esa-sceva/satcom-chunk-collection huggingface dataset:
# Internal Backpressure for Onboard Crospoint-Buffered Switches with Port Multiplexing
## 1 Introduction
Additionally, onboard switches must address single-event upsets (SEUs) and multiple-event upsets (MEUs), often requiring dual-module or triple-module redundancy to ensure high reliability. These redundancy techniques consume additional hardware resources, further complicating the design of satellite switches under constrained conditions. A scheme that multiplexes several input ports onto an internal bus [19] can effectively reduce the complexity and storage space requirements of crosspoint buffering and improve the efficiency and scalability of the system. For a system with a large number of ports and limited storage space, this undoubtedly constitutes a very valuable optimization scheme. Yet, this scheme is not immune to the issue of Head-of-Line Blocking (HOL) [20]. Specifically, the shared input-output bus among multiple ports, despite the high bus rate, may be constrained by one single output port rate. When multiple input ports occupy the same output bus going to the same output port, it will cause data to accumulate at the crosspoints. This congestion not only affects ports directly involved in conflicts but also indirectly impacts other ports that are not in direct conflict with the congested output port. Even if these ports are not overloaded, the inability to forward data through the crossbar architecture can compromise the system's Quality of Service (QoS) [21]. To address this issue, this paper proposes a crosspoint-buffered switching structure with internal backpressure (BP), primarily designed to reduce memory consumption and effectively alleviate internal congestion issues caused by buffer overload in a resource-constrained onboard environment. The main contributions of this paper are as follows:  
(1) We introduce a crosspoint-buffered switching architecture with port multiplexing that reduces resource consumption in resource-constrained scenarios like satellite switches. (2) To address the queuing blocking problem caused by port multiplexing, we propose a backpressure (BP)-based traffic control method for the first time. At the same time, we further develop four BP mechanisms with different traffic control granularities and conduct comparative simulation analysis. (3) The proposed BP mechanisms are simulated under multiple traffic scenarios, and the influence of different traffic parameters on network performance is considered. The simulation results show that the BP mechanism can significantly reduce the packet loss rate and improve the throughput rate compared to the case where no BP is applied. Among different BP mechanisms, we demonstrate that the BP load balance strategy has the best performance. The main contents of this paper are as follows: Section 2 summarizes the related work. Section 3 describes the overall design of the internal BP structure. In Section 4, the traffic model is introduced and analyzed. In Section 5, the simulation results of the actual simulation experiments are analyzed in detail. Section 6 concludes the paper.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Obtaining Pure Oxides and Metals  
To separate pure gallium and indium or their oxides from the solutions, additional technological procedures such as cementation or electrolysis are necessary. If the final product is not pure enough, it is also subjected to a refining process.
# The Emerging Role of Long Noncoding RNAs in Sorafenib Resistance Within Hepatocellular Carcinoma
### Secondary Resistance
#### 1.3.1 Autophagy  
Autophagy is a self-degrading system that controls cells to eliminate abnormal proteins and dysfunctional organelles. It plays a crucial role in maintaining homeostasis in cells under stress, such as nutritional deficiency or hypoxia [64]. Based on recent studies, autophagy is a double-edged sword in various cancers by suppressing tumor initiation or supporting their progression [65; 66]. This dual outcome mechanism further plays a crucial role in drug resistance, which enables tumor cells to maintain cellular viability under metabolic and therapeutic stress. A study by [PERSON] et al. showed that microRNA (miR-25) increases sorafenib resistance in HCC by inducing autophagy. In addition, this specific microRNA decreases the FBXW7 protein expression to regulate autophagy, which makes miR-25 a peculiar therapeutic target for HCC treatment [66; 67]. In another study by [PERSON] et al., the relationship between sorafenib resistance, CD24, and autophagy is described. CD24-associated sorafenib resistance is accompanied by autophagy activation, which can be blocked by inhibiting autophagy using pharmacological inhibitors or knocking down essential autophagy genes. CD24 is a glycoprotein predominantly expressed on the surface of B lymphocytes and other tumors. According to various authors, it is highly expressed in HCC tumor tissues compared with adjacent normal tissues. Furthermore, CD24 expression was significantly increased in patients with residual chemotherapy resistance after sorafenib treatment. Compared to the untreated patients, it suggested that CD24 participates in a sorafenib-induced resistance process, proving that CD24 overexpression in patients was accompanied by autophagy activation [68; 69]. lncRNAs are involved in physiological and pathological processes such as development, differentiation, apoptosis, autophagy, inflammation, and cancer. LncRNA HULC triggers autophagy via stabilizingSirt1 and attenuates the chemosensitivity of HCC cells [70]. LncRNA MALAT1 targets the FOXA1/CD24/Src pathway in human hepatocellular carcinoma [71]. Meanwhile, lncRNA IL21-AS1 facilitates CD24-induced phagocytosis inhibition in ovarian cancer [56]. These might be potential autophagy/CD24 associated lncRNA in a novel mechanism towards sorafenib resistance.
# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
###### Abstract  
Gross primary productivity (GPP) is the most basic variable in a carbon cycle study that determines the carbon that enters the ecosystem. The remote sensing-based light use efficiency (LUE) model is one of the primary tools that is currently used to estimate the GPP at the regional scale. Many remote sensing-based GPP models have been developed in the last several decades, and these models have been well evaluated at some sites. However, an accurate estimation of the GPP remains challenging work using LUE models because of uncertainties in the model caused by model parameters, model forcing, and vegetation spatial heterogeneity. In this study, five widely used LUE models, Glo-PEM, VPM, EC-LUE, the MODIS GPP algorithm, and C-fix, were selected to simulate the GPP of the Heihe River Basin forced using in situ measurements. A multiple-model averaging method, Bayesian model averaging (BMA), was used to combine the five models to obtain a more reliable GPP estimation. The BMA was trained using carbon flux data from five eddy covariance towers located at dominant vegetation types in the study area. Generally, the BMA method performed better than any single LUE model. From the case study in the study area, it is indicated that the trained BMA is an efficient method to combine multiple LUE models and can improve the GPP simulation accuracy.  
carbon flux; remote sensing; bayesian model averaging 2021  
10329
# The RND Efflux Pump Gene Expression in the Biofilm Formation of _Acinetobacter baumannii_
## 1 Introduction  
_Acinetobacter baumannii_ (_A. baumannii_) has emerged globally as an opportunistic multidrug-resistant (MDR) pathogen that is primarily linked with healthcare-associated infections, including pneumonia, meningitis, bacteremia and urinary tract infections [1]. _A. baumannii_ is characterized by its natural competence that makes it more capable of incorporating foreign DNA, which can subsequently increase its adaptability to hostile environments [2, 3]. The emergence of resistant isolates to widely prescribed antimicrobial agents leads to a high rate of treatment failure, and a higher mortality rate for infectionscaused by _Acinetobacter_ spp. The poor prognosis of infections caused by _A. buumannii_ puts this bacterium among the list of \"top critical threat\" pathogens [4]. Various mechanisms are associated with the multidrug resistance phenotype, including enzymatic degradation, alteration of a target site, decreased membrane permeability, biofilm formation, and increased expression of the efflux pumps [5]. It was reported that the efflux pumps play a dual role in antibiotic resistance either directly by extruding antibiotics or indirectly by biofilm formation. Hence, the correlation between the wide substrate specificity efflux pump systems and the bacterial ability to form biofilms has become an important area to explore [6].  
The efflux pumps implicated in bacterial multidrug resistance perform a variety of roles in the transition of planktonic cells to biofilms. These pumps actively extrude quorum-sensing auto-inducers, as well as antimicrobial agents and metabolic intermediates, resulting in direct and indirect regulation of biofilm formation and quorum sensing [7]. Some pumps also cause change in cell membrane composition, thus, altering biofilm formation [8].  
_A. baumaniii_ are characterized by six major efflux pump families, which are major facilitator superfamily (MFS), multidrug and toxic efflux (MATE), resistance modulation-division (RND), small multidrug resistance (SMR), ATP binding cassette (ABC) [9], and the novel proteobacterial antimicrobial compound efflux (PACE) family [10]. Among the efflux systems, RND pumps are the most prevalent systems in Gram-negative bacteria [11]. RND efflux system extrudes a wide range of substrates that are structurally unrelated [12]. Three _Acinetobacter_ drug efflux pumps (Ade) belong to the RND systems, namely Ade-ABC, AdeFGH, and AdelJK, which have been widely distributed in _A. baumaniii_[13]. In addition, these efflux pumps have common substrates, which are fluoroquinolones and chloramphenicol [11]. Little is known regarding the association between efflux pumps in _A. baumaniii_ and biofilm formation, hence, the aim of our study is to investigate the role of the RND efflux pump system in biofilm formation by _A. baumaniii_. This can open new avenues for the treatment of biofilm-related infections.
## 7 Security Considerations and Proposed Zero-Trust Security Model
### Security Considerations to Support Zero-Trust Model
#### 7.1.3 Discovery of Network-Connected Devices in OT Environments  
Enterprise Discovery tools are implemented for identifying every network-connected device or firewalled environment which will provide the capability to categorize them and also identify firewalls and unused subnets to reduce vulnerabilities. Figure 11 below shows the process flow of the device discovery. All subnets and VLANs are to be associated with a site code and create schedules for the discovery of network and approved OT subnet. This will enable the identification of devices, network addresses (IPs), and firewalled environments. The discovery result can then be utilized to create and update the visibility report for providing access to approved subnets/VLANs.  
The advantages include targeted incident and vulnerability response, providing awareness and input into other OT cybersecurity processes, and providing visibility into subnet/VLANs and all network-connected devices.  
Figure 11: Device discovery technique in a firewalled environment.  
Figure 10: Network segmentation workflow.
## 5 Methodology
### Pruning the Super Neural Network  
Figure 7 depicts the super neural network leveraged by Darts_Tiny. Compared with the one depicted in Figure 4, the super neural network used here is much lighter, where one stem together with three cells constitutes the backbone.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Archs**} & \multirow{2}{*}{**Cells**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} \\ \cline{3-8}  & & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** \\ \hline \multirow{4}{*}{SArch\_1} & 14 & 89.01 & 94.04 & 86.10 & 91.12 & 92.13 & 97.15 \\ \cline{2-8}  & 8 & 90.78 & 96.46 & 88.56 & 94.02 & 93.12 & 99.03 \\ \cline{2-8}  & 4 & 89.88 & 96.95 & 88.28 & 95.12 & 91.53 & 98.85 \\ \cline{2-8}  & 2 & 88.70 & 96.30 & 85.56 & 93.93 & 92.08 & 98.79 \\ \hline \multirow{4}{*}{SArch\_2} & 14 & 86.58 & 91.40 & 81.74 & 88.31 & 92.03 & 94.72 \\ \cline{2-8}  & 8 & 89.20 & 95.87 & 86.85 & 93.37 & 91.91 & 98.50 \\ \cline{2-8}  & 4 & 91.52 & 97.14 & 88.28 & 95.26 & 95.02 & 99.09 \\ \cline{2-8}  & 2 & 88.30 & 96.98 & 86.38 & 95.35 & 90.31 & 98.67 \\ \hline \multirow{4}{*}{SArch\_3} & 14 & 87.83 & 92.71 & 85.56 & 90.06 & 90.23 & 95.51 \\ \cline{2-8}  & 8 & 89.24 & 96.36 & 85.83 & 93.88 & 92.92 & 98.98 \\ \cline{2-8}  & 4 & 90.71 & 97.10 & 89.10 & 95.35 & 92.37 & 98.90 \\ \cline{2-8}  & 2 & 86.94 & 96.70 & 82.56 & 94.34 & 91.82 & 99.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results on SSDD after reducing the number of cells.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Archs**} & \multirow{2}{*}{**Cells**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**Precision**} \\ \cline{3-8}  & & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** \\ \hline \multirow{4}{*}{HArch\_1} & 8 & 76.4 & 90.9 & 70.3 & 85.6 & 83.5 & 97.0 \\ \cline{2-8}  & 4 & 76.1 & 90.4 & 71.3 & 85.8 & 81.7 & 95.4 \\ \cline{2-8}  & 2 & 73.1 & 91.1 & 66.7 & 85.8 & 81.0 & 97.0 \\ \hline \multirow{4}{*}{  
The customization for ship detection is conducted through dedicated pruning. In the original Darts settings, there are three standard convolution layers that constitute the two stem blocks. According to our analysis, they make part of the overfitting. Thus, in Darts_Tiny, we retain one stem block, where only a standard convolution layer is included. After the stem, we stack three cells. The three cells used here are uniform (no longer divided into normal cells and reduction cells). The cell's inner structure also follows the template depicted in Figure 2, and the concrete structure will be decided by our neural architecture search algorithm. Each of the three cells expands the number of channels while diminishing the size of feature maps. Finally, Relu6 and Conv_1\(\times\) 1 make up the detection head.
## 1 Introduction
Timely and accurate monitoring of vegetation NPP dynamics is essential for understanding vegetation ecological dynamics and formulating effective drought response measures [36]. This study aimed to quantify and evaluate the ability of SIF to monitor drought-induced dynamic changes in NPP using drought events in the Huang-Huai-Hai (HHH) Plain from 2010 to 2020 and explored the potential of SIF to monitor NPP dynamic changes during drought events. Most recent studies have explored the relationship between drought and NPP at an annual or monthly scale. However, an in-depth understanding of the relationship between drought and NPP at a finer time scale is lacking. Based on the analysis of the relationship between SIF and GPP, and NPP and GPP, we propose a new SIF/NPP ratio index to investigate drought-induced NPP dynamic changes on an 8-day time scale. We also quantified and evaluated the ability of SIF to monitor drought-induced NPP dynamic changes. Our study addressed three main issues: (1) whether SIF can monitor dynamic changes in NPP, (2) the ability of SIF to monitor dynamic changes in NPP during drought events, and (3) the response of SIF to other environmental factors affecting dynamic changes in NPP.
###### Abstract  
Preventing metal corrosion is one of the most pressing issues in the modern world. One of the most effective ways to combat corrosion is to use inhibitors. Currently, much research is devoted to the study of green corrosion inhibitors obtained from plant extracts and agricultural waste. In this study, banana peel extract, rice straw extract, and their mixture as green corrosion inhibitors of carbon steel samples in 1 M HCl solution were examined by weight loss, electrochemical, and scanning electron microscopy methods. The experimental results showed that the mixture of extracts (40:60 banana peel and rice straw) had a higher inhibitory capacity than the individual inhibitors. At 750 ppm after 24 h of immersion, the inhibition of banana peel, rice extract, and the suggested mixture was 77.87, 95.03, and 96.36%, respectively. Furthermore, the mixture exhibited a maximum synergistic inhibition value of 1.65, indicating a 65% increase in inhibition efficiency when using the mixture instead of the extract alone. Nyquist plots obtained from electrochemical experiments confirmed the optimum concentration value of 750 ppm for banana peel extract, rice extract, and their mixture. These tests also show that the diameter of the semicircles in the presence of the mixture was larger than in the case of the extract alone, indicating a higher inhibitory capacity of the mixture. Moreover, scanning electron microscopy analysis showed the formation of a stable protective film on the metal surface. Finally, adsorption analysis showed the presence of both physical and chemical adsorptions of all the extracts used, which was obtained using the Langmuir isotherm.  
green corrosion inhibitor; banana peel extract; rice straw extract; carbon steel; adsorption 2025
# A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base
## 5 Conclusions  
In this paper, a novel method for detecting APT attack based on the belief rule base model is proposed. The BRB-based APT detection model has the advantages of comprehensive utilization of information and generation of interpretable results. To overcome the difficulties of APT detection, we investigate the characteristics of APT attack in network traffic and construct the BRB model that can embed the expert knowledge and effectively express all kinds of uncertainty. Then, the BRB model is trained with small samples to obtain more accurate results. Two different algorithms are used to detect APT in a very small sample size to compare with the proposed method. The experiments show that the BRB-based method has a reliable acceptable accuracy. Further, the BRB-based APT detection model can help with computer forensics because the results have good interpretability and every rule can be regarded as an evidence. BRB combines expert knowledge with artificial intelligence, which is also a typical application of human-machine hybrid intelligence in the field of network security.  
Our work provides a reasonable method for APT attack detection in traffic, which can obtain valid test results in the case of limited samples. However, there is still some work to be done next. First, there are many parameters of rules to be set by experts, which takes a lot of time and effort from experts. Automatic generation algorithms should be used in the parameter setting of rules. Second, expert knowledge bias needs to be taken into account when setting rules. The source of objective expert knowledge is an issue. Third, the impact of standardization of expert knowledge on the results is to be considered. Finally, APT contains many different types. In this paper, how to detect APT attacks in traffic are discussed. The next step is to detect APT in both traffic data and host data.  
Conceptualization, G.W. and G.H.; methodology, G.W. and Y.C.; software, G.W. and G.H.; validation, G.W., Y.C. and G.H.; formal analysis, Y.C.; investigation, G.W., Y.C. and J.W.; resources, G.H.; data curation, Y.C. and J.W.; writing--original draft preparation, G.W.; writing--review and editing, G.W.; visualization, Y.C. and J.W.; supervision, G.H. and L.W.; project administration, G.H. and L.W.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Hainan Province under Grant 2019 CXTD405, China Postdoctoral Science Foundation under Grant 2020M673668, and Guangxi Key Laboratory of Trusted Software under Grant KX202050, and the key research and development plan of HaiNan under Grant ZDYF2019007.  
Not applicable.  
The data used during this study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
###### Abstract  
This study presents the investigation of the electromagnetic properties and resistance performance of electrically conductive fabrics coated with composition containing the conjugated polymer system poly(3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT:PSS). The developed fabrics were intended for electromagnetic radiation (EMR) shielding in microwave range and for absorbing microwaves in radar operating range, so as to act as radar absorbing materials (RAM). The measurements of reflection and transmission of the developed fabrics were performed in a frequency range of 2-18 GHz, which covers the defined frequencies relevant to the application. Four types of fabrics with different fiber composition (polyamide; polyamide/cotton; wool and para-aramid/viscose) were selected and coated with conductive paste using screen printing method. It was found that EMR shielding effectiveness (SE) as well as absorption properties depend not only the amount of conductive paste topped on the fabric, but also resides in the construction parameters of fabrics. Depending on such fabric structural parameters as density, mass per unit area, type of weave, a layer of shield (or coating) just sticks on the fabric surface or penetrates into fabric, changing the shield thickness and herewith turning SE results. Meanwhile, the fiber composition of fabrics influences mostly bonding between fibers and polymer coating. To improve the resistance performance of the developed samples, a conventional textile surface modification technique, atmospheric plasma treatment, was applied. Initially, before plasma treatment and after treatment the fabrics were evaluated regarding an aqueous liquid repellency test, measuring the contact angles for the water solvent. The influence of plasma treatment on resistance performance of coated fabrics was evaluated by subjecting the plasma treated samples and untreated samples to abrasion in the Martindale abrasion apparatus and to multiplex washing cycles. These investigations revealed that applied plasma treatment visibly improved abrasion resistance as a result of better adhesion of the coating. However, washing resistance increased not so considerably.  
conductive textiles; EMR shielding; plasma treatment; microwave absorbing material 1