esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
There is growing concern regarding the nutritional value of processed food products. Although thermal pasteurization, used in food processing, is a safe method and is widely applied in the food industry, food products lack quality and nutritional value because of the high temperatures used during pasteurization. In this study, the effect of pulsed electric field (PEF) processing on whey protein content and bacterial viability in raw milk was evaluated by changing the PEF strength and number of pulses. For comparison, traditional pasteurization techniques, such as low-temperature long-time (LTLT), ultra-high temperature (UHT), and microfiltration (MF), were also tested for total whey protein content, bacterial activity, and coliforms. We found that, after treatment with PEF, a significant decrease in total bacterial viability of 2.43 log and coliforms of 0.9 log was achieved, although undenatured whey protein content was not affected at 4.98 mg/mL. While traditional pasteurization techniques showed total bacterial inactivation, they were detrimental for whey protein content: \(\beta\)-lactoglobulin was not detected using HPLC in samples treated with UHT. LTLT treatment led to a significant decrease of 75% in \(\beta\)-lactoglobulin concentration: \(\beta\)-lactoglobulin content in milk samples treated with MF was the lowest compared to LTLT and UHT pasteurization, and \(\sim\)10% and 27% reduction was observed.  
mik; PEF; food nutrition; bacterial inactivation; whey protein; microbiological safety Article  
Effect of Pulsed Electric Field (PEF) on Bacterial Viability and Whey Protein in the Processing of Raw Milk  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]
Hypoxia-Induced _Fam13a_ Regulates the Proliferation and Metastasis of Non-Small Cell Lung Cancer Cells  
[PERSON]  
Institute of Human Genetics, Polish Academy of Sciences, Straszzynska 32, 60-479 Poznan, Poland; [PERSON] (M.P.); [PERSON]M.Z.); [PERSON]J.L.); [PERSON]I.); [PERSON]ERSON]R.)  
M. Podralska  
Institute of Human Genetics, Polish Academy of Sciences, Straszzynska 32, 60-479 Poznan, Poland; [PERSON] (M.P.); [PERSON]M.Z.); [PERSON]J.L.); [PERSON]I.); [PERSON]ERSON]R.)  
M. Zurazwek  
Institute of Human Genetics, Polish Academy of Sciences, Straszzynska 32, 60-479 Poznan, Poland; marta.  
90% of solid tumors due to limitations in diffusion and inefficient vasculature [3]. In the course of adaptation to hypoxia, tumors evolve more aggressive and therapy resistant phenotypes [4]. Oxygen deprivation induces many changes in gene expression, mainly through stabilization of the hypoxia-inducible subunits (HIFs) that regulate the cellular response to hypoxia. It is known that HIF-1 regulates thousands of genes [5]. However, there is a discrepancy in the predictive value of HIF-1 and its downstream genes expression, so there is a need for the development of gene expression signatures for assessing tumor hypoxia.  
We previously showed that FAM13A was significantly up-regulated in lung cancer cell lines (A549, CORL-105) and non-small cell lung cancer fragments cultured ex vivo, after exposure to chronic hypoxia [6]. _FAM13A_ (family with sequence similarity 13, member A, MIM 147582) gene encodes two main protein isoforms (1 and 2). Only isoform 1 contains the Rho-GAP domain that is important for the regulation of cell proliferation and survival [7]. It is known that Rho GTPases family proteins play an important role in the process of cell migration and several signaling complexes [8]. Perturbed Rho GTPase signaling is associated with several lung diseases, including lung cancer [9]. Previously, _FAM13A_ gene variants were indicated as risk factors of lung cancer [10], chronic lung diseases including chronic obstructive pulmonary disease (COPD) [11; 12] and cystic fibrosis (CF) [13]. Nevertheless, the biological function of FAM13A has not been fully elucidated and the mechanism of protein activity is poorly understood.  
The aim of the present study is therefore to investigate the biological role of hypoxia-induced FAM13A in non-small cell lung cancer (NSCLC) progression. We examined the effect of FAM13A knockdown on cell proliferation, cell cycle, migration, invasion and apoptosis in NSCLC cells cultured under hypoxic conditions in order to clarify the function of FAM13A.
###### Abstract  
Heavy metals (HMs) are widely recognized for their toxicity and have serious environmental implications as technology advances and public pressure mounts to guarantee the safest and healthiest environment. This study evaluates the phytoremediation potential of HMs i.e., Copper (Cu), Zinc (Zn), Lead (Pb), and Cadmium (Cd) by _Calotropis procera_ (Aiton) W.T. Aiton, also known as Sodom apple, along an urban--rural gradient and its effect on communities' diversity, forage and medicinal quality in semi-arid region of Khyber Pakhtunkhwa Pakistan. The HM concentration was investigated along with the urban--rural gradients by sampling _C. procera_ and soil samples. Acid-digested samples were tested for metal concentration using an atomic absorption spectrophotometer (AAS). We used principal component analysis and cluster analysis to identify the pattern of metal distribution in plants and soil. To comprehend the species' diversity of plant communities in polluted sites, the species' composition of _C. procera_ communities was explored. Our results showed that the concentration of HMs in the soil and plant decreased from Zn to Cd (Zn > Cu > Pb > Cd). Likewise, more than half of the soil metal accumulated in the roots and aerial part of the plant, indicating the bioaccumulation potential of the plant species for these metals. Zn, Cu, Pb, and Cd translocation ratio varied from root > stem > leaf > flower. Root to stem transfer of metal was poor, but strongly mobilized to the leaves when available in the stems. _Cartlannus lantus, Sonchus asper, Cyndon dactylon, Xanthium strumarium,_ and _Silybum marianum_ were the leading species in three groups of 36 plant species. [PERSON]'s correlation revealed a significant relationship between HM concentrations and diversity indices. Zn and Cu content in the soil influenced plant species richness, Shannon-Wiener index (H), and evenness index (Eh). Given the environmental toxicity of HMs, Cd concentrations in soil exceeded the permissible level, suggesting residents should be warned about potential health risks. As a result, the species chosen for this study can be employed as a biomonitor and phytoremediator of soil contaminated by these HMs, as it can accumulate HMs to a toxic level.  
heavy metals; phytoremediation; _Calotropis procera_; species diversity
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 6 Results and Discussion
### Scenario-1  
In this scenario, we have single IoT device (Z1 mote) with multiple sensors attached to it, and the data are posted to separate resources on the cloud server. Here, we show delay and energy consumption results on increasing transaction loads (in form of data sending interval) in Figure 10. The results were collected with varying packet sending interval from 2 to 0.5 s, with a step size of \(-0.5\). With an increase in data sending rate, we observe increase in the delay values. REST-based scheme with three resources subscribed suffers greatly and around 100 ms delay increase is observed when the data sending interval  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **IoT Device** & **Server** \\ \hline
**Hardware** & Zolertia Z1 MCU: MSP430F2617 & \\ \cline{2-3}  & Radio: CC2420 & \\ \cline{2-3}  & RAM: 8 kB & \\ \cline{2-3}  & ROM: 92 kB & \\ \hline
**Operating System** & Contiki OS & Ubuntu \\ \hline
**Development Tool** & Cooja, Eclipse & Python \\ \hline
**Libraries and Frameworks** & GraphQL client, Query Language & Firebase, GraphQL Runtime \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration details of Server and IoT devices used in experimental setup.  
Figure 9: Experimental scenarios for performance analysis of the proposed scheme.  
is reduced from 2 to 0.5 s. However, the proposed scheme results in the lowest delay for all the cases. Furthermore, we observe the increase in the delay for it is marginal with increase in data sending rate, which shows that the proposed approach is more robust and resilient, and hence more suitable for IoT sensing applications.  
Energy consumption results also exhibit a similar pattern. A significant increase in energy consumption is observed for the REST-based schemes, on all the data sending intervals and further increases when the data sending rate is increasing. The proposed approach outperforms the baseline REST-based schemes and achieves delivery of the same data using less than half energy cost. Packet Delivery Ratio (PDR) and throughput results for this scenario are not meaningful (hence, not reported), as there is no notable packet loss or throughput changes for this one device-based evaluation scenario. Spread of the values has been shown by error bars for both the metrics and we can see better performance confidence for proposed GraphQL-based approach.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 4 System Model  
The proposed _DRAMBR_ represents a fully integrated reputation solution designed to manage the reporting process effectively under challenging conditions in V2V communication.  
The aim is to enhance the trustworthiness of V2V communications by monitoring vehicle platoon behavior, detecting any misbehavior, and reporting it back to a central RS that assigns and periodically updates their RVs to demonstrate their trustworthiness.  
The system operates in two primary phases: Offline Evaluation (OE) and Online Reporting (OR): In the OE, vehicles assess each other's behavior and store MR locally without central connectivity, enabling continuous trust management. In the OR phase, upon reconnecting to the Internet, the vehicles send the accumulated MRs to the RS. The RS aggregates, classifies, and analyzes the MRs, reducing processing overhead.  
The system can be implemented in both urban (online) and rural (offline) areas. In urban areas, it reduces computational complexity on the RS by decentralizing the RV, thereby improving scalability and efficiency. In rural areas, vehicles benefit from sharing their RVs without relying on a central authority, ensuring continued reliability even in disconnected scenarios.  
In this section, we first outline the main system assumptions. Next, we explain the vehicle behaviors highlighting the threat model relevant in this study. Following that, we introduce the DRAMBR framework. These explanations set the stage for discussing the main phases of DRAMBR.
# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
###### Abstract  
_Aspergillus flavus_ produces dangerous secondary metabolites known as aflat, which are toxic and carcinogenic, and their contamination of agricultural products results in health issues and economic hardships in the U.S. and around the world. Early identification of aflatoxinigenic isolates of _A. flavus_ is the key in the management of these fungi. An emerging detection method for specific fungi identification involves the analysis of microbial volatile organic compounds (MVOCs) released by the fungi. Complicating this approach is the understanding that many factors influence metabolic production, including growth parameters, such as growth media, temperature, spore counts and oxidation stress. In addition, analytical and data analysis methods can also influence the results. Several growth and analysis methods were evaluated and optimized in order to better understand the effect of the methods on fungi MVOC signatures. The results indicate that carboxen/polydimethylsiloxane (CAR/PDMS) has the best extraction efficiency for the MVOCs emitted by _A. flavus_. Both chemical defined agar (CDA) and chemical defined liquid (CDL) are suitable growth media for MVOC emission studies. The highest MVOC production was found at 30 \({}^{\circ}\)C. Log transformation was considered one of the best data pretreatment methods when analyzing MVOC data and resulted in the best principal component analysis (PCA) clustering in the experiments with different growth media. This study aims to elucidate fungal volatile organic compounds (VOCs) differences due to variations in growth parameters as a first step in the development of an analytical method for the monitoring of aflatoxin _A. flavus_ contamination in crop storage facilities.  
_Aspergillus flavus_; microbial volatile organic compound (MVOC); solid phase microextraction (SPME); principal component analysis (PCA); partial least square analysis (PLS) +
Footnote †: journal: _Aspergillus flavus_
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### Sdn  
The ETSI refers to virtualization technologies such as SDN as key enablers for network slicing applications and recommends considering them within its MEC architecture. SDN differentiates the control plane and user plane functionalities. An SDN controller increases network traffic efficiency by providing management and a global view of the network topology. A survey of SDN-based MEC solutions for network slicing and V2X URLLC applications is presented in [82], and [PERSON] et al. [83] investigated SDN controllers and classified their work based on application needs.  
**OpenDaylight**[84] is one of the most popular open source SDN controllers developed by the Linux foundation. The OpenDaylight SDN controller supports the OpenFlow southbound interface protocol. To test this controller, a switch is required or an alternative solution such as Mininet can be used. OpenDaylight is based on a Java model controller that employs YANG as its modeling language. The configuration and usage of OpenDaylight are thoroughly documented.  
**Open Network Operating System** (ONOS) [85] is an open source tool developed using Bazel of Google. ONOS supports different southbound protocols of which OpenFlow is the primary one. An ONOS SDN controller can be hosted on a VM and integrated with Mininet. ONOS provides comprehensive documentation and prerequisites for its usage.  
[PERSON] et al. present a performance comparison of OpenDaylight and ONOS SDN controllers [86].
###### Abstract  
This study describes the basic design, material selection, fabrication, and evaluation of soft pneumatic actuators (SPA) for lower-limb rehabilitation compression therapy. SPAs can be a promising technology in proactive pressure delivery, with a wide range of dosages for treating venous-related diseases. However, the most effective design and material selection of SPAs for dynamic pressure delivery have not been fully explored. Therefore, a SPA chamber with two elastomeric layers was developed for this study, with single-side inflation. The 3D deformation profiles of the SPA chamber using three different elastomeric rubbers were analyzed using the finite element method (FEM). The best SPA-compliant behavior was displayed by food-grade silicone A10 Shore with a maximum deformation value of 25.34 mm. Next, the SPA chamber was fabricated using A10 Shore silicone and experimentally validated. During the simulation in FEM, the air pressure was applied on the inner wall of the chamber (i.e., the affected area). This is to ensure the applied pressure was evenly distributed in the inner wall while the outer wall of the chamber remained undeformed for all compression levels. During the inflation process, pressure will be applied to the SPA chamber, causing exerted pressure on the skin which is then measured for comparison. The simulation and experimental results show an excellent agreement of pressure transmission on the skin for the pressure range of 0-120 mmHg, as depicted in the Bland-Altman plots. The findings exhibited promising results in the development of the SPA chamber using low-cost and biocompatible food-grade silicone.  
compression therapy; finite element modelling (FEM); hyperelastic material; soft pneumatic actuator (SPA); lower limb; silicone elastomer; venous disease +
Footnote †: journal: Applied Sciences
# Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey
## 6 Data Structures Used in Blockchain Anomaly Detection  
Depending on the problem at hand, the implementation of unsupervised learning methods in blockchain anomaly detection is strongly related to the way the data are formatted and structured and the features that are used. These topics are discussed in the subsequent analysis.  
Herein, three basic types of data structures are analyzed, namely, tabular-based, sequence-based, and graph-based structures. Each of them appears to have certain characteristics while providing various convenient ways to apply unsupervised learning in detecting blockchain anomalies.
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Ablation Study
#### 4.4.1 Contribution of Each Module
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning** & **Datasets** & **LOLv1** & \
\begin{tabular}{c} **LOLv2-** \\ **Real** \\ \end{tabular} &
\begin{tabular}{c} **LOLv2-** \\ **Synthetic** \\ \end{tabular} & **DICM** & **MEF** & **NPE** & **ExDark** & **Avg** \\ \hline \multirow{2}{*}{Conventional} & MSRCR & 0.400 & 0.439 & 0.378 & 0.480 & 0.254 & 0.333 & 0.797 & 0.440 \\  & Dong & 0.508 & 0.537 & 0.340 & 0.692 & 0.349 & 0.493 & 1.539 & 0.637 \\ \hline \multirow{2}{*}{Supervised} & KinD++ & 5.042 & 6.278 & 4.294 & 6.617 & 2.977 & 4.225 & 4.093 & 4.789 \\  & RetinexNet & 1.209 & 1.219 & 0.804 & 0.961 & 0.934 & 0.685 & 3.538 & 1.336 \\  & Retinexformer & 0.806 & 0.510 & 0.467 & 0.750 & 0.688 & 1.054 & 2.124 & 0.914 \\ \hline \multirow{4}{*}{Unsupervised} & EnlightGAN & 0.990 & 0.295 & 0.165 & 0.284 & 0.178 & 0.219 & 0.535 & 0.381 \\  & Zero-DCE++ & 0.061 & 0.204 & 0.126 & 0.069 & 0.170 & 0.262 & 0.084 & 0.140 \\ \cline{1-1}  & CLIP-LIT & 0.149 & 0.236 & 0.577 & 0.269 & 0.453 & 0.866 & 0.279 & 0.404 \\ \cline{1-1}  & NeRCo & 0.386 & 0.410 & 0.271 & 0.484 & 0.669 & 0.888 & 0.262 & 0.482 \\ \cline{1-1}  & Colie & 0.759 & 0.760 & 0.820 & 1.190 & 1.313 & 1.625 & 1.637 & 1.158 \\ \cline{1-1}  & Ours & 0.346 & 0.390 & 0.324 & 0.418 & 0.381 & 0.324 & 0.825 & 0.430 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Runtime comparison (in second) results of (LOLv1, LOLv2-real, LOLv2-synthetic, DICM, MEF, NPE, and ExDark) datasets. The first, second, and third best scores are highlighted with red, blue, and green colors, respectively. sky region, causing detail loss and apparent distortion. Consequently, its PSNR and SSIM values are also lower. When all modules are integrated, the proposed method achieves a more balanced enhancement effect. As shown in Table 6, after integrating all modules, the proposed method significantly achieves optimal values across all indicators. Therefore, both qualitative and quantitative evaluations demonstrate that all modules in the proposed method have a positive impact on the enhancement results.
## 3 Results
The simple forcing efficiency of chemical composition \(i\) based on direct aerosol radiative forcing at the top of the atmosphere is represented as follows:  
\[\text{SFE}_{i}=-\frac{S_{0}}{4}T_{atm}^{2}(1-N)\Big{[}(1-\alpha)^{2}2\beta \text{MSE}_{i}-4\alpha\text{MAE}_{i}\Big{]}, \tag{15}\]  
where the unit of SFE is \(\text{Wg}^{-1}\), \(S_{0}\) is the solar constant (1370 W/m\({}^{2}\)), \(S_{0}\)/4 is the globally averaged incident solar flux at the top of the atmosphere, \(T_{atm}\) is the transmittance of the atmosphere above the aerosol layer (0.76), \(N\) is the fraction of sky covered by clouds (0.6), \(\lambda\) is the albedo of the underlying surface (0.15), and \(\lambda\) is the fraction of radiation scattered by aerosols into the upper hemisphere (0.125). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) & \(\mathbf{d}\) \\ \hline Ammonium sulfate & \(-30\sigma_{g}+83.33\) & \(-\sigma_{g}+3.5\) & 2.5 & \(-1\) \\ Ammonium nitrate & \(-15\sigma_{g}+46.67\) & \(-0.7\sigma_{g}+2.73\) & 2.5 & \(-1\) \\ Sea salt & \(-17\sigma_{g}+50\) & \(-0.8\sigma_{g}+2.9\) & 2.5 & \(-1\) \\ Elemental carbon & 12 & 0.2 & \(\sigma_{g}+0.5\) & \(0.4\sigma_{g}-2.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determined coefficients (\(a\), \(b\), \(c\), and \(d\)) of mass extinction efficiency in Equation (6) as a function of geometric standard deviation \(\sigma_{g}\). Figure 8 shows the comparison between the calculated SFE using the analytically approximated mass efficiency and that using the Mie theory with varying geometric standard deviations. The comparison is germane because calculations of radiative forcing by aerosols are of great interest in large-scale numerical models and hence climate studies. It is shown that the calculated SFE values using the analytically approximated mass efficiency and that using the Mie theory have large correlations (\(r^{2}>0.97\)) and small root mean square errors (RMSEs) for all aerosol species. These solid results do not depend on the variation of geometric standard deviations. For AMS, the \(r^{2}\) is 0.986 (0.981; 0.983) for the geometric standard deviation of 1.5 (2.0; 2.5). These are 0.984 (0.961; 0.985) for AMN, 0.987 (0.990; 0.988) for SS, and 0.994 (0.998; 0.997) for EC. Thus, it is shown that the newly developed method that approximates MEE has comparable accuracy compared with the Mie theory-based calculations. Figure 7: Comparison between the Mie theory-based calculation of mass absorption efficiency (MAE, solid black lines) and the approximated MAE (long-dashed red lines) of elemental carbon for geometric standard deviations (\(\sigma_{g}\)) of (**a**) 1.5, (**b**) 2.0, and (**c**) 2.5. The approximated mass extinction efficiency in each panel is calculated based on the MAE\({}_{S}\) (dotted blue line) and the MAE\({}_{L}\) (dotted orange line) in each panel using Equation (5). The determined coefficients \(c\) and \(d\) of MAE as a function of geometric standard deviation in Equation (6) for elemental carbon are shown in panel (**d**,**e**).