esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Environmental Evaluations and Measures to Reduce Environmental Impact
#### 3.2.1 Air Pollution and Climate Change  
Maritime transport emits around 940 million tonnes of CO2 annually and is responsible for about 2.5% of global greenhouse gas (GHG) emissions [10]. Road transport is still the biggest emitter of GHG emissions, and it must be noted its fundamental role when it comes to maritime freight transportation in the final door-to-door delivering service to companies. Carbon emissions are produced at all the stages of container processing, since the arrival of the ship at the port to the final delivery operation by container trailers from port to the hinterland facilities and vice versa. Scholars developed different models to estimate emissions at port terminal facilities, most of which depend on energy used by each mode (kWh or fuel consumption) and emission factors related to each pollutant [17]. These types of methodologies are applicable to any polluting emission into the atmosphere. In the case of CO2 emissions at container terminals, which we decided to analyse more in depth in our paper, a specific methodology is that elaborated by [PERSON] [18]. In Sim [18], total carbon emissions at the container terminal are calculated by the summation of carbon emissions from vessel maneuver, vessel at berth, container loading and unloading, container transportation, and container receiving and delivery. Each of these factors is evaluated according to carbon emission density of vessel/equipment (depending on the activity type) and the amount of containers in TEU, according to Equation (1) [18]:  
\[E_{CO2}=\sum CED_{i}\cdot TEU \tag{1}\]  
where:  
* TEU = the transported amount of containers  
In general, most effective measures of mitigation for air pollutants are those related to the use of alternative fuels for ship propulsion [17; 19]. However, site-based measures can be effective at reducing the impact for local communities, e.g., in the case of shore side electrical power (also known as cold ironing), providing an alternative power source to their own engines for vessels at berth; this might be considered a long-term solution with considerable costs of realization. Other economic measures might be effective, such as tax rewards and bonuses for vessels with clean fuel, even if it is important to have that port administrators find stakeholders' support in its implementation. The first step is, however, the installation of monitoring station to constantly evaluate emissions and air quality.
## 3 Magnetic Data Analysis  
### Main Principles  
The magnetic \(\Delta T\) anomaly distortions occur due to the inclination of the magnetization vector to the horizon plane and the different orientations of the horizontal magnetization projection concerning the body's axes, e.g., [33]. Besides the geomagnetic field inclination, the body's axes' orientation relative to the geomagnetic field's horizontal component is also significant. Therefore, the analysis of field graph maps is insufficient and often necessary to analyze the isolines maps [34].  
In the conditions of oblique (inclined) magnetization, the \"reduction to pole\" procedure is often used, i.e., the calculation of the pseudogravimetric anomalies [35]. However, the procedure is appropriate only when all interfering bodies in the studied area (1) are magnetized parallel to the geomagnetic field and (2) have sub-vertical dipping. Only in this case, the recalculated magnetic fields can be used for further quantitative analysis. Besides this, the \"reduction to pole\" cannot calculate such disturbing factors as the rugged terrain relief and practically always available superposition of the magnetic anomalies with the various wavelengths. Similar approaches based on the transformation of the observed magnetic field--analytic signal, e.g., [36]; wavelet transform, e.g., [37; 38]; and Euler deconvolution, e.g., [39; 40]--have the same limitations.  
The developed interpretation methodologies involve the application of methods elaborated specially for the quantitative analysis of magnetic anomalies in complex physical-geological environments [34; 41; 42]. Unlike other techniques, these methods (improved versions of tangents, characteristic points, and areal methods) are applicable in oblique (and reverse) magnetization, rugged relief, and unknown levels of the normal field (i.e., superposition of the magnetic anomalies of different orders). Five interpretation models are used: (1) thick bed, (2) thin bed, (3) horizontal circular cylinder (HCC), (4) sphere, and (5) thin horizontal plate. In addition, [PERSON] [42] showed that a representative class of models that take intermediate forms between the thick bed and the thin horizontal plate could be interpreted using the methodologies developed for the thick bed. Thus, advanced techniques can interpret magnetic anomalies from most geological targets under conditions of inclined magnetization, rugged terrain relief, and superposition of anomalies of different orders.
## 3 Results
### Correlation of Activity Concentrations with Soil Properties  
Table 6 lists the correlation coefficients between activity concentrations and soil properties. The results show that there was no significant correlation between activity concentrations and pH, EC, OM, and silt content (\(p>0.05\)), respectively, except for \({}^{226}\)Ra which had a positive correlation with EC (\(p=0.03\)). The amount of soluble salts in the soil can be determined by measuring the EC content of the soil solution. The presence of \({}^{226}\)Ra in the soil suggests that it may have been dissolved under highly soluble salt conditions [52]. The activity concentrations had a significantly positive correlation with clay content and a negative correlation with sand content. This is because the fine particles of clay contain mineral lattices with many nuclides, and the high specific surface area of the fine clay particles provides a stronger adsorption capacity for nuclides [22; 26; 39; 40; 53]. Some studies have found a strong correlation between clay content and activity concentrations [38]. While soil organic  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Location/Country**} & \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**\({}^{40}\)K**} & \multicolumn{2}{c}{**\({}^{226}\)Ra**} & \multicolumn{2}{c}{**\({}^{232}\)Th**} & \multirow{2}{*}{**References**} \\ \cline{3-3} \cline{5-6}  & & & **Ba/kg** & & \\ \hline \hline \multirow{4}{*}{Kenting Peninsula Soil around coal-fired power plant Soil in whole island Soil in grass field Soil in tobacco Soil Soil in tobacco Soil Soil in water 702 & Taiwan 303 & 344 & 18.6 & 26.5 & PS * \\  & 12 & 359 & 18.7 & 26.8 & [48] \\  & 50 & 370 & 17 & 27 & [7] \\  & 33 & 335 & & & [49] \\  & 9 & 702 & 36.3 & 55.1 & [41] \\  & 16 & 407 & 22.2 & 33.4 & [11] \\  & 13 & 436 & 24.2 & 32.1 & [16] \\  & 73 & 539 & 23 & 41 & [1] \\  & 66 & 591 & 30.9 & 45.4 & [27] \\  & \multicolumn{4}{c}{Worldwide Ho Chi Minh City/Vietnam Kamnataka/India 25} & 215 & 24.0 & 33.0 & [32] \\  & 25 & 238 & 28.1 & 21.6 & [44] \\  & 40 & 277 & 20.0 & 19.4 & [3] \\  & 14 & 304 & 19.0 & 30.5 & [50] \\  & 16 & 314 & 32.4 & 35.6 & [5] \\  & 58 & 438 & 19.1 & 21.9 & [25] \\  & 17 & 464 & 24.0 & 26.9 & [2] \\  & 12 & 583 & 163.0 & 143.0 & [51] \\  & 37 & 594 & 83.7 & 53.6 & [4] \\  & 36 & 643 & 82.0 & 123.0 & [33] \\  & 16 & 742 & 63.0 & 59.0 & [42] \\  & 420 & 32 & 45 & [14] \\ \hline \hline \end{tabular}  
* PS: present study.  
\end{table}
Table 5: Radioactivity in the sands studied in the present study compared with those from other countries.  
matter can affect radiation activity [3; 17; 30; 39], this study did not find a significant correlation between OM and soil radioactivity, as it was the case for references [5; 38]. This could be due to the complexity of the source of organic matter in the sampling area [5; 7; 11; 30].
## 5 Parameters Study  
### Influence of Moving Speed of Vehicle  
The influence of moving speed on the vehicle acceleration and its corresponding acceleration spectrum are shown in Figures 9 and 10, respectively. The results indicate that, at lower speed values, the frequency could be easily and clearly determined. As the vehicle speed increased, the influences of the driving frequency of the vehicle became more pronounced, and the peak spacing of the amplitude in the spectrogram became larger. Excessive vehicle speed led to the overlapping of peaks of adjacent order frequencies, making frequency identification challenging.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & **Test Values** & **FE (Free** & **FE (Force** & **FE (Force** & **Relative Error-Free** & **Relative** \\  & **Vibration**) & **Vibration-Bridge**) & **Vibration-Vehicle**) & **Vibration (\%)** & **Error-Vehicle (\%)** \\ \hline
1 & 1.39 & 1.40 & 1.38 & 1.40 & 1.06 & 0.72 \\
2 & 1.83 & 1.98 & 1.97 & 1.97 & 7.93 & 7.61 \\
3 & 2.39 & 2.61 & 2.58 & 2.58 & 9.12 & 8.14 \\
4 & 3.39 & 3.51 & 3.51 & 3.52 & 3.55 & 3.77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first 4 natural frequencies of the northern approach of Jiubao Bridge.  
Figure 8: Acceleration spectrum of the vehicle with different vehicle running times: (**a**) 1 span (\(t\) = 5.5 s); (**b**) 2 span (\(t\) = 14.0 s); (**c**) 3 span (\(t\) = 22.5 s); (**d**) 4 span (\(t\) = 30.3 s).  
Figure 10: The influence of the moving speed of the vehicle on the acceleration spectrum: (**a**) \(v=1\) m/s; (**b**) \(v=5\) m/s; (**c**) \(v=20\) m/s; (**d**) \(v=40\) m/s.  
Figure 9: The influence of the moving speed of the vehicle on acceleration: (**a**) \(v=1\) m/s; (**b**) \(v=5\) m/s; (**c**) \(v=20\) m/s; (**d**) \(v=40\) m/s.  
As the vehicle speed increased, the maximum acceleration of the vehicle also increased approximately linearly, as shown in Figure 11. The maximum amplitude of acceleration spectrum initially exhibited a linear increase within a small range and subsequently fluctuated between 0.2 and 0.4. Meanwhile, Figure 12 demonstrates that the maximum mid-span displacement of the composite beam did not consistently increase with the rise in vehicle speed. Instead, it exhibited a fluctuating upward trend.
# Estimating Destination of Bus Trips Considering Trip Type Characteristics
## 3 Data  
The data utilized in this study contain bus and subway boarding and alighting information, and comprise card ID, boarding stop and time, alighting stop and time, used route, etc. We utilized transportation card data for the whole year of 2020 to estimate the transportation card-based alighting stop considering the characteristics of the trip types. However, when a passenger boarded a bus and utilized a subway on the next trip, we fabricated the data utilizing the location coordinates of the stop to construct the trip-chain. The total number of data (based on buses) utilized in the analysis was 4,110,205,036 (approximately 4.1 billion/year), out of which approximately 17% had no alighting tag information (see Table 2). However, when we examine for each city or province, the percentage of the missing alighting tag data in the remaining regions after excluding the Seoul metropolitan and Daejeon & Sejong areas is approximately 63%, which limits utilizing the data for the analysis of real travel patterns in the public transportation. Therefore, alighting stop estimation is imperative.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Researcher** & **Method** & **Accuracy** & **Criteria for Evaluating Accuracy** & **Reference** \\ \hline [PERSON] et al. (2011) & Trip-chain & 75–85\% & Accuracy of OD Matrix & [6] \\ \hline [PERSON] (2013) & Trip-chain & 91.3–94.6\% & Within 2 stops from actual destination & [8] \\ \hline [PERSON] et al. (2014) & Trip-chain & 84.2\% & - & [16] \\ \hline [PERSON] et al. (2015) & Historical trip data analysis & 79.2\% & Within 400 m from actual destination & [20] \\ \hline [PERSON] et al. (2016) & Trip-chain & 86.6\% & Within 800 m from actual destination & [17] \\ \hline [PERSON] et al. (2016) & Trip-chain & 82.4\% & \
\begin{tabular}{c} Inferred and actual destinations \\ located in same zone \\ \end{tabular} & [5] \\ \hline [PERSON] and [PERSON] (2017) & Trip-chain & 93.6–94.0\% & Within 2 stops from actual destination & [10] \\ \hline [PERSON] et al. (2018) & Trip-chain & 90.5\% & Within 2 stops from actual destination & [13] \\ \hline [PERSON] et al. (2019) & Trip-chain and historical trip data analyses & 67.2\% & Within 2 stops from actual destination & [26] \\ \hline [PERSON] (2019) & Trip-chain and Gaussian Mixture Model & 86.1\% & Within 1 stop from actual destination & [19] \\ \hline [PERSON] et al. (2019) & Trip-chain and machine learning & 74.43\% &
\begin{tabular}{c} Inferred and actual destinations \\ located in similar zones \\ \end{tabular} & [25] \\ \hline [PERSON] (2020) & Spatial clustering & 80.0\% & Within 2 stops from actual destination & [22] \\ \hline [PERSON] et al. (2020) & Neural network & 86.4\% & Within 400 m from actual destination & [24] \\ \hline \hline \end{tabular} Note: The accuracy of trip destination inference refers to the percentage of trips where the inferred destination matches the actual destination.  
\end{table}
Table 1: Accuracy of destination inference results from previous studies.
## 4 Path to Clinic
### Oligonucleotide Drugs  
Oligonucleotide therapies can directly inhibit DUX4 expression, targeting based on the target sequence, realizing personalized treatment, and can be stopped at any time if adverse events are encountered. However, this therapy carries the risk of off-targeting and cytotoxicity, inefficient muscle cell uptake, and less efficient drug delivery to tissues except the liver. All oligonucleotide drugs have the potential for cytotoxicity and immunogenicity. miRNAs are inherently less specific than ASOs, increasing the risk of off-targeting. Oligonucleotide drugs are costly for the treatment of FSHD, and there is currently no evidence to demonstrate their long-term effectiveness.  
A variety of oligonucleotide drugs have been approved for the treatment of muscular atrophy such as DMD. This approach has reached a relatively advanced stage for treating clinical indications, and the development of oligonucleotide therapy for FSHD is actively underway. In the process of oligonucleotide therapy going to clinic, two problems that urgently need to be solved are the choice of delivery carrier and precise drug dosage. When drugs are delivered to patients through weakly targeted carriers, autoimmune reactions will cause serious inflammatory reactions such as myositis and myocarditis, and the liver toxicity caused by off-target needs particular attention. Studies have shown that high-dose injection of AAV9 drugs has caused serious toxicity in rhesus monkeys and piglets, and acute liver failure and shock have occurred [78]. Therefore, to alleviate the extramuscular reaction, it is necessary to optimize the delivery vector, including transforming capsid protein and modifying regulatory elements, which can not only improve the transduction efficiency and targeting of the vector but also reduce the immune response. At the same time, it is still necessary to strictly control the drug dosage and assess the off-target toxicity.  
In a word, to cure FSHD clinically with oligonucleotide drugs, the toxic effects must be minimized when providing therapeutic effects.
# Multi-Source Satellite Imagery and Machine Learning
## 8 Conclusions  
This study explored the impact of different combinations of geospatial and spectrometric data on the performance of classification algorithms in a geological context. By combining geographic parameters, OLI spectrometric parameters, and data from dimension reduction by PCA, the objective was to evaluate the influence of each type of data and their combination on algorithms, such as Random Forest, XGBoost, and Gradient Boosting. The results showed that OLI geographic and spectrometric data are most effective in improving overall model accuracy and performance, especially with Random Forest. These data provide nuanced and specific information that is crucial for geological classification. In contrast, PCA data, while helpful in reducing dimensionality and speeding up execution, did not show exceptional performance in accuracy compared to other datasets.  
Combining different data sources generally improves results, although information overload in some combinations, such as OLI and PCA, has shown less optimal performance.  
Thus, judiciously adding these parameters can result in significant gains in accuracy and MCC, but it is crucial to select the right combinations to avoid excessive complexity. We plan to extend this study to other regions or types of geological formations to verify the robustness and generalization of the models. In addition, we will collect more data to balance the representation of different types of geological formations. Adding additional data will help reduce bias related to underrepresented classes, thereby improving the performance and fairness of the models. In addition to current parameters, integrating data from hyperspectral, thermal, or radar sensors will be considered. These new sources of information will bring additional richness to the characteristics used by the algorithms, providing a better ability to distinguish complex geological formations and refine predictions.  
Conceptualization, K.J.A. and V.-C.J.S.; methodology, K.J.A. and V.-C.J.S.; software, K.J.A.; validation, P.G. and K.F.K.; formal analysis, K.J.A. and A.B.B.; investigation, K.J.A. and A.B.B.; resources, P.G.; data curation, K.J.A.; writing--original draft preparation, K.J.A., A.B.B., and K.F.K.; writing--review and editing, P.G.; visualization, V.-C.J.S.; supervision, P.G. and K.F.K.; project administration, P.G.; funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Dataset available on request from the authors.  
The authors thank the International Foundation for Sciences (IFS) for its logistical support for this work.  
The authors declare no conflicts of interest.
## 5 Experiments and Results
The inclination of the heat source has been retrieved in good qualitative agreement with the true angles, with uncertainties of about 9\({}^{\circ}\). It is worth noting that the retrieved \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & & \(w\) **(mm)** & \(h\) **(mm)** & \(d\) **(mm)** & \(\varphi\) **(deg)** & **Flux (kW/m\({}^{2}\))** \\ \hline
**Sample 1** & Fitted values & 2.3 & 1.2 & 0.38 & 69 & 66.5 \\ (Current: 15 A) & Real values & 2.1 & 2.1 & 0.4 & 60 & - \\ \hline
**Sample 2** & Fitted values & 2.5 & 1.4 & 0.26 & 38 & 61 \\ (Current: 10 A) & Real values & 2.2 & 1.8 & 0.28 & 30 & - \\ \hline
**Sample 3** & Fitted values & 2.8 & 2.6 & 0.24 & 9.7 & 80 \\ (Current: 10 A) & Real values & 2.5 & 2.2 & 0.27 & 0 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Real and retrieved sizes of the Cu films of the three samples used in the experiments. Figure 11: Photographs of the sample inside the coil for different orientations of the Cu slab with respect to the sample surface (painted in black): (**a**) \(\varphi<45^{\circ}\) and (**b**) \(\varphi>45^{\circ}\). fluxes seem sensible given the current values and orientation of the Cu slabs with respect to the coil axis. Samples 2 and 3 were excited with the same coil current but the retrieved flux is less for sample 2, as the Cu slab makes an angle with the coil axis. In sample 1, excited with a higher current, the retrieved flux is also higher. Even with the difficulty associated to the estimation of \(h\), the fittings of experimental results confirm that it is possible to evaluate the lateral dimensions, inclination and emitted flux of inclined planar heat sources. To the best of our knowledge, these are the first quantitative results reported on the geometrical parameters and flux of buried tilted heat sources from surface temperature data.
## 3 Results and Discussion
### Interpretation of CB Model  
The proposed CB model has a strong capability to predict the SS of RCDBs. Regardless, it is essential to interpret the model prediction in order to explore the impact of each input feature on the predicted output values. Therefore, the interpretation of the CB model was conducted herein, adopting the SHAP algorithm and PDPs analysis, which are discussed in Section 2.5.
## 4 Results
### SHM Plate with Stringer
#### 4.1.3 Tomographic Damage Imaging  
In this section, we apply the reconstruction algorithm for probabilistic inspection of damage (RAPID) described in [33] for the analysis of tomographic damage imaging. Instead of the signal difference coefficient used in the original implementation, we have used Equation (2) to weight the contribution of each transducer path. Analog to Ref. [11] we have considered only those measurements in the analysis where the transducer pairs are on opposite sides (top and bottom).  
Tomographic images for damage \(D_{1}\) and the case of 60 kHz are shown in Figure 12 for four different damage sizes. It can be clearly seen that the peak intensity increases with damage size. This observation confirms the previous results reported in Figures 10 and 11. In addition, processing all 65 tomographic images reveals a clear rising trend in the peak intensity at the damage location \(D_{1}\) as shown in Figure 13.  
Figure 10: Overall analysis of the complete dataset recorded from the _SHM plate_ with stringer using the signal energy of the differential signals. The normalization was performed using the maximum signal energy for all transducer pairs for a specific carrier frequency. The arrows on top indicate the baseline state (B) and the damage states \(D_{1}\) to \(D_{3}\). An increasing damage indicator can be observed for each structural condition due to the increasing damage size. Few black spots exist related to outliers especially for the actuator-receiver pair \(T_{9}\)–\(T_{10}\) (channel 61). In this representation, a channel means one actuator-sensor pair.  
Figure 11: Analysis of the normalized signal energy of the differential signal for three representative actuator-sensor pairs (horizontal axis is proportional to an increasing damage size). The normalization was performed using the maximum signal energy for each transducer pair. Damage can be identified best on the straight line between the respective actuator and sensor given by the highest score.  
Figure 12: Tomographic image reconstruction for damage \(D_{1}\) and the damage sizes 2, 6, 10 and 13. The peak intensity increases of larger damage sizes as shown by the colorbar. The scaling parameter of the RAPID algorithm was defined here as \(\beta\) = 1.1.
# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Convolutional Neural Networks (CNNs)
#### 2.1.2 Architectures and Applications  
Several CNN architectures have been proposed and widely adopted in medical imaging applications [64; 65; 66]. Some of the most notable architectures include LeNet [67], AlexNet [63], VGGNet [62], ResNet [53], and DenseNet [68]. These architectures have been applied to various medical imaging tasks, such as image segmentation, classification, detection, and registration [69; 70].
## 3 System Model
### Network Model and Assumptions
Once SNs receive their first WuC, all the clusters operate independently of one another. Furthermore, it is assumed that UAVs have enough energy to complete a single round (i.e., move to the FoI, make clusters there, collect data packets, and turn back) and the energy deficiency of the UAVs does not affect the continuity in data packet collection from ground SNs during a single round. For the sake of clarity, the following assumptions are made:  
1. All SNs have individually unique identification. 2. SNs and BSs are stationary. 3. SNs are not utilized again once dead, i.e., after full consumption of their battery. 4. All SNs must be equipped with WuR, i.e., they must have a WuRx with extremely low power consumption and MR for data packets transmission.