esa-sceva/satcom-chunk-collection huggingface dataset:
# Particle Formation Mechanism of TiCl\({}_{4}\) Hydrolysis to Prepare Nano TiO\({}_{2}\)
## 4 Results and Discussion  
### Particle Formation Process  
In a specific experimental condition, a mixture containing 1 mL of TiCl\({}_{4}\), 6 mL of C\({}_{6}\)H\({}_{15}\)NO\({}_{3}\), 15 mL of H\({}_{2}\)O, and 30 mL of C\({}_{2}\)H\({}_{5}\)OH is prepared and maintained at a pH of 7 and a temperature of 25 \({}^{\circ}\)C for a duration of 4 h. The formation and growth behavior of TiO\({}_{2}\) particles is observed to change over time. As depicted in Figure 2, the nucleation of TiO\({}_{2}\) particles primarily occurs between 65 and 80 s, reaching a peak count of 195 particles per cubic micron. As the reaction progresses, the initially formed TiO\({}_{2}\) nuclei undergoes a process of agglomeration and secondary nucleation. Over a span of 240 min, particles of varying sizes are formed, as indicated in Figure 3c. Figure 3a,b suggest that the first 5 min are crucial for the formation of TiO\({}_{2}\) monocytes, as these quickly enter the second stage of nucleation and growth, leading to the formation of TiO\({}_{2}\) particles of different sizes. The mass fraction curve centers around D\({}_{50}\) at this point, displaying a normal distribution with the highest mass fraction at 14.01%. However, at the 60 min mark, despite continued nucleation and growth, the number of small particles declines due to agglomeration. The mass fraction curve is primarily centered around D\({}_{50}\) = 0.769 nm, following a normal distribution, with the mass fraction reduced to 8.21%. Beyond 60 min, with the complete hydrolyzation of TiCl\({}_{4}\), the formation of small TiO\({}_{2}\) particles almost comes to a halt and the mass fraction of D\({}_{50}\) gradually decreases to zero. Therefore, it is determined that the optimal reaction stage falls within the first 60 min.  
Figure 2: The curve of the number of TiO\({}_{2}\) crystal nuclei over time.
## 6 Conclusions and Future Work  
In this research, we introduced a new approach for using PQC for the classic channel of QKD and presented an optimal combination for both encryption and authentication. The choice of the best security level for cryptographic algorithms like CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon ultimately depends on striking the right balance between the required level of security and the performance and limits on resources set by the intended use case. These cryptographic techniques each provide a range of security levels that are differentiated by many factors, such as key sizes, signature sizes, and computing demands. Higher security levels, including Kyber 1024, Dilithium 5, and Falcon 1024, often offer more powerful security assurances, making them desirable options for applications where strong security is crucial. It is important to understand, nevertheless, that this increased security frequently results in larger signatures and slower cryptographic procedures. The \"best\" security level must, thus, be specifically customized for the application in question's unique security needs and performance limits. For instance, Kyber 512 and Dilithium 3 represent well-balanced choice, offering a commendable compromise between security and performance. They can be widely adopted and considered secure for most practical purposes. However, we should consider that the Falcon worked well in terms of signing and verification but its performance was not good in the key generation phase.  
There are various unexplored options for future investigation. Future research could delve deeper into symmetric encryption methods in order to gain a more in-depth understanding of AES's security and its future among the other schemes. Additionally, an investigation into quantum secure communication in quantum repeaters is also significant. This study creates the groundwork for future study and allows researchers interested in the implementation of PQC schemes to contribute to the continuous advancement of knowledge in this field.  
Conceptualization, F.R.G., M.Y. and N.E.; methodology, F.R.G. and M.Y; software, F.R.G. and M.Y; validation, F.R.G., N.E. and M.Y; formal analysis, F.R.G. and M.Y.; investigation, F.R.G., N.E. and M.Y.; resources, M.Y. and N.E.; data curation, F.R.G.; writing--original draft preparation, F.R.G.; writing--review and editing, F.R.G., N.E., Y.A. and M.Y.; visualization, F.R.G., N.E. and M.Y.; supervision, M.Y.; project administration, N.E. and M.Y.; funding acquisition, N.E. All authors have read and agreed to the published version of the manuscript.  
Glasgow Caledonian University provided the funding for open access for this research.  
The authors declare no conflicts of interest.
## 2 Results and Discussion
Figure 4: Stacked column representation of the terpene composition of the three oil fractions (F1, F2 and F3) (expressed as mg/kg oil, average \(\pm\) S.D., data in Table 2), obtained from SPME/ GC-MS analyses. Figure 5: Overlapped chromatograms obtained from the SPME/GC-MS of mixed and diluted (Mix F1 \(\div\) 3 dil.) oil fractions and oil samples from MAE at 60 \({}^{\circ}\)C and 100 \({}^{\circ}\)C (Ext. 60, Ext. 100). All the other compounds were present in amounts that were higher by between 15 (8-3-carene) and 1100% (linalool) compared to the starting olive oil (Mix F1 \(\div\) 3 dil. ), with an overall increase of 70% and 86% in the total amount of terpenes found in the oils (Table 3), for Ext. 60 and Ext. 100, respectively. Although the trend seems to indicate that extraction at 100 \({}^{\circ}\)C gave a higher amount of terpenes than extraction at 60 \({}^{\circ}\)C, the differences were not statistically significant (see [PERSON]'s post hoc test groups for Totals in Table 3). The highest terpene total amounts in oils obtained from Bedrocan(r) and Bediol(r) by [PERSON] et al. (2019) were around 3700 and 1900 mg/kg, respectively, when _Cannabis_ infloresences were not exposed to the decarboxylation step before extraction. Around 1500 and 900 mg/kg, respectively, were obtained using the Romano and Hazekamp method after decarboxylation ([PERSON] and [PERSON], 2011), demonstrating the significant loss of terpene components. Considering that a total terpene amount of 11,424 mg/kg dry matrix is present in Bedrocan(r), the oil obtained (1 g inflorescences/10 mL of olive oil; d = 0.92 g/mL) without decarboxylation (3700 mg/kg oil) showed a terpene recovery of only 34%. As reported in Table 4, if we consider that the initial content of terpenes in inflorescences is 91,324.74 \(\pm\) 2774.36 mg/kg dry matrix, the recovery percentage was close to 58% after decarboxylation (predominantly monoterpenes), while it reached 98.48 \(\pm\) 6.83% and 107.51 \(\pm\) 7.28% after extraction at 60 and
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 2 Related Work
### Lidar Point Cloud Segmentation  
The task of point cloud segmentation describes the process of assigning a class label to every point of the input point cloud. As the input point cloud is a sparse, irregular 3D tensor, classic convolution neural networks cannot be directly applied to this form of input data. [4] therefore projects the data to a spherical image format to process it with 2D convolutions. The network consists of an encoder-decoder structure, to be able to generate a class label for every input pixel.  
Similarly, [5] projects the point cloud data to a 2D frame. The paper augments the previously mentioned approach by taking into account a full 360\({}^{\circ}\) point cloud. Furthermore, it performs additional filtering for projection errors and provides an efficient implementation open source.  
[PERSON] [6] is the first to segment point cloud data without performing an intermediate feature transformation. The paper introduces the pointnet network structure, which can process an arbitrary number of input points by processing the points individually. However, their network does not consider neighboring points, as it is standard in convolutional neural networks. Their continued work [7] enhances the implementation to create local features from neighboring points while still operating on point cloud data directly. The applied operations still only process one point at a time.  
[PERSON] [8] transfers the convolution approach from grid-based data to 3D point cloud data. The network processes a target point and its neighboring points by a defined number of kernel points analogous to 2D image convolutions. In this manner, spatial relationships can be learned directly from the input points by performing a so-called Kernel Point Convolution (KPConv).
## 3 Materials and Methods
### DRL-Based Dynamic Collaborative Resource Scheduling Algorithm
In other words, a random mini-batch of data \(<\)\(s_{t}\),\(s_{t+1}\),\(a_{t}\),\(r_{t}\)\(>\) are sampled to update DNN parameters by \(\theta_{t+1}\leftarrow\theta_{t}-\alpha\cdot\sum\limits_{t}d\xi_{t}/d\theta_{t}\). 8. Train DNN parameters using extracted data entries based on Bellman optimization equations, formulated as \(Q^{\pi_{t}}(s_{t},a_{t})=r(s_{t},a_{t})+\gamma\cdot max_{a_{t+1}}Q^{\pi_{t}}( s_{t+1},a_{t+1})\), where \(\gamma\) is a discount factor. The Bellman error, formulated as \(\xi=0.5\)-\(E_{(s_{t},a_{t})}\left[Q^{\pi_{t}}(s_{t},a_{t},\theta_{t})-r(s_{t},a_{t})- \gamma\cdot max_{a_{t+1}}Q^{\pi_{t}}(s_{t+1},a_{t+1},\theta_{t})\right]\), can be minimized to update DNN parameters by the gradient descent method. Then, the value of actions is fit well by DNN if the Bellman error converges after updating the DNN parameters. 9. Repeat the above steps until the Q-value function converges. When using the model, the current computational resource status on the edge data center is also inputted into the model along with user requirements, and the model will return a dynamic, collaborative resource scheduling strategy. Notably, to optimize the action space and facilitate the model training process, a heuristic algorithm called \"Pricing Thinking oriented Greedy Strategy based Computing Resource Scheduling Algorithm (PT-GSCRSA)\" is applied to the above proposed DRL-based algorithm. The main idea of PT-GSCRSA is to assign the most expensive computing payment of AI service to the lowest cost of the edge data center. In detail, when multiple AI services arrive, services and processing costs of edge data centers are sorted in descending order and ascending order, respectively. Then, we assign the AI service with the most expensive payment to the edge data center with the lowest cost for data processing. If the requested computing resources of AI services are larger than the computational resource capacity of the allocated edge data center, the optimal way to split AI training data into multiple data segments is to assign all remaining computational resources of the edge data center to the AI service. Otherwise, if the requested computing resources of AI services are smaller than the computational resource capacity of the allocated edge data center, all AI training data are assigned to the edge data center, and the remaining computational resources of the edge data center are updated. When the workload of the edge data center with the lowest cost is full after processing part of the AI training data, we assign the remaining AI training data to the edge data center with the second lowest cost. Repeat the above steps until all AI training data are allocated to multiple edge data centers. The flowchart of DRL-based dynamic computational resource scheduling algorithm is shown in Figure 2.
## 1 Introduction
### Related Work
Existing research results have so far only focused on a specific model [32]. For intelligent star clusters, satellites not only exchange local real-time information, but also exchange information published in the public network as a relay. Bidirectional relay (TWR) technology represented by PNC is one of the most promising technologies for improving the spectrum efficiency of relay networks. MIMO-TWRC not only reduces the number of independent streams received by the relay, but also simplifies the complexity of signal processing at the relay end. In order to achieve signal alignment, the authors of [33] first proposed a transmission scheme based on zero forcing (ZF) precoding. In the multiple access (MA) stage, the two source nodes use channel inversion and power control to decouple the MIMO-TWRC channel [34; 35; 36] and they are able to achieve signal alignment at the relay. It is noted that the ZF precoding scheme in [33] performs poorly when the channel conditions are poor. In [37], an improved phase alignment scheme is proposed, which improves performance through regularized channel inversion, vector perturbation, and power adjustment optimization techniques. In addition, ref. [38] proposes a feature direction alignment scheme. In this scheme, the source node precoding matrix is designed based on channel inversion, and the transmitted signal space is optimized by using the directional rotation based on eigenvectors. Based on the additional degrees of freedom obtained by redundant antennas, the authors of [39] proposed a generalized signal space alignment using space compression technology. This makes the application scenarios of signal space alignment more universal. However, the algorithm did not optimize the signal alignment space, and the improvement of bit error performance was limited.
## 3 Results
### Thematic Categories  
#### 3.2.1 Development Forecast  
Five studies examined the use of CDSSs as a tool to predict the evolution that patients may have after heart surgery [55; 56; 57; 57; 68]. All five positive results obtained on the use of support systems to assess the evolution of patients in ICUs by analyzing multiple biomedical parameters [55; 56; 57; 67; 68]. [PERSON] et al. stated that the use of CDSSs can allow us to detect a rapid deterioration of a patient and be able to predict and achieve better results of concordance compared to classic scales of the sequential organ failure (SOFA) score and the simplified acute physiology score (SAPS) II [55]. The use of CDSSs, through the analysis of variables and clinical data associated with the function of the organs, results in a 45% higher F-score than the more classic tools (SOFA and SAPS-II are 26 and 29%, respectively) [55].  
Another study by [PERSON] et al. involved the development of a CDSS for the prediction of periventricular leukomalacia, resulting in improved survival rates of neonates who underwent cardiac surgery. Survival rates were improved due to the average reduction in the time between surgery and diagnosis to six days. In addition, the results showed a high F-score (0.8) for the way the system ranks [56].  
[PERSON] et al. assessed the probability of needing readmission in ICUs with the development of a prediction model using automatic learning techniques. With the use of the machine-learning-derived model, a lower area of the curve (0.76) was obtained, which was more favorable than the \"Stability and Workload Index for Transfer \"(SWIFT) score (0.65) or the \"Modified Early Warning Score\" (MEWS) (0.58). The results showed that this model better predicted ICU readmission compared to the SWIFT score and the MEWS. In addition, it had a higher area under the receiver operating curve (AUC) for predicting readmissions (\(p<0.001\)) [68]. Other studies proved the effectiveness of these support systems in predicting the evolution of the patient in the weaning process and with the MIMIC-II [57; 67].
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Science and Technology Advances
#### 3.1.4 Evolution of Heritage Missions  
Heritage missions will continue weather and climate monitoring from geostationary (GEO) and low Earth Orbit (LEO). GEO visible/infrared (VIS/IR) new sensors at very high spatial and temporal resolution are now enhancing the observational capabilities of clouds and precipitation systems [136; 137; 138]. The development of PMW sensors for GEO satellites has been ongoing for more than a decade [139], but the concept seems at the moment to have been superseded by the smallsat constellations. However, it may come back in the near future, as some countries are continuing feasibility studies.  
LEO PMW missions are also evolving by adding additional high-frequency channels, especially for the observation of ice clouds [140]. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Space Agency (ESA) will soon launch the EUMETSAT Polar System-Second Generation (EPS-SG) satellite, which will host the Ice Cloud Imager (ICI) with frequencies from 183 to 664 GHz. ICI will observe cirrus clouds, cloud ice water path, cloud ice effective radius, and cloud altitude, while at the same time providing vertical humidity profiles and vertical profiles of hydrometeors (cloud ice, graupel and snow), as well as water vapor. One more effort aimed at covering high latitude water cycle components is currently being undertaken by the European Union (EU) with its Copernicus Imaging Microwave Radiometry (CIMR, [141]), designed for monitoring the rapidly-changing Arctic environment; the sensor will also contribute to the existing constellation for precipitation measurement.  
International organizations such as GCOS and the Global Ocean Observing System (GOOS; [[https://www.goosocean.org](https://www.goosocean.org)]([https://www.goosocean.org](https://www.goosocean.org)), last accessed 7 August 2019) have developed approaches to help coordinate existing efforts and the planning of future systems, but they lack the authority and funding necessary to establish and manage an observing system at the global scale. The Coordination Group for Meteorological Satellites (CGMS, [[https://www.cgms-info.org/index_php/cgms/index](https://www.cgms-info.org/index_php/cgms/index)]([https://www.cgms-info.org/index_php/cgms/index](https://www.cgms-info.org/index_php/cgms/index)), last accessed 7 August 2019) strives to match the strategies of the organizations that launch and maintain in orbit the meteorological satellite fleet worldwide. CGMS receives inputs and support from its International Precipitation Working Group (IPWG, [[http://ipwg.isac.cnr.it](http://ipwg.isac.cnr.it)]([http://ipwg.isac.cnr.it](http://ipwg.isac.cnr.it)), last accessed 8 August 2019) [142].
## 4 Algorithm Analysis
### Message Flow between Nodes
#### 4.1.2 Message Flow during Path Selection  
After clustering, cluster head nodes begin to build a connected path. When a node's distance to the sink node is less than \(d_{hot}\), such as \(p_{2}\) in Figure 6c, the node sends a request message to the sink node, and the sink node replies with a confirmation message to the node; the process is shown in Figure 8a. When a node's distance to the sink node is greater than \(d_{hot}\) and there are other cluster head nodes within its \(R_{c}\_max\), such as \(p_{6}\) in Figure 6c, it has a message exchange with a cluster head node, which is shown in Figure 8b. In a similar way, when a cluster head node has no neighbors within its \(R_{c}\_max\), such as \(p_{1}\) in Figure 6c, it has a message exchange with a non-cluster head node; the message flow between them is shown in Figure 8c.
## 4 Experimental Results and Analysis  
The dataset used in this study was constructed based on continuous satellite target tracking image data acquired from the Changchun Artificial Satellite Observation Station of the National Astronomical Observatories, Chinese Academy of Sciences. The dataset consisted of 1000 consecutive satellite tracking images, all resized to a resolution of 256 \(\times\) 256. A total of 50% of the dataset was used as the training set, and the remaining 50% was used as the test set. In this section, we quantitatively and qualitatively evaluate the performance of the proposed model.
## 3 Results and Discussions
### Damage Classification Using the Deep Learning Approach  
The analysis and processing of the AE signals collected during the tensile test have been explained in the previous section. These collected AE signals are analyzed by CWT in the time-frequency domain and visualized with the spectrogram. The frequency-time content made it possible to categorize the AE signals based on their similarities.  
This categorization is shown in Figure 15. The CNN is used to efficiently classify groups of AE signals based on their similarities. The first mode analysis is used to make a comparison between the CNN models considered in this work (AlexNet, SqueezeNet, the new model). The second mode of analysis represent the contribution of a new, simple CNN model, but with better configurations. Finally, the minimum set achieves a better classification of AE signals. The stochastic gradient descent algorithm with momentum is used to train the CNNs. The initial learning rate is set to 0.0001, the maximum number of epochs for learning is 50, the mini batch has 30 elements, and there are three validation frequency observations. In order to achieve a better comparison of the results, the training  
Figure 14: (**a**) Spectrogram of the AE signal from the Elastic Stage, (**b**) Spectrogram of the AE signal from the Plastic Stage.  
Figure 13: (**a**) Spectrogram of the AE signal from Tx, (**b**) Spectrogram of the AE signal from Ty, (**c**) Spectrogram of the AE signal from Tz, and (**d**) Spectrogram of the AE signal from T45.  
validation, and testing of the three different CNNs were performed in the same laptop with the configuration. The processor is 11 th Gen Intel(R) Core i7-1195G7 and has a speed of 2.92 GHz, with 16.0 GB of RAM.  
The total number of images from each training mode is separated into three parts: 50% for training the CNN, 25% for validating its accuracy, and 25% for testing to rule out the overfitting of results.
# Phase Plane Trajectory Planning for Double Pendulum Crane Anti-Sway Control
###### Abstract  
In view of the double pendulum characteristics of cranes in actual production, simply equating them to single pendulum characteristics and ignoring the mass of the hook will lead to significant errors in the oscillation frequency. To tackle this issue, an input-shaping double pendulum anti-sway control method based on phase plane trajectory planning is proposed. This method generates the required acceleration signal by designing an input shaper and calculates the acceleration switching time and amplitude of the trolley according to the phase plane swing angle and the physical constraints of the system. Through this strategy, it is ensured that the speed of the trolley and the swing angle of the load are always kept within the constraint range so that the trolley can reach the target position accurately. The comparative analysis of numerical simulation and existing control methods shows that the proposed control method can significantly reduce the swing angle amplitude and enable the system to enter the swing angle stable state faster. Numerical simulation and physical experiments show the effectiveness of the control method.  
crane anti-sway; phase plane trajectory planning; double pendulum system; input shaping; double-step acceleration +
Footnote †: journal: _Algorithms_