esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Scenario Analysis
### Analysis Results and Findings  
The simulation results of the base scenario represent the reality and the differences from the other scenarios compared together. Based on the three time-dependent performance measures, all scenario results are compared: Volume, speed, and queue length.  
* Volume-the throughput of traffic is counted at three locations: on the freeway, on-ramp, and off-ramp. Table 4 shows the total volume throughput results of each scenario and the difference of it from the base scenario as a percentage. In the mainline of the freeway, no recognizable difference exists in volumes because the input of the total volume into the program is fixed. Therefore, the change will appear in the on-ramp or off-ramp in general, as shown in the table. Aside from the AS-3-5V4R scenario, all scenarios show a decrease in the total volume around the study area. Since a large decrease in volume is not good in this area from an operational point of view, AS-3 and AS-4 look reasonable. However, AS-3-3V2R, AS-3-4V3R, AS-3-5V3R, and AS-3-5V4R produced reasonable results with less than 1% of reduction in total volume.
* Speed--for the average speed comparisons among scenarios in the VISSIM simulations, the Space Mean Speed (SMS) is calculated and presented in Table 5. The speed improvement of each scenario is given as percentage change from the base scenario. From the results, it is clear that all scenarios have shown improvement in the average speed from the base scenario, but the AS-1, AS-2, AS-3-2V2R, AS-3-3V3R, and AS-3-4V4R show more significant improvements (over 50%) than others.  
* Queue length-maximum and average queue lengths are collected at the stop line of the on-ramp. In VISSIM, the queue on the freeway is defined by speed and headway (spacing) as shown in Figure 6. It starts if the speed decreases to 5 km/h and ends if speed recovers to 10 km/h with a maximum headway (spacing) between successive vehicles being 10 m. Since the maximum  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Volume (veh/h)**} & \multirow{2}{*}{**BS**} & \multirow{2}{*}{**AS-1**} & \multirow{2}{*}{**AS-2**} & \multicolumn{8}{c}{**AS-3**} & \multirow{2}{*}{**AS-4**} \\ \cline{3-14}  & & & & **2V2R** & & & **3V3R** & & **4V3R** & & **5V3R** & & **5V4R** & & **5V5R** \\ \hline \multirow{2}{*}{Freeway} & \multirow{2}{*}{5447} & 54  
available storage length of the queue from the signal in on-ramp to the interchange is 310 m in the study area, the simulation results, which exceed this value, are unrealistic. The results in Table 6 show that only five scenarios have the queue length not exceeding the maximum storage length on the frontage road. They are AS-2, AS-3-4V3R, AS-3-5V3R, AS-3-5V4R, and AS-4; except for the AS-3-5V3R, they also have reductions in the queue length on the freeway.
## 2 Related Work  
### Advances in Encoder Research for Building Area Segmentation  
Building area segmentation faces numerous challenges, such as variations in building scale and noise interference. As the core modules of segmentation models, the encoder's primary function is to extract deep-level features of building areas from raw images, playing a crucial role in building area segmentation tasks. Currently, encoders commonly used for building area segmentation can be broadly classified into two types. The first type is based on CNNs [18], which extract features of building areas from images through convolution operations, excelling at capturing detailed information about building areas. However, owing to the inherent restriction of the receptive field in convolutions, CNNs are relatively weak at extracting global features of building areas. The second type is based on a transformer [19], which uses the self-attention mechanism to model the dependencies between input sequences. Compared to CNNs, transformers can more effectively extract global features of building areas. However, transformers have some drawbacks, including high computational costs, susceptibility to overfitting, and dependence on large-scale labeled datasets. Thus, we designed a CNN-based improved Pseudo-Siamese network, aiming to improve computational efficiency and enhance the ability to extract building area features from images.
## 5 Results and Discussion
### Baseline Methods  
To illustrate its effectiveness, we compare our approach with the baseline methods and peer-reviewed studies. The selected baseline classifiers are commonly used in this field, including k-nearest neighbor (k-NN), Random Decision Forest, and Support Vector Machines (SVM). All baseline methods used a 5-fold cross-validation method for comparison with our method. The features trained in the baseline methods included the PSD, the C0 complexity, the power spectrum entropy, the Lyapunov index, and the correlation dimension. We trained the baseline classifiers in two ways: training in segments, and training in trials. The dimensions of each subject's features matrix are (five kinds of features \(\times\) 32 channels) or (five kinds of features \(\times\) 60/length of the time window \(\times\) 32 channels). Principal component analysis is adopted to reduce the features' dimensions. All training processes are tested in the MATLAB (R2016a) environment. Here, we briefly describe the details and parameter settings used in those methods.  
k-NN: k-nearest neighbor algorithm (k-NN) is a non-parametric method used for classification. An object is classified by a majority vote of its neighbors. It is useful to assign weight to the contributions of the neighbors, so that nearer neighbors contribute more to the average than more distant ones. Therefore, the main parameters of the k-NN algorithm are the number of neighbors and the weighting scheme of giving each neighbor a weight. In this study, k is selected from the set (k = [5; 10; 15; 20; 25; 30]). Furthermore, the Chebyshev distance is adopted to calculate the distance between the object and the neighbors. The inverse of the distance gives the neighbor a weight, and the weight is used in the vote procedure.  
Random Decision Forest: Random Decision Forest (RDF) is an ensemble learning method for classification, which constructs a multitude of decision trees at training time. The training algorithm applies bootstrap aggregating, or bagging, to the tree learners. It selects a random subset of the features during the learning process. The main parameter setting of RDF is the number of the learners. Here, the number of learners is selected from the set (N = [5; 10; 20; 30; 40; 50]).  
SVM: SVM hyperparameters consisting of a regularization penalty parameter (C) and inverse of RBF kernel's standard deviation (\(\gamma\) = 1/\(\sigma\)) were selected by grid search through cross-validation on the training set (C = {0.01, 0.1, 1, 10, 100}, \(\gamma\) = {0.1, 0.2,..., 1, 2,..., 10}). For the reason of multiple classes, the One-vs.-One strategy is employed during the SVM training.  
CNN + RNN (without LSTM units): To show the memory effect of LSTM units in long-period data analysis, we designed a hybrid neural network structure including CNN and RNN without LSTM units. In this network structure, in addition to the RNN network layer not using the LSTM unit, the other network structure is the same as the CLRNN.  
For peer-reviewed studies, we chose the studies listed in Table 2 for purposes of comparison.
## 5 Conclusions  
Based on the experiments presented in this paper, the following conclusions can be drawn:  
* Oxygen Carrier Aided Combustion (OCAC) of biomass using manganese ore as bed material is a viable concept with some potential to improve performance of existing Circulating Fluidized Bed (CFB) boilers.
* From an operational point of view, manganese ore worked excellently as bed material. No problems were encountered related to factors such as attrition and agglomeration. In addition, the material was easy to handle, to fill into and remove from the boiler.
* From the temperature profile of the boiler it can be seen that the presence of the oxygen carrier facilitates fuel conversion inside the boiler, including in the dense bottom bed. The effect did not always translate to reduced emissions though which suggests that final combustion in the cyclone outlet was also influenced.
* Substitution 10% of the sand bed with manganese ore made it possible to reduce the air to fuel ratio considerably without generating large amounts of CO. This suggests that higher fuel flow could be feasible in existing facilities and that fan power and heat loss with flue gases could be reduced.
* The use of 100% manganese ore resulted in higher emissions of CO than the sand reference. However, when combined with sulphur feeding dramatic reductions in CO emissions with up to 90% was achieved.
* The method did not show great potential for reduction of NO emissions. For a given air-to-fuel ratio, NO emissions were always higher when manganese ore was included in the bed compared to when only sand was used.  
This research has received funding from Energiforsk (Varmerforsk project A38758) and the Swedish Energy Agency (project 38758-1). The support of research engineers [PERSON] and [PERSON] and the operators of Chalmers Research Boiler (Akademiska Hus AB) are also acknowledged.  
[PERSON] was project leader responsible for formulating the project, preparation and logistics of bed material, communication with funding agencies, participation in the experimental campaign and data evaluation. [PERSON] was responsible for planning of the campaign at the research boiler and communication with its personnel. [PERSON] and [PERSON] participated in the experimental campaign and contributed much to planning, methodology development, data gathering and data evaluation. [PERSON] wrote the article with input from the other three co-authors.  
The authors declare no conflicts of interest.
# Plasma Surface Modification of PDMS-Glass Microfluidic Chips for Oil Recovery Studies
## 2 Materials and Methods
### Plasma Laboratory Instrument and Chip Plasmaization  
To modify the surface, a plasma chamber was made (Figure 1), in which a dielectric barrier discharge was ignited. The design of this chamber was based on a typical scheme for the industry of sealing chips from PDMS: a grounded cathode is placed inside a vacuum flask with a glass wall, outside of which an anode is placed, to which voltage is pulsed, which leads to ignition of the plasma in the flask between the anode and cathode. To create a high voltage, we used the d'Arsonval apparatus DE-212 Karat (SMP LLC, Moscow, Russia) (Figure 2a). The plasma flask was made from a 175 mm diameter microwave glass pan with a wall thickness of 5 mm. A copper anode 1 mm thick was glued to its cover from the outside using KER-215 and ESO-15 epoxy resin (Himalyans LLC, Nizhny Novgorod, Russia), with a screw soldered in advance for connecting a high-voltage current source. The design involved inserting a cathode inside the chamber (a 2 mm thick stainless steel plate) and placing a fitting for air evacuation. To avoid mechanical processing of glass, it was decided to make the lower part of the pan by extrusion printing from PETG plastic (Bestfilament LLC, Tomsk, Russia) using the existing Hercules 3D printer (Imprinta LLC, Krasnoyarsk, Russia). Elite Double 22 silicone (Zhermack S.p.A., Badia Polesine, RO, Italy) was used to manufacture the sealing ring and seal the threaded connections. Air was evacuated from the chamber using a Laboport N820 membrane vacuum pump (KnF Technology Co. Ltd., Shanghai, China) to a pressure of 5 kPa.  
Figure 1: Scheme of the chamber for plasma processing of microfluidic chips **(a)** and photograph of the chamber in the process of plasma burning **(b)**.  
Figure 2: Features of plasma burning in the manufactured chamber. When placing a dielectric object of large size (relative to the size of the anode and cathode) and irregular shape **(a)**, the plasma burns more intensely in places near the inner corners (this area is indicated by orange arrows) **(b)**; the plasma burns more intensely along the edge of the object (this area is indicated by blue arrows), even if the edge extends beyond the cathode **(c)**.  
Plasma treatment of PDMS and glass plates took place at a temperature of 25 \({}^{\circ}\)C in humidified air. To do this, the sample was placed on the cathode with the treated side up. Water was poured to the bottom of the chamber under the cathode, the chamber was closed with a lid with an anode, and a vacuum pump with a vacuum manometer was started. Humidification of the atmosphere in the chamber was carried out by natural evaporation of water. When the pressure in the chamber was reached, at which the water boiled, the source of high-voltage pulsed voltage was turned on, the plasma was ignited in the chamber, and the processing time was recorded. After the time had elapsed, the plasma setup was turned off and air was let into the chamber to open it and remove the sample.
## 8 tRNAs and Antibiotic Persistence and Resistance
### Bacterial Resistance Mechanisms  
Resistance to antibiotics that interfere with tRNA biology can be mediated by a variety of different mechanisms. Some of these involve alterations in membrane permeability to impair the influx of the drug or to have an active efflux of the drug from the cell. Examples of these include chloramphenicol, erythromycincom, neomycin, streptomycin and spectinomycin [194; 195; 132]. Another highly prevalent mode of resistance is mutation of the target sites and occurs in most examples of antibiotics that affect tRNA function (as covered in this review); examples of which are: Blasticidin S, chloramphenicol, erythromycincom, linezoloid, neomycin, spectinomycin, streptomycin and viomycin [196; 197; 198]. Another example is that of structural modifications in EF-Tu (due to mutations in the _tuf B_ gene), which leads to kirromycin resistance. Of course, spontaneous resistance mutations can appear in aaRSs genes in organisms exposed to aaRS targeting antibiotics, which we recently reviewed elsewhere [70]. Of the tRNA-dependent aaRS inhibitors mentioned here, AN2690 resistant _C. albicans_ strains appeared as aresult of a single point mutation in the LeuRS CP1 editing domain [81]. While agrocin 84 resistant agrobacteria has been reported in the field through transfer of the pAgK84 plasmid, encoding the TM84 self-immunity LeuRS AgnB2 [108], from the biocontrol organism _Agrobacterium radiobacter_ strain K84 to the plant pathogen _A. tumefaciens_[200].  
Modification of the binding site of the antibiotic is another mechanism to decrease the affinity of the drug. For example, methylation of rRNA by rRNA methyltransferases confer resistance to aminoglycos, chloramphenicolicals, oxazolidinones and some macrolides [201,202]. This consequently obstructs tRNAs from their function in the ribosome.  
In order to lower the effective concentration of the drug, some microorganisms increase the expression of target or a mimic of the target so as to lower the effective concentration of the drug and thus leave some target molecules uninhibited. For example, overexpression of the rRNA fragment that resembles h34 of the 16S rRNA is involved in conferring resistance to spectinomycin [203]. Similarly, increased production of EF-Tu has been observed in response to the EF-Tu inhibitor amythiamycin [204]. Alternatively, proteins are produced that bind to the target site and protect it by decreasing the affinity of the drug. For example, Tet O and Tet M proteins are produced in response to tetracycline inhibition, which bind to the tetracycline-stalled ribosomes and sterically dislodge the antibiotic from the binding site [205,206]. Another mode of antibiotic resistance is by degrading or modifying the antibiotic itself. Chemical modifications, such as phosphorylation, acetylation, adenylation, glycosylation, and hydroxylation of antibiotics, have been reported for chloramphenicolicals, aminoglycos, macrolides and tetracycline. This results in a reduced affinity of the drugs to their ribosomal targets [207, 208, 209, 210, 211, 212].
## 2 Related Works  
### UAV-Assisted VANETs  
VANETs have paved the path to numerous road safety applications, which can share critical information about the automatic drive with security services [14]. However, the existing obstructions and the high mobility of vehicles can disturb the communication links between vehicles and RSUs, resulting in many disconnections [15]. Recently in modern cities, the number of UAVs has increased significantly. UAVs can be considered as suitable candidates to improve the connectedness of VANETs, which have been used to relay and process mobile messages for ground terminals [16; 17; 18; 19]. Based on this concept, the UAVs can form an aerial subnetwork in the sky and assist ground vehicles to communicate.  
Due to their flexible mobility, the works in [20; 21] proposed that the UAVs can act as the relay nodes when the ground VANETs encounter a disconnection. In [22], [PERSON] et al. designed a software-defined space air-ground-integrated network architecture for supporting diverse vehicular services in a seamless, efficient, and cost-effective manner. In [23], [PERSON] et al. proposed UAVs that can cooperate with existing ground vehicles, which can make the data delivery more reliable and guarantee robust paths. The above works in [20; 21; 22; 23] consider the UAV-assisted VANET is a fully connected network, which is unreasonable. This is because one of the characteristics of UAVs and vehicles is that they are highly mobile and therefore the UAV-assisted VANET has intermittent connectivity. Therefore, the authors of [24; 25] proposed that UAV-assisted VANETs can be regarded as the vehicular delay tolerant networks (VDTNs). UAVs can assist the ground vehicle in data transmission by the storage-carry-forward (SCF) method [26]. Additionally, the authors of [27; 28; 29; 30] proposed UAVs can be deployed to help ground vehicles find communication paths for their message. In addition, the researchers of [31; 32; 33] proposed that UAV-based remote sensing can apply deep learning techniques to UAV-assisted VANETs. Furthermore, [PERSON] et al., in [34; 35], study UAV-assisted MEC and aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. In [36], the authors proposed an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. The work in [37] addressed the challenges of a decentralized and heterogeneous UAV swarm deployment and proposed a Nash bargaining-based weighted intra-Edge processing offload scheme to mitigate the problem of heavy processing in some of the swarm members. The difference is that the  
Figure 1: A mobile edge computing (MEC) architecture in unmanned aerial vehicle (UAV)-assisted vehicular ad hoc networks (VANETs).  
UAV deployment problem is not considered in this paper. We consider a unidirectional road, where UAVs are randomly located along the road and investigate the MEC technique for UAV-assisted VANETs that aims at minimizing the task processing delay.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 2 Materials and Methods
### Feature Selection  
In this work, a feature selection method was used to select the most informative wavelengths. Due to the large amount of information contained along the wavelength range of the NIR spectra, chemometric analysis needs to reduce the data amount needed to build useful models [35]. For example, depending on the compound, the key wavelengths for identifying it could be different. Therefore, by choosing the most meaningful wavelengths, the resulting model could be more robust by only using those specific wavelengths and avoiding the noise of the other lengths [36].  
Generally speaking, feature selection techniques try to reduce the number of input variables while keeping the maximum variability. This reduction can be performed in many different ways, but they can be broadly framed as filter methods, wrapped methods and embedded methods [37]. Among all possibilities, filtered methods are the most frequently used ones. In general, these methods are based on the use of a threshold with a filter to choose a subset of the best wavelengths to develop a model [38; 39]. In this work, a correlation-based feature selection (CFS) filter proposed by [PERSON] [40] was applied. This method measures the correlation of each feature with the output variable and among each of the other features. To evaluate CFS, it is important to follow the hypothesis that a good feature is highly correlated with the desired output variable and, oppositely, it is lowly correlated to other features. The threshold used by the CFS in this work has been tested with values between 1 and 100.
# Dynamic Simulation of Underground Cable Laying for Digital Three-Dimensional Transmission Lines
## 3 Construction of Cable Laying System
### Application of Contact Force and Tension  
During the cable laying process, the cable and the laying reel will be partially entangled and will contact and collide with pulleys, cable conveyors, and other equipment. Therefore, relevant contact force needs to be added to the cable and traction ropes to truly simulate the cable laying process. However, suppose contact force is applied to all cables and traction rope micro-sections due to the large number of pulleys and excessive contact force. In that case, this will seriously affect the cable laying simulation speed and even cause simulation failure [13]. The higher the accuracy and the more micro-sections the cables are divided into in the micro-element method, the more truly the dynamic characteristics of the cable can be reflected. However, in the cable laying system, if the number of micro-segments of traction ropes and cables is greater, the higher the conditions of hardware facilities required for simulation, the lower the success rate of the simulation, which may even cause the termination of the simulation. Therefore, when adding contact force to the traction rope and cable, the positional status of the traction rope and cable micro-segment columns should be estimated according to the simulation time so as to minimize the number of contact forces and improve the success rate of the simulation [14].  
The contact force of a cable laying system is mainly composed of components such as friction and collision forces, where the friction force is the contact friction generated by the cable and the traction rope as they pass over the pulley several times [15]. The collision force is the positive pressure generated by the mutual collision between the pulley and the cable, which is usually defined using the IMPACT function [16]. The tension mechanism consists of the combined force of part of the cable's own gravity and a STEP function force, which ensures that the tension of the tension mechanism of the cable release disk remains constant [17,18].
## 2 State of the Art
For the evaluation of novel allocation algorithms, a simulation model realized in the MATLAB programming environment is presented in Section 4. The development of novel allocation algorithms, together with their assumptions, input parameters, functions of auxiliary calculations, and output parameters of algorithms, is specified. Subsequently, particular algorithms for static allocation, dynamic allocation, and the dynamic customized allocation of available WDM and OAM channel resources are characterized in detail. In Section 5, results of different allocation algorithms for different considered operating technologies in common optical fibers are presented based on simulation scenarios for the P2 MP OAM&WDM-PON architecture. Simultaneously, resource efficiencies for static, dynamic, and dynamic customized methods are determined. Subsequently, the evaluation of particular allocation algorithms for different operating technologies is executed. Obtained results are discussed in detail appropriately to other published works in Section 6. In the final Section 7, conclusions with future challenges and research directions are presented.