esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
### Software Design
#### 2.2.1 Local Data Preservation  
The purpose of the \"raw data storage\" thread is to sample raw acoustic data and save it into the SD card. Unfortunately, the STM32H743 only has one MCU, and the two tasks cannot be performed by one MCU simultaneously. Thanks to the direct memory access (DMA) mechanism which transmits digital data via bus without MCU interaction, the data from AD7768 via MCASP can be fetched to memory, then central processing unit (CPU) writes the data into SD card. Those two processes are performed simultaneously. The data flowchart of \"raw data storage\" thread is shown in Figure 6. There are two buffers which are named ping buffer and pong buffer, respectively. To avoid data loss when writing data onto the SD card, a ring buffer is used. The purpose of the ring buffer is to manage the writing speed of the SD card. When the ping buffer fetches the acoustic data which is occupied by DMA, the pong buffer is free, so the MCU copies the data to the ring buffer, and the MCU writes the data from the ring buffer to the SD card, and vice versa. The switching between ping buffer and pong buffer is performed by a DMA controller. The raw acoustic data are stored in waveform (WAV) format, and the name of the file is made up of time and date, which can be obtained from the RTC.  
This thread is defined as \"mode 1\".
# Rheology, Hydration, and Microstructure of Portland Cement Pastes Produced with Ground Acai Fibers
###### Abstract  
Acai (_Euterpe olerna_) is a Brazilian typical fruit that is enveloped by natural fibers. This work investigated the effect of incorporating ground acai fibers (in nature and chemically treated with NaOH and HCI) in 5-10 wt.% replacement of Portland cement on the rheology, hydration, and microstructure of pastes. Rotational rheometry, isothermal calorimetry, X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) were performed to evaluate the cement pastes, in addition to SEM-EDS, FTIR, zeta potential, and XRD for fiber characterization. The results showed that the chemical treatment reduced the cellulose and lignin contents in acai fibers while increasing its surface roughness. The addition of 5% of either fiber slightly increased the yield stress and viscosity of paste, while 10% addition drastically increased these properties, reaching yield stress and viscosity values respectively 40 and 8 times higher than those of plain paste. The incorporation of 5% in nature fibers delayed the cement hydration by about 2.5 days while 10% in nature fibers delayed it by over 160 h. The chemical treatment significantly reduced this retarding effect, leading to a 3 h delay when 5% treated fibers were incorporated. Overall, the combined NaOH/HCI treatment was effective for acai fibers functionalization and these fibers can be used in cementitious composites.  
acai fiber; cement pastes; rheology; hydration; microstructure +
Footnote †: journal: Applied Sciences
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 7 Conclusions  
This work proposed the D-DSME extensions, which improve the scalability and reliability of the DSME protocol. In particular, the Shareable D-DSME extension allows GTSs to be shared between multiple flows, while the Mirror D-DSME extension allows for multiple retransmissions on the same GTS. The strong point of the Shareable D-DSME extension is that it is beneficial to both periodic and aperiodic flows. The first benefit from the slot sharing among multiple flows is that it leaves room for accommodating in a multi-superframe of the same length a larger number of periodic flows than DSME. Aperiodic flows, instead, benefit from the reduced multi-superframe length that, in case of CAP reduction enabled, determines shorter queuing delays for the aperiodic messages waiting for transmission within the CAP. The strong point of the Mirror D-DSME is that it exploits the same GTS for both the message transmission and the relevant retransmissions, thus improving reliability without requiring additional slots. As shown in the paper, both the proposed extensions can be implemented on commercial devices without hardware modifications. Currently, approaches to optimize the Shareable GTS assignment algorithm are under study and will be addressed in future work. Moreover, future works will address the case for Software Defined Networking architectures [32; 33] as a mean to make scheduling and GTS allocation in Shareable D-DSME even more flexible.  
**Author Contributions:** Conceptualization, methodology, investigation, writing-original draft preparation, and writing-review and editing: F.B., M.C., L.L., L.L.B., G.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Ablation Studies
#### 4.4.3 Prediction Confidence Estimation  
To assess the impact of various prediction confidence estimation methods on segmentation performance, we compare five approaches based on the prediction probability distribution: Maximum Probability (Max), Entropy [45], Energy Score [46], Variance [47], and Kurtosis [48]. In addition, as a reference, SV-Seg [19], which employs pre-defined calibration values, is adopted as the baseline. As shown in Table 4, the kurtosis-based estimation method exhibits the most significant performance improvement on the ScanNet and S3 DIS datasets, with the HmIoU metrics increasing by 0.7% and 1.3%, respectively. Moreover, all methods that utilize prediction confidence for adaptive bias calibration achieve segmentation performances that are close to or exceed the baseline in terms of mIoU for unseen classes.
## 2 Materials and Methods
### Participants  
Twenty-two (22) healthy subjects took part in this experiment. Subjects were solicited through online invitations as game scene test volunteers. The entire test population had a mean age of 27.95 years (std = 13.24). Two of the participants were minors (10 and 13 years old). Data capturing and soliciting were carried out with the guidance of their parents. Of these participants, 5 subjects were randomly selected as test subjects for experiment 1, consisting of dynamic scenes, while the rest were assigned to experiment two. This group was further subdivided based on the visible gender differences. Group 1 consisted of 10 male subjects, and group 2 consisted of 7 female subjects. Of these, 3 data subjects were discarded due to incorrect data recording (2 males and 1 female subjects). All participants (or guardians in the case of minors) provided written consent following the approval procedures issued by the ethics committee of Gifu University.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Accuracy** & \
\begin{tabular}{c} **Prediction** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} \\ \hline Ensemble (RUS-Boosted Trees) & 0.939 & 0.03 & 74.93 \\ Ensemble (Bagged Trees) & 0.934 & 0.05 & 65.53 \\ KNN (Cubic) & 0.938 & 0.02 & 57.44 \\ KNN (Cosine) & 0.936 & 0.03 & 53.00 \\ SVM (fine Gaussian) & 0.938 & 0.01 & 38.67 \\ SVM (Linear) & 0.934 & 0.04 & 49.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Machine learning training and performance.  
In the experiment protocol, experiment one is performed as a proof of concept to validate the control scheme. Subjects in 2D experiments performed EMG-controlled scenes first and keyboard controls (certain individuals) much later to avoid pre-exposure to game mechanics. No skills or game requirements were needed in the selection criteria.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 1 Introduction
The objective of this paper is to establish an analogous remote sensing approach to detect crop termination within the season using high temporal and spatial resolution images. Built on the WISE approach, we developed a new within-season termination (WIST) algorithm. The WIST algorithm uses the local MACD approach to detect the downtrend in the Normalized Difference Vegetation Index (NDVI) time-series (smoothed and gap-filled) data and then estimates cover crop termination date using the original satellite observations. The WIST algorithm was assessed on fields at the Henry A. Wallace Beltsville Agricultural Research Center (BARC) in Beltsville, MD, using the experimental VENuS time-series (5 m, 2 day) and the operational Sentinel-2 time-series (10 m, 4-5 days). Results from VENuS and Sentinel-2 in 2019 and 2020 were evaluated and inter-compared using recorded termination/harvest operation dates for BARC experimental fields. A simulation of near-real-time mapping of cover crop termination was assessed using the VENuS time-series.
## 4 Data Extraction
### Search  
For each audio chunk, the recognized sequence of units/tokens sometimes features gaps between two consecutive units, which represent silent pauses. Gaps longer than 0.5 s are defined as potential _breaking points_. A _slice_ is defined as an audio chunk between two consecutive breaking points while any audio chunk comprising one or more consecutive slices is called a _segment_. This means that a segment might contain one or more breaking points inside of it. Data collection is performed by searching for the segment lasting between 3 and 10 s with the highest alignment similarity. When the highest similarity is attained by two or more segments, the longest one is chosen. Thus alignment similarity and length are the primary and secondary selection criteria, respectively. Note, that segments may or may not correspond to complete sentences; a segment could comprise just part of a sentence, parts of two sentences or even two or more full sentences.  
A single-pass search is conducted (with linear time complexity) to optimize similarity and length across segments that satisfy the duration constraints within an audio chunk \(U\). Due to these constraints, the method only needs to consider a finite number of slices beyond each starting slice, typically one or two. Once the optimal segment \(s^{*}\) within an audio chunk \(U\) is identified, the adjacent audio sub-chunks, \(U_{l}\) and \(U_{r}\), are independently subjected to recursive searches if they are non-empty (see Figure 3). Each recursive call yields a list of segments resulting in two lists, \(S_{U_{l}}\) and \(S_{U_{l}}\), which are then combined with the optimal segment \(s^{*}\) to obtain a single unified list \(S_{U}\). In this way, after processing all audio chunks and merging the resulting segment lists, we obtain the comprehensive list of segments \(S\). Note, that \(S\) will not encompass every audio segment considered during the search, as numerous short segments (not satisfying the duration constraints) are excluded from \(S\).  
The time complexity of this recursive procedure is in \(O(n\log n)\) on average, with \(n\) being the number of slices in an audio chunk \(U\). If \(K\) audio chunks are to be processed, the time complexity of the whole procedure will be in \(O(\sum_{k=1}^{K}n_{k}\log n_{k})\), \(n_{k}\) being the number of slices in the \(k\)-th audio chunk.  
Finally, note that we may keep all of the retrieved segments or only those segments for which the provided transcription best matches the speech contents, either those segments with the highest values of alignment similarity amounting to a given number of hours (e.g., 1000 h) or those segments with alignment similarity higher than a given threshold (e.g., 95%).
# Are Voltage Sensors Really Embedded in Muscarinic Receptors?
## 1 Introduction  
Muscarinic receptors are seven transmembrane cholinergic receptors. They possess an extracellular amino-terminus, seven membrane-traversing hydrophobic helices, and a cytoplasmic carboxyl terminus [1]. Agonists and antagonists binding to these receptors affect downstream signal transduction mechanisms, mainly by manipulating ion currents and via activation of heterotrimeric GTP-binding proteins [1, 2].  
Muscarinic receptors are modified by post-translational modifications affecting their binding to other proteins and lipids in the cell membrane [1]. In addition, the affinity of muscarinic receptors for their cholinergic agonists is modulated by membrane depolarization [1, 2]. Recently, this characteristic has been attributed to a depolarization-induced charge movement in the muscarinic receptor. However, this explanation is inconsistent with numerous findings that are briefly presented here. Unveiling the molecular mechanism underlying the voltage-dependent affinity modulation of muscarinic receptors for their agonists is of significant importance for targeting the physiological outcome of muscarinic receptor stimulation in a variety of signal transduction mechanisms [3].
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
###### Abstract  
Leakage in water supply pipelines remains a significant challenge. It leads to resource and economic waste. Researchers have developed several leak detection methods, including the use of embedded sensors and pressure prediction. The former approach involves pre-installing detectors inside pipelines to detect leaks. This method allows for the precise localization of leak points. The stability is compromised because of the wireless signal strength. The latter approach, which relies on pressure measurements to predict leak events, does not achieve precise leak point localization. To address these challenges, in this paper, a coherent optical time-domain reflectometry (\(\varphi\)-OTDR) system is employed to capture vibration signal phase information. Subsequently, two pre-trained neural network models based on CNN and Resnet18 are responsible for processing this information to accurately identify vibration events. In an experimental setup simulating water pipelines, phase information from both leaking and non-leaking pipe segments is collected. Using this dataset, classical CNN and ResNet18 models are trained, achieving accuracy rates of 99.7% and 99.5%, respectively. The multi-leakage point experiment results indicate that the Resnet18 model has better generalization compared to the CNN model. The proposed solution enables long-distance water-pipeline precise leak point localization and accurate vibration event identification.  
leakage detection; \(\varphi\)-OTDR; deep leaning; Mels spectrograms 2024
## 4 Methodology
### Examples of Localization Errors Caused by Non-Structural Elements  
Due to the reliance on geometric features of the environment, probabilistic-based location algorithms could easily report erroneous localization poses when the surface shapes of different objects are similar. For example, a sofa not presented in a map could be interpreted mistakenly as a wall due to its large and flat front surface, as shown in Figure 2a. Because of this error, the entire map will be incorrectly offset toward the wall. The green particle swarm around the robot represents the best guess of the AMCL algorithm regarding where the robot is, which is incorrect. There are fewer correct particles than incorrect particles, and thus, the algorithm chose an incorrect pose as its localization. Figure 2b shows another example of inaccurate AMCL localization due to closed doors being recognized as walls. The navigation routine could plan routes through the doors if the frames are left open as spaces. However, when doors are closed, their previously empty space in the map is filled, allowing the closed doors to easily be mistakenly interpretedas part of a contiguous wall. As a result, a robot may determine its pose to be in another location in the building where there are three doors and continuous walls along the hallway. This type of layout is very difficult for a robot when subjected to the \"kidnapped robot\" problem [30], which arises when a robot is placed within the building and not given a starting pose or is relocated to another place in the building. Our study attempts to address these limitations by recognizing the types of objects, estimating their poses, and augmenting the information in the AMCL map during real-time localization.
## 2 System Architecture
### MQTT Communication Protocol  
MQTT clients are very small and require minimal resources, making them suitable for use on small microcontrollers. MQTT message headers are designed to be small, optimizing network bandwidth. MQTT enables messaging between devices and the cloud. It can scale to connect with millions of IoT devices. Many IoT devices rely on unreliable cellular networks. MQTT simplifies encrypting messages using SSL and authenticating clients with modern authentication protocols. MQTT is an extremely portable communication protocol based on the transmission control protocol/internet protocol. MQTT uses a publish/subscribe operational mode. If a received service is initiated, the subscriber subscribes to the topic of the message through the MQTT broker. The publisher is responsible for sending the message, publishing it along with information on the topic to the MQTT broker, and, finally, publishing it to the subscriber [8]. In addition to these publishing and subscribing functions, the MQTT protocol also provides messaging services, which are classified into three types based on the quality of service (QoS): QoS 0, QoS 1, and QoS 2 [9]. The proposed home monitoring system uses the QoS 0 messaging service, namely \"at most once\". From the perspective of the subscriber, this service may entail the missing of messages or repeated delivery. According to the seven layers of network communication, the MQTT protocol belongs to the application layer, whereas the packet is handled by the next layer (transmission control protocol). However, the MQTT protocol cannot confirm that the network is successfully connected in the lower layer. Accordingly, such a service is well-suited for environmental monitoring because the correct delivery of transmitted data is not required. The features of MQTT include lightweight message headers, high fault tolerance, low power consumption, support for large-scale concurrent connections, high cross-platform compatibility, and firewall-friendliness. However, it lacks a user management interface and does not support group communication or group management. Figure 3 illustrates the three types of QoS for the MQTT protocol.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 3 Data Collection and Processing
### Data Observation
#### 3.4.3 Sample Window  
Using a too large sample window would cause the system to miss certain frequency behaviors, while using a too small sample window would cost additional computational power. From the Frequency Domain analysis, each frequency characteristic is around 1 kHz or 1000 samples or a frame of 0.02 s. Hence, the selected sample window is 1000 samples with a standard overlap of 50%.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 5 Experiments and Results
### Experiments with Different Datasets
The results obtained with the cropped images were good and stable between different runs, even though the dataset was small. From this point on, the experiments were performed using cropped images. The table shows that the other pre-processing methods brought no improvement to the results. In some experiments, there were good results, but they were not repeatable; thus, they probably show that pre-processing can actually make the process more unstable. The last experiments were performed with 1000 marked images, which were used to make datasets E and F. Dataset E was used to test whether there was a significant advantage using more annotated images. The results show that the YOLO model was able to achieve some of the best precision rates, and the results were repeatable, with only minor variations between each run. Afterward, more experiments were performed, using the same images and adding more data augmentation techniques, thus producing dataset F. As the table shows, there is no significant advantage in adding more images through data augmentation. As the results show, the standard training method is able to achieve good results, and the results are, in general, stable and repeatable. There are differences in the results because the initial conditions are random and, therefore, the learning process may converge faster or slower, or even converge to the local minima. Some experiments were run more than once in order to minimize the influence of random results in the conclusions. Figure 3: Example of the sunspot detection using the best weights obtained with the various network structures L, M and S ((**a**), (**b**) and (**c**), respectively). The image corresponds to a spectroheliogram taken on 19 April 2012.
## 2 Inter-Satellite Communication
### Small Satellite ISL Missions for IoT
To enable large coverage and short response time for sustainable IoT services, key parameters such as data rate and maximum path length are still constraints. As small satellites are limited in size and power, and as their antenna dimensions are constrained by physics, innovative radio spectrum usage and antenna design must be realized for future operational missions. In particular, in order to enable seamless M2M with terrestrial nodes, a proper IoT protocol which enables hand-off, mobility management, and low power low rate transmission must be implemented. A parameter overview of terrestrial Low Power Wide Area Network (LPWAN) protocols for IoT is given in Table 3. As their transmission power is limited by the regulatory Power Spectral Density (PSD) mask, high RX sensitivity (e.g., \(-140\,\)dBm for MIOTY\({}^{\text{TM}}\) and antenna gain are required. Alternatively, a gateway between IoT sensors and the satellites could use higher frequencies (e.g., Sband) and TX power to mitigate the tight link budget.