esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, we developed and characterized a novel material for wound dressing composed of starch, polyvinyl alcohol, glycerol, citric acid, and zinc-oxide nanoparticles. To fabricate these biodegradable composites, PVA was dissolved in deionized water; starch and ZnO nanoparticles, considering the recent research interests in metal nanoparticles [44], were mixed in DI water utilizing an ultrasonic mixer; based on the existing literature [16, 45, 46, 47], glycerol and citric acid were used as crosslinking agents that improved the films' flexibility and antibacterial activities. The resulting materials (S1-S4) had a rougher surface than the material (SPGC) without the ZnO nanoparticles, which promotes cell attachment and growth [48, 49]. The WVTR analysis revealed that the ZnO  
Figure 12: Antibacterial activity test of starch-based materials against _E. coli_ and _S. aureus_ in formulations with different weight ratios of ZnO nanoparticles: (**A,B**) images of S1–S4 samples tested against _E. coli_ and their control zones using Penicillin; (**C,D**) images of S1–S4 samples tested against _S. aureus_ and their control zones using Penicillin.  
Figure 13: Biodegradability feature of the starch-based wound dressings: (**A**) 24 h; (**B**) 72 h; (**C**) Day 14.  
nanoparticles had a sufficient porosity that can cause absorption of more wound exudates and promote the wound-healing process by providing the necessary oxygen around the wound area [50]. UV-Vis spectroscopy confirmed that the material average transmittance percentage decreases after adding the ZnO nanoparticles. These starch films demonstrated advantageous UV light barrier properties as well as significant antibacterial activity against _E. coli_ and _S. aureus_. Other research groups have reported the same UV-Vis spectroscopy results in similar ZnO applications over the past few years [38; 39; 51]. Utilizing SEM imaging, the surface morphology and structure of the wound dressings were examined in order to evaluate sponge formation when nZnO concentration was increased. The starch/PVA/nZnO composite generated by the casting method had good swelling, gel fraction, and mechanical properties to maintain a moist and antibacterial environment for wounds. The ZnO nanoparticles acted as fillers to reinforce the composite, leading to higher tensile strength and more stable and flexible wound dressings [7; 17].  
Moreover, the attained degradation physical characteristics of the starch/PVA/nZnO films showed that the results are the same as studied wound dressings' properties in the literature [52]. The prepared solution pH value was measured, and it was verified that the growth of bacteria could be obtained within the wound area. The future work on this research topic could be directed towards monitoring human and animal wound-healing steps after wound protection with this type of protective material, the investigation of increasing the number of ZnO nanoparticles, changing the quantities of other contents of the solution to the dressing solution, and building a mathematical optimization model to enhance these wound dressings' performance.
## 5 Training Methodology
### Intervention Classification Model  
Following the EUREKA-based semantic parsing described in Section 5.2, we implement a lightweight intervention classification module that operates as a secondary head over the multilingual T5 encoder. This component predicts an appropriate medical response (e.g., \"dispatch ambulance\", \"activate stroke protocol\") based on the encoder's representation of the dialogue and structured patient context.  
The classifier is implemented as a two-layer feedforward neural network (multi-layer perceptron) attached to the encoder output of the mT5 model. Specifically, the hidden state corresponding to the first token in the encoder's final hidden layer is extracted (serving as a pooled representation akin to a [CLS] token) and passed through a linear layer with 128 hidden units, followed by an ReLU activation and a final linear layer projecting to the number of intervention classes. This pooled vector captures the global context of the input dialogue and is used to infer the appropriate intervention decision.  
The model is trained using a supervised classification objective with cross-entropy loss and optimized using the Adam optimizer with a learning rate of \(1\times 10^{-4}\). A dropout probability of 0.1 is applied during training to mitigate overfitting, and inverse-frequency class weights are used to address label imbalance among intervention categories. While L2 regularization and early stopping are not employed in the current setup, the model exhibits stable convergence within 20 epochs when trained on synthetic and weakly labeled data using a batch size of 64.  
At inference time, the classifier operates in parallel with the mT5 text generation pathway. After the encoder processes the dialogue input, the classifier predicts the intervention label independently of the generated EUREKA frame. This design preserves modularity and supports the use of constrained decoding to ensure schema compliance for the structured output. This two-stage design supports multilingual instant triage by enabling the model to provide both a structured semantic frame and an immediate intervention recommendation. Moreover, decoupling parsing from classification enhances interpretability and enables independent refinement or replacement of either module.
## 3 Results and Discussion
### Particle \(\zeta\)-Potential
Note that electrokinetic charges (\(Z_{\rm c}\)) have the same sign of the \(\zeta\)-potential. According to Equation (2) a \(\zeta\)-potential value of \(-60\) mV implies \(Z_{\rm c}=-19\); i.e., on each nanoparticle, there are 19 unbalanced negative charges. Such a value is almost coincident with the 21 excess electrons calculated on the basis of a detailed numerical model, at an atomistic level, of NP growth in the PLAL plasma [19]. With such a large amount of platinum oxides on the surface, the fact that PtNPs have the very same number of charges per particle than gold nanoparticles, made of essentially pure Au\({}^{0}\), points to a minor (if any) role of the metal oxide in the building of negative charge sitting on the particles. On the other hand, the fact that the number of electrokinetic charges obtained is closely matched to the number of trapped electrons predicted by a theoretical model corroborates the hypothesis of an active role of excess electrons on the stability of NPs prepared by PLAL.
## 2 Materials and Methods
### Data Processing  
Data processing mainly followed the pipeline described in our previous work [25]. EEG signals are highly susceptible to external and internal noises, and this becomes even more prominent during long-term recording [27]. To increase the quality of EEG data, we applied two steps of pre-processing. Firstly, we filtered EEG data with band-pass (1-60 Hz) and notch (50 Hz) filters. High-pass filter restrained low-frequency artefacts caused by stray effects or breathing, while the low-pass filter reduced high-frequency components, usually related to muscle activity during everyday routine or seizure convulsions [28]. The notch filter suppressed 50 Hz noise from the power grid. Secondly, we used an artefact-removal approach based on independent component analysis (ICA) to mainly remove blinking artefacts.  
Further data processing included time-frequency analysis. For this purpose, we used continuous wavelet transform (CWT) with Morlet mother wavelet [29]. We calculated wavelet power (WP) as it is one of the most common CWT-based characteristics for describing the time-frequency structure of EEGs [30]. We chose 1-30 Hz as the frequency band of interest since it is acceptable for studying both normal and pathological EEG activity [31]. CWT can produce a lot of WP data that is difficult to process and use as features for ML methods, so we performed additional steps to reduce data complexity. Firstly, we reduced the spatial dimensionality by averaging the WP over all EEG channels. Secondly, we divided the WP time series into a set of non-overlapping 60 s intervals and calculated the median value inside each interval, hence providing downsampling in the temporal domain. Thus, we obtained WPs averaged over all EEG channels and time intervals and used this data for further feature engineering.
## 3 Findings and Discussion  
To date, extensive research has not established a solid correlation between the cognitive functions of humans and the RF-EMF exposure emitted from UE and base stations. This lack of evidence reinforces the acceptability of high-frequency deployments in conjunction with a smart environment. With rigorous safety standards and regulations in place, the focus can remain on the transformative benefits that such deployments bring, including increased efficiency, improved connectivity, and enhanced user experiences. Ongoing monitoring and scientific studies continue to evaluate the potential effects of RF-EMF exposure, ensuring the well-being of individuals while promoting the advancements enabled by the integration of high-frequency deployments within smart environments. Many studies have examined the impact of RF-EMF exposure from MPs and BSs on cognitive functions using various behavioral indicators, including reaction time (RT), brain, and memory performance. As depicted in Figure 4, 25% and 30% of studies showed no negative impact due to exposure from BSs and MPs on cognitive activities, respectively. The combined findings of several studies indicated that exposure to BS impaired cognitive function, including the motor screening task, reaction time (RT), memory performance, and spatial working memory (SWM). Figure 4a,b show the relative performance parameters in terms of cognitive function associated with BS and MP. These findings were taken from 17 relevant research articles.  
Figure 4: Impact of RF-EMF exposure on cognitive abilities.
## 2 Materials and Methods
### Obtaining Key Indicators  
Expert weights (\(\varphi_{i}\)) are used when determining key indicators. The Borda rule is applied to compare alternatives. For each indicator rank, a corresponding integer value is assigned. If the expert assigns the \(k\)-th rank for the indicator \(I_{i}\), the Borda count for that choice is \(b(I_{i})\)= \(k-1\), while the worst alternative, according to the expert's opinion, receives the value \(b(I_{j})\) = 0.  
This approach of ranking the alternatives helps avoid confusion in determining preferences that may arise during pairwise comparisons due to transitivity. The Borda count reflects the recommendations of the experts, which are then combined by weighting based on each expert's experience coefficient. The weighted Borda rule is:  
\[B(I_{i})=\sum_{k=1}^{\varkappa}\phi_{k}\cdot b(I_{i})/\sum_{k=1}^{\varkappa} \phi_{k}\,, \tag{2}\]  
where \(\varphi_{k}\) is the experience coefficient of the \(k\)-th expert, and \(\sum_{k=1}^{\varkappa}\phi_{k}=1\). The value of B(\(I_{i}\)) is used to select the key indicators from each group, with a lower value indicating lower importance of the indicator.  
The problem of reaching consensus among experts within the group requires further analysis before selecting key indicators. To address this, ordinal consensus is applied, which, based on [50], is assessed by determining the measure  
\[\rho^{(k)}=1-\left(\sum_{i=1}^{m}\left|r\left(b_{i}^{(k)}\right)-r(B_{i}) \right|\right)/\left(0.5*m*(m+1)\right),m>2, \tag{3}\]  
where \(r(b_{i}^{(k)})\) represents the rank assigned to indicator \(I_{i}\) by expert \(E_{k}\) by using the corresponding Borda count, while \(r(B_{i})\) is determined based on the weighted Borda count for the group of experts. The degree of satisfaction of the group is calculated as  
\[\rho=AVG\left(\rho^{(1)},\ldots,\rho^{(k)},\ldots,\rho^{(m)}\right), \tag{4}\]  
that is, as the arithmetic mean of the \(\rho^{(i)}\) values for individual experts. This value serves as a measure of the experts' consensus, indicating the level of agreement in their rankings. A higher \(\rho\) value implies a greater agreement among experts. This value should be at least 0.70.
## 3 System Model
### Cluster Construction  
The clusters are formed and renewed during the cluster construction period which occurs at the start of a communication round. During this period, SNs determine their energy with respect to others within its vicinity. The contention time is divided into mini-slots, within which each node announces their energy level and coordinates. Each mini slot is built of a short inter-frame space (SIFS) and a communication slot. A node waits a SIFS time before it transmits to avoid collisions. When all the nodes have sent their announcements, a cluster head is identified based on the remaining energy and the SN with higher energy level elects itself as a CH; then it announces its head status to other SNs in the next slot. The SNs close to the CH decide to join its cluster, using CSMA/CA contention on the allocated mini-slots. CH then forms and maintains a member list table as indicated by cluster formation frame in Figure 3. The cluster construction process is further illustrated by Algorithm 1.  
Figure 3: Cluster Construction.  
Figure 2: MAC Communication Round.
## 3 Transient Response Simulation
### Research on Incentive and Damping Parameters
#### 3.3.1 Transient Excitation Calculation
The propeller excitation torque usually considers the single-blade passage excitation of the propeller, and its excitation torque is shown in Equation (12) [31]. \[M_{p}=9550\beta\frac{N_{e}}{n_{e}}\left(\frac{n}{n_{e}}\right)^{2} \tag{12}\]  
where \(M_{p}\) is the single-blade passage excitation of the propeller; \(N_{e}\) is the rated power of the propeller; \(n_{e}\) is the rated rotational speed of the propeller; \(n\) is the rotational speed of the propeller shaft; and \(\beta\) is the excitation factor, with an empirical value of \(\beta=0.0075\). Utilizing Equations (9)-(12), the excitations due to gas, reciprocating inertia, and the propeller can be individually determined. The combined effect of gas and reciprocating inertia excitations constitutes the diesel engine excitation. Varied firing sequences lead to distinct firing angles, thereby introducing phase differences among these excitations. During numerical computation, these excitations are applied to the simplified mass points of each diesel engine cylinder, taking into account their respective phase angles. Furthermore, the text assumes that the propeller excitation has an initial phase angle of zero relative to the first cylinder of the diesel engine. The propeller excitation predominantly accounts for the excitation at the blade passing frequency and its multiples, which are imposed on the simplified mass points of the propeller model.
# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 3 Methodology  
The LGNMNet-RF model uses the ROI of MHI features acquired during the pre-processing step as input images. The Intersection Over Minimum (IML) labelling method is used as input labels. As illustrated in Figure 1, this network's overall structure comprisesa neck, head, and backbone network. First, looking into the LGNMNet-RF feature extractor, as illustrated in Figure 2, the MobileNet V3 network without the Softmax activation function serves as the backbone, which comprises several convolutional, inverted residual blocks and pooling layers. The neck network has three layers: batch normalisation (BN), ReLU6, and Conv2d convolutional layers with a \(1\times 1\) kernel function. The remainder of the neck network uses a dropout layer to reduce overfitting and a linear layer to eliminate redundant information after normalising the output of the first part of the network. Ultimately, the head network handles classification and loss.  
Inspired by [6], to enhance the model's capacity for feature learning and generalisability, a novel loss function \(L\) was developed by [PERSON] et al. [6] that used MagFace combined with MobileNet V2 to perform ME detection. This Function (1) is derived from a combination of cross-entropy loss (\(L_{\text{CEH}}\)) and MagFace loss (\(L_{\text{Mag}}\)). Specifically, the MagFace model's feature-embedding technique could successfully alter the feature distribution of AUs with varying intensities within the same class during training, drawing AUs with locations near apex frames, the peak intensity frames, towards the class's centres and AUs with locations near onset/offset frames towards the adaptive margin. Applying the one-dimensional labelling results mapped from the high-dimensional feature embedding results of the MagFace yields the predicted classification labels and confidence scores.  
\[L=L_{\text{CEH}}+L_{\text{Mag}} \tag{1}\]  
The classification task generally uses the fully connected layers and Softmax classifier. However, many researchers have neglected the machine learning classifier approach, which significantly improves the accuracy of the classification task. In this research, the Random Forest classifier has the advantage of improving data generalisation, handling the complex, non-linear relationships between features, and being robust to noise.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Significant Wave Height (SWH) Verification  
The SWH is one of the three basic parameters of altimeter measurements. The SWH information is obtained from the waveforms returned by a radar altimeter [17; 18; 25; 26]. SWH is estimated from the slope of the leading edge of the return pulse [26; 27; 28; 29; 30]. Many studies have been devoted to the validations of SWH data from TOPEX/Poseidon (T/P), Jason-1, Jason-2, ERS, and GEOSAT follow-up data and envisioned altimeters [31; 32; 33; 34].  
The quality of the three HY-2B/C/D satellites' SWH data was verified using NDBC buoy data and dual crossover point data between different satellites. In this study, the spatio-temporal matching required that the closest distance of the satellite nadir point from a buoy was less than 50 km, and the minimum observation time differences was 30 min [16; 35]. The HY-2B/C/D SWH data were evaluated by analyzing the SWH differences values at the crossover points.
# Blasting Fragmentation Study Using 3D Image Analysis of a Hard Rock Mine
## 2 Materials and Methods
### Kuz-Ram Model Analysis  
The particle size distribution of the blasting fragments considered the average size of the blocks generated (\(X_{50}\)) and the uniformity index (n). The Kuz-Ram model was used to predict the particle sizes of the fragments resulting from the blasting tests proposed in this study.  
The Kuz-Ram model considered three equations: the modified Kuznetsov equation (Equation (1)), first developed by [PERSON] [20] and modified by [PERSON] [22; 23; 24]; the Rosin-Rammler equation (Equation (2)) [25], and the Cunningham uniformity index (n) equation (Equation (3)) [26]. It is worth noting that the K-factor is also known as the powder factor, that is, the necessary factor for evaluating the rock fragmentation power in the desired granulometry.  
\[X_{50}=A_{r}K^{-0.80}Q^{0.167}\bigg{(}\frac{115}{RWS}\bigg{)}^{0.633} \tag{1}\]  
where \(X_{50}\) (cm) is the average size at which 50% of the particles pass the sieve; \(A_{r}\) is the characteristic factor of the rock [27]; \(K\) (kg/m\({}^{3}\)) is the loading ratio; \(Q\) (kg) is the mass of explosive per hole; and RWS (%) is the relative power by weight of the explosive relative to the energy of the ANFO (115 is the RWS of the trinitrotoluene (TNT) [24]).  
The Rosin-Rammler equation (Equation (2)) estimated the complete fragmentation distribution using the percentage of retained fragments [26] for a sieve opening size:  
\[P(x)=1-e^{-(\frac{X}{X_{c}})^{n}} \tag{2}\]  
Figure 3: Flowchart of equipment operation.  
where \(P(x)\) is the cumulative frequency passing (%) through the opening of the sieve of size \(X\); \(X\) (cm) is the size of the sieve opening; and \(X_{c}\) (cm) is the characteristic size. The characteristic size is the size at which approximately 63.20% of the material passed through [28], and it could be obtained using the following equation:  
\[X_{c}=\frac{X_{50}}{\left(0.693\right)^{n}} \tag{3}\]  
By defining the uniformity index using Equation (4), it was possible to prepare the particle size distribution curve [22; 23]:  
\[n=\left[2.20-14\left(\frac{B}{D}\right)\right]\cdot\sqrt{\left[\frac{\left(1+ \frac{S}{S}\right)}{2}\right]}\cdot\left[\left(1-\frac{W}{B}\right)\frac{L}{H} \right]\cdot\mathrm{P} \tag{4}\]  
where \(B\) (m) is the burden; \(D\) (mm) is the diameter of the hole; \(S\) (m) is the spacing; \(W\) (m) is the standard deviation of the perforation; \(L\) (m) is the height of the explosive charge; \(H\) (m) is the height of the bench; and \(P\) is the perforation factor for stepped loading, which is equal to 1.10 [2; 26].
## 2 Detecting Principle
### Theoretical Model
To make the model calculation results better match with the experimental data, the reflectivity calculated with Equation (18) should be multiplied by the spectral line function \(g(\lambda,\lambda_{0})\) of the laser and then integrated, as written in Equation (19). \[R\big{(}\lambda_{0},V_{p}\big{)}=\int_{-\infty}^{\infty}g(\lambda,\lambda_{0} )R(\lambda)d\lambda \tag{19}\]  
where \(\lambda_{0}\) is the central wavelength. A semiconductor laser was used in our experimental setup; hence, the spectral line function \(g(\lambda,\lambda_{0})\) of the laser was the Lorentzian function:  
\[g(\lambda,\lambda_{0})=\frac{\Delta\lambda}{2\pi\lambda_{1}\lambda_{2}\lambda ^{2}}\cdot\frac{1}{\left(\frac{1}{\lambda}-\frac{1}{\lambda_{0}}\right)^{2}+ \frac{1}{4}\left(\frac{\Delta\lambda}{\lambda_{1}\lambda_{2}}\right)^{2}} \tag{20}\]  
\[\lambda_{1}=\lambda_{0}+\frac{\Delta\lambda}{2} \tag{21}\]  
\[\lambda_{2}=\lambda_{0}-\frac{\Delta\lambda}{2} \tag{22}\]  
where \(\Delta\lambda\) is the bandwidth of the laser. It is worth noting that the parameter used to calculate the EOFM signal is the reflectivity variation \(\Delta R(\lambda_{0},V_{p})\) rather than the reflectivity itself; \(\Delta R\big{(}\lambda_{0},V_{p}\big{)}\) is calculated as shown in the following formula:  
\[\Delta R\big{(}\lambda_{0},V_{p}\big{)}=R\big{(}\lambda_{0},V_{p}\big{)}-R( \lambda_{0},0) \tag{23}\]  
where \(R\big{(}\lambda_{0},V_{p}\big{)}\) is the reflectivity obtained for a voltage level \(V_{p}\), and \(R(\lambda_{0},0)\) is the reflectivity obtained when there is no voltage applied. In the experiment, the modulated reflected light was converted into a modulating electrical signal by a photodetector. To directly compare the calculation results with the experimental results, \(\Delta R\) is converted into the experimentally measurable voltage value according to the following equation:  
Figure 3:0  
where \(V_{out}\) is the output signal level of the photodetector, \(\Re\) is the responsivity of the photodetector, \(G\) is the transimpedance gain of the photodetector, and \(P\) is the incident light power.