esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Analysing Methods
#### 2.3.3 Active Thermography  
Long-pulse thermography belongs to the measuring methods of active thermography, in which the measured object is first excited with a heat source for a few seconds. Then the infrared radiation returned by the measured object is recorded using a thermographic camera, see Figure 12. The thermal excitation causes a material-specific heat flow in the measured object, resulting in characteristic surface temperature distribution. The infrared camera then visualizes this temperature distribution in the form of thermograms [24; 25].  
During measurement, the specimens are clamped in a test rig, see Figure 13. Then, kinematics are used to correctly position the specimen in front of the thermographic camera and the excitation unit, consisting of two halogen lamps. To avoid anomalies caused by the thermal reflections of the excitation unit onto the curved specimen, the excitation unit is positioned below and above the specimen. The two halogen lamps, each with a power of 1 kW, are placed at an angle of approximately 90\({}^{\circ}\) to each other and have a distance to the specimen of \(k=204\) mm.  
Table 3 shows the excitation durations used, which differ depending on the type of defect and the specimen. Specimens that have defects deeper in the material must be excited for a correspondingly longer time since the heat must travel a longer path through the material from the surface to the defect and return. Thermographic imaging is performed with the VarioCam hr head from the company InfraTec, which has a temperature resolution of 0.035 K, an image format of 640 px \(\times\) 480 px, and operates in a wavelength range of 7.5-14 \(\upmu\)m. The frame rate is 10 Hz for a measurement duration of 30 s, so 300 images are available in total for the image evaluation. Table 4 summarizes the experimental parameters.  
Figure 12: Schematic representation of the measuring principle of active thermography.  
Figure 13: Photo of the experimental setup for clamping and examination of the specimens at a distance between the excitation unit and specimen of \(k=204\) mm.  
For the evaluation of the measured images, the last image of the series is subtracted from all previous images at first so that constant influences are subtracted. The differential raw image with the highest contrast, i.e., the highest intensity difference between a defect and a defect-free area, is selected from the recorded image sequence. According to previous experiments, the image is located within the first 10 thermograms, corresponding to the first second of the measurement acquisition for the parameters used.
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 2 Methods
### Multi-Space Discriminators
#### 2.2.2 Discriminator on Output Space  
Adversarial learning on the feature space ensures the high-level feature distributions of the source domain and target domain are consistent, and encourages the segmentation network to produce a structurally correct prediction. Nevertheless, this is not enough for accurate segmentation. In addition to the high-level semantics, low-level details also have a huge impact on the segmentation predictions. Thus, adversarial learning on the output space is further used to ensure the source-target consistency on the segmentation details.  
For the domain adaptation on the output space, we utilize the entropy map discriminator \(\text{D}^{e}\) to align the entropy map calculated by the pixel-wise mask. The entropy is the expected value that measures the uncertainty of the predicted mask (\(p_{x}^{m}\)). We calculate the entropy map with the formula:  
\[E_{x}=p_{x}^{m}\cdot\log(p_{x}^{m}). \tag{3}\]  
As a rule of thumb, the prediction on the target domain is more uncertain than the prediction on the source domain. In other words, the segmentation network optimized by the source samples would perform better on the source domain. Therefore, we enforce entropy maps of the target domain to approximate that of the source domain. Similarly to the feature discriminator, the alignment on the output space is completed when the entropy maps from the target domain and source domain are difficult to distinguish from each other. The loss function of the entropy map discriminator \(\text{D}^{e}\) is:  
\[\mathcal{L}_{D^{e}}=\frac{1}{M}\sum_{x^{e}\in X^{5}}\mathcal{L}_{D}(E_{x^{e}},1)+\ \frac{1}{N}\sum_{x^{e}\in X^{T}}\mathcal{L}_{D}(E_{x^{e}},0) \tag{4}\]
## 6 Conclusions
For stripmap or ScanSAR operation, the knowledge of the sensor position makes it possible to study the movement of the coverage area, providing an increase of the achievable coverage. Different study cases were defined to analyze the impact of OI orbit: two study cases based on TerraSAR-X with different orbit parameters and a study case based on a GPS satellite. * Incident power density and required sensitivity: The available power at the PR antenna was estimated as a function of the target-to-PR distance, proving the feasibility of PAZ as an IO from this point of view, allowing coverages of 15 km with affordable system sensitivities.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} NTNs & Non-Terrestrial Networks \\ LEO & Low Earth Orbit \\ IoT & Internet of Things \\ MEO & Medium Earth Orbit \\ GEO & Geostationary Earth Orbit \\ COTS & Commercial Off-The-Shelf \\ SLL & Side-Lobe Level \\ BFN & Beamforming Network \\ BGA & Binary Genetic Algorithm \\ MO & Multi-Objective \\ IWO & Invasive Weed Optimization \\ ADC & Analog-to-Digital Converter \\ DAC & Digital to Analog Converter \\ DBF & Digital Beamforming \\ EVM & Error Vector Magnitude \\ LO & Local Oscillator \\ PN & Phase Noise \\ PA & Power Amplifier \\ LNA & Low Noise Amplifier \\ BER & Bit Error Rate \\ QAM & Quadrature Amplitude Modulation \\ TX & Transmitter \\ RX & Receiver \\ C & Coupling \\ DSP & Digital Signal Processor \\ ENOB & Effective Number of Bits \\ VGA & Variable Gain Amplifier \\ PAE & Power-Added Efficiency \\ CDMA & Code Division Multiple Access \\ \end{tabular}
## 4 Implementation
### Proposed Scheme  
Figure 12 shows the format of the proposed scheme that we will discuss in this subsection. In this scheme, every packet contains many sub-packets to support the compatibility of the frame rate variations. The serial number of the sub-packet was created as an SN. The sub-packet in the same packet had the same data payload. In practice, depending on the combination of the packet rate of the transmitter and the frame rate of the camera, the SN was divided and controlled. When the frame rate was smaller than the packet rate, undersampling occurred. In contrast, when the frame rate was larger than the packet rate, oversampling occurred. The packet rate is the number of packets (containing different payloads) being constantly transmitted in one time period (e.g., 20 packets/s). The proposed data frame of our scheme contained data packet frames that contained smaller data sub-packets (DSs); every DS holds the payload data and an SN. The SN carries the sequence information of a data packet and the identity of the payload. Payload identification was useful in practice because it helped the receiver identify the arrival state of a new payload in the oversampling case and detect lost payloads in the undersampling case. An example of SN in practical studies [22] is where the 1-bit Ab is used in the oversampling case, and 2-bit Ab is used for the undersampling case. A disadvantage of this method is the confusion of the receiver in the decoding process and the limited missing payload that it can detect. The proposed scheme can overcome this disadvantage by using n bits of the SN. The length  
Figure 11: Comparison of bit error rate (BER) of the C-COOK scheme using a matched filter and zero-crossing techniques with the theoretical Bit Error probability of OOK modulation.  
of the SN can be changed based on the practical situation and the system parameters. Depending on the channel environment, the SN length is chosen suitably. In harsh environment or a bad camera quality, the missed packet is large, then we need the SN of large length. However, with a good camera quality, we can reduce the SN length to improve the system performance. In our paper, we used the SN length of 4 bits to implement the MIMO-COOK scheme.  
Same with the COOK scheme, Figure 12 shows how to use SN with oversampling and undersampling cases. Besides that, to identify the order of LEDs in MIMO-COOK, we proposed LED_ID, which helped combining each separated packet in each LED in the order of the images.
# Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province
###### Abstract
With a focus on data acquisition, we propose a new framework for hydropower functional zoning with crowdsourced geospatial data, which refer to a kind of open geospatial data published by non-professionals voluntarily on the Internet. Open Street Map (OSM) is a representative type of crowdsourcing geospatial data. OSM data are adaptable and quickly updatable, and they are widely applied to a variety of research fields, such as emergency mapping, disaster relief, location services, and so on [35; 36; 37; 38; 39]. However, OSM data are not subject to any kind of systematic quality control. The details of OSM data coverage varies across spaces [36]. Therefore, for the subsequent analyses, it is crucial to clean, review, analyze, and assess the various data features in OSM. Ref. [40] showed that the spatial-clustering method could be used to process the point data and evaluate the impact of hydroelectric development in the basin. The point data of hydropower stations provided by OSM was utilized to supply missing data in the study. To increase its accuracy, OSM data were combined with other types of data, including paper reports and geo-logical databases. The hydropower functional zoning was implemented using the aquatic-ecological-zoning theoretical framework and the spatial-clustering results of the assessment of the hydroelectric development. Sichuan Province is rich in water-energy sources and accommodates 20% of the large hydropower stations in the world. However, ecological problems such as soil erosion and land degradation are continuously threatening ecosystem security. The framework of hydropower functional zoning applied in Sichuan Province could reveal the ecological environmental problems in the hydropower functional zones, which could be helpful to find suggestions for eco-environmental protection in Sichuan Province.
## 3 Proposed System Architecture
### Abnormal and Normal Event Analysis  
In the analysis determining whether events are abnormal or normal, we first observe the features of \(VGP\) on \(x_{VGP}(t)\), \(y_{VGP}(t)\), \(d\), area and \(r\), starting with observed features for \(x_{VGP}(t)\)and \(y_{VGP}(t)\), as illustrated in Figure 6a. In the analysis for walking as shown in this Figure, \(x_{VGP}(t)\) decreases at each pixel location before the turning point. The turning point indicates where the person is walking from, or standing to, the right or the left, and then turns to the left or the right. After this turning point, \(x_{VGP}(t)\) again increases significantly. At that time, \(y_{VGP}(t)\) also decreases during a finite period before the turning point, and then increases for an extended period after the turning point. Therefore, the period of actions can be clearly analyzed. Comparisons between distance \(d\) and \(y_{VGP}(t)\) can be considered supportive \(VGP\) features in analyzing changes in the object's position, as shown in Figure 6b. Then, aspect ratio (\(r\)) is added as a feature to efficiently analyze the object's posture. The person remains  
Figure 4: Example action patterns obtained from constructing the virtual grounding point. (**a**) Getting up pattern by changing centroid (C) and virtual grounding point (\(VGP\)). (**b**) Pattern for sitting down by changing centroid (C) and virtual grounding point (\(VGP\)).  
Figure 5: Illustration for area, width and height of object. (**a**) Area of object. (**b**) Width (\(w\)) and height (\(h\)) of object.  
in the same posture for a period of the time, as shown by orange and blue dashed lines in Figure 6c. Therefore, determining whether events are abnormal or normal depends on distance (\(d\)) and aspect ratio (\(r\)), as performed using the three steps described in the following sub-sections.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 6 Attacks that Require Advanced Detection
### AS Path Poisoning  
AS path poisoning involves the intentional insertion of AS numbers into the AS path attribute of a BGP announcement to influence route propagation and prevent certain ASs from receiving it. This attack can create discrepancies in routing information observable from multiple vantage points. The technique of leveraging data from diverse points can enhance the detection of such poisoning, as inconsistencies in the AS path information across different parts of the Internet might indicate an attempt at manipulation. This criterion supports the inclusion of AS path poisoning for its potential to benefit from early detection through comprehensive global routing table analyses.  
While AS path poisoning itself does not require collaboration between ASs, its impact is distributed across the Internet, affecting routing decisions in multiple ASs. The distributed nature of the impact suggests a benefit from group-level analyses, albeit indirectly, as understanding the propagation of poisoned routes can aid in its detection. AS path poisoning directly involves complex interactions across ASs, as the poisoned AS path can mislead routers about the best path for traffic, affecting routing decisions globally. Detecting this attack requires understanding these interactions and the dynamics of AS relationships, aligning well with this inclusion criterion.  
This attack exemplifies the sophisticated manipulation of routing information. Attackers must understand BGP operations and the trust relationships between ASs to craft believable yet strategically poisoned AS paths. This level of sophistication in manipulating routing information strongly meets this criterion for inclusion. AS path poisoning can be relatively stealthy, designed to appear as legitimate routing information while achieving the attacker's goal of route manipulation. This stealthiness, coupled with the potential for such attacks to evade detection by conventional monitoring systems that may not scrutinize AS path attributes closely, aligns with the criteria for stealthiness and evasive techniques.
## 2 Theoretical Background
### Cluster Probabilities  
According to [PERSON] and [PERSON] (1974), the \(100(1-\mathrm{a})\%\) confidence ellipse for the centroid of sample \(i\) is a region in the two-dimensional space that has a \((1-\mathrm{a})\) probability of containing the \"true\" (relative) position of the centroid of sample \(i\). Since the \"true\" (relative) position of the centroid of sample \(i\) is, in fact, the (relative) position of the centroid of the population from which sample \(i\) comes, a confidence ellipse for sample \(i\) visualizes a range of values likely to contain the (relative) position of the centroid of population \(i\) in the MDS plot. Therefore, a confidence ellipse gives a qualitative picture of the uncertainty in the position of the centroid of population \(i\) in this plot. It is evident that the MDS confidence ellipses are related to the uncertainty of the distances used in MDS. When the uncertainty of the distances is small, that is, when the distances have small standard deviations, the confidence ellipses exhibit small areas, and the opposite holds when the distances have large standard deviations. The confidence ellipses provide qualitative information about the uncertainty in a MDS plot. For a quantitative evaluation of this uncertainty, we may proceed to estimate cluster probabilities obtained from HCA using data from the confidence ellipses. This estimation may be performed by extending the relevant study presented by [PERSON] and [PERSON] (2016) and [PERSON] and [PERSON] (2015). This procedure can also be used to estimate the most probable dendrogram that is related to these probabilities and the probability of its appearance.  
In specific, given a MDS map with confidence ellipses, a point is randomly selected from each ellipse, and all pairwise Euclidean distances among these points are calculated. This step is repeated many times (about 2000), generating a large dataset of Euclidean distances. Subsequently, all the dendrograms of these distances are estimated, allowing the determination of the most probable dendrogram. Then the number of times that each pattern in the most probable dendrogram appears in the dendrograms of the Euclidean distances is counted. The uncertainty in a pattern is assessed from the percentage of the appearance of this pattern in the dataset of the Euclidean distances, which gives the probability of the formation of this pattern. This procedure can also be used to estimate cluster probabilities for all pairwise clusters.  
As expected, when the uncertainty in a MDS plot decreases, the area of confidence ellipses decreases, and therefore, the probability of the most probable dendrogram increases along with the cluster probabilities.
# On the Use of Data Envelopment Analysis for Multi-Criteria Decision Analysis
## 4 Discussion
The resultant efficiency scores can be considered as a distribution associated with each management option, allowing the effects of uncertainty to be explicitly captured in the analysis. We find that the results (i.e., efficiency scores and relative rankings) are also sensitive to model choice. From the results using both data sets, the slacks-based model approach provided greater discrimination between management options (DMUs), with a smaller set of options identified as efficient. [PERSON] al. (2018) note a key challenge with radial models is that an identified efficient DMU may still exhibit lower levels of output in some indicators that others that are also identified as efficient. Slacks-based models directly assess these indicator-specific differences, although the loss of the proportionality assumption may mean that the ability of the options to achieve efficiency is limited if such a proportionality constraint exists (2018). However, as we are not interested in improving the efficiency of inefficient management alternatives but identifying which options are the most efficient in a multi-criterion environment then this is not necessarily a problem. The choice of which model to use to assess the management alternative may depend on the degree of independence between management outcomes. If management outcomes are perfectly independent, such that trade-offs between them are possible, then the slacks-based model may be more appropriate. Conversely, if the outcomes are not totally independent, such that increasing one will require in some increase in another (and vice versa), then the radial model may be more appropriate given the associated proportionality assumptions. In the hypothetical data set, each outcome was total independent, and hence the slacks-based model was able to provide greater discrimination between the hypothetical alternatives that produced them. For the waste management example, several outputs were correlated (e.g., toxins, acidic gases and nitrogen released in the water), suggesting the proportionality approach underlying the radial model may have been appropriate. For the waste management example, both models produced similar rankings and numbers of 'efficient' options, with the utilisation of the hierarchical structure more important in distinguishing the most efficient alternatives. The study assumes that the relative preferences for each of the management outcomes is unknown, and hence the models provide a 'benefit of the doubt' measure of the efficiency of the different alternative. Under this scenario, the outcomes are sensitive to what form of model is used and how (if any) hierarchy of criteria is employed. The addition of preference weights is possible in DEA as noted previously, and this may reduce these differences if the importance of each criterion to the decision maker was more explicit. This is an area for future consideration.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
###### Abstract  
Coal seam thickness prediction is crucial in coal mine design and coal mining. In order to improve the prediction accuracy, an improved Kriging interpolation method on the basis of efficient data and Radial Basis Function (RBF-Kriging) is firstly proposed to interpolate the cutting data that is obtained in pre-mining, especially at the edge of the geological surface of coal seam by taking into account the spatial structure and the efficient spatial range, ensuring the integrity of the edge data during the movement of structural elements. Subsequently, a structural element transition probability based Gaussian process progression (STTP-GPR) method is proposed to predict the coal seam thickness from the interpolated coal seam data. The experimental results demonstrated that the proposed STTP-GPR method has superior performance in coal seam thickness prediction. The average absolute error of thickness prediction for thin coal seams is 0.025 m, which significantly improves the prediction accuracy in comparison to the existing back propagation (BP) neural networks, support vector machine, and Gaussian process regression methods.  
Keywords:coal seam prediction; structural element; Kriging interpolation; transition probability +
Footnote †: journal: Applied Sciences