esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 6 Future Directions
### Opportunities and Future Outlook  
The rapid expansion of the Internet of Things and the demand for real-time, privacy-preserving analytics have fueled the drive to incorporate artificial intelligence into resource-constrained devices. By migrating inference and, in some cases, training from centralized cloud servers to embedded microcontrollers, this paradigm promises to reduce latency, conserve bandwidth, and protect sensitive data [12]. However, these benefits come at the cost of formidable technical challenges, as typical deep learning pipelines must be significantly re-engineered to fit within tight memory, compute, and power budgets. Researchers and practitioners have responded with a surge of innovations, including model compression (quantization, pruning, knowledge distillation), TinyML frameworks, hardware accelerators, federated learning, and advanced security mechanisms [23].  
This paper has explored the key challenges--spanning computational constraints, energy efficiency, real-time requirements, security, privacy, and reliability--that confront AI deployment in edge devices. We then reviewed state-of-the-art solutions, highlighting how the synergy of specialized architectures, optimized software stacks, and collaborative learning protocols makes on-device intelligence increasingly viable [24]. Moreover, by examining opportunities in healthcare, smart cities, agriculture, and Industry 4.0, we revealed the breadth of potential applications poised to benefit from localized AI inference. In parallel, we emphasized that ethical and sustainability considerations remain central to responsible AI adoption, particularly as billions of edge devices enter our homes, streets, and workplaces [28].  
Looking ahead, future directions such as dynamic model adaptation, co-design of hardware and algorithms, advanced security mechanisms, large-scale privacy-preserving learning, and rigorous benchmarking offer fertile ground for continued progress [32]. The development of robust, low-power, and secure AI solutions capable of thriving under real-world conditions will require not only breakthroughs in algorithms and hardware but also interdisciplinary collaboration among data scientists, embedded systems engineers, ethicists, and policymakers [50]. Overcoming these challenges will pave the way for a new generation of pervasive, intelligent devices that seamlessly integrate into everyday life while preserving privacy, efficiency, and sustainability. In doing so, edge AI stands to transform industries and societies, enabling us to harness the power of machine intelligence wherever data are generated, even in the most constrained environments [23].
# A Review of Modular Multilevel Converters for Stationary Applications
## 2 MMC Topologies  
### Submodule Topologies  
The generalized configuration of a three-phase MMC is comprised of a DC terminal, an AC terminal, and a converting kernel involving three phase legs, as shown in Figure 1. Each leg/phase has two symmetric arms referred to as the upper arm and lower arm. The upper arm and lower arm contain a group of identical submodules connected in series together with a shock inductor to suppress high-frequency components in the arm current. An MMC can achieve bidirectional power conversion.
# MARIE: A Context-Aware Term Mapping with String Matching and Embedding Vectors
## 3 Results
### Experiment Results  
Table 1 shows the top 1, 3, 5 and 10 mapping accuracies of MARIE in each of three target datasets. To provide objective comparisons, we also reported the mapping accuracy of the string matching methods and an embedding vector-based mapping method. For string matching methods, we used the Jaccard index, edit distance and R/O similarity. For the embedding vector-based mapping method, we calculated the similarity score between terms T\({}_{1}\) and T\({}_{2}\), based on the cosine distance of their respective embedding vectors E\({}_{1}\) and E\({}_{2}\). This embedding vector-based mapping is a variant of the concept normalization method investigated by [PERSON] et al. [41]. The only difference is that the embedding vectors were generated from BioBERT instead of word2 vec.  
The interplay of structural and contextual information within terms enabled MARIE to outperform other mapping methods across all target datasets. Considering the process involved in mapping terms, this outcome is not surprising. Let us assume that a correct mapping between datasets A and B resulted in a term T\({}_{1}\)\(\in\) A to be mapped to a term T\({}_{2}\)\(\in\) B. Unless T\({}_{1}\) and T\({}_{2}\) are identical strings, two types of discrepancies occur between these two terms. Given T\({}_{1}\), either the order of words in T\({}_{2}\) will be different, or T\({}_{2}\) will be missing some of the words or subwords in T\({}_{1}\). The term embedding vectors and string matching methods are effective in addressing each of these discrepancies, respectively. As an embedding vector of a term is defined by the average of its word vectors, the similarity score computed from the embedding vectors of BioBERT is invariant to the changes in the word orders within a term. Therefore, the contextual information of an entire term is preserved, regardless of the changes in its word order. On the other hand, similarity scores from string matching methods depend on the length of common characters or substrings. Consequently, as long as the lengths of missing words are short, compared with the overall lengths of the terms, string matching methods are effective in overlooking these missing words. As MARIE simultaneously resolves these two discrepancies, it achieves effective mapping performance. This characteristic is also evident in Table 2, which compares the actual mapping outcomes between SNUH local concepts and Observational Health Data Sciences and Informatics (OHDSI) Athena ([[https://athena.ohdsi.org/](https://athena.ohdsi.org/)]([https://athena.ohdsi.org/](https://athena.ohdsi.org/))) from MARIE, R/O similarity and the embedding vector-based mapping.  
\begin{table}  
\end{table}
Table 1: Experiment results in biomedical mapping terminologies between Seoul National University Hospital (SNUH corpus) and three artificial target datasets. Compared with other existing mapping methods, MARIE showed superior mapping accuracy, which was further improved by changing the value of \(\alpha\).
# Ceramic Cracks Segmentation with Deep Learning
## 3 Crack Segmentation of Ceramic Surface
### Data Pre-Processing  
Pre-processing became necessary due to differences in context in the images, as just a grayscale image binarization is not enough, as much of the area of interest in the image is lost, and in some cases, the cracks present in the image disappearedm as shown in Figure 2. With that, it is necessary to apply some techniques in addition to binarization. Several experiments were carried out regarding the detection of lines, edges, and objects through computer vision to find a generic pre-processing for this problem.  
The techniques used are listed below:  
1. Histogram equalization;
2. Gaussian filter (with kernel \(3\times 3\));
3. Light and contrast adjust;
4. Inversion;
5. Erode and dilate functions (with kernel \(5\times 5\));
6. Finally, Otsu thresholding.  
Figure 1: Architecture for crack segmentation in CCS model.  
Figure 2: Example of a pre-processing: (**up**) Original image, (**middle**) simple binarization, and (**down**) performed pre-processing on the original image.  
As seen in Figure 2, the pre-processing result highlights the area of interest, becoming much more in evidence, thus facilitating the neural network's performance in the extraction of characteristics.  
This configuration was obtained through several attempts to highlight the images' cracks, using digital image processing techniques until reaching an acceptable result, where it obtained a better learning result by the model. The histogram equalization was used to change the image values' distribution, allowing the sharp differences to be reduced and accentuating details not previously visible. The Gaussian filter was used to soften the image, blurring it to remove noise, using a 3 \(\times\) 3 kernel to make a smaller effect since the kernel's size influences the blurring power. Light and contrast adjustment was used to correct images with excessive lighting problems, not impacting those with standard lighting. The pixel inversion used in the images was necessary to comply with the standard established in the labeling of the images, where it was decided that everything white would be cracked surfaces and what was black would be parts of the ceramic, so the inversion made what was black turn white, and whatever was white turned black, since the grayscale highlighted the cracks in black. The erosion and dilation process was applied to solve the discontinuity of some cracks that broke during the blurring process, using a 5 \(\times\) 5 kernel to continue the cracks, and lastly, a threshold was used. Some other techniques were tested but, in analysis, no significant change was seen for the objective that wanted to be achieved (highlighted in the fissures) and did not significantly influence the learning of the model.
## 1 Introduction
Like the repeater, the repealite transmits received GPS signals, but the transmission on each antenna is continuous and delayed by different periods, which is the measurement of the user receiver [18, 19]. This paper proposes a new architecture for the pseudolite-repeater-based indoor positioning system. The main idea of the method is that each satellite's signal is extracted from the received GPS signals(commonly including multiple satellites' signal) and forwarded using one indoor transmitting antenna. This process is realized using a Receiver/Transmitter and includes collecting the GPS signal from the outdoor receiving antenna, demodulating the GPS signal in the receiver component, repeating each satellite signal in the transmitter component and then forwarding different satellite signals via indoor transmitting antennas. The measurement of the system is the difference of pseudoranges measured by the indoor terminal and outdoor receiver. The pseudorange difference corresponds to the distance between the indoor terminal and transmitting antenna, and a signal delay bias. Four such measurements are then used in the position calculation. The advantages of the system include simple clock synchronization and no/minor modification of the commercial GPS receiver. The system is described in Section 2. The positioning algorithm in the user terminal is introduced in Section 3. The simulation method and results are shown in Section 4. Conclusions of the paper are presented in Section 5.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion
### Estimation of the Channel Capacity
#### 3.5.1 Estimation of the Channel Capacity in the CENELEC A Band  
Figure 19 shows the results of the estimation of the channel capacity in the CENELEC A band i.e., from 35.9 to 90 kHz. The noise power, denoted as \(Pn\) in Equation (4), can be extracted from the average noise measurements of each site. In that case, it is important to fix the power of the transmitted signal (see the \(Pe\)-parameter in Equation (4)) according to the G3-standard. Finally, the number of subcarriers is constant (\(N=36\)) with the \(\Delta f\)-parameter (see Equation (4)) equal to 1.5 kHz.  
Figure 18: Calculated signal-to-noise (SNR) evolution depending on the frequency in the transformer substation.
## 5 Conclusions  
This work presents a novel near-infrared hyperspectral video tracking methodology grounded in capsule-based architecture, integrating background modeling with spectral-spatial to achieve robust target localization. The method distinguishes between the target and background in the current frame using spatial and spectral information. Additionally, the background target routing algorithm effectively handles rapid target movement and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variations** & **HFBE** & **BT** & **SIP** & **MobileNet** & **Success Rate** & **Precision** & **FPS** \\ \hline i & ✓ & ✓ & ✓ & ✗ & 0.703 & 0.884 & 40.5 \\ ii & ✓ & ✓ & ✗ & ✗ & 0.657 & 0.803 & 13.8 \\ iii & ✓ & ✗ & ✓ & ✗ & 0.513 & 0.624 & 50.2 \\ iv & ✓ & ✗ & ✗ & ✗ & 0.501 & 0.622 & 36.7 \\ v & ✗ & ✓ & ✓ & ✗ & 0.563 & 0.612 & 69.0 \\ vi & ✗ & ✓ & ✗ & ✗ & 0.526 & 0.637 & 39.3 \\ vii & ✗ & ✗ & ✓ & ✗ & 0.326 & 0.412 & 112.6 \\ viii & ✓ & ✗ & ✓ & ✓ & 0.498 & 0.610 & 132.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on NIR. ✓ indicates that the module is enabled, while ✗ indicates that the module is disabled.  
scale changes. Finally, a spectral information position prediction module is introduced to reduce the sensitivity of the network to search region selection, thereby enhancing tracking accuracy. The effectiveness of the proposed algorithm is validated by experimental results, with an accuracy of 88.4% and a success rate of 70.3% in NIR tests. However, our proposed algorithm currently underperforms when faced with BC in the NIR dataset. In the future, we aim to further improve the algorithm to overcome this challenge. In addition, due to the inherent complexity of capsule networks, which may hinder their real-time deployment, we plan to combine lightweight network architectures with capsule networks in the future to maintain high accuracy while improving speed and real-time deployment efficiency.  
Conceptualization, L.W. and M.W.; substantial contributions to the conception, M.W.; interpretation of data for the work, M.W., K.H. and W.Z.; drafting the work or reviewing it critically for important intellectual content, M.W. and W.Z.; final approval of the version to be published, M.W., K.H., W.J. and W.Z.; investigation, M.W., K.H., W.J. and W.Z.; resources, M.W.; data curation, M.W., K.H. and W.Z.; writing--original draft preparation, M.W.; writing--review and editing, L.W., W.Z. and J.L.; visualization, M.W., K.H. and W.Z.; supervision, D.Z., L.W. and J.L.; project administration, D.Z., L.W. and J.L.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Wuxi Innovation and Entrepreneurship Fund \"Taihu Light\" Science and Technology (Fundamental Research) Project under Grant K20221046, and in part by the 111 Project under Grant B17035.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Thanks are due to [PERSON] and [PERSON] for the valuable discussions.  
The authors declare no conflicts of interest.
## 6 Results and Discussions
### Results and Discussions of Open-Ended Player Responses  
To answer our **RQ 2** (_How does the game volume affect the player's gaming experience?_) in detail, we conducted a manual review of the primary reasons for player complaints. Out of the nine complaining players in phase one, two found the audio cue volume too low (e.g., _Players should be able to hear enemy footsteps when they are nearby._), four found it too high (e.g., _Sound effects are crucial in FPS games. After playing, my ears feel overwhelmed._), and three thought that the sound effects needed improvement. In contrast, out of the seven complaining players in phase two, five requested more realistic sound effects, particularly for footsteps (e.g., _It is recommended to modify the sound of the robot's movement._), which is not the main focus of this study, while only two felt that the audio cue volume was too high (e.g., _The volume is too loud, and it's uncomfortable because I don't know how to adjust the mouse sensitivity._).  
As a result, we argue that having more varied complaints in a DDA setting tends to indicate that the DDA setting provides a worse gaming experience. Regarding feedback about unrealistic sound, only one player in phase one specifically mentioned poor footstep simulation, which limits further discussion. However, complaints about the volume being too loud were very prominent, indicating that players generally dislike games with high volume levels. In addition, from the GUESS trend in phase two, we observed that once the volume exceeds 70, players' GUESS values drop sharply. Based on this and players' feedback, and given that we do not know the exact dB values for the players, we should keep the game volume below 70 in the absence of such information.
## 3 Results
Because the splitting, training, and testing process described above was performed randomly, it was repeated 30 times to avoid bias according to the central limit theorem,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**TRAINING SET**} \\ \hline  & \multicolumn{2}{c}{**ACCURACY**} & \multicolumn{2}{c}{**SENSITIVITY**} & \multicolumn{2}{c}{**SPECIFICITY**} & \multicolumn{2}{c}{**PRECISION**} \\
**LESION** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** \\ \hline BCC & 0.9998053 & 0.000557 & 0.991325 & 0.003305 & 0.999013 & 0.000478 & 0.993097 & 0.003319 \\ SCC & 0.996912 & 0.000734 & 0.985676 & 0.004545 & 0.998515 & 0.000531 & 0.989579 & 0.003716 \\ MEL & 0.997727 & 0.000607 & 0.990838 & 0.002973 & 0.998709 & 0.000528 & 0.990981 & 0.003639 \\ AK & 0.996932 & 0.000557 & 0.991146 & 0.00306 & 0.997758 & 0.00052 & 0.984448 & 0.003544 \\ BKL & 0.995689 & 0.00073 & 0.98531 & 0.004189 & 0.997167 & 0.00062 & 0.980236 & 0.004258 \\ DF & 0.997554 & 0.000714 & 0.987148 & 0.003966 & 0.990939 & 0.000378 & 0.993235 & 0.002618 \\ NV & 0.997815 & 0.000605 & 0.991293 & 0.003389 & 0.998745 & 0.000487 & 0.991221 & 0.003352 \\ VASC & 0.99912 & 0.000359 & 0.996388 & 0.001829 & 0.999511 & 0.000323 & 0.996589 & 0.00225 \\ MEAN \(\pm\) 1 SD & 0.997475 & 0.001002 & 0.98989 & 0.003684 & 0.998557 & 0.000754 & 0.989923 & 0.00524 \\ \hline \hline \multicolumn{10}{c}{**TESTING SET**} \\ \hline  & \multicolumn{2}{c}{**ACCURACY**} & \multicolumn{2}{c}{**SENSITIVITY**} & \multicolumn{2}{c}{**SPECIFICITY**} & \multicolumn{2}{c}{**PRECISION**} \\
**LESION** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** \\ \hline BCC & 0.999241 & 0.000712 & 0.996774 & 0.003976 & 0.999586 & 0.000548 & 0.997078 & 0.00388 \\ SCC & 0.998992 & 0.001031 & 0.994303 & 0.007762 & 0.999665 & 0.000483 & 0.997583 & 0.003562 \\ MEL & 0.999479 & 0.000826 & 0.998582 & 0.002601 & 0.999611 & 0.000737 & 0.997366 & 0.004978 \\ AK & 0.999253 & 0.000693 & 0.997617 & 0.003347 & 0.999483 & 0.00057 & 0.996359 & 0.003996 \\ BKL & 0.998471 & 0.001062 & 0.996698 & 0.003667 & 0.998732 & 0.00107 and the mean \(\pm 1\) standard deviation was calculated for each metric to determine the repeatability of each implemented method. An example of the subspace k-NN method's performance in each cycle is presented. In Figure 5, the ROC curve for the training and test sets from an example cycle of the subspace k-NN method is shown. Both curves have excellent performance for each of the eight classes. The confusion matrix for the training and testing of the example process of the subspace k-NN method are shown in Figure 6.
# Anti-Bacterial Action of Plasma Multi-Jets in the Context of Chronic Wound Healing
## 2 Materials and Methods
### Plasma Jet Devices
The third option consists in the generation of multi-jets in a bunch of individual helium flushed capillaries, each equipped with an inner needle-like powered electrode, and a grounded ring electrode surrounding all the 4 mm inner diameter, 3 cm long dielectric capillaries. The short distances between the needle tip and the capillary outlets (5 mm in this work) and the short gap from the capillary outlet to the samples (a few mm in this work) allow the generation of the multi-jets with voltage pulses of lower peak amplitudes of 2 kV. This low voltage operation enables the use of a more compact and lower power-consuming pulsed power supply and operation at a higher pulse repetition rate (20 kHz  
Figure 1: Schematic of the multi-jets devices (option 2) with pulse power supply, high voltage wire inside the silicone and center: multi-spot, and right: diffuse mode applicators. On bottom end on view of the applicators. When operated, the silicone tube connection for the diffuse mode applicator is the same as for the multi-spot applicator. in this work) while keeping the input power to the multi-jets applicator in the range of a few watts. Another key advantage of this third class of multi-jets, as was shown, is that the distance to the samples is much less critical for each individual jet's generation over non-flat targets, which is representative of chronic wounds. In a first step, this type of device has been used for the pilot study in a single jet configuration as will be described in the results section. The multi-jets configuration for the generation of four secondary jets is shown in Figure 2. This third class of multi-jet device is called the \"low voltage\" plasma applicator, as in the following. The Table 1 summarizes the main characteristics for the three plasma device options discussed in this section. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1 st Option** & **2 nd Option** & **3 rd Option** \\  & **Single Jet** & **Multi-jets from One Primary Jet** & **Bunch of** \\  & & & **Individual Jets** \\ \hline Voltage (kV) & 10–20 & 14 & 3 \\ \hline Repetition rate (kHz) & 20 & 2 & 10–20 \\ \hline Gas flow (L/min) & 0.5 & 4 & 3 \\ \hline Key & Simplest & Large surface treatment & Less sensitive to target distance \\ advantage & design & & topology \\ \hline Name of the plasma & & & \\ applicator & Single jet & Multi spot & Diffuse mode & Low voltage \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the plasma gun based devices. Figure 2: Top: schematic of the low voltage multi-jet applicator (option 3). Bottom: 4 and 9 low voltage multi-jets delivered on the author’s hand. Parameters: voltage 2.5 kV, repetition rate 20 kHz, gas flow 2 L/min.
## 4 The Use of Mycobacteriophages in Diagnostics and Therapy
### Phage-Antibiotic Combination Therapy  
Due to declining antibiotic efficacy, phage-antibiotic combination therapy (PAS--phage-antibiotic synergy) has gained attention [113]. This strategy can lower antibiotic dosages and slow resistance development [109]. [PERSON] et al. observed reduced M. smegmatis growth when rifampicin was combined with phages, outperforming antibiotics alone [106]. [PERSON] et al. showed synergy between phages and beta-lactams (azibactam + meropenem) against _M. abscessus_ GD276B [114]. However, aminoglycosics like kanamycin can inhibit phage infection, making their combined use inadvisable [110]. Some studies suggest a limited synergy. [PERSON] et al. found that phage plus rifampicin or isoniaizid yielded no improvement or worse outcomes than phage monotherapy, indicating a lack of synergy [115]. Despite these caveats, more than 10,000 mycobacteriophages have been identified, offering a vast therapeutic potential [116]. While resistance can develop, resistant strains often exhibit reduced virulence, slower growth, and increased antibiotic sensitivity [117].