esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup and Results
### Hyper-Parameters Tuning  
In our experiments, the model architecture was enhanced using hidden dimensions of 768 \(\times\) 100, with RoBERTa employed as the pre-trained encoder to generate contextual embeddings. A bi-directional LSTM layer was then applied, with a hidden size ranging from 100 to 200, enabling the model to effectively capture long-range dependencies in both temporal directions. Following this, a Conditional Random Field (CRF) layer was integrated on top of the LSTM outputs. While the CRF layer does not utilize traditional hidden units, it was optimized using negative log-likelihood loss, allowing it to model label dependencies and improve sequence tagging performance for nuanced propaganda detection. To mitigate overfitting, a dropout value of 0.5 was chosen, following [PERSON], et al. [65]. Additionally, the Hyperband algorithm, as described by [PERSON], et al. [66], was employed to compare its performance against popular Bayesian optimization methods for hyper-parameter optimization in ML-MC problems. The ML-MC model, configured with 10 epochs and batch sizes of 16, was tuned using an Adam optimizer. The learning rate was set within the range of 2-5 \(\times\) 10\({}^{-3}\) (with a maximum of 2 and a minimum of 5), and weight decay varied from 0.1 to 0.001 for other hyper-parameters. Table 8 illustrates the optimal hyper-parameter ranges for the specific ML-MC model.
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing
#### 3.2.2 Calibration  
The calibration step ensures that all systematic factors that can affect the geometrical accuracy of the point cloud are accounted for. These factors include misalignment angles between the IMU and the scanner mirror, mirror scale factors, leaver arms between the GPS antenna the IMU and the scanner mirror, range offsets, and intensity tables used to remove random walk in the ranges caused by stronger or weaker return signals [47].  
Basic calibration is achieved by flying three or four lines over the same area at different headings and producing point clouds from these. As a minimum two need to be in opposite headings and slightly offset from each other, with a third perpendicular to the first two. Ideally the area should be free from vegetation and contain hard surfaces with slopes at different orientations. By analyzing the differences between the point clouds from the different lines it is possible to determine correction factors that will make the point clouds match each other [50,51].  
The result of a properly calibrated sensor is that the point cloud from different flight lines of the same area will match properly; conversely if the system is not properly calibrated the point cloud from the lines will not match. This is illustrated with Figure 16 where the point cloud from one flight line over the core of Mayapan was intentionally rotated to simulate a roll bias, causing the data from that line to diverge from the rest (panels on the right of the figure). This is evident in the cross section on the right, where the points from the un-calibrated flight line are depicted in red. The DMS or DEM from a dataset which is not properly calibrated will appear noisy (not sharp and uniform), as compared to the DSM in the left of the image, with evident artifacts, including swath edges (marked with red arrows) as seen on the right side of the shaded relief image top right of Figure 16.
## 3 Results and Discussions
### Photocatalytic Activity
#### 3.5.4. Scavenger Test  
To further confirm the photodegradation process and production of reactive species, the photodegradation process of bentazon was assessed by trapping experiment under UV light. 2-propanol and ascorbic acid were used as scavengers for hydroxyl radicals (\"OH) and superoxide radicals (-O\({}_{2}\)\({}^{-}\)). A dramatic decrease in the photodegradation efficiency was observed, which suggests the dominant role of the ROS species in the degradation of bentazon as shown in Figure 8. Without a scavenger, the obtained degradation efficiency of the TiO\({}_{2}\) catalyst was 99%. However, it is observed that the degradation efficiency is reduced from 99% to 27% and 13% in the presence of 2-propanol and ascorbic acid, respectively, which confirmed that the reactive species (\"OH and -O\({}_{2}\)\({}^{-}\)) are produced and have a dominant effect in the photodegradation of bentazon.
# Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry
## 4 Proposed Approach
### Branch and Bound Algorithm
#### 4.1.1 Evaluation of \(C_{max}\)  
In order to determine a lower bound for a given solution, we need to calculate the makespan before proceeding to the resolution itself. Often, a job \(J_{i}\) cannot begin before their earliest start time \(r_{ij}\) and must be executed before the limited validity date \(v_{ij}\).  
Consider \(\Gamma=J_{1},J_{2},\ldots,J_{i},\ldots,J_{k}\)as the set of first supposed jobs to be executed in this order with \(k\leq n\) and U as the set with the rest of the jobs at any time.  
We have the following:  
**For \(i=1\):**  
\(\alpha_{11}=t_{11}+\theta_{11}\);  
\(\alpha_{12}=max(\alpha_{11},r_{12})+\theta_{12}\);  
\(\alpha_{13}=max(\alpha_{12},r_{13})+\theta_{13}\).  
**For \(1<i\leq k\)**  
\(\alpha_{i1}=max(\alpha_{(i-1)1},r_{i1})+\theta_{i1}\);  
\(\alpha_{i2}=max(\alpha_{(i-1)2},\alpha_{i1},r_{i2})+\theta_{i2}\);  
\(\alpha_{i3}=max(\alpha_{(i-1)3},\alpha_{i2},r_{i3})+\theta_{i3}\).  
We now consider the last job \(J_{n}\). The best case is that this job does not depend on the machine \(M_{2}\) and the machine \(M_{3}\). The makespan \(C_{max}\) is then expressed by the following equation:  
\[C_{max}=\alpha_{k1}+\sum_{i\in U}\theta_{i1}+\theta_{n2}+\theta_{n3} \tag{8}\]  
Figure 1: The input and the output of the propoed algorithms.  
This leads to the following equations, considering the shortest processing time:  
\[C_{1}\geq\alpha_{k1}+\sum_{i\in U}\theta_{i1}+\min_{j\in U}(\theta_{j2}+\theta_{ j3}), \tag{9}\]  
\[C_{max}\geq\alpha_{k2}+\sum_{i\in U}\theta_{i2}+\min_{j\in U}(\theta_{j3}), \tag{10}\]  
and finally  
\[C_{max}\geq\alpha_{k3}+\sum_{i\in U}\theta_{i3}. \tag{11}\]  
Based on the results expressed by Equations (2)-(4), the lower bound of \(C_{max}\) can be expressed by the following inequality, with \(j\in U\):  
\[C_{max}\geq\max\{C_{1};C_{2};C_{3}\} \tag{12}\]  
with:  
\[C_{1}=\alpha_{k1}+\sum_{i\in U}\theta_{i1}+\min_{j\in U}(\theta_{j2}+\theta_{ j3}) \tag{13}\]  
\[C_{2}=\alpha_{k2}+\sum_{i\in U}\theta_{i2}+\min_{j\in U}(\theta_{j3}) \tag{14}\]  
\[C_{3}=\alpha_{k3}+\sum_{i\in U}\theta_{i3} \tag{15}\]
## 3 Methods and Results
### Simulated Surgical Tasks  
The setup for conducting simulated surgical operations, such as lifting and dissecting cancerous tissues, as shown in Figure 18, which are fundamental steps in ESD, involved integrating a grasper into the effector over-tube. A circular silicone piece, approximately 27 mm in diameter, was affixed onto the silicone colon phantom to simulate a cancerous tissue on the colon. As shown in Figure 18b,c, the grasper was manipulated to approach and grasp the simulated lesion, subsequently lifting the simulated tissue. Additionally, Figure 18d,e demonstrates the robot's capability to execute a'sweep' motion, with the over-tube moving leftward and rightward to simulate the diathermy dissecting the lesion (Supplementary Materials). These basic simulated surgical operations were conducted to showcase the potential applicability of the robot in MISs. Furthermore, the robot's durability was evaluated through hours of testing without observed breakage, indicating its sufficient durability as a single-use surgical device.
# Aerodynamic Simulation of Helicopter Based on Polyhedron Nested Grid Technology
## 3 Validation of CFD Simulation Method
### Coaxial Rotor in Hover  
In this section, the flight test data [31] of a coaxial rotor provided by NASA are selected to verify the CFD simulation method and grid method proposed in this paper. In this simulation, the distance between the upper and lower rotors is 0.1 m.  
The parameters of the coaxial rotor model are shown in Table 5:  
Four different test states were selected for verification. The rotation speeds of the four states were all 3100 RPM, and the collective pitch of the upper and lower rotors were the same, which were 3\({}^{\circ}\), 6\({}^{\circ}\), 9\({}^{\circ}\), and 12\({}^{\circ}\). The total thrust coefficient and torque coefficient of the coaxial rotor system under each condition were calculated and compared with the test data. The calculation results are shown in Figure 13.  
Figure 12: Pressure coefficient distribution in the center line of the top fuselage. (**a**) \(\mu\) = 0.151. (**b**) \(\mu\) = 0.231.  
Figure 13: Variation of thrust coefficient and torque coefficient with collective pitch of the coaxial rotor in hover.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Blades** & **2 + 2** \\ \hline Plane shape of blade & Rectangle \\ Airfoil & NACA0012 \\ Chord length \(c\) & 0.06 m \\ Rotor radius R & 0.38 m \\ Undercut \(R_{cut}\) & 0.21 R \\ Rotor solidity \(\delta\) & 0.2 \\ Torsion angle & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detailed parameters of the coaxial rotor.  
It can be seen from Figure 13 that the calculation values are consistent with the test data and the error is within the allowed range, thus proving that the CFD simulation method and the grid method proposed in this paper are suitable for the simulation of the interference flow field of coaxial rotors in hover and can achieve a high calculation accuracy.
## 6 Conclusions
According to the terminology discussed in the introduction, our model is currently categorized as a digital model since it simulates pre-existing ocean environments using past data without automatic data exchanges with the real world. However, it is imperative to undertake this fundamental step towards developing a digital twin of an ocean for the eventual realization of fully developed ocean digital twins. We visualized the validated model results in the three-dimensional domain to demonstrate the concept of a digital twin of the SIS system. This visualization illuminated the bathymetric, circulation, and water temperature characteristics of the SIS and is expected to help various stakeholders, such as marine navigators, decision-makers, and citizens, understand the sea status both easily and intuitively. Finally, the structure of the modeling system was illustrated to guide the direction of our future study. The digital twin of the SIS discussed in this study is in the very early stage, and more modules will be added to represent wind waves, sediment transport and sedimentation, and particle tracking with the assimilation of oceanic data to produce and provide diverse information to various stakeholders. Then, a platform for the dissemination of this information will also be further developed. Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, J.-S.J. and H.S.L. ; software, visualization, J.-S.J. ; supervision, project administration, funding acquisition, H.S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The initial and boundary conditions and observed data are available from the authors upon request. FES2014 was produced by Noveltis, Legos and CLS and distributed by Aviso+, with support from CNES ([[https://www.aviso.altimetry.fr/](https://www.aviso.altimetry.fr/)]([https://www.aviso.altimetry.fr/](https://www.aviso.altimetry.fr/)) accessed on 4 October 2022). The Japanese Coastal Ocean Monitoring and Forecasting System (MRI. COM-JPN) developed by the Meteorological Research Institute of the Japan Meteorological Agency, Japan, was used. The ERA5 provided by the Copernicus Climate Change Dataset of ECMWF was used after the interpolation and calculation of the variables demanded by SCHISM. We appreciate the thoughtful discussions and useful comments from [PERSON] at DPRI, Kyoto University, and [PERSON] at Virginia Institute of Marine Science. We appreciate the anonymous reviewers for their constructive comments to improve our manuscript. The authors declare no conflict of interest. Figure 16: Time series of the observed and modeled temperatures at twelve stations. W6 was excluded because its location is close to W5. The locations of the stations are presented in Figure 1a.
## 3 The CSES Scientific Mission  
The High-Energy Particle Detector (HEPD) is a light and compact (40.36 cm \(\times\) 53.00 cm \(\times\) 38.15 cm, total mass \(\sim\)45 kg) payload designed and built by the Limadou team, the Italian branch of the CSES Collaboration. The apparatus is made up of a series of sub-detectors:  
* A tracking system, including two 213.2 mm \(\times\) 214.8 mm \(\times\) 0.3 mm double-sided silicon microstrip planes. Each silicon plane is divided into three identical independent sections, each of which containing two silicon sensors.
* A trigger system, consisting of one EJ-200 plastic scintillator layer segmented into six paddles (20 cm \(\times\) 3 cm \(\times\) 0.5 cm apiece), each one read out by two Photo-Multiplier Tubes (PMTs).
* A range calorimeter composed of two sections: The upper part is a tower of 16 EJ-200 plastic scintillator planes (15 cm \(\times\) 15 cm \(\times\) 1 cm), each one read out by two PMTs. The lower part is a 3 \(\times\) 3 matrix of Lutetium-Yttrium Oxyorthosilicate (LYSO) inorganic scintillator crystals--5 cm \(\times\) 5 cm \(\times\) 4 cm each; each of the nine crystals is read out by one PMT located at its bottom side; and
* an anti-coincidence (VETO) system composed of five EJ-200 plastic scintillator planes (0.5 cm thick), each one read out by two PMTs.  
The instrument is optimized to detect electrons in the 3 to 100 MeV energy range and protons between 35 and 250 MeV, as well as light nuclei. In these three years of flight, after a long period of calibration and testing, HEPD has been able to measure fluxes of low-energy galactic protons with great precision [40] and to observe the effects of the geomagnetic storm of August 2018 [41]. All the capabilities assessed in these years make HEPD well suited for the analysis of low-energy electrons and protons with good angular resolution and stability over time, which is particularly useful in highly anisotropic flux conditions like the ones encountered in SAA. More technical details can be found in [32; 42; 43].
# Bacon, a Lightweight Deep Reinforcement Learning Benchmark Library for Flow Control
## 1 Introduction  
In recent years, the area of deep-reinforcement-learning-based flow control has undergone a rapid development, with a surge of contributions on topics such as (but not limited to) drag reduction [1], collective swimming [2], and heat transfers [3]. Unlike traditional methods, deep reinforcement learning (DRL) enables the learning of complex control strategies directly from data, thereby alleviating the effects of local minima and the generalizability of the algorithm for other scenarios [4]. Yet the inherent reproducibility issues of DRL algorithms [5], as well as the variety of computational fluid dynamics (CFD) solvers and the possible variability of environment design among the different actors of the community, make it hard to accurately compare algorithm performances, thus hindering the general progress of the field. Moreover, the standard DRL benchmarks (such as the MUJOCO package [6], or the Atari games from arcade learning environments (ALE) [7]) have a limited relevance in the context of benchmarking DRL methods for flow control as their dynamics, observation spaces, computational requirements, and action constraints display substantial differences to those of numerical flow control environments.  
From a general point of view, flow control problems are characterized by a simulated physics environment spanning over at least two dimensions, possibly including time. The control is performed by an agent that modifies boundary conditions, source terms, or other components of the domain in order to optimize a given objective. A notable difficulty is, therefore, designing a robust and at the same time efficient environment that is able to cope with a wide range of actions while preserving low and stable runtimes [8]. One way of minimizing the computational cost is lumping the Navier-Stokes equations, the backbone of most fluid mechanics problems, by limiting the dimensionality and restricting its terms to the dominant ones for the problem at hand. This way, it is possible to retain the main features of the flow while tuning the schemes and discretizations towards higher performances.  
In the present contribution, we lay the first stone of a numerical flow control benchmark library which will allow DRL algorithms to systematically assess methodological improvements on physically and numerically relevant problems. The design of the test cases voluntarily limits the computational cost of the solvers, making this library a first benchmarking step before testing on more complex and CPU-intensive cases.  
The organization is as follows: a short presentation of the library and its general characteristics is proposed in Section 2, after which the environments are introduced in a systematic way in Section 3. For each case, the physics of the problem are described, followed by insights on the discretization. Then, the environment parameters and specificities are described, after which baseline learning curves and details on the solved environment are provided. Finally, the perspectives for the present work are exposed in a conclusive section.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components
#### 2.2.1 Device Layer  
Actuators are devices that can perform some actions based on the data generated by sensors, e.g., electronic motors, drug pumps, etc. On the other hand, according to their installation type, these devices can be divided into three categories (i.e., implantable, wearable, and fixed devices). These categories are briefly discussed here.  
Implantable Medical Devices are implanted into the human body as shown in Figure 4. The most common devices belonging to this category are the Pacemaker [42], Neuro-stimulators [43], Insulin Pumps, Glucose Monitoring Systems [44], Gastric Stimulators [45], Foot Drop Implants [46], Cochlear Implants [47], and Drug Pumps [48], etc.  
Fixed Medical Devices are related to the third category of devices that can be used for different tests in the laboratory, such as X-ray machines. The medical devices in this layer can be classified according to their functionalities or installation type. According to their functionalities, these devices can be divided into two types (i.e., sensors and actuators) briefly discussed here.  
Sensors generate data by sensing physical parameters from their environment, e.g., temperature, pressure, etc. [49], ventilators, and ECG machines installed in medical labs and diagnostics rooms. These machines are not mobile because of their sizeable physical dimension. These machines are computerized, controlled, and connected to the networks for remote monitoring. The data sent from these devices are not secure and prone to data-stealing threats. It is essential to ensure the security of these devices because these devices gather and forward the data of multiple patients on an hourly basis compared to the devices working remotely. Therefore, these devices draw more attention from the intruders. A summary of data transmission rate, frequency spectrum, range, etc., is given in Table 2.  
Figure 3: Five-Layer Architecture for IoHT.
## 4 Results of the Experiment
### Discuss  
This experiment compared the accuracy of four drought indices, namely TVDI, RVI, NDWI, and VTCI, in detecting vegetation drought in Laibin City. TVDI demonstrated a strong negative correlation in Pearson's correlation studies. Specifically, in March and April 2022, as well as January 2023, the Pearson correlation coefficients with the relative humidity of the 0-10 cm soil layer were \(-0.79\), \(-0.82\), and \(-0.774\), respectively; Kendall's tau-b correlation coefficients were \(-0.649\), \(-0.66\), and \(-0.628\). When considering the same months, the Pearson correlation coefficients with the 0-20 cm soil layer were \(-0.783\), \(-0.795\), and \(-0.525\); Kendall's tau-b correlation coefficients were \(-0.614\), \(-0.621\), and \(-0.68\). The other three indices exhibited a positive correlation, with their absolute values all being lower than that of TVDI. Therefore, it is evident that TVDI demonstrates a higher correlation in reflecting soil relative humidity. Compared to the other three drought indices, TVDI exhibits higher sensitivity and accuracy in capturing vegetation drought conditions, effectively reflecting the degree of drought in different regions of Laibin City.  
However, despite TVDI's advantages in detecting vegetation drought, it also faces some limitations in practical applications. The calculation of TVDI relies on vegetation coverage, and different vegetation types exhibit varying responses to drought, potentially leading to suboptimal performance of TVDI on specific vegetation types. Additionally, the spatial resolution of remote sensing data affects the accuracy of TVDI. At lower spatial resolutions, TVDI struggles to detect drought conditions in small areas, thereby compromising the accuracy of drought monitoring. The calculation of TVDI involves surface temperature, which is influenced by various factors such as atmosphere, soil, and vegetation, potentially causing errors in reflecting drought conditions. To address these shortcomings, future research can further optimize the calculation method of TVDI by incorporating measured soil moisture data, enhancing the temporal resolution and spatial coverage of remote sensing data, and integrating multiple drought indices for multi-source data fusion. This approach aims to improve the accuracy and practicality of vegetation drought detection.