esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reinforcement-Learning-Based Distributed Resource Selection Algorithm for Massive IoT
###### Abstract  
Massive IoT including the large number of resource-constrained IoT devices has gained great attention. IoT devices generate enormous traffic, which causes network congestion. To manage network congestion, multi-channel-based algorithms are proposed. However, most of the existing multi-channel algorithms require strict synchronization, an extra overhead for negotiating channel assignment, which poses significant challenges to resource-constrained IoT devices. In this paper, a distributed channel selection algorithm utilizing the tug-of-war (TOW) dynamics is proposed for improving successful frame delivery of the whole network by letting IoT devices always select suitable channels for communication adaptively. The proposed TOW dynamics-based channel selection algorithm has a simple reinforcement learning procedure that only needs to receive the acknowledgment (ACK) frame for the learning procedure, while simply requiring minimal memory and computation capability. Thus, the proposed TOW dynamics-based algorithm can run on resource-constrained IoT devices. We prototype the proposed algorithm on an extremely resource-constrained single-board computer, which hereafter is called the cognitive-IoT prototype. Moreover, the cognitive-IoT prototype is densely deployed in a frequently-changing radio environment for evaluation experiments. The evaluation results show that the cognitive-IoT prototype accurately and adaptively makes decisions to select the suitable channel when the real environment regularly varies. Accordingly, the successful frame ratio of the network is improved.  
reinforcement learning; multi-armed bandit; IoT; distributed channel selection +
Footnote †: journal: Applied Sciences
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 3 Anti-Biofilm Agents to Tackle _P. aeruginosa_  
Biofilms act as protective niches against immune defenses and antibiotics. Therefore, they play a major role in antimicrobial resistance and the establishment of chronic diseases. The steady increase in knowledge about biofilms is guiding the development of ABAs of interest. Several in vitro models have been described as well as various ways to evaluate the anti-biofilm activity of compounds. Firstly, the biofilm can be grown in static conditions and formed in microplates or small surfaces like membranes or coupons. Secondly, dynamic conditions in which biofilm settles in flow cells can be implemented. These independent chambers allow biofilms to grow in the presence of a shear force and a continuous nutrient supply.  
The quantification of biomass is then carried out following one of the following methods: (i) crystal violet (CV) staining and optical density (OD) measurement, (ii) biofilm visualization by fluorescence microscopy, (iii) adherent cell isolation followed by OD measurement or counting of colony forming units (CFUs)/mL. The biofilm is grown in microplates in the presence of the tested compounds, in conditions that favor its formation (supplementation of the culture medium with essential cations). After planktonic cell elimination and rinsing of the wells, the biofilm is stained with a CV solution, then dissolved and quantified by UV/Visible spectrometry (OD\({}_{595}\)). The biofilm is grown in microplates or on surface units in the presence of the tested compounds and different fluorescent probes which allow the evaluation of the production of its components. This monitoring can also be realized by measuring (i) the fluorescence from a modified strain that secretes a fluorophore like the green fluorescent protein (GFP) or (ii) the luminescence from a bioluminescent strain expressing for instance the luciferase gene. The biofilm is grown in microplates or surface units in the presence of the tested compound. Adherent cells are removed, diluted in an appropriated medium and inoculated on agar plates. The counting of the colonies allows the estimation of bacterial concentration in CFU/mL reflecting the initial biomass [43].  
It should be noted that the efficacy of ABAs described in the literature is difficult to compare due to the diversity of experimental procedures. In this review, the promising ABAs will be classified according to their target involved in the signaling pathways or the scaffolding of biofilms, and then according to the chemical structure of their pharmacophore (Figure 3). Particular attention will be paid to key interactions between compounds and their pharmacological target studied by molecular docking, in particular those which appear crucial for the anti-biofilm activity.
# Extended Reality in Applied Sciences Education: A Systematic Review
## 3 Results
### Challenges and Limitations in the Educational Application of XR Technologies  
Figure 9 illustrates the various limitations associated with the application of XR technologies in the educational domain. While some studies did not explicitly address these constraints, others highlighted multiple dimensions of challenges. The most commonly reported limitations are spatial and temporal constraints, with VR accounting for 10 studies ([13; 16; 17; 19; 24; 26; 30; 35; 37; 44]), AR for 4 studies ([7; 13; 16; 63]), and MR for 1 study ([9]).  
The second most frequently mentioned limitation is small sample size, noted in 12 studies, including 7 that employed VR ([11; 20; 25; 27; 29; 40; 43]), 4 using AR ([12; 50; 57; 62]), and 1 utilizing MR ([64]). Variations in student acceptance were highlighted in 9 studies, with 7 studies on VR ([11; 17; 18; 24; 28; 37; 43]) reporting issues such as dizziness or discomfort when using head-mounted devices, while AR ([48]) and MR ([64]) were each mentioned in one study.  
Figure 9: Key limitations in the application of XR technologies in applied sciences education.  
The high cost of XR technologies was primarily associated with VR, discussed in four studies ([11, 13, 35, 44]), with AR mentioned in one study ([12]). Finally, limited battery life was cited in two studies, one involving VR ([22]) and the other AR ([48]). This issue can impact extended class durations or experiments requiring continuous operation, as battery depletion or device overheating may force interruptions in educational activities.
# Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel
## 1 Introduction  
Many storage technologies have been proposed to satisfy the increasing demand for cloud storage. Particularly, research and development of holographic data storage (HDS) is being focused on as the next-generation storage technology [1, 2]. In HDS, the data are stored as two-dimensional (2D) data pages. Thereafter, in the read process of the HDS system, the 2D data pages are captured as an image with a charge-coupled device (CCD) sensor [1]. Thus, the read-back data are distorted by 2D inter-symbol interference (ISI), pixel blur, misalignment, and noise [3].  
To reduce the noise and improve the signal-to-noise (SNR) ratio, [PERSON] et al. investigated the optimization of apertures [4]. In addition, to reduce the 2D ISI, other optical methods have been introduced for phase modulation [5, 6] and polarization [7]. In addition, modulation and error correcting codes have been proposed to solve the 2D ISI [8, 9, 10, 11]. Specifically, in signal processing, the equalizer combined with the partial response maximum likelihood (PRML) reduces the 2D ISI significantly [3, 12, 13, 14].  
Generally, PRML supplies coefficient interference to the soft output Viterbi algorithm (SOVA) to detect the received signal [15]. However, SOVA is designed to handle one-dimensional (1D) ISI. Therefore, to obtain an effective SOVA, the 2D ISI needs to be converted into 1D ISI. The 2D ISI includes the interference from the upper, lower, left, and right directions. Therefore, the interference from the upper and lower directions can be removed to create a 1D ISI from the horizontal direction. Further, the interference from the left and right directions can be removed to create a 1D ISI from the vertical direction. Therefore, in this study, we proposed an interference estimator for estimating and removing interference to create the 1D ISI. In addition, we used the general partial response (GPR) target, which is an advanced version of PRML [16]. Moreover, we utilized a parallel structure to exploit horizontal interference (HI) and vertical interference (VI). Thereafter, we combined the information from HI and VI using an average function. During experiments, our proposed model significantly improved the bit error rate (BER) performance of HDS.  
The remainder of this paper is organized as follows: Section 2 explains the 2D equalizer and GPR target. Section 3 presents an interference estimator. The proposed model ispresented in Section 4. Section 5 presents and discusses the results of the simulation. Finally, Section 6 concludes the study.
# A Multi-Site Anti-Interference Neural Network for ASD Classification
###### Abstract  
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that can reduce quality of life and burden families. However, there is a lack of objectivity in clinical diagnosis, so it is very important to develop a method for early and accurate diagnosis. Multi-site data increases sample size and statistical power, which is convenient for training deep learning models. However, heterogeneity between sites will affect ASD recognition. To solve this problem, we propose a multi-site anti-interference neural network for ASD classification. The resting state brain functional image data provided by the multi-site is used to train the ASD classification model. The model consists of three modules. First, the site feature extraction module is used to quantify the inter-site heterogeneity, in which the autoencoder is used to reduce the feature dimension. Secondly, the presentation learning module is used to extract classification features. Finally, the anti-interference classification module uses the output of the first two modules as labels and inputs for multi-task adversarial training to complete the representation learning that is not affected by the confounding of sites, so as to realize the adaptive anti-interference ASD classification. The results show that the average accuracy of ten-fold cross validation is 75.56%, which is better than the existing studies. The innovation of our proposed method lies in the problem that the traditional single-task deep learning ASD classification model will be affected by the heterogeneity of multi-site data and interfere with the classification. Our method eliminates the influence of multi-site factors on feature extraction through multi-task adversarial training, so that the model can better adapt to the heterogeneity of multi-site data. Meanwhile, large-scale 1 DconV is introduced to extract features of brain functional network, which provides support for the interpretability of the model. This method is expected to take advantage of multiple sites and provide reference for early diagnosis and treatment of ASD.  
Keywords:anti-interference neural networks; multi-site data; autoencoder; large-scale 1 DconV; ASD +
Footnote †: journal: 2023, 16, 315. [[https://doi.org/10.3390/a16070315](https://doi.org/10.3390/a16070315)]([https://doi.org/10.3390/a16070315](https://doi.org/10.3390/a16070315))
## 2 Materials and Methods
### High-Pressure Liquid Chromatography Size Exclusion for Molecular Mass Distribution (HPLC-SEC)  
HPLC-SEC was applied to the enzymatically hydrolyzed and lyophilised samples of the common buckwheat extracts before and after in vitro digestion. A Dionex Ultimate 3000 HPLC (International Equipment Trading Ltd., Mundelein, IL, USA) equipped with a column oven with integrated size-exclusion Ghpak SB-802 HQ (8 \(\times\) 300 mm (8 \(\upmu\)m)) (Shodex, Munich, Germany) and Ultrahydrogel 500 (7.8 \(\times\) 300 mm (10 \(\upmu\)m)) (Waters, Wilmslow, UK) columns were used. Sugars were detected with the RI detector Opilab T-rEX (Wyatt, Santa Barbara, CA, USA). As a standard, in order to identify the degree of polymerization (DP), the following monomeric units were used: DP1-D-(+)-glucose (\(\geq\)99.5%; G8270; Sigma, St. Louis, MO, USA); DP2-D-(+)-mannose (\(\geq\)95%; 92,683; Supelco, Bellefonte, PA, USA); DP3-malotriose (\(>\)99%; GLU313; Elictily S.A, Crolles, France) DP4-matotetarose (\(>\)99%; GLU314; Elictily S.A, Crolles, France). The following main parameters were applied: 0.05 M NaNO\({}_{3}\) eluent, a flow rate of 0.5 mL min\({}^{-1}\), the temperature of 40 \({}^{\circ}\)C, work pressure of 50-52 bar, RI detector, and an injection volume of 50 \(\upmu\)L. A sample solution containing 2 to 20 mg g\({}^{-1}\) (1% DMSO-dimethylsulphoxide as internal standard) of the lyophilized matter was used for the test. The samples were stirred with a magnetic stirrer for 30 min. The required sample volume (\(\sim\)1 mL) was filtered through a 0.45 \(\upmu\)M syringe filter into a chromatographic flask (the filtrate had to be precise). Results were obtained by analyzing the chromatograms. In this case, the retention times (tR) of each peak and internal standard (DMSO) were essential. These values and the chromatogram data were exported in the csv format and other required data (baseline and coordinates) were generated in a standard calculation file. The molecular weight distribution curve was plotted against sample concentration curves based on regularly checked and updated calibrations.
## 8 Conclusions  
UHS in the Baltic Basin presents a promising but complex opportunity. The geological formations offer significant potential, but technical and economic challenges must be addressed to realize this potential. Ongoing research and pilot projects are essential to refine storage techniques, ensure safety and reliability, and establish economic viability. The synergy between geological studies, engineering innovations, and policy support will be critical in advancing UHS in the Baltic Basin.  
The findings of the preliminary simulation study concerning UHS in Lithuanian geological formation showed promising results with good potential for UHS. It is observed that during the H2 injection phases, gas saturation (Sg) increased as H2 occupied the pore spaces, displacing water and causing a decrease in Sw. Over time, the average Sg increased, indicating net H2 accumulation in the reservoir, while Sw decreased, showing persistent water displacement. These trends highlight the significance of continuously monitoring and adjusting injection and production cycles to optimize H2 storage and recovery and reduce environmental impact in Lithuanian geological formations. Additionally, the study suggests the usage of distinct wells for injection and production to increase overall efficiency. Apart from this, the period cycle of the injection and production can be optimized. By optimizing these cycles, we may improve the performance and stability of the H2 recovery process.  
Overall, UHS appears to be a suitable solution for meeting Lithuania's predicted growth in H2 output. Future research will focus on understanding how H2 interacts with rocks, minerals, and reservoir fluids, as well as its effects on the surface environment. Environmental aspects require comprehensive impact assessments to evaluate effects on groundwater, ecosystems, and local communities, monitoring induced seismicity and integrating renewable energy sources for sustainable H2 production. Moreover, regulatory aspects necessitate analyzing existing frameworks, developing guidelines for safe UHS operations, and engaging with stakeholders to build support and address public concerns. By addressing these multifaceted aspects, Lithuania can establish a robust and sustainable UHS infrastructure, supporting its transition to a cleaner and more resilient energy system.  
The conceptualization of the work presented is by, [PERSON] and [PERSON]; [PERSON] developed the methodology presented in the paper; [PERSON] performed simulations and volumetric analysis; [PERSON], [PERSON] and [PERSON] contributed equally to writing the original draft preparation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Lithuanian Research Council under grant No. P-PD-23-090 for postdoctoral research fund.  
Not applicable.  
The data used in the publication of this article have been collected from reports, published reviews, and research articles available online.  
The authors would like to acknowledge the support from UAB Minijos Nafta for sharing geological data of the Lithuanian reservoirs.  
The authors declare no conflicts of interest.
## 2 Methodology
### RGSM Decoder  
Slim-neck introduces a lightweight convolution system called GSConv [27], whose structure is illustrated in Figure 3. Depth-wise separable convolution can reduce the number of parameters and the computational overhead but does not effectively integrate spatial and channel information. The GSConv structure first applies a standard convolution operation to the input feature map to obtain the first set of feature maps. Then, these feature maps undergo depth-wise separable convolution. Specifically, a separate convolutional kernel is applied independently to each input channel. Subsequently, a \(1\times 1\) convolutional kernel is used to perform linear combinations between the channels, generating the second set of feature maps. Subsequently, these two sets of feature maps are concatenated and subjected to channel shuffle operations, enhancing the flow of feature information in both the spatial and channel dimensions while reducing the network's computational complexity.  
The role of the decoder is mainly to fuse the downsampled feature maps with the upsampled feature maps to restore the detailed image information. Due to the small and irregular nature of PCB soldering defects, the detailed defect information is severely reduced after multiple rounds of feature extraction. Effective feature fusion operations are required in the decoder to restore the features effectively. To address this issue, this paper proposes a residual GSConv module (RGSM) based on GSConv and incorporating residual connections for use in the decoder. The RGSM structure, as shown in Figure 4, takes as input a feature map obtained by combining downsampled and upsampled feature maps. First, a \(1\times 1\) GSConv is employed to effectively fuse the features and exploit the channel shuffling feature, recovering detailed information from the shallower features and aiding in feature restoration from upsampled features. Through the above operations, features of different depths but the with same scale, which were simply concatenated, are fused to form new features. Subsequently, ReLU activation is applied to enhance the nonlinearity of the features, and GSConv is used again to enhance the feature fusion capabilities. Secondly, a residual connection structure combines the features obtained from  
Figure 3: The GSConv structure.  
another \(3\times 3\) standard convolution branch, followed by ReLU activation to enhance the nonlinearity of the combined feature maps, thereby strengthening the feature expression capabilities. Additionally, the RGSM effectively reduces the number of model parameters, decreases the network's computational costs, and alleviates the problem of the parameter increase caused by deepening the network in the encoder, thereby facilitating overall model optimization.
###### Abstract  
The aim of this study was to evaluate the contact pressure distribution of two different nickel-titanium (NiTi) endodontic rotary instruments against the root canal walls and to virtually predict their centering ability during shaping with finite element analysis (FEA). Resin blocks simulating root canals were used. One was shaped with ProGlider and ProTaper Next (PTN) X1-X2 and one with ScoutRace and BioRace (BR) 1, 2 and 3. Both resin blocks were virtually replicated with computer-aided design (CAD) software. The endodontic instruments ProTaper Next (PTN) X2 and BioRace BR3 were also replicated with CAD. The NiTi instruments and the shaped blocks geometries were discretized and exported for FEA. The instrument rotation in the root canals was simulated. The finite element simulation was performed by applying an insertion and extraction force of 2.5 N with a constant rotational speed (300 rpm). To highlight possible differences between pressure distributions against the root canal portions outside and inside the canal curvature, the parameter _Var_ was originally defined. _Var_ values were systematically lower for PTN X2, revealing a better centering ability. FEA proved effective for the virtual prediction of the centering ability of NiTi instruments during an early design phase without the use of prototypes.  
CAD; endodontics; engineering; finite element analysis (FEA); computer simulation; dental informatics/bioinformatics 2020 10.3390/app10082981
# Effect of Compression Tights on Skin Temperature in Women with Lipedema
## 2 Materials and Methods
### Data Collection  
Skin temperature was assessed employing an infrared camera (Flir E60 bx, FLIR Systems Inc., Wilsonville, EEUU) with a resolution of 320 \(\times\) 240 pixels, with noise equivalent temperature difference (NETD) <0.05 \({}^{\circ}\)C and measurement uncertainty of \(\pm\)2 \({}^{\circ}\)C or 2%. The camera was checked before starting the experimental phase using a blackbody (BX-500 IR Infrared Calibrator, CEM, Shenzen, China) with a target emissivity of 0.95 and resolution of 0.1 \({}^{\circ}\)C. The blackbody presents a measurement uncertainty of \(\pm\)0.8 \({}^{\circ}\)C and stability of \(\pm\)0.1 \({}^{\circ}\)C at a temperature between 50 and 100 \({}^{\circ}\)C. Therefore, adjusting the blackbody to a temperature of 50 \({}^{\circ}\)C, the cameras registered 50.0 \(\pm\) 0.6 \({}^{\circ}\)C.  
During the measurements, the camera was situated perpendicularly to the regions of interest (ROI) assessed, and it was always located at the same distance (1 m) [16]. The infrared camera was turned on 10 min before the measurement for electronic stabilization [16]. At the same time, the participants conducted a thermal room adaptation of 10 min in a stand position [29]. Room temperature and relative humidity were monitored using a digital thermo-hygrometer (TFA Dostmann, Wertheim-Reicholzheim, Germany), obtaining the following environmental temperature and relative humidity: 21.1 \(\pm\) 0.7 \({}^{\circ}\)C; 46.5 \(\pm\) 7.2% (Test 1) and 20.2 \(\pm\) 1.1 \({}^{\circ}\)C; 35.6 \(\pm\) 6.8% (Test 2). No differences in environmental conditions were observed between tests (\(p\) > 0.05). All images were collected in an area without sunlight, 5 m away from electronic equipment, electric light, and people. The reflected temperature was measured according to the standard method ISO 18434-1, 2008. All the images were taken at the same time of the day.  
Six ROIs were determined in each lower limb in the study according to the anatomical points shown in Figure 4: (1) anterior thigh, (2) posterior thigh, (3) knee, (4) popliteal fossa, (5) anterior leg and (6) posterior leg. Mean skin temperature and maximum skin temperature were obtained for each ROI using thermographic software (ThermaCam Researcher Pro 2.10, FLIR Systems, Wilsonville, Oregon, EEUU). The same researcher carried out the entire thermographic analysis to ensure consistency. Images were processed using a skin emissivity factor of 0.98. Variations in skin temperature (difference between post and pre-test) were calculated for mean and maximum skin temperature.  
Figure 3: Average compression values of the prototypes measured in the system MST MK V, Swiss-lastic Ag St, Gallen, Swiss. Reference points of each measurement point extracted from [12].