esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Integrated Methodology for the Elaboration of Geospatial Exposure Databases
#### 2.3.2 Plan Regularity  
The Polsby-Popper index [63] was calculated for the building footprints polygons to assess the degree of plan irregularity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group of** **Attributes** & **Attribute** & **Data Source (San Jose)** & **Open Access Alternatives** \\ \hline \multirow{4}{*}{Exterior attributes} & Building position within a block & Footprints digitized from National 1:1000 orthophotographs [57] & Bing or OpenStreetMap footprints/Bing/ESRI/Google imagery [52; 58] \\ \cline{2-3}  & \multirow{2}{*}{Shape of the building plan} & Footprints digitized from National 1:1000 orthophotographs [57] & Bing or OpenStreetMap footprints/Bing/ESRI/Google imagery [52; 58] \\ \cline{2-3}  & \multirow{2}{*}{Structural irregularity} & Footprints digitized from National 1:1000 orthophotographs [57] & Bing or OpenStreetMap footprints/Bing/ESRI/Google imagery [52; 58] \\ \cline{2-3}  & & Exterior walls & imagery [52; 58] \\ \hline \multirow{4}{*}{Roof, floors, and foundation} & Roof & MASTER imagery [59] & Sentinel-2 or ASTER imagery [55; 56] \\ \cline{2-3}  & Floor & & \\ \cline{2-3}  & \multicolumn{2}{c}{Foundation system} & \\ \hline \hline \end{tabular}  
* Attributes with gray background color are those included in this case study. (*) Building height is not usually available in data-scarce cities with high seismic risk, as in San José, implying an acquisition cost. (**) OpenStreetMap still offers very limited coverage in terms of building heights across most cities and populated areas worldwide.  
\end{table}
Table 2: _Cont._\[\text{Polsby}-\text{Popper index}=\frac{4\cdot\pi\cdot\text{Area}}{\text{Perimeter}^{2}} \tag{1}\]  
This index, ranging from 0 to 1, measures how regular a shape is based on its area and perimeter. A value of 1 corresponds to a perfect circle, the most compact shape for a given perimeter. Based on this index, buildings can be classified according to the regularity of their plan.  
In seismic design, shapes like squares or rectangles (with one side up to twice the length of the other) are generally considered regular. Buildings with a Polsby-Popper index between 0.65 and 0.85 are classified as very regular. Those with an index between 0.35 and 0.65, or between 0.85 and 1.00, are considered regular. Buildings with an index below 0.35 are considered irregular in plan (example in Figure 3). The 0.35 threshold marks the point where plan irregularity may significantly affect the building's seismic performance, particularly by introducing torsional effects due to complex or asymmetrical footprints.
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 1 Introduction
Accordingly, three contributions are made: (1) adaptive sparse image representations with fast orthogonal dictionary learning will be introduced to improve the denoising performance with updated reference images; (2) L\({}_{0}\) norms are introduced to regularize not only sparsity but also the data fidelity, resulting in stronger sparse constraint for images and robustness to outliers introduced by salt and pepper noise; and (3) the impulse denoising model is solved with a feasible numerical algorithm. The rest of this paper is organized as follows: The typical L\({}_{1}\)-L\({}_{1}\) regularization model is reviewed in Section 2.1. The proposed method is presented in Section 2.2. Experimental results are analyzed in Section 3. Discussions are made in Section 4. Finally, Section 5 presents the conclusions.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Reducing Network Traffic by Data Aggregation and Fusion  
Data aggregation and fusion are the processes of aggregating the data from multiple sensor nodes and providing fused data to the BS. Data aggregation and fusion methods eliminate redundant transmissions, hence saving bandwidth and energy.  
In densely deployed CR-WSNs, many sensors produce very similar data. Transferring similar data by all CR-WS nodes may generate network-wide massive traffic that wastes network bandwidth and other resources. In clustering, CR-WS nodes send data to the CH that keeps localized traffic within the cluster. Each CH aggregates data, and transmits the fused data to the BS or another CH. Generally, CHs form a tree structure to transmit aggregated data via multihops through other CHs, which results in significant energy savings [45].
## 1 Introduction
[18] described in their study that inoculated raw skinless chicken fillet treated with UV light up to 0.192 J/cm\({}^{2}\) had significant (\(p<\) 0.05) reductions of 0.76, 0.98, 1.34, 1.76, and 1.29 log CFU/g for _C. jejuni_, _E. coli_, _Salmonella_ Enteritidis, total viable counts, and _Enterobacteriaceae_, respectively. Additionally, [PERSON] et al. [19] studied the effectiveness of pulsed ultraviolet light for the destruction of _Salmonella_, _E. coli_, and _Campylobacter_ on the surface of chicken thigh meat, and on the thigh skin surfaces, the variables of study are the distance (8 and 13 cm) and treatment time (0, 5, 15, 30, and 45 s). The authors concluded that pulsed UV light exposure for 5 and 45 s on thigh skin surfaces resulted in log10 reductions of 1.19 and 1.96 for _E. coli_; 1.08 and 1.85 for _Campylobacter_; and 0.90 and 1.82 for _Salmonella_, respectively. The exposure to PUV light for 5 and 45 s on lean surface thighs resulted in higher log10 CFU/cm\({}^{2}\) reductions (1.22 and 2.02 for _E. coli_, 1.45 and 2.09 for _Campylobacter_, and 1.55 and 2.42 for _Salmonella_, respectively). Poultry meat can be potentially contaminated on its surface by _Campylobacter_ due to its capability to easily adhere [20], mainly during slaughtering and deboning practices. The objective of this study was to assess the effect of pulsed light (including UV, representing 10-15% of total spectra) treatments using a central composite rotatable design (CCRD) with a binomial combination of the variables, voltage and distance to the source of the pulsed light lamp, with the same number of pulses, responsible for the production of total energy delivered to the sample (fluence, J/cm\({}^{2}\)). We intend to choose the best variable combination that could reduce the contamination of _Campylobacter jejuni_ on poultry meat with less impact on colour and also on those volatile aldehydes related to meat flavour.
## 2 Materials and Methods
### Test Equipment and Methods
#### 2.5.2 Evaluation Indicators  
According to the determination standard of the qualified sowing depth range (h \(\pm\) 10) mm in the agricultural industry standard NY/T 1768-2009 [44], the qualified sowing depth range of this test is set to 40-60 mm, and the calculation formula of each index is as follows:  
\[\eta_{1}=\frac{n_{1}}{N_{1}}\times 100\% \tag{17}\]  
\[\overline{h}=\frac{\sum{h_{i}}}{N} \tag{18}\]  
\[S_{h}=\sqrt{\frac{\sum{\left({h_{i}-\overline{h}}\right)^{2}}}{N}} \tag{19}\]  
\[V_{h}=\frac{S_{h}}{h}\times 100\% \tag{20}\]  
where \(\eta_{1}\) is the qualified rate of the sowing depth, %; \(n_{1}\) is the number of the qualified sowing depth; \(N_{1}\) is the total number of the sowing depth measurement points; \(\overline{h}\) is the mean value of the sowing depth, mm; \(h_{i}\) is the sowing depth measurement value, mm; \(S_{h}\) is the standard deviation of the sowing depth, mm; and \(V_{h}\) is the variation coefficient of the sowing depth, %.  
In addition, according to the control principle of the downforce and the sowing depth, the situation in which the sowing depth is qualified but the compaction of the seed groove is too high is likely to occur when the downforce is overloaded. Especially for the no-till, the compactness of the soil before sowing is much higher than the soil condition of the tilled land, and the compaction of the sidewall of the seed groove should be reduced as much as possible during the no-till sowing. In order to comprehensively measure the suitability of the sowing depth and sidewall compaction, combined with the qualified compaction test data of no-till land [45,46], this test set the \"seed groove environmental qualification rate\" (SFEQ), and the method to determine whether the groove environment is qualified is if the sowing depth of a measurement point is within the set sowing depth (\(h\pm\) 10) m and the compaction data are within the interval of [\(c\),(1 + 20%)\(c\)] of the post-sowing soil compactness. The point is judged as the \"seed groove environmental qualification rate\" (SFEQ). Where \(c\) is the pre-sowing soil compaction/kPa, obtained by measuring the soil compaction data below \(H\) (the set sowing depth) at the tire marks of the gauge wheel, and one measurement point corresponds to one pre-sowing soil compaction data.  
The seed groove environmental qualification rate (SFEQ) is calculated by the following formula:  
\[\eta_{2}=\frac{n_{2}}{N_{2}}\times 100\% \tag{21}\]  
where \(\eta_{2}\) is the seed groove environmental qualification rate (SFEQ), %; \(n_{2}\) is the number of the qualified seed groove environment; and \(N_{2}\) is the total number of measuring points of the seed groove environment.
## 3 Methodology
### Experimental Setup (Sample Preparation and SC CO\({}_{2}\) Injection)  
Sample preparation plays a crucial role in experimental studies especially when the analytical procedure is based on a comparative study of samples. In order to minimize the effect of rock heterogeneity, the mineralogical (OpM-SEM) and geochemical (XRF) characterization of the samples before and after CO\({}_{2}\) exposure was conducted on neighbouring areas of various blocks of 27 cm\({}^{3}\). Although the thin sections and the SEM specimens of before-after samples obviously could not be prepared of exactly the same sample area, they were taken from surfaces as close to each other as possible (a few millimetres of distance). For the geochemical comparative analyses neighbouring blocks were used. The sample blocks selected for CO\({}_{2}\) exposure were saturated with brine in cycles of 72 h under atmospheric conditions.  
The experiment was conducted in a static Autoclave chamber (without CO\({}_{2}\) flow during the test) at 7.5 MPa pressure, 35 \({}^{\circ}\)C temperature, during 970 h. The configuration of the experimental setup (chamber, pumps, CO\({}_{2}\) cylinders, connections, etc.) was based on other systems described by [15; 20; 26; 28]. In general terms, two cylinders of commercial CO\({}_{2}\) of 99.8% purity were connected via 5 mm stainless steel tubes to (i) the chamber of 3 dm\({}^{3}\) where the samples were placed and (ii) to a piston pump also connected to the chamber. The first cylinder, directly connected to the chamber, makes it possible to fill the chamber with CO\({}_{2}\) at atmospheric temperature and 4.5 MPa pressure. The second cylinder allows the user to pressurize the chamber applying a controlled gas flow until the desired pressure value (i.e., 7.5 MPa). The inside material of the chamber is Hastelloy C276 alloy.  
Figure 2: Process of sampling (**a**) and sample preparation (**b**) for mineralogical and geochemical analyses before and after SC CO\({}_{2}\) exposure (**c**).  
The temperature inside the chamber (35 \({}^{\circ}\)C during the experiment) is controlled by a temperature regulator. All of the elements and sensors (of pressure and temperature) of the system are connected to a central computer which controls and regulates the system setup by the Hel 5.1 software (HEL Ltd., Borehamwood, UK).  
Six previously saturated rock samples were placed into the chamber and covered with 0.3 dm\({}^{3}\) of brine. For the dry experiment the samples were not saturated and no brine was introduced into the system. In both wet and dry configurations, the test consisted of three distinct phases: (i) the fill-in phase, filling the chamber with CO\({}_{2}\) until reaching supercritical conditions (3 h); (ii) the experimental phase, during which the supercritical pressure and temperature values are maintained steady; and (iii) the evacuation phase, during which the system is de-pressurized and the fluid and solid samples are collected (3 h). All the experimental runs for this study were carried out in the Laboratories of the IGME in Tres Cantos, Madrid, Spain.
# A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
## 6 Results and Discussion
### Engineering Use Case: Crash Box Design
#### 6.3.1 Key Performance Indicators (KPIs)  
When evaluating the crashworthiness of crush boxes, a number of Key Performance Indicators (KPIs) are often examined [56; 57]. These KPIs provide quantifiable metrics to assess the effectiveness of a crash box under crushing loads. An essential indicator is the total Energy Absorbed (EA). This quantifies the work performed on the structure to induce deformation due to the impact and is defined by Equation (34):  
\[EA=\int_{0}^{L_{c}}F(x)dx, \tag{34}\]  
where \(L_{c}\) is the crushing length and \(F(x)\) denotes the resultant impact force over the displacement \(x\). Maximizing EA is a common objective function, as long as it does not overweight the structure. To avoid this problem, it is often replaced by the Specific Energy Absorbed (SEA), which is the absorbed energy per unit mass of material:  
\[SEA=\frac{EA}{m}, \tag{35}\]  
where \(m\) is the sum of the mass of the crash box and the bumper beam in this case. Another important indicator is the mean crushing force, which is given by Equation (36):  
\[F_{m}=\frac{EA}{L_{c}}. \tag{36}\]  
The mean crushing force is a required element for the calculation of the Undulation of Load Carrying Capacity (ULC). This indicator evaluates the stability of the structure under crushing and is given by Equation (37):  
\[ULC=\frac{\int_{0}^{L_{c}}|F(x)-F_{m}|dx}{EA}. \tag{37}\]  
Finally, we introduce Crushing Force Efficiency (CFE), which relates the maximum peak resistance force \(F_{max}\) to the average force \(F_{m}\):  
\[CFE=\frac{F_{max}}{F_{m}} \tag{38}\]  
These two critical parameters shown in Equation (38) directly affect the deceleration experienced by vehicle passengers during a collision. Ideally, an absorber will have a CFE equal to one, meaning that the crush box absorbs energy uniformly throughout the deformation process and behaves in a controlled manner under crash conditions.
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
## 1 Introduction
[PERSON] and [PERSON] [12] presented a study showing good agreement between the measured and the predicted strains by resetting the measured strains to zero every day to avoid the accumulated errors caused by the temperature drift. Based on the data recorded by the resistance strain gauge, this study proposed a simple data-processing method to eliminate temperature-induced drift. The advancement of the study is to provide an easy but effective way to quantitatively determine the mechanical strain. Wireless ERS gauges were adopted in the study. The research performed tests to study how temperature changes affected the electrical resistance responses of strain gauges attached to steel and concrete specimens without restraint to obtain the temperature-resistance effect of the gauges. Then, strain monitoring tests for steel beam and concrete beams were conducted for a number of consecutive days, during which diurnal and nocturnal temperature changes and load changes were experienced. Using the proposed innovative strain data analysis method, temperature effects were eliminated to obtain the mechanical strain generated by load changes. Finally, the feasibility of using the proposed strain data analysis method to monitor the residual strain generated by a huge load within a short time was examined. Figure 1: The ambient temperature and measured strain relationship.
## 2 Method
### Multi-Stream Expansion Based on Location-Aware Attention Mechanism  
As shown in Figure 1, in the above structure, the encoded features of the acoustic information from the main encoder and the encoded features of the auxiliary encoder's pronunciation prior knowledge are calculated separately. In this process, the correlation between pronunciation prior and phonetic-specific is not considered. We propose a multi-stream expansion method by introducing the attention mechanism to calculate the correlation between the main and auxiliary inputs, obtain enhanced information streams, and provide richer sources for subsequent information fusion. The location-aware attention mechanism [25] introduces the attention weight calculated in the previous step as location awareness in the general attention calculation. In this paper, we use the attention weight \(a_{o}\), used to calculate the origin stream output \(r\), as the location awareness. It serves as a constraint to stabilize the process of enhancing features.  
Specifically, the output \(h_{t}\) of the acoustic signal encoder is used as the \(Key\) and the \(Value\). The output vector \(r_{l}\) obtained by Equation (2) after the prior encoder. \(r_{l}\) is used as the \(Query\) to calculate the attention weight as in Equation (8).  
\[a_{lt}=\text{ LocAttention }(r_{l},h_{t},a_{o}). \tag{8}\]  
By using this weight to perform a weighted summation on the encoder output in the original stream, we obtain the enhanced acoustic vector \(r_{e}\) as follows:  
\[r_{e}=\sum_{t=1}^{T}a_{lt}h_{t}. \tag{9}\]  
In the same way, the enhanced pronunciation prior vector can be obtained. This process can be more intuitively understood from Figure 2.
# Fast Support Vector Machine for Power Quality Disturbance Classification
## 3 Design of the Fast SVM (FSVM)
### FSVM Classification System  
Figure 2 shows the design architecture of the proposed FSVM. For a power system, Energy Management System (EMS) is generally implemented in the control center on top of SCADA, where the voltage and current are readily available. FSVM links to SCADA/EMS to get existing measurements, i.e., the voltages and currents. There are pre-selected observation locations with equipment installed to get measurements, namely Obs-1, Obs-2,..., Obs-q. Observation locations mean the buses of interests where harmonics exist. The bus voltage measurements received at a regular interval will be sent to a Data Processor for translation. The input signal of FSVMs is the amplitude of one cycle of the distorted wave. Receiving the distorted wave, each FSVM will perform pattern recognition to generate an output value. The _fdis_ will output the decision according to the score of each class, the class with the highest score is the PQD. Some examples can be seen in [3,11] for disturbance classification including magnitudes.
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Automatic Identification System  
The AIS transponders communicate over the air without any authentication or integrity checks, which allows hackers to use it to spread fake massages [24]. As stated in [11], software-defined radio is used by attackers to instigate fake \"man-in-the-water\" signals, making the ship unnoticed, and transmitting fake weather reports. Trusting in possibly inaccurate data can lead to poor choices and disastrous results. AIS data are also freely accessible to the public through websites such as Vessel Finder Limited  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Systems** & **USE** \\ \hline \hline \multirow{6}{*}{Automatic Identification System (AIS)} & - Vessel traffic monitoring and assistance \\  & - Avoid a collision \\  & - Notify ports and maritime authorities of the ship’s location \\  & - Calculate the distance between the ship and the other ships \\  & - Ensure sea safety by monitoring traffic \\  & - Accident investigation and search and rescue operations \\ \hline \hline Electronic Chart Display & - Collect and combine data from electronic navigation sensors \\   Information System (ECDIS) & - Shows the position of the ship in real time \\ \hline \hline GPS and GNSS & - Displays the position of the ship \\  & - Displays the speed \\  & - Displays the route and time \\ \hline \hline Radar & - Provides information about the ship’s surroundings \\  & - Detection of the position and speed of objects \\ \hline \hline Global Maritime Distress & - Broadcast the distress messages related to safety issues \\   System (GMDSS) & - Sending and receiving critical safety alerts \\ \hline \hline Global Industrial Control & - Assist in reducing human errors \\   Systems (ICSs) & - Increase resource productivity \\  & - Extend the life of the equipment \\  & - Control and monitor parameters on board a ship \\ \hline \hline Very Small Aperture & - Uses a satellite network to send and receive data \\   Terminal (VSAT) & - Offer a variety of communication and security services \\ \hline \hline Propulsion and machinery & - Monitor and regulate onboard machinery \\   management and power & - Monitor and regulate propulsion \\   control systems & - Monitor and regulate steering \\ \hline \hline Video Surveillance System & - Monitor transport operations in large storage areas \\  & - Monitor transport operations in large vessels \\ \hline \hline IT Network Systems & - Used for internal/external processes to send, receive, and store data \\  & - Used for crew welfare \\  & - Used for crew personal devices (BYOD) \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the main automation systems in modern and autonomous ships.  
([[https://www.vesselfinder.com/](https://www.vesselfinder.com/)]([https://www.vesselfinder.com/](https://www.vesselfinder.com/))) and Marine Traffic [28]. In this context, the IMO criticised the disclosure of information about ships and their itineraries because this information can be very useful in the event of a targeted attack.