esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Production of bio-alcohols is one of the approaches used in the development of alternative energy. Pervaporation is a promising option for the separation of bio-alcohols from the fermentation mixture. A serious problem in the process of continuous extraction of biobutanol from the fermentation broth is the contamination of the membrane, which leads to a decrease in its permeability over time. In this work, the transport properties of composite membranes based on polyheptylmethylsiloxane (PHeptMS), polydecylmethylsiloxane (PDecMS), and a commercial membrane MDK-3 were studied during separation of a real ABE-fermentation broth in vacuum pervaporation mode. The study was performed before and after continuous contact of the membranes with the fermentation broth for one month. Visually and by scanning electron spectroscopy, the presence of membrane surface residue and its effect on the wettability of the membrane selective layer by the components of the ABE broth were determined. The sediment composition was evaluated by energy dispersive analysis and infrared spectroscopy. According to the pervaporation separation of the ABE-broth using PHeptMS, PDecMS, and MDK-3 membranes, the butanol flux was 0.029, 0.012, and 0.054 kg/(m\({}^{2}\)-h), respectively. The butanol-water partition factor was 41, 22, and 13 for PHeptMS, PDecMS, and MDK-3, respectively. After one month of incubation of the membranes in ABE-fermentation broth during the separation of the model mixture, a decrease of 10 and 5% in permeate flux and separation factor, respectively, was observed for all membranes. Temperature dependences (30-60 \({}^{\circ}\)C) of permeate flux, permeability, and selectivity were obtained for the membranes after clogging. The most promising in terms of minimal negative changes as a result of fouling was demonstrated by the PHeptMS membrane. For it, the clogging dynamics during separation of the real fermentation broth for 216 h were investigated. Two characteristic steps of decrease in transport and separation properties were observed, after 28 and 150 h of the experiment. After 216 h of experiment, a 1.28-fold decrease in total flux through the membrane, a 9% decrease in butanol permeability, and a 10% decrease in n-butanol selectivity were found for PHeptMS.  
fouling: ABE-fermentation broth; pervaporation; polyalkylmethylsiloxane; composite membrane +
Footnote †: journal: Applied Sciences
## 3 Establishment of the Model
### Model Hypothesis  
It is assumed that in the process of transporting goods from the starting city to the destination city, there are highway transport and railway transport. The carbon dioxide emissions generated by different transport modes are different because of their different transport time, distance, and cost. At this time, multimodal transport operators need to meet the time needs of customers, combined with the carbon emission limits of logistics enterprises. An optimal transportation path from the starting point to the destination is obtained. Transportation is the key to logistics cost management and savings, and the cost value of multimodal transportation is a key reference indicator for operators to adjust transportation solutions. Transportation time is the key indicator in multimodal transport issues. Goods arrive early or late and need to pay storage or delay costs. Goods that arrive at the end time must meet the end time window constraints; otherwise, user satisfaction will be affected. With the increasing socio-economic growth, low-carbon green development also puts forward new requirements for the transportation industry, which means that achieving low-carbon transportation in multimodal transport is of great significance to the improvement of transportation quality. On this basis, this paper selects transportation cost, transportation time, and carbon emissions as the optimization objectives. To describe the  
Figure 1: Network diagram of random path layout for multimodal transport.  
transportation problem more appropriately and to facilitate the construction and solution of the model in this paper, the following assumptions are made for the problem.  
(1) The capacity limitations of transport routes and transfer nodes are not considered;  
(2) In the process of transportation, the freight volume remains fixed regardless of the increase or decrease of goods;  
(3) The mode of transport between the two adjacent nodes is inseparable; that is, only one mode of transport can be chosen;  
(4) The transfer of goods only occurs at the node, and the transit process occurs at most once at a node.  
(5) Damage to goods, road congestion, and natural disasters are not taken into account in transportation.  
The symbols of the parameters and decision variables involved in this question are as follows (Table 2):
## 2 Materials and Methods
### Quantification of Free MC by ELISA
#### 2.4.2 Quantification of Free MC in Bivalves' Tissues  
Free MC were extracted from 10 mg of freeze-dried bivalve tissue with 1 mL of 80% methanol. Samples were placed in an ultrasound water bath for 15 min at 35 kHz (UP200S, Bioblock scientific, Strasbourg, France), and then subjected to three cycles of centrifugation-probe sonication (sonication: 1 min, 30% amplitude and 50% duty cycle; centrifugation: 10 min, 2500 rpm). The supernatant was kept at 4 \({}^{\circ}\)C and another millilitre of 80% methanol was added to the pellet in order to repeat the extraction as described before. Then both supernatants were pooled together and stored at \(-80\)\({}^{\circ}\)C until analysis in duplicate at various dilution factors (10, 50, 100) using ELISA test (Abraxics LLC, Warminist, PA, USA). To determine the recovery rate of MC in each species, unexposed mussels were sacrificed and removed from their shell. Tissues were freeze dried, grinded and spiked with 1, 10 and 100 \(\upmu\)g of MC-LR standard, and analysed as described previously. The mean percentages of extraction and detection of free MC on these freeze dried tissues was \(56.4\pm 14.1\%\) for the digestive glands of _A. anatina_ and \(58.9\pm 4.6\%\) for whole _D. polymorpha_. Measured concentration in mussels were then corrected by the recovery rate.
## 3 Positioning System Experimental Platform
### WinBus
#### 3.1.1 LiDAR  
Lidar is an optical sensing technology that illuminates a beam of light. Lidar measures the time interval between the emitted and received beam signals to count and calculate the distance to the target. It predicts the shape of the target using the reflected signals of multiple beams. It can also use the reflectivity of the received beam to determine the target surface material. The WinBus deploys two 3D lidars on the vehicle roof. 3D lidar sensors are also known as 3D laser scanners. This sensor captures their surroundings almost continuously. Therefore, 3D lidar sensors are ideal for collision protection for autonomous vehicles or for the detection of objects. In the previously discussed literature, 3D lidars were used as sensors for SLAM. The lidar mounted on the WinBus is used to record the scene point cloud. The system creates a recorded point cloud by overlaying the map. The completed map is the point cloud map data of the comparison ends. During the comparison, the lidar scans the scene environment in real-time. The SLAM algorithm compares the current point cloud with the point cloud image. This method finds feature locations that match the current point cloud, and then calculates the vehicle coordinates.
## 2 Materials and Methods
### Hybrid Control System Architecture
#### 2.1.3 LSTM-Based Real-Time Model Identification and Challenges  
The proposed system employs a Long Short-Term Memory (LSTM) network architecture specifically designed for real-time model identification of dynamic systems. While this approach offers significant advantages, several challenges need to be carefully addressed to ensure optimal performance.  
Computational Complexity Analysis  
The LSTM network's computational complexity can be expressed as O(W), where W represents the total number of weights in the network. To maintain real-time performance, we implement the following optimization strategies:* Network Pruning: removal of redundant connections, reducing the number of parameters by approximately 35%.
* Quantization: using 16-bit floating-point representation instead of 32-bit.
* Batch Processing: optimized batch size of 32 samples to balance throughput and latency.  
These optimizations result in a 45% reduction in computational overhead while maintaining model accuracy within 98% of the full-precision network.
## 4 Conclusions  
The development of versatile measurement systems is crucial to the advancement of both EC and PEC sensor technologies, which are highly dependent on advances in materials science. This work presents a novel and versatile measurement system that is applicable to both types of sensors. The system incorporates a potentiostat circuit with a current measurement range of 1 nA to 18 mA and a high precision of 99%. It also features an external excitation light source comprised of seven monochrome LEDs with user-selectable wavelengths (385 nm, 405 nm, 470 nm, 525 nm, 590 nm, 630 nm, and 720 nm). The power for these LEDs is supplied by a current source controlled by a DAC signal. The experiment chamber, designed to investigate various EC and PEC sensor materials with sample volumes below 2 mL, facilitates easy experimentation and saves the sample solution. The wireless measurement system communicates via BLE with the Android-based user interface, where measurement results are stored as CSV files on the device's memory. Furthermore, to demonstrate the system functionality for PEC applications, we employed \(CuO\) as the photoactive material and \(Fe(CN)_{6}^{3-}/Fe(CN)_{6}^{4-}\) as the target analyte for a representative experiment. The development of this flexible EC-PEC system lays the foundation for further research on these promising sensor technologies. One potential application could involve comparing the sensing properties of different materials in electrochemical and photoelectrochemical techniques for a specific target object, comparison of the effectiveness of each method in this scenario and demonstration of the advantages of one approach over the other.  
Conceptualization, A.H.H.V., T.T.T. and V.C.T.; methodology, A.H.H.V., M.H.H.T., T.N.T. and A.D.N.; software, A.H.H.V.; validation, M.H.H.T., V.C.T. and T.T.N.; formalism analysis, A.H.H.V.; investigation, A.H.H.V., T.N.T. and A.D.N.; resources, T.T.T., V.C.T. and T.T.N.; data curation, A.H.H.V. and T.T.N.; writing--original draft preparation, A.H.H.V.; writing--review and editing, V.C.T. and T.T.T.; visualization, A.H.H.V. and T.T.T.; supervision, M.H.H.T., V.C.T. and T.T.N.; project administration, T.T.N.; funding acquisition, T.T.T., V.C.T. and T.T.N. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2022-20-01/HD-KHCN.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study. The authors are grateful to [PERSON] of University of Hawai'i at Manoa, Honolulu, HI 96822, USA for his guidance during the early stages of this study and his valuable assistance with the experiments  
The authors declare no conflicts of interest.
# Finite Element Models of a Benchmark Footbridge
## 2 Description of the Benchmark Footbridge
### Monitoring Systems  
During construction, the Streicker bridge was equipped with two fiber optic-based monitoring systems embedded in the concrete deck [12] with the aim of transforming the bridge into an on-site laboratory for research and educational purposes. The system was conceived for global structural monitoring based on discrete fiber Bragg-grating (FBG), long-gauge strain and temperature sensors and a Brillouin-scattering based distributed sensing system used for integrity monitoring. Since the bridge is almost doubly symmetrical in plan, the sensors were installed on half the main span and on the south-east leg, which is the longest one (Figure 4). Only the FBG system is relevant for this research work due to its capability for dynamic monitoring. In particular, measurements from parallel sensors are taken into account in this study. These sensors are installed in couples parallel to the center line of the deck close to the axis of symmetry of each cross-section, one sensor at the top and one at the bottom. This allows for curvature calculations based on the strain measured at the top and bottom sensors. The specifications of the SHM system can be summarized as follows: resolution 1.7 \(\upmu\)e, repeatability \(<\)1 \(\upmu\)e, typical gauge length 60 cm (30-120 cm), dynamic range from \(-\)5000 to 7500 \(\upmu\)e and maximum measurement frequency 250 Hz [31].
## 1 Introduction  
In order to understand the aroma of wine and make a marketable product, it is necessary to separate, identify, and quantify the chemical compounds that impart these aromas. Aromas to note are the primary (varietal aroma), secondary (aromas due to fermentation from yeasts), and bouquet (aromas due to aging and storage) aromas. The pleasant volatiles in wines are due to the presence of higher aliphatic alcohols, ethyl esters, and acetates [1,2]. Wine aroma increased in complexity after malo-lactic fermentation (MLF), which produces changes in the carbonyl compounds [3]. Wines that have undergone MLF can be associated with herbaceous aromas from aliphatic aldehydes [4,5] or buttery aromas from diacetyl [6]. Wine can also have off odors due to volatile sulfur compounds, described as garlic, onion, or cabbage [7]. Vinylphenols have been described as phenolic, medicinal,smoky, spicy, and clove-like [8; 9]. An experienced palate can often distinguish the \"foxy\" characteristic of the main North American vines (_Vitis labrusca_ and _Vitis rotundifolia_) from vinifera vines caused by methyl anthranilate [9; 10].  
Cold climate grapes are newer, and the aroma profiles are less characterized than Vinifera varieties. The identification and quantification of the most aromatic compounds can help the industry maximize the aroma quality in these wines. The varietal flavor profile was used to demonstrate good examples of wine production and the best grape growing and innovative vinicitation techniques. The first step in the aroma analysis of wines is to extract volatile organic compounds (VOCs). Solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is useful in extracting and pre-concentrating VOCs in wine. The inception of GC-olfactometry (GC-O) in 1964 allowed researchers to link an aroma descriptor to these separated compounds [11]. Although many detectors have been used to identify and quantify aroma compounds from wine, MS is the most widely used [12]. A review and summary of many experimental parameters are available elsewhere [12; 13].  
In this research, an automated headspace SPME-GCMS-O method was developed and the aroma profiles of seven cold-hardy wine samples were investigated. The chemicals in selected cold-hardy wines were isolated and tentatively identified by matching the mass spectral and aroma character. The grape and wine industry has expanded exponentially in cold climates. Therefore, there is a need to research aroma compounds and their links to grape growing and wine making practices in cold climates. Such information can be used for the monitoring of fruit maturity, developing the best viticultural and wine making practices, and the development of appropriate wine styles specific to cold climates. The flavor and aroma profiling of cold-hardy wine enables the development of high quality and unique wines. Therefore, a method was developed to evaluate the full chemical and sensory aroma profile of wine from cold climate grapes.
## 3 Push-Pull Converter Sizing and Parameterisation
### Transformer Sizing and Parametrisation
#### 3.1.6 Magnetising Resistance  
For the calculation of magnetising resistance (\(R_{Nu}\), Figure 2), it is necessary to know the core losses. In the scientific literature, there are different analytical models for the estimation of transformer core losses considering Eddy current losses and hysteresis losses, depending on the type of magnetic core and topology [37; 38]. These models are complex to apply and require information obtained from experimental tests on the transformer, which makes them difficult to apply in this study. There are simpler and reasonably accurate traditional models that do not require prior testing to estimate power losses. These simplified models are based on generalised experimental equations, such as the experimental Steinmetz equation (OSE), its modified version (MSE), its general version (GSE) or the improved generalised Steinmetz equation (IGSE) [39; 40]. For this study, since real transformers are not available for testing and analysis, to obtain the best possible approximation, core losses were estimated from TDK Electronics(r) transformer and inductance design software(EPCOS Magnetic Design Tool Ver. 5.5.1.127, compiled on 18 April 2018). The power loss density in the transformer core was calculated considering it to be of the ferrite type, push-pull topology with a duty cycle of 0.45 and a rate operating temperature of 80 \({}^{\circ}\)C (usual operating temperature under rate conditions). Considering the power loss density (\(P_{L}\)) at the rated operating voltage of the transformer and considering the voltage drops in the primary windings of the transformer negligible, the magnetising resistance of the transformer core is given by the expression of the active power dissipated in a resistor, given by (16).  
\[P_{L}\cdot V_{e}=\frac{V_{L_{N_{U}}}^{2}}{R_{Nu}}\to R_{Nu}=\frac{V_{L_{Nu}}^{2} }{P_{L}\cdot V_{e}} \tag{16}\]
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Optimization Criteria
#### 5.1.1 Cost  
Costs can be related to computation, communication, resource usage (other than CPU and network), and energy consumption.  
Computation cost. Performing a task on a fog node or device is called computation cost. It covers the time, energy, and resources used to complete the computation [15]. Depending on the difficulty of the task and the fog node's capabilities, computation costs might change.  
In the Cloud-Fog system, carrying out a task results in costs tied to processing, memory consumption, and bandwidth utilization. The cost estimated when node \(N_{i}\) processes the task \(T_{k}\) is expressed as [33]:  
\[Cost(T_{i}^{k})=c_{p}(T_{i}^{k})+c_{m}(T_{i}^{k})+c_{b}(T_{i}^{k})\]  
Figure 5: Publisher of the selected papers.  
Figure 6: Category of performance metrics used in the optimization criteria.  
Processing cost is defined as:  
\[c_{p}(T_{k}^{i})=c_{1}*Exect(T_{k}^{i})\]  
where \(c_{1}\) is the CPU usage cost per time unit in node \(N_{i}\), and \(\text{Exect}(T_{k}^{i})\) is the execution time of \(T_{k}\) processed in node \(N_{i}\). Letting \(c_{2}\) be the memory usage cost per data unit in node \(N_{i}\) and \(\text{Mem}(T_{k})\) be the memory required by task \(T_{k}\), the memory usage cost is:  
\[c_{m}(T_{k}^{i})=c_{2}*Mem(T_{k}^{i})\]  
Task \(T_{k}\) processed in node \(N_{i}\) needs an amount of bandwidth \(Bw(T_{k})\), which is the sum of input and output file size. Let \(c_{3}\) be the bandwidth usage cost per data unit; the bandwidth usage cost is defined as follows:  
\[c_{b}(T_{k}^{i})=c_{3}*Bw(T_{k}^{i})\]  
Total cost for all tasks to be executed in Cloud-Fog system is calculated as follows:  
\[TotalCost=\sum_{T_{l}\in NodeTasks}Cost(T_{k}^{i})\]  
Communication cost. Tasks may need to be transferred between fog nodes for processing in a distributed system like IoT and fog computing [64]. Communication cost includes the overhead of transferring data, such as bandwidth usage, latency, and energy consumption between nodes [40].  
Resource cost. Fog nodes have constrained CPU, memory, and storage capabilities. In the context of fog computing, resource cost entails efficiently assigning and utilizing computing resources essential for the effective execution of tasks. This encompasses the utilization and consumption of a range of resources, such as computational power, storage, and communication bandwidth. The term reflects the comprehensive expenditure needed to ensure efficiency within a fog computing environment.  
Energy cost. Energy efficiency is a crucial factor for many IoT and fog computing devices as they are battery-powered. The cost of energy consumption is dependent on the amount used for communication and processing [56].
## 4 Simulation Setup
### Simulation Parameters  
The simulated network consists of multiple APs connected to a CPU, serving multiple UEs distributed across the coverage area. The simulation parameters are summarized in Table 3[3, 6, 16].  
The CNN model was trained using the Adam optimizer with a batch size of 64 and a learning rate of \(1\times 10^{-3}\). A validation set comprising 20% of the training data was used for early stopping, and training was continued for up to 1000 epochs. All training samples were generated under random user distributions with nonuniform clustering to enhance generalization.