esa-sceva/satcom-chunk-collection huggingface dataset:
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### Towards Microsecond Real-Time XPCS at Storage-Ring Sources
#### 4.1.5 XSVS for Radiation-Sensitive Samples  
The increasing coherent flux at modern X-ray sources challenges the application of XPCS experiments on radiation sensitive samples. Here, XSVS may offer an alternative approach to study the sample dynamics. Most importantly, the repetition rate of the detector does not define the accessible time scale because shorter exposure times can be used in many cases. Instead, electronic triggers may enable accessing much faster timescales. An example was given by [PERSON] et al. using the Eiger 4M and Eiger 500k detectors [140] with exposure times down to 10 \(\upmu\)s. However, the authors found a peculiar detection error of the detector, which resulted in too many 2-photon events. While this is not relevant for many applications, low-intensity XSVS relies on the correct counting of all photons. Nevertheless, the authors presented a correction algorithm which allowed the calculation of speckle contrasts.  
Advantages of XSVS for longer time scales were shown in [65]. Therein, the dynamics of ionic liquids were studied around their structure factor peak close to their glass transition. Independent from the exposure time, the sample volume experienced the same dose, resulting in count rates below 10\({}^{-2}\) photons per pixel. The exposure times were chosen between 0.05 s and 1 s. These times would be as well accessible by standard sequential-mode XPCS, however, much higher doses would have been needed. On the other hand, the correlation functions are built from many single measurements taking several minutes to hours which does not allow to access out-of-equilibrium processes or real-time experiments.
## 3 Potential of Intelligent Algorithms for Hydrodynamic
### Machine Learning (ML)
In hydrodynamic noise research, reinforcement learning has been widely applied to tasks such as noise control, flow field optimization, and intelligent regulation, providing new solutions to improve the effect of noise suppression and system adaptive capabilities [79]. In recent years, the application of reinforcement learning in hydrodynamic noise prediction and control has been gradually developed, especially in the active control of fluid noise sources and flow structure optimization, showing good prospects. For example, deep reinforcement learning combines the feature extraction capability of deep neural networks and the decision optimization capability of reinforcement learning, which can be used to construct efficient noise suppression strategies, enabling the control system to learn and adapt to different flow conditions autonomously. [PERSON] et al. [80] designed a reinforcement algorithm combining DQN training with deep neural networks (DNNs) to allow reinforcement learning (RL) in complex high-dimensional environments such as speech recognition. In order to solve the problem of inefficient channel bandwidth utilization in heterogeneous underwater sensor networks, [PERSON] et al. [81] proposed an optical and acoustic dual-channel multiple access protocol (OA-DLMA) based on deep reinforcement learning (DRL). The method finds the optimal channel access strategy through self-learning of agent nodes and enhances the data transmission rate through a differentiated reward strategy. These studies show that, compared with traditional numerical optimization methods, reinforcement learning can autonomously adjust control parameters and achieve dynamic optimization under complex boundary conditions and nonlinear flow characteristics, reducing the reliance on physical experiments and high-cost computation. Reinforcement learning methods have great potential for application in hydrodynamic noise optimization, and in the future, they are expected to be further combined with multi-intelligent body systems, adaptive exploration mechanisms, and efficient numerical simulation techniques to promote the development of fluid noise control in the direction of intelligence and automation.
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 4 Materials and Methods
### Methods to Develop Ensemble-Learning-Based Models for WaQuPs
#### 4.4.1 Default Parameter  
As depicted in Figure 4, the implementation of the first scenario encompasses a sequence of essential steps. It commences with the retrieval of the dataset employed in this research, followed by the removal of less pertinent columns, such as location names. Subsequently, the dataset undergoes ROS to achieve class balance. The next stage involves a feature selection process to identify and retain the top 10 features within the dataset.  
Feature selection is instrumental in enhancing the efficiency and efficacy of our machine learning models. Specifically, we employed the coefficient correlation method, employing a predefined threshold of 0.25. This threshold functions as a filtering criterion, allowing us to retain features exhibiting a significant correlation with the target variable while eliminating redundant or less informative attributes. As a result, we identified and retained ten highly correlated features, which were deemed pivotal in driving the accuracy and overall performance of our machine learning model. The selected features, along with their respective correlation values, are comprehensively detailed in Table 3), offering valuable insights into their relevance and influence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Selected Feature** & **Value Correlation** \\ \hline Organic & 0.6175501741325587 \\ TDS & 0.47393967056911174 \\ Manganese & 0.43650780057849137 \\ Chloride & 0.3974162772157505 \\ Turbidity & 0.385749632688686897 \\ Fluoride & 0.325764243146723 \\ pH & 0.29762644622409384 \\ Total hardness & 0.2941555317484847 \\ Sulfate & 0.2684154921972715 \\ Temperature & 0.26050365661276903 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected features with high correlation.  
Figure 4: Flowchart for scenario 1.  
After that, the dataset is then divided into distinct test and training sets. The initial scenario culminates with the evaluation of performance metrics, specifically encompassing accuracy, precision, recall, and the F1-score.
## 2 Review of Cryogenic-Cooling Benefits
### Thermal Parameters
#### 2.2.2 Thermal Conductivity Standard Debye Model
As an approximation to the Debye model, the following equation is often used for any \(T\)[41]:  
\[C_{V}=\left[\frac{9 nN_{A}k_{B}}{M}\right]\cdot\frac{1}{1+\frac{5}{4\pi^{4}} \left(\frac{\theta_{D}}{T}\right)^{3}} \tag{34}\]  
If we take the limit as \(T\to 0\), we find the following equation  
\[C_{V}\simeq\left(\frac{12\pi^{4}}{5}\right)\cdot\,\left(\frac{nN_{A}k_{B}}{M} \right)\left[\frac{T}{\theta_{D}}\right]^{3} \tag{35}\]  
This equation correctly predicts the \(T^{3}\) dependence of the specific heat for low temperatures, and is known as the Debye-\(T^{3}\) law. In Figure 3, we show a plot of \(C_{V}\) for the undoped laser material \(Y_{3}Al_{5}O_{12}\), as a function of temperature. We used Equation (32) for calculating \(C_{V}\) using the Debye model, and the YAG Debye temperature of 760 K [29]. Also shown are published data for YAG [29], as well as the approximate results obtained using Equation (34). The Debye model matches the experimental data fairly well, while the approximate model over estimates the specific heat for temperatures starting at about 180 K.  
Rather than Equation (26), an alternative approach to calculating the thermal conductivity was used by Klemens [42], whose model we will discuss in more detail in the next section of this paper. The following more sophisticated relationship for k can be employed:  
\[C=C_{V}\ \rho \tag{27}\]0  
where \(S(\omega)\) is defined by Equation (29), and \(v_{s}\) and \(\tau\) have been previously defined. The use of \(\tau(\omega)\) rather than the mean free path makes it simple to introduce phonon scattering from impurity atoms introduced into the crystal lattice, as we will review in the following section. If we take \(S(\omega)\) in the high temperature limit, we find the following relationship:  
\[C=C_{V}\ \rho \tag{27}\]1  
where we have used the following expression for the Debye frequency:  
\[C=C_{V}\ \rho \tag{27}\]2Inserting Equation (37) into Equation (36), and assuming that the phonon speed \(v_{s}\) is a constant, we obtain  
\[C=C_{V}\ \rho \tag{27}\]3  
This equation has been used by Klemens [42] to calculate k when scattering defects are doped into a crystal. It should be noted however that this equation is strictly valid only when \(T>>\theta_{D}\). For most laser materials, and particularly in the temperature range of interest here, about 77-300 K, this condition is violated but nevertheless often used because it can provide analytical results.
## 2 Physical Layer's Challenges for 6G
In this manner, a hybrid analog-digital beamforming system can be implemented, where each RF module feeds a directive sub-array (block), ensuring dense mmWave beamforming. In turn, multiple similar blocks may comprise the required MIMO system. This redesigned architecture will align with the emerging implementation paradigm, significantly reducing power losses in RF hardware. While hardware evolution is gradual, substantial improvements in energy efficiency and performance are anticipated in the mid-term future.
# Simultaneous SAXS/SANS Method at D22 of ILL: Instrument Upgrade
###### Abstract  
A customized portable SAXS instrument has recently been constructed, installed, and tested at the D22 SANS instrument at ILL. Technical characteristics of this newly established plug-and-play SAXS system have recently been reported (_J. Appl. Cryst._ 2020, 53, 722). An optimized lead shielding arrangement on the SAXS system and a double energy threshold X-ray detector have been further implemented to substantially suppress the unavoidable high-energy gamma radiation background on the X-ray detector. The performance of the upgraded SAXS instrument has been examined systematically by determining background suppression factors (SFs) at various experimental conditions, including different neutron beam collimation lengths and X-ray sample-to-detector distances (SDDX-ray). Improved signal-to-noise ratio SAXS data enables combined SAXS and SANS measurements for all possible experimental conditions at the D22 instrument. Both SAXS and SANS data from the same sample volume can be fitted simultaneously using a common structural model, allowing unambiguous interpretation of the scattering data. Importantly, advanced in situ/real time investigations are possible, where both the SAXS and the SANS data can reveal time-resolved complementary nanoscale structural information.  
small angle X-ray scattering; small angle neutron scattering; SAS; SANS; SAXS; nanomaterials Article
# Moodle LMS Integration with Amazon Alexa: A Practical Experience
## 3 Method
### Moodle Web Services  
For the implementation of our prototype we are required to connect programmatically to Moodle. To be more precise, our prototype took in use the REST protocol, which becomes available automatically when enabling the Mobile Web Services. Mobile Web Services is a built-in web service designed to enable the interaction between mobile applications and Moodle [38]. This web service is required for running the official Moodle Mobile app, or if a third-party app explicitly requires it. By utilizing the Mobile Web Services, we are capable of exploiting the same information as the official Moodle app uses to provide features such as browse the content of courses, track personal progress, or view course grades [38]. It is important to notice that the Moodle release used for our test case was \"3.7.4+ (Build: 20200117)\".  
For our prototype, we initially took in use three core API functions:  
* \"core_webservice_get_site_info\", which returns site info, user info, and list web service functions.
* core_enrol_get_users_courses, which gets a list of course ids that a given user is enrolled in.
* core_calendar_get_calendar_events, which gets calendar events for a given user.  
We aimed to combine the use of the above-mentioned Moodle functions in order to retrieve enough information to present to a user with the specific events related to courses and other user-related matters. Later in this chapter, there is a more detailed explanation of how these functions were used and integrated into our Alexa skill.
###### Abstract  
Carotenoids astaxanthin and \(\beta\)-carotene are widely used natural antioxidants. They are key components of functional food, cosmetics, drugs and animal feeding. They hold leader positions on the world carotenoid market. In current work, we characterize the new strain of the green microalga _Bracteacoccus aggregatus_ BM5/15 and propose the method of its culturing in a bubble-column photobioreactor for simultaneous production of astaxanthin and \(\beta\)-carotene. Culture was monitored by light microscopy and pigment kinetics. Fatty acid profile was evaluated by tandem gas-chromatography-mass spectrometry. Pigments were obtained by the classical two-stage scheme of autotrophic cultivation. At the first, vegetative, stage biomass accumulation occurred. Maximum specific growth rate and culture productivity at this stage were 100-200 mg.L\({}^{-1}\).day\({}^{-1}\), and 0.33 day\({}^{-1}\), respectively. At the second, inductive, stage carotenoid synthesis was promoted. Maximal carotenoid fraction in the biomass was 2.2-2.4%. Based on chromatography data, astaxanthin and \(\beta\)-carotene constituted 48 and 13% of total carotenoid mass, respectively. Possible pathways of astaxanthin synthesis are proposed based on carotenoid composition. Collectively, a new strain _B. aggregatus_ BM5/15 is a potential biotechnological source of two natural antioxidants, astaxanthin and \(\beta\)-carotene. The results give rise for further works on optimization of _B. aggregatus_ cultivation on an industrial scale.  
photobioreactors; astaxanthin; \(\beta\)-carotene; carotenoids; carotenogenic algae; _Bracteacoccus_
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 3 Task Allocation
### Complexity Analysis of the Task Allocation Algorithm  
Given the information of the operating environment and configurations, the proposed task allocation algorithm can provide solutions at low complexity. Assuming there are \(N\) UAVs and \(M\) targets in the environment. In Algorithm 1, each allocation round will assign one target, it requires \(M\) allocation rounds to allocate all the targets. In each allocation round, step 2 in Algorithm 1 requires \(N\) iterations. For step 3-5 in Algorithm 1, they undergo \(M\) iterations for the worst case. The complexity for the consensus process [19] in Algorithm 1, step 8 is \(\left(N-1\right)^{2}\) for the worst case. In Algorithm2, at most \(N\) elements are sorted, the complexity of the Algorithm 2 is \(N\log(N)\). Summing up the numbers analyzed above in each allocation round, the complexity of the proposed task allocation algorithm in the worst case can be computed as \(NM+\left(N-1\right)^{2}+N\log(N)\). For \(M\) targets (line 1 in Algorithm 1), the complexity of the proposed task allocation algorithm in the worst case can be expressed as \(M^{2}N+M(N-1)^{2}+MN\log(N)\).
## 2 Related Works
The above two approaches [9; 11] can be compared in terms of segment embedding and clustering algorithms. First, the two approaches entail different segment embedding methods. In a previous study [9], phoneme-level segments were represented as fixed-length vectors using the LSTM architecture. In contrast, the previous study [11] extracted grapheme-level segments obtained using a single HMM state with GMM and then applied a clustering algorithm to each frame. Here, processing each frame may be insufficient to represent spontaneous speech with various spectral patterns. On the other hand, the LSTM architecture, which converts a segment consisting of various frames into a latent vector, can better represent spontaneous speech by using long-term dependency. Second, these two approaches used different clustering algorithms: k-means clustering [12] and decision tree-based clustering [4]. K-means clustering is the most common clustering algorithm using Euclidean distance. On the other hand, decision tree-based clustering splits the data by using top-down greedy splitting, which considers the left phoneme, the right phoneme, and the central phoneme. We did not search the literature to compare these two clustering algorithms to derive phoneme-like units. However, we expect that decision tree-based clustering performs better than k-means clustering as the acoustic model training process usually uses a decision tree-based clustering algorithm. The proposed method builds new subword units by using the segment embedding method of the previous study [9] and a decision tree-based clustering method performed on phoneme-level segments. Our approach in this paper differs from that in [11] because we use segment-based clustering instead of frame-based clustering, and our approach differs from that in [9] because we apply decision tree-based clustering instead of k-means clustering. In addition, the decision tree-based clustering method differs from the approach in [11], which uses the HTK toolkit [23], in that the questions for node splitting are automatically generated by top-down binary data clustering rather than by hand.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 7 Experimental Tests
### 67n.s1 One-Phase to Ground with Compensated Neutral  
For the fault protection 67n.s1, single-phase to ground with compensated neutral, a maximum fault clearing time of 450 ms is imposed. Consequently, considering a fault detection time of 57 ms and a 60 ms breaker trip time, there is a Maximum Blind Waiting time \(T_{MBW}\) of 333 ms within which the PR can wait for a Blind message before tripping its CB. This time interval is much greater than that required in the case of fault 50.s3; thus, it is possible to relax the stringent constraints on the design of the LoRa packet. In other words, with fault 67n.s1, it is possible to adopt larger \(T_{packet}\) values and use higher SF values that considerably improve the receiver sensitivity and link budget.  
In Table 14, the nominal time duration of \(T_{packet}\) reported in Table 10 are again considered and compared with the Maximum Blind Waiting time of the fault 67n.s1. In particular, it can be observed that by choosing the SF and BW pairs (10;125), (11;250), and (12;500) for the configuration of the LoRa transmission packet, a maximum \(T_{packet}\) of 209.85 ms is obtained, which is lower than the Maximum Blind Waiting time required for fault 67n.s1. The SF and BW pairs (10;125), (11;250), and (12;500) correspond to receiver sensitivities, respectively, of: \(-132\), \(-131.5\), and \(-131\) dBm. The configuration that allows for the best receiver sensitivity and the greatest link budget is the one with the SF and BW pair (10;125), which was chosen for the subsequent tests.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study** & **Interval** & **Measured Value [s]** \\ \hline Fault clearing time for fault 50.S3 between SR and PR1 & \(T_{SR\_TRIP}\) & 0.03297 \\ Fault clearing time for fault 50.S3 between PR2 and PR3 & \(T_{PR2\_TRIP}\) & 0.03296 \\ Reclosure time for fault 50.S3 between SR and PR1 & \(T_{IB\_CLOSED}\) & 0.10796 \\ Reclosure time for fault 50.S3 between PR2 and PR3 & \(T_{IB\_CLOSED}\) & 0.07085 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Time intervals for fault clearing and interconnection reclosure for the fault 50.S3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Interval** & **Measurement Method** & **Max Value [s]** & **Measured Value [s]** \\ \hline \(T_{SR\_BLIND}\) & \(t^{\prime}_{SR\_RXDone}\)-\(t^{\prime}_{FAULT}\) & 0.03300 & 0.01827 \\ \(T_{PR\_BLIND}\) & \(t^{\prime}_{PR\_RXDone}\)-\(t^{\prime}_{FAULT}\) & 0.03300 & 0.01827 \\ \(T_{PR2\_TRIP}\) & \(t^{\prime}_{PR2\_CB}\)-\(t^{\prime}_{FAULT}\) & 0.03300 & 0.03296 \\ \(T_{PR3\_TRIP}\) & \(t^{\prime}_{PR3\_CB}\)-\(t^{\prime}_{FAULT}\) & - & 0.05149 \\ \(T_{IB\_CLOSED}\) & \(t^{\prime}_{TR\_IB}\)-\(t^{\prime}_{FAULT}\) & - & 0.07085 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Estimated time intervals during the fault 50.S3 between PR2 and PR3.  
Figure 14 shows the time-frequency diagram of one of the LoRa packages, with a nominal duration of 209.85 ms, SF = 10, and BW = 12, obtained through the real-time spectrum analyzer Tektronix RSA 3408A.
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
###### Abstract  
Angle-of-arrival (AoA) estimation in localized hybrid arrays suffers from phase ambiguity owing to its localized structure and vulnerability to noise. In this letter, we propose a novel phase shift design, allowing each subarray to exploit difference beam steering in two potential AoA directions. This enables the calibration of cross-correlations and an enhanced phase offset estimation between adjacent subarrays. We propose two unambiguous AoA estimation schemes based on the even and odd ratios of the number of antennas per subarray \(N\) to the number of different phase shifts per symbol \(K\) (i.e., \(N/K\)), respectively. The simulation results show that the proposed approach greatly improves the estimation accuracy as compared to the state of the art when the ratio \(N/K\) is even.  
hybrid array; localized subarrays; angle-of-arrival estimation; mmWave communications +
Footnote †: journal: Sensors  
**Keywords:**