esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Isolation and Morphological Characterization of Microbial Community (Fungi and Bacteria)  
By using nitrocellulose membranes as a sampling method, coupled with cultural analysis, 62 fungi and 20 bacteria were isolated from the stained areas of the oil painting. A quantitative microbial risk assessment was evaluated via a direct count of colonies on Petri dishes. The results of the bacterial count were of 0.7 CFU/dm\({}^{2}\) on the reverse side and 1.7 CFU/dm\({}^{2}\) on the obverse side. The results showed a higher concentration of colonies on the obverse side of the painting, especially on sample points 15F, 17F, 19F, and 25F. As regards fungal counts, 0.14 CFU/dm\({}^{2}\) on the reverse and 2.9 CFU/dm\({}^{2}\) on the obverse sides were recorded. In general the microbial count on both obverse and reverse sides of the canvas case study was not particularly high [23; 38]. This can be explained by the use of a non-invasive sampling technique using the nitrocellulose membrane and the possibility that not all the microbial strains might have been cultured due to the limits of the cultivation methods [39].  
According to the results obtained by the cultural analysis, a greater level of bacterial and fungal contamination was observed on the obverse side of the canvas painting. This is in contrast with what is reported in the literature, where a greater microbial contamination is usually found on the reverse of the canvas [38; 40]. The more consistent colonization of the obverse rather than the reverse sides could be due to the way in which the canvas has been stored over the years, probably stacked with other paintings.  
In order to get an insight into the biodiversity corresponding to the stained areas of the painting, bacterial and fungal isolates were clustered according to their morphology (Table S2). Among the 13 different bacterial clusters, Clusters I and VII were the most numerous (Figure S1a). Twenty-four different fungal clusters were identified as well; among these, 12 were represented by only one isolate, while the most abundant was Cluster IV, counting 17 colonies collected on the obverse side of the painting (Figure S1b).
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
###### Abstract  
A novel transmission technique--namely, a DFT-spread spectrally overlapped hybrid OFDM-digital filter multiple access (DFMA) PON based on intensity modulation and direct detection (IMDD)--is here proposed by employing the discrete Fourier transform (DFT)-spread technique in each optical network unit (ONU) and the optical line terminal (OLT). Detailed numerical simulations are carried out to identify optimal ONU transceiver parameters and explore their maximum achievable upstream transmission performances on the IMDD PON systems. The results show that the DFT-spread technique in the proposed PON is effective in enhancing the upstream transmission performance to its maximum potential, whilst still maintaining all of the salient features associated with previously reported PONs. Compared with previously reported PONs excluding DFT-spread, a significant peak-to-average power ratio (PAPR) reduction of over 2 dB is achieved, leading to a 1 dB reduction in the optimal signal clipping ratio (CR). As a direct consequence of the PAPR reduction, the proposed PON has excellent tolerance to reduced digital-to-analogue converter/analogue-to-digital converter (DAC/ADC) bit resolution, and can therefore ensure the utilization of a minimum DAC/ADC resolution of only 6 bits at the forward error correction (FEC) limit (\(1\times 10^{-3}\)). In addition, the proposed PON can improve the upstream power budget by \(>\)1.4 dB and increase the aggregate upstream signal transmission rate by up to 10% without degrading nonlinearity tolerances.  
orthogonal frequency division multiplexing (OFDM); digital filter multiple access (DFMA); DFT-spread OFDM; intensity modulation and direct detection (IMDD); passive optical network (PON) +
Footnote †: journal: _Sensors_**2021**, 21, 5903. [[https://doi.org/10.3390/s21175903](https://doi.org/10.3390/s21175903)]([https://doi.org/10.3390/s21175903](https://doi.org/10.3390/s21175903))
## 4 Experiment and Results
### Performance Measures  
We used Accuracy, Precision, Recall, and F1-score measures to compare classification performance. These measures are defined as follows:
# Laser Induced Aggregation of Light Absorbing Particles by Marangoni Convection
## 1 Introduction  
Light absorbing particles (APs) are usually immersed in the fluid in vivo for applications of diagnosis, in vivo imaging, etc. [1; 2; 3]. They normally move without control, which reduces their functionality and efficiency. Optical trapping [4; 5; 6] is a useful technique for the non-contact and non-invasive manipulation of small particles, such as cells [7; 8; 9], organelles [10; 11; 12], nanoparticles [13; 14; 15], colloidal particles [16; 17; 18], and oil drops [19; 20; 21]. There is also an intensive study on the use of optical tweezers to drive and control the micro-particles into aggregation [22; 23; 24; 25; 26]. Optical tweezers can control transparent particles due to the balance between the gradient force and scattering force [27]. However, optical tweezers always push the APs away from the focus of laser beam [28; 29; 30]. The optical scattering force on APs is always much larger than the optical gradient force. Furthermore, the motion of the APs is affected severely by particle absorption and the thermal process. Therefore, it is difficult to trap and collect the APs into aggregation via the optical gradient force.  
There may be a promising alternative method to control the APs in a solution with a fluidic flow generated by a thermal gradient. The thermal gradient may cause thermal convection [31], thermophoresis [32], and Marangoni convection [23] to drive the particles to migrate. These methods are quite attractive because particles can be trapped over long ranges by the convective flow--consider concentrating DNA [33] and directly assembling micro- and nanoparticles [34; 35; 36; 37; 38]. In most of above studies, the particles were dispersed in a thin chamber, which had a light absorbing surface to generate the local thermal gradient. However, the metal coated surface is complicated to fabricate and rarely used for microscopic observation. In this paper, we exploit laser-induced convection to collect the APs in a thin chamber with common coverslip substrate. The thermal gradient is generated by continuous laser heating of the AP aggregate itself.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 4 Experimental Methods  
### Dataset  
The ASVspoof 2019 challenge provides a standard database for anti-spoofing [27]. The LA subset of the provided dataset includes a real voice and different types of TTS and VC spoofing attacks. The training and development sets share the same six attacks (A01-A06), including four TTS and two VC algorithms. In the evaluation set, there are 11 unknown attacks (A07-A15, A17, and A18), including combinations of different TTS and VC attacks. The evaluation set also includes two attacks (A16 and A19) that use the same algorithms as the two attacks in the training set (A04 and A06) but are trained using different data.  
Figure 5: Schematic of incremental learning. The circle in the figure represents the features. Triangles, rectangles, and different colors represent category labels, sample data, and different categories, respectively.  
When training the voice spoof detection model, we used the LA subset's training, development, and validation sets in ASVspoof 2019 as the training, validation, and test sets for our experiments.  
We slice and dice the LA subsets when training the voice spoof detection model. Because A04 and A16 use the same algorithm and A06 and A19 use the same algorithm, we grouped A04 and A16 into class A04 and A06 and A19 into class A06. Subsequently, all classes A1 to A17 were divided into a training set, validation set, and evaluation set at a ratio of 3:1:1.
## 2 Registration and Analysis of the Smartphone Recordings  
The cosmic ray CREDO Detector application for smartphones is available freely at Google Play. The active users send their records to the CREDO database. They have access in turn to all the data from other users to download, study, and analyze for their own Citizen Science efforts. In the CREDO database, there are approximately a million images registered with the smartphone camera's lens obscured.  
Such recorded images should consist mostly of black pixels. When this condition is met, the CREDO Detector application starts working. If a particle of secondary cosmic radiation, muon (or possibly a particle of local radiation source), passes through the active layer of the smartphone camera it will stimulate some of the pixels. A few to several dozen pixels, distributed in a cluster of shapes that range from circular to extended lines, should then appear brighter on the roughly homogeneous black background. In one 24-h period there can be anything from one to several hundred detections.  
Events in which very long traces are visible can potentially be the tracks of cosmic ray muons that passed through the camera at large angles. This possibility is, in theory, easily verifiable. The zenith distribution of such incident angles of single incoherent muons is well established, and is actively measured by small, even portable, muon telescopes often created and operated by students [15; 21; 22; 23; 24] as a demonstration of their detectors. In this paper, we will show that the majority of CREDO registered events are due to real muons with a recovered zenith angle distribution as expected.  
The data used to obtain the results presented below consist of more than 100,000 CREDO database registrations obtained by one (very active) user with only one camera. The CREDO application draws a lot of power, and practically can only be used for extended periods when connected to a charger. Therefore, the cameras actively connected to the CREDO network usually rest permanently in the same place, which means that once placed horizontally they remain in this position permanently, and we do not have to be concerned of blurring the distributions by uncontrolled changes in the camera matrix plane. The registrations are available as PNG files cropped from the whole camera frame to 60 \(\times\) 60 pixels around the brightest pixel in the frame.  
Some events in the database are caused by the single pixel noise signal near the edge of the camera, and the image does not consist of 60 \(\times\) 60 pixels around the brightest pixel. This reduces the number of pictures used in the present analysis from \(1\times 10^{5}\) to \(6\times 10^{4}\). Examples are shown in the top row in Figure 1. Signals are roughly proportional to the ionization energy loss in the particular matrix pixels, but it should be noted that some corrections (unknown in practice) have been applied to the photos by the internal camera software. The box 2D-histograms are given in the middle row in Figure 1 which scale in size with the ionization energy. The bottom row is the process by which the main axis of the \"muon tracks\" are identified.
## 3 Overview of Information Systems
### Case Study  
Case Study: \"Advancements in Healthcare: Development of a Comprehensive Medical Information System with Automated Classification for Ocular and Skin Pathologies--Structure, Functionalities, and Innovative Development Methods\" published in _Applied System Innovation_ Journal, MDPI, 2024 [12].  
Background: The scope of the article was to present the development and implementation of a medical information system with an automated pathology detection module.  
Implementation: The implementation platform was Salesforce, which featured an automated classification module for ocular and skin pathologies using Google Teachable Machine. The project had two primary objectives: first, to create a classification module that could integrate with the platform where the MIS was developed, and second, to develop the MIS itself. These two aspects were combined by embedding a medical image classification system directly into the information system. This integration optimized the workflow, benefiting both the medical team and patients by providing real-time, instant information about the presence of a pathology from an acquired image or video. The architecture of the MIS is presented in Figure 13.  
Outcome:  
* Enhanced Patient Care: The system allowed for seamless access to patient records across different departments, reducing errors and improving diagnosis and treatment accuracy.
* Operational Efficiency: Automated workflows and integrated systems reduced administrative burdens and improved resource allocation.
* Cost Savings: Improved data management and streamlined processes resulted in significant cost reductions over time.  
This article is the first in a series that will be followed by several papers emphasizing the results of the pilot project evaluating the developed medical system in real-life scenarios in a medical institution. This pilot is crucial for gathering authentic user feedback and practical case studies. We anticipate publishing multiple papers in the near future that will elaborate on these findings.  
Although detailed case studies and comprehensive user feedback will be included in future publications, preliminary feedback from the pilot users has been positive. The users have highlighted the system's intuitive interface, improved data management capabilities, and enhanced workflow efficiency as key benefits.
## 3 Adaptive LQG-FF Current-Voltage Controller
### State Observer  
A state observer was designed to reduce the number of sensing elements; in this application, it enables us to process the control strategy only measuring the current (\(i_{sz}\)), as shown in Figure 7. In addition, the observer needs the duty cycle (\(d\)), which is known. The output of the observer is the vector of estimated states, which are the inductor currents and capacitor voltages; these were defined in Equations (13) and (14).  
For the small-signal model obtained in Equations (14) and (15), and the feedback state law defined in (20) [30], the linear state observer is formalized as follows:  
\[\dot{\hat{x}}=A_{m}\cdot\hat{x}+B_{m}\cdot d-\ell\cdot(y-\hat{y} )=(A_{m}+\ell\cdot C_{m})\cdot\hat{x}+B_{m}\cdot d-\ell\cdot y \tag{22}\] \[\hat{y}=C_{m}\cdot\hat{x} \tag{23}\]  
where \(y\) corresponds to \(i_{sz}\), as shown in Figure 7.  
In Equation (22), the observer gain vector (\(\ell\)) defines the dynamic behavior of the error estimation, ensuring the observer convergence to the system states. This observer gain (\(\ell\)) is calculated solving an optimization problem to ensure two characteristics: the best  
Figure 7: State observer.  
estimation of the states, and minimizing the effect of measurement noise in \(i_{sz}\). Similarly to the feedback state vector (\(K\)), the observer gain vector (\(\ell\)) in Equation (24) is calculated by solving the Riccati differential equation given in (25) [29]; here, \(S_{o}\) corresponds to the optimal error covariance matrix of the state observer, and \(\gamma\) is a scalar to adjust the convergence speed of the observer states [31].  
\[\ell=S_{o}\cdot C_{m}^{T}\cdot\gamma \tag{24}\]  
\[A_{m}\cdot S_{o}+S_{o}\cdot A_{m}^{T}-S_{o}\cdot C_{m}^{T}\cdot\gamma^{-1}\cdot C _{m}\cdot S_{o}+B_{m}\cdot B_{m}^{T}+S_{o}=0 \tag{25}\]
## 2 Materials and Methods
### Dataset Organization  
The MMD-MSD dataset is organized into six main folders, which contain the raw data (physiological signals, pictures, videos), annotated data (pictures with markers, timestamps for the physiological recordings), and data descriptors (angles and features). The folder names correspond to the type of information they contain:  
* Physiological_Signals_raw--with 100 subfolders, one folder per participant;
* Pictures_raw--pictures in :jpg SOOC (straight-out-of-camera);
* Pictures_with_markers--either 10 or 2 posture-specific markers, manually placed;
* Video--overhead-recorded videos;
* Angles--computed based on manually adjusted markers;  
Figure 4: Important postural angles computed for the needs of MSD detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Angle of Measurement** & **Reference Values** \\ \hline The craniovertebral angle (CVA) is identified as the intersection of a horizontal line passing through the C7 (2) spinous process and a line joining the midpoint of the tragus of the ear (1) to the skin overlying the C7 spinous process. & CVA \(<\) 48–50\({}^{\circ}\) is defined as forward head posture \\ \hline Rounded shoulder angle: the angle formed by the horizontal line passing through the middle of the humerus (3) and the line from the midpoint of the humerus to the spinous process of C7 (2). & Rounded shoulder angle \(\leq\) 52\({}^{\circ}\) \\ \hline Coronal shoulder angle: also known as the anterior shoulder alignment, this is defined as the angle between a horizontal line and a line joining the coracoid processes (2); it is used to determine whether the left and the right shoulders are level or not. & Its normal value should be 180\({}^{\circ}\) \\ \hline Thoracic kyphosis angle: the angle between the segments of C7 (2) and T11 (5). & 20\({}^{\circ}\) to 45–50\({}^{\circ}\) \\ \hline Normal lumbar lordosis is characterized by an average lumbosacral angle. These included measurements from the inferior endplate of T11 (5) to the superior endplate of S1 (7). & 39–53\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Position of markers on the human body.  
Features--computed from the physiological signals PPG, EDA, and ST. The root folder also contains three files:
* QuestionnaireResponses.csv--responses of all 100 participants to the 29 questions of the MSD questionnaire (available at: [[http://isr.tu-varna.bg/ergo/index.php/resursi](http://isr.tu-varna.bg/ergo/index.php/resursi)]([http://isr.tu-varna.bg/ergo/index.php/resursi](http://isr.tu-varna.bg/ergo/index.php/resursi)), 5 April 2024);
* StrooptestPerformance.csv--Stroop tests' data, including participant ID, question number, task complexity (L = low, H = high), participant's response, correct answer, and reaction time (test 1; test 2);
* Dataset Description.dox--technical documentation of MMD-MSD. Details about the file names and formats are available in Appendix A.
## 6 Results and Discussion
### Group Specific Discussion  
Hereinafter, the RSSIs and the SNRs will be analyzed, for each SF and for each test trial, by modeling them as discrete random variables. Indeed, the Gateways sample those quantities in a discrete fashion. In particular, the RSSIs take values in the set  
\[\mathcal{R}=\{-137,-136,\ldots,-1,0\}\]  
where the elements of \(\mathcal{R}\) are expressed in dBm, while the SNRs take values in the set  
\[\mathcal{S}=\{-20.0,-19.9,\ldots,19.9,20.0\}\]  
where the elements of \(\mathcal{S}\) are expressed in dB. Since the measured RSSIs at the Gateways side can be considered as the power of the received signals, a comparison between the values of RSSIs that were recorded during the tests and the value of \(P_{RX}\) reported in Table 3 can be accomplished. The former ones were lower than the latter due to either the limited clearance of the first Fresnel zone (see Table 4) and to the fact that the sea behaves as a huge ground plane because of the salinity of the water. Such phenomena cause a difference between the theoretical values and the measured ones of slightly more than 20 dBm. However, this did not notably affect the performance of the wireless communication link. Along with those variables, also the percentage of the received packets for each SF for each test group will be evaluated.
# Numerical and Experimental Research on the Laser-Water Jet Scribing of Silicon
###### Abstract
The micro-groove with sawtooth morphology was found on the side wall, which could be attributed to the asymmetric propagation of LWJ within the groove. The adoption of parallel passes could eliminate this phenomenon effectively. Our research group also previously conducted relative investigations. Under the guidance of numerical and experimental studies, [PERSON] et al. fabricated micro-grooves with DTWR of up to 13.6 on SiC/SiC ceramic matrix composites using single-row scribing with the assistance of an argon atmosphere [13]. The heat-affected zone, recast layer, and fiber damage were absent on the cross-section of micro-grooves. The experimental results of [PERSON] et al. [14] suggested that the water pressure should be controlled in a limited range to avoid extra damage. Moreover, laser pulse energy occupied a dominant role in promoting scribing efficiency. In view of the literature review, the feasibility of LWJ in scribing materials has been supported. The influence of machining parameters on structure morphology has been clearly revealed as well. However, most of the studies hold the view that the default ablation mechanism is the laser-induced thermal effect. Few attempts were made to investigate the influence of LWJ-induced stress on material ablation. Moreover, a micro-groove with large DTWR on hard and brittle materials, such as the monocrystalline silicon, can hardly be realized with both high quality and efficiency. In this study, the theoretical model describing the interaction between LWJ and silicon was firstly established. The temporal evolution of temperature and stress fields were analyzed through numerical simulations, from which the silicon ablation mechanism was interpreted. On these bases, the evolution of ablation morphology under different incident laser pulses was obtained from the aspect of simulations and experiments. Further experiments were carried out to identify the influence of machining parameters on groove morphology as well as to discover the most appropriate scribing strategy for fabricating micro-grooves with large DTWR. The achievements of this study helped pave the way for future applications of LWJ in the potential industrial fields.