esa-sceva/satcom-chunk-collection huggingface dataset:
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 3 Porous Carriers Used for the Immobilization of Lipase  
### Pore and Particle Sizes  
The presence of pores increases surface areas for enzyme adsorption [36; 37; 38; 39]. Studies showed that the presence of micropores of silica aerogels protects lipase against alterations of the microenvironment and increases surface areas [40; 41]. Porous carriers can be divided into three main categories, known as microporous, mesoporous, and macroporous materials [36; 37]. Each type of support has different capabilities and disabilities [36]. The pore diameters on microporous supports are too small for enzyme diffusion, even though the supports have strong adsorption and large surface areas [37; 38; 39]. Mesoporous supports are commonly used to immobilize lipases and peroxidase [42; 43]. Despite high surface areas and stability, the confinement of enzymes in mesopores with small pore size or non-open pore structures can decrease the enzymatic activity [44]. Mesoporous supports can cause major resistance during enzyme immobilization and diffusion [45; 46]. Macroporous materials have a pore size at a micrometer scale that can significantly reduce the diffusional limitation [47]. The enzymes can be quickly immobilized on macroporous materials but also leach out quickly, mainly when the pH values of the media vary [48]. Macroporous supports only possess a small surface area and yield a low loading amount of enzymes, which is one of the significant problems in industrial applications [37; 49].  
The porosity of a support plays a significant role in the efficiency of immobilization [36; 45]. The presence of pores increases the surface area of the support for lipase adsorption [46]. Pore exposure and particle size affect the binding of the substrate on the lipase [48]. Then, they affect the enzymatic reaction and the productivity of the reaction [50; 51]. Polypropylene beads are well-known supports for the efficient immobilization of hydrolytic enzymes [52]. Generally, a macroporous polypropylene support (Accurel MP) consists of many pores on its surface [53; 54; 55]. A polypropylene support has a large surface area compared with other types of carriers, such as beads and nonporous supports [56]. Macroporous properties will increase the adsorption of lipase in the pores and surface of the support [52; 57]. However, some lipases are desorbed from the support during the synthesis and continuous reaction [49]. A study was conducted to retrieve a support from immobilized papain but not an enzyme [12]. The scanning electron microscopy images showed the Ethylene glycol-bis [succinic acid \(N\)-hydroxysuccinimide] (EG-NHS) aggregates were smaller in size but had a larger surface area than bigger carrier free cross-linked enzyme aggregates using glutaraldehyde (GLA) [13]. Thus, more active sites at a large surface area are responsible for higher-mass transfer and activity enhancement [36; 57].
## 4 System Model and Proposed Framework
### Proposed Architecture  
In this research, a two-lane traffic scenario was considered that resembles real-time traffic. The RSU was placed in the middle of the road to cover all portions of the road. A hybrid hierarchical model was used, which can maintain load balance even in dense traffic. Each RSU can handle the control plane request. If the RSU is not able to handle the request in a situation where the route is not available or the destination node is not known, it sends the request to control plane layer 2. The two network interfaces are LTE for RSU to vehicle (R2V) communication and high-speed low-range Wi-Fi for vehicle to vehicle (V2V) communication. The cloud server was implemented at the top of all layers. It stores all the software updates, the logical view of global topology, and control policies. It also facilitates the RSUs. If the RSU is not able to handle the request, it sends the request to the cloud server. The cloud server has an overall topological view. Therefore, it responds to the request of the RSU, and the RSU further disseminates it to the nodes as per the request. All RSUs are connected to a high-speed wired network to communicate with each other. Figure 1 illustrates the system model broadly.  
The proposed framework can be divided into three planes.  
* Data Plane: This plane of the framework contains the data-forwarding module. The vehicles are equipped with high-speed Wi-Fi to forward the data, based on IEEE 802.11 ac standards [45]. However, like the normal SDN, these data planes also have the capability to store the route locally for a given time period. If the sender node starts sending data, it first searches the locally stored routes. If the route is valid, it starts transmitting data; otherwise, it goes to the control plane for the new rules.
* Control Plane: The control plane is divided into two parts to reduce the load on the control plane. Control plane layer 1 covers the logical local topology of the network. RSU serves as layer 1 of the control plane. Layer 2 collects all the information from the RSUs in its region, and finally, it keeps the global topological view of its region.  
It may communicate with other control plane layer 2 stations. Control plane layer 2 is also capable of performing analyses and making decisions. This layer can be used for machine learning and AI-based utilities in SD-VANETs. The cloud server has been used to act as a control plane layer 2.
* Application Plane: The application plane of the framework provides facilities like network security policies, user-based applications, the internet, etc. Control plane layer 2 interacts with the application plane using the northbound API of the OpenFlow protocol [46].
## 4 Experimental Setup
### Interaction Environment  
To evaluate the impact of the RJAS on user engagement, we designed an interactive environment where the participants played the game \"Odds and Evens\" with the robot [PERSON]. This game has been used because of its simplicity and suitability for maintaining user engagement without demanding excessive cognitive effort from the user. Its intuitive rulesmake it very unobtrusive: the user can focus on the interaction with the robot rather than fiddle with complicated mechanics, which makes an optimal scenario for RJAS evaluation.  
The game has distinct steps (see Figure 3), starting with the robot _presenting_ itself and _explaining the rules_. This is followed by a _calibration_ phase, where the user is prompted to display different number combinations with their fingers, helping them to learn how to correctly present their hands to the robot, which is then able to recognize the gestures accurately. During the _selection and reminder_ phase, the robot either randomly invites the user to choose whether to play as \"odds\" or \"evens\" or makes the choice itself and then provides a brief reminder of the rules. In each round, the robot actively joins in the interaction by performing a _countdown_, showing its choice on the tablet screen, and asking the user to _display their hands_. If the user does not show their hands on time or if there are problems in detection, the robot restarts the round. Otherwise, it determines the outcome and _checks the winner_. This loop runs five times per match for two matches. Finally, the robot _concludes the game_ with a farewell.  
The auditory and visual stimuli were triggered at various game stages, such as the selection and reminder, countdown, and check-winner phases, to make scenarios similar to real life and test the robot's ability to respond to the user's distractions. These stimuli were thought to temporarily catch the user's attention to challenge RJAS in re-engaging the user effectively. To ensure replicability while reducing the risk of learning effects, distractors were presented at predefined non-consecutive rounds across the two gameplay sessions. Their timing was deliberately spread across different game phases to minimize predictability and avoid repetitive sequences.  
The auditory stimuli comprised sounds like drum beats, which lasted for 2 s, bells lasting 5 s, glass breaking lasting for 1 s and repeated twice, and an alarm clock sounding for 5 s. Visual stimuli, presented as animated GIFs, included a moving vehicle and a cartoon chase scene. These animations appeared within the user's peripheral vision on a monitor and remained on until the round was over. Both types of stimuli were selected according to the normative literature about human attention capture. Typically, auditory stimuli are more effective than visual ones [59]. The duration and timing of the stimuli were empirically adjusted to avoid overloading the user while allowing them to stay focused on the game.
###### Abstract  
The present paper is focused on the post processing of the data coming from the Blade Tip-Timing (BTT) sensors in the case where two very close peaks are present in the frequency response of the vibrating system. This type of dynamic response with two very close peaks can occur quite often in bladed disks. It is related to the fact that the bladed disk is not perfectly cyclic symmetric and the so called \"mistuning\" is present. A method based on the fitting of the BTT sensors data by means of a 2 degrees of freedom (2 DOF) dynamic model is proposed. Nonlinear least square optimization technique is employed for identification of the vibration characteristics. A numerical test case based on a lump parameter model of a bladed disk assembly is used to simulate different response curves and the corresponding sensors signals. The Frequency Response Function (FRF) constructed at the resonance region is compared with the traditional Sine fitting results, the resonance frequencies and damping values estimated by the fitting procedure are also reported. Accurate predictions are achieved and the results demonstrate the considerable capacity of the 2 DOF method to be used as a standalone or as a complement to the standard Sine fitting method.  
tip-timing method; blade vibration; dual mode resonance; parameters identification; mistuning
## 3 Results
### Delay Tolerance for Optical Hybrid Processing  
One of the challenges of the proposed optical modules was finding the optimal delay to mitigate the CD. This happened because we used the stochastic gradient descent with adaptive moment estimation to update the delay and the weights of the NN at the same time. This can make the convergence process more difficult. To avoid this slow convergence and simultaneously simplify the optical structure, we investigated the performance of the hybrid systems for a fixed delay at 12 km of fiber transmission. Figure 9 shows the tolerance to CD in this scenario. The hybrid two-stage SD has a CD tolerance similar to the case in which the optimal delay is calculated. In other words, almost no penalty is introduced by fixing the delay. In contrast, the hybrid two-stage SD/SC with fixed delay  
Figure 8: Simulation results to compare the hybrid two-stage SD/SC and hybrid two-stage SD/SC modules. The receiver topology considered in Figure 2 is highlighted inside the parentheses in the legend of the figure.  
Figure 7: Simulation results to analyze the impact of delay and phase shift in the hybrid two-stage SD/SC structure. The receiver topology considered in Figure 2 is highlighted inside the parentheses in the legend of the figure.  
showed lower CD tolerance. This might be an indication that the sensibility in the delay of the full optical structure (SC/SD) is mainly related to the SC section.  
Comparing both modules with a fixed delay and 0 dB SNR penalty at KP4 FEC, the hybrid two-stage SD/SC shows a transmission of \(\approx\)370 ps/nm of accumulated CD (23 km of equivalent SSMF). The hybrid two-stage SD shows a transmission of \(\approx\)300 ps/nm of accumulated CD (18 km of equivalent SSMF).  
Considering only the hybrid two-stage SD, there was no penalty for the distance from 7 to 18 km. Below 7 km, the performance with a fixed delay was higher, which could indicate a missing interpolation operation from the NN to adapt to a better time delay. For distances from 18 to 23 km, we note that the fixed delay had less penalty. However, the curve with optimal delay showed a higher standard deviation, which indicates a more difficult convergence for the trainable variables. Fixing the delay would mean simplifying the convergence for the NN and, for this specific amount of CD, it showed better performance. Considering the hybrid two-stage SD/SC, we note that there was no penalty from 8 to 13 km. Outside this range, the penalty increased faster compared to the optimal delay.
# Metrics for Polyphonic Sound Event Detection
## 2 Background  
### Classification and Detection of Sound Events  
_Sound event classification_ in its simplest form requires assigning an event class label to each test audio, as illustrated in Figure 1A. Classification is performed on audio containing isolated sound events [10; 13; 14] or containing a target sound event and additional overlapping sounds [23]. In classification, the system output is a class label, and there is no information provided about the temporal boundaries of the sounds. Audio containing multiple, possibly overlapping sounds can be classified into multiple classes--performing audio _tagging_, illustrated in Figure 1B. Tagging of audio with sound event labels is used for example for improving the tags of Freesound audio samples [24], and has been proposed as an approach for audio surveillance of home environments [25]. Single-label classification is equivalent with tagging, when a single tag is assigned per test file.  
_Sound event detection_ requires detection of onsets and offsets in addition to the assignment of class labels, and it usually involves detection of multiple sound events in a test audio. This results in assigning sound event labels to selected segments of the audio as illustrated in Figure 1C. For overlapping sounds these segments overlap, creating a multi-level segmentation of the audio based on the number and temporal location of recognized events. Sound event detection of the most  
Figure 1: Illustration of sound event classification, tagging and detection.  
prominent event at each time provides a monophonic output, which is a simplified representation of the polyphonic output. These cases are presented in Figure 2, together with the polyphonic annotation of the audio. Polyphonic sound event detection can be seen as a frame by frame multi-class and multi-label classification of the test audio. In this respect, polyphonic sound event detection is similar to polyphonic music transcription, with sound events equivalent to musical notes, and the polyphonic annotation similar to the piano roll representation of music.
## 2 Materials and Methods
### Toxicity Evaluation of Cr(VI) Reduction Products in Batch Treatment  
To evaluate the change in its toxicity after treatment, 300-mg L\({}^{-1}\) Cr(VI)-containing wastewater was treated by the CUST210-1 strain or chromate reductase in batch mode. The initial concentrations of the CUST210-1 strain and the enzymatic activity were \(3\times 10^{7}\) cfu mL\({}^{-1}\) and 10.8 U mg-protein\({}^{-1}\), respectively. The treatment time for the CUST210-1 strain and chromate reductase was set for 2 h and 0.5 h, respectively. Toxicity was evaluated using _V. fischeri_ and _P. parwa_, and toxicity units are expressed as EC\({}_{50}\) and LC\({}_{50}\). The analysis data were evaluated using a Probit analysis in IBM SPSS, version 20 (IBM Corp, Armonk, NY, USA).  
\begin{table}
\begin{tabular}{c c} \hline
**Targeted Gene** & **Sequence Primers (5\({}^{\prime}\)-3\({}^{\prime}\))** \\ \hline _apCA_ & \begin{tabular}{c} F ATGAGTATCGCTACCTAAMATCC \\ R TACTGCATTGCACCCGACAAC \\ \end{tabular} \\ \hline _nfSA_ & \begin{tabular}{c} F ATCGAATTCACAGCTGAAGGCTCACTTTGC \\ R ATGCCCGATCCAGTAACGCTTGTCGGT \\ \end{tabular} \\ \hline _nfSB_ & \begin{tabular}{c} F GTAGGATCCCATATTCTGCTGC \\ R ACTGAATTCTTACACTTCCGTTAAGGTG \\ \end{tabular} \\ \hline _yieF_ & \begin{tabular}{c} F AGCTCATTTAAATGCATGG \\ R ATCAAGGAAATGTCGGCAA \\ \end{tabular} \\ \hline _azr_ & \begin{tabular}{c} F AATACGTTAAGCGCGCAGCG \\ R ATATATGTAAACCTATTTG \\ \end{tabular} \\ \hline _crS_ & \begin{tabular}{c} F CATATGGCCTTGCCTTTCCACCCCCTCGAACTC \\ R GAATTCCTAAAACCCCT \\ TTGGTACTGGGGGGGTAC \\ \end{tabular} \\ \hline _mreG_ & \begin{tabular}{c} F ATCACTTCCGGAACTGGGTGT \\ R TACCCCGCAACACCTGTA \\ \end{tabular} \\ \hline _hydC_ & \begin{tabular}{c} F CCTCTTATCTTTAACAAAGGGTGCAGGCE \\ R GGGTGGCAGGCTTCTTTTGGG \\ \end{tabular} \\ \hline _mtrC_ & \begin{tabular}{c} F AGATCTGTTGGCGCTAGACATAG \\ R GGGGCCGCCATAATAGCTTCCGTTCCAAATTGT \\ \end{tabular} \\ \hline _omCA_ & \
\begin{tabular}{c} F AGCCGTATGGCCTG \\ R TACTGCAGGAAATACGGCG \\ \end{tabular} \\ \hline _chrR_ &
\begin{tabular}{c} F ATGTCTGATACGTTGAAAGTTTGTTA \\ R CAGGCCTTACCGCGTTA \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Oligonucleotide primer pairs used to amplify the chromate reductase gene.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
## 3 Results
#### 3.1.1 Laser Power  
As shown in Figure 2a,\(e\), the antifeedant percentages and mortality rates of _P. rapae_ larvae increased sharply as the laser power increased. All treatments showed significant differences in repellent activity at 24 h (\(F\) = 214.66, df = 4, 10, \(p\) < 0.001), 48 h (\(F\) = 297.28, df = 4, 10, \(p\) < 0.001) and 72 h (\(F\) = 289.40, df = 4, 10, \(p\) < 0.001) (Figure 2a). The mortality rate of 100% was detected 48 h posttreatment with a laser power of 5 W, and the same results were obtained with 7.5 W and 10 W (Figure 2e). However, when the laser power was greater than 7.5 W, no significant differences in the antifeedant percentages after 24 h (\(F\) = 0.004, df = 1, 4, \(p\) = 0.952), 48 h (\(F\) = 0.003, df = 1, 4, \(p\) = 0.960) and 72 h (\(F\) = 0.001, df = 1, 4, \(p\) = 0.971) (7.5 W vs. 10 W) or mortality rates (7.5 W vs. 10 W: after 24 h, Same, all 96.67%; 48 h: Same, all 100%) were detected. The antifeedant percentage (24 h, 15.84%; 48 h, 22.21%; 72 h, 49.56%) and mortality rate (24 h, 16.66%; 48 h, 63.33%; 72 h, 83.33%) obtained with a laser power of 1 W were extremely significantly different from those obtained with the other laser powers (Figure 2a,_e_). Therefore, the laser power range was adjusted to 1.5-7.5 W, and values within this range were used in the various combinations of different working parameters tested in subsequent tests.
## 2 Literature Review
Utilizing the proposed approach, the authors detected heart arrhythmias from 12-lead ECG recordings and obtained a validation accuracy of 68.2% and a testing accuracy of 51.4%. The authors of [29] introduced a DL method for the classification of arrhythmias utilizing 12-lead ECG signals. In their proposed approach, the authors presented a modified ResNet with SE blocks. Additionally, they applied zero padding to extend the signal to 4096 samples and downsampled them to 257 Hz. Utilizing custom weighted accuracy measure and 5-fold cross validation, they obtained a validation accuracy of 68.4%, and a test accuracy of 52%. In [30], the authors proposed a novel approach using a Wide and Deep Transformer Neural Network for the detection of cardiac abnormalities utilizing 12-lead ECG recordings. In their methodology, they combine two features: transformer neural network features and random forest handcrafted ECG features. The utilized approach achieved an impressive accuracy of 58.7% on the validation dataset and 53.3% on the test dataset.
# Airport pavement Maintenance Decision-Making System with Condition Cases Optimization
## 2 Airport Pavement Management System Architecture
### Condition Index: Serviceability Level (SL)  
In accordance with the perspectives of various stakeholders such as airport managers, pilots, and passengers, the demands placed on airport pavement vary significantly. Airport managers prioritize efficient budget allocation and pavement maintenance, while pilots focus on the safe takeoff and landing of aircraft, and passengers prioritize a comfortable driving experience. To accommodate these diverse considerations, the decision-making model adopted in this study is based on the serviceability level (SL) developed through collaborative research between the Korea Airports Corporation (KAC) and the Federal Aviation Administration (FAA). This unique index takes into account the specific conditions in Korea, departing from the mega-index SL concept introduced by the FAA [23] and tailored to the domestic context [24].  
SL is expressed as a numerical value ranging from a minimum of 0 to a maximum of 100, characterized by hierarchy and comprehensiveness. It is constructed from a perspective that seeks facilities that are safe, sustainable, and comfortable. Factors influencing safety (SAFE) are subdivided into categories related to material or construction-induced distress (MCD: material and construction related distress) and skid resistance (F: friction). Sustainability (SUS) comprises categories related to design-related distress (DD: design-related distress) and structural stability (SS: structural stability). Structural stability is calculated as a weighted sum of deflection indices, such as base layer index (BLI), middle layer index (MLI), and D8 (measured deflection from the sensor located at a 60 inch offset from the loading plate), using the deflection measurements collected from the heavy weight  
Figure 1: Proposed airport pavement management system architecture.  
deflectometer (HWD) test data, with adjustments based on daily and monthly temperature change. When DD and SS values are evaluated as poor, extensive maintenance--such as full-depth repairs or repavement methods--is necessary. Lastly, comfort evaluates longitudinal profile (P: profile) as an indicator affecting aircraft vibrations. The analytical hierarchy process (AHP) was chosen as the methodology for determining the weightings between these constituent elements. Developed by [PERSON] in 1980, AHP simplifies complex problems into pairwise comparisons, enabling informed decision making [25]. Weighted averages were applied to the responses of 18 Korean and 3 U.S. highway/airport pavement experts. The final weights were then used to derive the SL formula (Equations (1)-(4)) [24].  
\[SL=0.640*SAFE+0.234*SUS+0.126*COMF \tag{1}\]  
\[SAFE=0.661*MCD+0.339*F \tag{2}\]  
\[SUS=0.550*DD+0.450*SS \tag{3}\]  
\[COMF=1.000*P \tag{4}\]