esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### ABTS Radical Scavenging Activity  
ABTS radical scavenging activity is among the most used methods employed to determine antioxidant capacity [16]. Enzyme content, degree of hydrolysis, solubility of hydrolysates, types of peptides, and free amino acid content are some of the factors that could have effect on the ability of hydrolysates to scavenge ABTS radicals [45].  
As for DPPH, the ABTS activities of extracts are closely related to the hydrolysis time and type of enzyme. The variation with the enzyme treatment could be due to the changes in the structures of hydrolysate proteins due to the different cleavage sites of the enzymes [19]. In this way, alcalase hydrolysates displayed higher levels of ABTS than papain, and the values were also better as time increased [10]. In the present study, these increases resulting in an increment of 20.0% and 26.0% for papain and alcalase, respectively. Alcalase also gave rise to hydrolysates with antioxidant activity in other porcine tissues (appendix, colon, lung, pancreas, and rectum). In this regard, [PERSON] et al. [46] confirmed that hydrolysates obtained from these porcine by-products can also generate antioxidant activity, showing ABTS values even higher than those obtained for liver.  
An opposite effect was observed in bromelain, since the activity decreased as the hydrolysis time increased, meaning that hydrolysis for 4 h would be sufficient to obtain the higher activity of the extracts obtained with this enzyme. Better activities were also found by other authors in protein hydrolysate extracted from porcine liver when hydrolysis time was increased, with increments of inhibition percentages until 47.0% and 49.0% for papain and alcalase hydrolysates, respectively, for 0-6 h of reaction time [10].  
The ultrafiltration step also significantly affected the activities of the samples analyzed. ABTS radical scavenging activity increase as the size of the peptide increased. This was in contrast with the results found in previous studies, where the fractions smaller than 3 kDa reached higher values (795 uM TEAC/mg) [44]. This behavior could be related to the type of enzyme used, which in addition to conditioning the bioactivity of the hydrolysates obtained, could influence the composition of the extracts obtained, and therefore the filtration process. This may affect the fouling mechanisms that occur in polymeric membranes, such as the one used in the present study, decreasing permeate fluxes through the membrane, and consequently the separation performance and the ABTS activity [41].
## 2 Materials and Methods  
### Prototype Observation Results  
Jinping-I Dam, which is the highest dam in the world (305 m) by far, is located on the downstream of the Yalong River in southern China. The layout of the tunnel of Jinping-I project is depicted in Figure 1. The length downstream the lock chamber is about 800 m; the drop of bottom elevation is about 141 m; and the height and width of the cross-section are 17 m and 13 m, respectively. Three air ducts are installed between the tunnel gate and outlet. Four aeration devices are employed in the tunnel to prevent cavitation damage. Recently, prototype observation has been conducted to examine the ventilation capacities of air ducts and air entrainments of aerator. Prototype observation was conducted [23], and the results can be seen in Table 1. The gate opening percentages are 25%, 50%, 75% and 100%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Gate Opening Ratio**} & \multirow{2}{*}{**Flow Rate (m\({}^{3}\)/s)**} & \multirow{2}{*}{**Air Demand of Air Ducts (m\({}^{3}\) s\({}^{-1}\))**} & \multicolumn{3}{c}{**Average Aeration Concentration of Cross Section (\%)**} \\ \cline{3-7}  & & & **Duct No. 1** & **Duct No. 2** & **Duct No. 3** & **Aerator No. 2** & **Aerator No. 4** \\ \hline
25\% & 793 & 933.59 & 1402.13 & 892.08 & 27.79 & 32.19 \\
50\% & 1460 & 1258.51 & 1805.52 & 1119.53 & 20.72 & 27.00 \\
75\% & 2140 & 1543.3 & 2161.08 & 1495.33 & 17.35 & 22.11 \\
100\% & 3220 & 1313.09 & 2097.1 & 1812.71 & 12.57 & 16.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Air demand and aeration concentration data from prototype observation.
## 4 Results and Discussion  
This section evaluates the performance of nine models--DT, KNN, RF, SVC, XGBoost, GRU, LSTM, TCN, and Transformer--for activity recognition within _Intelligent Transportation Systems (ITS)_. Effective activity recognition is crucial for enhancing safety and efficiency in ITS applications. The analysis compares model performance across two sensor configurations (two-sensor and four-sensor) and two data segmentation strategies (non-overlapping and 50% overlapping), focusing on key performance metrics such as accuracy, precision, and recall to assess their viability for real-time applications in ITS.
# RCM: A Remote Cache Management Framework for Spark
## 6 Conclusions  
To solve the problem of cache management optimization of a big data platform, we propose RCM, a remote cache management framework for Spark, including a cache data weight generation module (CWG), cache replacement module (CREP), and data placement module (CPL). CWG establishes a new cache data weight model according to the task characteristics of the big data platform. The initial weight is obtained by considering the response time of the query database, the number of queries, and the data size. Then, the time loss function is given to define the weight change caused by time loss. CREP uses the weight generated by CWG to define the priority of cache replacement. When cache resources are insufficient, low-priority cache data are replaced by high-priority data. CPL dynamically matches the best cache server based on the KM algorithm to improve cooperation efficiency. To verify the effectiveness of RCM, we implement RCM based on Redis and deploy it on eight computing nodes and four cache servers. Three kinds of experimental datasets are selected from SNAP [34] datasets, and the benchmark jobs are PageRank, K-means and WordCount. The experiment proves that RCM has a more obvious cache acceleration effect on the big data platform than advanced cache management strategies. Compared with MCM, SACM and DMAOM, the execution time of RCM is reduced by 42.1% at most.  
Because RCM is independent from the Spark platform, it is easy to perform a remote cache management component in other big data platforms. In the future work, we wil try to deploy RCM on other big data platforms, such as Hadoop [35], Flink [36], and Storm [37].  
All authors made contributions to the manuscript. Conceptualization, methodology, software, validation and writing, Y.S. and H.L.; software and visualization, B.L.; validation and investigation, X.H. and J.W.; writing and preparation, J.Y. and R.Z.; funding acquisition, J.Y. and X.H. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Job execution time.  
**Funding:** This work was supported by Henan Province Science and Technology R&D Project (Grant No: 212102210078) and Henan Province Major Science and Technology Project (Grant No: 201300210400).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank all the anonymous reviewers for their helpful comments and suggestions to improve the manuscript.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Antiderivative Antialiasing for Stateful Systems +
## 6 Conclusions and Outlook  
The presented approach for aliasing reduction generalizes the approach of [10] to all nonlinear systems that can be cast in a way that the nonlinearity takes scalar or very-low-dimensional input. This includes, but is not limited to, all models of circuits with a single one-port nonlinear element. If the system contains a delay-free loop, it has to be re-cast such that the nonlinearity is defined as the solution of the delay-free loop. Then, the delay introduced by applying the method of [10] to the nonlinearity can be compensated by adjusting the system's coefficients, even if the nonlinearity is part of a feedback loop.  
As is to be expected, the achieved aliasing reduction is comparable to that of [10], allowing to significantly reduce the required oversampling especially for systems which introduce strong distortion, while the additional computational load is modest. Assuming lookup tables are used for \(f(u)\) (in general being implicitly defined) and its antiderivative \(F(u)\), the main price to pay is in terms of memory used.  
It should be noted that the extensions to higher-order antiderivatives as proposed in [14] or [15] should be straightforward, following the same principle. A more interesting future direction would be to lift the restriction on the nonlinear function to have only scalar or very-low-dimensional input. If the method of [10] (or even the higher-order extensions of [14] or [15]) could be generalized to nonlinear functions with multiple inputs without requiring excessively large lookup tables, the method proposed in the present paper would immediately generalize to all stateful nonlinear systems.  
This research received no external funding.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 8: Output spectra of various model configurations for the tube screamer distortion circuit when excited with a single sinusoid of 1 V amplitude at 1244.5 Hz. Crosses mark expected harmonics.
## 3 Results and Discussion  
### Experimental Results  
In [26; 27], the experimental results for the gear machining were presented, with different cutting modes with the given acquisition systems located in the points specified in the previous sub-chapter.  
In Table 2, some results referring to the chosen points' displacements, recorded using Bruel and Kjaer the analyzer, were given [26].  
Table 3 shows the results recorded for the torque moments of the tool shaft corresponding to the same situation as in Table 2. In Figure 4, the corresponding diagrams are presented.  
From the moment diagrams (Figure 4), the pulsating nature of the cutting moments can be observed. The torque moments can be decomposed into an average constant component and a variable one (Figure 4a). The average component is balanced by the motor torque moment of the driving motor. The variable component leads to the maintenance of vibrations in the kinematic rolling chain. The variation law of the variable component can be written in harmonic form with the Fourier series, the errors introduced being insignificant (Figure 4b). In the moment diagrams, the transformation benchmark is \(3000\)mV = 48.75 Nm.  
When performing the frequency analysis of the torque moments, the first five pulsations can be observed, but the first two are much more obvious, which is why they were taken into account [27]; the same procedure was utilized in [36].  
Regarding the deviation from the geometrical shape of the teeth's involute profile, the experimental analysis demonstrated that the displacements were within admissible limits (20%) and did not have major influences on the quality of the processed surfaces [27].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hob Module**} & **Rotational Speed** & **Feed Rate** & **Point 1** & **Point 2** & **Point 3** & **Point 4** \\
**m** & **Speed** & **V\({}_{\mathbf{f}}\)** & **Displacements** & **Displacements** & **Displacements** & **Displacements** \\
**(mm)** & **n\({}_{\mathbf{h}}\)** & **(mm/min)** & **s\({}_{\mathbf{hy}}\)** & **s\({}_{\mathbf{hz}}\)** & **s\({}_{\mathbf{gz}}\)** & **s\({}_{\mathbf{gy}}\)** \\  & **(rpm)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** \\ \hline
3.5 & 95 & 4 & \(-1.29\dots 2.44\) & \(-1.31\dots 1.01\) & \(-1.71\dots 1.29\) & \(-1.17\dots 1.02\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results.  
Figure 4: Graphs for the torque moment variation: (**a**) torque moment vs. time; (**b**) torque moment vs. frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Module**} & **Rotational Speed** & **Feed Rate** & **Maximum Torque Moment** & **Torque Moment Expression** \\
**m** (mm) & **n\({}_{\mathbf{h}}\)** (rpm)** & **v\({}_{\mathbf{f}}\)** (mm/min)** & **Mtmax (Nm)** & **Mt (Nm)** \\ \hline
3.5 & 95 & 4 & 52.96 & 18.207 + 9.224 sin 0.233t + 4.955 sin 2.623t \\ \hline \hline \end{tabular}
\end{table}
Table 3: The measured numerical results for the torque moment.
# Harnessing Artificial Intelligence for Wildlife Conservation
## 2 Methodologies
### Data Collection  
Conservation AI employs a comprehensive and methodologically rigorous data collection strategy, utilising both camera traps and drones. By partnering with leading conservation organisations globally, including Chester Zoo, the Endangered Wildlife Trust, and the Greater Mahale Ecosystem Research and Conservation team, Conservation AI is able to amass a diverse array of datasets. This carefully crafted dataset creation process is what distinguishes Conservation AI from other organisations, ensuring that the data collected are not only extensive but also highly relevant and precise, thereby enhancing the effectiveness of the AI models used in conservation efforts.  
The collected data encompass both visual spectrum and thermal infrared imagery, including still images and video recordings of wildlife in their natural habitats. Camera trap data provide critical insights into species presence and behaviour within specific locales, while drone-acquired data expand observational capabilities to cover larger and more remote regions. This dual-method approach facilitates the capture of comprehensive wildlife activity patterns, encompassing both diurnal and nocturnal behaviours.  
These meticulously curated datasets are utilised by Conservation AI to develop region-specific models tailored to various regions, such as Sub-Saharan Africa, the Americas, Asia, the U.K., and Europe. Specialists in data collection, filtering, and quality control manage the data to optimise model performance. The deployment of a robust data processing pipeline ensures the production of high-quality datasets, which are essential for training AI models that are both accurate and reliable (repeatable).
## 1 Introduction  
Vehicle to everything (V2X) communications have been recently introduced with significant attention [1; 2]. V2X networks provide the projected benefits such as reducing logistical costs for operating vehicular fleets, decreasing traffic-related fatalities, and introducing a variety of new business models [3; 4]. To develop advanced techniques of 5G-enabled V2X networks in vehicular networks, coexistence of various kinds of V2X communications exists, and they may share the same wireless medium for data transmissions. In particular, vehicular to infrastructure (V2I) communications benefit vehicles that are associated with a central entity. Efficient and effective coordination of different types of V2X communications are analyzed in order to improve network performance under such a complex and heterogeneous topology as in [5; 6; 7; 8]. The latest developments in the standardization of 802.11 bd and V2X techniques are presented in [9].
## 4 Performance Evaluation  
To assess the performance of our proposed Doppler estimation and signal recovery system, two types of evaluations are discussed, namely, Doppler and detection evaluations. The first evaluation measures the SNR against different Doppler values, and the accuracy of the module is then calculated. The second evaluation measures the system SNR against the probability of detection. These evaluations offer a valuable understanding of how effective the proposed module is in practical and real-world applications.  
Prior to evaluating our proposed algorithm, it is important to explain the reasoning behind categorizing Doppler values into low, medium, and high intervals, as well as the criteria used to select these intervals. Note that all calculations are performed in the S-band.
# The Effect of Isomorphic Pitch Layouts on the Transfer of Musical Learning +
## 4 Discussion
### Semi-Structured Interviews  
Interview responses provided further insight to the effects described above. There was a greater overall preference for isomorphic pitch layouts. However, this effect was largely driven by those who played on Wicki-like layouts and more so by musicians than non-musicians. In contrast, there was a greater preference for the non-isomorphic layout amongst those who played on Bosanquet-like layouts, which was largely driven by non-musicians. Findings from responses under _Reason for layout preference_ shed further light on this. The sub-theme _Layout structure_ was the most prominent (it contained the greatest number of statements) and comprised statements primarily made by musicians rather than non-musicians. Statements under this sub-theme also primarily corresponded to those with a preference for the Wicki-like isomorphic layout. However, all statements corresponding to non-isomorphic layouts related to the Bosanquet-like arrangement and, with the exception of one statement, referred to how many pitches were contained on each row. The difference in the patterns of layout preferences across layout types hints at the presence of additional factors that influence learning. The commonalities between statements made by those with a preference for the non-isomorphic Bosanquet-like layout provide support for the previous suggestion that one such factor is likely the number of pitches per row.  
The second most prominent sub-theme under _Reason for layout preference_ was _Practice effects_, which largely comprised statements made by non-musicians. Additionally, musicians made more statements for this sub-theme than any other sub-theme indicating that, for non-musicians, _Practice effects_ were the most influential factor when learning on a novel musical interface. This is in contrast to musicians who made the highest number of statements for the sub-theme _Layout structure_. The differing proportions of musicians and non-musicians across layout preferences suggests differences between how base learning and advanced learning occur.  
As for _Reason for layout preference_, the sub-theme _Layout structure_ was also the most prominent of the theme _Challenging aspects_. The consistency of this sub-theme's level of prominence within its respective themes indicates that structure--that is, note positions, note groupings, and sequence patterns--is the most important factor contributing to an individual's experience with a pitch layout.
## 2 Materials and Methods  
### 1D Consolidation by PINN with Layer-Wise Locally Adaptive Activation  
#### 2.1.1 1D Terzaghi Consolidation with Continuous Drainage Boundaries  
The traditional 1D Terzaghi consolidation equation is  
\[\frac{\partial u}{\partial t}=C_{\mathrm{v}}\frac{\partial^{2}u}{\partial z^{2 }},\ z\in[0,H],t>0 \tag{1}\]  
where \(t\) is the time, \(z\) is the depth, \(u\) is the excess pore water pressure, \(H\) is the soil layer thickness, and \(C_{\mathrm{v}}\) is the consolidation coefficient, which is expressed as  
\[C_{\mathrm{v}}=\frac{k_{\mathrm{v}}}{m_{\mathrm{v}}\gamma_{\mathrm{w}}} \tag{2}\]  
where \(k_{\mathrm{v}}\) is the permeability coefficient, \(m_{\mathrm{v}}\) is the coefficient of volume compressibility, and \(\gamma_{\mathrm{w}}\) is the weight per unit volume of water. The traditional 1D Terzaghi consolidation boundary conditions are either fully drained or fully undrained. However, in the practice, the boundary conditions are between fully permeable and fully impermeable, and all 1D consolidation models in this paper use a time-dependent continuously drained boundary [32]. Figure 1 shows the schematic diagram of the 1D consolidation of a soil layer with a continuously drained boundary under a uniform instantaneous vertical loading \(q\).  
The boundary conditions for the upper and lower interfaces are taken, respectively.  
\[u(t,0)=qe^{-bt} \tag{3}\]  
\[u(t,H)=qe^{-ct} \tag{4}\]  
Figure 1: Schematic diagram of 1D consolidation with continuous drainage boundary.  
where \(q\) is the top applied load, \(b\), \(c\) are the drainage parameters at the top and bottom boundaries, and additionally, the initial conditions are set to  
\[u(0,z)=q \tag{5}\]  
In order to speed up the neural network solution, the input and output data of the network need to be converted into dimensionless form in advance [33], and the following normalized parameters are defined:  
\[\overline{u}=\frac{u}{q},Z=\frac{z}{H},T_{\mathrm{v}}=\frac{\mathrm{C_{v}}t}{ H^{2}},\alpha=\frac{bH^{2}}{\mathrm{c_{v}}},\beta=\frac{cH^{2}}{\mathrm{C_{v}}} \tag{6}\]  
Thus, the governing equation of the soil consolidation and fixed solution conditions in dimensionless form can be obtained:  
\[\frac{\partial\overline{u}}{\partial T_{\mathrm{v}}}=\frac{\partial^{2} \overline{u}}{\partial Z^{2}} \tag{7}\]  
\[\overline{u}(Z,0)=1 \tag{8}\]  
\[\overline{u}(0,T_{\mathrm{v}})=e^{-aT_{\mathrm{v}}} \tag{9}\]  
\[\overline{u}(1,T_{\mathrm{v}})=e^{-\beta T_{\mathrm{v}}} \tag{10}\]
# Direct Measurement of Aerosol Liquid Water Content: A Case Study in Summer in Nanjing, China
###### Abstract
In addition, sophisticated optical instruments, such as polarized lidar [32] or a three-wavelength humidified turbidimeter, use measured aerosol light-scattering coefficients and backscattering coefficients to estimate the ALWC [33]. It should be noted that the methods above are often complicated and expensive, and some of them are not applicable for the measurement of real air, therefore limiting their widespread use. In this regard, our group previously established a novel, simple, and low-cost method [34] that showed that the \"bias\" in the measured PM\({}_{2.5}\) mass concentration caused by a high RH in a turbidimeter from that measured at dry air conditions can be used to determine the ALWC. In this work, we further improved this method and for the first time, applied it to directly quantify the ALWC in ambient PM\({}_{2.5}\) in Nanjing during the summer of 2023. Our findings are also insightful for our understanding of ambient aerosol chemistry and toxicology in similar large cities like Nanjing with relatively significant air pollution issues [35; 36; 37].
# Management Platforms and Protocols for Internet of Things: A Survey
## 5 IoT Network Management Platforms
### OpenDayLight  
The OpenDayLight (ODL) is an open-source Web-platform for network management as SDN. It uses open protocols to allows centralized control and network device monitoring [41]. The ODL supports OpenFlow and offers ready-to-install modular network solutions. There is support for a wide range of network protocols, including SNMP, NETCONF, RESTCONF, OVSDB, Border Gateway Protocol (BGP), Path Computation Element Protocol (PCEP), Locator ID Separation Protocol (LISP), and more. OpenDaylight is slightly different from other controllers because it offers other protocols such as southbound interfaces, e.g., OpenFlow, BGP, and PCEP. In addition, OpenDayLight offers interfaces with OpenStack and Open vSwitch (OVSDB).  
OpenDaylight is a micro-service that uses the sharing of YANG-based (NETCONF) data structures for messages exchange and data storage, as shown in Figure 14. According to [PERSON] et al. [60], through a model addressed to the Model Driven Service Abstraction Layer (MD-SAL), can aggregate any application or function to a service and loaded by the controller.  
_Pros: It supports the interoperability of the networks and many native protocols._  
_Cons: No basic security._  
Figure 14: Illustration of the OpenDayLight architecture.  
Figure 13: Illustration of the OpenNMS architecture.