esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Fetal doses measured in each clinical scenario in Hospital 1 are listed in Table 4.  
Since, as expected, measured fetal doses were always below 1 mGy if the fetus was not in the primary beam, only clinical scenarios with a fetus in the primary beam were additionally investigated. Fetal doses in all clinical scenarios with the fetus positioned in the primary beam are presented in Figure 2 for all three hospitals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clinical Scenario** & **Imaging Modality** & **Unit** & \begin{tabular}{c} **Fetal Dose—Head** \\ **(mGy)** \\ \end{tabular} & \begin{tabular}{c} **Fetal Dose—Body** \\ **(mGy)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Non-specific \\ abdominal pain \\ \end{tabular} & Radiography & \begin{tabular}{c} Siemens Luminos dRF \\ Max \\ \end{tabular} & 1.6 & 1.9 \\ \hline \begin{tabular}{c} Nephrostomy tube \\ placement \\ nephro/urolithiasis \\ \end{tabular} & Fluoroscopy & \begin{tabular}{c} Siemens Uroskop \\ Omnia Max \\ \end{tabular} & 0.5 & 0.1 \\ \hline \begin{tabular}{c} Nephrolithiiasis, \\ appendicitiss, \\ abdominal pain \\ \end{tabular} & CT & GE Revolution Frontier & 23.7 & 19.6 \\ \hline \
\begin{tabular}{c} Polytrauma \\ \end{tabular} & CT & GE Revolution Frontier & 34.7 & 32.5 \\ \hline
\begin{tabular}{c} Pulmonary embolism \\ \end{tabular} & CT & GE Revolution Frontier & 0.3 & 0.2 \\ \hline \multirow{2}{*}{Ischemic stroke} & CT & GE Revolution Frontier & 0.05 & 0.03 \\ \cline{2-4}  & Fluoroscopy and DSA & Philips Azurion & 0.0 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absorbed dose for each specific clinical scenario measured in the fetal head and body. CT—computed tomography, DSA—digital subtraction angiography.  
The highest measured absorbed dose in the fetus was measured in a polytrauma CT scan scenario (consisting of head, cervical spine, thorax/abdomen, and pelvis CT) resulting in absorbed doses to the fetal head and to the fetal body of 45.7 mGy and 47.4 mGy, respectively.
## 1 Introduction
### The FO Wave Energy Project  
FO started with Wave Energy in 2000, and, in 2004, the Wave Energy Converter _Buldra_, built as a platform with multiple point absorbers, was launched. Since then, FO has tested out various concepts and built several different prototypes, all based on the point absorber concept. The series of experiments have led to the single body point absorber concept, as realized by our latest prototype _Lifesaver_. Point absorbers are not the most efficient when measured in terms of captured energy, but have nonetheless proven to be successful on total performance and energy costs.  
Until now, FO has operated four WECs based on the single body point absorber in real sea conditions. The first, named _B33_, was a small proof-of-concept device that was operated outside Akland, Norway during the autumn of 2007 and the winter of 2008. B33 showed good results, leading to the second device, named _B22_, which was equipped with a full-scale control, communication and production system. B22 was operated outside Risor, Norway from the summer of 2008 until the spring of 2009. Based on these experiences, FO started the next development phase in collaboration with a selected few European companies and universities through the Sustainable Economically Efficient Wave Energy Converter (SEEWEC) project. This work led to the full-scale system Bolt(r)Bolt that was installed outside Risor, Norway in June 2009. Bolt is pictured in Figure 4 on site and has, since 22 December 2010, produced 3360 kWh of energy ([PERSON] _et al._) [5].  
Figure 4: FO’s Wave Energy Converter _Bolt®_, located outside Risor, Norway has been in operation since June 2009.  
Based on the success of Bolt(r), FO decided to use the knowledge and experience gained so far to proceed to the next generation of design. An agreement with several UK companies was made with funding from the UK Technology Strategy Board (TSB). The goal of the project was to improve the Bolt(r) concept towards a commercial level where it can be launched at Wavehub [6], and hence the project name _Bolt2 Wavehub_. The project resulted in the full-scale system Lifesaver, consisting of a 16 m toroidal floater with five individual all-electric PTO systems. Lifesaver was installed on the test site _Fabtest_ in April 2012 and is planned to be in operation until March 2013, when it has to be brought ashore due to the strict UK regulations. Fabtest is a UK test site for pre-commercial WEC concepts located in Falmouth Bay outside of Cornwall, England. The exact location of Fabtest is shown in Figure 5.  
Thus far, FO has not had a single serious event with any of the WEC systems, which has allowed for continuous long-term testing in real sea conditions. This has proved invaluable for building a knowledge base with wave energy. However, the problems and issues that arise in real sea conditions are multifaceted and thus exceed the scope of the undertaken tests.
## 4 Results and Discussion
### Logistic Regression Modeling
The ROC curve is shown in Figure 7.  
Through ROC analysis, it was found that the AUC of flood vulnerability considering only topographic factors and that including rainfall were 0.848 and 0.866, respectively, and both were evaluated as \"very good\" as shown in Table 4. Further, the precision was calculated to confirm the rate at which flood occurrence was detected for the flood vulnerability, which was classified into five classes. This can be obtained by using the number of samples classified as positive in a 2 \(\times\) 2 confusion matrix as shown in Equation (16), and the range is [0, 1] (perfect value is 1) [49; 50]. The precision for each class was calculated as shown in Figure 8 and, in both cases, it was found that floods were detected at a rate of more than 50% in the very high class, and more than 40% in the high class. \[Precision=\frac{TP}{TP+FP} \tag{16}\]  
Based on the results of this analysis, it is considered that the logistic regression model detects flood occurrence well in the study area. Although the inputting of the hydrological factor did not make a distinct difference, it can be assumed that the vulnerability will change and the detection rate will improve if new rainfall data are input, even without topographic changes. To confirm this, the performance of the logistic regression equation was evaluated using the maximum 1-h rainfall and inundation trace maps in 2010 and 2011. Figure 7: ROC curves of flood vulnerability and inundation traces.
## 4 Anchor Bolt Pull-Out Analysis
### Measures against Insufficient Distance from the Free End with PCM  
As clarified in the previous section, if the anchor bolt installation position is close to the free end, the pull-out strength will decrease. We investigated a method that does not reduce the pull-out strength as much as possible, even when anchor bolts are unavoidably attached near the free end by using PCM (Polymer Cement Mortar).  
PCM is a simple method of spraying reinforced mortar onto the existing concrete of an aged RC structure, and it has already been used in many ways [23; 24]. In this study, we calculated how much the anchor bolt pull-out strength can be prevented from decreasing by PCM method when the anchor installation position is close to the free end.  
The analysis model of this simulation is shown in Figure 29, where a PCM reinforcement layer with 3-mm or 6-mm thickness is added to the existing concrete surface. In Figure 29a, the PCM area is smaller, but it is able to cover the projection of the anchor bolt on the lateral surface of the concrete block, while, in Figure 29b, the PCM area is sufficient to cover sufficient the corn failure domain. The material parameters of PCM is shown in Table 6. The boundary conditions are the same as the previous section, where the three sides of the upper surface displayed in black in the figure are fixed.  
Figure 30 shows the crack pattern obtained in SPH analysis, and Figure 31 shows the load-displacement relationship. It is recognized from Figure 30a,b that, since the strength of the PCM material is higher than that of existing concrete, the cracks can be seen growing under the PCM area. Thus, this change in the crack growth path causes a slight increase in the pull-out strength of the anchor bolt.  
On the other hand, in the case of Figure 30c,d, when the PCM reinforcement area is wide, a crack occurred in the PCM layer, which has a high tensile strength than that of existing concrete, and a clear effect was seen, such as an improvement in pull-out strength of about 40%. To summarize these results, it is possible to improve the pull-out strength of anchor bolts with insufficient distance from free end by using the PCM method. To achieve a better performance of the PCM reinforcement layer, the reinforced area by PCM should be large enough to cover the assumed cone failure domain of the concrete.  
Figure 28: Relationship between maximum load and ratio of distance free edge and embedment depth \(s/l_{e}\).  
Figure 27: Load-displacement relationships with different distance to free edge.  
Figure 31: Load-displacement relationships with PCM reinforcement layer.  
Figure 30: Crack patterns with PCM reinforcement layer.  
Figure 29: Analysis model with PCM reinforcement layer. **(a)** Model with PCM layer of small area. **(b)** Model with PCM layer of large area.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 4 Using ECADLL Information for Spoofing Detection
### Classification as Spoofer Presence  
The generic impairment detection is the first step of the detection algorithm. Once it has been classified as impairment, the goal is to recognize whether or not it can be labeled as a spoofing attack.  
Figure 5: Figure of the proposed ECADLL algorithm. The procedure is repeated for all of the satellites, and it is able to classify the signal tracked by Unit1 as either impairment or spoofing.  
In order to effectively declare the impairment as a spoofing signal, different aspects need to be taken into account.
## 3 Results and Discussion of the Current Study
### Effect of Cement Content on the Specific Gravity of Contaminated Soil  
The value of the specific gravity of soil solids (Gs) is considered one of the material properties used to classify the soil and compaction process. The higher value of Gs indicates fine-grained soil and vice versa. The results exhibited a reverse proportion between the crude oil content and the unit weight of the soil solids. As shown in Figure 11, the value of Gs for the natural soil was recorded to be 2.7 and remarkably reduced to 2.64, 2.60, and 2.54 when the soil was contaminated with 5, 10, and 15% crude oil content. The agglomeration behaviour of the soil particles controls the size of the spaces due to the larger thickness of the crude oil at which the particles are more granulated, and then reduced specific gravity is generated.  
In the treatment stage, the cement was added to the contaminated soil in three percentages: 3, 5, and 7%. For all crude-oil-contaminated soil, adding the cement material increases the specific gravity values of the contaminated soil, as depicted in Figure 12. The cement has a relatively higher unit weight compared to the contaminant substance; thus, the unit weight of the soil solids will be more considerable as the cement is added to the soil. The results have demonstrated that the cement could increase the specific gravity by 10% for all the contaminated soils, and its effectiveness does not vary frequently when the crude oil content changes.  
Figure 11: Variation of specific gravity of soil solids with crude oil content.
## 2 Materials and Methods
### Machine Learning Segmentation Approach
#### 2.2.3 Fuzzy c-Mean (FCM) Segmentation  
The FCM clustering algorithm is an iterative distributing technique that yields optimal c-partitions. This technique calculates the cluster centers and produces the class membership matrix. The generalized least-squared error function is minimized via the optimal fuzzy c-partition. The initial random membership matrix is generated through the algorithm of FCM. This random membership matrix is used as the weight for each sample to fit in each cluster. Then, the centroid for each cluster is calculated using Equation (5). The membership matrix is updated through the generated new cluster centers. Then, the updated membership matrix is compared to the previous membership matrix. If the difference exceeds a certain threshold, then another iteration is generated. Otherwise, the algorithm is stopped [12].  
\[J_{m}(U,v)=\sum_{k=1}^{N}\sum_{i=1}^{C}(U_{ki})^{m}\parallel Y_{k}-V_{i} \parallel^{2}A \tag{5}\]  
where the \(J_{m}(U,v)\) is the minimized FCM, meanwhile, \(Y=\{y_{1},y_{2},\ldots y_{n}\}\subset R^{n}\) is the data set. \(C\) is the number of clusters in \(Y\); \(2\leq c<n\), and m is the weighting exponent. Then, \(U\) is the fuzzy c-partition for \(Y\). While \(\parallel Y_{k}-V_{i}\parallel A\) is an induced a-norm of \(R^{n}\).
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 3 Results
### Influence of Canopy Openness on the Number of Observation Satellites and Signal Quality
#### 3.1.2 Influence of Canopy Openness and Equipment on Carrier-to-Noise Density
Try to choose dual-frequency full-mode smartphones. Smartphones  
Figure 10: Horizontal distance root mean square error (DRMS) of SPP model under different openness. are not competent for measuring forest boundaries, forest resource surveys, and other work requiring high accuracy.
## 3 Results
### Power Supply Considerations  
Nanosensors require a reliable and continuous power supply to operate effectively. The power supply considerations depend on the sensor type, location, and the availability of power sources in the bridge infrastructure.  
For embedded nanosensors, power can be supplied through batteries or energy harvesting techniques, such as solar cells or vibrational energy harvesters [83]. Battery-powered sensors offer mobility and independence from external power sources but require periodic maintenance and replacement [84]. Energy harvesting techniques utilize the ambient energy available in the bridge environment, such as vibration or sunlight, to generate power for the sensors.  
In some cases, it may be feasible to connect the nanosensors to the existing power infrastructure of the bridge, such as electrical cables or power lines [85]. This option provides a continuous power supply but requires careful planning and coordination with the bridge maintenance and electrical systems.  
Consideration should also be given to power management and optimization strategies to prolong the sensor's battery life or reduce power consumption. Techniques such as duty cycling, data compression, or adaptive sampling can be employed to minimize power usage while still capturing relevant data [86].
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 2 Related Work
### SR Based on Deep-Learning Algorithms  
Recently, SR based on deep-learning algorithms have been proven to achieve great success. Many learning models have attracted increasing attention due to their powerful capabilities. To enrich the image details, a multiscale dictionary is presented by [PERSON] et al. [27]. The related methods for SR tasks originate in compressed sensing [28; 29]. Under the mapping between the LR and HR images, [PERSON] and [PERSON] propose a deeper network architecture to increase the accuracy, relying on a high degree of complexity [30]. In [PERSON] et al., the authors present a generative model with a Laplacian pyramid network (LAPGAN) that is similar to our pre-trained module. The RefineNet [15] fuses finer-grained features with semantic information through a multipath architecture, which generates semantic HR images, as given by [PERSON] et al. This method also facilitates gradient propagation during end-to-end training. The skip-connections [23; 31] as an effective design are used to tackle the gradient vanishing problem. Meanwhile, it is beneficial for skip-connections to carry significant information during the forward propagation. For these reasons, the DRRN adopts many skip-connections between the convolutional layers to improve the SR performance. In addition, [PERSON] [32] et al. present feature matching to accelerate convergence using the mean squared error (MSE). This assists the discriminator in handling some images that are poor in high-frequency details. In [PERSON] et al. [18], the authors use a perceptual loss function based on the VGG19 network to generate more photo-realistic images than typical algorithms. According to related work, an enhanced architecture is crucial for the GAN algorithm.
# Reducing Q-Value Estimation Bias via Mutual Estimation and Softmax Operation in MADRL
## 2 Research Background
### Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (Matt3)  
The training modes of multi-agent deep reinforcement learning include Decentralized Training Decentralized Execution (DTDE), Centralized Training Centralized Execution (CTCE), and Centralized Training Decentralized Execution (CTDE), as shown in Figure 2.  
The MATD3 [10] algorithm extends the single-agent TD3 algorithm to a multi-agent environment, adopting the centralized training decentralized execution reinforcement learning framework. During the behavior execution process of the agents, each agent inputs their local observation information into the policy network, outputs deterministic actions, and during the training process, the agents can obtain additional information from other agents to evaluate their own actions.  
The problem of Q network estimation bias will have a more severe impact when transferring policy gradient algorithms with Q-functions to a multi-agent scene, because the estimation bias of the Q network can increase the variance of the neural network gradient and affect the update process of the policy network neural network. In a multi-agent environment, as the number of agents increases, the difficulty of updating the policy function in the correct direction increases exponentially. The MATD3 algorithm simply extends the TD3 algorithm to a multi-agent environment, and there are still problems with the underestimation of Q values. In a multi-agent environment, this bias will have a greater impact on the agent's strategy learning.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Scientific and Technical Goals and Payloads
#### 2.4.3 Redundancy Concept  
For the radio communication to work, it is important to always have only one master node active, that controls the communication. To ensure redundancy two master nodes are present but one active one is determined by the hardware switches in the power unit.  
The two master nodes are connected to the power by so-called keep-off switches. These switches keep their off-state if they receive regular pulses on their control input. If these pulses are lacking for a certain period they turn to the on state. Now one master node controls the switch of the other and vice versa.  
The active master node sends keep-off pulses to the other node's switch, when it determines itself to be healthy and working. This ensures only one active master at a time. The generation of the pulse is placed in a piece of code that only executes when the radio communication is reliably working. This is the code that processes received frames. Other nodes may only send frames when they receive a sync frame from the master, so if the master receives a frame from another node, this shows reliable operation of the master's sender and receiver. The flow of the signals is shown in Figure 3.  
If anything fails, the keep-off pulses stop and the other master node boots after the timeout. If this node is working it will itself send a keep-off to the first node and disable it.  
On power-on both nodes will boot at the same time. To ensure only one node stays on both master nodes are programmed to send a keep off command at start-up with a random delay. This selects one node randomly to be active and ensures no node is preferred in case it has unforeseen problems that cause this mechanism somehow to fail.  
The active master node manages the redundancy of other nodes in the satellite in software by listening for status messages sent over radio. As the master node is responsible for controlling the power unit it is able to switch all other nodes on and off as needed.
## 4 Initial Trajectory Generation
### Unconstrained Polynomial Trajectory Generation  
The techniques that are used for unconstrained trajectory optimization are faster than constraints optimization. In [35], the researchers extended minimum-snap trajectory generation as an unconstrained QP. According to their findings, minimum-snap works well for small segments size. For higher-order polynomials with varying segment sizes, minimum-snap becomes ill-conditioned. Thus, an unconstrained QP was proposed. After substituting (19) and (20) into (18), \(J(\Gamma)\) can be reformulated as:  
\[J(\Gamma) =\underbrace{\begin{bmatrix}\mathbf{d}_{1}\\ \vdots\\ \mathbf{d}_{m_{d}}\end{bmatrix}}_{\mathbf{d}}^{T}\underbrace{\begin{bmatrix} C(T_{1})\\ &\ddots\\ &C(T_{m_{d}})\end{bmatrix}^{-T}}_{C^{-T}}\underbrace{\begin{bmatrix}Q(T_{1})\\ &\ddots\\ &&Q(T_{m_{d}})\end{bmatrix}}_{\mathcal{Q}} \tag{21}\] \[=\begin{bmatrix}\mathbf{d}_{1}\\ \mathbf{d}_{p}\end{bmatrix}^{T}\underbrace{SC^{-T}QC^{-1}S^{T}}_{K}\begin{bmatrix} \mathbf{d}_{f}\\ \mathbf{d}_{p}\end{bmatrix}=\begin{bmatrix}\mathbf{d}_{f}\\ \mathbf{d}_{p}\end{bmatrix}^{T}\begin{bmatrix}R_{ff}&R_{fp}\\ R_{pf}&R_{pp}\end{bmatrix}\begin{bmatrix}\mathbf{d}_{f}\\ \mathbf{d}_{p}\end{bmatrix},\]where \(\mathbf{d}\) contains fixed derivatives (\(\mathbf{d}_{f}\)) and free derivatives (\(\mathbf{d}_{p}\)), and \(S\) is a permutation matrix (ones and zeros), which is used to correct the order. Then, \(\frac{dI(\Gamma)}{d\mathbf{d}_{p}}=0\) yields the optimal value for \(\mathbf{d}_{p}\):  
\[\mathbf{d}_{p}^{*}=-R_{pp}^{-1}R_{fp}^{T}\mathbf{d}_{f}. \tag{22}\]  
Once \(\mathbf{d}_{p}\) is determined, a polynomial that corresponds to each segment can be recovered.
## 3 Methodology
### NN Sensitivity Tests  
In this section, we use a test dataset of three SAR Sentinel-1 images and three corresponding Landsat-8 reference surface water maps to make several sensitivity tests in order to optimize the performance of the NN classification (see details of the test data sets in Table 1). Three different sensitivity tests were carried out: (1) selecting the best threshold of the NN output to classify land/water surface; (2) understanding the effect of the equalization of the water and non-water pixels in the NN training dataset; (3) finding the most important satellite NN inputs. The NN performances have been evaluated based on: spatial correlation between the SAR and Landsat-8 surface water maps, overall accuracy of the NN, as well as higher values of true positive (TP) and true negative (TN) percentages. True positive value indicates the NN ability to correctly detect water pixels, while true negative value illustrates its ability to correctly detect non-water pixels (compared to the Landsat-8 surface water maps).