esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Chemical Properties
#### 2.2.2 Element Content Analysis  
In order to further study the changes of chemical aging effects of silicone rubber with aging time, element content analysis was performed, with the help of X-ray photoelectron spectroscopy (XPS) conducted by an ESCALAB 250 Xi from Thermo Fisher Scientific Company. Test results are listed in Table 6.  
Table 6 shows the following:  
(1) With the increase of aging time, the relative content of Si and C elements gradually decreases, which leads to a decrease of Si-O-Si absorption peak altitude and Si-(CH\({}_{3}\))\({}_{2}\) absorption peak altitude.  
(2) Combined with the analysis in Section 2.2.1, the hydrolysis reaction in the surface material of silicone rubber will lead to an increase of the relative content of the O element as aging time increases.  
(3) With the increase of aging time, there are few changes in the relative contents among the A\({}_{1}\), Fe and Pt elements in the samples. Some fluctuations result from the differences among the fillers of composite insulators with different voltage levels.  
(4) Based on the test results of XPS, the relative contents of Si, C and O elements (denoted \(X_{Si}\), \(X_{C}\) and \(X_{O}\), respectively) are taken as parameters to characterize the aging degree of composite insulators.
# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
## 5 Limitations and Challenges  
In the utility, industrial, commercial, and residential sectors, an EMS is essential for optimal operations. Its objectives are to limit greenhouse gas emissions, optimize the scheduling of energy resources, and reduce energy consumption. Monitoring and data analysis are facilitated by the integration of supervisory control and data acquisition (SCADA) and human-machine interface (HMI) in an EMS. This involves energy production from generation sources, weather forecasts, load demand, and the latest energy prices. This information is used by the EMS to improve system performance at the generation, transmission, and distribution levels.  
Careful selection of an EMS method is essential to maintaining stable and reliable operation for nanogrids. The choice of a specific EMS should take into account the characteristics of the deployed system, such as operating modes, topologies, and structure. However, the choice of one approach does not necessarily imply the unreliability of the other. The main challenge is to match the chosen method with the studied constraints and the fixed objective related to the control strategy, aiming to identify its utility in the deployed system.  
Nanogrids' EMS operation is mainly divided into two layers: the day-ahead layer and the real-time layer. In order to account for forecast errors, the day-ahead layer is further divided into smaller time segments. Reference values are sent to LCs in real-time using communication links. Implementation and validation for EMSs are conducted using diverse solution methodologies, applied in different domains, using simulation validation [92; 93; 94; 95] and experimental validation [96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107].  
There have been efforts to establish standards and guidelines for the development of both AC and DC microgrids. The standards framework for AC microgrids is at a more advanced stage of development than that for DC microgrids. However, research is still being developed to address the standards and guidelines for DC microgrids, indicating continued progress in this area.  
While significant progress has been made in the field of hybrid nanogrids for embedded systems, there are still challenges to address. Designing an EMS for hybrid nanogrids that is scalable and adaptable to different scales of embedded systems needs further research and development efforts to optimize system designs, improve component efficiency, enhance integration and interoperability techniques, and achieve cost-effective solutions. Moreover, the need for real-time decision-making poses a challenge, especially in embedded systems where rapid adjustments are required. Developing algorithms that can make  
Figure 5: Example of competitive scores for storage technologies.  
quick and accurate decisions to respond to changing conditions while ensuring secure and reliable communication within the hybrid nanogrid is essential.
# Verification of the Scots Pine (_Pinus sylvestris_ L.) Crown Length Model
## 2 Materials and Methods
### Biometric Measurements
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**Model Validation Data**} \\  & **Mean \(\pm\) SD (Range Min–Max)** \\ \hline \multicolumn{2}{c}{**Stands**} \\ Total number of the sample plots & 20 \\ Total number of trees per sample plots & 10 957 \\ Age of stands (years) & 34.65 \(\pm\) 2.78 (28–40) \\ Number of trees per ha (\(N\), ha) & 2191\(\pm\) 452 (1224–3132) \\ Number of trees per sample plot (\(N\), 0.25 ha) & 547.85 \(\pm\) 112.96 (306–783) \\ Stand basal area (\(BA\), m\({}^{2}\) ha\({}^{-1}\)) & 27.77 \(\pm\) 6.43 (16.69–40.66) \\ Diameter at breast height (\(DBH\), cm) & 12.29 \(\pm\) 3.24 (9–32) \\ \hline \multicolumn{2}{c}{**Sample trees**} \\ Number of trees cut down (\(N\), pcs.) & 300 \\ Height of cut trees (\(H\), m) & 13.99 \(\pm\) 2.67 (8.33–19.79) \\ Diameter at breast height (cut trees) (\(DBH\), cm) & 13.27 \(\pm\) 2.75 (9.0–21.0) \\ Diameter of the trees at ground height & 14.97 \(\pm\) 2.75 (10.7–22.7) \\ (\(DGH=DBH+1.7\); cm) & 5.12 \(\pm\) 1.11 (2.31–8.63) \\ Crowns length (\(CL\), m) & 13.27 \(\pm\) 1.11 (2.31–8.63) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stands and sample trees data used to verify the crown length model. Figure 2: Distribution of all the tree diameters at breast height (\(N\) = 10,957) across 20 plots. The red line indicates probability density of normal distribution function with expected value estimated by arithmetic mean and standard deviation in population estimated by standard deviation in sample.
## 4 Societal Benefits and Impact
### Contributions to Smart City and Community Initiatives
#### 4.3.1 Integration with Existing Smart City Infrastructure  
The HARISM framework is designed to complement and enhance existing smart city infrastructures rather than replace them. This integration can occur at multiple levels. The physical layer of our proposed system can interface with existing sensor networks that many cities have already deployed. For instance, cities with established traffic monitoring cameras, environmental sensors, or public Wi-Fi infrastructure can repurpose these assets as data collection points for our HAR-enabled safety monitoring. This repurposing maximizes return on investment while minimizing the need for redundant sensor deployments.  
Most progressive cities have implemented some form of a data center or urban management platform. Our IoPVT system is designed with standard API interfaces that allow for bidirectional data exchange with platforms like IBM's Intelligent Operations Center (IOC) [96] or open-source alternatives like FIWARE [97]. This interoperability ensures that the safety insights generated by our system can flow into existing urban dashboards while also consuming relevant contextual data from other city systems.  
Real-time alerts and predictive analytics from our safety monitoring framework can be injected directly into established emergency response systems. For example, when potential hazards are detected, automated notifications can be routed through existing Computer-Aided Dispatch (CAD) systems used by police, fire, and emergency medical services, accelerating response while maintaining established protocols and workflows.
## 2 Related Work  
Efficient semantic sentence embedding is crucial for many natural language processing tasks. While several approaches have been proposed, Sentence BERT (SBERT)-based methods [1] have gained popularity due to their superior performance in terms of accuracy compared with existing methods such as GloVe [3], InferSent [4], and Universal Sentence Encoder [5]. Additionally, SBERT-based approaches are much faster than BERT-based cross-encoder approaches, making them a promising solution for large-scale NLP applications.  
SBERT is a BERT [2]-based approach that uses two siamese BERTs and pooling layers to generate sentence embeddings. The network is fine-tuned to ensure that similar sentences have close embedding vectors. Intuitively, the Semantic Textual Similarity (STS) dataset is one of the appropriate datasets for fine-tuning SBERT, as it consists of two sentence  
Figure 1: Overview of our approach based on multiple monolingual models.  
pairs with their semantic similarity scores. One of the most widely used STS datasets for fine-tuning and testing is the SemEval STS dataset [12; 13; 14; 15; 16; 17]. Note that there are many different versions of BERT, such as BERT-large, RoBERTa-base [6], etc. The performance of SBERT greatly depends on not only the datasets used but also the pre-trained model used.  
While most sentence embedding techniques are designed for English, there are also widely used approaches for Korean, such as KLUE [8]. KLUE offers pre-trained Korean models and datasets, including models based on BERT and RoBERTa, which are trained on extensive Korean corpora. The Korean corpora, which have been tokenized using a morpheme analyzer and a BPE-based tokenizer, amount to approximately 62.65 GB. This size is larger than the data used to train the original BERT (16 GB) but smaller than the data used to train RoBERTa (160 GB). Additionally, KLUE-STS provides training and development sets for STS tasks. KorSTS [18] is also a popular Korean STS dataset, which provides training, development, and test datasets for Korean STS tasks by translating the STSb [12] English dataset.  
There are also approaches to create a single multilingual model that can map sentences from multiple languages into the same embedding space, such as Knowledge Distillation [7]. The Knowledge Distillation approach first prepares an English model (teacher model) and then trains the multilingual model in such a way that the translated sentences are mapped to the same embedding space as the original sentences. Surprisingly, it has been found that this multilingual approach outperforms even the Korean monolingual model for the STS task.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 1 Introduction
### Structure of the Paper  
The rest of this paper is organized as follows. Section 2 presents related work on the routing protocols in MANETs and their performance in heterogeneous environments, focusing on the key literature addressing various protocols, like AODV, OLSR, BATMAN, and DYMO. Section 3 discusses the research methodology, detailing the experimental setup using the OMNeT++ simulation tool, as well as the design and performance metrics used to evaluate the selected routing protocols.  
In Section 4, the system evaluation and test results are presented, analyzing the impact of heterogeneous nodes on the performance metrics, including the end-to-end delays, throughput, and packet delivery ratios. Section 5 presents the research findings. Finally, Section 6 concludes the paper by summarizing the main findings and suggesting potential areas for future research to enhance the routing performance in MANETs.
## 3 Optical Properties
### The Impact of Indium Composition and Average Layer Thickness on Photoluminescence Spectra
In the case of the samples formed by the deposition of In\({}_{0.5}\)Ga\({}_{0.5}\)As, the increase in QD density revealed by TEM (Figure 2c) is confirmed by PL studies. In the low-temperature PL spectrum, one can see that peaks of QWDs and QDs have comparable intensities. With temperature increase up to 100 K, the contribution of shorter-wavelength peak (QWD) is vanished almost completely; however, further temperature increase (up to 300 K) results in recovery of its intensity (Figure 6c). The interpretation of such behavior, as well as in the previous case, is based on the existence of one-way carrier transport from QWDs to QDs in a middle-temperature range. However, in the case of 50% indium concentration, the QD density is higher than that of QWDs, and, correspondingly, the intensity of QWD related PL peak is relatively weak. Separate peaks due to different kinds of quantum-sized objects were also revealed in [18]. An extra emission was observed from the 2\({}^{\circ}\) and 15\({}^{\circ}\) off samples, and attributed to the photoluminescence from quantum wires and pyramidal self-controlled quantum-dots, respectively. These peaks were absent at the PL spectrum on 0\({}^{\circ}\) surfaces. Thus, we can conclude that the increase of In composition of deposited InGaAs layer results in a systematic increase of the QD density in comparison to the density of QWDs, which agrees well with the data obtained by TEM studies. The impact of the increase in InGaAs thickness on QWD PL spectra is shown in Figure 7. The spectra were recorded at 100 K when the QD peak is the most pronounced. If the InGaAs thickness amounts to 7 ML, only the QWD peak is seen in the spectrum. With the increase in the nominal thickness of deposited InGaAs, the intensity of the QD-related PL peak also increases that reflect the growth of QD density, which is confirmed by TEM studies [36]. For the 10 ML thickness, the QD PL peak becomes as intensive as that from QWDs. Our results qualitatively agree with [37], where In\({}_{0.15}\)Ga\({}_{0.85}\)As QWs were grown on a GaAs(001) vicinal substrate with a miscut of 6\({}^{\circ}\) towards (111)A. Optical and structural investigation revealed the presence of step bunches at the upper interface of the QWs with thickness 20 nm and 30 nm. As step bunches were not present in the thinner QW (10 nm), the authors can conclude that the phenomenon is activated above a critical thickness of the InGaAs layer that depends on the growth parameters and the growth rate. To conclude this part, tuning the InGaAs layer composition and thickness makes it possible to control QWD-to-QD density ratio and, correspondingly, optical properties of the samples.
## 2 The Proposed Method
### Original Features Extraction
#### 2.1.1 Wpt  
When wavelet packet transform (WPT) is decomposing the low frequency part of the signal, it can also decompose the high frequency part more meticulously at the same time, and there is neither redundancy nor omission in the decomposition. Therefore, WPT can provide better time-frequency analysis than wavelet transform for the mechanical vibration signal containing both medium and high frequency information. The steps to extract wavelet packet energy features mainly include [22]:  
(1) Extract signals in each sub-band  
Recorded the wavelet function \(\mathrm{W(k)}\) and scaling function \(\varphi(\mathrm{k})\) as \(\mu_{1}=\mathrm{W(k)}\) and \(\mu_{0}=\varphi(\mathrm{k})\), respectively, then  
\[\left\{\begin{array}{l}\mu_{2n}(t)=\sqrt{2}\sum\limits_{k\in Z}h(t)\mu_{n}(2t -k)\\ \mu_{2n+1}(t)=\sqrt{2}\sum\limits_{k\in Z}g(t)\mu_{n}(2t-k)\end{array}\right. \tag{1}\]  
where \(\mathrm{g_{k}}=(-1)^{\mathrm{k}}\mathrm{h_{1-k}}\) is a biorthogonal filter, \(\mathrm{n}=2\mathrm{l}\) or \(\mathrm{n}=2\mathrm{l}+1,1,0,1,2,\cdots\) The recursively defined function \(\mu_{\mathrm{n}}\) is called the wavelet packet determined by orthonormal scaling function \(\mu_{0}=\varphi(\mathrm{k})\).  
(2) Calculate the energy of each sub-band  
Set the signal energy corresponding to the reconstructed signal \(\mathrm{c_{jk}}\) of the jth frequency band of the kth layer after the wavelet packet decomposition as \(\mathrm{E_{jk}}\), then  
\[E_{jk}=\int\left|c_{jk}(t)\right|^{2}dt=\sum\limits_{m=1}^{N}\mathrm{x_{jm}} \tag{2}\]  
In which m is the discrete point of the reconstructed signal \(\mathrm{c_{jk}}\) of the jth frequency band of the kth layer, while \(\mathrm{x_{jm}}\) stands for the amplitude of the discrete points of the reconstructed signal \(\mathrm{c_{jk}}\).  
(3) Constructing wavelet packet feature vector  
The feature vector of the wavelet packet can be obtained through normalizing the characteristic parameters calculated by the following formula:  
\[\mathrm{e}=\left\{E_{jk},E_{j1},\cdots,E_{ji}\right\}/E,\;l=2^{j}-1 \tag{3}\]  
where \(\mathrm{E}=\sum\limits_{k=1}^{N}\mathrm{E_{jk}}\) is the total energy of the signal that equals to the sum of the energy of each sub-band.  
After selection, this paper extracted 14 WPT original features for further research.
## 2 LMMSE Estimator within GNSS Augmentation Systems
### Pseudorange Measurement Error Modeling  
The pseudorange DC or measurement error for each satellite \(i\) and RS \(j\) at every epoch \(n\) can be written as [12]:  
\[\eta_{i,j}=c(\delta t_{j}-\delta t_{i})+E_{i,j}+I_{i,j}+T_{i,j}+\epsilon_{i,j} \tag{1}\]  
Figure 3: Satellite-based train control system (3 InSat project).  
where \(c\) is the speed of light, \(\delta t_{j}\) and \(\delta t_{i}\) are the reference station and satellite clock bias from a standard time reference as GPS Time (GPST), \(E_{i,j}\) is the ephemeris error, \(I_{i,j}\) is the ionosphere delay bias, \(T_{i,j}\) is the troposphere delay bias, and \(\epsilon_{i,j}\) is the error in unmodeled effects, modeling errors, and measurement errors. The pseudorange measurement error is the result of several independent error sources, which are individually modeled by an overbounding zero mean normal distribution characterized by a standard deviation \(\sigma_{CE}\) for satellite clock and ephemeris errors, \(\sigma_{I}\) for ionosphere errors, \(\sigma_{T}\) for troposphere errors and \(\sigma_{w}\) for unmodeled noisy errors. Due to the noisy term \(\epsilon_{i,j}\) in the expression of the pseudorange DC, it is possible to separate the expression in two terms, that is  
\[\eta_{i,j}=\xi_{i,j}+\epsilon_{i,j} \tag{2}\]  
where \(\xi_{i,j}=c\delta t_{j}+E_{i,j}+I_{i,j}+T_{i,j}\) is the true total correction to be estimated. It is modeled by a normal distribution with zero mean and a variance of \(\sigma_{\xi}^{2}=\sigma_{CE}^{2}+\sigma_{I}^{2}+\sigma_{T}^{2}\). Considering a number of RSs of known position, performing pseudorange measurements along the railway line, at each epoch \(n\), and for each satellite \(i\), the sequence of \(\xi_{i,j}\) can be written in vector form as \(\mathbf{\xi}=[\xi_{i,1},\xi_{i,2},\cdots,\xi_{i,N_{RS}}]\), where \(N_{RS}\) is the number of RSs (in \(\mathbf{\xi}\) the symbol \(i\) is omitted to simplify the notations). To take into account the spatial correlation among \(\xi_{i,j}\) and their random nature, \(\mathbf{\xi}\) is modeled as a stochastic vector defined by a Probability Density Function (PDF) as \(\mathbf{\xi}\sim\mathcal{N}(\mathbf{\mu}_{\mathbf{\xi}},\mathbf{R}_{\mathbf{\xi}})\). The modeling and measurement errors which make up the noisy term are independent of time and space and other RSs. Therefore, the vector of noisy terms relative to each RS is modeled by a zero mean jointly Gaussian random process with uncorrelated components in time and a diagonal covariance matrix in space, defined by a PDF as \(\mathbf{\epsilon}\sim\mathcal{N}(\mathbf{0},\mathbf{R}_{\mathbf{w}})\) and \(\mathbf{R}_{\mathbf{w}}=\sigma_{w}^{2}\mathbf{I}\), where \(\mathbf{I}\) is the identity diagonal matrix. The resulting pseudorange DC is then represented by vector \(\mathbf{\eta}\).
## 1 Introduction
The proposed antenna uses advanced parametric optimization techniques and innovative material selection strategies to enhance its gain and efficiency while maintaining a compact structure. By integrating rigorous performance evaluations and experimental validations, this research contributes to the development of practical, high-performance mmWave antennas for next-generation wireless networks. The proposed design aims to bridge the gap between single-element and array configurations, offering a viable alternative for mmWave applications, in which size, cost, and efficiency are critical considerations.
## 3 Simulation Results
### Computational Complexity Analysis
needed for tasks like training and inference for each method, we aim to provide insights into their efficiency and suitability for practical implementation. In our proposed LSTM method, the total number of multiplications required to estimate the channel is 17,572 per OFDM symbol. Meanwhile, for the MMSE estimator, the total number of multiplications required to estimate the channel is 528,384 per OFDM symbol. Therefore, the MMSE estimator incurs a significantly higher computational delay compared to the LSTM estimator. This analysis facilitates an understanding of the computational trade-offs between LSTM networks and traditional MMSE techniques, enabling informed decisions in designing and deploying communication systems.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Data Preprocessing
This ensures that the model is exposed to a wider range of scenarios and can generalize better to unseen data. Textual data preprocessing is essential for ensuring the accuracy and efficiency of AI models. Missing values in numerical or categorical data can be dealt with by replacing them with the mean, median, mode, or user-defined parameters. Categorical data may require encoding techniques such as one-hot encoding, label encoding, or developer-specific algorithms to transform them into a format suitable for analysis [106]. Dimensionality reduction techniques such as principal component analysis (PCA) or LDA are employed to mitigate the \"curse of dimensionality\", enhancing computational efficiency. Outliers in text data are addressed using methods based on the Z-score or interquartile range (IQR). Additionally, windowing techniques can be applied to maintain time dependency in text datasets, with the size of the time window being either static or dynamic [8]. Balancing text data is crucial for addressing class imbalances. One common approach is oversampling, where instances of the minority class are duplicated or synthetically generated using techniques such as SMOTE (Synthetic Minority Oversampling Technique). Undersampling involves reducing the number of instances in the majority class to match the minority class, though it risks losing valuable information. Data augmentation can also be employed, in which new text samples are created through paraphrasing, translating, or substituting words with their synonyms to increase the dataset's diversity. Algorithmic approaches adjust class weights during training to penalize misclassification of the minority class more heavily, encouraging the model to learn from these instances. Combining these preprocessing methods can significantly enhance a model's performance and robustness. By integrating these preprocessing techniques, AI models can better handle the complexities and variations inherent in textual data, leading to more accurate and reliable outcomes.