esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
High-frequency sampling is a preferable option when designing wind farm prognosis systems, as it allows to better capture the dynamics of the variables involved in wind turbine monitoring [8; 10]. Low frequency sampling (5 min) commonly used in the SCADA system removes much of the information from the measured signals, so that information is lost. This is the case, for example, of the power signal, in which the information provided by the SCADA system contains already less information compared to the original one because the medium and high frequency components have already been lost due to the long sampling period. However, this does not imply that these variables cannot be used for wind turbine prognosis, as has been shown in other studies [4; 9; 12; 17].  
When high-frequency sampling is not an option or not available, or if a simpler system has to be designed, to process, store, and send smaller amounts of data, aggregation to longer time window frames is a possible and useful option. This option has been explored in this work, and experimental results show that models using 60-min aggregated data outperform results obtained with the original 5-min data. Three different experiments demonstrate that better predictions were obtained using 60-min data, which simplifies the model and the amount of data to store, process, send, and/or display.  
To our knowledge, this is the first work investigating this option and the results make it interesting and potentially useful. Our experimental results suggest that averaging over long periods of time improves performance because the variability of a signal is usually reduced. In future work we will deeply investigate that, and will consider other subsystems and target variables, along with the possibility of extending the models beyond normality models.  
Conceptualisation: [PERSON], [PERSON], and [PERSON]; methodology: [PERSON] and [PERSON]; software: A.-B.M., [PERSON]-P., and [PERSON]-[PERSON]; validation: [PERSON] and [PERSON]; formal analysis: [PERSON], [PERSON] and [PERSON]; investigation: [PERSON], [PERSON], and [PERSON]; resources: [PERSON]; data curation: A.-B.M.; writing--original draft preparation: [PERSON] and [PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON], and [PERSON]; visualisation: [PERSON] and [PERSON]; supervision: [PERSON] and [PERSON]; project administration: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from Smartive.  
The authors would like to thank Smartive ([[http://smartive.eu/](http://smartive.eu/)]([http://smartive.eu/](http://smartive.eu/)) (accessed on 1 June 2021)) for providing the data used in the experimental part.  
The authors declare no conflict of interest.
## 3 The Method Presented in This Paper
### Cold Wave Recognition Process
#### 3.1.2 Cold Wave Recognition Under Small Sample Conditions  
Imbalanced distribution and insufficient sample size may prevent the classification model from fully learning the sample characteristics during training, leading to the reduced reliability of the forecasting results. This issue is particularly pronounced for small-sample events. Cold wave events are typical examples of small-sample events. To address this issue, this paper uses GANs to generate data samples based on the existing characteristics of cold wave samples.  
GANs are deep learning architectures comprising two adversarial neural networks: the Generator and the Discriminator [38]. The structure of the GANs is shown in Figure 6. The generator receives random noise, such as Gaussian or uniform noise, as input, and generates cold wave samples that closely match the real data distribution. Through learning, the generator makes it difficult for the discriminator to distinguish between generated and real samples, thereby continuously improving the quality of the generated data.  
As a classification neural network, the discriminator's goal is to accurately distinguish whether the input data come from real samples or cold wave samples generated by the generator. The generator and discriminator have opposing objectives: the generator aims to maximize the discriminator's classification error, while the discriminator seeks to minimize the error rate. In this adversarial process, the two networks optimize each other, continuously improving their performance.  
Through this adversarial learning process, GANs are capable of generating cold wave samples consistent with the distribution of real data. Ultimately, the generator is able to produce data highly similar to that of real cold wave samples, providing sufficient cold wave sample data for subsequent classification model training and significantly alleviating the issue of insufficient cold wave samples.
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## Materials and Methods
### Drug Content and Encapsulation Efficiency  
SVT quantification was carried out by high performance liquid chromatography with detection in the ultraviolet range (HPLC-UV), using a previously validated method [30]. The analysis was performed with a Shimadzu HPLC system (Kyoto, Japan) with detection at 238 nm and using a column Phenomenex Lichrosphere(r) C18 (4.6 \(\times\) 250 mm, 5 \(\upmu\)m). The composition of the mobile phase was 65% acetonitrile and 35% sodium dihydrogen phosphate buffer (25 mM, pH 4.5), the flow rate was 1.0 mL-min\({}^{-1}\), with an injection volume of 100 \(\upmu\)L. Calibration curves (\(n=3\)) were prepared to determine the linearity (\(R>0.99\)) in the concentration range from 0.1 to 20 \(\upmu\)g-mL\({}^{-1}\). The drug content in the formulations was determined by diluting a precise volume of nanoparticle suspension (100 \(\upmu\)L) in 10 mL of the mobile phase. The samples were then sonicated for 30 min and filtered through a 0.45 \(\upmu\)m membrane (Millipore(r), Billerica, MA, USA) before being assayed by HPLC-UV. Free simvastatin was determined in the ultrafiltrate after ultrafiltration-centrifugation (Ultrafree-MC, cut-off of 30 kDa, Millipore) at 2688\(\times\)_g_ (Scilogex D3024, Rocky Hill, CT, USA) for 15 min and quantification by HPLC-UV. Encapsulation efficiency (EE) as a percentage was calculated by the difference between the total and free, i.e., non-encapsulated, drug amount, divided by the total drug amount multiplied by 100. All analyses were performed for triplicate batches (\(n=3\)).
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Used Datasets  
The available data for experimentation play a determinant role in the scientific research. Even in spite of the fact that the datasets used come from very different sources (Figure 5a), DARPA-KDD datasets (traffic collected in 1998 and 1999) are very popular and still being used today (note that all studied papers in the NTARC database have been published after 2000, 2012 being the median year of publication). However, such datasets have not been representative of network traffic for a long time, both in terms of attack classes and legitimate traffic forms. Such insight reveals a common lack of reliable, high-level network data for science, a problem that has been reported several times [122; 123]. Issues related to data privacy, enterprise security, and governmental interests minimize the possibilities for data sharing. CAIDA [124] and MAWI [125] are organizations that lead initiatives for publishingnetwork data for research purposes; however, even in spite of these efforts, available data are preprocessed and shared without payloads and with anonymized IP addresses, considerably reducing the value for investigation. In this sense, the Canadian Institute for Cybersecurity [126] is also worth mentioning, perhaps the most active group in the creation and publication of high-quality IDS datasets (e.g., the ISCX and CIC dataset families).  
Figure 4(a) also shows a disturbing ratio of datasets that are not publicly available (\"private\" or \"lost sources\"), a fact that hinders reproducibility and honest research.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 4 Preliminaries  
The main objective of this research is to generate collision-free (i.e., congestion-aware) and energy-efficient routing for transmitting the emergency messages over the WSN-IoT and also transmit the alert messages using the IoT devices to the respective destination with a minimum delay. A multi-tier hierarchical framework based on subdivision technique for node deployment that aims to reduce long-distance communication. The proposed multi-tier framework can support load balancing and scalability. Scalability is a significant aspect in planning a well-organized routing protocol for WSN-IoT. A decent transmitting procedure has to be accessible and adaptive to alterations in the network configuration.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 5 Experimental Results
### Control System Performance Analysis of the Aerial Manipulator  
The performance validation of the aerial manipulator control system was conducted, with a primary focus on stability and disturbance resistance analysis. Moreover, the system was compared to the quaternion-based PID algorithm in the open-source Pixhawk flight controller ([[https://docs.px4.io/main/zh/config_mc/pid_tuning_guide_multicopter_basic.html](https://docs.px4.io/main/zh/config_mc/pid_tuning_guide_multicopter_basic.html)]([https://docs.px4.io/main/zh/config_mc/pid_tuning_guide_multicopter_basic.html](https://docs.px4.io/main/zh/config_mc/pid_tuning_guide_multicopter_basic.html)), accessed on 8 June 2023). The motion of the manipulator during tasks, such as target object grasping, transportation, and delivery, generates reactive forces that introduce disturbances. Additionally, the state estimation process of the flight robotic arm introduces measurement noise. Therefore, this study performed trajectory tracking experiments for the aerial manipulator's periodic swinging during straight-line UAV flight in the presence of dynamic disturbances. Two joint servos of the aerial manipulator swung periodically between \(\theta_{1}=0^{\circ}\), \(\theta_{2}=70^{\circ}\), and \(\theta_{1}=115^{\circ}\), \(\theta_{2}=45^{\circ}\) (Figure 17).  
Compared to the quaternion-based cascaded PID algorithm used by Pixhawk, the aerial manipulator platform exhibited significant improvement in positional error in all three coordinate axes. When the manipulator underwent periodic swinging during UAV flight, the UAV's positional and attitude errors using the proposed method were noticeably lower than those of the Pixhawk algorithm. These experiments demonstrate the effectiveness and robustness of the control method proposed in this paper.  
Figure 16: X, Y, Z direction positional tracking of the aerial manipulator system.  
Figure 17: Position and attitude analysis of aerial manipulator. (**a**) Position analysis, and (**b**) attitude analysis.
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 3 Principle of Operation
### Numerical Simulation  
We developed a numerical simulation of the present scheme based on the model presented in Section 2.2 and on the operation procedures described in Sections 3.3 and 3.4. Our simulation neglects both the effects of phases accumulated through propagation and the effects of noise.  
In the following, the simulation is both proposed as a comparison for real experiments and employed to study configurations with more light sources than what is currently possible in our laboratory.
## 2 Materials and Methods
### Design Phase
#### 2.2.6 Modeling of CP Models  
After selecting CP as a promising approach for solving scheduling problems during the analysis phase, the specific solvers still needed to be determined. There were a wide variety of solvers available for CP, and as the solution quality of traditional scheduling algorithms varied depending on the problem to solve, so did the performance of different CP solvers. The MiniZinc Challenge compared different solvers on a set of problem instances. MiniZinc is a solver-agnostic modeling language for defining and solving combinatorial satisfaction and optimization problems. The MiniZinc Challenge provided an overview of the current most successful solvers. OR-Tools is one of the top solvers, winning gold in the international constraint programming competition for several categories every year since 2013. OR-Tools is an open-source software suite for optimization developed by Google [59]. For solving CP problems, they provide two solvers: The CP-SAT solver, a CP solver that uses SAT methods, and the original CP solver. The CP-SAT solver is the primary OR-Tools solver for constraint programming and uses techniques for solving SAT problems along with CP methods. The job-shop scheduling problem is a common problem for the CP-SAT solver. Additionally, another promising solver is CP Optimizer, developed by IBM, which has the goal of focusing on industrial optimization problems. Due to their recent developments and the successful implementation of these solvers in other studies, this study selected OR-Tools and CP Optimizer for optimizing the scheduling problems.  
The OR-Tools implementation for the JSSP and FJSSP was programmed with the Python API and used the CP-SAT solver provided by Google. The version used was 9.4.1874. CP Optimizer is a constraint optimization solver that is part of the IBM ILOG CPLEX Optimization Studio, with a focus on scheduling problems. This study used the  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Group** & **Feature** & **Explanation** \\ \hline Instance-size-related & Jobs & Number of jobs (n) \\  & AGVs & Number of AGVs (k) \\  & Operations & Number of operations (i) \\  & Job–AGV ratio & Job-to-AGV ratio (n/k) \\  & Skewness & Max. of job-to-AGV ratio and AGV-to job-ratio: max(n/k, k/n) \\  & Processing time & Travel time of AGV for each operation \\ Statistical features & Mean job & Arithmetic mean of job completion times \\  & Min job & Minimum job completion times \\  & Max job & Maximum job completion times \\  & Deviation job & Standard deviation of the job completion times divided by the corresponding \\  & Min operation & Minimum operation duration time within the entire instance \\  & Max operation & Maximum operation duration time within the entire instance \\  & Horizon & The estimated total length of a schedule, calculated by summing the estimated \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature description for scheduling problem instances.  
Python API to program the optimization in Python with the CP Optimizer interface, CPLEX version 22.1.0.0, and docplex version 2.23.221.
## 4 Discussion  
### Volumetric Response and Moisture Dynamics  
The observed volumetric instability in untreated expansive soils subjected to cyclic wetting-drying (WD) aligns well with established behaviours documented in highly plastic clays [5; 7]. Notably, the progressive reduction in swelling from approximately 14% to a range of 8-10% over 15 cycles suggests a significant degree of structural degradation and pore collapse, resulting in a densified matrix with diminished water retention capacity. The pronounced shrinkage strains, ranging from \(-2\%\) to \(-7\%\), coupled with observable crack propagation, underscore the potential durability concerns arising from seasonal hydric fluctuations [13].  
In contrast, cement-treated soils exhibited a distinctly different behaviour, with swelling strains increasing from 22% to 30% across successive cycles, despite an initial high strength. This behaviour can be attributed to internal structural evolution, characterised by microcracking resulting from repeated moisture ingress and the precipitation of expansive hydration products, such as ettrinigite, which further promotes volumetric expansion [37]. The increase in saturation moisture content to 43% highlights an increase in macroporosity resulting from the destabilisation of cementitious bonds and the leaching of soluble phases. Notably, the residual swelling appeared to plateau at approximately 15%, indicating the formation of semi-stable matrices capable of retaining volumetric memory through cycles.  
Guar gum-treated soils demonstrated volumetric responses similar to untreated soils regarding swelling-shrinkage amplitude; however, they exhibited distinct moisture migration behaviours. The delayed saturation observed in guar gum-treated soils (\(-6\) days compared to 4 days for untreated soils) and the reduced severity of cracks point to the beneficial role of the polysaccharide in enhancing particle bridging and minimising capillary connectivity [38]. This behaviour is consistent with previous studies indicating that biopolymer treatments effectively limit surface evaporation and manage pore pressure buildup, thereby mitigating damage driven by desiccation [27]. While the mechanical improvements were modest, guar gum-treated soils' enhanced integrity and crack resistance underscore their potential to mitigate hydraulic fatigue. This is evident from the X-ray CT grayscale images of soil columns in Figure 10.
## 2 Methodology
### The Principles of Robustness Computation
#### 2.2.1 Principles of Watermarking Algorithm  
* Principle 1: The watermark is repeatedly embedded into data.  
The volume of the remote sensing image is commonly much larger than the length of binary watermark sequence, thus the watermark is usually embedded more than once into data for robustness enhancement. It is well known that this principle has been applied in current watermarking algorithms [8; 38; 39; 40; 41]. In fact, the repeat embedding strategy can be regarded as the spread spectrum [15], in that the former is a point of view in the spatial domain and the latter a strategy in the transformation domain. Considering that image cropping is conducted mainly in the spatial domain, thus the similar principle can be adequately used to express the strategy in the same domain for convenience.  
Specifically, repetition is represented by \(N\), which means the watermark has been embedded \(N\) times in the host image or that watermark information has been spread \(N\) times in the transformation domain. To simplify the analysis principle and robustness computation model, \(N\) is constrained as positive integers. Figure 3 illustrates the concept of Principle 1.  
* Principle 2: The watermark is distributed uniformly.  
Because of the large size of the remote sensing image together with Principle 1, the watermark is equally distributed throughout the image. If the watermark energy is concentrated in a small part of the spatial domain or the narrow band in the transformation domain, the image fidelity, both in this area, together with the watermark robustness in other parts, will greatly degrade. Hence, Principle 2, which have been also adopted widely [8; 10; 41; 42; 15], represents the tradeoff between watermark imperceptibility and robustness.  
Quantitatively, if a watermark of \(L\) bits length is repeatedly embedded into the remote sensing image, for \(N\) times; in such a situation, Principle 2 suggests the watermark extraction will extract \(a\times L\)\(\times\)\(N\) bits of the watermark sequence from \(a\) percentage amount of the remote sensing image. This is made apparent in Figure 4. It should be pointed out, however, that it is not consistent for the extracted watermark bits in Figure 4 to be exactly situated, in the lower right-hand corner of the watermark information, as can be seen in Figure 3. As noted in Principle 2 the watermark information is not necessarily embedded sequentially. In Figure 4, the extracted watermark bits are regarded as randomly chosen from the whole watermark bits.  
Figure 3: Illustration of Principle 1.
## 3 Experimental Results
### Mitigation of Noise Effects: Ultrasonic System with in-Air Propagation
This can be explained by analyzing the trends over time, for example in Figure 11: The residual variability is related to an oscillation and not to white noise; as shown in [22], this variability of the signal results directly from the limited bandwidth of \(\delta(t)\), the approximated delta function, and thus from limitations of the identification technique, and is not associated with random noise; therefore, it cannot be removed either with filtering techniques such as those proposed in this paper or by averaging over repetitions of the experiment. If we want to draw conclusions about the comparison between the two methods, we can say that, where the noise level is very low, the two methods are equivalent; the method proposed in this work, in addition to not assuming characteristics of stationarity of the system, allows to obtain better results in the presence of high noise variance compared to the signal level.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 2 Moments from 3D Shapes
### Basis Function: Laplace Spherical Harmonics  
If the domain is the sphere \(\mathbb{S}^{2}\subset\mathbb{R}^{3}\), a point \(\boldsymbol{p}\) is characterized by its inclination angle \(\theta\) and azimuthal angle \(\phi\) only. A function \(f\) on \(\mathbb{S}^{2}\) can then be represented through its spherical harmonics. They form a Fourier basis on a sphere much like the familiar sines and cosines do on a line. The spherical harmonic \(Y_{l}^{m}\) is defined by  
\[Y_{l}^{m}(\theta,\phi)=N_{l}^{m}P_{l}^{m}(\cos\theta)\mathrm{e}^{im\phi}, \tag{4}\]  
where \(N_{l}^{m}\) is a normalization factor:  
\[N_{l}^{m}=\sqrt{\frac{2l+1}{4\pi}\frac{(l-m)!}{(l+m)!}},\]  
and \(P_{l}^{m}\) are the associated Legendre polynomial. \(Y_{l}^{m}\) is defined for \(l\) non negative integer and \(m\) integer, such that \(-l\leq m\leq l\). It is enough, however, to compute the spherical harmonics for \(m\geq 0\), as we have the relationship,  
\[\overline{Y_{l}^{m}(\theta,\phi)}=(-1)^{m}Y_{l}^{-m}(\theta,\phi).\]We note the definition of the harmonic polynomial,  
\[e_{l}^{m}(\mathbf{x})=r^{l}Y_{l}^{m}(\theta,\phi), \tag{5}\]  
where \(\mathbf{x}\) is the point with spherical coordinates \((r,\theta,\psi)\). The spherical harmonics are orthonormal:  
\[\int_{0}^{\pi}\sin\theta\mathrm{d}\theta\int_{0}^{2\pi}\mathrm{d}\phi Y_{l}^{m }(\theta,\phi)\overline{Y_{l^{\prime}}^{m^{\prime}}(\theta,\phi)}=\delta_{mm^{ \prime}}\delta_{ll^{\prime}}. \tag{6}\]  
The spherical harmonics moments \(g_{l}^{m}\) are defined as:  
\[g_{l}^{m}=\int_{0}^{\pi}\sin\theta\mathrm{d}\theta\int_{0}^{2\pi}\mathrm{d} \phi f(\theta,\phi)\overline{Y_{l}^{m}(\theta,\phi)}. \tag{7}\]  
Note that these moments are complex, as the spherical harmonics are complex.  
Just like Fourier series are complete, the spherical harmonics are complete on the sphere. It is important to notice, however, that they cannot be computed on a volumetric shape without some modifications, such as the use of solid harmonics [55], or with the introduction of Zernike polynomials, which are presented in the following subsection.