]'s method, and distance of each voxel from the ventricles was calculated. These features were extracted using several window sizes such as \(3\times 3\times 3\), \(5\times 5\times 5\), \(7\times 7\times 7\), and \(9\times 9\times 9\), and segmentation accuracy was separately calculated for each window size (supplementary material, Table S1). The segmentation accuracy using window size of \(5\times 5\times 5\) was higher than that achieved using other window sizes, therefore, we reported results using window size of \(5\times 5\times 5\).  
**Transfer learning features:** A CNN model that was pre-trained on 1.2 million 3-channel images of the ImageNet LSVRC-2010 (imagenet_vgg_f [28]) was adapted. The model was provided by the VLfeat library [29] as part of their MatLab toolbox (MatConvNet) for computer vision applications. This CNN utilizes multilayer Perceptrons with hidden layers and is a type of deep feed forward neural network. To apply the pre-trained model to our data and extract transfer learning features, a 2-step process was adopted. The first step was to create 3-channel/sliced images using T1, T1-FLAIR, and FLAIR images for each given patient. For instance, for T1, T1-FLAIR, and FLAIR images of size mxnxk = \(180\times 192\times 256\), k = 256 3-channel images of size mxnx3 = \(180\times 192\times 3\), where 3 slices/channels of each image were obtained from the corresponding slices in T1, FLAIR, and T1-FLAIR images, were generated. In the second step, window size of \(11\times 11\times 3\) was considered around each voxel and MatConvNet model was applied, yielding 4096 features for each \(11\times 11\times 3\) size window, thereby resulting in 4096 features per voxel.
## 3 Results and Discussion  
### BMP Tests  
Methane production during the BMP test was regular for batches fed with the MW or the mixture between the former and AS (Figure 2 and Table 4). Final BMP values were very similar: 421 \(\pm\) 13 NmL-gVS\({}^{-1}\) for MW and 420 \(\pm\) 23 NmL-gVS\({}^{-1}\) for the mixture respectively; the very low standard deviation witnesses the uniformity of the production among batches. Moreover, results confirm those reported in the scientific literature for similar substrates [27; 28], corroborating that MW is an excellent substrate for anaerobic digestion. Notably, due to the supply of nitrogen from the inoculum, the benefit of optimizing the C/N ration by adding the AS is not evident in batch tests.  
The results relative to batches fed uniquely with AS are completely different, even if the difference was expected. At the end of the experiment, their production was, on average, lower than that of the blank (inoculum); in this case, the standard deviation was also very limited, thus confirming the consistency of the results. The most likely reason for the observed behaviour is due to the presence of \(d\)-limonene in the substrate. In fact, the initial concentration of the latter at the beginning of the experiment was of about 4870 mg\(\cdot\)L\({}^{-1}\) and therefore well above the level severely inhibiting anaerobic digestion [14].  
Figure 2: Methane cumulative production during BMP tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Reactor 1** & **Reactor 2** & **Reactor 3** & **Reactor 4** \\ \hline
**Reinoculation (end of start-up phase)** & YES & YES & NO & YES \\
**Loading (regime phase)** [gVS-L\({}^{-1}\)-day\({}^{-1}\)] & 1 & 0.5 & 1 & 0.5 \\
**Market Waste (TS basis)** & 100\% & 100\% & 95\% & 95\% \\
**Anchovy Sludge (TS basis)** & - & - & 5\% & 5\% \\
**C/N substrate** & 36.3 & 36.3 & 24.7 & 24.7 \\
**Substrate addition [g-d\({}^{-1}\)] (regime phase)** & 10.00 & 5.00 & 10.10 & 5.05 \\
**Expected regime \(d\)-limonene conc. [mg\(\cdot\)L\({}^{-1}\)]** & - & - & 680  \\
**Addition of GAC—10 g\(\cdot\)L\({}^{-1}\) (days)** & - & - & 74–83 & - \\ \hline \end{tabular}
\end{table}
Table 3: Semi-continuous experiments.  
The analyses on digestate at the end of the experiments (Table 4) further confirm these trends: while the reactors fed with MW and with the mixture perform similarly (and similarly to the positive control fed with cellulose data; not shown) in terms of residual COD, ammonium ion and chloride concentration, and VFA, the batches fed with AS only present a higher residual COD (+63%) and a high concentration of VFA (+510%). The latter indicates that the conversion of the substrate to VFA occurs, but the high concentration of _d_-limonene severely affects the methanogens, triggering the accumulation of VFA up to toxic values [29; 30; 31]. The results of the batch tests indicate that AS, even in the presence of quite high amounts of _d_-limonene, is potentially a good substrate for co-digestion with carbonaceous feedstocks.