esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Indoor Setup  
A glass tank (55 \(\times\) 55 \(\times\) 10 cm) placed underneath a south-facing window was used for the indoor experiment. The tank received only natural light during the day, as no other illumination was used (sunrise at 7:30 UTC + 1, sunset at 16:30 UTC + 1, average  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Unit** & **Method** & **Value** \\ \hline Total nitrogen & mg N L\({}^{-1}\) & UNI EN 12260: 2004 [68] & 25.2 \\ Ammonium & NH\({}_{4}\)\({}^{+}\)-N L\({}^{-1}\) & APAT CNR IRSA 4030 A1 Man 29 2003 [69] & 24.4 \\ Nitrate & NO\({}_{3}\)\({}^{-}\)-N L\({}^{-1}\) & APAT CNR IRSA 4020 Man 29 2003 [70] & 1.4 \\ Nitrite & NO\({}_{2}\)\({}^{-}\)-N L\({}^{-1}\) & APAT CNR IRSA 4050 Man 29 2003 [71] & 0.16 \\ Total phosphorus & mg L\({}^{-1}\) P & UNI EN ISO 15587-2: 2002 ISO 17294-2: 2016 [72; 73] & 22.1 \\ COD & O\({}_{2}\) mg L\({}^{-1}\) & ISO 15705 par 10.2: 2002 [74] & 80 \\ BOD\({}_{5}\) & O\({}_{2}\) mg L\({}^{-1}\) & APHA Standard Methods for the Examination of Water & 19 \\  & & and Wastewater 23 rd 2017 5210 [75] & 19 \\ Total suspended solids & mg L\({}^{-1}\) & APAT CNR IRSA 2090 B Man 29 2003 132 114 [76] & 48 \\ Al & mg L\({}^{-1}\) & ISO 15587-2: 2002 + UNI EN ISO 17294-2: 2016 [72; 73] & 1.33 \\ Cr & mg L\({}^{-1}\) & ISO 15587-2: 2002 + UNI EN ISO 17294-2: 2016 & \textless{}0.02 * \\ Cr (VI) & mg L\({}^{-1}\) & APAT CNR IRSA 3150 C Man 29 2003 [77] & \textless{}0.02 * \\ Cu & mg L\({}^{-1}\) & ISO 1187-2\_2002 + UNI EN ISO 17294-2: 2016 [73; 78] & 0.021 \\ Hg & mg L\({}^{-1}\) & ISO 1187-2\_2002 + UNI EN ISO 17294-2: 2016 & \textless{}0.001 * \\ Pb & mg L\({}^{-1}\) & ISO 1187-2\_2002 + UNI EN ISO 17294-2: 2016 & \textless{}0.005 * \\ Ni & mg L\({}^{-1}\) & ISO 1187-2\_2002 + UNI EN ISO 17294-2: 2016 & \textless{}0.01 * \\ Zn & mg L\({}^{-1}\) & ISO 15587-2: 2002 + UNI EN ISO 17294-2: 2016 & 0.06 \\ _Escherichia coli_ & CFU/100 mL & UNI EN ISO 9308-1:2017 [79] & 61,100 \\ \hline \end{tabular}
\end{table}
Table 2: Characterization of the WW and analytical methods employed for the analyses. * indicates values below detection limit.  
PAR during daytime: 20 \(\upmu\)mol\({}_{\text{photons}}\) m\({}^{-2}\) s\({}^{-1}\)). The average temperature in the room was 19 \(\pm\) 2 \({}^{\circ}\)C. A 4 L amount of _Chlorella_-like culture, cultivated as above, and 16 L of WW were mixed to obtain a final volume of 20 L and a target OD\({}_{750}\) of 0.3. In the following days, the culture was mixed manually before collecting the samples.
## 3 Experimental Results  
This section provides a comprehensive overview of a series of experiments conducted to implement a novel synchronous wireless Structural Health Monitoring (SHM) framework. The framework utilizes wireless, low-cost, and readily available components. The primary objective of this study is to bridge the gap in the utilization of low-cost off-the-shelf sensors, shifting the focus from mere accuracy comparisons with high-grade accelerometers.  
The initial validation involves assessing the performance of a TCP/IP socket programming-based mimic broadcasting mechanism. This mechanism aims to achieve initial alignment and ensure consistent performance across each low-cost sensor. This validation step is crucial for establishing the reliability and effectiveness of the wireless SHM framework.  
Central to the employed framework for wireless communication is the TCP/IP socket programming-based mimic broadcasting. This framework serves as the backbone for data collection from three RPi0-2W sensor nodes. All nodes are controlled seamlessly through a personal computer using the SSH (Secure SHell) protocol. Notably, the operation of the RPi0-2W sensor nodes can be easily managed, with continuous operation halted upon user interruption via keyboard input. This wireless control mechanism offers convenient start and stop control of the sensor nodes.  
Additionally, the section delves into the utilization of low-cost sensors in a Three Degrees of Freedom (3 DOF) structure to differentiate between structural parameters in two distinct structures with varying stiffness properties. This investigation aims to shed light on the potential of low-cost sensors in effectively identifying and characterizing structural parameters. By showcasing the capability of low-cost sensors in discerning between different structural properties, the study underscores their practical utility in SHM applications. This contribution advances cost-effective monitoring solutions, fostering progress in the field of structural health monitoring.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Method  
This study combined SAM with overlap prediction, cropland masking, and post-processing to extract 10 m parcels. Then, 0.65 m imagery was used to assess the internal complexity of each parcel through histogram and edge features, determining the need for further segmentation. The accuracy was enhanced through histogram equalization and adaptive parameter settings, ultimately yielding 0.65 m parcel results. The final parcel mapping was achieved by combining results from both resolutions. The overall experimental flow is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Resolution** & **Area** & **Date** \\ \hline \multirow{3}{*}{Sentinel-2} & \multirow{3}{*}{10 m} & Youyi County & 09/09/2022 \\  & & A1 & 16/08/2022 \\  & & A2 & 11/06/2022, 10/08/2022, 09/09/2022, 26/09/2022 \\ \hline \multirow{3}{*}{GF-7} & \multirow{3}{*}{0.65 m} & Youyi County & 27/09/2022 \\  & & A1 & 01/09/2022 \\ \cline{1-1}  & & A2 & 27/09/2022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The satellite data used in this study.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets  
In the previous section, we modeled combinatorial optimization problems directly, a minimum vertex cover problem, and a graph partitioning problem with binary quadratic nets and formulated them as marking problems. However, they are exceptional cases. In general, we can meet the conceptual gap between combinatorial optimization problems and target binary quadratic nets. Note that the problem is more serious in the direct formulation of the Ising or QUBO models.  
Our approach attempts to minimize the gap by converting problem-domain Petri net models, represented by timed Petri nets and colored Petri nets, into the corresponding binary quadratic nets. This section proposes a method for constructing target binary quadratic nets from problem-domain Petri net models.
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Papers** & **Models** & **Stations** & **Dataset** & **Normalization** & **Ensemble Type** \\ \hline
[1] & CNN, GCN & Single & Korean & – & Convolutional \\
[2] & TEAM–Transformer & Multiple & Japan, Italy & – & – \\
[3] & GCN, BNGCNN & Multiple & Southern California & Batch & Convolutional \\
[5] & K-means clustering & Single & Global Seismic Data & – & – \\
[11] & EEPM & Single & India, Kenya, and Nepal & – & Machine learning \\
[6] & CNN & Multiple & South Korea & Batch, attention & – \\
[7] & Graph partitioning & Single & Waveform Data & – & No \\
[20] & Transformer & Multiple & Africa & Attention & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work with models using normalization, attention, or ensemble methods. Figure 1: The architecture of the stacked and parallel ensemble techniques. (**a**) Parallel ensemble; (**b**) stacked or sequential ensemble.
## 2 Laser-Generated Ultrasound  
With the development of advanced ultrasound technologies, LGUS technique has played an important role in biomedical ultrasound imaging and industrial nondestructive evaluation. There are various investigations on LGUS transmitters including mathematical modeling and analysis, effective absorbing materials, fiber-optic transmitters for extensive applications, and ultrasound beam focusing and shaping (as will be discussed in Sections 2.2.2 and 2.2.3). Efficient LGUS transmission could significantly increase the pressure, effectively improve penetration depth, and even provide useful thermal and mechanical effects on tissues. High-efficiency LGUS transmission strongly demands the novel materials with high laser absorption, low heat capacity, and large thermoelastic expansion. For instance, an AuNPs-PDMS nanocomposite is utilized to enhance the efficiency of LGUS transmission, which has three orders of magnitude improvement compared with an aluminum (Al) thin film [39]. The recent interest in CNT materials has led to exceptional transduction performance of LGUS transmitters and has expanded the application of LGUS to therapeutic ultrasound [30]. New materials as LGUS transmitters are typically tested in the form of a thin film during initial exploration. To meet specific applications, further development is required. LGUS transmitters built on optical fibers featuring a compact size facilitates interventional ultrasound applications such as intravascular ultrasound (IVUS) imaging. More recently, high-power laser-generated focused ultrasound based on an acoustic lens has drawn surging interest, which has the potential to open up promising applications in therapeutic ultrasound. Ultrasound beam focusing and beam shaping may also be realized through array implementation of LGUS transmitters. Traditional ultrasound transducers face challenges with modern applications, while AOUSTs combining LGUS transmitters and ODUS detectors fill the gap by unique performances such as broad bandwidth and possibly noncontact characteristics. Besides medical imaging and therapy, LGUS technique also demonstrates broad applications for material characterization and evaluation such as PDMS thin film characterization, crack inspection, and investigation of phase transitions in metals (as will be discussed in Section 2.3).
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 1 Introduction
### Importance of Chaotic Time Series Forecasting
#### 1.2.4 Chaotic Time Series Forecasting in Other Systems  
The distributed control system and information technology, which comprises supervisory information technology and management information systems, are commonly used technologies in thermal power plants [20]. The real-time data collected from power plant equipment and personnel controls using these technologies are a chaotic time series. Further, the instantaneous generator output power is critical to indicate the adjusting and controlling equipment's status. As a result, predicting the immediate generator power time series could provide decision-making, maintenance, and incident-handling information. Further, it positively impacts plant production, optimal operation, and problem detection and maintenance technology.  
Natural hazards, such as earthquakes, severe floods, fires, and volcanic eruptions, and the destruction they create are worldwide issues that impose a high cost in terms of human lives and financial damages [21]. The wireless sensor networks monitor the urban river levels and other natural environmental conditions for predicting the floods before they occur so that the people at risk evacuate in time.  
Similarly, the nonlinear spacecraft system contains various fields with advanced technology, and it has a significant impact on national economies, research, and technology. Faults in the spacecraft system are challenging to detect and rectify. As a result, studying the trend of spacecraft telemetry metrics and the variation law is essential for the early prediction of spacecraft problems.
## 3 Audiovisual Interaction with Real-World Movable Objects
### System Configuration  
Here, we describe the process of the entire system, from object recognition to impact sound presentation, in VR that reproduces the real world. The configuration of the system is shown in Figure 4.  
Figure 4: System configuration for audiovisual interaction with movable objects.  
Figure 3: Rod-shaped object with VR controller.  
Figure 2: Equipment for experiment.  
In generating the visual information of the object, the position and shape of the movable object were reproduced in the VR space, based on the information from the RGB-D camera attached to the VR headset. At the same time, image processing divided the components into images and generated impact sounds for each component. Next, the impact sounds were associated with the generated 3D object data. The details of the generation of visual information will be presented in a future report, due to space limitations. Here, we describe the division into component images in Section 4, and the generation of impact sounds in Section 5.  
As stated earlier, the objective of this research is to dynamically generate VR content that reflects the real world, and the impact sound, in the VR system that needs to acquire movable objects in real time. For this purpose, it is first necessary to acquire object images. Our object image acquisition is premised on acquisition from the RealSense, attached on the front of the HMD. First, the surface image of the material is acquired by performing object component segmentation using color information. There are also methods of decomposing an object into its components, based on the point cloud [20,21], but in this research, we emphasize real-time characteristics and, thus, adopt a more efficient image-based method.  
The system recognizes a real-world object by image recognition using the deep learning model YOLO v3 [22], acquires a point cloud of the recognized object with the RGB-D camera, and displays it in real time in VR space.  
Next, material estimation was performed using CNN as intermediate information for associating the material with sound, and the material class was estimated from the texture image of the object component. Using the estimated results, a spectrogram image was generated, based on the material class by the trained generative model. The input to the generative model was a concatenation of the categorical variable c (one-hot vector), which represented the attributes of the estimated material, and the random noise z. The generator generated a spectrogram image, based on this input label; the image is converted into waveform data, and the sound is presented to the user. By using the above system, the user can acquire the dynamic impact sound in the virtual environment with the object image as the input.
## 4 Experimental Results
### Performance Across Different Load Domains on CWRU Dataset
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training** & **Predicted** & **TICNN** & **Ensemble** & **LPC-NN** \\
**Sub-Dataset** & **Sub-Dataset** & **TICNN** & **TICNN** & **LPC-NN** \\ \hline
2 & 3 & 99.1 & 99.5 & 98.5 \\
2 & 4 & 90.7 & 91.1 & 91 \\
3 & 2 & 97.4 & 97.6 & 97.8 \\
3 & 4 & 98.8 & 99.4 & 97 \\
4 & 2 & 89.2 & 90.2 & 93.4 \\
4 & 3 & 97.6 & 98.7 & 98.1 \\ Average accuracy & 95.5 & 96.1 & 96 \\ \hlineAs can be seen, the best accuracy value is given by the 50-50-10 structure model with the number of LPC coefficients at the NN input equal to 50, which we found earlier based on the criteria for determining the model order. Note that this model, unlike the others, requires a little more time to train. The training time for N = 12 is slightly less due to the smaller number of neural network parameters. The accuracy of this model 12-50-10 is lower, which is explained by the fact that the LPC model with N = 12 cannot capture all the informative features of the signals. The time for training the network with N = 100 is significantly less, which is explained by the larger capacity of the model 100-50-10 and the possible rapid overfitting of the network. This also explains the low accuracy of this model on the test dataset. Thus, reducing the number of LPC coefficients is not advisable, since it leads to a decrease in the accuracy of the model and provides almost no improvement in time. Increasing the number of LPC coefficients requires a more powerful neural network. This will probably provide good accuracy but will require more training time.
## 5 Case Study
### Experiment Results
#### 5.4.4 Discussion  
The sum of the two types of error weight coefficient is 0.97525. We should pay attention to the fact that this sum is not equal to 1 because this study only considers multipath effects, NLOS transmission, and atmospheric disturbances, and not all interference factors. In fact, there exist other types of interferences in indoor environments, such as the time between UWB equipment [14] asynchronies, etc., which are not considered in this study. Furthermore, Figures 13 and 14 indicate that the maximum deviation in the experiment area is 1.4 m, which is considered high for most UWB systems. The dynamic error propagation model did not improve the positioning accuracy, therefore, 1.4 m in the experiment was only used to analyze the weights of all the error factors.  
Focusing on the weights of the positioning position error, it can be found that compared with the multipath effect and non-line-of-sight propagation, atmospheric interference has a very small proportion of the influence value on the total error. However, it is still significant for the quantization impact of atmospheric interferences on the total positioning error, which was completed in this study. In an intelligent workshop, chemical workshop, textile workshop, and other indoor environment with a large amount of dust and other particles, floating and sediment in the air will have a great impact on the signal refractive index, which should be included according to this DEP model in this study.  
In fact, the error weight determination depends on environmental conditions and the layout of the obstacles, on the lab experimental data. The coefficients are constant after calibration in a given environment. Further, the weight coefficient under different scenarios should be calibrated again in each new environment.
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Timestamping Services  
#### 5.4.1 KSI Blockchain  
_KSI Blockchain_ is a globally distributed infrastructure for issuing and verifying _KSI signatures_. These signatures serve as a solid proof of _when_ and _by whom_ some data item of arbitrary size and format was generated. A user interacts with the KSI system by submitting the hash-value of the data to be signed. Then, the KSI system returns to the user a signature providing cryptographic proof of the _time of signature_, the _integrity of the signed data_, and the _data origin_, i.e., which entity generated the signature.  
Unlike traditional digital signature approaches, such as the Public Key Infrastructure (PKI), which depend on asymmetric key cryptography, KSI uses only hash-function cryptography. Thus, the generation and verification of KSI signatures is based solely on the security of hash-functions and the availability of a public ledger, commonly referred to as a blockchain. KSI core technology is based on [PERSON] and [PERSON]'s work [78].  
The main benefits of the KSI system can be summarized as follows:  
* **Unlimited Signing Rate:** KSI signatures can be generated at a practically unlimited rate. Even if an exabyte (i.e., \(10^{18}\) bytes) of data are generated around the planet per second, and data recordshave an average size of 1 MB, KSI is able to provide signatures for all one trillion (\(10^{12}\)) data records per second with negligible computational, storage, and network overhead.
* **Portability:** A KSI signature can be verified even after its respective data has crossed geographic or organizational boundaries and service providers.
* **Data Privacy:** KSI does not ingest any user data; data never leaves the user's premises. Instead, the system is based on one-way cryptographic hash functions that result in hash values uniquely representing the data while being irreversible. That is, one cannot start with the hash value and reconstruct the data, or come up with some alternative data that has the same hash value. Data privacy is guaranteed at all times.
* **Quantum Immunity:** The cryptography behind KSI signatures ensures that they will never expire and that they remain quantum-immune, i.e., secure even after the realization of quantum computing.
* **Independent Verification:** The properties of the signed data can be verified locally, without relying on a trusted authority.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Communication Subsystem
#### 4.1.1 Hardware Specification  
For testing purposes, the multi-UAV system network is physically implemented in XBee Pro 900 HP S3B modules connected to Raspberry Pi type computers with Linux operating system, embedded in a quadrotor UAV type Phantom 3 equipped with Global Positioning System (GPS), autopilot and maximum flight time of about 25 min. The UAV model characteristics are: maximum take-off weight (MTOW) of 21.5 kg; the payload of 10 kg; fight autonomy of over 20 h (with a full tank of 7500 cc; environment protection against rain and snow; embedded power generator rated at 80 W [27].  
The XBee modules have a range of 6.5 km for a transmission rate of 200 kbps for transporting data in a network IEEE 802.15.4 (ZigBee) with a power consumption of 2050 mW [46]. The BS is a personal computer connected to an XBee device, running the server to exchange messages with the network nodes. To evaluate the network architecture as proposed for the multi-UAV system, a squadron of quadrotor helicopters is employed according to Figure 7. Figure 7a shows the UAV model Phantom 3, while Figure 7b represents the XBee module connected to power bank. Figure 7c corresponds to the BS. The BS employed in the assessed scenarios to validate the FATNET employed in this work acts as the UAV hub in the squadron to sweep the rocket impact area.  
Figure 6: Architecture for the Multi-UAV system.  
[PERSON] et al. [20] presents an aircraft flight and navigation control system using VHF for uplink and S-Band for downlink. There are some advantages associated with ZigBee transmission devices over those using S-band, among the most important ones is the communication range, which is directly related to the operating frequency of the modules. ZigBee devices use a lower operating frequency than S-band, which means less signal attenuation and therefore a greater ability to ensure reliable system communication. Another important advantage is the cost. More sophisticated equipment operating at higher frequencies tends to be more expensive due to increased manufacturing complexity. Finally, another determining factor for choosing ZigBee is that the S-band is restricted to military operations, environmental satellites, TV and other applications. For these reasons, it is feasible to use ZigBee, which is approved by ANATEL (Brazilian National Telecommunications Agency.
## 3 Simulation Results

Table 3: Comparison between the proposed PSO-based ANC system and existing approaches [23; 24; 35] in terms of computational complexity. Figure 9: Comparison between approaches when a change in the secondary path occurs. Figure 11 depicts the residual noise and the power spectrum when a broadband noise is used as undesired-noise. As can be observed from Figure 11, the proposed algorithm achieves an average noise reduction of 11.54 dB by using broadband noise as a reference signal.