esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Inner Speech Recognition via Cross-Perception Approach in EEG and fMRI
## 3 Proposed Targeted Improvements
### EEG Signal Processing Enhancements  
Singular Spectrum Analysis (SSA) for EEG Decomposition  
To address the non-stationary nature of EEG signals during inner speech, we employ Singular Spectrum Analysis (SSA). SSA decomposes the original EEG signal into interpretable components, allowing for more effective feature extraction.  
Given an EEG signal \(X=(x_{1},\ldots,x_{N})\), we first construct the trajectory matrix:  
\[\mathbf{Y}=\begin{bmatrix}x_{1}&x_{2}&\cdots&x_{K}\\ x_{2}&x_{3}&\cdots&x_{K+1}\\ \vdots&\vdots&\ddots&\vdots\\ x_{L}&x_{L+1}&\cdots&x_{N}\end{bmatrix} \tag{1}\]  
where \(L\) is the window length, and \(K=N-L+1\).  
We then perform Singular Value Decomposition (SVD) on \(\mathbf{Y}\):  
\[\mathbf{Y}=\mathbf{U}\mathbf{S}\mathbf{V}^{T} \tag{2}\]The eigentriples \((U_{i},\sqrt{\lambda_{i}},V_{i})\) are used to reconstruct the components:  
\[\tilde{X}^{(i)}=\sum_{j=1}^{L}\frac{1}{|A_{j}|}\sum_{(l,k)\in A_{j}}u_{li}^{(i)} \sqrt{\lambda_{i}}v_{ki}^{(i)} \tag{3}\]  
where \(A_{j}=\{(l,k):l+k=j+1\}\).  
To capture the temporal dynamics of EEG signals, we transform them into image representations. While several time series image encoding methods exist, such as Recurrence Plots (RPs) and Gramian Angular Fields (GASFs and GADFs), we chose Markov Transition Fields (MTFs) for their unique advantages in the context of inner speech recognition.  
Recurrence Plots, while effective for visualizing repeating patterns in time series data, may not adequately capture the subtle, non-repetitive nature of inner speech-related EEG signals. Gramian Angular Fields (GASFs and GADFs) preserve temporal correlations but can be sensitive to noise and may not effectively represent the state transitions crucial for distinguishing different inner speech categories. In contrast, MTFs offer several advantages for our task. They capture both the temporal dynamics and state transition probabilities of EEG signals, which are crucial for representing the complex patterns associated with inner speech. MTFs preserve more temporal information compared to GASFs and GADFs, allowing for better representation of the subtle temporal patterns in inner speech EEG data. The probabilistic nature of MTFs makes it more robust to the noise and non-stationarity often present in EEG signals during cognitive tasks.  
We transformed them into Markov Transition Field (MTF) images. This allows us to leverage powerful image processing techniques for feature extraction. Given a quantized EEG signal \(q=q_{1},q_{2},\ldots,q_{N}\), we construct the transition probability matrix \(W\):  
\[w_{ij}=\frac{\#\{(q_{s},q_{s+1})|q_{s}=i,q_{s+1}=j\}}{\#\{q_{s}|q_{s}=i\}} \tag{4}\]  
The MTF is then defined as  
\[M_{ij}=[w_{q;q_{i}}]_{i,j=1,2,\ldots,N} \tag{5}\]  
Figure 2 illustrates the process of transforming EEG signals into MTF images. This transformation allows us to capture the temporal dynamics of EEG signals in a format that can be effectively processed by convolutional neural networks, enabling more robust feature extraction for inner speech recognition.
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 3 Data and Methodology
### Data and Variables  
The variables used in this work were Cobalt futures prices, Nickel futures prices, and WTI Crude Oil Futures prices, taken from Bloomberg data services. The intention for using the futures rather spot prices was due to the increased practical viability of the study. The data were considered on a daily basis from a historical period spanning from 7 March 2013 to 12 December 2022. From this basic data, the returns of Cobalt and Nickel future prices were generated and, finally, a time series was obtained describing the evolution of the volatility of Cobalt and Nickel returns, the main variables of the study. Volatilities were estimated through GARCH (1, 1) models.  
As can be seen from Figure 1a, there was surge in the prices of Nickel, especially after the onset of the Russia-Ukraine War in early 2021, which was followed by a huge spike. They have remained highly volatile thereafter. Cobalt prices, however, remained quite stable throughout the duration of study (Figure 1b).  
When it came to crude oil prices, high fluctuations could be observed during the COVID-19 pandemic, with a spike, and they have stabilized in recent years with production increases from various OPEC members.  
Figure 1: _Cont._
## 3 Results and Discussion
### The Coating on Different Substrates  
OSER can be applied on various substrates to protect the surface. The influence of the substrate on the scratch resistance was investigated. As shown in Figure 9, there were evident scratches on the coatings with a 9H pencil, but no scratches with an 8H, which meant that both DETA-OSER and MXDA-OSER were still ultra-scratch-resistant, even on iron substrates with a rough surface. Besides, owing to their inert and hydrophobic properties, OSER could be used as a protective coating to prevent the substrate from being scratched or becoming wet. Additionally, due to its high transparency, it had almost no effect on the pattern of the substrate.
## 3 Models
### Continuous Model Simulation Details  
We consider symmetries inherent to each case that are evident when the RVE is partitioned into nine equally sized subdomains for case (i) (Figure 3a) and four equally sized subdomains in case (ii) (Figure 3b). Each (square) subdomain is prescribed a length \(L=10\); thus, to capture internal symmetries, the RVE is \(30\times 30\) for case (i) and \(20\times 20\) for case (ii). The state of the system at time \(t\) is the subdomain in which the particle is located (1-9 in case (i) and 1-4 in case (ii), Figure 3). State changes, which are vertical or horizontal transitions between neighboring subdomains, are tracked to determine transition probabilities for this surrogate model (see Sections 3.2.2 and 3.3.2).  
Additional assumptions are as follows: (a) our single particle has prescribed radius \(R_{\text{part}}\) and moves with a speed \(v=8\); (b) at \(t=0\), the particle is placed at the center of subdomain 5 for the equally spaced obstacles model and at the center of subdomain 3 for the multisize obstacles model; (c) at each time step of prescribed temporal step size \(\Delta t=0.05\), a new direction is chosen, via uniform sampling, from \(\theta\in[0,2\pi]\), and the particle is advanced a distance of \(\Delta x=v\Delta t\) in this direction; (d) collisions with the obstacles are perfectly elastic, i.e., the particle bounces off the obstacle at the (reflected) incidence angle relative to the local tangent line at the point of collision.  
We simulate many realizations over \(N_{t}=1\times 10^{7}\) time steps to generate plots of the mean-squared displacement \(\langle r^{2}\rangle\) versus time. For all simulations considered in this study, 2000 realizations on a high-performance cluster computer were sufficient for the slopes of these linear curves to reach stationary values.
## 3 Case Study
### Spatial Distribution of Indicators Based on the 3D Geological Model
#### 3.2.2 Spatial Distribution of Assessment Indicators  
The geotechnical test dataset is integrated from the collected geotechnical investigation reports to create the 3D geological model attributed with the data of geotechnical indicators \(c\), \(\varphi\), \(k\). The Kriging method is employed to interpolate the data onto the 3D geological structure model. For hydrogeological indicator \(q\), it is assigned the zoning grades based on YSW in the study area, as shown in Figure 5. Thus, each node inside the 3D geological model is assigned by its zoning grade in the aquifer (sandy and gravel layers) and 0 in the aquitard (clayey layers). Soft soil and gravel with coarse sands are the main negative geological layers in the study area. The spatial distance indicators \(ds\) and \(dg\) are calculated based on the 3D geological structure model.  
Finally, each node inside the 3D geological model is associated with the normalized value of indicators calculated by Equations (2) and (3), as shown in Figure 6.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 2 Related Work
### Character Recognition in Different Characters Using YOLO
#### 2.1.3 YOLO for Greek Papyri Character Recognition  
This study advances the recognition of characters in Greek papyri manuscripts by integrating modern object detection and representation learning methods. The authors utilize a dataset of 153 annotated training images sourced from 108 manuscripts, along with 34 external test images from the Oxyrhynchus Papyri and AL-PUB collections. The detection framework is based on YOLOv8 for real-time recognition, while prediction refinement is achieved using a DeiT (Data-efficient Image Transformer) model. A ResNet-50 network, pre-trained with SimCLR, extracts high-quality feature representations from limited labeled data. To improve localization accuracy, the authors employ Weighted Box Fusion (WBF) with hard majority voting. Building on prior work in historical document analysis [7; 8; 9], the study proposes a robust pipeline for both character recognition and author identification. From this research, we identify several areas for future investigation, including improving recognition accuracy for deteriorated texts, refining paleographic dating methods, and enabling scalable transcription. Key areas of emphasis include expanding the dataset to encompass a broader variety of manuscript types, fostering collaboration with historians to improve transcription precision, and integrating linguistic metadata to aid in long-term preservation and support extensive scholarly analysis.
## 4 Key Application Areas for Passive Wake-Up Radio
### Asset Tracking  
Asset tracking in WSN is the employment of mobile wireless sensors on each of the assets to enable asset managers to track asset location and to indirectly monitor the asset usage status in real time. For cargo dispatching companies, an essential aspect of their business is the real-time status of their active assets. While some companies still use the manual tracking which is unreliable with higher overhead cost, the majority employs the smart asset tracking method. The RFID is most often used for this purpose with technologies, such as the ZigBee IEEE 802.15.4, adopting and improving upon the RFID functionality to enhance the asset tracking capabilities. RFIDs in the form of wake-up radios are used to track the products by their ID and the main radio handles the communication of other main information.  
Asset tracking application specific quasi-passive wake-up radios are proposed in [122]. The wake-up radio design requirement for asset tracking should be able to operate for the long-term with bidirectional communication and high energy efficiency. Asset tracking system using the passive wake-up radio is shown in Figure 11. Although communication between two passive wake-up radios is considered for shorter range due to their low relatively low transmission power and receiver sensitivity. However, in Figure 11 the communication is initiated by a high transmission power base station, which  
Figure 10: Description of Passive wake-up radio for smart grid application.  
can start the wake-up radio even at longer range. After the main radio is triggered to start-up by the wake-up radio, the main radio can send its information back to the base station over the long distance with high transmission power since it is active. The passive wake-up radio protocols proposed in [49; 63; 64] are suitable candidates for use in this application because of their low maintenance cost and long-term operation capability.
## 4 Mathematical Model
### Constraint  
This problem includes several conventional constraints (Constraint 1, 2, 4) and a special constraint (Constraint 3).  
**Constraint 1** is expressed as follows: each order should be picked and only picked once, which can be guaranteed by Equations (13) and (14).  
\[\sum_{m=1}^{M}\sum_{k=1}^{N}x_{nmk}=1 \tag{13}\]  
\[\sum_{w=1}^{W}y_{nw}=1 \tag{14}\]  
**Constraint 2** is expressed as follows: the usage of the device cannot exceed its available limit, which can be guaranteed by Equations (15) and (16).  
\[1\leq m\leq M \tag{15}\]  
\[1\leq w\leq W \tag{16}\]  
**Constraint 3** is expressed as follows: the orders belonging to the same pallet can only be picked by the same picking station.  
To mathematically express Constraint 3, we first define a function \(f(p,n)\) to represent the subordination relationship between pallet \(p\) and order \(n\), which is equal to one iff the order \(n\) belongs to the pallet \(p\). Next, we structure a matrix \(\mathbf{Y}(p)\) as shown in Equation (17). Finally, the matrix \(\mathbf{Y}(p)\) is constrained by Equation (18) to ensure that Constraint 3 holds.  
\[\mathbf{Y}(p)=\begin{pmatrix}f(p,1)\cdot y_{11}&\dots&f(p,N)\cdot y_{\rm N1} \\ \vdots&\ddots&\vdots\\ f(p,1)\cdot y_{1W}&\dots&f(p,N)\cdot y_{1W}\end{pmatrix} \tag{17}\]\[\prod\limits_{p=1}^{p}r(\mathbf{Y}(p))=1 \tag{18}\]  
**Constraint 4** is expressed as follows: pallet-picking tasks that are simultaneously open on the picking station cannot exceed the upper capacity limit of the picking station.  
Due to each order belonging to a unique pallet, the picking operation time window for each pallet can be obtained based on the order arrival schedule of each picking station as described in Section 4.2. We use \(TP_{wi}=\left[T_{wi},T_{wi}\right]\) to represent the picking operation time interval of pallet \(i\) on picking station \(w\), use \(MP\) to represent the upper limit of the picking station capacity, and use \(TP_{w}\) to represent the set \(\left\{TP_{wi}\right\}\). When \(card(TP_{w})>MP\), we use \(TP_{n}=\left\{TP_{n_{1}},TP_{n_{2}},\cdot\cdot,TP_{n_{MP+1}}\right\}\) to represent any subset of \(TP_{w}\) with a size of \(MP+1\). To sum up, Constraint 4 can be guaranteed by Equation (19):  
\[card(TP_{w})\leq MP,\text{ or }card(TP_{w})>MP\text{ and }\underset{i=1}{ \cap}TP_{n_{i}}=\varnothing \tag{19}\]
# Impact of LGE-MRI in Arrhythmia Ablation
## 2 Use of LGE-MRI for the Detection of Fibrosis
### Image Acquisition for Patients with Cardiac Devices  
Most patients scheduled for VT ablation are individuals with established cardiomyopathy-opathy who carry an implantable cardioverter defibrillator (ICD) implanted for primary or secondary prevention. This issue represents a major limitation for MRI because of security and also because of hyperintense image artefacts that can be caused by the device.  
Regarding safety, several studies have shown an extremely low risk of device-related complications in patients who undergo cardiac MRI, not only in MRI conditional devices, but also in the case of theoretical MRI nonconditional devices. With adequate intraprocedural programming of the device, MRI is safe in patients with MRI nonconditional devices [33; 34; 35]. In this sense, the main issue with patients with cardiac devices is the quality of MRI images due to the possible artefacts related to the ICD. Artefacts can occur when metallic ICD components distort the magnetic field [36; 37] making it difficult to obtain clear images using LGE-MRI. The effect of ICD artefacts are most prominent in the anterior wall, and in patients with left sided devices, in the anterior and apical left ventricle. Some studies have suggested that limited spectral bandwidth of the inversion pulse used in LGE-MRI is the primary cause of device-related artefacts [38; 39]. To avoid those artefacts, particular wideband MRI sequences have been recently developed, increasing the bandwidth of the inversion and excitation pulses. Therefore, the use of wideband sequences can minimize device-related artefacts and, subsequently, overcome the image artefact, making LGE-MRI robust for myocardial characterization [33; 40; 41]. Many centres including ours are now applying these wideband sequences, avoiding device-related artefacts and achieving high quality images, even in areas closed to the ICD. In fact, our group has recently proved a strong correlation between wideband LGE-MRI and electroanatomical maps [42]. In this study [42] the accuracy of wideband sequences to detect CCs previously located in EAMs was analysed for 13 patients with an ICD and a wideband sequence. The accuracy of CCs identified was 85.1% and the positive predicting value was 92.5%.
## 3 Results and Discussions
### Yeast Films Characterization
#### 3.3.3 Color and Transparency of Yeast Films  
The CIELab color parameters are presented in Table 3.  
The lightness of YFs was higher than that of the control YF samples without CLEO, and this can be explained by the light scattering effect of the CLEO droplets. The positive a values of all yeast films point toward films' reddish color, while the positive b values show the yellow tint of the yeast films, as can be observed in Figure 4. The brownish tint of yeast films can be explained by the Maillard reaction products formed by the yeast cell wall's proteins and carbohydrates. Other researchers [39] reported an increase in a* and b* values of the films after the addition of EOs, which was consistent with our results. The differences in color (\(\Delta\)E) for all YFs were higher than 12, indicating a visible color difference between the control samples without CLEO and the yeast films with CLEO. The lowest concentrations of glycerol and Tween, at 1% (\(w/v\)) each, contributed to the casting of the most transparent yeast film--YF1.  
Figure 3: SEM micrographs of yeast films: (**a**) top view; (**b**) cross-section at a 1000\(\times\) magnification; and (**c**) cross-section at a 5000\(\times\) magnification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **L** & **a** & **b** & **\(\Delta\)E** & **Transparency** \\ \hline YF1 & 59.89 \(\pm\) 0.02 **\({}^{\text{a}}\)** & 0.88 \(\pm\) 0.04 \({}^{\text{b}}\) & 24.06 \(\pm\) 0.02 \({}^{\text{a}}\) & 24.61 \(\pm\) 0.03 \({}^{\text{b}}\) & 51.99 \(\pm\) 0.05 \({}^{\text{a}}\) \\ YF2 & 60.40 \(\pm\) 0.02 \({}^{\text{b}}\) & 0.81 \(\pm\) 0.02 \({}^{\text{c}}\) & 23.67 \(\pm\) 0.02 \({}^{\text{b}}\) & 25.69 \(\pm\) 0.02 \({}^{\text{a}}\) & 48.66 \(\pm\) 0.04 \({}^{\text{a}}\) \\ YF3 & 59.34 \(\pm\) 0.00 \({}^{\text{c}}\) & 0.89 \(\pm\) 0.03 \({}^{\text{a}}\) & 23.93 \(\pm\) 0.00 \({}^{\text{c}}\) & 24.56 \(\pm\) 0.05 \({}^{\text{b}}\) & 47.48 \(\pm\) 0.04 \({}^{\text{f}}\) \\ YF1c & 82.27 \(\pm\) 0.00 \({}^{\text{d}}\) & 0.39 \(\pm\) 0.01 \({}^{\text{e}}\) & 14.11 \(\pm\) 0.01 \({}^{\text{d}}\) & & 52.78 \(\pm\) 0.05 \({}^{\text{b}}\) \\ YF2c & 86.54 \(\pm\) 0.00 \({}^{\text{e}}\) & 0.38 \(\pm\) 0.01 \({}^{\text{e}}\) & 7.62 \(\pm\) 0.00 \({}^{\text{e}}\) & & 51.64 \(\pm\) 0.05 \({}^{\text{c}}\) \\ YF3c & 81.37 \(\pm\) 0.00 \({}^{\text{f}}\) & 0.60 \(\pm\) 0.00 \({}^{\text{d}}\) & 13.20 \(\pm\) 0.00 \({}^{\text{f}}\) & & 50.48 \(\pm\) 0.04 \({}^{\text{d}}\) \\ \(p\)-value & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 \\ \hline \hline \end{tabular} \({}^{\text{a}}\)The values are the mean of duplicate analyses. On the columns, different letters indicate significant diferences (\(p<0.05\)) between films determined by ANOVA and [PERSON]’s post hoc test.  
\end{table}
Table 3: Color and transparency of YFs.  
The brownish tint of the yeast films might not necessarily antagonize consumers, since recent studies have shown that cream, brown, or green colors are associated with higher sustainability and are embraced by consumers [40].
## 3 Selection of Materials for Wearable Antennas
### Flexible Conductive Material for Wearable Antennas  
In wireless applications, achieving conductive patterns with excellent electrical conductivity ensures optimal gain, efficiency, and bandwidth. Furthermore, the conductive material should also possess resistance to degradation caused by mechanical deformation. Nanoparticle (NP) inks, such as silver and copper, are commonly used for making antennas that are flexible because they have a high level of electrical conductivity. Silver nanoparticle ink surpasses copper nanoparticles because it forms oxides at a slower pace [55]. Nevertheless, high conductivity and stretchability are not compatible attributes. One possible approach to address is that this issue involves the implementation of one-dimensional nanostructures, including metal nanowires and carbon nanotubes affixed to a stretchable fabric [56].  
Currently, the conductivity values under significant strain continue to be insufficient to meet the demands of practical applications. In [57], a conductive composite material composed of rubber fibers and silver nanoparticles that enables the creation of exceptionally stretchable circuits via a fabrication method is presented. Figure 4 shows the properties of choosing conductive materials, considering the physical features of the pattern, such as adherence to the substrate, and the desired physiochemical properties of the inks, such as compatibility with the printing process. One example is the restrictions placed on the size of the printing nozzle for nanoparticles, as well as problems with aggregation, stability, rheology, and electrical and mechanical properties [52].  
Figure 4: Features of conductive materials required for design.  
Table 5 describes the various flexible conductive materials utilized in wearable antennas for the ISM band, exhibiting properties such as thickness, conductivity, and specific material composition. These materials are selected based on criteria including high conductivity, low resistivity, deformability, weather resistance, and high tensile strength, tailored to the design and application requirements of the wearable antenna [57].  
The wearable antenna employs both a ground and top radiator. Conductive materials can be assessed based on their resistivity, conductivity, malleability, resistance to weathering, tensile strength, and compatibility with flexible materials. Several inflexible conductive materials have been employed for wearable antennas. These materials possess excellent conductivity, are inexpensive, and may be incorporated into textile substrates utilizing adhesive laminated materials, eliminating the need for embroidery and stitching techniques. However, their inflexible structure limits their applicability for flexible applications [58].