esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.3 The LCA of an LPG-Powered Ship  
In this paper, LPG is used onboard a purse seiner in a dual-fuel engine suitable for LPG. It is assumed that ships operate only in a dual-fuel mode with 95% LPG (\(x_{LPG}\)) as the second fuel and 5% diesel (\(x_{\text{P-}LPG}\)) as the pilot fuel. The fuel consumption of an LPG-powered ship  
Figure 7: Processes included in the LCA of an LNG-powered ship.  
is calculated by Equations (11) and (12). Specific fuel consumptions are obtained from [96]. The processes included in the LCA of an LPG-powered ship are presented in Figure 8.  
The environmental impact of the engine is the same as that of the diesel engine in Section 3.2.1, while TTW emissions are calculated based on Equation (13), with emission factors for LPG and diesel presented in Table 3. The WTT phase processes of LPG and diesel as pilot fuels are the same (Section 3.2.1), except the process of fuel production in the Croatian refinery. LPG is assumed to be produced in Europe and then transported to Croatia via tank truck for 2000 km.
## 2 Materials and Methods
### Experimental Design  
The lactic acid adsorption study comprised 6 different series of experiments, investigating the effect of (i) contact time; (ii) initial pH; (iii) adsorbent dose; (iv) initial lactic acid concentration; (v) temperature and (vi) batch kinetic studies on the adsorption process. The effect of contact time was tested for a period of 24 h by using a model fermentation broth without initial pH (\(\sim\)2.35) corrections with samples withdrawn at different time intervals (0, 2, 4, 6, 8 and 24 h). The adsorption capacity and efficiency (i.e., % lactic acid adsorbed onto the adsorbent) under equilibrium conditions were calculated using Equations (1) and (2), respectively:  
\[q_{e}=\frac{(C_{0}-C_{e})\ \times\ V}{m} \tag{1}\]  
\[E=\frac{C_{0}-C_{e}}{C_{0}}\times 100 \tag{2}\]  
where \(q_{e}\) = amount of adsorbate in the adsorbent at equilibrium (mg/g), \(E\) = lactic acid recovery efficiency (%), \(C_{0}\) = initial concentration of lactic acid (mg/L), \(C_{e}\) = equilibrium concentration of lactic acid in the solution (mg/L), \(V\) = volume of the solution (L) and \(m\) = mass of the adsorbent (g).  
The effect of the initial pH was tested at 5 different initial pH (2.0, 2.35, 3.86, 5.0 and 6.5) conditions, both above and below the \(pK_{a}\) (3.86) value of lactic acid. The degree of ionization of lactic acid depends on the pH and can be calculated using Equation (3):  
\[\%\ Ionized=\frac{100}{1+exp^{(pK_{a}-pH)}} \tag{3}\]  
The effect of the adsorbent dose was tested to determine the appropriate adsorbent dose (\(w/v\) basis) by varying the dose from 5% to 30% (\(w/v\)) with an increment of 5% in each step. The effect of the lactic acid concentration was tested for 9 different lactic acid concentrations, varying between 1.5 and 36.3 g/L. This range was particularly selected to represent the lactic acid production from various homolactic or heterolactic fermentation processes with low concentration of lactic acid in the fermentation broth [8; 18].  
Lactic acid adsorption was investigated at various temperatures (303.15-353.15 K). The thermodynamic parameters including Gibbs free energy (\(\Delta G^{0}\)), enthalpy (\(\Delta H^{0}\)) and entropy (\(\Delta S^{0}\)) are the indicators of the adsorption process and their values indicate the spontaneity and favorability of the process [25; 26]. The \(\Delta G^{0}\) can be calculated from Equation (4), whereas \(\Delta H^{0}\) and \(\Delta S^{0}\) can be calculated by using the Van't Hoff equation (Equation (5)), i.e., from the slope and intercept of the linear plot of \(ln\ K_{L}\) versus \(1/T\):  
\[\Delta G^{0}=-RTlnK_{L} \tag{4}\]  
\[lnK_{L}=\frac{-\Delta G^{0}}{RT}=-\frac{\Delta H^{0}}{RT}+\frac{\Delta S^{0}}{R} \tag{5}\]  
where \(K_{L}\) = Langmuir isotherm constant (L/mol), \(R\) = universal gas constant (8.31 J/(mol K)), \(T\) = temperature (Kelvin), \(\Delta G^{0}\) = Gibbs free energy (kJ/mol), \(\Delta H^{0}\) = enthalpy (kJ/mol) and \(\Delta S^{0}\) = entropy (kJ/(mol K)).
# Green Scalar Function Method for Analyzing Dielectric Media
## 2 Theoretical Background
### 2D Green Function  
As in 3D systems, the scalar Green's function in 2D geometries will only depend on the relative position between the source and the observer, so it is convenient to define the relative coordinates.  
\[\mathbf{r}-\mathbf{r^{\prime}}\equiv\mathbf{\rho}-\mathbf{\rho^{\prime}}=(\rho\cos\phi,\rho \sin\phi,0)=(x-x^{\prime},y-y^{\prime},z-z^{\prime}) \tag{6}\]  
Thus, it is reducing the three-dimensional problem to one in the plane. In the 3D case, the scalar Green's function refers to a point source of electromagnetic fields, which are located in \((x^{\prime},y^{\prime},z^{\prime})=(0,0,z^{\prime})\). However, the 2D case refers to the field generated by an infinite linear charge source observed in a plane \(z=\text{const.}\) whose axial dependence for with the term \(\exp{(ik_{z}z)}\). So, one can obtain this 2D Green's function, \(g_{\text{2D}}(\mathbf{r},\mathbf{r^{\prime}})\), as the line integral of this linear source  
\[g_{\text{2D}}(\mathbf{r},\mathbf{r^{\prime}})=\int_{-\infty}^{\infty}g_{\text{3D}}( \mathbf{r},\mathbf{r^{\prime}})e^{ik_{z}z^{\prime}}dz^{\prime}=\int_{-\infty}^{\infty }\frac{e^{ik\sqrt{2^{\prime}+y^{2}+(z-z^{\prime})^{2}}}}{4\pi\sqrt{x^{2}+y^{2} +(z-z^{\prime})^{2}}}e^{ik_{z}z^{\prime}}dz^{\prime}=-\frac{i}{4}H_{0}(k_{\rho }\rho)e^{ik_{z}z} \tag{7}\]  
where \(H_{0}(k_{\rho}\rho)\) denotes the Hankel function of the first kind, \(k=|\mathbf{k}|=|(k_{\rho},k_{z})|\) is the wave vector modulus, and \(k_{\rho}=|k_{\rho}|\) is the transverse wave vector modulus, respectively.
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 2 Problem Formulation
### Task Modeling  
According to the payloads they represent, the OINTS problem's tasks exhibit particular behaviors. Four major requirements must be met in order to schedule these jobs. To begin, we assume that tasks are non-preemptive. This means that once a work has begun, it must be finished satisfactorily within a certain time frame. This is the scheduling problem's initial requirement.  
Second, jobs are normally performed within a defined time frame. This means that a minimum and maximum duration between task activations must be specified. This is the scheduling problem's second requirement.  
Third, tasks must start a minimum number of times during an orbit to complete mission criteria. An upper constraint can restrict this number if a job must only be executed once. This is the scheduling problem's third requirement.  
Finally, tasks can optimize their optimal start and completion timings to ensure the highest potential mission value. This is the scheduling problem's fourth requirement. Another key feature for ensuring service quality is scheduling based on each task priority \(u_{j}\). Table 1 presents an overview of such QoS parameters.  
These requirements are critical for modeling operations such as environmental data collecting, image capture, or other high-level activity carried out by dedicated hardware, namely sensors, transponders, payloads, cameras, and similar, which substantially influence the satellite's energy consumption.  
Constellation Requisites  
Mission planning, or task scheduling, for a group of nanosatellites requires careful consideration of various factors. For instance, each satellite can fulfill constellation duties; hence, it is important to specify a simultaneity factor for each task. This is accomplished through constraints that limit the minimum and maximum number of subsystems required to perform a task at any given time. Additionally, lower and upper bounds for the number of startups must be specified for constellation task assignments. This is identical to the previously mentioned limitations, except now that all satellites in the constellation are examined.  
Network behavior is another key factor to examine. Some jobs may benefit from running synchronously over the entire constellation simultaneously, and this can be enforced via a simultaneous start and stop. This can be accomplished by imposing constraints requiring all satellites to perform the same task simultaneously.  
To plan a constellation mission with redundant task execution, one can add a binary flag made equal 1 whenever two jobs should run simultaneously in distinct spacecrafts and 0 otherwise. The purpose of this is to ensure mission reliability by redundancy. Additionally, the minimum and maximum global number of startups for each task must be optimized.  
Therefore, the ONTs problem in a nanosatellite constellation involves a balance between coordination among the satellites, meeting mission requirements and power constraints while ensuring a high level of reliability and redundancy.
## 2 Materials and Methods
### System Mechanism  
In this study, a mechanism to detect cracks in steel material is proposed, and the results of stating different wave parameters are analyzed to point out differences based on crack locations. The study is strengthened by multi-physics finite element model (FEM) simulations (performed using Abaqus software) of the system.  
There are three main blocks of a photoacoustic system: an excitation laser system, a sensing laser system, and a control system, as depicted in Figure 2. As the name suggests, the control unit, which consists of a computer system, is responsible for the arbitration process for excitation and sensing systems to simultaneously start their tasks. The excitation system comprises of a pulsed laser and is responsible for providing the target material with a precise amount of thermal energy to initiate the thermal expansion phenomenon. This excitation generates acoustic waves in the material, which travel through the length of the material. Then, the sensing system measures the acoustic waves at designated sensing points. Data are sent to the control unit PC for image generation and further analysis. If the energy of the excitation laser is too high for the target material, then ablation, melting, or plasma formation can occur, which are not suitable in the current research scenario. Therefore, we are interested in designing the input laser power that produces thermal expansion, which results in acoustic waves due to pressure differences in the surroundings. Finally, the sensing laser measures these acoustic waves to estimate the composition of the target object, and the observed data are used to generate images for further analysis.  
Figure 1: Effects of different energies of the excitation laser on the target material causing thermal expansion or ablation/melting.
## 4 Results
### SAM vs. SAM 2
#### 4.1.3 Outliers Analysis  
Leaving the outliers out of the plot maintains the focus on the main distribution of the data. However, it is essential to note that the outliers are not necessarily errors or anomalies in the data. They could be valid data points that are more extreme than the rest. In this case, they represent the most significant improvements (and declines) in the segmentation results. The last step in analyzing these data would be to investigate these outliers. Figure 8 shows the normalized deltas for each dataset and metric, sorted from best to worst values.  
The Coronacases and Radiopaedia datasets show many extremely positive deltas for the Jaccard index and Dice score. Although extreme negative deltas exist, they are less frequent. Most deltas are close to zero, meaning the results will remain the same. However, the odds of improvement are higher than the odds of decline. The Montgomery dataset,  
Figure 8: SAM 2 vs. SAM deltas in descending order.  
on the other hand, shows no outliers for both metrics because the improvements are less infrequent than in the other two datasets. Most deltas are positive, meaning the odds of performance improvement are higher than the odds of decline.  
SAM 2 improves SAM performance because the model segments are better. There is no correlation between the size of the masks and model performance. The Pearson correlation coefficient is close to 0.20 for both the Jaccard index and Dice score when testing the Coronacases and Radiopaedia datasets and around 0.07 when testing the Montgomery dataset. These low values mean there is no linear correlation between the mask size and the delta values.
## 3 Environment
### Anomaly Detection in the Network  
In an OCC, time-distance diagrams display the pre-planned timetable and the realized timetable to operators in real time. Based on the track vacancy data, the supervision system updates regularly the actual traffic situation in the HMI. The pre-planned schedule is a graph for each train. Based on the operational data, the information system draws the realized graph and tries to estimate the future traffic situation. The database of conflicted zones--single track sections, switching zones, following trains--is static data implemented in the information system. The pre-planned timetable contains the pre-planned route order of each train (and other necessary data, such as planned arrival, departure time, etc.). Due to the actual and planned schedule, the route conflicts appear--well in advance--in the time-distance diagram in real time. In some cases, a delay of a train does not lead to route conflict. It is a deviation from the timetable but does not requires rescheduling or reordering task performed by the dispatcher. If the planned timetable--based on the actual data--leads to a route conflict, it is a disturbance. It is necessary to alert the dispatcher visually through the time-distance diagram.  
The automatic route setting system (ARS) gives route setting orders based on the planned data. The interlocking system does not perform the route setting orders--coming from the ARS--in the case of a conflicted situation to not lead to a traffic violation. The advanced scheduling systems recognize these situations early and alert the operators.  
At this time, the dispatcher may take corrective measures. The operator can reorder and reschedule the affected trains.
# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
## 2 Problem Definition and Related Work
### Existing Concept Drift Detection Methods
Three steps are used to describe this method: (i) this method labels the patterns from the test set using an unsupervised method; (ii) from the training and test sets, some statistics will be computed, conditioned to the given class labels the training set; and (iii) a multivariate hypothesis test used to compare the training and testing statistics. Finally, based on this test, DetectA attempts to detect drift on the test set before the real labels are obtained. NN-DVI: This method proposed by the authors of [21] uses a regional-density estimation as a drift detection method, named nearest neighbour-based density variation identification (NN-DVI). This method consists of three components: (i) k-nearest neighbour-based space-partitioning schema (NNPS), which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation; (ii) the density discrepancies are accumulated by a distance function in these subspaces and quantifies the overall differences; (iii) the statistical test defines the confidence interval to detect drift. Note that two windows of data are used by NN-DVI and these are compared to detect drift. HLFR: This method by [22] uses Hierarchical Linear Four Rates. HLFR runs using two layers: the responsibility of detecting potential drifts belongs to the first layer, whereas the second layer validates the detected drift and communicates this information back to the first layer. Layer one observes the same four rates of the confusion matrix. Consequently, if a drift is detected, a permutation test is applied by layer two to confirm if the detected drift is true or false. However, if the drift is false, the testing process restarts. FPDD, FSDD, and FTDD: These methods are proposed by [23]. Each of these methods uses a different statistical test and then measures the difference in errors rather than correct predictions. For example, when the number of errors or correct predictions is smaller than five, the [PERSON]'s Exact test will be used by FPDD. On the other hand, instead of using the test of equal proportions, the chi-square test for homogeneity of proportions is applied by FSDD.
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 3 Results
### Parcellation Granularity Effect on Fingerprinting  
Finer parcellation granularities offer a more detailed representation of the functional connectome by dividing the brain into more regions. However, this comes with a trade-off, as the time series for each region may be less reliable due to being derived from a smaller number of voxels. Hence, FCs fingerprints are expected to be dependent on the parcellation granularity of the data. Figure 2 displays the within-condition fingerprinting results of maximum-rank ST-HOSVD decompositions (i.e, for parcellation 614, the brain parcellation rank \(R_{1}\) and participant rank \(R_{2}\) were chosen to be 614 and 426, respectively) for parcellation granularities of \(114,214,314,414,514,614,714,814,\) and \(914\). As the parcellation granularity increases, matching rates plateau for all fMRI conditions starting at parcellation granularity of 414. Therefore, we give a stronger emphasis to parcellation granularity of 414 in the remaining analyses of this study. It is noteworthy that the longest (resting-state) and shortest (Emotion) fMRI conditions display, respectively, the highest and lowest matching  
Figure 1: Graphic representation of the participant identifiability framework. For an input tensor \(\mathbf{\mathcal{X}}\) consisting of FCs from one imaging session, we compute its Tucker decomposition to obtain a core tensor \(\mathbf{\mathcal{G}}\), two identical brain parcellation factor matrices \(B\), and a participants’ factor matrix \(\mathbf{P}\). Fixing \(\mathbf{\mathcal{G}}\) and \(B\) factor matrices, we estimate the participant’s factor matrix \(\mathbf{Q}\) for the FCs from another imaging session \(\mathbf{\mathcal{Y}}\). We then obtain an identifiability matrix \(\mathbf{I}\) by computing row-wise [PERSON]’s correlation between \(\mathbf{P}\) and \(\mathbf{Q}\), and obtain a matching rate score through \(\mathbf{I}\).  
rate values across all parcellation granularities. However, tasks with intermediate scanning lengths do not follow such trend.
# Generating Super Spatial Resolution Products from Sentinel-2 Satellite Images +
## 2 Materials and Methods
### Dataset  
In deep learning, data functions as a critical component concerning the model's quality and efficiency. Deep neural networks necessitate a significant volume of training data to understand intricate patterns. The vast and multifarious nature of data resolves numerous issues associated with model execution and performance decline [11].  
We utilized archived WorldView-2/3 images in the European Space Agency (ESA) as ground truth to prepare the LR-HR image pairs. These images were sensed by the satellite from February 2009 to December 2020, covering several areas of Earth. An example is shown in Figure 1. Care was taken to select scenes with minimal cloud cover or no cloud cover for the use of archived images. It was also ensured that at the time of image acquisition, a Sentinel-2 image was available within a week or less from that time. Finally, 46 large images were selected, covering various areas with urban, rural, agricultural, maritime, and other land uses. However, considering the structural differences in densely populated urban areas and the lack of images in these regions, they were excluded from the training data.  
After properly selecting the Sentinel-2 and Worldview image pairs, initial preprocessing steps were performed, such as converting the Digital Number (DN) to radiance. Additionally, the Sentinel-2 images were co-registered with their corresponding Worldview images, which have a higher spatial resolution. Then, the Worldview bands were resampled to a spatial resolution of 2.5 m. Finally, the Sentinel-2 and Worldview images were respectively divided into 64 \(\times\) 64 and 256 \(\times\) 256 patches and then augmented based on the frequency of each land use.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Employed Methods to Monitor Snow Cover with SAR Data
#### 3.3.1 Wet SCE Detection
Another approach is using the average of several images originating from a similar sensing period [137]. However, the temporal distance between reference images has to be taken into account, ensuring that longer intervals between observations do not introduce additional uncertainties [121]. [PERSON] et al. [138] selected one image observed during the melting period \(\sigma_{i}^{0}\) and two reference images (one acquired at the beginning of melting period \(\sigma_{w}^{0}\), and another acquired after melting period \(\sigma_{g}^{0}\)) to estimate the snow-free ground \(F_{g}\):  
\[F_{g}=100\times\frac{\sigma_{i}^{0}-\sigma_{w}^{0}}{\sigma_{g}^{0}-\sigma_{w}^ {0}}\ \% \tag{2}\]  
[PERSON] et al. [139] used the same formula in an approach proposed as the linear interpolation phase step. They applied the algorithm not pixel-based but at a bigger scale to eliminate the influence of SAR speckles. They also tested the usability of multi-year reference images; they concluded that the reference image does not necessarily need to be sensed in the same year as the classified melting season. Namely, a reference image from the past may be used to estimate future snow cover conditions. Thanks to these improvements, \"[PERSON]'s method\" has remained the most commonly applied algorithm in the past 20 years. As numerous new satellites with different wavelength designs have been launched since \"[PERSON]'s method\" was published in 2000, the \(-3\) dB threshold value for the ratio map segmentation has also been customized for different sensors and locations [121; 140; 141; 142; 143]. However, as this approach cannot be applied to L-band SAR (because the backscattering values from the snow-covered scene and the reference scene containing no or dry snow are similar [144]) and can only detect wet SCE, other SAR-based algorithms have been explored as well.
## 3 Method
### Extracting Component  
We can map the distorted image with the corrected image and then extract the watermark from the corrected image after applying STN. This component extracts the watermark from the watermarked image with screen shooting attacks. The watermarked image is transformed into the DCT domain before the watermark is extracted given that the watermark is embedded in the DCT domain. Since the watermark is embedded in the transform domain, the extraction component contains a copy of the transform layer used to embed the module in order to represent the watermarked image on the same basis. Meanwhile, other network layers learn to extract watermarks in the dct domain. The specific steps of the watermark extraction procedure is described in Algorithm 2.  
```
0: Attacked image \(I_{a}\) size of \(M\times M\).
0: Watermark \(w_{e}\).
1: Apply spatial transformation network to the attacked image \(I_{a}\) size of \(M\times M\) and output corrected image.
2: Divide corrected image size of \(M\times M\) into blocks of size \(h\times g\) and obtain a tensor of size \(b\times h\times g\).
3: Input the tensor into the DCT transform layer and output DCT coefficients tensor.
4: Fed the output of DCT transform layer into extracting layers, and then obtain watermark \(w_{e}\).
```  
**Algorithm 2** Extracting algorithm.