esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 BBN Model Validation
### Model Validation Based on Predictive Inference  
Case \"Zlatni Zal\"--Based on the propositions in Section 2.1 and the accident report for \"Zlatni Zal\", the instantiation of evidence is introduced. The root nodes were assigned the values specified in the report of the grounding \"Zlatni Zal\". The node values \"weather\", \"day/night\", \"day\" and \"season\", \"sea state\", \"wind force\", \"situational awareness\", and \"visibility\" form the evidence set e. According to Equation (3), average causal influence amounts to a negative value, \(\Delta\)P\({}_{\text{e}}\) (\"grounding\" = \" yes\") = \(-\)2%, where negative values indicate the increased likelihood of the occurrence of the accident (Figure 9).  
Case \"Cicero\"--Based on the propositions in Section 2.1 and the accident report for \"Cicero\", the instantiation of evidence is introduced. The root nodes were assigned the values listed in the Cicero marine casualty report: weather, \"day/night\", \"day\", \"season\", \"visibility\", \"special caution area\", \"sea state\", and \"wind force\" (Figure 10). Ac  
Figure 9: Model of the grounding of a small passenger ship “Zlatni Zal” according to the report on the maritime accident.  
cording to Equation (3), the average causal influence amounts to a negative value, \(\Delta\text{P}_{\text{e}}\) (\"grounding\" = \" yes\") = \(-22\%\), where negative values indicate an increased likelihood of the occurrence of the \"Cicero\" accident.  
If the developed BBN model had been used immediately before both accidents, on the available evidence, its response would issue a warning, which is in line with one of the model development objectives
## 3 Results and Discussion
### KEGG Analysis of Differential Metabolites  
The KEGG database can reveal the biological pathway information enriched by metabolites [46]. KEGG analysis revealed (Figure 7B) that the differential metabolites were involved in 82 KEGG pathways. The significantly different metabolic pathways included four pathways: glutathione metabolism, purine metabolism, nucleotide metabolism, and ketosugar unit biosynthesis. Redox reactions are fundamental to cellular energy conversion and are also important factors in regulating cellular responses to environmental stimuli. Glutathione is a crucial antioxidant, providing redox stability to cells and serving as an interface for metabolic reactions and signaling pathways, providing fuel for the growth and development of organisms. Additionally, it can regulate plant defenses and assist in the biosynthesis of sulfur-containing metabolites through various mechanisms [47]. In the glutathione metabolism pathway, seven metabolites were identified, and the level of gamma-glutamylcysteine in CN was significantly higher than in DBG. In purine metabolism, 18 metabolites were identified, with uric acid, deoxyguanosine 5\({}^{\prime}\)-monophosphate (dGMP), 2\({}^{\prime}\)-deoxyinosine, adenosine 5\({}^{\prime}\)-diphosphate (ADP), 2\({}^{\prime}\)-deoxyinosine triphosphate, and adenosine monophosphate levels significantly lower in CN than in DBG. In the nucleotide metabolism pathway, 20 metabolites were identified, and the levels of uridine, xanthosine, hypoxanthine, inosine, cytidine-5\({}^{\prime}\)-monophosphate, thymidine-5\({}^{\prime}\)-diphosphate, 2\({}^{\prime}\)-deoxyadenosine, guanine, guanosine, and 2\({}^{\prime}\)-deoxyadenosine-5\({}^{\prime}\)-monophosphate were significantly higher in CN than in DBG. In the ketosugar unit biosynthesis pathway, four metabolites were identified, and the level of dTDP-3-0-methyl-beta-L-Hammose in CN was significantly higher than in DBG. Interestingly, the majority of these differential metabolites were concentrated in nucleotides and their derivatives. Research has shown that when plants face abiotic stress, some nucleotides and their derivatives act as signaling  
Figure 7: **(A)** Heatmap of differential metabolites of non-volatile substances; **(B)** Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment map of differential metabolites.  
molecules, bridging the gap between perception and adaptation, coordinating cellular and physiological responses, and enabling better adaptation to environmental changes. Additionally, some nucleotides and their derivatives have recently been shown to play a central role in pathogen defense [48]. Therefore, we speculate that nucleotides and their derivatives play a critical role in the environmental adaptation and quality formation of _B. roseoflavus_.
## 4 Proof Examples (two MIHP on GHT(\(m\), \(n\), \(n\)/2) )
### m _Even_, n \(\geq\) 4; y1 = y2, Odd; (x2 - x1) Odd  
The basic Lt. and Rt. Hamiltonian path patterns in a GHT(2, 12, 6) network can have no collision except for end nodes such as Figure 4.  
Figure 4: MIHP performance may exist in HT(2)/GHT(2, 12, 6).  
Figure 3: _Cont._  
Both X-extensions, noted as the subscript x, are in consistent directions. Node blocks are deemed as special nodes. On the Lt. nodes 07, 09.10, 08, 19, 17.18, 20, 22.21 keep lagging, and nodes 23, 12, 14, 16, 03, 01.02, 24, 11, 13, 15, 04 keep leading. For nodes embedded, we need only check those of R-emb. Assume L-emb. \(\leq\) R-emb. first. Nodes within _08,09_ of the Lt. keep lagging behind their compared nodes within _04.15_ of the Rt. Nodes within _15.04_ of the Lt. keep leading their compared nodes within _04.15_ of the Rt. Nodes within _20.07_ of the Rt. keep leading their compared nodes within _16,03_ of the Rt. Nodes within _03,16_ of the Rt. keep lagging behind their compared nodes within _16,03_ of the Lt. Nodes within _23,12_ (_24,11_) of the Lt. keep leading their compared nodes within _12,23_ (_11,24_) of the Rt. We change the direction of swing expansions (upward \(\leftrightarrow\) downward) of embeddings. If L-emb. \(\geq\) R-emb. _01,02_ can definitely get more nodes per embedding. It is noted that the swing expansion close to the location of X-ext. needs to be adapted to get more nodes in the highest or lowest extension.  
This example of end nodes being adjacent, can have relatively fewer links to start establishing MIHP. Nevertheless, it shows the possibility of scalable MIHP performance in honeycomb tori, HT(\(m\)) or GHT(\(m\), 6\(m\), 3\(m\)) \(m\geq 2\).
## 4 Electrochemical and Bio-Electrochemical Applications
### Electro-Fenton and Electrochemical Catalysis  
The electro-Fenton process combines the electrochemical generation of H\({}_{2}\)O\({}_{2}\) with a Fenton-like reaction to produce -OH radicals to degrade contaminants [22]. SDB can beused as an electrocatalyist in these systems, contributing to the electrochemical generation of H\({}_{2}\)O\({}_{2}\) and its subsequent activation [62,63]. For instance, a cathode coated with SDB has been utilized to facilitate in situ H\({}_{2}\)O\({}_{2}\) generation from oxygen, which then initiates Fenton reactions for organic pollutant degradation, integrating both electrochemical and catalytic treatment approaches [64]. The quinone groups in the SDB promote electron transfer to oxygen, producing H\({}_{2}\)O\({}_{2}\), while its iron content promptly converts the peroxide into -OH radicals. This integrated approach enables pollutant mineralization without requiring external H\({}_{2}\)O\({}_{2}\) addition. Given that the electro-Fenton membrane enabled excellent self-cleaning ability and stability, [PERSON] et al. [65] designed Fe/N co-doping SDB ultrafiltration membranes, which achieved 100% tetracycline removal within 0.749 min under electro-assisted degradation, while also mitigating membrane fouling (shown in Figure 2). The abundant oxygen functional groups (C-O/C=O ratio) and nitrogen species (i.e., pyridinic and pyrrolic N) played a key role in boosting H\({}_{2}\)O\({}_{2}\) formation and accelerating the Fe(III)/Fe(II) cycling process. An alternative approach involves utilizing SDB as a conductive adsorbent in electrochemical reactors, where it captures organic contaminants and subsequently transfers [62,63] them to the electrode for direct oxidation, effectively serving as a bridge between adsorption and electro-oxidation. Based on this concept, while still a lab-scale study, this could provide a basis for improving the contact between contaminants and electrodes in flow-through reactors.  
In summary, the use of SDB in electrochemical applications is a growing trend. As a sustainable electrode material, it enhances adsorption and catalytic functions in electrochemical water treatment. Its applications range from boosting power generation and pollutant removal in MFCs to facilitating energy-efficient desalination through CDI. By repurposing sludge into a valuable energy- and resource-generating material, these advancements align with circular economy principles.
## 6 Parametric Study
### The Effect of the Penetrating Depth of Cut-Off Wall  
In instances where the depth or thickness of the aquifer is considerable, the containment of groundwater flow by the cut-off wall of a foundation pit often adopts a suspended configuration, thus failing to achieve complete containment. In such cases, determining the appropriate penetrating depth stands as one of the most important design parameters. Various factors can influence the efficacy of groundwater level reduction at varying depths of cut-off wall embedment, including the hydrogeological conditions of the aquifer and boundary recharge conditions. Nevertheless, given the determination of other parameters, delineating the relationship between the penetrating depths and the corresponding aquifer water level drawdown serves to ascertain the optimal embedment depth required. Figure 8 plots the distribution of dimensionless drawdown at the top of the confined aquifer at different penetrating depth ratios of the cut-off wall after dewatering the strip excavation. As the cut-off wall increases the seepage path, the deeper the cut-off wall is in the confined aquifer, the more obvious the barrier effect on the groundwater flow, and the smaller the drawdown outside the strip excavation pit. As shown in Figure 8, it demonstrates that the change in drawdown is more obvious with the increase in the penetrating depth when \(n_{1}<0.6\), while the drawdown changes slightly when \(n_{1}\in[0.6,\ 0.8]\). In other words, as the penetrating depth is large enough, increasing the penetrating depth could result in an uneconomic design for dewatering. Therefore, it is necessary to determine a reasonable penetrating depth ratio (\(n_{1}\)) to obtain a balance between the barrier effect and economy.  
Figure 8: The distribution of dimensionless drawdown along the \(x\) direction with different penetrating depth ratios from \(n_{1}=0.2\) to \(0.8\).  
Figure 7: The distribution of dimensionless drawdown in the \(x\) direction with different anisotropy for the cases where \(k=1\), \(10\) and \(100\).  
Figure 9 displays the variation in the dimensionless drawdown with the penetrating depth of the cut-off wall at different distances. As shown in Figure 9, the influence of the penetrating depth on the dimensionless drawdown decreases as \(x_{\rm D}\) increases. So, focusing on the curve for when \(x_{\rm D}=1.0\), it can be seen that the rate of change of the dimensionless drawdown drops with the rise of the penetrating depth ratio \(n_{1}\). When \(n_{1}\geq 0.7\), the rate of change is less than 0.1069, which means a negligible slight growth in the barrier effect. Therefore, it is thought that there is an optimum penetrating depth ratio when determining the parameters. This conclusion is similar to the results obtained from the finite element method [37].
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 1 Introduction
### Our Contribution  
We propose decentralized data distribution approaches supported by a hierarchical and multi-tier network structure [15]. The proposed solution combines local and decentralized management with centralized decisions to efficiently use the available network resources and meet the requirements of Industry 4.0 applications. Contrary to the recent theoretical and abstract simulative comparisons, we  
1. Analyze the characteristics of four protocol stacks for data distribution in wireless industrial networks, built mainly from proposed standards such as IEEE 802.15.4 [16], IETF RPL [17] and IEC WirelessHART [18], and adopt either a centralized (data accessed only at the central network controller) or distributed (data replicated in _proxies_) approach. We implement the methods in OMNeT++ Network Simulator by extending and customizing the simulator according to the needs of our study. We evaluate the performance of the four methods via a detailed simulation analysis. The use of OMNeT++ reveals detailed performance insights about industrial data distribution. We show that a limited number of data _proxies_--carefully placed in order to limit delay in data retrieval--effectively helps in fulfilling latency constraints.
2. We implement two test cases so as to highlight the applicability of (mobile and stationary) distributed lightweight data management within an actual industrial environment. In order to demonstrate how we can apply the data management concepts and showcase their feasibility, we designed and implemented a small-scale demonstration at the premises of a smart manufacturing experimentation facility (IK4-Tekniker), requiring robust data exchange between a heterogeneous set of autonomous factory machines. The core of this concept is to demonstrate how we can use smart data distribution at the manufacturing experimentation facility so as to achieve cost-effective operations, fault tolerance and dynamic, plug-and-play smart data distribution solutions.
## 3 Materials and Methods
### Transfer Learning  
In conventional machine learning paradigms, the assumption often prevails that the feature spaces of both training and test datasets remain identical. However, starting the training and testing processes from scratch can be highly time-consuming and resource-intensive [33]. To alleviate this predicament, transfer learning (TL) has emerged as a viable strategy, aiming to achieve precise classification outcomes even with constrained training data. Transfer learning involves leveraging knowledge from one model to improve the performance of another model in a target domain. Its primary objective is to enhance efficiency in the target domain. This approach proves particularly effective when dealing with relatively small target domain datasets, as it can leverage datasets from related source domains [34]. By utilizing transfer learning, this study can address such situations more robustly.  
Figure 4 illustrates the TL workflow employed in this study, where original deep models trained on ImageNet are fine-tuned on target datasets like ISIC2018. Pre-trained models (AlexNet, VGG-11, GoogleNet, ResNet50, ViT, SwinViT, and SkinSwinViT) leverage knowledge from ImageNet, and their weights are optimized on comprehensive datasets to enhance feature extraction. Additionally, the prediction layer (fully connected layer) is modified in this paper to accommodate the 7 target categories for training.
## 2 Material and Methods
### Nitric Oxide Syntase like (NOS-like) and Nitrate Reductase (NR) Activity  
NOS-like activity was determined by the method described by [PERSON] et al. [40] after homogenising the sample (1 g) in a buffer containing of 100 mM HEPES-KOH (pH 7.5), 1 mM EDTA, 10% glycerol (\(v/v\)), 5 mM Dithiothreitol, 500 \(\mu\)M Phenylmethylsulphonyl fluoride, 0.1% Triton X-100 (\(v/v\)), 1% polyvinylpyrrolidone (\(w/v\)) and 20 \(\mu\)M flavin adenine dinucleotide (FAD). The homogenate was centrifuged at 12,000 rpm for 20 min at 4 \({}^{\circ}\)C, and the NOS-like activity was determined in the supernatant according to [PERSON] et al. [41]. A 200 \(\mu\)L aliquot of the supernatant was used to detect the NOS-like activity in 1 mL of reaction mixture containing 100 mM phosphate buffer (pH 7.0), 1 mM I-Arg, 2 mM MgCl\({}_{2}\), 0.3 mM CaCl\({}_{2}\), 4 \(\mu\)M BH\({}_{4}\), 1 \(\mu\)M FAD, 1 \(\mu\)M flavin mononucleotide (FMN), 0.2 mM DTT, 0.2 mM NADPH. The decrease in absorbance as a result of NADPH consumption was determined at 340 nm for 5 min. NOS-like activity was calculated using the extinction coefficient of NADPH (\(\varepsilon\) = 6.22 mM\({}^{-1}\)cm\({}^{-1}\)) and expressed as nmol NADPH used g\({}^{-1}\) fresh weight of seeds.  
The NR activity was determined in vivo by following the protocol given by [PERSON] [42]. The chopped tissue (250 mg) was mixed in the reaction mixture containing 100 mM phosphate buffer (pH 7.2), 25% isopropanol and 200 mM potassium nitrate and kept undisturbed at 30 \({}^{\circ}\)C for 2 h. The nitrite formed in the reaction mixture after incubation was measured at 540 nm following azo-coupling through naphthalenediamine-dihydrochloride and sulphanilamide. The NR activity was represented as nmol NO\({}_{2}\) g\({}^{-1}\) FW of seeds h\({}^{-1}\).
## 6 High Frequency Characterization and Modelling
### High-Frequency Characterization of UTC Photodiodes
#### 6.2.2 De-Embedding (Extraction of Parasitic Elements)  
The process flow of the on-wafer RF measurements and the parameter extraction is summarized in Figure 17. First, the \(S\)-parameters of the open and short test structures are measured up to the targeted frequency. Then, one-port S-parameters of the UTC-PDs are measured under various bias conditions. This is followed by an open-short de-embedding process to extract the intrinsic frequency-dependent behavior of the UTC-PD.  
The intrinsic admittance of a device (\(Y_{int}\)) can be extracted from the measured total admittance of the device (\(Y_{tot}\)), by subtracting the admittance of the measured open (\(Y_{open}\)) and short (\(Y_{short}\)) test structures. The following extraction step is the model optimization of the magnitude and the phase of the \(S_{11}\) under different bias to extract the frequency as well as the bias dependence of the junction-related parameters of the UTC-PD. One-port \(S\)-parameters, measured up to 40 GHz under a bias range of 0 to \(-\)2 V, are shown in  
Figure 16: Measured \(S_{11}\) for open (blue) and short (red) patterns.  
Figure 17: The flow chart of measurements and parameters extraction process.  
Figure 18 for the three geometries under test, comparing the de-embedded experimental data and the compact model simulation, affirming a good model accuracy over the entire frequency and bias range.
## 2 Current Cellulosic Ethanol Production Process in Commercial Plants
According to [PERSON] [20], the plant has reached the targeted 70 gallons per ton of biomass and further optimization is in progress. One of the world's largest cellulosic ethanol production facilities, the Beta Renewables plant officially opened at Crescentino (Italy) in 2013 but it has been shut down since 2017 due to a restructuring effort of the parent chemical company Mossi & Ghisolfi [26]. The plant had an annual capacity of 40 kt of ethanol produced from wheat straw, rice straw, and giant reed (Arundo donax). On November 1, 2018, Eni's chemical subsidiary Versalis acquired the Mossi Ghisolfi Group's green portfolio and it is currently in the process of defining an action plan to restart the activities of the Crescentino plant [18]. Two commercial scale plants are in operation in Brazil. One is the Bioflex 1 plant from Granbio that began production in September 2014, with a current production capacity of about 65 kt per year; the other one is Raizen's Costa Pinto Unit with an annual production capacity of 36 kt of ethanol from bagasse using a technology developed by Iogen Energy, a joint venture of Raizen and Iogen Corp. According to USDA GAIN Brazil report [9], this is the only one producing at relatively large scale. Other projects have been announced for the production of cellulosic ethanol, mainly in the EU. The construction of a new full scale commercial cellulosic ethanol plant has been announced in 2017 by Enviral, the largest producer of bioethanol in Slovakia, that recently signed a license agreement to use Clariant's Sunliquid@technology [27]. The plant is planned to be integrated into the existing facilities at the Enviral's Leopoldov site (in Slovakia) producing 50 kt per year of ethanol from agricultural
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks  
[PERSON]  
multi-hop WMSNs of embedded devices is still open and largely unexplored. WMSNs produce a large amount of video data; therefore, the probability of congestion in WMSNs is more than that in low-speed wireless sensor networks. Congestion degrades the overall performance of the network and affects the reliability due to the packet loss. Therefore, wireless transmission for multimedia with guaranteed packet delivery in WMSNs is of substantial significance due to higher data rate requirements.  
This paper focuses on the most challenging case in which multimedia data can be transferred from a source node to sink node through multi-hop communication. We describe efforts that involve system design and implementation of an embedded platform based on raspberry pi (RPi) sensor nodes. In this paper, we discuss the design and implementation of IVSP deployed for surveillance applications, and our intention is to ensure the reliable delivery of video data from collections of sensors to a sink, while avoiding congestion collapse. This video monitoring data may need to be played back later on. Therefore, when transmission fails, data packets should not be simply discarded. To the best of our knowledge, video surveillance application has not been addressed in this context before.  
The major contributions of this article are summarized as follows: (1) We design a cost-effective and computationally intelligent wireless video sensor network platform over multi-hop WMSNs. Our platform consists of 7 WMSN nodes, and each node is built by a raspberry pi (RPi); (2) To get the precise measurement of congestion, we introduce a combination of two different congestion indicators to differentiate congestion levels and handle them correspondingly; (3) We present a method in which each source node maintains its data sending rate and periodically updates its data sending rate according to its parents' congestion level. Each node has a unique IP address and can establish a routing path from itself to the sink through multi-hop communication; (4) We propose different retransmission mechanisms for different packets. Lost packets can be stored temporarily and retransmitted when congestion situation is improved; (5) The proposed platform is flexible, scalable, and suitable for wireless monitoring of buildings, open terrain, remote areas, etc.  
The remainder of this paper is organized as follows. Section 2 gives the related work. Section 3 discusses the description of WMSNs system. Section 4 briefly describes the design implementation. Section 5 provides an experimental environment that includes parameter selection, optimization, and performance evaluation. Section 6 concludes our work.