esa-sceva/satcom-chunk-collection huggingface dataset:
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 4 Enrichment of Low-Grade Pyrophyllite Ore
### Attrition-Scrubbing  
The Attrition-scrubbing technique is a simple beneficiation process in which the mineral particle is scrubbed under a high-slurry-flow speed, which also allows the particles to be affected by each other. This attrition leads to friction and collisions between the particles themselves. The attrition cell walls, impellers, and deflectors cause scrubbing, abrasion, and particle disintegration. This technique has wide applications for removing clay minerals from low-grade ones and has proven to be efficient in enriching several mineral ores, such as uranium and sand [103]. Moreover, this technique was shown to effectively concentrate low-grade pyrophyllite ore by significantly increasing the Al\({}_{2}\)O\({}_{3}\) content and decreasing the SiO\({}_{2}\) content [104]. In another study, the attrition-scrubbing method was used to upgrade pyrophyllite ore that contains quartz and kaolinite as gangue minerals (Table 6). The study showed a significant improvement in the Al\({}_{2}\)O\({}_{3}\) grade, where a very high Al\({}_{2}\)O\({}_{3}\) grade of 29.33% was obtained for the finest size (\(-\)75 microns) compared to 21% for the Al\({}_{2}\)O\({}_{3}\) grade in the feed, alongside a decrease in SiO\({}_{2}\) content to 61%, indicating high purity rates for pyrophyllite. Further, this study indicated that the attrition-scrubbing technique is effective for separating quartz and kaolinite from pyrophyllite [94]. Figure 3 presents an XRD analysis showing that the pyrophyllite was concentrated at a size of 75 microns under attrition-scrubbing. The major crystalline phase was pyrophyllite, which was increased via scrubbing compared to raw pyrophyllite, in which quartz was a major phase. Attrition-scrubbing clearly gives excellent results in separating clay minerals associated with pyrophyllite, which in turn improves the processing economy and energy consumption at the level of industry.  
Figure 3: Comparison of the XRD patterns of raw pyrophyllite and selected pyrophyllite concentrates obtained by attrition-scrubbing. P: pyrophyllite; Q: quartz; K: kaolinite [94].
## 2 Methods
### Cryptosystem  
Based on the above principles, we will describe the pure digital encryption process as shown in Figure 4a in detail. Firstly, eight QR codes (Q\({}_{i}\), i = 1, 2, 3, 4, 5, 6, 7, 8) which record eight images are superimposed in bit plane mode as shown in Equation (13) to obtain a superimposed image Q. Secondly, the superimposed image Q is randomly scrambled by pixels into image D, and then randomly divided into four images (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\) and D\({}_{4}\)) similar to random decomposition of numbers (e.g., \(255=80+66+57+52\)). Thirdly, each image (D\({}_{1}\), D\({}_{2}\), D\({}_{3}\) and D\({}_{4}\)) is multiplied by the phase of the PP (P\({}_{1}\), P\({}_{2}\), P\({}_{3}\), P\({}_{4}\)) in Figure 2d to obtain four complex values. Finally, according to EMD principle, each complex value is decomposed into two parts, one part is four ciphertexts (C\({}_{1}\), C\({}_{2}\), C\({}_{3}\) and C\({}_{4}\)), and the other part is four keys (K\({}_{1}\), K\({}_{2}\), K\({}_{3}\) and K\({}_{4}\)).  
In the process of decryption, as shown in Figure 4b, first, the added ciphertexts and keys are loaded on the front focal plane of the lens L in Figure 4b by SLM, and it can be considered to load the ciphertexts and keys, respectively, with the help of 4\(f\) imaging system. Second, the square root of the intensity distribution obtained in the far field is normalized to [0, 255], which is image D. Third, the normalized image D is inversely scrambled to obtain image Q, and the Q is decomposed into eight QR codes in bit plane mode as shown in Equation (14). Finally, the corresponding original image can be obtained by scanning each QR code.  
\[\text{Q}=2^{0}\text{Q}_{1}+2^{1}\text{Q}_{2}+2^{2}\text{Q}_{3}+2^{3}\text{Q} _{4}+2^{4}\text{Q}_{5}+2^{5}\text{Q}_{6}+2^{6}\text{Q}_{7}+2^{7}\text{Q}_{8}, \tag{13}\]  
Figure 3: The schematic diagram of EMD for illustration of the relationship between \(I(x)\), \(M_{1}(x)\), and \(M_{2}(x)\).  
\[\begin{array}{l}\text{Q}_{8}=floor\big{(}\text{Q}/2^{7}\big{)},\\ \text{Q}_{7}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}\big{/}2^{6}\big{]},\\ \text{Q}_{6}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}-2^{6}\text{Q}_{7}\big{/}2^{5 }\big{]},\\ \text{Q}_{5}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}-2^{6}\text{Q}_{7}-2^{5} \text{Q}_{6}\big{/}2^{4}\big{]},\\ \text{Q}_{4}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}-2^{6}\text{Q}_{7}-2^{5} \text{Q}_{6}-2^{4}\text{Q}_{5}-2^{3}\text{Q}_{4}\big{)}/2^{2}\big{]},\\ \text{Q}_{2}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}-2^{6}\text{Q}_{7}-2^{5} \text{Q}_{6}-2^{4}\text{Q}_{5}-2^{3}\text{Q}_{4}-2^{2}\text{Q}_{3}\big{)}/2^{ 1}\big{]},\\ \text{Q}_{1}=floor\big{[}\text{Q}-2^{7}\text{Q}_{8}-2^{6}\text{Q}_{7}-2^{5} \text{Q}_{6}-2^{4}\text{Q}_{5}-2^{3}\text{Q}_{4}-2^{2}\text{Q}_{3}-2^{1}\text {Q}_{2}\big{)}/2^{0}\big{]}\end{array} \tag{14}\]  
where _floor_ is rounded down to the closest integer.
## 7 Conclusions
This allows the researches to personalize the configurations according to their needs, without having to recompile the applications. In order to validate the usability, we conducted a number of tests involving 10 users equally distributed in terms of age and gender. The tests showed that a very short time was needed to understand how to use the applications and start using them. The application scenarios of the proposed suite are numerous and mostly focus on human monitoring (see Section 2). The idea of implementing a suite for recording and labeling signals from smartphone and smartwatch sensors arose because of the difficulties we encountered in acquiring a dataset of accelerometer signals related to human activities and falls [16]. This dataset, as well as others in the state of the arts, are used to develop machine learning algorithms that are then able to predict whether a person's activity is a race or a walk. To obtain robust machine learning algorithms, a huge amount of data is needed, and more importantly, data needs to be correctly labeled. The manual work for data labeling would be tedious and noise-prone. One of the possible application scenarios of the proposed suite would be the following: needing to acquire signals from the accelerometer and the gyroscope of a smartphone. The idea is to record signals related to \(n\) activities (walking, running, etc.) performed by a group of \(m\) humans. Each activity is performed following a given story board: walking for 5 min, running for 3 min, and so on. Each person performing the activity holds the smartphone for data recording in his/her pocket and the smartphone for data labeling in his/her hands. Using the proposed labeling suite running on the latter smartphone, the human is able to easily label the activities that he/she is performing. Future developments will include a simplification of the process of selecting sensors when both the smartphone and the smartwatch have been selected, as suggested from the tests. We plan to substitute the current tab layout with a simple activity that includes two buttons: one button is used to show a popup from which the subject can select the smartphone's sensors; the other button is used to show a popup from which the subject can select the smartwatch's sensors. Moreover, we plan to include in both the applications additional functionality that will allow the researchers/users to define the content of the configuration files so that they do not have to edit files in JSON format. Finally, we will explore the possibility to integrate voice recognition as a way to ease the process of starting, stopping, and labeling activities. This improvement will release the user of the burden of manually signaling the beginning of each activity in the predefined list operation mode and of manually selecting and signaling the beginning and end timings of each activity in the free choice operation mode. **Funding:** This research received no external funding. **Acknowledgments:** The authors wish to thank [PERSON] and [PERSON] for their support in developing the applications. **Conflicts of Interest:** The authors declare no conflict of interest.
# Olive Oils Classification via Laser-Induced Breakdown Spectroscopy
## 3 Results and Discussion  
The LIBS spectra of the 36 olive oils samples were collected using the previously described experimental conditions and procedures and they were used for the training and the evaluation of the different predictive models examined here. In Figure 1b, some representative olive oil LIBS spectra are presented, using the same color code followed in Figure 1a, to facilitate the designation of the different geographical areas of origin. All samples' LIBS spectra are presented in Figure S2 at the Supplementary Material (SM) section. As can be seen from Figure 1b and Figure S2, all the LIBS spectra of the studied olive oils are very similar, exhibiting as expected the same spectral features, due to their identical elemental composition. Several atomic and molecular origin spectral features are easily identified, as for example the atomic oxygen, O(I), lines at 777 nm, the atomic hydrogen lines (belonging to the Balmer series) H\({}_{\alpha}\) and H\({}_{\beta}\) at 656.3 and 486.1 nm respectively and the atomic line of carbon C(I) at 247.9 nm. In addition to the above atomic emissions, the progression of the molecular bands of the B-X violet system of neutral cyanogen CN (v = -1, 0, +1), around 400 nm and the progression of the C\({}_{2}\) molecular bands (Swan bands) arising from the fragmentation of the different olive oil's constituents (as for instance the fatty acids: oleic, linoleic and palmitic acids, etc.) under the plasma conditions [14]. As can be seen, the high resemblance of these spectra makes their classification, at least by visual inspection, very difficult if not impossible. Hence, the implementation of machine learning techniques is becoming necessary for the discrimination and classification of the different olive oils based on their LIBS spectra.
## 2 Related Activities
### RF-EMF Modelling Tools  
RF-EMF modelling tools are able to compute the fields generated by radiating sources; however, they may exhibit limitations in modeling capabilities, availability, and computational performance. For instance, finite-difference time-domain (FDTD) tools require box-like discretization of the geometry, which can induce modeling errors on generally curved geometries. There are also techniques that allow the management of surfaces not aligned to the grid. Considering the changing exposure landscape, it is urgent to develop a framework of open-source tools to fill the gaps not covered by the commercial software by adding new multi-physics models (high-performance computing (HPC)-platform-compatible) to improve accuracy and detail.  
NextGEM will develop next-generation, open-source computational tools for RF-EMF environmental levels and body-absorbed energy measurements. New numerical algorithms will be tested and validated to improve numerical efficiency and accuracy. New interfaces (multiple wave planes with human-body geometry and a full-wave EM solver) and capabilities (coupling EM with other physical models) will be developed, further advancing RF-EMF modeling in complex environments. These new algorithms will be implemented in ERMES [8]. ERMES is an open-source finite element code in the frequency domain that implements in C++ a simplified version of the weighted regularized Maxwell equation method. This finite element formulation produces well-conditioned matrices which can be solved efficiently with low-memory-consuming iterative methods. Furthermore, thanks to the null kernel of its differential operator, it can operate distinctly in quasi-static and high-frequency regimens.
## 4 Enabling Technologies for FSO Communication
### Cooperative Relaying  
In this section, we present relay-assisted transmission as a method to enhance communication reliability and coverage. By utilizing intermediate relay nodes, this technique helps overcome obstacles and extend the range of transmission, ensuring more robust and efficient communication links. We will explore the principles, advantages, and implementation strategies of relay-assisted systems in various communication scenarios.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 5 Simulation Experiments
Under the threshold of false alarm probability \(P_{f}<10^{-5}\), detection probabilities \(P_{d}\) of each algorithm under different \(E_{b}/N_{0}\) are counted, respectively. Compared with Hou\({}_{1}\) and Hou\({}_{2}\), DUWDC has more points involved in related operations and better detection performance. It can be seen from Figure 13 that when \(E_{b}/N_{0}\) is at \(-3\) dB to 1 dB, DUWDC performance is about 0.8 dB better than Hou\({}_{2}\) under the same detection probability. Figure 12: False alarm probability of DUWDC under different SNR. Figure 13: Relationship curve between the detection probability and SNR. At the same time, in order to better compare the detection performance of DUWDC under high \(E_{b}/N_{0}\), Figure 14 gives the relationship curve between the missed detection probability and \(E_{b}/N_{0}\). It can be seen that the missed detection probability of DUWDC is significantly lower than that of Hou\({}_{1}\) and Hou\({}_{2}\). In addition, when \(E_{b}/N_{0}\) is at 1 dB to 6 dB, DUWDC performance is about 1 dB better than Hou\({}_{2}\) under the same missed detection probability. To verify the robustness of the algorithm, based on DUW TDMA signal, DUWDC, Hou\({}_{1}\) and Hou\({}_{2}\) are simulated and compared. Simulation conditions: normalized frequency offset \(f_{e}T=0.1\), \(E_{b}/N_{0}=-1\)dB, the total number of UW is 48 symbols and the number of symbols inserted in each group is 2, 4, 6, 8, 12 and 16. Monte Carlo experiments are carried out on this signal. Under the threshold of false alarm probability \(P_{f}<10^{-5}\), the detection probabilities \(P_{d}\) of each algorithm under different UW distribution structures are counted, respectively. The experimental results are shown in Figure 15.  
It can be seen from Figure 15 that under the same SNR, detection probability of DUWDC is at the same level for DUWs with different distributi  
Figure 14: Relationship curve between the missed detection probability and SNR. Figure 15: Influence of different UW distribution structures on algorithm detection performance. \(\text{Hou}_{1}\) and \(\text{Hou}_{2}\) it fluctuates greatly. \(\frac{1}{n}\sum_{i=1}^{n}{(x_{i}-x)}^{2}\) is used to calculate the variance of the curves in the figure; \(\text{Hou}_{1}\) and \(\text{Hou}_{2}\) are 0.0133 and 0.009, respectively, and DUWDC is \(1.56\times 10^{-5}\), which is 99.83% lower than \(\text{Hou}_{2}\). This shows that DUWDC has good stability. The analysis of robustness of the algorithm in Section 4.1 is verified quantitatively.
###### Abstract
For this purpose, there is a need for a topology optimization that integrates the three porous areas which occur along the print direction depending on the part surface (see Figure 1 left). Since the three porous areas could be considered as three materials, a multi-material topology optimization can be considered. Due to the specific dependency of the areas on the part surface, a single-material approach is suitable as well. Such an approach based on filters for the consideration of surface coatings in a density-based topology optimization was investigated by [PERSON] et al. [19]. [PERSON] et al. [20] also analyzed, based on filters, the consideration of a surface layer resulting from the AM of metallic materials in a topology optimization. In each case, it was shown that the consideration of a surface layer, which has different material properties compared to the rest of the part, has a significant influence on the optimization result. However, the authors considered the entire surface of the component as coated and did not implement any differentiation, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Laser Power** & **Scanning Speed** & **Hatching Distance** & **Layer Thickness** \\ \hline
250 W & 900 mm/s & 150 \(\mu\)m & 30 \(\mu\)m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Process parameters for the investigation of different porosity distributions. Figure 1: Exemplary representation (cut through \(x\)-\(y\) plane) of the three porous areas (**left**) and CT scan to visualize the porosity distribution in one print layer (**right**) adapted from [17]. for example, in the print direction, as is necessary for the investigated SLM under the given process parameters. In order to respect the print direction of AM and its effects on the part, process simulations can be employed. Thereby, the printed paths are simulated using the G-code, and an FE model is built up element by element and linked with thermal models [21]. Thus, predictions for the resulting material properties can be made and are mainly used for the determination of emerging residual stresses. However, these coupled process simulations are computationally expensive [21]. Therefore, the iterative use within a topology optimization is not considered to be purposeful due to the high computation time. In this contribution, an approach for the iterative consideration of shape-dependent material properties in a 3D topology optimization is presented. The three porous areas of the investigated SLM (see Figure 1) are distinguished and taken into account depending on the print direction. The optimization aims to achieve the highest possible stiffness under a given volume constraint and will be used in the future as a basis for the extension by a global strength constraint to maintain the maximum permissible stress within the three areas. The results are used to discuss the fundamental structure of the approach and the necessity of its extension. Furthermore, its potential for design finding in the product development process is analyzed.
## 1 Introduction
This method entailed the synthetic generation of trajectory features designed to fulfill various reward-related criteria. Moreover, we incorporated a human behavior model to accurately represent human feedback and decision-making within the framework of the system, providing demonstrations and responses. Our experimental setup within the CARLA environment involved training the optimal policy using the soft actor-critic (SAC) algorithm [18; 19], focusing on the reward weights. We define the optimal strategy as the autonomous vehicle's ability to navigate from a starting point to a final destination. Firstly, we generate trajectory queries based on empirical strategies, then develop preferences according to human intentions as shown in Figure 1, and subsequently construct a belief model based on those preferences. To refine the belief model as shown in Figure 2, we adopted the No-U-Turn Sampler (NUTS) [20] approach, thus eliminating the need for manual adjustments in the sampling step size and iteration count, as required by the traditional Markov chain Monte Carlo (MCMC) techniques [21; 22; 23], including the Metropolis-Hastings algorithm. The NUTS algorithm's adaptive and self-tuning nature enhances user-friendliness and significantly reduces the influence of human-related variability in sampling efficiency. Furthermore, we conducted a comparative analysis, measuring the cosine similarity between the user and belief models. Our approach demonstrated a substantial performance improvement compared to conventional MCMC methods. Our investigation revealed that the APRL-formulated reward function significantly accelerates the policy learning process, facilitating the rapid attainment of optimal policies within a reduced temporal frame. This sophisticated reward function enhances the model's adaptability to complex task environments. Remarkably, it also ensures higher success rates, even in tasks with stochastic initial and final positions. This paper offers the following contributions:  
1. The application of an active preference learning methodology to generate reward functions using human preference updates to learn about reward weights. 2. The employment of the No-U-Turn Sampler (NUTS) to update the belief model for reward weights, a highly efficient and adaptable MCMC method free from manual tuning, particularly useful in dealing with complex probabilistic models and multi-modal distributions. 3. The adoption of the mutual information method as an active preference optimizer to produce informative and comprehensible questions. This is achieved through maximizing information gain, resulting in effective queries for reward learning that better reflect human preferences. 4. The demonstration that the reward weights produced by our method achieve a high success rate in enhancing policies for autonomous driving navigation tasks, even under conditions involving randomized initial positions and without detailed routing information or high-precision mapping. Our approach solely relies on input from the autonomous vehicle's forward-facing monocular camera and internal sensors.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 4 Undersea Application and Experimental Results
### Experimental Results  
In June 2017, the UAC gateway and the observation node were deployed in the South China Sea, as shown in Figure 10. The depth of the seafloor was 1750 m. The heights of the gateway node and the observation node from the seafloor were 2 m and 140 m, respectively. Both nodes employed a single-hydrophone receiver and their horizontal distance was 2.5 km. The communication bandwidth was 6-10 kHz, which was divided into 120 subcarriers for transmission. For each block, the number of information bits is 60, and the duration of the cycle prefix was 10 ms. The observation node is equipped with two sets of batteries, which, together with power consumption under different working modes, are listed in Table 2. When the update cycle is half an hour and the gateway works continuously, the battery power can last for one year.  
The measured channel is normalized and further statistically counted in Figure 11, where the probability density function (PDF) of the measured channel energy (i.e., \(|H_{i}|^{2}\)) is shown with that of the Rayleigh fading channel as a reference. From the figure, the fading degree of the actual channel is lower compared with the Rayleigh fading channel. Thus, during the communication interval, the IrCC is redesigned according to the statistical results to match the real channel. The BER curve of the proposed scheme is drawn in Figure 12, where one observes that reliable communication is achieved when \(E_{b}/N_{0}>9.2\) dB. The proposed ITE algorithm achieves a performance gain of 0.8 dB. The IrCC redesign based on the statistical results brings 1 dB performance gain to the system. The value of the strategy of designing codewords to match channel conditions has been proved. Based on the non-coherent UAC  
Figure 9: Schematic of the calculation model.