esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of the Effects of Different Metal Ions on the Extracellular Amylase Activity  
For determining the effects of various metal ions on the extracellular amylase activity, CaCl\({}_{2}\), MgCl\({}_{2}\), MnCl\({}_{2}\), CuCl\({}_{2}\), FeCl\({}_{2}\), and HgCl\({}_{2}\) (Sigma Aldrich, Hamburg, Germany) were each individually mixed with crude extracellular amylase enzyme solution at a final concentration of 0.0015 M and incubated at 60 \({}^{\circ}\)C for 30 min. The extracellular amylase activity was then measured by DNSA assay as described previously in Section 2.9. All experiments were conducted in triplicate. The crude amylase activity in the absence of metal ions was used as the control and considered 100% activity.  
Detection of Raw Cassava Starch Hydrolysis by Crude Extracellular Amylase of NW2 Isolate Using the Iodine-Starch Test  
Cassava starch was purchased from a local market. Different concentrations of cassava starch (0.5%, 1%, 2%, 3%, and 4%) suspension were prepared by dissolving it in 0.05 M sodium phosphate buffer (pH 8.0). An amount of 0.1 mL of crude enzyme was mixed with 0.9 mL of each different cassava starch suspension separately and incubated for 4 h at 60 \({}^{\circ}\)C. Then, 0.1 mL of iodine solution (2% I\({}_{2}\) and 0.2% KI) was added into each reaction mixture and mixed well to observe color differences. A control reaction prepared using 1% cassava raw starch solution without the addition of crude enzyme was included for comparison.
# Decision Framework for Predictive Maintenance Method Selection
## 2 Problem Exploration
### Problem 3--Financial Justification for a Specific Maintenance Approach Is Not Clear  
The final problem identified in the case study is the financial justification for the implementation of the maintenance method. As _problem 2_ shows, there is not always the right fit between the ambition level and the available data. To solve this, a company can either reduce its ambition level or collect the right data. The latter is often associated with substantial investment costs. Therefore, although it often seems to be difficult to (financially) justify these investment costs, a positive business case is important for the successful implementation of the selected maintenance method.  
The benefit of MMs to asset performance can often (primarily) be found in reducing the number of unplanned failures while not conducting too many repairs or replacements. When, for example, a health monitoring system detects an anomaly, it will create a prognostic identification and estimate the remaining useful life (RUL), allowing the user to take preventive action before the system fails [31]. Such a maintenance technique creates timely knowledge about failures and improves flexibility in managing the asset. The challenge is to express this benefit in financial terms, to enable a comparison to the required investment costs, or to find another (non-financial) way to weigh costs and benefits. It was observed that many companies do not make such an assessment before starting the development of an advanced maintenance approach.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 3 Problem Definition
### Problem Statement  
Initially, a single PM could be sufficient to host all of the VMs needed for a service. However, it will be necessary to expand the service as the number of customers continuously increases or additional functionalities are needed to provide more efficient service. In these cases, more computing resources will be required so additional VMs must be created. If the amount of resources available on the currently used PM is not sufficient, additional VMs will be created on other PMs.  
For example, as illustrated in Figure 1, a service was hosted on nine VMs that were placed on one PM when the service was released, but the service is later expanded by creating additional VMs on other PMs. Figure 2a shows that both highly dependent and less dependent VMs exist in each PM. Even among the VMs that belong to the same service, the traffic volume and the dependency between the pairs of VMs can be different: some VMs are highly dependent on each other (and have high levels of data traffic between them) while other VMs have less communication dependency on each other (and have less data traffic between them).  
In conventional strategies, the communication dependency between VMs is not considered when a service is expanded by creating additional VMs. Thus, highly dependent VMs may be placed in different PMs, which will significantly increase the data traffic between PMs. This will negatively impact the service performance because the data traffic between the service and the customer flows through the same physical network. Moreover, traffic growth in physical network increases operating cost of the data center. As shown in Figure 2, data traffic between VMs in different PMs flows through both the virtual and physical networks of the data center. At the same time, data traffic between VMs in a same PM flows through only the virtual network. In this regard, it is desirable to minimize the traffic volume in physical network to improve the service performance.  
To improve the service performance, it is desirable to minimize the traffic volume in physical network of the data center. To this end, we propose an efficient VM-placement strategy to determine the appropriate placement location for each VM by considering traffic dependency. The VM placement by the proposed strategy is illustrated in Figure 2b.  
Figure 1: System description.
## 2 Discussion
### Limitations, Safety and Challenges
Nevertheless, a dose-response investigation revealed that whilst concentrations up to 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of nanoceria do not interfere with basal levels of ROS, higher concentrations (400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) of nanoceria induce ROS production, resulting in mitochondrial membrane depolarisation, DNA damage, and activation of the caspase cascade, characteristic of apoptotic pathways [106]. These findings underscore the complexity of nanoparticle interactions within ocular tissues, suggesting a critical need for further nuanced investigation into ROS-mediated effects to fully comprehend their implications for ocular health and potential therapeutic applications. Accurately replicating in vivo conditions presents multifaceted challenges, encompassing factors such as the dynamics of aqueous fluid and patient-specific variations like immune responses, which complicate the extrapolation of data to humans. Moreover, conducting human trials necessitates specialised laboratory facilities and skilled personnel to obtain and propagate HCECs while adhering to rigorous regulatory safety standards. Despite these challenges, the favourable safety profile observed with MNPs in conjunction with CECs, alongside the global demand for innovative treatments for endothelial diseases, highlights the need for further exploration and potential implementation of this approach in human trials. Ensuring the biocompatibility of nanomaterials, including a thorough evaluation of dose-dependent effects, remains crucial despite the widespread adoption of new nanotechnologies. There is a significant gap between the rapid integration of these materials into various applications and the limited scope of comprehensive nanotoxicological assessments. This emphasises the necessity for meticulous evaluation to guide decisions regarding their safe and effective use in clinical settings.
## 2 Architecture and Protocol
### Protocol Design
In order to maximize the UL, the multi-user frame accommodation channel access of each VR device should be prohibited. The channel access may be prohibited by setting EDCA parameters, including AIFSN and CW, to very large values, and maximum values are particularly recommended. Setting the EDCA parameters does not require any modification to IEEE 802.11 standards, and manufacturers could configure the EDCA parameters easily.
## 4 Spatiotemporal Evolution Characteristics
### Diverse Changes in Spatial Influencing Factors  
Considering the inherent characteristics of the study subjects, and after conducting multiple comprehensive field surveys, it was determined that the most pertinent explana  
Figure 6: Kernel Density Estimation Analysis of POI for Xi’an City Center Paomo Restaurants. tory variables for the study encompass the spheres of transportation, workplace, residential living, and commerce. These broader categories were broken down into a total of 14 detailed secondary indicators to provide a nuanced understanding of the factors influencing the spatial distribution of the entities being studied. These indicators include the proximity to main roads and subway stations, which represent the transportation aspect; the density of corporate, financial, government, and social institutions, capturing the workplace dynamics; and the density of residential facilities, which reflects the living conditions in the area. Additionally, this study examined the density of catering, scientific and educational, medical, leisure, and shopping facilities, which are integral to understanding the residential and commercial quality of life. For a more comprehensive view of the commercial environment, the density of tourism, hotel, and service facilities was also included. All these indicators (as detailed in Table 3) were selected based on their relevance and potential impact on the study's focus, ensuring a robust framework for analyzing the complex interactions between urban development and the spatial distribution of the study objects. This study uses the Analytic Hierarchy Process (AHP) to determine the weights of the indicators. Five experts who have lived in Xi'an for a long time were invited to score each element. The weights were finalized through individual ranking of the indicators, with all weights summing up to 1. This methodological approach allows for an in-depth exploration of the urban fabric and provides insights into how different urban factors contribute to shaping the dynamics of city spaces and the behavior of their inhabitants. The analysis is structured around several key urban factors--traffic, workplace, living, and trade--each assigned a specific weight reflecting its relative importance. The table provides a nuanced view into how each factor's sub-indicators have changed over the study period, and here we break down and interpret these changes. There was a significant improvement (17.87% increase) in the proximity of Paomo restaurants to main roads by 2023, suggesting enhanced accessibility. Conversely, the increase in distance to subway stations (11.20% decrease) could indicate a shift in restaurant locations away from the immediate subway vicinity, possibly due to rising property costs in these areas. Despite mixed results
# The Exploitation of Lysosomes in Cancer Therapy with Graphene-Based Nanomaterials
## 4 The Transport of GNMs to Lysosomes
Chemical bonds between GNMs and drugs or polymers in graphene-based DDSs can be easily broken, but there is only indirect evidence of pure GNMs degrading in lysosomes. [PERSON] et al. demonstrated that phagocytosed graphene may be digested in macrophages, but the degradation mechanism remains unclear [49]. Additionally, [PERSON] et al. showed that GO can be degraded by lysosomal enzymes neutrophil and eosinophil peroxidase in cell-free conditions [50; 51]. Conversely, [PERSON] et al. demonstrated that GO cannot be digested in lysosomes. Instead, they accumulate in them, causing steric hindrance [9]. The accumulation of GNM in lysosomes may have both harmful and beneficial effects in cancer therapy. On the one hand, if anticancer drugs are attached to GNMs, their lysosomal accumulation might prevent drugs from reaching their site of action, which would reduce their anticancer effects [52]. On the other hand, it may block lysosomal  
Figure 2: The entry of GNMs and graphene-based DDSs into cells. In phagocytosis, large GO are engulfed in a phagosome, which fuses with the lysosome. In clathrin-mediated endocytosis, small GO nanosheets and GQDs are bound to cell surface receptors and internalized into clathrin-coated vesicles that mature into late endosomes and eventually fuse with the lysosome. Small hydrophobic graphene sheets and flakes enter the cytoplasm directly by penetrating the plasma membrane and can become entrapped in autophagosomes, which subsequently fuse with lysosomes in the process of autophagy. GQDs can also cross cell membranes by caveolae-mediated endocytosis, which occurs through the formation of caveolae vesicles, which may fuse with endosomes. activity, including protective autophagy degradation, leading to cell death and enhancing the therapeutic effect [9; 10; 11; 53].
## 4 Result and Discussion
### Battery Based Power Levelling During Solar Radiation and Temperature Change
#### 4.4.2 Temperature Change  
Figures 30 and 31 show the change of battery power to compensate the PV power fluctuation caused by the temperature change. As can be seen from Figure 30, the temperature is changed  
Figure 32: Battery charging.  
from 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, which reduces the PV power from 1.74 MW to 1.55 MW. During this period, the battery injects from zero to 0.2 MW power to fill up the shortage, which in turn maintains the MMC1 delivered power constant. Similarly, battery power is reduced from 0.2 MW to zero, whereas PV power is increased from 1.55 MW to 1.74 MW during the temperature change from 50 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. The delivered real power of MMC1 encounters negligible overshoot during such change. In both cases, battery compensates the shortage of PV power due to temperature fluctuation.
## 4 Problem Formulation
### Sdr  
Now, SDR will be applied for solving problem (P2). It is known that MRT is the optimised transmit beamforming solution for problem (P2), i.e., \(\mathbf{w}^{*}=\sqrt{P}\frac{(\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}+ \mathbf{h}_{d}^{H})^{H}}{||\mathbf{h}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_ {d}^{H}||}\), where \(P\) is the transmitted power of AP. Here, \(\mathbf{w}^{*}\) is replaced by [4]:  
\[\underset{P,\beta}{\text{min}} \quad P\] (17) s.t. \[\quad P||\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_ {d}^{H}||^{2}\geq\gamma\sigma^{2} \tag{18}\] \[\quad 0\leq\theta_{n}\leq 2\pi,\forall n. \tag{19}\]  
Hence, reducing the transmitted power is equal to increasing the combined channel gain power and is expressed as:  
\[\underset{\theta}{\text{max}} \quad||\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_{d} ^{H}||^{2}\] (20) s.t. \[\quad 0\leq\theta_{n}\leq 2\pi,\forall n. \tag{21}\]  
Let \(\mathbf{v}=[\mathbf{v}_{1},\cdots,\mathbf{v}_{N}]^{H}\) where \(\mathbf{v}_{n}=e^{i\theta_{n}},\forall n\). Then, let the change of variables \(\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}\triangleq\mathbf{v}^{H}\Phi\) where \(\Phi=diag(\mathbf{h}_{r}^{H})\mathbf{G}\in C^{N\times M}\), we have \(||\mathbf{h}_{r}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_{d}^{H}||^{2}=|| \mathbf{v}^{H}\Phi+\mathbf{h}_{d}^{H}||^{2}\). Thus, problem (4.21) is equal to [4]:  
\[\underset{\mathbf{v}}{\text{max}} \quad\mathbf{v}^{H}\mathbf{\Phi}\mathbf{\Phi}^{H}\mathbf{v}+ \mathbf{v}^{H}\mathbf{\Phi}\mathbf{h}_{d}+\mathbf{h}_{d}^{H}\mathbf{\Phi}^{H} \mathbf{v}+||\mathbf{h}_{d}^{H}||^{2}\] (22) s.t. \[\quad|\mathbf{v}_{n}|^{2}=1,\forall n. \tag{23}\]  
Here, this problem (22) and (23) is still a non-convex quadratically constrained quadratic program (QCQP), which can be rewritten as a homogeneous QCQP [4]. So, considering a new auxiliary variable \(t\), the problem (22) and (23) is then equivalent to  
\[R=\begin{bmatrix}\mathbf{\Phi}\mathbf{\Phi}^{H}&\mathbf{\Phi}\mathbf{h}_{d} \\ \mathbf{h}_{d}^{H}\mathbf{\Phi}^{H}&0\end{bmatrix},\bar{\mathbf{v}}=\begin{bmatrix} \mathbf{v}\\ t\end{bmatrix}.\]  
Note that \(\bar{\mathbf{v}}^{H}\mathbf{R}\bar{\mathbf{v}}=\text{tr}(\mathbf{R}\bar{ \mathbf{v}}\bar{\mathbf{v}}^{H})\). Let \(\mathbf{V}=\bar{\mathbf{v}}\bar{\mathbf{v}}^{H}\), which has to be satisfied \(\mathbf{V}>0\) and \(rank(\mathbf{V})=1\). So, it is given by:  
\[\underset{\mathbf{V}}{\text{max}} \quad\text{tr}(\mathbf{RV})+||\mathbf{h}_{d}^{H}||^{2}\] (24) s.t. \[\quad\mathbf{V}_{n,n}=1,\quad n=1,\ldots,N+1, \tag{25}\] \[\quad\mathbf{V}\succeq\mathbf{0}. \tag{26}\]  
Problem (24)-(26) can be solved optimally by a well-known convex optimisation problem solving tool such as CVX [32], since it is a convex semidefinite program (SDP). As the relaxed issue (24) typically does not have a rank-one solution, i.e., \(\text{rank}(\mathbf{V})=1\), it follows that the problem (24)'s optimal objective value merely serves as the upper bound (22).  
Note that the single-user system developed above can be extended to multi-user scenario following the approaches in [4].
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Carbon Fluxes Production  
The BEPS model has been shown to be capable of assessing the impact of changes in the leaf area index (_LAI_) on the carbon cycle [37; 48]. In the BEPS, _GPP_ is calculated as:  
\[GPP=GPP_{sultlit}LAI_{sultlit}+GPP_{shade}LAI_{shade} \tag{1}\]  
where _GPP\({}_{sultlit}\)_ and _GPP\({}_{shade}\)_ are _GPP_ of sun leaves and shade leaves per unit area, respectively. _LAI\({}_{sultlit}\)_ and _LAI\({}_{shade}\)_ are the LAIs of sun leaves and shade leaves, respectively  
\[LAI_{sultlit}=2\times\cos\theta\times\left[1-\exp\left(-0.5\times\Omega\times \frac{LAI}{\cos\theta}\right)\right] \tag{2}\]  
\[LAI_{shade}=LAI-LAI_{sultlit} \tag{3}\]  
where \(\Omega\) is the clumping index derived from MODIS data at a 500 m resolution [49] and \(\theta\) is the daily mean solar zenith angle. Moreover, the validation results using flux tower _GPP_ demonstrated that this dataset has satisfactory precision in almost every biome [41]. In the BEPS model, _NEP_ is calculated as:  
\[NEP=GPP-AR-HR \tag{4}\]  
where _AR_ is autotrophic respiration and _HR_ is heterotrophic respiration, both of which make up the total ecosystem respiration. _AR_ consists of growth respiration (_GR_) and maintenance respiration (_MR_). _GR_ is assumed to be 25% of _GPP_, the _MR_ of plant carbon pools is estimated based on their size, temperature, and reference respiration rate, and the simulation of the _HR_ part utilizes the CENTURY model approach [50]. Yearly BEPS carbon fluxes with a spatial resolution of 0.072727\({}^{\circ}\)\(\times\) 0.072727\({}^{\circ}\) during the 1985-2018 period were used in this study ([[http://nesdc.org.cn/](http://nesdc.org.cn/)]([http://nesdc.org.cn/](http://nesdc.org.cn/)), accessed on 6 January 2022) [51; 52].
###### Abstract  
Improving the quality of service (QoS) and meeting service level agreements (SLAs) are critical objectives in next-generation networks. This article presents a study on applying supervised learning (SL) algorithms in a 5G/B5G service dataset after being subjected to a principal component analysis (PCA). The study objective is to evaluate if the reduction of the dimensionality of the dataset via PCA affects the predictive capacity of the SL algorithms. A machine learning (ML) scheme proposed in a previous article used the same algorithms and parameters, which allows for a fair comparison with the results obtained in this work. We searched the best hyperparameters for each SL algorithm, and the simulation results indicate that the support vector machine (SVM) algorithm obtained a precision of 98% and a F1 score of 98.1%. We concluded that the findings of this study hold significance for research in the field of next-generation networks, which involve a wide range of input parameters and can benefit from the application of principal component analysis (PCA) on the performance of QoS and maintaining the SLA.  
5G/B5G service classification; dimension reduction; ML; PCA; QoS; SLA +
Footnote †: journal: Future internet
## 1 Introduction
### Available Containerization Frameworks  
During the analysis of the IEEE Xplore, Scope, and Google Scholar papers on the matter of embedded software containerization, the following container engines were found:  
* MicroPython;
* Jerry Script;
* Singh;
* Velox VM;
* Toit;
* Femto containers;
* Wasm3;
* Golioth;
* Others.  
After conducting a top-level analysis and reviewing it, it became clear that each container engine is more or less based on the WebAssembly principles of real-time code translation. WebAssembly was initially developed by the W3C organization as the translator for web-technologies and applications. Later on, because of its popularity, it became a good choice for the cross-platform engine for non-web applications too. Partially its popularity is driven by the support of the native to the typical embedded software programmings languages such as C, C++, and Rust. As of today, there are more than 35 implementations of high-performance WebAssembly machines, roughly 50% of which are actively supported and continue their lifecycle. During the desktop analysis of the above-mentioned WebAssembly implementations of the container engines, the main focus was on three things: ease of migration and adoption of the most commonly used hardware platforms; the existence of already completed ports for easy-to-get COTS hardware platforms; and, last but not least, the royalty-free nature of the engine, so the concept of COTS components of CubeSats is still kept. As the basis of the solution of the container's engine implementation on the typical CubeSat hardware, the WASM3 [39] interpreter engine was chosen. WASM3 was initially created to deliver outstanding performance for the low-performance targets and thus fully supports the energy-efficient and low-performance hardware of CubeSats. The other neither qualitative nor quantitative selection factor was that WASM3 was developed and actively supported in Ukraine. This will allow the authors to establish a direct connection with the WASM3 development team and by this ensure further work on the use of containers for the CubeSat applications. To be able to better understand how containerization could help CubeSat development teams, let us look into a typical CubeSat system structure and identify the conceptual way of implementing containers for CubeSat projects.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 5 Prototype Implementation
### Synthetic Dataset for Evaluation  
To systematically test the system, we created a synthetic dataset that emulates key aspects of real patient data. This approach allows controlled variation in conditions to evaluate performance under different scenarios (normal, anomalous, high load, etc.). The dataset was generated as follows:  
* Vital Signs Generation: We generated time-series for heart rate (HR), blood oxygen saturation (SpO\({}_{2}\)), blood pressure (BP), and body temperature over a period of 24 h with 1 min resolution. Baseline values and circadian patterns were introduced (e.g., HR slightly lower at night). We added random noise to simulate sensor variability.
* Event Injection: We injected specific events as shown in Table 1. These events were spaced out and combined in various ways to create different test scenarios (some with single anomaly, some with multiple concurrently, etc.).
* Multiple Patients: We created five synthetic patient profiles with slight variations in baseline and event patterns to test multi-user handling. For example, Patient A might have a known condition that causes frequent tachycardia, whereas Patient B might be generally healthy except one fall event. This allowed us to see if our cloud agent could adapt thresholds or responses per patient.  
For validation against reality, we based our ranges on known clinical data (e.g., normal HR 60-100, tachycardia threshold \(\sim\)100; normal SpO\({}_{2}\)\(\sim\)95-100%, where <92% might be hypoxic; fever threshold \(\sim\)38 \({}^{\circ}\)C). We did not use real patient data due to privacy and complexity, but our synthetic data approach is sufficient for evaluating system performance metrics like detection accuracy and false alarm rates because we have ground truth (we know when we inserted an event).