esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiments  
#### 2.3.1 Hypothesis  
To validate whether a teleoperation intermediary using MR-subspace-enhanced spatial mapping of human motion facilitates control of robotic arm-hand systems by unskilled operators, a set of spatial manipulation tasks, shown in Figure 4, including pick-and-place and assembly, were developed. A user study was designed and conducted to verify task performance and user experience with the proposed HRI system compared to two typical teleoperation modes.  
Figure 4: An overview of spatial manipulation tasks and command input methods. (**a**) Pick-and-place task of two cubical and cylindrical objects given a goal position with different heights; (**b**) assembly task involved grabbing one LEGO subassembly from a predefined spot and stacking it onto a fixed LEGO base on the table; (**c**) isometric-rate HRI scheme using the 6-DOF input device; (**d**) isotonic-position imitative HRI scheme using the Vive motion controller.  
The null hypothesis (H0) for a repeated-measures ANOVA is that the MR-subspace-enhanced imitation-based motion mapping approach for telemanipulation and the two alternatives (commonly used teleoperation methods) have identical effects on task efficiency, user performance, and system usability for unskilled users, in terms of effectiveness, intuitiveness, and learnability.
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Interaction between Factors  
In this subsection, the interaction between factors identified in Section 3.1. is shown. The objective is to identify the factors that have more effect on fish performance, both directly and indirectly. These factors will be included in our smart control system.  
We have identified 13 factors for the Production Domain, 18 factors for the Abiotic Domain, 20 factors for the Species Biotic Domain, 12 factors for the External Biotic Domain, and 23 factors for the Control Domain, a total of 86 factors. In order to simplify the visualization of information, the parameters are reduced to 67, and those without interaction with other factors are deleted from this analysis. In addition, some parameters are fused since their relationship with other parameters is similar. The combination of those 67 factors supposes a total of 2144 possible interactions. The relation between parameters has been classified as strong, medium, weak, and none. Figure 7 outlines the relationship between factors. There have been identified 190 interactions between factors, 59 strong interactions, 86 medium interactions, and 55 weak interactions.  
Figure 7: Interaction between factors of included domains. In this graphic, we can see the relationship between factors, the brighter the line, the greater the amount of interaction between factors. Note that some factors have been excluded from this analysis if they have no interaction, and some factors might be combined due to similar interactions with other factors. The color of the factors indicates the Domain.  
The factors with more interactions are the water temperature, the fish detecting and counting (from the control Domain), the tank and cage dimensions, the feed quality (from the Production Domain), and the feeding demand (from the Species Biotic Domain). The first factor from the External Biotic Domain is biofouling, which interacts with many of the monitoring and control factors. Figure 8 summarizes the identified number of interactions for each factor in descending order. It must be noted that the number of interactions is not indicative of the importance of the individual factor. Some factors, such as wind or water flow, which appear with a low number of interactions, are essential for feeding success.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
## 2 Common Frequency Domain Methods
### Reference Standard-Time Domain Analysis  
In order to obtain the applicability of various frequency-domain methods to different spectral width parameters, it is necessary to compare the estimation results of different frequency-domain methods, which requires obtaining an accurate solution as the comparison standard. In this paper, the life result TRF calculated by the rain current counting method combined with the Miner cumulative damage criterion is selected as the reference standard, so as to achieve the purpose of comparison. Before applying the rain current counting method, since the simulated spectral curve is a frequency domain curve, it needs to be converted into a time history curve. In this paper, the inverse Fourier transform method is selected for time-frequency conversion. Since the simulated spectrum is in the form of power spectral density and does not contain phase information, but for a stationary random process, if it is assumed that its random phase angle is uniformly distributed in the interval [0, 2\(\pi\)][38], in the end, consistent statistical characteristics can be obtained, and the lifetime results are also equivalent.  
In order to obtain a more accurate time-domain result, when applying the time-domain method, not only the influence of rain flow amplitude but also the influence of rain flow mean value must be considered [39]. Therefore, the Goodman equation is added to the time domain method to obtain the rain flow life estimation \(T_{RF}\) in Equation (27):  
\[D_{RF}=\sum\frac{1}{N_{i}}=\sum\left(\frac{s_{u}s_{u}}{s_{u}-s_{m}}\right)^{ \alpha}/C \tag{26}\]  
\[T_{RF}=\frac{1}{D_{RF}} \tag{27}\]  
In Equation (26), S\({}_{u}\) is the ultimate tensile strength, S\({}_{u}\) is the stress amplitude, S\({}_{m}\) is the average stress, and S\({}_{f}\) is the fatigue limit amplitude.
## 4 Conversion Method for CCA-Secure IBKEM  
### IBKEM  
#### 4.1.1 Syntax  
An ID-based key encapsulation mechanism IBKEM consists of four algorithms (IBKEM.Setup, IBKEM.KeyGen, IBKEM.Encap, and IBKEM.Decap). The setup algorithm (PP, MSK) \(\overset{\$}{\leftarrow}\) IBKEM.Setup\((\lambda)\) takes as input the security parameter \(\lambda\) and outputs public parameters PP and a master secret key MSK. The key generation algorithm \(\text{SK}_{\text{ID}}\overset{\$}{\leftarrow}\) IBKEM.KeyGen(PP,MSK,ID) takes as input public parameters PP, the master secret key MSK, and an identity ID, and outputs an identity secret key \(\text{SK}_{\text{ID}}\). The encapsulation algorithm \((\text{K},\text{CT})\overset{\$}{\leftarrow}\) IBKEM.Encap (PP, ID) takes as input PP and ID, and generates a key K and a ciphertext CT. The decapsulation algorithm \(\text{K}\leftarrow\text{IBKEM.Decap(PP,CT,\text{SK}_{\text{ID}})}\) takes as input public parameters PP, a ciphertext CT, and an identity secret key \(\text{SK}_{\text{ID}}\), and then outputs a key K.
## 2 Materials and Methods
### Determination of Emulsification Activity  
To determine the potential of biosurfactants in promoting the emulsification of different oils, emulsification activity tests were included in the working plan of the present study, along with the determination of their stability under extreme conditions of pH, salinity, and temperature. The oils were grape seed oil and sweet almond oil acquired commercially in the city of Recife, Brazil. Oil-in-water (O/W) emulsions were prepared by 1:1 (\(v/v\)) homogenization of the lipid phase and aqueous phase in tubes using a vortex mixer for2 min for subsequent measurement of the height of the emulsified phase (cm). The aqueous phase consisted of solutions of the biosurfactant at concentrations of 1% CMC, CMC, and 2 CMC [18]. The emulsification index (E24) was determined as the ratio between the height, measured in cm with a caliper, of the emulsified phase and the total height of the tube content, as described by [PERSON] [21].  
To study the effect of temperature, biosurfactant solutions were placed in a water bath at different temperatures (40, 60, 80, and 100 \({}^{\circ}\)C) for one hour, followed by the formation of emulsions. After this time, the tubes were cooled to room temperature. As for the effect of pH, the biosurfactant solutions were placed in a set of beakers at pH values in the range of 2 to 12 by addition of 5 M HCl or NaOH solution. The solutions were then transferred to test tubes for the preparation of the O/W emulsions. Finally, the effect of salinity was investigated using NaCl solutions at concentrations ranging from 2 to 12% in test tubes and vortexed for 2 min, followed by the preparation of the O/W emulsions.
## 4 Mitigating Quantization Quality Degradation Based on the Observation
### Overview of Adapting Our Techniques for Quantization  
In this section, we introduce two practical methods--Quantization-free Module (QFeM) and Quantization-free Prefix (QFeP)--that can be applied independently or jointly to mitigate activation spike-induced quantization errors. An overview of these methods is illustrated in Figure 3, which summarizes the key ideas behind QFeM and QFeP. QFeM is applied in a layer-wise manner: after selecting an LLM with a GLU-based architecture and a layer-wise quantization method, QFeM evaluates the quantization efficiency for each linear layer based on activation spike severity. Layers with strong spikes are excluded from activation quantization to reduce degradation. The scoring and selection process is detailed in Section 4.2. QFeP is a token-level method applicable regardless of GLU usage or layer-wise quantization. It identifies tokens that trigger strong spikes and preemptively inserts them as a prefix to be stored in the KV cache, effectively suppressing spikes during inference. For tested LLMs listed in Table 2, preselected prefixes can be used directly; otherwise, a prefix can be searched and applied according to the technique described in Section 4.3.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Model** & **Prefix** & \(\mathbf{\alpha}\) & \(|\mathbf{M_{unq}}|/|\mathbf{M}|\) \\ \hline LLaMA-2-7B & [BOS] all. & 6.68 & 17/128 \\ LLaMA-2-13B & [BOS] then, & 12.91 & 6/160 \\ LLaMA-2-70B & [BOS] I\({}^{\prime}\) & 9.16 & 25/320 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications for QFeM and QFeP used in experiments. \(|\mathbf{M}|\) denotes the total number of linear layers in the LLM, and \(|\mathbf{M_{unq}}|\) represents the number of unquantized layers for QFeM.  
Figure 3: Overview of QFeM and QFeP. (**Left**): QFeM excludes the modules whose \(r^{(m)}\) is larger than the hyperparameter \(\alpha\) from quantization. (**Right**): QFeP computes in advance the prefix of activation spikes and solely utilizes their KV cache during the quantization phase, effectively preventing further activation spikes in subsequent sequences. In both figures, the y-axis represents transformer layers where \(i<j<k\), indicating early, middle, and late layers, respectively. The x-axis corresponds to the input sequence, where tokens with yellow highlights are general tokens, and those with red highlights are the tokens that trigger activation spikes.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Simulation Setup  
The simulations have been performed using an Ubuntu 22.04 operating system on NS3 ([[https://www.nsnam.org](https://www.nsnam.org)]([https://www.nsnam.org](https://www.nsnam.org)) (accessed on 25 May 2025)) (version 3.36.1) as the network simulator. To evaluate the proposed scheme, a comprehensive simulation has been performed. The simulation environment has been configured to add nodes equipped with IoT sensors. Table 2 provides a summary of the simulation parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Modules** & **Parameters** & **Values** \\ \hline \multirow{3}{*}{\begin{tabular}{l} Operating \\ system \\ \end{tabular} } & \multirow{3}{*}{Ubuntu} & 22.04.1 \\ \cline{3-3}  & & NS3 (ns-3.36.1) \\ \hline \multirow{3}{*}{\begin{tabular}{l} Network \\ topology \\ \end{tabular} } & Node placement & Random disc position (radius = 300 m) \\ \cline{2-3}  & Traffic pattern & Constant Bit Rate (CBR) \\ \cline{2-3}  & Communication range & 250 m \\ \cline{2-3}  & Number of nodes & 50-250 \\ \cline{2-3}  & Mobility model & ConstantPositionMobilityModel \\ \hline \multirow{3}{*}{\begin{tabular}{l} Node mobility \\ \end{tabular} } & Base station mobility & Constant position \\ \cline{2-3}  & Node mobility & Constant position \\ \cline{2-3}  & Position allocation & Random disc position allocator \\ \hline \multirow{3}{*}{\
\begin{tabular}{l} Propagation \\ \end{tabular} } & Packet size & 1000 bytes \\ \cline{2-3}  & Constant rate & 100 Kpps \\ \cline{2-3}  & Physical layer modulation and \\ \cline{2-3}  & coding scheme & DsssRate11 Mbps \\ \cline{2-3}  & Data rate & 5 Mbps \\ \cline{2-3}  & Propagation loss model & [PERSON] propagation loss model \\ \hline \multirow{3}{*}{
\begin{tabular}{l} Wi-Fi \\ configuration \\ \end{tabular} } & Wi-Fi standard & 802.11b \\ \cline{2-3}  & Transmit power start & 20.0 dBm \\ \cline{2-3}  & Transmit power end & 20.0 d \\ \cline{2-3}  & Receiver gain & 30 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Nodes collect QoS metrics and reliability data over sliding observation windows of 10 s, which are repeated at intervals of 60 s. The 10 s window is sufficient to capture recent performance trends without causing excessive computation or communication overhead. Nodes perform trust computations only at each interval of 60 s, which reduces the frequency of evaluations while maintaining responsiveness to behavioral changes. To avoid reliance on outdated data, old observations are discarded after each trust computation cycle. This ensures that trust scores reflect the most recent behavior without cumulative bias.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 4 Application Scope
### Industrial Products
#### 4.1.3 Usability and Ease of Use  
In studies of product usability and ease of use, surveys of attractiveness and satisfaction are key elements of user research. Eye-tracking activities and electroencephalograms (EEGs) are associated with user preferences for products, which reflect user experience-related activities at the visual (physical) level and the psychological (cognitive) level. For example, there is a positive correlation between the number of fixations and visual complexity, and a positive correlation between the average duration of fixations and user pleasure [55]. [PERSON] et al. [56] measured the usability evaluation of satisfaction with packaging for soft drink PET bottles using eye-tracking and EEG measurements, which showed that users' psychological cognitive and behavioral processes can be captured by EEG.  
[PERSON] and [PERSON] used EEG to explore users' views on product usefulness, ease of use, and fun. The results showed that the EEG asymmetry in the alpha band was highly correlated with emotion, and which was significantly correlated with pleasure and interest during learning [57; 58]. Beta, as an important indicator of immersive user experience [59], has been shown to be related to emotions in immersive environments [60]. [PERSON] et al. [61] applied the theory of asymmetry of frontal a-waves to compare the energy changes in left and right brain a-waves, and screened out users' preferred product elements combined with emotional scales. On this basis, [PERSON] et al. [62] compared car pictures with real cars as reference groups, and investigated how people perceive car design in different scenarios. The results showed that the main differences between the most negative or positive activation electrode differences were observed in the alpha and beta bands, among which the left-right inversion in the alpha band implied a difference between user experience under photo and real car cases, and its left-right inversion can be regarded as the result of different immersive user experiences under two situations. This study is significant for exploring users' demand for virtual and real products.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 4 Solar Array Fault Classification Model
### Model Training  
The 35,000 preprocessed data points were split into 80% for the training set and 20% for the testing set. Figure 24 shows the accuracy and loss curves after 300 iterations, with the model achieving a final training accuracy of 99.9%. It is observed that before 15 iterations, the accuracy and loss change rapidly, and the model converges quickly. After surpassing 15 iterations, the accuracy and loss gradually stabilize. Based on the results in Figure 24, the optimal number of epochs is set to 100.  
Figure 23: Architecture of the Solar Array Fault Classification Model.  
Figure 22: Multi-input Model Architecture.  
To visually assess the classification results of the trained model, a confusion matrix is used to display the relationship between predicted results and actual labels. The results for the test set are shown in Figure 25, where the numbers indicate the count of data points for each predicted label, with the diagonal representing correct predictions for each category, including 'no' for normal, 'ag' for aging faults, 'ps' for shading faults, 'PID' for PID faults,'sc' for short-circuit faults, 'hs' for hot spot faults, and 'ck' for crack faults. The model predicted all test data correctly except for one instance where a hot spot fault was misclassified as normal. These results demonstrate the high recognition accuracy of the method.
# Advances in Tissue Engineering for Disc Repair
## 2 Biological Approaches
### Molecular Therapies
#### 2.1.2 Gene Therapy  
A strategy to overcome the short half-life limitation of growth factors is to provide a sustained supply of growth factors within the IVD [29]. The therapeutic effect of gene therapy is based on the induction of target gene upregulation or downregulation. These genes are transferred using viral or non-viral vectors, which are either directly injected into the degenerated discs or transduced into cells [16]. Another strategy used for intradiscal gene therapy is gene expression downregulation, which is detrimental to the physiological balance of the disc and may, thus, lead to IVDD [16]. Hence, if gene therapy is performed properly, it can provide many benefits, including a more sustained target gene expression and long-term biological effects. Promising targets for gene therapy include both anabolic regulators, such as TGF-\(\beta\), latent membrane protein 1(LMP-1), and SOX-9, and anticatabolic regulators, such as anti-ADAMTS (a disintegration and metalloproteinase with thrombospondin motifs)-5, and TIMP (tissue inhibitor of metalloproteinase)-1 [13]. In terms of non-virus vector-mediated gene transfer, ultrasound targeted microbubble transfection method has been reported to improve the transfection efficiency of plasmid DNA in NP cells, and polyplex micelles made from a vector carrying miRNA-25-3p were used in an IVDD rat model [30]. Clustered Regulatory Interspaced Short PalindromicRepeats-Associated Cas9 (CRISPR/Cas9) is an innovative technology that can be used to target other genes for IVDD treatment. Using the CRISPR/Cas9 gene-editing system in AF cells from patients with chronic LBP, knock out of the transient receptor potential vanilloid type 4 (TRPV4) gene induced the reduction in inflammation [30]. However, the use of gene therapy for IVD regeneration is currently limited to in vitro and in vivo animal studies due to safety concerns. There are no ongoing human clinical trials of gene therapies for IVD regeneration.
## 3 Adaptive Background Calibration Method
### Mismatch Coefficients Estimation  
In order to estimate the mismatch parameter \(\mathbf{C}\), the error signal in the input-free band is filtered out to drive the FxLMS algorithm. A high-pass finite impulse response (FIR) filter \(h[n]\) is exploited in our design because the input-free band stemming from oversampling locates around the Nyquist frequency. However, the input-free band can be any where within the Nyquist band. The error signal filtered out by \(h[n]\) is:  
\[\varepsilon[n]=y_{c}[n]\times h[n]=\mathcal{T}_{n}^{T}\mathbf{C}(\bar{p}_{y[n]}^{f }+\bar{R}_{\varepsilon[n]}^{f})-\bar{T}_{n}^{T}\hat{\mathbf{C}}[n]\bar{p}_{y[n]}^{f} \tag{18}\]  
where \(x[n]\) is removed by the high pass filter \(h[n]\) and:  
\[\bar{R}_{\varepsilon[n]}^{f}=\bar{p}_{x[n]}^{f}-\bar{p}_{y[n]}^{f}, \tag{19}\]  
where:  
\[\bar{p}_{x[n]}^{f}=[x^{2}[n]\times h[n]\ x^{3}[n]\times h[n]\ \cdots\ x^{L}[n]\times h[n]] \tag{20}\]  
and:  
\[\bar{p}_{y[n]}^{f}=[y^{2}[n]\times h[n]\ y^{3}[n]\times h[n]\ \cdots\ y^{L}[n] \times h[n]] \tag{21}\]  
representing the high pass filtered version of the power terms \(\bar{P}_{x[n]}\) and \(\bar{P}_{y[n]}\), respectively.  
The power of the cost function \(\varepsilon[n]\) is minimized when the estimated coefficient \(\hat{\mathbf{C}}[n]=\mathbf{C}\). Estimating the nonlinear parameter is an identification problem that can be solved by exploiting the FxLMS algorithm with the iteration function:  
\[\hat{\mathbf{C}}[n+1]=\hat{\mathbf{C}}[n]+\mu\varepsilon[n](\bar{T}_{n}\bar{p}_{y[n]}^ {fT}). \tag{22}\]  
The principle diagram of the nonlinear coefficients estimation is shown in Figure 5. The error reconstruction box in Figure 5 is the dashed box in Figure 4.
## 5 Pavement Performance Evaluation  
### Skid Resistance  
The friction coefficient and texture depth were used to evaluate the skid resistance of asphalt pavement at the different spraying amounts of nano-TiO\({}_{2}\) photocatalytic coatings. The friction coefficient of the sprayed nano-TiO\({}_{2}\) asphalt mixture was measured using the pendulum friction meter (Shuyang highway equipment plant, Suqian, China) according to the test method of T0964 in the Chinese Field Test Methods of Subgrade and Pavement for Highway Engineering (JTG E60-2008) [27] and the surface texture depth was determined using the test method of T0731 in the Chinese Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011) [24]. The test results are shown in Figure 10.  
It can be seen from Figure 10, both the friction coefficient and the texture depth of the asphalt mixture were reduced with the increase of the spraying amount of photocatalytic coating. It indicated that the skid resistance of the asphalt mixture was decreased due to spraying the nano-TiO\({}_{2}\) photocatalytic coating on the sample surface. When the nano-TiO\({}_{2}\) photocatalytic coating spraying amount was greater than 550 g/m\({}^{2}\), the friction coefficient was close to the critical lower limit value (58 BPN) of the technical requirement in the Chinese Specifications for Design of Highway Asphalt Pavement (JTG D50-2006) [28]. Therefore, it is recommended that the spraying amount of nano-TiO\({}_{2}\) photocatalytic coating should not exceed 550 g/m\({}^{2}\) in practical projects.
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 3 Hierarchical Phoneme Classification  
This section analyzes the characteristics of clustered phoneme groups. We explain the similarities and differences between phoneme groups, and the detailed implementation of group-specific models. By combining the group-specific phoneme classification models, a hierarchical phoneme classification framework is proposed. All individual group models are implemented by deep neural networks. The neural network automatically finds and recognizes the characteristics of speech signals. For sequential inputs such as speech signals, modeling temporal patterns is necessary. A recurrent neural network (RNN) is a type of neural network that can effectively learn temporal data. Therefore, phoneme classification model in this paper is built using RNN. More specifically, bi-directional long short-term memory (BLSTM) is adopted [8; 42; 43].