esa-sceva/satcom-chunk-collection huggingface dataset:
# A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis
## 4 Case Study
### Experimental Results
#### 4.2.3 Evaluation of Performance  
Note that ECC techniques have been widely integrated with standard SRAM and DRAM devices using customized circuits, while the physical implementation of PCA is not the focus of this work. For a fair comparison of performance, software emulated ECC encoder/decoder and PCA techniques are adopted to quantify the difference in amount of computation.  
Figure 12a depicts the overall emulated execution time of proposed error confinement technique on images with different sizes from 256 \(\times\) 256 to 1024 \(\times\) 1024 with the ECC technique. In the proposed method, sub-block size of 256 \(\times\) 8 is chosen with two remaining principal components according to Table 1, in order to achieve sufficient PSNR with relatively small execution overhead and memory footprint.  
For small images, those techniques take similar execution time, but with the increment in image size, the proposed technique takes significantly less time compared to SECDED ECC. Such speed-up is caused by the faster block-level processing of our approach compared with the relatively slower word-level encoding and decoding in ECC. When the size of the image is large, the proposed technique is even more efficient. The overhead of execution time caused by fault injection is reflected in the process of correcting erroneous data with the average vectors. Compared with feature extraction and matrix multiplication in the process of data reconstruction, the time consumed by correcting erroneous data can be simply ignored and the results in Figure 12 are almost consistent with different error rates.  
Another interesting comparison is the difference regarding memory usage. In Figure 12b, the proposed method uses far less memory compared to SECDED ECC technique are shown. In particular, for an image of size 1024 \(\times\) 1024, the proposed technique reduces memory usage by 68%, which attributes to block-level processing of our approach and ECC requiring six parity bits for encoding each 32-bit code word.
# Innovation Potentials and Pathways Merging AI, CPS, and IoT
## 1 Introduction  
Innovation advances in the areas of Cyber-Physical Systems (CPS) and Industry 4.0 in the production area [1; 2; 3; 4; 5], Physical Internet (PI) and Internet of Things (IoT) in the logistics and transportation area [6; 7; 8; 9; 10] as well as Artificial Intelligence (AI) in the informatics field [11; 12; 13; 14; 15; 16] have brought about a tremendous growth and innovation potential for global value chain setups. The question is not _if_ further innovation and automation will happen, but when--sooner than later--and in which specific way. Independent of physical production innovations (e.g., additive manufacturing) information integration and decision autonomy tendencies will drive new supply chain and customer interaction designs and business models.  
But it is a core interest of research and business technology forecasting to identify important developments as early as possible as in this way adequate preparation (e.g., training, investment) can be secured. Therefore, as specific technologies and applications are not yet known, it is prudent and the only way forward to use existing descriptions and state-of-the-art technology concepts as basis for extrapolation methods.  
This article presents a technology forecast result based on extensive descriptions of developments by field as well as interaction traits. Therefore, three intertwined research questions are pursued, namely  
* RQ1: How can the status and state-of-the-art of development in the areas of production (CPS, additive manufacturing, Industry, 4.0 etc.), transportation (PI, IoT) and artificial intelligence (AI) be described?* RQ2: Applying the method of technology forecasting and evaluation, what looming innovations and prognoses can be derived from that?
* RQ3: What interactions and innovation potentials can be assumed bringing the three areas of production, transportation and artificial intelligence together?  
To comply with these objectives, the contribution is structured as follows within the subsequent sections: Section 2 provides an extensive state of the art and literature analysis for the three sectors addressed (RQ1). Section 3 outlines the method of technology forecasting and evaluation and applies that to the three sectors discussed herein (RQ2). Section 4 brings together a brainstorming including the interactions and linkages between the three areas towards new innovation fields and concepts (RQ3). Section 5 describes the suggested innovations with the example of autonomous truck driving and maintenance. Section 6 concludes with an outlook.
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 4 Conclusions  
This paper proposed a method of extracting the stress wave reflection period based on image processing, and was applied to the length detection of buried metal piles. In the experiments, the vibration hammer was used to strike the buried pile and the stress wave reflection signal was received by a PZT sensor. The time-frequency spectrogram of the reflected signal was obtained after applying wavelet transformation, quadratic self-correlation, and STFT operations. The trough distribution map of the reflection signal was obtained by performing edge detection and image binarization on the time-frequency spectrogram. The valley distribution curve was obtained by calculating the mean value of the column pixels. The curve was processed using FFT and the main frequency was calculated. The reflected wave was then calculated based on the main frequency. Finally, taking the reflection period into the formula for estimating the wave reflection distance, the length of the metal buried pile was calculated. The experiments validated the effectiveness and accuracy of the proposed new algorithm. The average relative error is within 1.1% based on measuring the different lengths of two buried metal piles in the soil. This average relative error can meet the accuracy requirement in industry applications. In addition, the concept of transforming signals into time-frequency domains and analyzing them through image processing has promising applications for health monitoring of other structures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Calculated Length (m)** & **Error Rate (\%)** & **Average Error Rate (\%)** \\ \hline
1 & 1.9896 & 0.52 & \\
2 & 1.9880 & 0.60 & \\
3 & 2.0127 & 0.63 & \\
4 & 1.9864 & 0.68 & \\
5 & 2.0191 & 0.95 & \\
6 & 1.9900 & 0.50 & \\
7 & 2.0210 & 1.05 & \\
8 & 1.9868 & 0.66 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different depth test results of the 2.00 m buried metal piles.  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea and developed the algorithm; [PERSON] and [PERSON] conducted the data acquisition; [PERSON], [PERSON] and [PERSON] performed the data analysis; [PERSON]. prepared the manuscript; [PERSON] and [PERSON] made critical revisions to the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Natural Science Foundation of China, grant numbers 51978079 and 61901059, National Key R&D project, grand number 2016 YC0303703, National Major Science and Technology Special Projects, grand number 2017 ZX05019001 and Hubei Provincial Natural Science Foundation of China, grand number No. 2019 CFB233.  
**Acknowledgments:** The authors thank the anonymous reviewers for their constructive suggestions, which helped improve the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Preliminaries
Two modules \(\mathcal{P}_{1}=\langle P_{1},I_{1},O_{1}\rangle\) and \(\mathcal{P}_{2}=\langle P_{2},I_{2},O_{2}\rangle\) are _compositional_ if  
\(O_{1}\cap O_{2}=\emptyset\), and  
\(O_{1}\cap C=\emptyset\) or \(O_{2}\cap C=\emptyset\) for every strongly connected component \(C\) of \(P_{1}\cap P_{2}\). The _join_ of \(\mathcal{P}_{1}\) and \(\mathcal{P}_{2}\) is the module \(\mathcal{P}_{1}+\mathcal{P}_{2}\) defined as:  
\[\langle P_{1}\cup P_{2},(I_{1}\setminus O_{2})\cup(I_{2}\setminus O_{1}),O_{1} \cup O_{2}\rangle. \tag{7}\]  
According to the Module Theorem, if \(\mathcal{P}_{1}\) and \(\mathcal{P}_{2}\) are compositional, then (a) their join \(\mathcal{P}_{1}+\mathcal{P}_{2}\) is defined and (b) its answer sets can be obtained from the answer sets of \(\mathcal{P}_{1}\) and \(\mathcal{P}_{2}\) as follows:  
_A set \(X\) of ground literals is an answer set of \(\mathcal{P}_{1}+\mathcal{P}_{2}\) iff \(X=X_{1}\cup X_{2}\) for answer sets \(X_{1}\) and \(X_{2}\) of \(\mathcal{P}_{1}\) and \(\mathcal{P}_{2}\) respectively, such that \(X_{1}\cap(I_{2}\cup O_{2})=X_{2}\cap(I_{1}\cup O_{1})\)._  
Two very important ramifications of the definition of compositionality and of its role in the Module Theorem are that (i) all rules defining a (ground) literal must belong to the same module, and that (ii) all positive rule cycles must remain confined within individual modules. These requirements impose significant restrictions on the applicability of incremental computations in clingo. Consider for instance the following program:  
\(\#program\;base\). \(\#external\;r_{1}\). \(\#external\;r_{2}\). \(s\gets r_{1}\). \(s\gets r_{2}\). \(\#program\;mod_{a}\). \(\#external\;r_{3}\). \(s\gets r_{3}\). Furthermore, consider the sequence of clingo calls \(ground(base)\); \(solve()\); \(ground(mod_{a})\), which intuitively grounds and solves the _base_ module, and then attempts to ground module \(mod_{a}\). The second call to the _ground_ function will trigger an error, because \(s\) is defined in multiple modules.
## 2 Background and Literature Review
### Word Embeddings Vectors: ELMo and Gloves
ELMo uses big datasets to make deeply contextualized word representations: (1) understanding the complicated parts of language use, like syntax and semantics, and (2) telling the difference between word meanings that change in different language contexts, like representing polysemy [52]. ELMo's unique feature is its ability to understand context, allowing it to create distinct representations for homonyms, i.e., words that share the same spelling but have different meanings; therefore, it offers a unique approach compared with traditional models by considering the entire input phrase, resulting in a more advanced understanding of word usage [52]. The technology behind ELMo is a bidirectional LSTM (BiLSTM), which is trained on large amounts of text using a language modeling method. BiLSTM efficiently analyzes sentences and can predict word sequences in both directions by combining forward and backward LSTM parts. This makes a two-way language model a useful tool for many NLP applications because it uses BiLSTM processing to improve language comprehension through word embeddings. [PERSON] et al. (2023) [53] proposed an ML-based approach for spam detection on social media and used various features and classifiers to distinguish spam messages from legitimate ones. And, [PERSON] et al., (2021) [54] used ML for spam detection in social networks, and the proposed Voting Classifier algorithm achieved a high classification accuracy rate of 97.96%. Nevertheless, these studies concentrated on the English language, while our study delves into the impact of contextualized embeddings such as ELMo for detecting social media spam on various platforms such as Twitter and YouTube. The evaluation offers recommendations on the best neural components for diverse informal content. In addition, integrating platform-specialized LSTM models balances performance and generalization ability while accommodating stylistic variations in real-world, nonstandard text. Through a methodical analysis of cutting-edge representations and structures, our approach provides valuable insights for implementing spam filtering systems. In a nutshell, these previous studies have proposed that the still representation performs very well in the discriminate spam study of content on social media and also in real-time view analysis compared with any other form of context. These studies just scratch the surface in trying to analyze the context in the contextual representation of social media spam. Therefore, we need to reveal the critical factors in differentiating the contextual representations from the contemporary representations, and subsequent implementation of the results can help such industries in the fulfillment of user expectations and build a secure environment through the benefits brought by contextualized study. This research goes into the depths of the foundational works and later provides support to justify user intention and also provides much-needed computational consideration.
## 3 Results and Discussion
### 3.2. Mechanical Analysis
#### 3.2.3 Flexural Resistance and Flexural Modulus of Elasticity  
The flexural resistance and flexural modulus of elasticity (MOE) for all the composites are shown in Figure 10. The flexural resistance and flexural MOE for the PA6 control sample were 105 MPa and 2.45 GPa for dry samples and 90.19 MPa and 2.08 GPa for 48-h conditioned samples respectively. The flexural MOE had significant improvements and the flexural resistance had gradual improvements with respect to increase in cellulose composition.  
Figure 10: (**a**) Flexural resistance and (**b**) flexural modulus of elasticity (MOE) of the pulp cellulose composites.  
Figure 9: Stress-strain curves of neat PA6 and cellulose pulp-PA6 composites.  
The highest values of flexural resistance and flexural MOE were obtained for 25% composition with the values being 113.6 MPa and 3.84 GPa for dry samples and 106.5 MPa and 3.13 GPa for conditioned samples respectively. These values correspond to an 8% increase in flexural resistance (Figure 9(a)) and 57% increase in flexural MOE (Figure 9(b)).
## 3 Proposed System Model  
In this study, specific hardware and software were utilized to collect environmental data. The collected data were processed and analyzed using predefined methods for training machine learning algorithms, with Python 3.11 employed for data processing and analysis. The materials used were systematically evaluated at each stage to ensure the reliability of the results. The steps taken to ensure the reliability and consistency of the data were carefully planned and integrated into the methodology, aligning with the objectives of this research.  
Figure 1 presents an integrated system architecture developed for environmental quality prediction in a library environment. This system collects real-time environmental parameters through IoT sensors and user feedback, analyzes the interaction between the environment and the occupancy levels, and evaluates the gathered data. The collected data undergo preprocessing and are subsequently analyzed using machine learning algorithms, generating real-time environmental quality predictions. This multi-layered approach aims to optimize environmental monitoring and management processes in the library by providing a more effective, data-driven, and user-oriented model. The methodology seeks to offer high accuracy and efficiency in environmental quality assessment, ultimately fostering a healthier and more productive academic environment.
## Appendix A Proof of Theorem 1  
Goal 1 indicates that we want to build clusters that include as many UAVs as possible (limited by \(n_{max}\)). To achieve Goal 1, the merge of two clusters should always increase the intra-cluster communication link correlation. Given cluster \(C_{k}\) with \(n_{k}\) UAVs, then the intra-cluster communication link correlation of cluster \(C_{k}\) is \(f^{c}(C_{k})=\sum_{i\in C_{k}}\sum_{j\in C_{k}|(i,j)\in F}f^{c}_{ij}\). Adding another cluster \(C_{p}\) with \(n_{p}\) UAVs to cluster \(C_{k}\), then we have \(f^{c}\big{(}C_{k}\cup C_{p}\big{)}=\sum_{i\in(C_{k}\cup C_{p})}\sum_{j\in(C_{ k}\cup C_{p})(|i,j)\in F}f^{c}_{ij}\). It can be decomposed as \(f^{c}\big{(}C_{k}\cup C_{p}\big{)}=\sum_{i\in C_{k}}\Big{[}\sum_{j\in C_{k}|(i, j)\in F}f^{c}_{ij}+\sum_{j\in C_{p}|(i,j)\in F}f^{c}_{ij}\Big{]}+\sum_{i\in C _{p}}\big{[}\sum_{j\in C_{k}|(i,j)\in F}f^{c}_{ij}+\sum_{j\in C_{p}|(i,j)\in F }f^{c}_{ij}\Big{]}\). After simplification, we obtain that \(f^{c}\big{(}C_{k}\cup C_{p}\big{)}=f^{c}(C_{k})+f^{c}\big{(}C_{p}\big{)}\)\(+2\sum_{i\in C_{k}}\sum_{j\in C_{p}|(i,j)\in F}f^{c}_{ij}\). Since \(f^{c}_{ij}>0\), we can deduce that \(f^{c}\big{(}C_{k}\cup C_{p}\big{)}>f^{c}(C_{k})+f^{c}\big{(}C_{p}\big{)}>f^{c} (C_{k})\), thus concludes the proof.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description  
Instead of introducing the mixed audio signal (main speaker and other speakers around him) directly to the VoIP system, a system called Speaker Separation and Identification (SSI) is inserted between the microphone and the VoIP system. Figure 2 illustrates the main process of SSI block. The first steps of processing are converting real-time mixed audio signal into a recording one in format of WAV to be processed in speaker separation system. Then, all separated speakers are fed to the speaker identification system to identify the target speaker. During that time, the main speaker is ready to send over IP to start the call process without any other human voices or noise around him. The proposed model integrated with VoIP system is depicted in Figure 3.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 5 Opportunities and Cross-Cutting Themes  
### Healthcare and Wearables  
Healthcare stands as one of the most promising domains where resource-constrained AI can have a transformative impact [17]. Wearable devices, such as smartwatches, fitness bands, or specialized medical sensors, are increasingly incorporating machine learning capabilities to track vital signs, detect abnormalities, and offer personalized health recommendations [29]. By performing AI inference locally, these wearables can protect sensitive health information from being routinely transmitted to the cloud, significantly reducing privacy risks and potential regulatory hurdles [73]. For patients with chronic conditions like diabetes or cardiac arrhythmia, continuous on-device monitoring can detect early warning signs and trigger timely interventions.  
Nevertheless, wearables must maintain battery life over extended periods to remain unobtrusive and user-friendly. Achieving this goal requires efficient AI architectures that can run quickly and minimize power drain [50]. Additionally, user comfort and adoption hinge on the devices' physical form factor, which limits the potential for large heat sinks or bulky batteries. Innovations in model compression and hardware-software co-design can help address these constraints, as can synergy with other medical IoT components like smart patches or implantables [22]. Over time, these converging solutions may result in integrated healthcare ecosystems where personalized diagnostics and treatment plans are partly executed on ultra-low-power devices worn by patients in real-world settings [32].
## 1 Introduction
Previous studies have attempted different solutions: coherent target analysis has been used for block-based coherence estimation [31], and local control points have been employed to merge adjacent blocks [32; 33]. Some studies [33; 34] have adopted a regular grid partitioning strategy with overlapping regions, using common PS points in overlapping areas for block merging [35]. Nevertheless, PS point distributions are highly heterogeneous (e.g., dense urban areas vs. scattered villages), which can lead to the formation of multiple disconnected components within individual blocks. Existing methods lack mechanisms to identify these connected components, making it difficult to merge isolated blocks during the global least squares adjustment. This can result in a rank deficiency and reduce the mathematical completeness of the inversion model. To overcome these limitations, we present a novel PSInSAR method based on regular grid partitioning and connected component constraints. At the partitioning strategy level, a dynamic regular grid partitioning algorithm was designed to adaptively adjust the grid size and extend block boundaries, achieving an optimal balance between memory usage and computational efficiency. At the merging model level, a weighted least squares adjustment model was constructed using common PS points in overlapping regions, leveraging the spatial continuity of phase observations (deformation rate and elevation residuals) to eliminate systematic biases between blocks. At the mathematical completeness level, a graph-theoretic connectivity analysis algorithm was introduced to identify independent connected components within each block by constructing an adjacency matrix. This enables parallel inversion across connected subdomains, resolving the issue of isolated blocks and rank deficiency caused by uneven PS point distributions. The key breakthrough of this method lies in the first-ever integration of connected component theory into PSInSAR block processing, providing a new paradigm for the automated, high-efficiency processing of large-scale PS networks at the urban scale. The structure of this paper is as follows. Section 2 describes the proposed method in detail. Section 3 introduces the study area and datasets. Section 4 presents the experimental results and analysis. Section 5 provides a discussion. Finally, Section 6 concludes this paper.
# JFET Integration Using a Foundry SOI Photonics Platform
## 4 Conclusions  
We have demonstrated the monolithic integration of photonics and electronics components using a commercial foundry SOI photonics technology provided by Singapore's A*STAR Institute of Microelectronics (IME). In particular, we have investigated the realization of JFETs integrated with standard optical components. Using available implant parameters and Si film thicknesses, doping levels in the film were estimated and used to design our JFETs. The dimensions were designed in accordance with the technology's layout rules. Working \(n\)-channel JFETs were successfully demonstrated, although the measured pinch-off voltage was much higher than expected. The frequency response of the JFET amplifiers should be adequate for many important applications in SOI-based photonic sensing. With the optimization of the bandwidth, gain, and noise of the transimpedance amplifier, our design could be useful for optical receivers in fiber telecommunications and data centers.  
Conceptualization, S.L. and N.G.T.; methodology, S.L. and N.G.T.; validation, S.L.; formal analysis, S.L., N.G.T., and W.N.Y.; investigation, S.L.; resources, S.L. and W.N.Y.; data curation, S.L. writing--original draft preparation, S.L.; writing--review and editing, N.G.T. and W.N.Y.; visualization, S.L.; supervision, N.G.T. and W.N.Y.; project administration, N.G.T. and W.N.Y.  
This research was funded by National Science and Engineering Research Council of Canada and Canada Research Chairs Program.  
This project was fabricated by IME with the support of the Silicon Electronic Photonic Integrated Circuits (SiEPIC) workshop program through CMC Microsystems.  
The authors declare no conflict of interest.