esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Testbed Environments--Physical and Virtualized
### Virtual Environment--Docker  
To use Docker containerization, firstly, we build an image or use an existing one that contains all the prerequisites needed for the application to work. Docker allows to change the runtime environment and installed programs very conveniently. This allowed the GCC compilers in versions 9.3.0 (OpenMPI 3.1.1) and 7.4.0 (OpenMPI 2.1.1) to be compared with each other. Due to the use of two different versions of the GCC compiler, as in the host environment, two separate images (Dockerfiles) have been prepared.  
Created images were based on the very light Linux version for containerization--Alpine. The next step was to download and install the GCC compiler and the OpenMPI library in appropriate versions. It was also necessary to create a new user, configure its settings, and set up SSH communication, which included server installation, use of previously generated encryption keys and port forwarding.  
The virtual test environment was built using the same, all physical machines as the Host test environment. The Docker containerization engine has been installed on every computer. Before running the tests, only one container was running on each computer at a time. Each container had access to all host hardware resources (CPU and RAM were not limited in any way). All containers (17) were connected with each other into one computing cluster using the Docker Swarm [27] tool, creating an Overlay network. Two computing containers have never run on a physical host at the same time.
## 6 Open Concerns and Potential Developments
### Challenges  
The following are the challenges faced in IoT-based smart healthcare management:  
1. Scalability: In the healthcare system, scalability is crucial. This implies a system's ability to meet shifting demands and adapt to developments that will become more significant in the future. According to the literature review, some of the suggested methods for HIoT systems can work on a limited scale, and only a few nodes or devices can vouch for their authenticity. Scalability is an important element. However, it appears to be a challenge because the offered ways were generally applied in confined contexts.
2. Interoperability and Standardization: In the HIoT, interoperability is crucial for the transposition of information and resources between patients and smart objects. Open-source frameworks with reliable connections are the main obstacle to interoperability; standards must be established so that horizontal platforms can be operable, programmable, and communicable, regardless of the model or the manufacturer, among devices, operating systems, and applications. It is also important to acquire dynamic and flexible architectures that are interoperable with large-scale IoT applications, and to be able to interface with nonhomogeneous data centres and smart devices with scalable architecture. The interoperability and standardization of HIoT systems are the primary unresolved issues.
3. Mobility: The literature has paid less attention to mobility as one of the biggest obstacles in HIoT. When it comes to IoT healthcare systems, mobility means providing patients with the ability to connect to the gateway whenever and wherever they want. A mobile network improves the quality of service, makes information accessible no matter where users are, and makes the network fault-tolerant. It is essential in the healthcare industry that mobility protocols are dependable so that packet losses, network failures, and end-to-end delays are minimized. Therefore, mobility presents an intriguing research problem.
4. Real Testbed Environment: Only 24% of the examined research was carried out in actual testbeds, while the rest was assessed using simulation tools. The HIoT methodologies suggested in these studies should be enacted in real situations. To be completely honest, all of the suggested methods need to be put to the test in actual settings in order to determine whether or not they can deliver a system of healthcare that is adequate. Real testbed implementation is difficult as a result.
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 2 Sample Structures and Experimental Procedures
A holographic spectroscope (HBS) was used to divide the pump beam into two beams to form dynamic grating in the sample. Two interfering beams generated against HBS were used to construct simple temporal and spatial adjustments, which enabled easy adjustment of the dynamic grating period by replacing HBS with another grating with different spacing. This new FWM tool is commonly used, reproducible, and easy to operate. In the experimental set-up shown in Figure 2, \(\theta\) refers to the angle between two incident beams in a pump beam. Another study showed other details of non-degenerate ps FWM using HBS components [27]. Excitation of crystal-free carrier gratings with optical interference patterns shows advantages over those of other probe excitation techniques. Given the temporal and spatial modulation of optical properties of matter by incident light I(x) = I\({}_{0}\) [1 + cos(Kx)], the disequilibrium carrier current distribution of spatial modulation was generated N(x,t) = N\({}_{0}\)(t) + \(\Delta\)N(x,t) cos(Kx), where N\({}_{0}\) and \(\Delta\)N refer to unmodulated and modulated carrier densities, respectively, along the grating vector K = 2\(\pi\)/\(\uplambda\) with the cycle of \(\uplambda\). Free carrier gratings were generated by repeating the modulation of instantaneous electrical properties by the  
Figure 1: Scheme of InGaN/GaN sample structures used in this work. modulation values \(\Delta\)n(x,t)-\(\Delta\)N(x,t) with refractive index \(n\)[28]. The time interval of beam detection corresponded to the delay in grating efficiency of \(\eta\)(t)-exp\((-\Delta t/\tau_{\rm G})\), which paralleled the time interval of probe beam \(\Delta t\ =\ \tau_{\rm G}\), in which the \(\eta\) value decreased by e\({}^{2}\), whereas carrier modulation decreased by e in this interval. Similar sampling was repeated in other grating periods, and the corresponding \(\tau_{\rm G}\) value was determined. The plot of inverse grating decay time \(1/\tau_{\rm G}\) versus \((2\pi/\Delta)^{2}\) (or the so-called angular dependence of decay time) determined the diffusion coefficient and carrier recombination time. One of the first two parameters was determined by the grating period, as shown below:  
\[\frac{1}{\tau_{\rm G}}\ =\ \frac{1}{\tau_{\rm R}}+\left(\frac{2\pi}{\Delta} \right)^{2}\rm D \tag{1}\]  
where \(1/\tau_{\rm R}\) denotes the recombination time, and \(\tau_{\rm D}=\Lambda 2/(4\pi 2\rm D)\) indicates the diffusion time of the grating erasure.
# Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light
## 2 Methods and Clinical Cases  
### Remote PPG System  
To monitor blood perfusion in cortical microvessels, a cbPPG system consisting of a video-recording block synchronized with a digital electrocardiogram was used. Detailed description of the system was previously published [24], including a layout of the video-recording block that consisted of 8 light emitting diodes and a digital monochrome complementary metal-oxide-semiconductor (CMOS) camera. The cortex was illuminated by incoherent linearly polarized green light (wavelength \(530\pm 40\) nm) from a distance of about 70 cm. Each video with duration of 40 s was recorded at 39 frames per second with pixel resolution of \(752\times 480\) (one video was recorded before surgical intervention and another after it). The recorded frames were saved into the hard disk of a personal computer. A digital electrocardiogram at the data acquisition frequency of 1 kHz was used to record an electrocardiogram (ECG). To synchronize ECG and video recordings, synch pulses of each frame were recorded in one of the ECG channels providing synchronization accuracy of one millisecond. During video recording, the main illumination in the operating room was turned off, providing an intensity of light-emitted diode (LED) illumination at the cortex (the mean value of about 3 mW/cm\({}^{2}\)) at least 10 times higher than the intensity of remaining ambient light.
## 3 Results and Discussion
### Pollutant Disappearance Experiments
#### 3.1.3 Oil Disappearance  
Creosote and oil are complex pollutants that cause serious environmental problems [11; 12; 15; 57]. The use of the ligninolytic fungi for the remediation of the soils contaminated by such mixtures has been discussed [10; 11]. We studied the ability of the fungi to metabolize oil to assess their mycoremediation potential. Most wood-inhibiting basidiomycetes actively utilized oil; the disappearance was 72 to 92% during 14 days of cultivation (Figure 3A). The exceptions were _B. adusta_, _S. murashkinskyi_, and two members of the genus _Trametes_ (_T. ochracea_ and _T. gibbosa_). Litter-decomposing _S. rugosaannulata_ removed up to 90% of oil. The studied ascomycetes remove oil actively, too. The disappearance varied from 60 to 84% with the exception of _G. candidium_ (the decrease did not exceed 46%). Weak activity of different strains of _G. candidium_ towards oil hydrocarbons has been shown before [58].  
An increase of biomass of the fungi was observed in all treatments; the mycelium grew in the form of pellets of various sizes. Figure 3C shows the growth of the fungi _P. ostreatus_ D1 and _S. rugosaannulata_ in the basidiomycetes-rich environment with oil after 14 days.  
Figure 2: Anthraquinone dyes decolorization after 2 days’ cultivation of the studied fungi: 1—_P. ostreatus_ f. Florida, 2—_P. ostreatus_ MUT2977, 3—_P. ostreatus_ 336, 4—_P. ostreatus_ D1, 5—_P. ostreatus_ LEBIN0432, 6—_B. adusta_, 7—_Len. betulium_, 8—_St. murashkinskyi_, 9—_Sch. commune_, 10—_Str. rugosaannulata_, 11—_T. versicolor_ DSM11372, 12—_T. versicolor_ MUT3403, 13—_T. hirsuta_, 14—_T. maxima_, 15—_T. ochracea_, 16—_T. gibbosa_, 17—_Lec. aphanocladii_, 18—_F. oxysporum_, 19—_G. candidium_, 20—_C. herbarum_.  
Figure 3: Oil disappearance by studied fungi (**A**), disappearance of fractions of oil degradation during growth of _P. ostreatus_ D1 (**III**) and _Str. rugosaannulata_ (**\(\square\)**) in basidiomycetes-rich medium (**B**), and the fungal growth (**C**).  
The study of changes of fraction composition of oil revealed some differences among the fungi used. Wood-inhabiting basidiomycete _P. ostreatus_ D1 utilized all fractions of oil with the exception of naphthenes. Their disappearance varied from 52 to 66% (Figure 3B). Whereas soil-inhabiting _S. rugososanmulata_ more actively degrade alkanes and naphthenes, fractions of low- and high-molecular-weight aromatic hydrocarbons were less accessible to this fungus (Figure 3B). This can result from the low activities of ligninolytic enzymes produced by _S. rugososanmulata_ in comparison with _P. ostreatus_.
# Asymmetric Distance in K-Means Clustering Enhances Quality of Cells Raman Imaging
###### Abstract  
Raman microspectroscopy is a powerful, label-free technique for the biochemical characterization of cells, but its complex spectral data require advanced computational methods for meaningful interpretation. Clustering analysis is widely used in spectroscopic imaging to extract meaningful biochemical information. Traditional methods, such as K-means clustering with Euclidean distance, often struggle to capture subtle spectral variations, leading to suboptimal segmentation. Alternative distance metrics, including cosine and Mahalanobis distances, have been explored to enhance cluster separability, yet challenges remain in distinguishing chemically relevant features while minimizing redundancy and noise. In this study, we introduce an asymmetric metric distance matrix with a tunable eccentricity parameter to improve clustering performance in Raman hyperspectral imaging. Our results demonstrate that suitable eccentricity values enhance the identification of subcellular structures while requiring fewer clusters than Euclidean-based approaches. Compared to polar metrics, the proposed asymmetric metric achieves better stability and reduced noise, leading to more accurate segmentation. Future research could explore its application in other clustering techniques and machine learning frameworks, as well as its application in broader spectral imaging techniques where the distance metric plays a fundamental role.  
Raman imaging; biochemical microspectroscopy; asymmetric metric matrix; K-means clustering analysis; cellular spectral imaging Article
# Argininemia: Pathophysiology and Novel Methods for Evaluation of the Disease
## 4 Novel Methods of Evaluation
### Novel Evaluation Methods
#### 4.2.2 Use of Metabolomics and Machine Learning  
Emerging diagnostic methods for metabolic disorders leverage untargeted metabolomics data and rely on disease-specific networks derived from profiling data [6; 82; 83]. A group of researchers, with the aid of untargeted metabolomic analysis, identified multiple novel potential biomarkers for ARG1-D, that could prove useful when it comes to the monitoring of the treatment's efficacy [6]. Another notable achievement in this field involved the development of an automated computational method, which enables accurate diagnosis of 16 different IEMs, including argininemia [83].  
This novel model appears promising particularly for cases where patients remain undiagnosed using current available methods. It quantifies the similarity of individuals' metabolite perturbations with patterns observed in various diseases, allowing for the recommendation and ranking of candidate diagnoses. Furthermore, the model proves valuable in interpreting variants of uncertain significance identified through exome sequencing. This computational method offers competitive diagnostic accuracy when compared to rule-based biomarker modeling approaches, has the potential to replace pathway-based modeling approaches, and significantly improves the speed and confidence with which clinical laboratory directors make diagnostic and treatment decisions [83].
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 2 Background and Related Work
### Privacy Concerns  
FL makes a step toward protecting data generated on individual devices by sharing model updates, e.g., gradient information, instead of raw data. However, sending model updates throughout the network during the training process can expose sensitive information, either to third parties or to the central server [22].  
Although recent methods aim to improve the privacy of FL using tools such as secure multiparty computation (SMC) or differential privacy, these approaches often provide privacy at the cost of reduced model performance or system efficiency [17; 22]. Understanding these tradeoffs, both theoretically and empirically, is a significant challenge in implementing private FL systems [5].  
FL is already a case study for many applications. [PERSON] et al. [23] presented a study on AI-driven autonomous vehicles, and in [24], an energy efficient distributed analytics solution for IoT was proposed. In addition, [PERSON] et al. [25] used FL for the transmission of sensor-data transformations.  
The next section details the implementation of a mobile FL framework using Android devices as a case study, focusing on the impact of unreliable participants and selective aggregation methods in a multimobile computing application. Since this is the focus of the work, we do not explicitly address some of the challenges mentioned above, such as client heterogeneity or privacy concerns.
## 5 Conclusions  
The results presented in this work add to our understanding of the relationship between vegetation indices and the biochemical composition of mangrove by showing which multispectral and hyperspectral indices best explain the variation in chlorophyll concentration at the leaf and canopy level. We tested the ability of broad band and hyperspectral VIs to predict mangrove CC at different scales. At leaf level indices with spectral bands around the red edge (705-753 nm), Vogelmann indices and the MTCI were the most sensitive to mangrove leaf CC (\(R^{2}>0.5\)). A key finding was that at ESU level, the best performing Landsat 8 VI was NDVI green, which explained 80% of the variation in CC. The linear model describing the relationship between CC and NDVI green was used to map the spatiotemporal variability of CC in the mangrove landscape. The study demonstrated that the multispectral, medium-resolution Landsat 8 can be used to quantify CC in mangrove forests where ground networks and other possible tools cannot be applied and the use of mapping techniques based on satellite data is absolutely necessary. A practical application of this result is that future efforts to estimate CC in mangrove forests using multispectral remote sensing should consider the use of Landsat 8 NDVI green. The findings also corroborated the utility of the red edge spectral bands to predict mangrove CC at leaf and ESU level. This has implications for the improvement of mangrove monitoring using upcoming technology such as Sentinel 2, which will include two spectral bands around the red edge position. This spectral band arrangement will allow for the computation of VIs highly correlated with CC tested in this work at finer spatial and temporal resolution. It is recommended that future research should focus on testing existing and newly developed algorithms to estimate CC in mangrove forests using the new generation of satellites that outperform the capabilities of current sensors.
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 3 Methods
### Regression with AutoRegressive Integrated Moving Average Errors
#### 3.1.2 Inclusion of Explanatory Variables  
ARIMA models are univariate and cannot readily incorporate explanatory variables. However, as mentioned before these effects can be particularly relevant for retail sales forecasting where factors such as promotions and other marketing activities substantially affect demand. We may extend ARIMA models in order to include explanatory variables by considering regression with ARMA errors as follows:  
\[y_{t}=\delta_{0}+\sum_{k=1}^{K}\delta_{k}x_{k,t}+\eta_{t}, \tag{1}\]  
where \(y_{t}\) is the target time series, \(x_{1,t},\ldots,x_{K,t}\) are the \(K\) explanatory variables, \(\delta_{0},\delta_{1},\ldots,\delta_{K}\) are their coefficients and \(\eta_{t}\) is the error series that follows an ARMA model. All ARMA and regression parameters can be estimated simultaneously by maximun likelihood estimation. Prior to estimating the model \(y_{t}\) must be stationary, that is achieved via differencing. For the explanatory variables we need to consider the phenomenon we are modelling and their statistical properties prior to deciding whether to difference or not (for a detailed discussion see [57]). In our case, differencing the explanatory variables when \(y_{t}\) should be differenced is meaningful, and therefore we simplify the process by differencing all variables when \(y_{t}\) is deemed to be non-stationary. For instance, the change in sales (differenced \(y_{t}\)) is explained by the change in prices and not the price at period \(t\).
## 2 Materials and Methods
### Numerical Modeling
#### 2.2.1 Geometry, Boundary Conditions, and Contact Modeling  
The ASP was modeled as a quarter-symmetric model, as shown in Figure 3, due to the symmetric nature of the simulation. This approach reduces computational time by up to four times compared to a full model. Therefore, the quarter model of the ASP had dimensions of \(L=350\) mm, \(W=625\) mm, and \(H=100\) mm. Symmetric boundary conditions in \(x\) and \(y\) directions were applied to the ASP side in the \(y-z\) and \(x-z\) planes, respectively, while the outer face sheets were fixed on the other two sides. The face sheets and auxetic core were modeled using a fully integrated shell with five and two integration points, respectively, to minimize the hourglass energy. For the data sampling process, the thickness of both face sheets was set to 5 mm. The mesh convergence analysis was performed to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geometry** & **Independent Variable** & **Dependent Variable** & **Constraint** \\ \hline \multirow{3}{*}{REH} & \multirow{3}{*}{\(N_{y}\) (\(w\)), \(N_{z}\) (\(h\)), \(\theta\), \(t\)} & \(l_{1}=h/\left(2\cos\theta\right)\) & \\  & & \(l_{2}=(w+h\tan\theta)/2\) & \(\theta<\tan^{-1}(w/h)\) \\  & & \(d=(w-h\tan\theta)/2\) & \\ \hline \multirow{3}{*}{DAH} & \multirow{3}{*}{\(N_{y}\) (\(w\)), \(N_{z}\), \(\theta\), \(t\)} & \(h=[H+(N_{z}-1)(w/2)\tan\theta]/N_{z}\) & \\  & & \(l_{1}=(w/2)\sec\theta\) & \\  & & \(l_{2}=\sqrt{h^{2}+(w/2)^{2}}\) & \(\theta<\tan^{-1}(2h/w)\) \\  & & \(d=h-(w/2)\tan\theta\) & \\  & & \(\theta^{\prime}=\tan^{-1}(2h/w)-\theta\) & \\ \hline \multirow{3}{*}{SH} & \multirow{3}{*}{\(N_{y}\) (\(w\)), \(N_{z}\) (\(h\)), \(\theta\), \(t\)} & \(l_{1}=w/(4\cos\theta)+h\tan\theta/(4(\cos\theta+1))\) & \\  & & \(l_{2}=h/(4\cos\theta)+w\tan\theta/(4(\cos\theta+1))\) & \\  & & \(d_{1}=2(l_{1}\cos\theta-l_{2}\cos\theta)\) & \\  & & \(d_{2}=2(l_{2}\cos\theta-l_{1}\cos\theta)\) & \\ \hline CH & \multirow{2}{*}{\(N_{y}\) (\(w\)), \(N_{z}\) (\(h\)), \(r\), \(t\)} & \(l_{1}=\sqrt{w^{2}-4r^{2}}/2\) & \\  & & \(l_{2}=\sqrt{h^{2}-4r^{2}}/2\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of geometric parameters for each auxetic geometry.  
Figure 2: Geometric parameters of the unit cell for (**a**) re-entrant honeycomb (REH), (**b**) double-arrow honeycomb (DAH), (**c**) star honeycomb (SH), and (**d**) tetra-chiral honeycomb (CH).  
obtain the mesh size for the auxetic core, which converged at a mesh size of 3 mm, while 10 mm was used for the face sheets [52]. AUTOMATIC_SINGLE_SURFACE was applied to all parts of the ASP for self-contact, while AUTOMATIC_SURFACE_TO_SURFACE was applied between the auxetic core and both face sheets to prevent penetration. The upper and lower nodes of the auxetic core was assumed to be fully connected with both face sheets using TIED_NODES_TO_SURFACE. The Static and dynamic coefficients of friction for all contact were 0.3 and 0.2, respectively.
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 9 Discussion
A possibility that was investigated in this paper is empowering non-health-specialist volunteers and school teachers to perform vision screening where the intervention can be performed without a vision specialist, achieved by using technologies [14; 98]. Professionals utilize traditional, instrumental, and computerized tools and technologies to conduct vision screening. Although traditional methods of screening are the gold standard, instrumental screening is gaining the attention of vision experts and is recommended by the American Academy of Ophthalmology (AAO), the American Academy of Pediatric (AAP), the American Association of Certified Orthoptists (AACO), and the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) for children under certain conditions [48; 55]. Computerized technology, especially eye-tracker devices integrated with computer software, shows promising applications for collecting objective evidence of the eye movements that can be utilized to assess children's functional vision [33; 132]. While using ET, some factors such as calibration setup, the sitting position of children, and the adjustment for children with special needs can influence data quality. Still, research studies have demonstrated that adopting various techniques and algorithms could optimize data quality and minimize the mentioned challenges [136].