esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Analysis  
### Pressure Distribution  
Figure 7 shows the pressure distribution in the second stage impeller and diffuser of the multistage mixed-transfer pump at 10%, 50%, and 90% blade height under a 20% IGVF. The pressure distribution  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditions** & **Head\_CFD (m)** & **Head\_EXP (m)** & **Variation (\%)** \\ \hline IGVF = 0 & 79.63 & 78.82 & 1.03\% \\ IGVF = 5\% & 74.75 & 73.55 & 1.63\% \\ IGVF = 10\% & 65.28 & 63.42 & 2.93\% \\ IGVF = 15\% & 53.79 & 51.71 & 4.02\% \\ IGVF = 20\% & 43.25 & 41.26 & 4.82\% \\ IGVF = 25\% & 35.05 & 32.63 & 7.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Head data of the simulation (CFD) and experiment (EXP) under different inlet-gas-volume fractions (IGVFs).  
Figure 6: Head and efficiency performance in the simulation and experiment.  
at the front and back of the blades at different heights was uniform but the pressure of the impeller blade varied with different blade heights. This result shows that the supercharging capacity of the impeller was different from the front cover to the back cover but the change trend of the pressure gradient in each passage of the impeller was similar. Thus, the design of the impeller was reasonable. The pressure at the front cover of the impeller blades was greater than that at the back cover, indicating that the pressure-boosting capacity at the front cover was stronger than that at the back cover. The pressure variation at the inlet of the first stage impeller and diffuser changed sharply, directly leading to a large pressure pulsation at the blade head of the impeller and diffuser. This result was consistent with the results of the pressure fluctuation frequency domain diagram.
## 2 Mechanism of Using the ESS to Suppress Electromechanical Oscillation  
### Equivalent Model of an SG Dominated Power System with ESS  
As shown in Figure 1, the regional power grid is described by an equivalent synchronous generator (SG) and connected to the utility grid with an approximately infinite capacity. In order to achieve better performance of oscillation suppression, the ESS is connected to the power system at the center of the transmission line [19; 20]. In Figure 1, \(\mathrm{u_{dc}}\) is the DC side voltage of ESS. \(E\), \(V\), and \(U\) are, respectively, the voltages of the equivalent SG, the connection point, and the utility grid; \(Z_{\mathrm{T}}\), \(Z_{\mathrm{L}}\), and \(Z\) are the equivalent impedances of the transmission lines; \(P_{\mathrm{m}}\) and \(P_{\mathrm{e}}\) respectively represent the input mechanical power and the output electromagnetic power of the equivalent SG.  
When the power system operates steadily, the input and output power of the equivalent SG are balanced, and the power grid frequency can be stably maintained. When the power grid is disturbed and the rotor power is unbalanced, electromechanical oscillation occurs in the power system. Since the inertia of the SG is large, the electromechanical oscillation period after the system disturbance is also longer. When electromechanical oscillation occurs in the power system, the rotor speed and/or the power angle of the SG can be fed back to the ESS. An ESS with a PID (or PD) controller can equivalently change the inertia effect, damping capacity, and synchronization of the power system, thereby modifying the electromechanical oscillation [14].  
According to the conclusion of the research in [19], when the ESS suppresses the electromechanical oscillation of the power grid, there are two main types of feedback signals, the power angle of the SG (or the transmission line power) and the rotor speed of the SG (or the grid frequency). In view of the fact that the power angle of the SG is hard to measure, and the ESS with a PD controller cannot affect the system inertia when the power angle is fed back, the ESS with the feedback signal of rotor speed is adopted in this paper, as shown in Figure 2. \(\mathrm{U^{*}_{dc}}\) is the rated value of the DC side voltage of the ESS, \(\omega_{0}\) is the rated rotor frequency, and the PLL of the grid phase detection module adopts the synchronous reference frame-based open-loop phase locking scheme (SRF-OLPL) [21; 22].
# UV-Curable Silicone-Modified Polyurethane Acrylates for Food Freshness Monitoring
For example, a membrane with a visible color change at different pH conditions of 2-12 was developed by immobilizing pH-sensitive dyes onto ethyl cellulose nanofibers, which exhibited good stability for up to 7 days at room temperature [17]. A pH-sensitive material for monitoring the freshness of pork for 12 days was also fabricated by the covalent grafting of oregano essential oil and black rice bran anthocyanin onto a chitosan matrix [18]. Neutral red (NR), an easily available water-soluble, pH-sensitive dye in the pH value range of 4-6 with relatively low toxicity, has been widely used [19]. [PERSON] et al. [20] developed pH-sensitive films with visible color variation at a pH value of 2-10 by covalent bonding NR onto cellulose nanofibrils. Their work revealed that NR is a good candidate for covalent bonding onto a polymer matrix to develop pH-sensitive intelligent materials. UV-cured technology has gained much attention in the coating industry because of its high efficiency, environmentally friendly, and energy-saving merits [21]. The outstanding performance of UV-curable silicone-modified materials such as non-toxicity and excellent chemical and thermal stability makes them broadly used in electronics, sensors, and coatings [22; 23]. In this paper, UV-curable pH-sensitive intelligent silicone-modified coatings for monitoring the freshness of high-protein food such as shrimp and pork were prepared. The materials have a good pH-sensitive property and long-term color stability, which have potential usages in monitoring the freshness of high-protein food.
# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 4 Experiment  
### Data  
We validated our model on two highway traffic datasets, PeMS04 and PeMS08, collected in California. Both datasets were collected by the Caltrans Performance Measurement System (PeMS) in real time every 30 s, and the traffic flow data were aggregated once every 5 min. The system has more than 45,514 detectors deployed on highways in the major metropolitan areas of California. The datasets involve three kinds of traffic measurements: flow, occupancy, and speed. We selected the data from Californian areas 4 and 8 as the dataset for this study.  
PeMS04 comprises traffic data from the San Francisco Bay Area, collected by 3848 detectors on 29 roads in this area over 59 days from January to February 2018. The data were collected every 30 s and aggregated every 5 min. We selected the data from the first 45 days as the training set, and the remaining data as the test set.  
PeMS08 is traffic data from San Bernardino, collected by 1979 detectors on 8 streets in this area over 62 days from July to August 2016. The data were collected every 30 s and aggregated every 5 min. We selected the data from the first 48 days as the training set, and the remaining data as the test set.  
As shown in Figure 4, through visual processing of the two data sets, we found that the traffic flow is the most direct indicator of intersection congestion, and that change of traffic flow can help us to predict congestion.
## 4 Results and Discussion
### Experimental Validation
#### 4.2.1 Step Response  
When the input was a step signal with an amplitude of 20 N, the response curve of the output force obtained was as shown in Figure 16.  
The experimental results coincided with the simulation results. The BPNN-SP-PID controller was the fastest at the beginning stage, which needed \(\sim\)30 ms of action time, while BPNN-PID and PID took -70 ms and \(\sim\)80 ms, respectively. At the approaching stage, BPNN-SP-PID was still the fastest to reach the set value of 20 N and to enter the ideal  
Figure 14: Schematic diagram of the experimental platform.  
Figure 15: Software interface diagram in Labview.  
range. The adjustment times of the BPNN-SP-PID, BPNN-PID and PID were 0.16 s, 0.46 s and 0.48 s, respectively. The three controllers also presented different degrees of overshoot. The overshoot of BPNN-SP-PID was the smallest, being only 1.6%, while the overshoots of BPNN-PID and PID were 4.9% and 9.7%, respectively. At the stable stage, the residuals of the three were different from the ideal scenario in the simulation, but the steady state error could also be guaranteed to be within 0.2 N.  
By comparison with Section 4.1.1, it can be found that the experimental results were a bit worse than the simulation due to the influence of uncontrollable nonlinear factors under the actual experiment. BPNN-SP-PID still presented the best performance among the three controllers in the experiment, which could significantly improve the reaction speed while maintaining stability.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Embedded System Software  
To establish communication between hardware components and the cloud platform, which is installed on a dedicated server, it is necessary to build data packets. This is done by coding a certain set of instructions that permits the data to flow as is needed to send and receive adequate information.  
As shown in Figure 11, the processing board is programmed with the main firmware, inside which different sets of libraries were developed for its correct functioning. The first set of libraries corresponds to the ultrasonic water level sensor which contains the code needed to carry out operations to measure the distance between the sensor and the riverbed. The second set of libraries contains the commands needed to establish a connection between the processing board and the cellular modem board. This set is divided into three libraries: the first one includes instructions for the setup routine of the modem, the second one belongs to GPS commands, and the third one includes the 3G connection commands that are needed to send packets through the cellular network.  
Figure 11: RiverCore firmware architecture.  
At the beginning of the program, the processing board sets up the hardware environment to connect to the cellular network, then it retrieves GPS and water level sensor data, and finally, it forms a JSON string to send it through the 3G cellular network.  
All the instructions used as AT commands are stored in cellular modems' memory, as a command set. All the communication between processing and modem boards is made through the UART (Universal Asynchronous Receiver-Transmitter) serial protocol.
## 3 Results and Analysis
### Reduction of Power Line Interference  
#### 3.1.1 Analysis of ECG Recordings  
The time-domain waveform and spectrum of the ECG signal of a representative subject collected under NLC are shown in Figure 6. It can be seen from the ECG waveform that Method 1 reflects a series of sine wave that superimposes the ECG signals and thus prevents the ECG recordings from being obvious. However, Methods 2, 3 and 4 produced obvious ECG signals, whose time-domain waveforms are better than that of Method 1. Furthermore, the difference amongst Methods 2, 3 and 4 is not obvious, probably because of their mixture with other biopotential signals such as EMG. This finding suggests that the waveforms of ECG signals acquired with the proposed shielded drive technology would be better than that of the conventional unshielded drive approach.  
By analyzing the ECG spectrum, it can be seen that the 50 Hz frequency in the ECG signal collected by Method 1 is as high as 13.6 dB; the 50 Hz power line frequency amplitude of Method 2 is \(-\)5.1 dB, which is 18.7 dB lower than that of Method 1; the 50 Hz power frequency amplitude of Method 3 is \(-\)9.6 dB, which is 23.2 dB lower than that of Method 1; while Method 4 records a 50 Hz frequency amplitude of \(-\)17.0 dB which achieves a reduction of 30.6 dB in comparison to Method 1, and it is much better. In addition, the harmonics of 50 Hz in the spectrum of Method 4 are also eliminated. So  
Figure 5: EMG acquisition setup with the introduction of electrode lead jitter.  
it could be concluded that the use of shielded drive can effectively suppress power line interference during ECG signal recording, especially when utilizing Method 4.  
The plot on the second row in Figure 6 shows the time domain waveform and spectrum of the raw signal (the signal gotten from Method 1) after undergoing filtering via a 50 Hz notch filter. Its waveform is similar to those of the shielded drive methods, but the spectrum has a large depression near 50 Hz. Because ECG signal frequency lies in the range of 0.05-100 Hz, the useful components of the recordings would be lost. In contrast, the hardware-shielded results are seen to be better.
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 2 Materials and Methods
### Mass Variation Measurement  
To monitor potential mass variation from UV-induced degradation or leaching, the tensile specimen mass was recorded before and after exposure. A precision balance (Mettler TG50 Thermobalance, Mettler-Toledo, Columbus, OH, USA) with an accuracy of \(\pm\)0.0001 mg was used to measure each specimen. The data allowed for comparison of pre- and post-exposure conditions to identify any material loss. All mass measurements were performed in a humidity-controlled environment maintained at 23 \(\pm\) 2 \({}^{\circ}\)C and 50 \(\pm\) 5% relative humidity to minimize moisture fluctuation and ensure consistent readings.
## 3 General Opportunities, Challenges, and Applications
### Opportunities
#### 3.1.3 Scalability  
At its core, FL is designed for expansion. Its structure, which encourages the addition of devices to its network, is inherently scalable. Each device that becomes part of this network augments the collective intelligence without ever directly sharing its raw data with a centralized entity. This architecture ensures that, as the network grows, so does its diversity and richness in data, all while maintaining a lightweight and efficient structure [23].
# Locally Scaled and Stochastic Volatility Metropolis-Hastings Algorithms
## 3 Experiments
### Real World Application
#### 3.4.3 Datasets  
The datasets that we use in this paper are outlined in Table 1. The dataset consists of financial market data that we use to calibrate the jump diffusion process model and real-world classification datasets that we model using Bayesian logistic regression.  
We calibrate the jump diffusion process to four datasets. These are single stock, cryptot-currency, stock index, and currency datasets. These datasets are daily closing prices from 1 January 2017 to 31 December 2020 retrieved from Google Finance [48], which we converted into log returns. The datasets were: _MTN dataset_ which is a JSE listed stock, _Bitcoin dataset_ which is a crypto-currency (in USD), _S&P 500 dataset_ which is a stock index and _USDZAR dataset_ which is a currency. Note that the formula used to calculate the log-returns is given as:  
\[r_{i}=\log(S_{i}/S_{i-1}) \tag{18}\]  
where \(r_{i}\) is the log return on day \(i\) and \(S_{i}\) is the stock or currency level on day \(i\). The descriptive statistics of the dataset are shown in Table 2. This table shows that the USDZAR dataset has a very low kurtosis, suggesting that it has very few (if any) jumps when compared to the other three datasets.  
There are four datasets that we modeled using Bayesian logistic regression. All the datasets have two classes and thus present a binary classification problem. The specifics of the datasets are:  
* _Heart dataset_--This dataset has 13 features and 270 data points. The purpose of the dataset is to predict the presence of heart disease based on medical tests performed on a patient [49].
* _Australian credit dataset_--This dataset has 14 features and 690 data points. This dataset aims to assess applications for credit cards [49].
* _South African fraud dataset_--This dataset is of audit findings of South African municipalities [50]. The dataset has 14 features, which are financial ratios, and 1560 data points. For this dataset, the aim is to classify the local government entities with fraudulent financial statements [51].
* _German credit dataset_--This dataset has 25 features and 1000 data points. This dataset aimed to classify a customer as either good or bad credit [49].  
The jump diffusion process datasets used a time series of log returns as the input. The features for the Bayesian logistic datasets were normalised. We used 90% of the data to train the models and 10% to test the models. The train-test split on the time series datasets is based on a cutoff date that confines 90% of the data into a training set. The prior distribution over the parameters for all the jump diffusion process datasets was a standard normal, while for the logistic regression datasets the prior was a Gaussian with standard deviation equal to 10 as in Girolami and Calderhead [4].
## 5 Discussion
### Limitations  
Due to the limitations of the SM datasets and precipitation datasets, the study period only covered 16 years from 2003 to 2018. In the future, longer time spans and higher precision SM data will improve the application of soil moisture information in the remote sensing integrated drought index. The SPI constructed with precipitation data of long time series is also more reliable. This study did not assess the TVMPDI by land cover. We did not use the lag time of meteorological drought index, GPP and agricultural drought for analysis, which may be different in different land covers and needs further study.  
In addition, the study did not take into account the influence of human activities on drought changes in the eastern and western parts of the study area. In future studies, we will assess the influence of human activities on the occurrence of drought and the response of the TVMPDI and the Land Water Storage (LWS).
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 5 Traffic Balance Design
The second part is based on the previous step, and factors are set to a step of 0.01. The operation state of DCS should be controlled by \(DC\) (_lat_, _t_). In an _idle_ or _busy_ state, CubeSat can be used as a space data collector. On the contrary, it is disabled for ground devices. Moreover, onboard process functions can be introduced to increase system efficiency. Limited storage capacity can also be improved by information preprocessing. Invalid data is deleted in the preprocessing function. Valid data is compressed and returned to the ground data center. To increase system efficiency, the above strategy can be embedded in a unified hardware platform. A uniformed software-defined radio (SDR) platform is preferred.
## 2 Materials and Methods
Specifically, a Shimmer EMG unit (Realtime Technologies Ltd., Dublin, Ireland) was used; it is equipped with a bipolar sensor engineered to register superficial muscle activity. Each sensor included two channels and functions at a sampling rate of 1024 Hertz, with a signal resolution of 24 bits. The pre-gelled electrodes had a diameter of 10 mm [20; 21; 22]. Data acquired from the Shimmer unit were transmitted to a mobile application, which then forwarded the information to a cloud service for simultaneous analysis. This system facilitated the generation of comprehensive reports based on the collected data. The electrodes were placed on the AH surface, providing valuable insights into muscle function by quantifying the electrical activity generated during muscle contractions. To position the sensors on the AH muscle, the skin was thoroughly cleaned to remove any oils or lotions, ensuring optimal adhesion of the sensors. The origin of the AH muscle was identified, located near the base of the first metatarsal on the medial side of the foot. sEMG sensors were then positioned directly over the AH muscle, specifically centered on the medial aspect of the foot, approximately midway along the first metatarsal, according to the visual instructions provided by the device itself. Following sensor placement, the participants were asked to perform muscle contractions to verify that the sensors were accurately detecting electrical activity. In this study, the analysis focused on two primary metrics: Root Mean Square (RMS), a standard measure utilized to assess muscle activity during functional movements, which reflects the average electrical activity over time, and Maximum Voluntary Contraction (MVC), which indicates the maximum strength that a muscle can exert while performing a movement. Ethical approval was granted by the Ethics Committee of Bari University Hospital (n.903/2023). All participants provided informed consent. All procedures were performed in accordance with the Helsinki Declaration principles of 1975, as revised in 2008.
## 5 Formal Modeling and Analysis of SAPS
### User Anonymity  
The user's identity, ID, cannot be stored in plaintext at the user level, nor at the TS, and it can be transmitted via the login request. In our scheme, user identity is masked in both \(M_{A}\) and \(P_{A}\), after the original identity registration, session request, and end-user joining request is sent to the TS in the encrypted form. The secret parameters are generated by party (A) and party (B) by choosing secret number \(d_{A}\), \(d_{B}\), respectively, from \(E\) with large order \(n\) as shown in steps I-III. It is not feasible for any third party to get these secret parameters and it is almost impossible to recover messages \(M_{A}\), \(M_{B}\) to get identities without knowing the one-way hash function. It is not viable for an adversary to compute the original identities of the participant. The TS has private and public key pairs and the participants use their MAC for message integrity, masked-IDs, nonce number, and time stamp (T) for message freshness. At the trusted sever, a randomized symmetric encryption technique is used to conceal the random number \(\{R_{TS}\}\) generated by the TS instead of using the XOR. Before communicating the session request of party(A) to party(B), the TS first verifies the original identity of the requested party and the message freshness by computing the inverse functions as shown in Protocols II and III. It generates a temporary public ID of each participant instead of using their original identities. The public identity, say IDV, can run the protocol. Therefore, on the basis of the above analysis, it is impossible for the adversary to get the user's identity from the proposed protocol during authentication, forwarding, the joining request, or the session key computing procedure. Therefore, we know that the proposed protocol is able to provide user anonymity.