esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
###### Abstract  
Ultrasonic guided wave (UGW) systems are broadly utilised in several industry sectors where the structural integrity is of concern, in particular, for pipeline inspection. In most cases, the received signal is very noisy due to the presence of unwanted wave modes, which are mainly dispersive. Hence, signal interpretation in this environment is often a challenging task, as it degrades the spatial resolution and gives a poor signal-to-noise ratio (SNR). The multi-modal and dispersive nature of such signals hampers the ability to detect defects in a given structure. Therefore, identifying a small defect within the noise level is a challenging task. In this work, an advanced signal processing technique called split-spectrum processing (SSP) is used firstly to address this issue by reducing/removing the effect of dispersive wave modes, and secondly to find the limitation of this technique. The method compared analytically and experimentally with the conventional approaches, and showed that the proposed method substantially improves SNR by an average of 30 dB. The limitations of SSP in terms of sensitivity to small defects and distances are also investigated, and a threshold has been defined which was comparable for both synthesised and experimental data. The conclusions reached in this work paves the way to enhance the reliability of UGW inspection.  
Keywords:signal processing; SNR; split-spectrum processing; ultrasonic guided waves +
Footnote †: journal: Applied Sciences
## 5 Experimental Result
### Testbed
#### 5.1.1 System-on-Chip  
We implemented and deployed the entire SHM pipeline on an STM32F407 VG6 MCU. It is a system-on-chip manufactured by STMicroelectronics, which features an ARM Cortex-M4 with a floating-point unit. The core comes with 192 KB RAM and 1 MB FLASH memory and supports a maximum clock frequency of 168 MHz. The STM32F407 VG6 supports multiple low-power modes and various clock frequency configurations, which allow a fine-grained tuning of the system performance depending on the application needs. For the sake of this work, we apply the following policies:  
* When the system is in RUN mode, the system-on-chip is always clocked at the maximum allowed clock frequency, equal to 168 MHz.
* When the system fetches data from the MEMS sensor over SPI, the transfer is performed via DMA with the core in SLEEP mode. Before entering SLEEP mode, the MCU core is clocked down to 16 MHz to minimize SLEEP current consumption.
* When the system-on-chip is not working, it is put in STANDBY mode, switching off the voltage regulator to achieve the lowest consumption possible. Notice that we can apply such an aggressive power-saving policy since we assume our SHM application does not need to retain any state before one LSNN inference and the next inference.  
Considering the software stack employed to implement the SHM application, we interfaced the on-board peripherals using the Hardware Abstraction Layer provided by STMicroelectronics. Instead, the mathematical processing (e.g., FFT computation, FFT-to-spike conversion) is implemented using the CMSIS-DSP library primitives. It is a set of routines developed by ARM to deliver DSP-like functions optimized to run on Cortex cores.  
Figure 8: Architecture of the hardware-in-the-loop test system. The PXI system comprises a PXIe-4309 Analog Input module to monitor current consumption in each stage of the pipeline. The PXIe7858R FPGA, along with the PXIe-8880 Windows 7 controller, implements the Virtual Sensor and allow loading MEMS readings from a CSV file in real-time, emulating a real buffered digital MEMS node in hardware. The whole application runs on a STM32F4 development board featuring a Cortex-M4 ARM core.
# The Effect of Seed Removal and Extraction Time on the Phenolic Profile of Plavac Mali Wine
## 3 Results and Discussion
### Plavac Mali Wines' Phenolic Profile
#### 3.1.3 Flavan-3-ols  
The total flavan-3-ol concentration was strongly influenced by the extraction time and seed removal with the highest concentrations achieved by 49 and 21 days of prolonged maceration followed by 49 days of prolonged maceration without seeds. These data are in accordance with earlier studies [11; 44; 45] showing a positive influence of longer maceration times on primarily (+)-catechin, procyanidin B2, and (\(-\))-epicatechin-gallate. Interestingly, the smallest difference was noted in the concentration of (+)-gallocatedin and procyanidin B1 whose presence, as reported by [45], are exclusively connected to grape skins and seeds removal thus had no marked effect. On the other hand, the (+)-catechin, procyanidin B2, and (\(-\))-epicatechin-gallate concentrations were much lower in the variants where seeds were separated, proving that their absence can strongly influence the phenolic profile of wines. This corresponds to the previously published data [12; 45; 46] showing that a greater relative proportion of individual flavan-3-ols existed in the seeds. Moreover, data presented in this work correspond to those previously published results [32] pointing out Plavac mali variety as one with a large share of flavan-3-ols placed in the skin from which they are readily extracted and which, in this case, resulted in more or less equal levels in wines produced with 7 day maceration times compared to prolonged maceration wines with seed removal (Table 1). The impact of the year was not pronounced which is in accordance with work by [47] pointing out that temperature and thermal increases have little impact on tannins whose accumulation in skins and seeds occurs predominantly before veraison [48].
## 3 Wind Turbine Power Curve Monitoring  
The measurement of power output is fundamental to the wind industry since it forms the basis for the wind turbine's power production warranty. A calculation of the power efficiency of a wind turbine consists of calculating the simultaneous wind speed in front of the turbine and the turbine's power output, described by power curves. The WTs' power curve reflects a dynamic, non-linear and roughly cubic relationship between hub height wind speed (measured by anemometer) and electrical power output below rated power. An anemometer looks like a weather vane and has four cups, so it can more accurately measure wind speed. The end of the horizontal arm is connected to each cup which is fixed on a central axis. The arms rotate the axis as wind drives through the cups. The faster the wind, the faster the axis rotates through the cups. Figure 1 shows typical power curves for 8 MW and 9.5 MW rated WTs. Generally, the power curve is classified into three wind speed ranges: (a) cut-in wind speed where the WT starts to rotate and produce power, (b) cut-out speed is usually when rotor blades are stopped to prevent the damage caused by high winds, and (c) rated wind speed at which maximum power is produced, or rated power. Theoretically, the power that is extracted from the wind by the turbine is described by the following equations [21],  
\[P_{w}=0.5\rho\lambda r^{2}\mu^{3} \tag{1}\]  
\[P_{m}=C_{p}\left(\lambda,\beta\right)P_{w} \tag{2}\]  
where \(P_{w}\) = power in the wind (W); \(P_{m}\) = mechanical power output of the turbine (W); \(\mu\) = wind velocity (m/sec); \(C_{p}\) = power coefficient of WT; \(\rho\) = air density (kg/m\({}^{3}\)); \(r\) = radius of the rotor (\(m\)); \(\lambda\) = tip speed ratio (defined as the ratio of the speed of the blade at its tip to the speed of the wind).
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 2 Related Work  
Rising concerns about women's safety have led to the development of IoT-based safety devices [7, 8]. With the assistance of the global positioning system (GPS), tracking users' locations and timelines has become feasible. Mobile applications on smartphones or smartwatches allow users to send emergency alerts to law enforcement. However, in critical situations, manually operating these applications may not be practical. Therefore, an advanced application to automatically detect threats is essential for crime prevention.  
Fall detection technology has primarily been used for older adults, demonstrating that machine learning techniques--such as convolutional neural networks (CNNs) combined with time-series data--are effective for early detection and prevention. Wearable devices equipped with this technology can provide real-time alerts when a fall occurs or predict potential falls in advance, enabling proactive intervention. These advancements significantly improve emergency response capabilities [9, 10, 11]. However, while falling is a specific behavior that can be relatively straightforward to detect using estimators, distinguishing high-risk movements from ordinary ones is more complex. This requires extensive research, including data collection, in-depth analysis, and the development of a new predictive model.  
In public safety, deep learning models are employed to identify hazardous situations in CCTV footage. Systems like SpotCrime autonomously analyze surveillance video to detect potential threats. These models utilize relational inference, which assesses object relationships (e.g., proximity and interaction), allowing accurate risk evaluation even in cases of low object detection accuracy. Such technology is increasingly integrated into smart cities and surveillance infrastructures to enhance public security [12, 13].  
Human behavior recognition through sensor data has applications beyond security, extending to user interaction systems. Deep learning models are widely used to classify human behavior, from simple gestures to complex activities. Our research explores the effectiveness of neural networks in recognizing human movements from sensor data, demonstrating their ability to accurately detect even subtle actions in real time [14, 15].  
This study focuses on policing science with a particular emphasis on women's safety, as they constitute the majority of personal protection requests. It is crucial to develop machine learning models capable of detecting high-risk movements using motion data from women. Unlike previous systems that primarily collect and analyze static data, this research employs a one-dimensional convolutional neural network (1D CNN) to continuously process accelerometer data. This approach enables real-time recognition of hazardous movements, enhancing personal safety for women in both public and private settings. By leveraging machine learning techniques, this method improves both the accuracy and response speed of threat detection.
## 2 Materials and Methods  
### Chemicals  
The standards including asiatic acid, corosolic acid, betulin, betulinic acid, and oleanolic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA). Madecassic acid was purchased from Toronto Research Chemicals (Ontario, ON, Canada). Hederagenin and ursolic acid were obtained from Tokyo Chemical Industry (Tokyo, Japan). Maslinic acid, gallic acid, 3,4-dihydrobenzoic acid (3,4-DHBA), and \(p\)-coumaric acid were obtained from Chengdu Biopurify Phytochemicals Ltd. (Chengdu, China). Catechin, epigallo-catechin gallate (EGCG), myricetin-3-\(O\)-glucose, myricetin-3-\(O\)-rhammoside, quercetin-3-\(O\)-glucose, and quercetin-3-\(O\)-rhammoside were obtained from Extrasynthese SAS (Genay Cedex, France). Quercetin and quercetin-3-\(O\)-arabinoside were purchased from ChemFaces Biochemical Co., Ltd. (Wuhan, China). All standard compounds have a purity of \(\geq\)95%, except ursolic acid (purity \(>\) 90%). HPLC-grade acetonitrile, methanol, and formic acid were purchased from Sigma-Aldrich.  
Preparation of Standard Solutions  
Each standard was prepared at a concentration of 1.0 mg/mL in methanol (stock solution). Then, standard mix series including triterpenoid, flavonoid, and phenolic acid series were prepared from the stock solutions and diluted to the appropriate range for calibration curves. All solutions were stored in a brown vial at \(-\)60 \({}^{\circ}\)C until analysis.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 4 Electric Vehicle Battery Chargers Categories  
Electric vehicle battery chargers can be classified according to their charging capabilities, physical configurations, and intended applications as on-board or off-board chargers. On-board chargers are incorporated within the vehicle, and their capacity determines their charging rate. They are commonly found in electric vehicles and are compatible with various charging infrastructures. On the other hand, off-board chargers are external units, and their specifications determine the charging rate. They are used in public charging stations and private charging points where on-board charging is not possible or convenient. On-board charging, while convenient for many EV owners, may not always suffice in certain situations, leading to the need for off-board chargers. Challenges such as long-distance travel, lack of personal charging facilities, and the demand for faster charging options highlight the limitations of on-board charging. Off-board chargers, including fast-charging stations along highways, public locations, and commercial premises, address these challenges by offering quick and accessible charging solutions for EV users.
# Performance Analysis of ICA in Sensor Array
## 2 Hypotheses, Definitions and Notations
### Condition Number of Matrix  
Consider a system of linear equations:  
\[\mathbf{x}=\mathbf{As} \tag{9}\]  
The system is featured by the coefficient matrix \(\mathbf{A}\), \(\mathbf{x}\) is the vector of data, and \(\mathbf{s}\) is the solution vector to be estimated.  
Figure 2: Illustration of the case \(P=N=2\).  
**Definition 1**[34]:  
The condition number of a non-singular matrix \(\mathbf{A}\), denoted as \(\mathit{cond}(\mathbf{A})\), can be calculated via:  
\[\mathit{cond}(\mathbf{A})\triangleq||\mathbf{A}||\Big{\|}\mathbf{A}^{-1}\Big{\|} \tag{10}\]  
where \({}^{\prime}||\bullet||^{\prime}\) indicates certain kind of matrix norm. For singular matrix \(\mathbf{G}\), \(\mathit{cond}(\mathbf{G})=\infty\).  
The condition number measures the singularity of a matrix or equivalently, the sensitivity of the solution of corresponding linear equation system to errors in the data. Hence, it is a valid indicator of the accuracy and numerical stability of results from matrix inversion and linear equation solution. When a minor perturbation in the observation only leads to a small error in the solution vector, the matrix \(\mathbf{A}\) is considered to be well-conditioned, and inversely, if minor perturbation of \(\mathbf{x}\) results in large error in the estimation of \(\mathbf{s}\), then the matrix is said to be ill-conditioned.  
The connotation of the condition number can be understood in a more straightforward way, that is, it measures the amplification between errors in \(\mathbf{x}\) (denoted as \(\Delta\mathbf{x}\)) and that in the estimation of \(\mathbf{s}\) (denoted as \(\Delta\mathbf{s}\)). In fact, it has been proved in [34] that:  
\[\frac{||\Delta\mathbf{s}||}{||\mathbf{s}||}\leqslant\mathit{cond}(\mathbf{A}) \frac{||\Delta\mathbf{x}||}{||\mathbf{x}||} \tag{11}\]  
In this manuscript, we adopt the Frobenius norm for the calculation of the condition number, which is defined as [34]:  
\[||\mathbf{A}_{P\times N}||_{F}\triangleq\left(\sum_{i=1}^{P}\sum_{k=1}^{N}|a_{ ik}|^{2}\right)^{\frac{1}{2}} \tag{12}\]  
in which \(a_{ik}\) is the element of \(\mathbf{A}\) in the \(i\)-th row and \(k\)-th, \({}^{\prime}|\cdot|^{\prime}\) indicates the module value.  
Combining Equations (8), (10) and (12), the condition number in the \(2\times 2\) array receiving case is obtained as:  
\[\mathit{cond}(\mathbf{A})=\frac{4}{\sqrt{2-2 cos[2\pi d\cdot(\frac{cosh_{2}cos _{2}}{\lambda_{2}}-\frac{cosh_{1}cos_{1}}{\lambda_{1}})]}} \tag{13}\]
## 8 Conclusions & Future Directions
The following abbreviations are used in this manuscript:  
3 GPP Third Generation Partnership Project  
5 GAA 5G Automotive Association  
ADAS Advanced Driver Assistance Systems  
AMQP Advanced Message Queuing Protocol  
AN Accident Notification  
BSM Basic Safety Message  
CA Cooperative Awareness  
CAGR Compound Annual Growth Rate  
CAMM Cooperative Awareness Message  
CAMINO vehiCulAr coMmunIcation maNagement framework  
CAV Connected Autonomous Vehicles  
CDF Cumulative Distribution Function  
C-ITS Cooperative Intelligent Transport Systems  
CSMA/CA Carrier-Sense Multiple Access with Collision Avoidance  
DEN Decentralized Environmental Notification Message  
DSRC Direct Short Range Communication  
EVA Emergency Vehicle Alert  
FDD Frequency Division Duplex  
FDR Frame Delivery Rate  
GNSS Global Navigation Satellite System  
GPS Global Positioning System  
HMI Human Machine Interface  
IEEE Institute of Electrical and Electronics Engineers  
IP Internet Protocol  
IVI Infrastructure to Vehicle Information Message  
KPI Key Performance Indicators  
LOS Line of Sight  
LTE Long Term Evolution  
MNO Mobile Network Operator  
MQTT Message Queuing Telemetry Transport  
NR New Radio
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Coordination Constraints of OCRs
#### 2.3.6 Summary of the Proposed ASPC Approach for OCRs in a DG-Based Distribution Network  
Figure 5 shows a flow-chart of the ASPC approach for OCRs in the DN with DGs. There are five basic steps as the following:  
* Step 1: Input the maximum fault-current values detected by each OCR corresponding to four different fault types (e.g., ph-ph, 1 ph-G, 2 ph-G, and 3 ph-G) that occurred at the remote end of the protective zones/lines.
* Step 2: Select the pick-up and tripping/fault currents for the OC relays and define the tripping time of 50P and 50G functions, as shown in Table 1.
* Step 3: Based on the objective function \(Z\) in Equation (2), determine \(A\), \(B\), and \(C\) factors and \(TDS\) multiplier of 51P and 51G functions in each OCR by using three meta-heuristic algorithms, GSA, hybrid PSO&GSA and GA.
* Step 4: Check the tripping time of 50P/G and 51P/G functions and the constraint conditions of CTI between the primary and backup relays for the OCR pairs and the OCR triples as referred to Equations (3)-(5).
* Step 5: Select the appropriate protection coordination results for OCRs which are satisfied with the constraint conditions; and update the setting parameters to the related OCRs in the DG-based DN.  
\begin{table}
\begin{tabular}{c c c} \hline
**Different OC Functions** & **Selection of Fault/Tripping Currents, \(I_{f,rk}\)** & **Descriptions** \\ \hline _50P: Definite-time phase OC protection function_ & \(I_{f\_rk\_F50P}=I_{f,p,ph-ph}^{\max}\) & \(k\) is a given fault location at the remote end of the protected line/zone; and \\ \hline _51P: Inverse-time phase OC protection function_ & \(I_{f\_rk\_F51P}=I_{f,p,ph-ph}^{\max}\) & \(I_{f,r,p,ph-ph}^{\max}\) is the maximum ph-ph with \(r=1\ldots N\) & fault current. \\ \hline _50G: Definite-time ground OC protection function_ & \(I_{f,rk\_F50G}=\min\left(I_{f,r,3 ph-G}^{\max},I_{f,r,2 ph-G}^{\max},I_{f,r,1 ph-G}^ {\max}\right)\) & \(I_{f,r,3 ph-G}^{\max},I_{f,r,2 ph-G}^{\max}\) and \(I_{f,r,1 ph-G}^{\max}\) are the \\ \hline _51G: Inverse-time ground OC protection function_ & \(I_{f,rk\_F51G}=\min\left(I_{f,r,3 ph-G}^{\max},I_{f,r,2 ph-G}^{\max},I_{f,r,1 ph-G}^ {\max}\right)\) & maximum sph-G, 2 ph-G, and 1 ph-G fault currents for a given fault location, respectively. \\ \hline \end{tabular}
\end{table}
Table 1: Fault-current selection for the 50 and 51 OC functions.
# A Data-Driven Approach to Discovering Process Choreography
## 4 Materials and Methods
### Process Choreography Discovery
#### 4.3.3 Method 9: Specification of the IOBP and the Process Choreography  
The construction of the IOBP model and the process choreography representation are defined using the results (data and models) generated by implementing methods 7 and 8. Therefore, the exchange of messages is specified on the IOBP model based on the data discovered in the previous methods, enabling the process choreography to be visualized through the formalization of the interaction between the participants of the collaboration. The IOBP model and process choreography are built through the following steps:  
* Let us consider a pair of BPMN process models from participants \(BPMNI=(i,o,T,G,\)\(E_{m})\) and \(BPMNR=(i,o,T,G,E_{m})\).
* The set of nodes \(T\in BPMNI\) and \(T^{\prime}\in BPMNR\) refers to the set of events contained in the event registers \(L\) and \(L^{\prime}\), considering that each node in \(T\) and \(T^{\prime}\) has a label with the name of the events in \(L\) and \(L^{\prime}\), respectively. This means that a \(\subseteq T\) is represented by the events \(a\in PME\) and \(\subseteq T^{\prime}\) is represented by the events \(b\in PME\).
* The inter-organizational business process (IOBP) is generated by adding the process models \(BPMNI\) and \(BPMNR\), each model within a _pool_ element identified with the _name_ of the participant.
* With the set of task event pairs of type message and its subtype (\(PME\)), the message flow connection (\(Mfc\)) between the subsets of nodes \(\subseteq T\) and \(\subseteq T^{\prime}\) is specified. \(Mfc\) represents the links that relate message-type tasks (events) between the pools of the _IOBP_ model. Connectors \(Mfc\) are added to the \(IOBPmodel\gets Mfc\), representing the process choreography that supports the message exchange logic between the process models \(BPMNI\) and \(BPMNR\).
* The direction of each flow connector \(Mfc\) is given by the following conditions (1): \[Mfc\leftarrow\left\{\begin{aligned} &(a\in PME)x(b\in PME)\ |\ |a \to b|=1,&\text{if }a.subtype=send\\ &\textbf{and }b.subtype=receive,\\ &(a\in PME)x(b\in PME)\ |\ |b\to a|=1,&\text{if }a.subtype= receive\\ &\textbf{and }b.subtype=send\end{aligned}\right.\] (1)
## 4 Procedure and Analysis of Range Differentiation in 3D MMW Imaging
### Imaging Complex Targets: A Discussion on Physical Limitations of the Imaging System
These enhancements, including the use of smaller GDDs and larger mirrors, would significantly improve the system's ability to reconstruct complex or curved objects with finer details, thereby enhancing the spatial resolution and overall image clarity in future implementations. In our imaging setup, the knife was positioned diagonally within the rectangular image plane to maximize coverage of its distinct structural features. Although the scanning area is technically 8 \(\times\) 8, the step sizes for vertical and horizontal movements must be carefully adjusted to accommodate the object's dimensions. When the knife is placed strictly in a horizontal or vertical orientation, the resulting MMW image does not effectively capture the dimensional differences between the blade and handle. Instead, the image appears similar to that of a flat metal plate, with only minor variations due to orientation rather than structural contrast. By positioning the knife diagonally, we ensure that a greater portion of the object is covered during scanning, allowing for a clearer representation of its shape and depth variations. This approach provides a more meaningful visualization  
Figure 12: Grayscale images reconstructed from the MMW imaging system for a knife and a gun-shaped object (**right**) alongside photographs of the actual objects used for imaging (**left**). of how the system detects object edges and structural changes, which is reflected in the imaging results presented in the manuscript.