esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 4 Numerical Results
### The Performance of BLEU and Sentence Similarity
When comparing multiple n-gram scores, the advantage of LLM-SC over other methods heightens with higher n-grams at high-SNR levels. Figure 3 presents a comparison of sentence similarity metrics. In Figure 3a, all methods exhibit the same increasing trend in sentence similarity as the SNR increases. Traditional methods such as Huffman with Turbo coding achieve a BLEU (1-gram) score of 0.2 under 9 dB, yet the sentence similarity approaches 0. This indicates that even if some words are received, the sentence cannot be properly understood. Machine learning-based methods like DeepSC and LLM-SC show consistency between sentence similarity and BLEU scores. At low SNRs, DeepSC has higher similarity than LLM-SC, but DeepSC has an upper bound across the entire SNR range and cannot achieve error-free transmission similar to the BLEU  
Figure 2: BLEU scores versus the SNR for the same bps, with [PERSON] coding with RS (30,42) in 64-QAM, 5-bit coding with RS (42,54) in 64-QAM, [PERSON] coding with Turbo coding in 64-QAM, 5-bit coding with Turbo coding in 128-QAM, [PERSON] coding with Turbo coding in 8-QAM, the DNN-based JSCC [44] trained over AWGN channels and Rayleigh fading channels, DeepSC trained over the AWGN channels and Rayleigh fading channels [5], and finally, our proposed LLMS-SC. score. In contrast, LLM-SC demonstrates better competitiveness at high SNR, capable of transmitting complete semantic information. In Figure 2(b), under the Rayleigh fading channel, all methods experience significant degradation compared to the AWGN channel, indicating that channel fading has a substantial impact on semantic transmission. In this scenario, LLM-SC still maintains a high advantage at high SNRs, achieving near-error-free transmission, but performs slightly worse than DeepSC and JSCC at low SNRs due to the impact of channel fading.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 3 Experiment and Results
### Evaluation Metrics  
In order to better evaluate the performance of the algorithm, mean absolute error (MAE) and mean absolute percentage error (MAPE) are chosen as the evaluation metrics, and the computation of MAE and MAPE are shown in Equation (16) and Equation (17), respectively:  
\[\text{MAE}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\lvert\text{BPM}_{\text{ est}}(\text{i})-\text{BPM}_{\text{true}}(\text{i})\rvert \tag{16}\]\[\text{MAPE}=\frac{1}{\text{N}}\sum_{\text{i=1}}^{\text{N}}\frac{\left|\text{BPM}_{ \text{est}}(\text{i})-\text{BPM}_{\text{true}}(\text{i})\right|}{\text{BPM}_{ \text{true}}(\text{i})}\times 100\% \tag{17}\]  
where N denotes the total number of windows for each set of data, \(\text{BPM}_{\text{est}}(\text{i})\) denotes the heart rate estimate for the i-th window, and \(\text{BPM}_{\text{true}}(\text{i})\) denotes the heart rate reference value for the i-th window.
## 3 Localization Algorithm
### Tightly Coupled Hybrid Algorithm  
In the tightly coupled version of the hybrid algorithm, raw measurement results from the BLE anchors and ranging sensors are fused. For this purpose, the EKF is used. The assumed models and implementation of the time-update and measurement phase are almost identical to the BLE-based EKF presented in Section 3.1 (1)-(7). The major differences are the forms of the measurement vector \(z\) and the sensor model \(h_{k}(x_{k})\).  
\[z_{k}=\left[RSS_{1}\quad\cdots\quad RSS_{n}\quad d_{1}\quad\cdots\quad d_{n}\right] \tag{14}\]  
\[h_{k}(x_{k})=\left[RSS_{1}(x_{k})\quad\cdots\quad RSS_{n}(x_{k})\quad d_{1}(x _{k})\quad\cdots\quad d_{n}(x_{k})\right] \tag{15}\]  
The measurement vector contains both RSS measurement results and measured distances. In the sensor model \(h_{k}(x_{k})\) the RSS is estimated as in (10), whereas ranging results \(d_{n}\) are estimated as the distance of the sensor from the predicted location.  
The other difference is verification and correction of the predicted state vector value. As in the loosely coupled version, if any of the proximity sensors detect the user and the predicted location is outside the sensors beam, it is pulled to the closest point on the arc corresponding to the measured distance.
## 3 Results
### Cultural Differences by Demographic Variables
#### 3.2.2 Differences by Work Location (Facility vs. Headquarters)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Spanish Nuclear Industry**} & \multicolumn{6}{c}{**NPP**} & \multicolumn{6}{c}{**NPC**} \\ \hline  & \multicolumn{2}{c}{**Facility**} & \multicolumn{2}{c}{**Headq.**} & \multicolumn{2}{c}{**Facility**} & \multicolumn{2}{c}{**Headq.**} & \multicolumn{2}{c}{**Facility**} & \multicolumn{2}{c}{**Headq.**} & \multicolumn{2}{c}{**Facility**} & \multicolumn{2}{c}{**Headq.**} \\ \hline
**OCI Styles** & **M** & **SD** & **M** & **SD** & **Cohen’s** \& **M** & **SD** & **M** & **SD** & **Cohen’s** \& **M** & **SD** & **M** & **SD** & **Cohen’s** \& **S** \\ \hline Humanistic-Encouraging & 3.61 & 0.85 & 3.38 & 0.92 & 0.27 & 3.65 & 0.85 & 3.61 & 0.82 & 0.05 & 3.30 & 0.82 & 2.98 & 0.93 & 0.38 \\ Affilative & 3.69 & 0.89 & 3.43 & 0.90 & 0.29 & 3.73 & 0.90 & 3.62 & 0.85 & 0.13 & 3.38 & 0.80 & 3.11 & 0.89 & 0.32 \\ Achievement & 3.40 & 0.61 & 3.30 & 0.71 & 0.16 & 3.43 & 0.60 & 3.46 & 0.63 & \(-\)0.04 & 3.20 & 0.64 & 3.04 & 0.77 & 0.24 \\ Self-Actualizing & 3.39 & 0.70 & 3.25 & 0.78 & 0.20 & 3.41 & 0.70 & 3.40 & 0.72 & 0.01 & 3.25 & 0.71 & 2.98 & 0.80 & 0.35 \\ Approval & 2.83 & 0.64 & 2.70 & 0.63 & 0.21 & 2.82 & 0.65 & 2.60 & 0.61 & 0.34 & 2.91 & 0.54 & 2.86 & 0.63 & 0.09 \\ Conventional & 3.30 & 0.65 & 3.26 & 0.69 & 0.05 & 3.28 & 0.66 & 3.16 & 0.68 & 0.18 & 3.40 & 0.59 & 3.44 & 0.68 & \(-\)0.06 \\ Dependent & 3.26 & 0.56 & 3.24 & 0.57 & 0.04 & 3.25 & 0.56 & 3.16 & 0.57 & 0.16 & 3.34 & 0.52 & 3.38 & 0.56 & \(-\)0.07 \\ Avoidance & 2.28 & 0.74 & 2.26 & 0.74 & 0.03 & 2.26 & 0.75 & 2.11 & 0.67 & 0.21 & 2.42 & 0.69 & 2.51 & 0.78 & \(-\)0.13 \\ Oppositional & 2.79 & 0.46 & 2.67 & 0.43 & 0.26 & 2.80 & 0.47 & 2.74 & 0.41 & 0.14 & 2.74 & 0.45 & 2.56 & 0.45 & 0.40 \\ Power & 2.62 & 0.64 & 2.56 & 0.65 & 0.08 & 2.61 & 0.65 & 2.49 & 0.64 & 0.19 & 2.66 & 0.59 & 2.69 & 0.66 & \(-\)0.05 \\ Competitive & 2.13 & 0.87 & 2.03 & 0.84 & 0.12 & 2.12 & 0.89 & 1.91 & 0.84 & 0.24 & 2.28 & 0.71 & 2.25 & 0.79 & 0.03 \\ Perfectitious & 3.11 & 0.61 & 3.06 & 0.61 & 0.09 & 3.11 & 0.61 & 3.09 & 0.62 & 0.04 & 3.13 & 0.57 & 3.01 & 0.60 & 0.21 \\ Constructive & 3.52 & 0.69 & 3.34 & 0.77 & 0.26 & 3.55 & 0.69 & 3.52 & 0.69 & 0.05 & 3.28 & 0.67 & 3.03 & 0.79 & 0.36 \\ Passive/Defensive & 2.92 & 0.49 & 2.86 & 0.53 & 0.11 & 2.90 & 0.50 & 2.76 & 0.51 & 0.29 & 3.02 & 0.42 & 3.05 & 0.54 & \(-\)0.06 \\ Aggressive/Defensive & 2.67 & 0.49 & 2.58 & 0.48 & 0.17 & 2.66 & 0.50 & 2.56 & 0.49 & 0.21 & 2.70 & 0.43 & 2.63 & 0.47 & 0.16 \\ \hline \hline \end{tabular} \({}^{*}\)Note: Mean = M; Standard Deviation = SD; Sample = N.  
\end{table} 
Table 7: Descriptive statistics of the OCI organizational cultures and styles by work location.
## 3 Results
### Effects of LPA Species on hBM-dMSCs Proliferation  
XTT measurements were performed to investigate the possible cytotoxicity and to evaluate the effects of 18:1, 18:2, and 16:0 LPA on the proliferation of hBM-dMSCs. The cells were treated with increasing concentrations of phospholipids alone or in combination with HSA. After 24 h of treatment, none of the three LPA species showed cytotoxic effects up to 10 \(\upmu\)M concentration. 18:1 LPA in 1, 3, and 10 \(\upmu\)M concentrations significantly increased cell proliferation compared with the control group, but solely when administered in the presence of HSA (Figure 2A). In addition, 18:2 LPA significantly enhanced the proliferation of hBM-dMSCs in combination with HSA in 1, 3, and 10 \(\upmu\)M and when examined alone in 3 and 10 \(\upmu\)M concentrations (Figure 2B). A significant elevation in the cell proliferation was caused by 0.3, 1, 3, and 10 \(\upmu\)M 16:0 LPA treatment, exclusively in combination with HSA (Figure 2C). Interestingly, among the examined three LPA species, only 18:2 LPA in 3 and 10 \(\upmu\)M concentrations significantly increased cell proliferation when administered without HSA (Figure 2B).
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing
#### 3.2.3 Point Cloud Production and Project Binning  
On having correct calibration factors, it is then possible to use the LiDAR georeferencing equation that combines the range, scan angle and SBET observations to produce the point cloud (PC). As illustrated in Figure 17a a single point cloud file is produced for each flight line, usually called a flight strip or just a strip. Because of swath overlap, the laser returns for a given area might be contained in several strips. For a project with long flight lines a strip might contain an extremely large number of returns, making it impractical to further process the data on a strip-by-strip basis due to computer memory limitations. Therefore the entire project coverage is broken down into smaller manageable sections that can be called tiles or bins (Figure 17b). The points from the different flight lines are distributed among the tiles. Depending on the format used to save the tiles each point might retain information from which flight strip it originally came from. With all the information of a given area contained in the individual tiles they are further processed in a variety of ways depending on the intended final application. For archeology, the next step is usually to classify the returns as coming from the ground or other objects (vegetation, manmade structures).  
Figure 16: Sensor calibration (**a**) DSM from a properly calibrated dataset, the red dashed line represents the location of the cross sections; (**b**) DSM from simulated bad calibrated dataset; (**c**) Cross section from the properly calibrated dataset; (**d**) Cross section from the improperly calibrated dataset.
## 5 Applications Across Diverse Sectors
### Inventions in 3D Printing Technologies
#### 5.2.2 Digital Twins  
Lately, progress in additive manufacturing has converged with digital twin technology, which digitally replicates physical assets or systems for immediate tracking and improvement. [PERSON] and colleagues presented a case study that focused on developing and training a digital twin of a robot arm. This involved training with artificial intelligence (AI) in a virtual environment and then applying what was learned to real-world settings [81]. This method combined a digital environment with a real one by using a 3D-printed copy of the virtual robot arm. The digital twin concept enhances AM processes by optimizing parameters, detecting defects, monitoring machine health, and managing big data. This emerging research area has the potential to unlock AM's full capabilities for demanding applications, necessitating further developments in modeling, databases, machine learning, equipment integration, and predictive algorithms [46; 82; 83; 84].
# Mechanical Behavior of Bi-Layer and Dispersion Coatings
## 4 Discussion
As regards composites with carbon nanotubes and copper, hardness was determined as above 600 GPa for CNTs-1 Cu composites in [66], hardness was of 1.1-1.4 GPa and [PERSON]'s modulus 96-108 GPa in CNTs-Cu nanocomposites [67], and H of 85-96 MPa and E of 9-10 GPa in other work of the same team was reported [68], copper-based hybrid nanocomposites with 4 wt. pct. of SiC and MWCNTs showed hardness increased from 0.96 to 1.61 GPa, and [PERSON]'s modulus from 98 to 120 GPa for pure copper and nanocomposite, respectively [69], for Cu-10 MWCNTs the hardness was about 1 GPa and [PERSON]'s modulus about 100 GPa in [70] and for HAp-CNTs (20 wt. pct.) the hardness and [PERSON]'s modulus were 13.3 GPa and 189.5 GPa, respectively [71]. Here, determined values of hardness and [PERSON]'s modulus are lower than the above-reported values which may be attributed to the specific microstructure of tested coatings, relatively thick and containing not closely packed carbon nanotubes. Figure 8 shows the comparison between the yield pressure parameter for each coating deposited, both on Ti and Ti alloy substrate. It could be observed that also, in this case, different relationships for coatings deposited either on Ti or Ti alloy appear. For Ti alloy, the nanocopper addition improved the resistance to plastic deformation under indenter tip load, but it also caused cracking in the MWCNTs coating. For Ti substrate, the cracks were invisible, but the MWCNTs-nanocopper coating achieved a lower yield pressure parameter than for the MWCNTs-titania coating. This shows that the substrate affects the properties of deposited coatings. Both coatings have been developed for the medical implantology field, for long-term titanium implants. It is well-known that high mechanical stresses may appear during implantation surgery. Therefore, the coatings need to be both elastic and resistant to plastic deformation, well adjacent to the titanium surface, and possess Young's modulus close to that of a bone. The present results show that the MWCNTs-titania coating, never before investigated at the present setting, is especially plausible for such application. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Coating** & **Substrate** & **S\({}_{\mathbf{a}}\) (\(\mathbf{\mu}\)m)** & **H (GPa)** & **E (GPa)** & **h\({}_{\mathbf{max}}\) (\(\mathbf{\mu}\)m)** & **PDE (n)** & **ERE (n)** & **H/E\({}_{\mathbf{r}}\) 10(6)** \\ \hline MWCNTs & Ti & 0.29 & 0.032 & 3.41 & 3.58 & 7.06 & 0.958 & 7.1 \\ MWCNTs & Ti13 Nb13 Zr & 0.34 & 0.101 & 14.17 & 2.07 & 3.88 & 0.378 & 5.3 \\ MWCNTs\_TiO\({}_{2}\) & Ti & 0.56 & 0.183 & 10.11 & 1.55 & 5.01 & 0.644 & 13.7 \\ MWCNTs\_TiO\({}_{2}\) & Ti13 Zr13 Nb & 0.65 & 0.137 & 7.69 & 1.81 & 5.87 & 0.722 & 13.1 \\ MWCNTs/Cu & Ti & 0.36 & 0.079 & 3.51 & 2.55 & 19.23 & 6.821 & 16.8 \\ MWCNTs/Cu & Ti13 Zr13 Nb & 0.41 & 0.213 & 10.83 & 1.43 & 3.53 & 0.688 & 14.7 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of mechanical properties of two coatings deposited on different substrates: Ti (present work) and Ti13 Nb13 Zr (previous research [47]).
## 4 Numerical Example  
In this study, our first objective was to minimize the total cost of production and inventory by determining the economic planning of production to meet the varying demands (Tables 1 and 2), and secondly, to minimize the total maintenance and quality cost for different process scenarios. To resolve the joint model of production, maintenance, and quality, we used Mathematica software with its integrated algorithms due to the complexity of the problem. The optimal results are represented by the best production plan and a numerical procedure for the maintenance and quality problem to determine the optimum value of sample size, sampling interval, the control limits, and the optimal number of perfect preventive maintenance actions corresponding to integrated policy.  
In this numerical example, we consider two practical cases to validate our proposed model's efficiency and robustness.
## 2 Materials and Methods
### Isolation and Extraction of Cellulose Nanocrystal  
The extraction of the cellulose nanocrystal was carried out using the acid hydrolysis procedure as described in references [28; 29] with slight modifications. Firstly, raw oil palm trunk fibres were ground into fine powder using the Riken grinder with a 1.5 mm filter screen. The fibre extractives were dewaxed for 4 h through the Soxhlet extraction method using ethanol/toluene [2:1 (\(\
icefrac{{v}}{{v}}\))] and were dried overnight in the oven at 40 \({}^{\circ}\)C. Secondly, about 20 g of the dried fibre sample was bleached in a sodium chlorite (NaClO\({}_{2}\)) solution under an acidic condition (10% acetic acid solution) at 70 \({}^{\circ}\)C under agitation of 70 rpm in a water bath incubator shaker for 1 h, and this step was repeated 4 times. The bleached fibres were washed vigorously with dH\({}_{2}\)O until the fibres change to a white colour. The hemicelluloses region of the fibres was eliminated by soaking the fibres in 6 wt% potassium hydroxide (KOH) for one day at 4 \({}^{\circ}\)C. Then, the fibres were repeatedly washed with dH\({}_{2}\)O to neutralize the pH of the samples and were further processed using the acid-hydrolysis technique. About 210 mL of sulfuric acid solution (64%; H\({}_{2}\)SO\({}_{4}\)) was added to the sample and was agitated at 45 \({}^{\circ}\)C for 1 h. Subsequently, about 400 mL of cold water was added to the hydrolys cellulose fibres, followed by washing and rinsing using dH\({}_{2}\)O at least 15 times. Any residual sulfuric acid was further eliminated by subjecting it to a dialysis process for at least 3 days in order to neutralise the pH. The samples were then homogenised and sonicated before being subjected to a freeze-dried process. The dried CNC was stored at RT until further use.  
Characterisation Using TEM Analysis  
The structure and size of the CNCs were observed using transmission electron microscopy (TEM) (EFTEM Libra 120, Carl Zeiss, Germany). This analysis was conducted at the Electron Microscopy Unit, School of Biological Sciences, Universiti Sains Malaysia. The measurement of the CNCs was done using ImageJ software (Version 1.53) and the average value of the data was reported.
###### Abstract  
The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.  
energy efficient; network functions virtualization; open radio access network; software-defined network; industry internet of things +
Footnote †: journal: Proceedings
## 2 Materials and Methods
### Classification Methods  
#### 2.5.1 Support Vector Machines  
Support Vector Machines (SVMs) are widely used machine learning methods in data-driven research fields. They aim to find the optimal hyperplane that separates different classes in classification problems [37; 38; 39; 40]. SVM maximizes the margin between support vectors of different classes, ensuring optimal class separation. In non-linear datasets, SVMs cannot create a linear hyperplane. Therefore, kernel functions are used to transform the data into a higher dimensional space where linear separation can be performed [41].
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Dictionary Construction
#### 3.3.2 Selection of Additional Atoms  
Additional atoms can be incorporated to the dictionary in order to increase its flexibility in representing different ECGs. These atoms should be constructed using highly correlated waveforms with respect to the remaining candidates (to obtain representative dictionary atoms), as well as with low absolute correlation with respect to already selected waveforms (to avoid similar atoms). Here, we propose to use the following procedure to select a total of \(K\) representative waveforms:  
1. Set the number of accepted atoms equal to one (\(k=1\)), the pool of candidate waveforms as \(\mathcal{C}=\{1,\ldots,\ell_{0}-1,\ell_{0}+1,\ldots,Q^{\prime}\}\) (i.e., all the waveforms except for the one selected for the first atom), the pool of accepted waveforms as \(\mathcal{A}=\{\ell_{0}\}\), and construct a reduced correlation matrix by removing the row and column corresponding to the first atom selected from the global correlation matrix \(\mathbf{C}\): \[\mathbf{C}_{r}=\left[\begin{array}{cc}\mathbf{C}_{1:\ell_{0}-1,1:\ell_{0}-1 }&\mathbf{C}_{1:\ell_{0}-1,\ell_{0}+1:Q^{\prime}}\\ \mathbf{C}_{\ell_{0}+1:Q^{\prime},1:\ell_{0}-1}&\mathbf{C}_{\ell_{0}+1:Q^{ \prime},\ell_{0}+1:Q^{\prime}}\end{array}\right].\] (14)
2. \(\mathtt{WHILE}\ k<K\): 1. Select, from the remaining waveforms in the pool of candidates, the one with the highest average correlation (in absolute value) with respect to all the other candidate waveforms in the pool, i.e., \[\tilde{\ell}_{k}=\arg\max_{i=1,\ldots,Q^{\prime}-k}\ \sum_{j=1}^{Q^{\prime}-k}| \mathbf{C}_{r}(i,j)|,\] (15) and obtain the associated index, \(\ell_{k}\), in the original set of candidate waveforms.  
* Compute the maximum correlation (in absolute value) between the selected candidate and all the already accepted atoms, \[\rho_{\max}=\max_{i=0,\dots,k-1}|\mathbf{C}(\ell_{k},\ell_{i})|.\] (16)
* Remove the \(\ell_{k}\)th waveform from the pool of candidates (i.e., set \(\mathcal{C}=\mathcal{C}\setminus\{\ell_{k}\}\)), and construct a new reduced matrix (\(\mathbf{C}_{r}\)) by removing the \(\tilde{\ell}_{k}\)th row and column from the current \(\mathbf{C}_{r}\).
* IF \(\rho_{\max}<\gamma\) (with \(0\leq\gamma\leq 1\) denoting a pre-defined maximum correlation threshold), THEN add the selected waveform to the pool of accepted atoms (i.e., \(\mathcal{A}=\mathcal{A}\cup\{\ell_{k}\}\)), and set \(k=k+1\).  
END  
Note that the value of \(\gamma\) sets an upper bound on the number of waveforms that can be accepted for a given set of candidate waveforms. Therefore, \(K\) is the maximum number of waveforms that can be accepted in the previous algorithm, but the number of waveforms selected can actually be smaller than \(K\). See Section 4.1 for a detailed description of the values of \(\gamma\) and \(K\) used in this work.