esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Whole Exome Sequencing  
WES was performed by the University of Arizona Center for Applied Genetics and Genomic Medicine. Isolated DNA from patient adenoma tissue will be quantified using the Qubit quantitation system with standard curve, as per the supplier protocol (Thermo Fisher Scientific). All samples were further tested for quality using the Fragment Analyzer (Advanced Analytical), following the manufacturer-recommended protocols. Whole exome sequencing (WES) was performed by array capture and approximately 60 Mb of exome target sequence, using the SureSelectXT Human All Exon V6 enrichment (Agilent) or equivalent (which one was used). All exome library builds were quantified via qPCR and subsequently sequenced to a minimum 20X coverage, using paired-end chemistry on the Illumina NovaSeq platform. Whole exome sequencing (WES) was performed by hybridization capture of approx. 35 Mb of the exome target sequence, using the Swift Exome Hyb Panel (Swift Biosciences 83216). All exome library builds were quantified via qPCR and subsequently sequenced to a minimum 20X coverage, using paired-end chemistry on the Illumina NextSeq500 or NovaSeq platform (Illumina). DNA reads were trimmed, filtered by quality scores and aligned to the human genome (hg38) with Burrows-Wheeler Aligner with default parameters. Picard ([[http://broadinstitute.github.io/picard](http://broadinstitute.github.io/picard)]([http://broadinstitute.github.io/picard](http://broadinstitute.github.io/picard)) (accessed on 22 December 2021)) was used to mark duplicates. Germline single nucleotide variants (SNV) were called using the Genome Analysis Tool Kit (GATK), using the given guidelines. Mutations were annotated using ANNOVAR for coding sequences. Variants that passed the quality filter were further investigated for similarity. Concordance between tissue and organoids was calculated using Jaccard similarity index (\(\mathrm{J_{ij}=M_{ij}/(M_{i}+M_{j}-M_{\bar{i}})}\) where \(\mathrm{M_{i}}\) is the number of variants in tissues, \(\mathrm{M_{j}}\) is the number of variants in organoids, and \(\mathrm{M_{ij}}\) is the number of identical variants in both tissue and organoid.
## 2 Related Work
Clustering ensures energy efficiency as it employs trust-based FL for identifying malevolent nodes and choosing a trusted path for data transmission. The proposed scheme involves reduced energy and delay, thus offering improved network lifetime, PDR, and security. [PERSON] et al. [25] focused on ensuring the trusted exchange of data and reliable device communication by offering a trust-based optimization scheme. This scheme manages trust in routing protocols by using Dempster-Shafer Theory (DST), a decision-making algorithm to ensure reliable clustering, and uses Whale Optimization Algorithm (WOA) to support routing. It ensures reliability as well as energy efficiency by confirming trustworthiness in ideal cluster and path selection. It offers an improved network lifespan, throughput, and energy efficiency. [PERSON] et al. [26] proposed the Trust-based and energy-aware Secure Routing Protocol (TSRP), a multi-dimensional trust assessment framework. The proposed system assesses trust that is indispensable for guaranteeing the security of nodes. Rather than trusting exclusively in encryption, it allocates trust to relay nodes and obtains the reputation from neighboring nodes. It efficiently finds trusted relay nodes when reducing the effect on resources. The proposed authentication scheme involves an advanced critical production scheme and guarantees resource efficacy by calculating multi-dimensional trust. It aids in creating an equilibrium between enhancement in resource usage and the reinforcement of security. It ensures energy efficiency, reduces energy fluctuations, and offers increased speed. Considering all the above existing studies in the literature, it is evidently clear that an efficient design of a congestion-aware secure routing method using hybrid KHM and GSA will overcome all the significant challenges faced by these studies. A clustering scheme is included in the proposed secure routing protocol. Further, priority-based routing is incorporated to effectively avoid network congestions and promote the free flow of network traffic.
## 2 Materials and Methods
### Quantitative Criteria for the Evaluation
#### 2.5.1 Cloud to Cloud Comparisons  
Comparisons between high resolution datasets are a key issue in change detection studies [34]. Examples of applications are for the assessment of natural surfaces altered by erosion, and for measuring the extracted volume of material in quarry management. Techniques based on the comparison of digital elevation models DEMs in a geographic information system GIS environment (DEM of differences, DoD) can be substituted by cloud to cloud (C2C) or cloud to a triangulated mesh (C2M) comparisons any time a comparison between complex-shaped and not-quite-plane scenes has to be taken into account. In 2013, [PERSON] et al. proposed the Multiscale Model to Model Cloud Comparison algorithm (M3C2) [35], which is implemented in CloudCompare as a plugin. It starts by estimating the normal surface and orientation in 3D at a scale consistent with the local surface roughness. Then, it measures the mean surface change along the normal direction and explicitly calculates a local confidence interval. M3C2 directly compares two point clouds by using a sample of core points (a subset of the original one, to speed up the computations).  
With respect to C2C and C2M methods, M3C2 provides an easier and quicker workflow because it does not require a meshing process and it reduces the uncertainty related to point cloud roughness by local averaging. Moreover, M3C2 provides signed (and robust) distances between two point clouds [36]. For these reasons, we adopted the M3C2 algorithm to analyse the IMMS datasets in order to obtain statistical parameters about the signed distances, as well as an interactive 3D view of the dataset comparison. This supports the data quality assessment, which is of course related to the test field shape and characteristics.  
An aspect complicating the comparison is that, in general, datasets acquired with any IMMS do not completely overlap with the reference, since many high surfaces are not scanned. Therefore, it is important to set a threshold value beyond which the distance is not computed. In the M3C2 CloudCompare procedure, a \"maximum allowable distance\" can be specified to eliminate unrealistic results when the compared clouds have different extensions or when datasets are spatially discontinuous: in this study, a distance of one metre was assumed.  
All of the data comparisons are stored as scalar fields and are presented hereinafter by mapping the values onto the IMMS tested. The same colour scale is adopted for all the figures in the Results section. The colour scales are presented (in metres) on the right of the figures; the range scale of the signed distance values varies from \(-1\) m to \(+1\) m but, to improve their readability, the rainbow colour scale only considers a \(-8\) cm to \(+8\) cm range.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Retrieval and Processing of Orthoinagery from Geoportal
#### 3.4.2 Tile Division and Optimization  
The bounding box area was divided into 10 m by 10 m tiles. This approach simplified the process of handling high-resolution images. Tiles not intersecting with the road segment were removed from the tile list, reducing unnecessary data transfer and processing, as can be seen in the exemplary Figure 10.
## 4 Experimental Results  
In this section, we compare the performance of the 24 detection models in Figure 1 through experiments with the two kinds of data sets discussed in the previous section, including 25 data sets for restaurants and 33 data sets for tourist attractions. To investigate the performance more precisely, the precision, recall, F1 score, and balanced accuracy of the models were compared. For each model with a data set, five-fold cross-validation was performed at each run, and the average score of five runs was used for the performance comparison. Experiments were conducted on Google Colab with a V100 GPU and 40 GB of memory.
## 4 Discussion
### Prospectives of Lactic Acid Adsorption  
The strongly basic resin AMB400 showed a higher lactic acid removal efficiency and capacity compared to GAC and RLX425 at a pH above the \(pK_{a}\) value of the lactic acid (3.86) that is suitable for most of the fermentation processes to recover lactic acid. The thermodynamic analyses shows a stable lactic acid adsorption in the range of 303.15-353.15 K for AMB400 compared to GAC and RLX425. So, this enables AMB400 resin to recover lactic acid on a wide range of operating temperatures. In addition, the effects of competitive adsorption of the lactic acid need to be assessed in the presence of other constituents present in a fermentation broth, such as sugars, organic acids, salts and amino acids.  
The current study shows that the strongly basic resin AMB400 is a better adsorbent than GAC and the weakly basic resin RLX425 for a model fermentation broth containing only lactic acid. However, the amount of the lactic acid adsorbed to the adsorbents cannot be used as a sole parameter to choose an appropriate adsorbent [18]. In addition, the adsorption capacity of the resins can decrease after various cycles of adsorption and desorption and thus long term performance of the resins is vital to select the most economical and feasible resin [18]. Desorption of lactic acid from the adsorbents is an important factor that needs to be examined as a downstream processing strategy, but was beyond the scope of this study. According to previous studies, the adsorbed lactic acid onto GAC and anionic resins can be desorbed by various chemicals, depending on the pH, temperature and functional groups present on the adsorbents as well as the nature of the desorbent, i.e., acetone, methanol, 1 M NaCl, NaOH (2-4%), NH\({}_{4}\)OH (2-4%), Na\({}_{2}\)CO\({}_{3}\) (4-8%) or 1 M H\({}_{2}\)SO\({}_{4}\)[13; 22; 25].  
In addition to desorption studies, it is important to scale-up the batch experiments with fixed-bed adsorption columns along with analysis of the lactic acid adsorption breakthrough curves in order to evaluate the most economical and feasible adsorbents for the lactic acid removal from the fermentation broth. The optical purity of the lactic acid can be achieved >99% by desorbing it with organic solvents (e.g., acetone and methanol) [22]. The optical purity of the lactic acid will slightly decrease when it is desorbed with HCl, NaOH and NaCl. In addition, the optical purity of the acid will be impacted by the presence of other constituents in the model or real fermentation broth. The contamination can be reduced by filtration or acid/base treatments. Accordingly, further investigations can optimize integrated or even in situ recovery of organic acids from a real fermentation broth used for _T. neapolitana_ fermentation.
## 3 Methodology
### Filter-Based Feature Selection Using SelectKBest with ANOVA F-Test  
SelectKBest with the ANOVA _F_-test, categorized as a filter method, evaluates the relevance of features based on statistical methods. Specifically, it employs the ANOVA _F_-test, a univariate measure that quantifies the significance of each feature concerning the target variable. The goal is to select the 'k' best features, where 'k' is a user-defined parameter [30].  
The ANOVA _F_-test function is utilized to compute the ANOVA _F_-statistic between each feature and the target variable. This statistic measures the degree of linear dependency between the feature and the target, enabling the identification of features most likely to be informative for classification. The formula for the _F_-statistic is as follows:  
\[F=SSB/(k-1)/SSW(n-k) \tag{4}\]  
where the sum of squares between (\(SSB\)) measures the variance between classes, the sum of squares within (\(SSW\)) measures the variance within each group, \(k\) is the number of classes, and \(n\) is the total number of data points [30]. SelectKBest is a feature selection algorithm, and when combined with the ANOVA _F_-test, it becomes SelectKBest with the ANOVA _F_-test. The primary purpose of this method is to evaluate the significance of individual features concerning a target variable. It operates as a filter method, meaning it ranks features based on statistical measures without involving the learning algorithm. The ANOVA _F_-test, in this context, is a statistical test that assesses whether the means of different groups are equal. In feature selection, it helps quantify the relationship between each feature and the target variable. The methodology employed in this study was designed to explore and evaluate the effectiveness of various feature selection and machine learning methods for emotion classification using EEG brainwave data.
## 3 Findings and Discussion
### Challenges and Opportunities
Monitoring exposure levels and adhering to established guidelines can help minimize any potential health risks. Implementing safety protocols, such as maintaining safe distances from network infrastructure and utilizing advanced antenna technologies, can further mitigate potential concerns [54]. Another challenge is addressing public perception and fostering trust [55]. Transparent communication, providing accurate information, and addressing concerns openly are vital to building public confidence in the safety of high-frequency networks. Educational initiatives that raise awareness about scientific evidence, risk assessments, and safety measures can help individuals make informed decisions and alleviate any unfounded fears. Moreover, the equitable deployment of high-frequency networks should be a priority. Ensuring access to reliable and high-speed connectivity for all segments of society, regardless of socioeconomic factors, is crucial to prevent the exacerbation of existing digital divides. Bridging this gap requires collaboration between governments, industry stakeholders, and community organizations to provide affordable and accessible network infrastructure and promote digital literacy. Furthermore, the environmental benefits of deploying high-frequency networks should not overshadow the potential environmental impact of network infrastructure itself. While aiming for a greener environment, it is important to consider the energy consumption and sustainability of network infrastructure components. Implementing energy-efficient practices, utilizing renewable energy sources, and adopting responsible waste management strategies can help mitigate any adverse environmental effects [56]. Hence, deploying high-frequency networks to establish a green environment presents opportunities for environmental sustainability and resource management. However, challenges related to potential effects on human cognitive functions must be carefully addressed through ongoing research, adherence to safety standards, and open communication. Balancing environmental benefits, public health, and equitable access to connectivity will be key to successfully deploying high-frequency networks in a responsible and sustainable manner.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
###### Abstract  
Autonomous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the case in urban environments. Thus, the prediction of integrity for specific routes or trajectories is of interest. The carrier-to-noise density ratio (C/N0) reported by the GNSS receiver offers important insights into the signal quality, the carrier phase availability and subsequently the RTK solution integrity. The ultimate goal of this research is to investigate the predictability of the GNSS signal strength. Using a ray-tracing algorithm together with known satellite positions and 3D building models, not only the satellite visibility but also the GNSS signal propagation conditions at waypoints along an intended route are computed. Including antenna gain, free-space propagation as well as reflection and diffraction at surfaces and vegetation, the predicted C/N0 is compared to that recorded by an Septentrio Altus receiver during an experiment in an urban environment in Hannover. Although the actual gain pattern of the receiving antenna was unknown, good agreements were found with small offsets between measured and predicted C/N0.  
GNSS; network RTK; urban navigation; integrity monitoring Article  
Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments  
154850[[https://doi.org/10.3390/rs15194850](https://doi.org/10.3390/rs15194850)]([https://doi.org/10.3390/rs15194850](https://doi.org/10.3390/rs15194850))
## 1 Introduction
### Contribution of the Paper  
In summary, this study aims to optimize resource utilization (technical, economic, and human) through systematic parameterization of a Transformer-based neural network, with three key objectives:  
Signal Reliability Assessment:To evaluate whether statistical and technical tools can effectively determine if secondary variables provide reliable predictive information about process behavior, thereby creating signal redundancy.  
Computational Efficiency Optimization:  
To optimize neural network hyperparameters for maximum computational efficiency, minimizing training/retraining resource requirements, while benchmarking performance against simpler methodologies.  
Practical Implementation Evaluation:  
To assess the suitability of this forecasting tool for integration into maintenance plans, considering operational complexity and computational demands relative to alternative, simpler approaches.
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
###### Abstract  
Twenty-one experienced runners completed three treadmill running sessions on different days. Each session consisted of three consecutive 2 min trials at self-selected speeds (RPE = 3, 5, and 7). An eight-camera marker-less motion capture system and instrumented pressure treadmill (TM) collected data over the final \(\sim\)25 s at each speed. Lower extremity joint angles (ankle, knee, and hip) and segmental angles (pelvis and trunk) were computed for each trial with foot contact and toe off being kinematically determined. Spatiotemporal metrics (ground contact time, step length, and cadence) were measured via TM and compared to their kinematically derived counterparts. All spatiotemporal metrics demonstrated excellent agreement (ICCs > 0.98). Both intra-trial and inter-session variability, averaged across the entire running cycle, for all lower extremity joint angles in all planes were low (intra-trial: sagittal = 2.0\({}^{\circ}\), frontal = 1.2\({}^{\circ}\), and transverse = 1.9\({}^{\circ}\); inter-session: sagittal = 1.4\({}^{\circ}\), frontal = 0.8\({}^{\circ}\), and transverse = 1.3\({}^{\circ}\)). Discrete measures of lower extremity joint and segmental angles were evaluated for inter-session reliability at foot contact, toe off, and peak value during the stance phase. On average, discrete measures demonstrated good reliability (ICC\({}_{\text{sagittal}}\) = 0.85, ICC\({}_{\text{frontal}}\) = 0.83, and ICC\({}_{\text{transverse}}\) = 0.77) with average standard error of measurement < 1\({}^{\circ}\). Marker-less motion capture reliably measured treadmill running kinematics in a group of runners demonstrating heterogeneous foot strike patterns (13 rearfoot strike and 8 non-rearfoot strike) across a range of speeds (2.67-4.44 m/s).  
kinematics; marker-less; reliability; foot strike detection Article
# The Effectiveness of Pump Techniques and Compages: A Systematic Review
## 3 Results
### Qualitative Synthesis
#### 3.3.2. Pump Techniques Versus Conventional Rehabilitation  
Six studies [38; 41; 42; 51; 53; 55] were identified comparing osteopathic pump protocol versus standard rehabilitation at short-term follow-up. Conventional rehabilitation included treatments such as standard pulmonary rehabilitation (tapotement, pursed-lip breathing, saline nebulizer and rest) [41], supervised and unsupervised exercise (breathing, coordination and walking) [53], nebulization [38], standard cardiorespiratory rehabilitation [51], incentive spirometry [55], and drugs (bronchodilators, antibiotics, expectorants, sedatives, and parenteral fluids) [42]. The outcomes assessed were pain, pulmonary function, and length of hospitalization.  
* _Pain._ Two studies [51; 53] assessed this outcome in patients operated on for heart surgery at short-term follow-up. In both studies, there was a statistically significant decrease in mean pain intensity in the treatment group compared to the control group. In the first study [51] there was also a statistically significant decrease in breathing difficulty for the treatment group compared to the control group.
* _Pulmonary function._ Four studies [38; 41; 42; 56] were identified comparing osteopathic pump protocol versus other conservative treatments, at short-term follow-up. In the first study [38] on patients with pulmonary pathology, a statistically significant increase in FVC appeared in both groups. No significant changes emerged for other lung functions. In the second study on healthy subjects [41], osteopathic techniques were compared with standard pulmonary rehabilitation [SPR]. In the treatment group, rib-raising and lymphatic pump led to a change in FEV1 and FVC although not statistically significant. In the control group, saline treatment was associated with significant decline in lung function. All other techniques did not significantly change lung function. In the third study on patients with pulmonary pathology [42], in the treatment group an osteopathic protocol was added to a nebulization, while the control group was submitted only to nebulization. Statistically significant post-treatment improvements in FEV1, FVC, VC, and FEV1/FVC emerged in the experimental groupcompared to the control group. In the fourth study [56] on patients operated on for cholecystectomy, an osteopathic pump protocol was compared with incentive spirometry. This study showed a significant increase in FVC and FEV1 in the treatment group compared to the control group at second and third postoperative days.
* _Length of Hospitalization._ Only one study in patients operated on for heart surgery [51] assessed this outcome. Hospitalization was significantly shorter for the treatment group compared with the control group.
* _Functional capacity._ Only one study in patients operated on for heart surgery [51] assessed this outcome. This study showed a statistically significant increase of walking distance for both groups, without significant difference between the treatment group and the control group.