esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study  
### Set Up  
In this experiment, a mooring area of 2 nautical miles \(\times\) 2 nautical miles was defined on the MATLAB, and the water depth value was 20-40 m. For the convenience of the mooring area detection research, the following assumptions were made:  
1. The influence of wind, current and anchoring ship on the anchoring was not taken into consideration, that is, the anchoring dropping point coincided with the anchor position, \(L_{0}=0\);
2. The anchor position was directly in front of the ship, that is, on the ship's course line;
3. The trim angle of the ship was assumed to be \(\alpha=0.5^{\circ}\);
4. The value range of coefficient \(\tau\) was [2;3]. In this study, the cargo ships passing through the anchorages were general cargo ships, so the value of \(\tau\) was 2;
5. In this experiment, the selected ship type was a general cargo ship, \(\sigma=1\);
6. The positioning error of GPS/GNSS/BDS was not taken into account.  
In this study, three general cargo ships were selected, and the specific ship sizes are shown in Table 2. Where \(L_{S}\) is the designed length of the ship (m); \(B\) is the moulded breadth of the ship (m); \(D_{A}\) is the moulded depth of the ship at the chain hole (m); \(L_{SA}\) is the distance from the chain hole to the bow (m); \(k\) is the distance from the chain hole to the centerline of the fore-aft axis (m); \(d_{F}\) is the bow draught of the ship (m); and CB is the block coefficient of the ship.  
The anchorage circle radius models were modified in various studies, summarized as shown in Table 3. Note that \(R\) represents the radius (m) of a single anchored mooring ship.  
According to the experimental data in Table 2 and the improved safety distance model of anchoring ships, the simulation of the anchorage area detection was carried out using MATLAB. The existing ships and the ships to be anchored in the anchorage area belong to the above three types. The distance of existing ships in the anchorage meets the safety distance requirements for anchoring ships. Additionally, the experimental data contain 10 sets of data in two categories. The first category comprised the detection data of five groups of anchorage areas with a wind power of less than or equal to 7, and the anchorage water depths were 20 m, 25 m, 30 m, 35 m and 40 m, respectively. Then, the second category of data comprised the detection data of five groups of mooring areas with a wind force of greater than 7, and the anchorage water depths were 20 m, 25 m, 30 m, 35 m and 40 m, respectively. Tables 4 and 5 include the extracted data of the first group in the first category and the data of the first group in the second category, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(L_{s}\) & \(B\) & \(D_{A}\) & \(L_{SA}\) & \(k\) & \(d_{F}\) & CB \\ \hline Ship No. 1 & 192 & 22.6 & 18 & 8 & 8 & 6.5 & 0.809 \\ Ship No. 2 & 225 & 32 & 25 & 10 & 12 & 7.5 & 0.823 \\ Ship No. 3 & 333 & 60 & 30 & 12 & 17 & 10 & 0.834 \\ \hline \end{tabular}
\end{table}
Table 2: Ship type parameters selected for the simulation experiment (unit: m).
# Fuel-Water Emulsions as an Alternative Fuel for Gas Turbines: A Project Summary
## 3 Research Results and Discussion
### The Influence of the Presence of an Emulsifier
#### 3.4.2 Deposits in the Engine  
As a result of the combustion process of fuel-water emulsion enriched with surfactant in engines, two types of deposits were identified. The first of them is a deposit resembling varnish, while the second type of deposit is accretions resembling white chalk dust [31; 32]. In both cases, the formation of deposits was attributed to the emulsifier. In order to verify these hypotheses, an inspection of the GTM120 gas turbine was carried out after tests in which an emulsifier was added to the base fuel, and a visual observation of the unit was carried out during tests in which it was fed with a fuel-water emulsion.  
After a series of tests in which fuel with the addition of a surfactant in an amount of up to 5% of the mixture mass was burned, numerous, clearly visible, mostly glassy green deposits were recorded. They were visible, among others, on the outside of the lower part of the engine exhaust nozzle (Figure 18). The most likely reason for their occurrence in this place was that excess fuel had accumulated in this area during a failed start of the unit. Apart from the outlet nozzle, similar deposits formed on the turbine blades and its guide as well as on the walls of the combustion chamber and evaporators. Their formation should be attributed entirely to the presence of a surfactant because in the test series in which they were created, water was not used as a fuel additive. No deposits resembling chalk dust or other deposits were recorded. More extensive photographic documentation of the mentioned sediments can be found in [63].  
Chalk-dust-like deposits were observed on turbine blades and turbine guides after a series of tests in which a fuel-water emulsion was burned. There is no photographic documentation of these elements with the characteristic white deposit.  
Based on the above, it is hypothesized that the surfactant is responsible for the glassy \"varnish-type\" deposits, while the characteristic white deposits resembling chalk dust are  
Figure 18: GTM120 gas turbine outlet nozzle with the marked places indicating glassy deposits.  
caused by the presence of water. Perhaps they are created through the synergistic interaction of water and an emulsifier, but without the presence of water, they are not produced.
## 2 Materials and Methods
### Statistical Analysis  
A Kolmogorov-Smirnov test was used to examine the normal distribution of the data. [PERSON]'s test was used to test for sphericity and Greenhouse-Geisser was used when the assumption of sphericity was not met. A \(t\)-test for paired samples was used to compare the critical speed, performance time from 4 \(\times\) 50 m and 100 m, blood lactate concentration, heart rate, and rate of perceived exertion before (Pre) and after (Post) training period testing procedures. [PERSON]'s \(d\) using the pooled SD as a denominator [24] was used for the calculation of effect size. The effect size categorization according to [PERSON]'s \(d\) was (a) 0.20 (small), (b) between 0.20 and 0.80 (medium), and (c) greater than 0.80 (large) [24]. The 95% confidence limits were also calculated for the mean differences between Pre and Post values. One-way analysis of variance in one factor (time points of measurement) was used to examine the measured variables during the 6-week training period. To estimate the size of the main effects and interactions, the partial eta-squared (\(\eta^{2}\)) values from the analysis of variance were used. A post hoc analysis of statistical power was also calculated using G-Power 3.1.9.2 software. The partial eta-squared and effect size for the sample size in the present study (\(N=8\)) resulted in a power of analysis corresponding to 0.6 [25]. Pearson correlation was used to examine relationships between \(\Delta\) mean values of performance time,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Characteristics** & **Intervention Training Days** & **Other Training Days** \\ \hline Warm-up & 1000 m & 1200 m \\ \hline \multirow{4}{*}{Intervention set} & 2 sets \(\times\) 4 repetitions of 50 m (front & \\  & crawl and preferred stroke, starting & \\  & every 2 min with 5 min passive & \\  & recovery between sets) & \\ \hline \multirow{2}{*}{Other training sets} & \multirow{2}{*}{zone 1: 1000 m} & zone 1: 800–1000 m \\  & & zone 2: 600–1000 m \\  & & zone 3: 600–1000 m \\ \hline Technique & 800 m & 800–1000 m \\ \hline Overall training volume & 3000 m & 3400–5200 m \\ \hline Zone 1 corresponds to 95–97% of critical speed, zone 2 corresponds to 99–101% of critical speed, and zone 3 corresponds to 104–107% of critical speed. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training characteristics during the 6-week training period.  
blood lactate concentration, and heart rate. Data are presented as mean \(\pm\) SD. Statistical significance was set at \(p\) < 0.05.
# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
## 3 Vector-CFNN
### Defuzzification Layer  
This layer carries out a defuzzification operation, in which the crisp values are computed by aggregating the results of each rule. Subsequently, to determine the output probability for classification, the softmax function is applied. The defuzzification and softmax equations are, respectively, presented as follows:  
\[d_{i}=\sum_{j=1}^{r}R_{j}w_{ij} \tag{8}\]  
\[y_{i}=Softmax(d_{i})=f(d_{i}) \tag{9}\]  
\[f(d_{i})=\frac{e^{d_{i}}}{\sum_{j=1}^{n}e^{d_{j}}} \tag{10}\]  
where \(d\) is the crisp output computed by the defuzzification operation; \(R_{j}\) denotes the firing strength of \(j\)th fuzzy rule; \(w\) represents the learnable weight; \(y_{i}\) is \(i\)th class probability; and \(f(\cdot)\) is the function for normalizing the value to be within 0 and 1.
## 3 Materials and Methods
### Proposed Method
#### 3.3.4 Model Design  
For pest detection and classification, the YOLOv8 architecture is adopted. It is based on an anchor-free model with a decoupled head to process objectness, classification, and regression tasks independently. The scalability and modularity of the YOLOv8 architecture make its design easily customizable in terms of parameter characteristics such as anchor boxes, input size, and complexity. This hierarchical architecture provides the ability to learn many object classes and to accurately detect objects in challenging images too [51]. It is a fast one-stage object detection procedure which is characterized by an input segment, a  
Figure 7: Boxplot comparing the distribution of the image cardinalities in the classes for the different datasets (clean, filtered, and balanced dataset).  
backbone, a neck, and an output segment [45]. The input segment carries out mosaic data augmentation, adaptive anchor calculation, and adaptive grayscale padding on the input image. The backbone carries out the extraction of hierarchical features at different scales while the neck aggregates and refines the features extracted by the backbone by focusing on enhancing the spatial and semantic information across different scales. Multiple Conv and C2F (cross-stage partial bottleneck with two convolutions) modules process the input image for feature extraction maps at diverse scales. The output feature maps are analyzed by the Spatial Pyramid Pool Fusion (SPPF) module, which performs pooling with different kernel sizes. The obtained results are fed as input to the neck layer which incorporates the Path Aggregation Network (PANet) and Feature Pyramid Network (FPN) to improve the feature fusion procedure. The detection part of Yolov8 distinguishes the classification head from the detection head (bounding box regression). Binary cross-entropy loss (BCE Loss) and distribution focal loss (DFL) with complete intersection over union (CIoU) are employed as the loss functions for classification and detection tasks, respectively, [52]. Starting from YOLOv4, MOSAIC data augmentation has been introduced in the training pipeline. This technique is an improvement of the Cutmix augmentation, in which 4 training images are combined into a single one [53]. In more detail, MOSAIC resizes each of the 4 images, creates the combined image, and finally takes a random cutout to obtain the final image. The final image could therefore contain only small portions of original images and objects. MOSAIC is used to help the model generalize and avoid overfitting, since it is forced to also recognize objects from partial views with reduced features.
## 2 Methodology
### Sparse Tomography  
The inversion problem of rock ultrasonic velocity tomography can be expressed as the minimization problem:  
\[\widetilde{\mathbf{s}}=\underset{\mathbf{s}}{\text{argmin}}\frac{1}{2}\| \mathbf{A}\mathbf{s}-\mathbf{T}^{\text{obs}}\|_{2}^{2} \tag{7}\]  
where \(\mathbf{A}\) is the sensitive kernel matrix, representing the length of rays in the spatial grid; \(\mathbf{s}\) is the slowness in each grid; and \(\mathbf{T}^{\text{obs}}\) is the observed travel time. Inversion problems such as (7) are often ill-conditioned due to limited observation angles and noise interference, which makes them very difficult to solve. To obtain a stable and unique solution, a regularization term \(\mathbf{\Omega(s)}\) could be added to the right side of Equation (7) [54,55]. The most classical regularization method is Tikhonov regularization [56], whose regularization term is the 2-norm of the model. However, its solution is too smooth. If we take the regularization term as the 0-norm of the model or its approximation (such as total variation, TV), we can obtain a sparse solution. Then, the minimization problem (7) becomes:  
\[\underset{\mathbf{s}}{\text{argmin}}\frac{1}{2}\|\mathbf{A}\mathbf{s}-\mathbf{ T}^{\text{obs}}\|+\lambda\int\|\
abla\mathbf{s}\|_{1}^{1}dV \tag{8}\]  
where the second term is the TV regularization term and \(\lambda\) is the regularization parameter, which balances the fits of the data and the weights of the regularization term. Since this method adopts the sparse regularization term, we call this method sparse tomography. The work in [57] proved that this method can be used to reconstruct hydraulic fracturing fractures, so we used this method to obtain the 3D ultrasonic velocity imaging in the sample.
## 3 Occurrence, Distribution and Environmental Impacts of MPs
### MPs in Air and Precipitation  
The main sources of MP in the air are urban dust, synthetic textile fibers, tire erosion and the abrasion of plastic materials. Due to their small size and light weight, MP particles can easily be transported to distant and sparsely populated areas over hundreds of kilometers by air masses. German scientists recently reported significant amounts of MPs in snow samples from the Arctic and the Alps, which proves the long-range transmission of MPs by air and precipitation. About 14,400 MP particles were found per liter of melted arctic snow, which is a strangely high amount for such a remote region with an extremely low population [43]. MP fibers were also found in a remote lake located within the Masurian Landscape Park in Poland, which suggests that MPs are carried by the wind and rain and enter freshwater isolated from sewage outlets [44]. A study on airborne MPs conducted in France reports five times higher concentration of MPs in city dust after rain and other precipitation [45]. [PERSON] et al. (2019) [46] collected rainwater samples at eight locations across the mountains of Front Range in Colorado, USA, to track the trajectory of airborne nitrogen that is being deposited in the national park. A microscopic examination of filtered rain discovered MP particles in 90% of the samples, mostly blue fibers that are likely originating from synthetic clothing.
# Password Guessability as a Service (PGaaS)
## 2 Background and Related Work
### Password Security  
The main objective of a password is to restrict the access of unauthorized persons to the system, and therefore we always want them to be sufficiently secure [17]. We have two common approaches to model password guessability starting from this definition. One uses large databases from which the strength can be estimated, while a newer approach uses neural networks. One example of the latter is the work proposed in [18]. The authors developed a Javascript-based tool that uses a small print neural network model for password guessing. As stated by the authors, such an approach generates and compresses a neural network model that users will use on the client's side. However, the model would need to be retrained and the compressed version updated to support modification to the learning dataset. Modifications might arise from how users create their passwords and might arise from the language used by users to write their passwords. The former approach uses password databases as a source for strength-guessing algorithms. One such tool is PESrank, which is in charge of estimating the password's strength through ranges that allow us to determine how likely a password is to be found. All passwords are decryptable. However, having a higher strength will cause attackers to take more resources and more time to extract them. When compared with both approaches, the proposal from this paper usesdynamically updated databases to reflect possible changes that might arise from language or password creation styles.  
We can find dictionary attacks and brute force attacks among the most common attack techniques. A dictionary attack prioritizes attack speed, although this leads to a smaller spectrum of possibilities, while a brute force attack covers many more possibilities by considerably reducing speed. A dictionary attack uses a set of common words to be tested, while brute force tests all possible character combinations to form character strings [19]. It is possible to use both techniques according to the information available about the passwords, such as a particular pattern that can be replicated. Dictionary attacks are the most commonly used because they help save time, and dictionaries are created with leaked and common passwords that different people have used. Thanks to this, researchers have created many dictionaries over the years with which these types of attacks are carried out. Recently, applications that generate password dictionaries such as PassGAN are becoming known. Side-Channel Attack (SCA) is one of the easiest and most potent methods to execute against cryptographic implementations, and their targets range from primitives, protocols, modules, and devices to uniform systems [20]. They pose a severe threat to the security of cryptographic hardware products. In this way, solutions to key enumeration problems and rank estimation problems are used to carry out these attacks.
# Localization of Radio Sources Using High Altitude Platform Station (HAPS)
## 4 Discussion
### Localization of Multiple Sources
In the location estimation scenario assuming mobile terminal localization, where a total of 10 transmitters with the same frequency and power were deployed, one per cell, better results were obtained for larger cell sizes, as shown in Table 10. The reason for this is that, as previously mentioned, the smaller the cell size, the more likely the transmitters are to be located close to each other. Even in the absence of buildings, the estimation probability does not reach 100%, which is also attributed to the close proximity of the transmitters. Furthermore, the estimation accuracy for a cell size of 6 km per side was better than that for 10 km. This is because close-proximity situations occur less frequently, and the positioning error due to distance becomes a dominant factor. From the cumulative distribution, when the cell size is small (particularly when the cell side length is 1 km), peak overlap occurs, making it difficult to accurately detect individual peaks. When the element spacing was increased for localization in the case where the cell size was 1 km per side, the estimation results improved. As shown in Figure 24, it was confirmed that the resolution improvement allowed for better peak identification. The estimation probability also improved due to the influence of angular resolution. However, several cases were observed where the positioning error increased significantly. Specifically, when transmitters were in extremely close proximity, even if the number of transmitters was correctly estimated using the AIC method, the accuracy of angular resolution might not be sufficient to distinguish the peaks. When this phenomenon occurs, positioning accuracy deteriorates significantly, leading to cases where the positioning error becomes large.
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 4 Discussion
While responses for turbidity were similar in the two lakes, patterns in Chl-a were different, with Lake Mweru showing greater and faster responses and with an overall increase in Chl-a six months after the major fire occurrence (+16%) while Lake Mukenge had a Chl-a decrease (\(-22\%\)). We observed that on occasion turbid conditions in lakes did not lead necessarily to successive Chl-a increases. Although lake turbidity might increase after wildfires as a result of increasing runoff and transport of burned material to the lake, this will not necessarily correspond to a related input of nutrients, as the majority of materials (debris, ashes, suspended material) will not be suitable for direct use by phytoplankton. Moreover, the increased water turbidity could reduce light availability for phytoplankton growth, resulting in an overall Chl-a decrease, as observed in our study for different lake typologies. Other studies investigating the effects of wildfires on lakes reported significantly reduced lake water transparency primarily due to increases in dissolved organic carbon [26; 28] and in turbidity. Subsequent changes in primary productivity would be mediated by both light availability and nutrient conditions, making the ultimate response of lake water quality to wildfires unpredictable. We have also to acknowledge that hydro-morphological features should be integrated to consider soil properties, such as type, moisture, and texture, as they have been shown to be important in determining the hydrological responses of landscapes following wildfires [73]. However, as our study had a more ecological rather than a hydrological modelling approach, the lack of inclusion of soil properties was considered less crucial.
## 6 Results
### Influence of Oblique Images  
Two datasets were processed for camera and lever-arm calibration. The first dataset combines nadir and oblique images of Flights s2-n50 and s2-n50. The inclusion of oblique images eases the correlation between the focal length and the lever-arm as well as between the focal length and the camera height (\(\delta_{LA_{z},f}\) = 0.18 and \(\delta_{C_{z},f}\) = 0.18). However, when no GCP is included in BBA procedure, the \(z\) component of lever-arm remains highly correlated with the camera height (\(\delta_{C_{z},LA_{z}}\) = 0.99). By including one GCP (Point B, as shown in Figure 3), the \(z\) component of lever-arm is better decorrelated from the height of camera (correlation coefficient \(\delta_{C_{z},LA_{z}}\) = 0.80). With the added information, more constraints are applied within the BBA procedure and the correlations between the focal length and other two parameters increased (\(\delta_{LA_{z},f}\) = 0.84, \(\delta_{C_{z},f}\) = 0.68). The internal photogrammetric accuracy is also slightly improved (\(\sigma\) is decreased by 0.3 \(\upmu\)m). Moreover, the RMS of residuals on CPs decreases considerably, especially along \(z\)-axis. This gain is mainly due to the proper estimation of the \(z\) component of lever-arm. We can see that the RMS of residuals on CPs decreases by 7.6 cm on vertical direction while the estimated lever-arm vector has an increase of 7 and 6.1 cm on \(z\) axis for nadir and oblique flight, respectively.