esa-sceva/satcom-chunk-collection huggingface dataset:
## Framework
### Dictionary Construction
#### 2.3.1 Sub-Dictionary Learning Approach  
The SCC online dictionary learning algorithm [24] shown in Algorithm 1 extracts eigenvalues from each cluster and builds the corresponding sub-dictionary. The dictionary and sparse code are initialized and denoted as \(\mathbf{D}_{1}^{1}\), \(\mathbf{z}_{i}^{0}\) = 0, i = 1, 2,... n respectively. The general expression of the sparse code is \(\mathbf{z}_{i}^{k}\) = 0, i = 1, 2,... n, k = 1, 2,... n and learning rate \(\eta_{1}^{1}\) = 1. The number of epochs and the index of data points are represented as superscript k and subscript i respectively. It acquires an image patch \(\mathbf{x}_{i}\), when \(k\) = 1 and \(i\) = 1. The sparse code \(z_{i}^{k}\) is updated by a few steps of coordinate descent (CD):  
\[z_{i}^{k}=CD\big{(}\mathbf{D}_{i}^{k},\mathbf{x}_{i},\mathbf{z}_{1}^{k-1}\big{)} \enspace, \tag{3}\]  
The \(j\)-th coordinate \(z_{i,j}^{k-1}\) of \(\mathbf{z}_{i}^{k-1}\) (\(0\leq j\leq m\)) is updated as follows:  
\[b_{j}\leftarrow(d_{i\eta_{j}^{k}})^{T}(x_{i}-\mathbf{D}_{i}^{k} \mathbf{z}_{i}^{k-1})+z_{i,j}^{k-1}\enspace, \tag{4}\] \[z_{i,j}^{k-1}\gets h_{\lambda}(b_{j})\enspace, \tag{5}\]  
where \(h_{\lambda}\) is a soft threshold shrinkage function [27; 28] and \(b_{j}\) is a descent parameter that can be calculated by Equation (5). An updating cycle is equivalent to one step of coordinate descent. Dictionary D is updated by stochastic gradient decent (SGD):  
\[\mathbf{D}_{i+1}^{k}=P_{B_{m}}(\mathbf{D}_{i}^{k}-\eta_{i}^{k} \
abla_{\mathbf{D}_{i}^{k}}f_{i}(\mathbf{D}_{i}^{k},\mathbf{z}_{i}^{k}))\enspace, \tag{6}\]  
where \(P\) denotes the projection operator, and \(B_{m}\) is the feasible set of D that is defined as follows: \(B_{m}=\{D\in\mathbb{R}^{m\times n}:\forall j=1,\ldots,m,||d_{j}||_{2}\leq 1\}\). The learning rate is an approximation of the inverse of the Hessian matrix \(\mathbf{H}=\sum_{k,i}z_{i}^{k}(z_{i}^{k})^{T}\). The gradient of \(\mathbf{D}_{i}^{k}\) can be obtained as follows:  
\[\
abla_{\mathbf{D}_{i}^{k}}f_{i}(\mathbf{D}_{i}^{k},\mathbf{z}_{i}^{k})=( \mathbf{D}_{i}^{k}\mathbf{z}_{i}^{k}-x_{i})(\mathbf{z}_{i}^{k})^{T}\enspace, \tag{7}\]  
This allows \(i=i+1\). If \(i>n\), set \(\mathbf{D}_{1}^{k+1}=\mathbf{D}_{n+1}^{k}\), \(k=k+1\) and \(i=1\). Then it repeats the previous calculating process. When \(k>m\), the calculation stops, m is preset value and usually \(10\leq m\leq 15\). The SCC only runs a few steps of CD to update the sparse codes and SGD algorithm is conducted to update the dictionary.  
All sub-dictionaries \(\mathbf{D}_{1}\), \(\mathbf{D}_{2}\),..., \(\mathbf{D}_{n}\) are learned by using SCC. These sub-dictionaries are used for describing the underlying structure of each image patches cluster.
## 5 Testing the Proposed Algorithm
### The Benchmark Data Set  
To test the performance of the proposed algorithm, we decided to use the same data set used by other researchers that dealt with the Greek HSTP, which we also used in our previous publications. Using the same data set gives an insight about the relative strength of the algorithm in quite a fair manner. The data set we used consisted of ten input files, with six of them constituting the so-called \"Beligiannis' data set\" [1; 44; 45]. It is true that the input data files of the above-mentioned data set may be considered of small to medium size. Nevertheless, we insisted on them firstly for historical reasons and for a fair comparison to other published algorithms, as we explained in the beginning of this section. Secondly, despite their size, these instances were not easy to solve. The interested reader is referred to [PERSON] et al. [12], where a typical integer programming method was found unsuccessful in solving all of them. At this point, we would like to state that three of the ten input files, albeit not being the most difficult ones to solve, were used at the Third International Competition, in 2011. This competition was dedicated to the school timetabling problem. Three of these files, among others, were utilized as a benchmark for testing the competition finalists' algorithms. Next, in Table 3, we present some main characteristics of these input files. They are numbered from 1 to 11, with the file numbered by 6 being absent. This is because the corresponding file had some documentation deficiencies. This fact caused the exclusion of file 6 from several published works. We followed the same exclusion practice. Files 8, 9, and 10 were presented by [PERSON] and [PERSON] [21], while files 8, 9, 10, and 11 were used by [PERSON] et al. [20]. Finally, the above set of files were used, except from [PERSON] and [PERSON] [21] and [PERSON] et al. [20], by [PERSON] et al. [27], [PERSON] and [PERSON] [8; 9; 10], [PERSON] and [PERSON] [46], [PERSON] et al. [28], [PERSON] et al. [11], and [PERSON] et.al. [12] in corresponding research works.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Parameter** & **Meaning** & **Value** \\ \hline HCW & Hard constraints’ weight & 10 \\ BASE & Exponential base & 1.3 \\ IDWC & Class dispersion weight & 0.95 \\ IDWT & Teacher dispersion weight & 0.6 \\ TEPW & Teacher gaps weight & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values used for the fitness function of the Greek HSTP.  
One may easily conclude that the used benchmark data set is quite interesting, since seven files out of ten include teacher unavailability cases, while four out of the six files of the [PERSON] data set include unavailability cases along with co-teaching cases. As was obvious in Section 5.2, where the performance of the proposed algorithm is presented and compared to other algorithms, files 7, 1, 2, and 4, in this order, were the most difficult ones. On the other hand, files 5 and 8 were quite easy to solve.
## 5 Performance Analysis for Fixed-Gain Relaying  
### Outage Probability  
For fixed-gain relaying, the outage probability is expressed by replacing \(\gamma\) with \(\gamma_{th}\) in (31), which is given by:  
\[P_{out}^{F}(\gamma_{th})=F_{\gamma_{eq}^{c}}(\gamma_{th}) \tag{41}\]
## 3 Results and Discussion
### Tensile Strength
The red column of Figure 5B shows the tensile strength of the ternary composite of E51 + E-POSS (10%) + GF (16%); it was obvious that the ternary composite exhibited the highest tensile strength among the E51, E51 + E-POSS, and E51 + GF composites. The tensile strength of the E51 + E-POSS (10%) + GF (16%) composite was 214.9 MPa (Table 1), increasing by around 257.6% compared with the E51 sample. The epoxy groups of E-POSS and E51 reacted with the amino groups of PEPA to form a three-dimensional cross-linking network, and a large number of polar groups such as hydroxyl and secondary amino groups (-NH) were formed around the cross-linking bonds. The hydroxyl polar groups were also produced on the surface of GF due to the hydrolysis of the chemical bonds of silica. These polar groups on the surface of the glass fiber and in the epoxy resin matrix could form strong hydrogen bonds, thus improving the compatibility of the epoxy resin matrix and glass fiber. It can be inferred that simultaneous introduction of E-POSS and GF into E51 resin may yield a synergistic effect on matrix modification for improving the tensile strength of the E51 + E-POSS (10%) + GF (16%) composite.
# The Potential of Visible and Far-Red to Near-Infrared Light in Glaucoma Neuroprotection
## 5 Visible and Far-Red to Near-Infrared Photobiomodulation in Neuronal Differentiation
recently demonstrated that the optic nerve lamina region (ONLR) contains a neural progenitor cell (NPC) niche, which may have a role in both postnatal optic nerve development and in adult optic nerve support and repair [56]. Unmyelinated retinal ganglion cell axons originating in the retina pass through the ONLR, the most anterior portion of the optic nerve, before myelination occurs in the more distal portion of the optic nerve. However, the precise sites of postnatal RGC axonal growth and the mechanism by which myelination occurs at sites of newly formed axonal membrane late in postnatal growth are still obscure [56]. It is well known that ONLR has a number of unusual characteristics: it inhibits intraocular myelination, enables postnatal optic nerve myelination of growing axons, modulates the fluid pressure differences between eye and brain, and is the primary lesion site in age-related open-angle glaucoma. It remains to be investigated whether PBM therapy with red light can effectively stimulate NPC and at least in part regenerate the tissue damaged by glaucoma.
## 5 Implementation and Experiments
### Assessment through Personalized Content  
We conducted a number of experiments to evaluate the delivered eLearning system. The implemented eLearning system is provided as a cloud service and hence the only environmental constraint for accessing the system is the use of a web browser.
## 2 Materials and Methods
### Bayesian Optimization  
Bayesian optimization is a strategy for the global optimization of black-box functions. A black-box function is an unknown analytic expression of the function, and the evaluation of the function is restricted to a sample at each point [37]. The core components in Bayesian optimization are the objective function, the surrogate model, and the acquisition function. The objective function is the black box function used to maximize compliance with some parameters. For example, in the classification problem, accuracy can be used as the objective function. The surrogate model is a model to approximate the objective function. The Gaussian processes and Parzen-Tree Estimator are the most popular models for the surrogate model [38]. The acquisition function is used to determine where to evaluate the objective function next. There are many options for acquisition functions, such as the Probability of Improvement (PI), Expected Improvement (EI), and upper confidence bound [38].  
The Bayesian algorithm is as follows [39]:  
1. Choose a surrogate function to approximate the objective function and define its prior. The choice of this function is optional; the algorithm is convergent, and the choice of the prior function can help increase the speed of convergence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Dropout Size**} \\ \hline Dropout on & 0.00000 & 0.10000 & 0.20000 \\ outputs & 0.02050 & 0.01970 & 0.01960 \\ inputs & - & 0.01996 & 0.02000 \\ recurrent-outputs & - & 0.01950 & 0.09136 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation MSE for different dropout size (Loc. 3).  
Figure 6: The left-hand side is the general CNN architecture, and the right-hand side is the convolutional operation.  
Given a set of observations, [PERSON] rule is used to obtain the posterior distribution over the objective function. [PERSON] rule is given by Equation (10): \[P(A|B)=\frac{P(B|A)P(A)}{P(B)}\] (10) where \(A\) and \(B\) are models and new observations, respectively; \(P(A)\) and \(P(B)\) are the probabilities of the prior distribution (probability that \(A\) is true) and the probability that \(B\) is observed, respectively; \(P(A|B)\) is the posterior distribution, and \(P(B|A)\) is the likelihood of the event \(B\) occurs if model \(A\) is true.
3. Use an acquisition function \(\alpha\) which is a function of the posterior distribution, to determine the next sample point as \(x_{t}=\underset{x}{\operatorname{argmax}}\ \alpha(x)\).
4. Add the new sample to the observation set and go back to step 2 until convergence or budget has elapsed.  
In this paper, Bayesian optimization [40] is used to minimize the loss function in terms of batch size, dropout size, learning rate, and decay. Figure 7 shows the validation loss during Bayesian optimization for Loc. 3.
## 5 Measurements and Results
### Mirrors on Calcium Fluoride Substrates  
Calcium fluoride is a widespread material in IR spectroscopy in the wavelength range 0.15-9 \(\upmu\)m. The CaF\({}_{2}\) crystal has high mechanical strength (withstands high pressure) and is non-hygroscopic. It is believed that it is sensitive to thermal effects (although the melting point is rather high at 1148 \({}^{\circ}\)C). At room temperature, it is optically isotropic. The difference between the refractive indices of the extraordinary and ordinary rays is very small (\(n_{e}-n_{o}\)) \(\lesssim\) 0.001.  
Our measurements of the FP cavity with mirrors on calcium fluoride substrates showed that its integral characteristics (finesse and contrast) are practically kept in a wide range of temperatures, from room to helium. A slight drop in the contrast 20% was observed during the passage beyond the mark of 60 K. (It should be noted that the thermal conductivity of calcium fluoride in the region from 60 K to 40 K increases from \(\sim\)1 W/cm\(\cdot\)K up to \(\sim\)10 W/cm\(\cdot\)K [28]. Perhaps this could change the deformation balance in the mirror substrate and destroy the mode matching.)  
This is reflected in Figure 6, where the cooling curves for the two optical pump powers 10 mW and 350 mW are presented. When warming up, the contrast value was restored with a small hysteresis, which is the same as for the mirrors with other substrates studied above.  
Figure 5: Splitting of optical resonance with cooled sapphire mirrors: T = 190 K, splitting of peaks \(\sim\)120 kHz, width of coupled resonance \(\sim\)600 Hz (green curve, observed peak; blue curve, model peaks; and red curve, synthesized curve).
## 2 Methods
### Algorithm Design  
LSTM is suitable for processing time-series data and can accurately predict changes in driver's vigilance by modeling long-term dependencies. Attention-based Fusion assigns and dynamically adjusts weights to features for adaptive feature weighting according to the inputs.  
The driver recognition model (A-LSTM in a later section) uses LSTM to process EEG, eye movement, and ECG indicators through two LSTM layers to extract time-related features. The first LSTM layer (LSTM1) captures temporal dependencies from the input data, followed by a dropout layer and Batch Normalization (BN) layer to prevent overfitting and ensure dimensional stability. The processed features are then passed through a second LSTM layer (LSTM2) to further refine temporal relationships.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & \multicolumn{2}{c}{**Indicator**} \\ \hline EEG & Theta Power & Alpha Power \\  & Low Beta Power & Theta/Alpha Power Ratio \\ \hline Eye movement & Head Heading & Head Pitch \\  & Eyelid opening & Pupil meter \\ \hline  & Standard Deviation of NN & Root Mean Square of the Successive \\  & intervals (SDNN) & Differences (RMSSD) \\ ECG & Number of PPG Peak Interval & Number of PPG Peak Interval \\  & Differences \textless{} 20 ms (N20) & Differences \textless{} 50 ms (N50) \\  & Heart Rate (HR) & Low-Frequency Power (LF) \\  & High-Frequency Power (HF) & Low/High Frequency Ratios (LHR) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physiological indicators summary.  
After the LSTM layers, an attention mechanism is applied to assign importance weights to the features from each physiological modality, allowing the model to prioritize the most relevant patterns for detecting changes in driver vigilance. These weighted features are subsequently fed through another BN layer to ensure dimensional appropriateness before entering the fully connected layers.  
Finally, the model accomplishes the recognition task by making decisions based on the combined features of all input signals through two fully connected layers.  
To address data imbalance, the model employs a cross-entropy loss function with category weights. It uses the Adam optimizer with an initial learning rate of 0.001 and includes a learning rate scheduler that reduces the learning rate by 0.1 if the loss does not significantly decrease within 5 epochs. The model undergoes a total of 50 training sessions, each consisting of 50 epochs.
## 3 Results and Discussion
### Identification of LAB Resistant to Pasteurization Process  
After enumeration, presumptive LAB (Gram-positive and catalase negative) were isolated, purified and analyzed by RAPD-PCR in order to recognize the different strains that overcome the thermal treatment. RAPD analysis showed the presence of 4 different  
Figure 3: pH decrease during 7-days of curd acidification. Empty symbols: control curds. Full symbols: experimental curds. Results indicate mean values \(\pm\) SD of the four determinations (carried out in duplicate for two independent productions) of all trials.  
strains (Figure 4a) from a total of 25 presumptive LAB isolates that formed four distinct phenotypical groups 1 for rods and 3 for cocci (Figure 4b).  
The sequencing of 165 rRNA gene indicated that the LAB community resistant to pasteurization process was represented by the species _Enterococcus casselfiavus_, _Enterococcus faecalis_, _Enterococcus faecalis_ and _Lactobacillus fermentum_ (recently reclassified as _Limosilactobacillus fermentum_) [38]. All these species are part of the non-starter LAB community implicated in the ripening process of cheees [39] and represent an essential part of the microbiota of raw ewes' milk cheees. In particular, enterococci are often isolated from raw ewe's milk [40], while _Ls. fermentum_ are during cheese ripening [41].
## 2 Related Work
### Decentralized Storage Solutions  
Research in the field of decentralized storage solutions explores mechanisms to overcome the limitations of traditional centralized storage systems, such as vulnerability to censorship and risks of data breaches. Recent studies have examined various approaches and technologies aimed at enhancing the security, efficiency, and resilience of blockchain-based storage solutions.  
Article [12] provides an overview of centralized and decentralized storage methods, focusing on technologies such as IPFS, Swarm, and Sia. A comparative analysis revealed that each solution had specific strengths and weaknesses depending on the requirements of various blockchain applications. The authors emphasize that decentralized systems offer greater fault tolerance, enhanced privacy, and improved scalability compared to centralized solutions.  
In [13] the author focuses on Filecoin, a blockchain-based storage system built on IPFS. It analyzes the unintended centralization introduced by its reward mechanism, which favors large storage miners capable of mobilizing significant resources. Although Filecoin aims for full decentralization, the results show that large enterprises with excess storage capacities dominate block creation, limiting the competitiveness of smaller nodes.  
The author in [14] provides a technical overview of next-generation peer-to-peer data networks such as IPFS, Swarm, and Arweave. IPFS is highlighted as a groundbreaking solution due to its content-based addressing and deduplication capabilities. These features enable secure and efficient data sharing while facilitating integration with blockchains for various applications, including IoT and content distribution.
## 2 Many-Objective Optimal Power Flow (MaOPF) Problem
### Objective Functions
#### 2.1.1 Fuel Cost  
To reduce the system operation cost, the fuel cost function is considered to be minimized which is formulated as given below:  
\[f_{1}(x,u)=\sum_{i=1}^{N_{g}}\left(a_{i}P_{gi}^{2}+b_{i}P_{gi}+c_{i}\right) \tag{4}\]  
where \(f_{1}\) is the fuel cost function; \(N_{g}\) is the number of generators; \(P_{gi}\) is the active power of the \(i\)th generating unit; and \(a_{i}\), \(b_{i}\), and \(c_{i}\) are the fuel cost coefficients.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 4 Q-RPL: Q-Learning-Based Routing Protocol Design
### Q-Learning Algorithm  
Q-learning is one of the most popular RL algorithms [44]. It is an off-policy learner that seeks to find the best action to take given the current state. It is known for its simplicity and effectiveness, especially in environments with a finite number of states and actions. In Q-learning, a Q-function is used to measure the quality of a state-action pair, based on the observed reward. The following equation shows how the Q-values are updated when action \(a\) is taken by a node \(i\) in state \(s\), yielding a reward \(r\).  
\[Q(s^{i},a^{i})\leftarrow(1-\alpha)Q(s^{i},a^{i})+\alpha[r+\gamma\max_{a^{i}}Q( s^{j},a^{i})] \tag{1}\]  
where \(Q(s^{i},a^{i})\) is the value of the current state-action pair, \(\alpha\in[0,1]\) is a parameter called learning rate, \(r\) is the reward received after taking action \(a\), \(\gamma\in[0,1]\) is another parameter named discount factor, and \(\max_{a^{j}}Q(s^{j},a^{j})\) is the maximum reward expected from all the possible actions at the next hop candidate \(j\).  
The learning rate determines how much new information affects the existing Q-value. A higher learning rate allows the model to adjust more quickly to changes but can cause the learning process to be unstable. Meanwhile, the discount factor balances the importance of immediate and future rewards. A higher discount factor makes the agent more forward-looking by emphasizing the potential future benefits of current actions.  
Other key elements in the design of any RL solution based on the Q-learning algorithm are the formulation of the state-action space, reward, and policy update. The next subsections provide more details about these specific components in our design solution for routing in AMI.
# Measurement of Head-Related Transfer Functions: A Review
## 4 Multi-Loudspeaker-Based Fast HRTF Measurement Methods
### Continuous Measurements
#### 4.2.1 Continuous MESM  
In contrast to the step-wise mechanism, [PERSON] and [PERSON] [123] proposed a method in which a subject rotates continuously using a turntable while loudspeakers play back exponential sweeps. The sweep signals are consecutively played back over loudspeakers with a certain delay relative to each other in an overlapped form [158]. After the last loudspeaker starts playing signals, the first loudspeaker should be restarted with an overlap. This means that the exponential sweep is played back repeatedly via each speaker while the subject rotates continuously. The total measurement duration is clearly reduced compared to the step-wise MESM approach because of the repositioning time saved at each azimuth angle.  
The influence of a rotating subject on the frequency shift (Doppler effect) and the changes in measurement positions have to be taken into account, which may affect the quality of measurement results. [PERSON] and [PERSON] [123] demonstrated that the possible frequency shift caused by the rotation of a human subject is clearly lower than the just noticeable difference (JND) by assuming a fast rotational speed of 15\({}^{\circ}\)/s. Thus, the Doppler effect can be neglected if HRTFs are continuously measured with a typical rotational speed. In the case of the step-wise measurement, the HRTFs obtained from different loudspeaker directions have the same azimuth angle at each step. For the continuous measurement system, the azimuth angle changes continuously when the subject rotates during the measurement. Moreover, the changes in the azimuth angle are frequency-dependent since the excitation signal (exponential sweep) varies its instantaneous frequency with time. [PERSON] and [PERSON] [123] corrected the frequency-dependent offsets by the interpolation of measured HRTFs in the SH domain. In general, the measurement accuracy decreases with the increasing rotational speed. With a rotational speed of 3.8\({}^{\circ}\)/s, there was almost no audible difference to the step-wise measurement system [123; 161].