esa-sceva/satcom-chunk-collection huggingface dataset:
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 4 Methodological Approach  
The research is organized according to the following methodological phases:  
1. Extensive overview of the current state of cold ironing in European ports, which includes the following: 1. Search and analysis of data on cold ironing usage across EU ports, 2. Selection of a sampling group of 10 ports for the development of further investigations, 3. Estimation of power demand of ships at berths, 4. Estimation of emission/energy [g/kW/h] for auxiliary engines and electricity production,
2. Quantification of the time spent by ships in ports, based on a survey on a selected sampling group of ports, which includes the collection of MarineTraffic(r) data for 2 weeks on dwell time in the sampling group of ports with cold ironing in operation,
3. Estimation of the potential reduction of gaseous emissions of various typologies of ships in ports achievable by cold ironing implementation, which includes thecalculation of emissions from auxiliary engines in a reference scenario (without cold ironing) and during cold ironing in full operation (auxiliary engines switched off),
4. Estimation of global potential benefits, implementation challenges, and identification of technological and operational solutions,
5. Assessment of potential solutions and identification of further research needs.
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 3 Influence Analysis of Time-Domain Anti-Jamming on Burst Signal
The diagram shows that the smaller the CNR loss margin of the receiver, the longer convergence time and more significant data loss are caused. The BDS is still in the demonstration and development stage. For example, the transmit/receive time-sharing technology in low-orbit constellation communications is still under research. In addition to the satellite-ground link with the T/R time-sharing period of 3 s, other system links are still under construction, which may adopt different periods to achieve T/R time-sharing. When the time requirement is less than the satellite-ground link period, the time-domain anti-interference may impact other link burst signal systems more. Figure 7 shows the proportion of data loss under different receiving time slots and analyzes the influence of signal receiving time slots on the signal quality of the navigation receiver. It can be seen from the figure that as the signal reception interval decreases, the receiving quality of the burst navigation signal becomes more affected by anti-interference. As the CNR loss margin of the receiver decreases, the time-domain anti-interference has more apparent damage to the quality of the navigation signal.
## 3 Data Processing Methods
### SAR Images Formulation Based on ULA Communication Signals with the BPA
#### 3.2.2 The Principle and Procedure of the BPA  
The BPA originated from computed tomography technology and is classified as a time-domain imaging method. [PERSON] was the first to introduce computed tomography techniques into the field of SAR, providing a detailed exposition of the method [56; 57]. As shown in Figure 4, the fundamental concept of the BPA involves projecting each azimuth echo onto a pre-partitioned spatial grid to generate sub-images. Ultimately, all azimuth echo data are coherently stacked to form the focused image [58]. The BPA is characterized by its high imaging accuracy and phase preservation capability, thereby establishing a solid foundation for interferometric processing.  
The complete procedure of the BPA for SAR imaging is outlined below [59; 60; 61; 62; 63]:  
1. Range compression: Pulse compression processing is applied to the echo data in the range direction to achieve high resolution. This is typically accomplished using a matched filter. In this experiment, the PDP of the communication signal is equivalent to the result obtained from the radar echo signal after pulse compression, thereby rendering this step unnecessary.
2. Imaging area gridding: The imaging area is divided into a grid under an appropriate coordinate system, with the grid size being slightly smaller than the resolution unit of the system.
3. Calculation of the two-way time delay: The two-way slant range and time delay between each grid point and the antenna phase center are calculated for all azimuth times.
4. Back projection and phase compensation: Based on the time delay for each grid point, the echo data at each point are interpolated from the corresponding azimuth echo, and phase compensation is applied to all points. The phase compensation term is dependent on the two-way slant range between the grid point and the antenna phase center at the current azimuth time. Ultimately, the echo data at each azimuth time are back projected to produce a sub-image.
5. Coherent superposition: The focused SAR image is obtained by coherently superposing the sub-images generated at all azimuth times
## 4 Time-Optimal Path Planning
Figure 2: Large-angle maneuver angular velocity path planning. The change in Euler angles over time is expressed as follows:  
\[\Phi(t)= \begin{cases}\frac{1}{2}a_{\omega}t^{2}&0<\ t<t_{1}^{\prime}\\ \Phi-\frac{1}{2}a_{\omega}(t_{2}^{\prime}-t)^{2}&t_{1}^{\prime}\leq\ t\leq t_{2}^ {\prime}\end{cases}  \tag{16}\]  
In scenarios with no uniform velocity phase, the maximum angle for the maneuver occurs at \(t_{1}^{\prime}\) when the angular velocity is \(\omega_{\max}\). At this point, \(\Phi_{\max}=\omega_{\max}t_{1}^{\prime}=\omega_{\max}^{2}/a_{\omega}\). Therefore, if \(\Phi>\omega_{\max}^{2}/a_{\omega}\), the angular velocity path includes a uniform velocity phase. If \(\Phi\leq\omega_{\max}^{2}/a_{\omega}\), the angular velocity path does not include a uniform velocity phase. Based on the Euler angles at each moment in time, the corresponding error quaternion is calculated as follows:  
\[\Delta q(t)=\left[\cos\frac{\Phi(t)}{2},\ e_{x}\sin\frac{\Phi(t)}{2},\ e_{y} \sin\frac{\Phi(t)}{2},\ e_{z}\sin\frac{\Phi(t)}{2}\right]^{\mathrm{T}} \tag{17}\]  
Therefore, the path of the satellite's attitude quaternion is as follows:  
\[q(t)=q_{c}\otimes\Delta q(t) \tag{18}\]
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 2 Methods
### Study Case
#### 2.3.3 Three-Dimensional Geometric Characterization and Mesh  
The 3D design was based on the topography records and cross sections of the dam (Figure 3). The model was simplified by eliminating galleries, bottom drainage, crest railings, stairs, and the board over the spillway.  
Figure 4 shows the dam ground plan, which consists of 3 arches with 2 different curvatures, covering 16 blocks, and abutments at each end in straight alignment, covering another 6 blocks.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Block** & **Monitoring Device** & **X (m.)** & **Y (m.)** & **Z (m.)** & **Elevation Above Foundation (m.)** \\ \hline
0 & Leveling screw P5 & 146.2 & 83.1 & 79.0 & 39.8 \\
1 & Pendulum PI-B1-GH & 164.8 & 79.3 & 58.3 & 18.3 \\
1 & Pendulum PI-B1-GP & 164.8 & 79.3 & 45.0 & 5.0 \\
2 & Pendulum PI-B2-GH & 133.8 & 83.0 & 60.2 & 18.9 \\
2 & Pendulum PI-B2-GP & 133.8 & 83.0 & 45.3 & 4.0 \\
3 & Leveling screw P6 & 174.2 & 75.5 & 79.0 & 34.7 \\
6 & Leveling screw P4 & 117.2 & 81.7 & 79.0 & 33.9 \\
7 & Leveling screw P7 & 196.9 & 62.1 & 87.0 & 36.2 \\
7 & Pendulum PI-B7-GP & 196.1 & 63.8 & 63.9 & 13.1 \\
8 & Leveling screw P3 & 96.0 & 75.0 & 87.0 & 39.5 \\
8 & Pendulum PI-B8-GH & 103.8 & 76.8 & 61.7 & 14.2 \\
8 & Pendulum PI-B8-GP & 103.8 & 76.8 & 53.1 & 5.6 \\
14 & Leveling screw P2 & 62.2 & 50.3 & 87.0 & 24.3 \\
14 & Pendulum PI-B14-GP & 64.1 & 50.6 & 67.7 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement monitoring devices at the dam.  
Figure 3: Dam design profile (m).  
The ground not immediately adjacent to the dam was simplified, while detailed ground definition, with contour lines every meter, was included adjacent to the dam.  
Tetrahedra with 10 nodes were used for the mesh, and a quadratic element order and a growth ratio of 1.1 (Figure 5). The rest of the parameters were established to maintain a compromise between the calculation time and the quality of the result. All the mesh features have been extracted from a mesh convergence study previously tested in similar cases under the same numerical model [15]. The mesh has 462.570 elements and 713.295 nodes, based on maximum mesh sizes of 1.12 m for the dam and 11.2 m for the bedrock, which were considered optimum in a similar case [15].  
Figure 4: Dam ground plan.  
Figure 5: Half dam and terrain meshes.
## 2 Proposed Approach
### Input Features
#### 2.1.2 Additional Spectral Information  
Various spectral features can be extracted through additional calculations on a spectrogram. In this paper, spectral centroid [25] (1 st order), spectral bandwidth [25] (1 st order), spectral contrast [26] (7 th order), spectral flatness [27] (1 st order), and spectral rolloff [28] (1 st order) are concatenated to provide 11-dimensional spectral information at a frame. The spectral centroid is a frequency where the center of mass of the spectrum at a frame is located. This has a strong correlation with the brightness of sound [29]. The spectral bandwidth indicates the dispersion of the spectrum that is computed from the spectral centroid. The spectral contrast is the average of the difference between peak and valley energies in each sub-band after dividing the spectrum into many sub-bands [26]. A high-contrast value means a clear narrow-band signal whereas a wide-band noise signal provides a small contrast value. We use six sub-bands in the experiments. The spectral flatness is a measure that quantifies the degree to which the spectrum is similar to white noise [27]. The spectral rolloff provides a frequency at which the spectral energy contained at and below that frequency is a certain percentage of the total energy at a frame.
###### Abstract  
Fatigue resistance quantification of modified asphalt is typically time consuming and may not correlate well with mixture fatigue test results. In this paper, the applicability of a multiple stress creep and recovery (MSCR) test on asphalt binder's fatigue resistance was evaluated. Six binder types with a variety of modifiers and different modifier contents were characterized. The MSCR test was conducted and its sensitivity to binder type and additive content under different aging durations was evaluated. Mixture fatigue tests including a semi-circular bending- Illinois flexibility index test and indirect tensile strength were conducted using control base asphalt and SBS modified asphalt. A ranking between the binder MSCR and mixture fracture test results was conducted to check if the MSCR result was representative of the mixture's fatigue resistance. Results indicate that the MSCR test at intermediate temperatures (20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C) can be performed with good repetitions. It was also found that the MSCR test was sensitive enough to differentiate the fatigue resistance among different binder types and additive contents. The ranking analysis shows that the binder MSCR test at intermediate temperatures showed a similar ranking to the mixture's fatigue tests, indicating that the binder MSCR test could be potentially utilized to represent a mixture's fatigue resistance where binder selection is a major concern. It was also found that the SBS modified binder showed the best crack resistance and was less affected by aging.  
modified asphalt; fatigue resistance; MSCR; intermediate temperature +
Footnote †: journal: Applied Sciences
# Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection
## 1 Introduction
dataset, or at a class level, applicable to specific classes. Alternatively, they may provide explanations at a local level, meaning for each individual image. Moreover, XAI methods vary in their target audience for explanation. Each group has distinct requirements for machine explanations and varying levels of comprehension in the relevant field. To investigate the reliability of automated predictions individually, clinicians require local and highly user-centric explanations from a diagnosis help system. On the other hand, most deep learning software libraries, including post-hoc XAI techniques, do not require expert-annotated data [12], leading to their widespread adoption. Nevertheless, these solutions are frequently dismissed as inadequate for addressing the issue of openness in the medical field since they provide a potential risk of generating confirmation bias during the interpretation of the explanations [13]. Transfer learning and Generative Adversarial Networks (GANs) have been employed to recognize and classify skin cancer [14; 15]. Although there has been notable advancement, the classification of skin cancer remains a challenging endeavor. This can be attributed to the scarcity of annotated data and the limited diversity between different classes. Varieties of skin lesion size, color, shape, and contrast, along with additional artifacts, such as hair and veins, further complicate the process. This article investigates the impact of incorporating a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), into a CNN. A single CSAM improves the performance of pretrained CNN models, but when the proposed CSAM is integrated twice, called Spiking Attention Block (SAB), the performance of pretrained models is boosted further. Deep learning architectures classify images by acquiring knowledge of the prominent characteristics and relationships. SAB enhances performance by directing its attention exclusively towards pertinent regions of the input; it also enhances the transparency of the image classification process for medical staff and effectively identifies and maps the specific sections of the input that the network relies on to classify the image. As a result, this mechanism increases the level of trust in the classification model. The remainder of the article is structured such that Section 2 provides the literature review, Section 3 provides an in-depth review of the proposed approach, Section 4 provides an overview of the classification and comprehensibility of the proposed method, and Section 5 concludes the article by discussing potential future directions.
## 3 Results
### HE Uregulates the Expression Levels of Growth Factors and DP Signature Genes in HDP Cells
These findings suggested that CM100 contains diverse chemokines involved in the migration of epithelial keratinocytes such as HaCaT cells. Figure 5: Evaluation of the effects of conditioned media from HE-treated HDP cells on HaCaT keratinocytes. (**a**) The protein expression levels of secreted VEGF, IGF1, FGF2, and FGF7 in conditioned media supplemented with 2% FBS were analyzed by Western blotting. Ponceau staining of the membrane was performed as a loading control. (**b**) HaCaT keratinocytes were incubated for 48 h with serum-free DMEM, CM0, or CM100, and cell migration was assessed using a chemotactic migration assay. Arrows indicate migrated cells, and quantitative analysis of the number of migrating cells was performed using ImageJ software v. 1.52a. (**c**) HaCaT keratinocytes were treated with serum-free DMEM, CM0, and CM100 for 48 h, and the gene expression levels of _VEGF_, _IGF1_, and _IL-1\(a\) were analyzed using RT-qPCR. The expression levels of each gene were normalized to that of _Cyclophilin_. Each bar represents the mean \(\pm\) SD from three independent experiments. Statistical significance was analyzed using one-way ANOVA followed by [PERSON]’s post hoc test. * \(p\) < 0.05, *** \(p\) < 0.001 compared with the control group. n.s. means non-significant. HDP, human dermal papilla; HE, heterotrophic-cultured _S. deserticola_ JD052 extract; RT-qPCR, real-time quantitative polymerase chain reaction. Since HE-treated conditioned media upregulated the secretion level of growth factors and the migration of HaCaT keratinocytes, the expression levels of intracellular-hair-growth-related genes were analyzed using RT-qPCR analysis. As shown in Figure 5c, we observed that CM100-treated HaCaT keratinocytes exhibited a significant upregulation in the expression levels of _VEGF_ and _IGF1_ and decreased interleukin-1\(\alpha\) (_IL-1a_) compared to CM0-treated cells. In conclusion, these data indicate that HE treatment in HDP cells facilitate hair growth through the paracrine mechanism.
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 3 Data Acquisition and Attack Vector Generation  
Considering the wide spread adoption of drones for smart cities, to undertake operations and render timely services, associated cyber threats need to be highlighted. Once an intruder is successful in gaining access to a UAV, it can carry out other attacks such as data theft (of sensitive video and image recordings from drone), data corruption, Denial of Service (DoS) or signal jamming, with a motive to sabotage a UAV in flight or to modify its flight path and fly it to an unwarranted waypoint. A compromised drone can be forced to crash through GPS spoofing techniques or by deliberately powering-off the motors in an attempt to rapidly drop its altitude, with an intent to incapacitate (or completely damage) the drone.  
Detecting anomalies in a drone through the analysis of its on-device data is, therefore, imperative to foster a secure drone flight and to prevent a cyber attack from occurring. To our knowledge, there does not exist a robust and labeled flight dataset for UAVs to introduce an anomaly detection exercise, and therefore, we proposed a methodology wherein abnormal (outlier) events are found through analysis of flight logs of a DJI Phantom drone are reverse engineered to construe attacks.  
Several references discuss the presence of anomalies in drone data. We examine references [19; 20; 21] to interpret the possibility of anomalous flight patterns in drones. In [21], motor temperature monitoring is undertaken to identify beyond-normal patterns. In [19], drone in-flight faults that could cause a crash have been highlighted. To detect such faults, motor speeds and drone altitudes are examined to identify potentially anomalous events in drone logs. Similarly, drone log analysis techniques described in [22] illustrate the use of drone flight logs for detecting a drone crash. The proposed scheme adopts the analysis of data obtained from the accelerometer, drone motor and altitude, to detect a crash. Based on this, it can be concluded that flight logs are an important source of information, which comprise both normal and anomalous drone flight operation data.  
As part of our proposed attack model, we first identify the potentially anomalous flight operations using exploratory data analysis and then identify potential scenarios that might closely match cyber attacks.
## 3 Results and Discussion
### Principal Component Analysis (PCA)
In the PCA, the residual levels of OCPs (\(\gamma\)-HCH, DDT, and their metabolites) in maize grain formed three groups based on fertilization treatments and years of the study (Figure 9b). The content of OCPs in grain was similar in treatments fertilized with MBM at 2.0 t ha\({}^{-1}\) and 3.0 t ha\({}^{-1}\), regardless of year. These treatments were characterized by the highest supply of nutrients and the highest grain yield. [PERSON] et al. [68] found that soil DDT levels are influenced by plant growth rates, which stimulate microbial activity and affect the availability of residual pesticides. In the present study, three treatments with similar grain contamination levels, i.e., the unfertilized treatment (2016) and 1.0 t ha\({}^{-1}\) MBM treatments (2016 and 2017), formed the smallest group. Similarly, in the analysis of organochlorine contaminant concentrations in MBM and soil, the first two components explained 57.94% (PC1) and 27.23% (PC2) of the variance, again total  
Figure 9: Results of PCA exploring the effect of temperature, precipitation, grain yield, and grain lipid content on the residual levels of organochlorine pesticides in grain. (**a**) projection of variables and treatments onto the first and second principal components, (**b**) \(\gamma\)-HCH—\(\gamma\)-hexachlorocyclohex, DDT—1,1,1-Trichloro-bis-2, [4-chlorophenylophyll]-ethane, DDD—dichloroethylene, DDE—dichloroethylene. total variance. The residual levels of DDT, its metabolites (DDE and DDD), and \(\gamma\)-HCH in MBM were positively correlated with DDE concentration in soil. The levels of DDT and its metabolites (DDE and DDD) in MBM were negatively correlated with the concentrations of DDT and DDD in soil. The concentration of \(\gamma\)-HCH in MBM was positively correlated with \(\gamma\)-HCH levels in soil and negatively correlated with DDT and DDD levels in soil (Figure 10a). When examining the influence of organochlorine contamination in both soil and grain, PCA results showed that the first two components explained 65.9% of the total variance, with 36.56% attributed to PC1 and 29.34% to PC2. Soil contamination with DDT, DDE, and DDD was positively correlated with the content of \(\gamma\)-HCH in grain (Figure 10b). A negative correlation was found between \(\gamma\)-HCH concentrations in soil and grain. [PERSON] et al. [69] suggested that the rate of DDT absorption by plants was determined by DDT concentration in soil. They reported a weaker correlation between the residual levels of \(\gamma\)-HCH in soil and plant tissues.