esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SLIC Method  
Superpixel was first proposed by [PERSON] et al. [62], and was used for image segmentation. In general, a superpixel in the image is a group of pixels that have continuous depths. The following properties for the superpixel are generally desirable: Superpixels should adhere well to image boundaries, and Superpixels should be fast to compute, memory efficient, and simple to use. Therefore, in the recent years, many superpixel algorithms, such as simple linear iterative clustering (SLIC) [63], superpixels extracted via energy-driven sampling (SEEDS) [64], Lattices [65], and GMMSP [66], have been proposed for various applications.  
In this paper, the superpixel algorithm is selected as a preprocess step to segment tiny regions, as shown in Figure 1, the SLIC is the best choice due to its two important properties: (1) The number of distance calculations in the optimization is dramatically reduced by limiting the search space to a region proportional to the superpixel size. This reduces the complexity to be linear in the number of pixels \(N\) and independent of the number of superpixels \(k\). (2) A weighted distance measure combines color and spatial proximity, while simultaneously providing control over the size and compactness of the superpixels. By default, the only parameter of the SLIC algorithm is k, which is the desired number of approximately equally-sized superpixels. For a given color image in the CIELAB color space, to get superpixel segmentations the following steps are required:  
1. Initialize cluster centers \(C_{i}=\left[\begin{array}{cccccc}l_{i}&a_{i}&b_{i}&x_{i}&y_{i}\end{array} \right]^{T}\), which are sampled on the regular grid spaced \(S\) pixels apart.
2. Move the cluster centers to the lowest gradient position in a \(3\times 3\) neighborhood.
3. Compute the distance \(E\) between each cluster center \(C_{k}\) and pixel \(i\) in a \(2\text{S}\times 2\text{S}\) region around \(C_{k}\), if \(D<d(i)\) then set \(d(i)=D\), \(I(i)=k\).
4. Compute new cluster centers \(C^{\prime}_{k}\) and residual error \(E\).
5. Repeat Step 3 and Step 4 until the residual \(E\) less than the threshold.
## 1 Introduction
### Objectives and Research Questions  
The objectives of this research were twofold: (i) investigate the gap between customer needs and technology developers' perceptions of these needs and (ii) validate the value propositions offered by the new LV grid solution developed in the project. The solution under investigation was a comprehensive package of smart grid technologies, developed to improve the operation and management of the electricity distribution network. The paper introduced a sense and respond (S&R) methodology to identify the gap between the technology developers' perceptions and the target customer needs (DSOs). The S&R method was further complemented by a customer survey to validate the relevance and urgency of the value propositions. The objectives can be broken down into four specific research questions:  
1. What are the challenges, status and practices of LV grid management?
2. How relevant and urgently needed are the value propositions of the RESOLVD project?
3. How prepared are the DSOs to deploy the new technology?
4. Is there any regulation barrier/challenge which hinders RES integration at the LV grid level?
# A Study of Emergency Aircraft Control During Landing
## 5 Aircraft's Model Identification
### Model Identification Process  
The direct objective of the aircraft's model identification, as described in (5) and (8), was to estimate all the dimensional aerodynamic derivatives present in the state and input matrices and related to the deflection of the thrust levers.  
The values of the flight parameters in (9) may change according to flight conditions. It concerns, in particular, the changes in flight altitude, airframe configuration, and the aircraft's mass loss related to fuel consumption. The knowledge of the flight parameters' behaviour is crucial both for correct model identification and for further designing the control system. The correlation between flight conditions and selected flight parameters was determined by the observations made in simulator tests and is presented in Table 2.  
Out of the listed parameters, the change in altitude affects only the required position of the thrust levers in equilibrium (level flight). Its lack of noticeable effect on the other flight parameters is in accordance with the principles of flight mechanics. In flight at Mach speeds below the critical Mach number, all aerodynamic forces and moments acting on the aircraft appear proportional to the square of the equivalent airspeed (EAS). This implies that at a given equivalent airspeed, the aircraft's controllability and responsiveness, as well as the aerodynamic loads acting on it, are almost constant and equal to the states occurring at a standard sea level, regardless of the actual altitude. Consequently, the steady states at different flight altitudes, with a frozen aircraft's configuration, are represented by a constant EAS value.  
For the 757-200 model configured with a take-off weight of 177,000 lb, including a fuel weight of 30,000 lb, the all-fuel consumption results in a change of approximately 13% in the total weight of the aircraft. Its effect on the dynamics and flight performance was neglected under the established design principles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight Parameter** & **Change in Altitude** & **Fuel Mass Loss** \\ \hline \(\mathrm{U}_{0}\) & no correlation & weak correlation \\ \(\mathrm{A}_{0}\) & no correlation & weak correlation \\ \(\Theta_{0}\) & no correlation & weak correlation \\ \(\delta\mathrm{T}_{0}\) & strong correlation & moderate correlation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of changing flight conditions on selected parameters.
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results for Interacting Models
#### 7.4.1 Results from SOPT
However, the low-energy excitations may exhibit non-Fermi liquid behavior. To our knowledge, this phenomena has not yet been explored. The lack of a sharp mobility edge due to interactions may also be interpreted as a delocalization of states that would have otherwise been localized by disorder. Further support for such a role of interactions is also found in the increase of the critical disorder, \(W_{c}(U)\) with increasing \(U\). In Figure 41, the integrated typical DOS for \(N_{c}=38\) as a function of disorder for various interaction strengths is seen to decrease sharply and vanish at a critical disorder strength, \(W_{c}\), whose value depends on \(U\). The inset shows that the \(W_{c}(U)\) increases with increasing \(U\). Using the TMT with an NRG impurity solver, Byczuk et al. had also found the same result [126]; however, since the TMT is a local theory, and hence corresponds to \(N_{c}=1\), it was not clear if their result was robust against inclusion of non-local dynamical correlations due to disorder and interactions. The TMDCA results for \(N_{c}=38\), which fully incorporate these correlations, shown in Figure 41 confirm that, indeed interactions can screen disorder effects, and hence a larger disorder value is needed to localize the system in the presence of interactions. Figure 41: Screening of disorder effects by weak interactions in the 3D Anderson-Hubbard model: The main panel shows the momentum integrated typical DOS, TDOS(**R** = 0; \(\omega=0\)) for \(N_{c}=38\) as a function of disorder, \(W\) for various \(U\) values (units \(4t=1\)). The inset shows that the critical disorder value, \(W_{c}(U)\) increases with increasing \(U\) for three cluster sizes. Interestingly, we also found a dip in the DOS at the chemical potential, akin to a pseudogap, at disorder values that were very close to the critical disorder. Since this is the weak coupling regime, this pseudogap could be a precursor of the Efros-Shklovskii Coulomb gap [176]; however, the present model has purely local interactions, while the Coulomb gap is found for long-range interactions, which have not been explored yet.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Opportunities of Data Fusion for SAR-Based Snow Cover Detection  
#### 5.6.1 SAR Flight Direction (Ascending and Descending)  
In [PERSON]'s study [140], merging ascending and descending SAR observations was mentioned as a crucial future development, which was also discussed in the Sentinel for science (SEN4 SCI) scientific workshop [122]. It was also suggested that this combination could reduce the dependency of LIA [135; 191]. However, only four studies utilized this solution, although [PERSON] and [PERSON] already tested this approach in their pioneer paper [127]. In their paper, two ratio maps were masked with shadow and layover areas as well as regions containing extreme LIA. Then they combined the images pixel-wise based on the LIA value. [PERSON] et al. [65] applied a similar approach but considered potential thawing. The general problem when combining different flight paths is the ground temperature difference between sensing times may lead to a change of the snowpack condition [67]. Thus, [PERSON] et al. [65] recommended a separate classification threshold for each direction.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
## 4 Results
### Simulation Results
#### 4.3.4 Flow Length  
Figure 10 represents a histogram of a sample mean of packet count in a single IP fragment flow. The expected number of fragments is 2, while the most extreme cases reach 8 packets and more. This follows the findings from [PERSON] et al. [1]. The higher number of packets in the fragment series most likely indicates an invalid flow (it can be a part of a DDOS attack or a firewall evasion technique). Fragment flows with packet count 1 are incomplete and cannot be reassembled.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ratio** & **High Slope** & **Low Slope** & **Mean Slope** & **Min. \(r^{2}\)** \\ \hline
0.10 & 0.46589 & 0.30953 & 0.40379 & 0.97732 \\
0.25 & 0.46042 & 0.39083 & 0.42903 & 0.98409 \\
0.50 & 0.46382 & 0.41744 & 0.44497 & 0.98323 \\
0.75 & 0.46755 & 0.44189 & 0.45782 & 0.97932 \\
0.90 & 0.46907 & 0.45781 & 0.46386 & 0.97625 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linear fit results for Figure 9.  
Figure 10: Histogram of the sample mean and the confidence intervals of the fragment count. Each data series represents a different ratio \(R\).  
The distribution of the flow length can be used to resolve the question from Section 3.1.1 about the fragment list size. Since the expected fragment length seldom exceeds 8, the list length of 8 should suffice for practical implementation.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 2 System Overview  
The inspection system consists of two major stages in a coarse-to-fine manner: defects detection and classification. The pipeline of the metallic surface defect inspection architecture is shown in Figure 2. The original images are obtained by industrial microscope under bright field imaging. The size of the capturing image is 2720 \(\times\) 2040 \(\times\) 3 pixels. Since this paper focuses on the defect inspection algorithms, the detailed image acquisition process will not be mentioned.  
For detail, the goal of the detection module is to segment and localize accurate defects. The input original image is firstly transformed to a prediction mask based on CASAE. Secondly, the threshold module is used to binarize the prediction result to obtain an accurate defect contour. Thirdly, defect regions that are considered as the input of the next module are extracted and cropped by a defect region detector. In the classification module, these defect regions are classified into their specific classes via a compact CNN. This compact CNN is intended to speed up the whole process of defect inspection. The entire inspection process consists of online detection in an actual industrial environment.
## 6 Results and Discussion
### Combining GAN-Based Methods with Undersampling  
To explore the impact of undersampling with GAN-based methods, we compared the prediction performance of GAN-based methods with GAN-based methods combined with undersampling. Furthermore, the Wilcoxon effect size was utilized to quantify the difference between the performance of GAN-based methods against GAN-based methods combined with undersampling. Tables 7 and 8 present the MCC values of GAN-based models combined with undersampling and the result of the Wilcoxon effect size, respectively.  
As shown in Tables 7 and 8, combining GAN with undersampling to overcome the imbalance problem caused a negligible negative impact on the prediction performance on  
Figure 3: Ranking of all methods across all datasets.  
70% of the defect datasets. Eclipse-2.0, JM1, and prop-43 were positively affected by the combination of GAN and undersampling. On the contrary, combining CWGANGP with undersampling showed a negative effect on all defect datasets, except on MC1, Lucene, and Eclipse-2.0 datasets. There was a large positive impact of combining CWGANGP with undersampling on MC1 and Eclipse-2.0 datasets and a negligible positive impact on the Lucene dataset. It is worth noting that the prediction performance on all defect datasets was negatively affected by combining CTGAN with undersampling, except on JM1 and prop-43 datasets.  
Integrating undersampling with GAN-based methods using the proposed approach caused a negative impact on 70% to 80% of the defect datasets. This can be linked to information loss, which is one of the drawbacks of undersampling. We can summarize the answer to the third research question, \"**RQ3**. _Will combining GAN-based methods with undersampling yield enhanced defect prediction performance?_\", as follows:  
**RQ3** answer. On most defect datasets, combining GAN-based methods with undersampling using the proposed approach caused a degradation in the defect prediction performance. Undersampling can result in the removal of some important samples, which affects the overall prediction performance negatively. Thus, based on the empirical evidence, we do not recommend combining GAN-based methods with undersampling.
###### Abstract  
Soils with low permeability are widely used in solid waste landfills to prevent leakage of leachate into groundwater. By adding nanomaterials to clay soils, the permeability of the clay can be reduced as well as the retention of pollutants in the leachate. In this study, three different nanomaterials, iron oxide, aluminum oxide, and OIto clay, were added to kaolin at two different rates (1% and 5%), and the effect of nanomaterials on permeability and heavy metal (iron, manganese, zinc, copper, and lead) removal rate was investigated. According to the experimental results, permeability decreased, and the heavy metal removal rate increased with increasing nanomaterial content in kaolin. With the addition of 5% iron oxide, 5% aluminum oxide, and 5% OIto clay to kaolin, the average permeability decreased by 63%, 81%, and 96%, respectively. Iron (90-93%), manganese (47-75%), zinc (39-50%), copper (33-41%), and lead (36-49%) removal rates of nanomaterial-added kaolin samples were found to be higher than the removal rates of kaolin without nanomaterial addition. OIto clay, which has the smallest size and high surface area, performed better than aluminum oxide and iron oxide in reducing the permeability of kaolin and retaining heavy metals.  
compacted clay; heavy metal; leachate; nanomaterial; permeability +
Footnote †:
## 3 Materials and Methods
### Vehicle Behavior Spatio-Temporal Interaction  
#### 3.2.1 Dynamic Multi-Level Relationship Diagram Modeling  
As shown in Figure 4, after generating the spatial scene graph, the dynamic multi-level relationship graph modeling method is introduced. This method is based on a multi-level structure that represents the relationships between vehicles at different spatial and temporal granularities, thereby capturing and understanding the dynamic interactions of vehicles in a more comprehensive manner. Compared with static spatial scene graphs, the dynamic multi-level relationship graph can update the interactions between vehicles in real time, improving the model's prediction accuracy and robustness for vehicle lane-changing behavior. By representing relationships at different spatio-temporal granularities,the model better adapts to complex traffic situations, including rapidly changing vehicle behaviors on highways.  
The interaction between vehicles and road objects such as traffic signs and obstacles can be modeled in complex traffic scenarios using the following equation:  
\[G=(V,E) \tag{4}\]  
where \(V\) represents the set of nodes, including vehicles and road objects, and \(E\) denotes the edges, reflecting the relationships between these nodes.  
In the case of multi-level relationships, each level is characterized by a specific type of interaction. For instance, the first hierarchical level represents direct proximity, the second reflects extended proximity, and the third captures more distant or complex relationships. The overall relationship diagram of the transportation scenario can be represented as a combination of the relationship diagrams at each level:  
\[G=\sum_{t=1}^{L}G^{l}=\sum_{t=1}^{L}\left(V,E^{l}\right) \tag{5}\]  
where \(L\) represents the total number of relationship layers.  
For dynamic edge weights, each edge \(e_{ij}^{l}\), connecting nodes \(v_{i}\) and \(v_{j}\) on layer III, is assigned a time-dependent weight \(w_{ij}^{l}(t)\). This weight is dynamically computed using the following equation:  
\[w_{ij}^{l}(t)=\frac{1}{1+exp\left(-\left(\alpha d_{ij}(t)+\beta\alpha v_{ij}( t)+\gamma\Delta a_{ij}(t)\right)\right)} \tag{6}\]  
where \(d_{ij}(t)\) denotes the distance between vehicles \(v_{i}\) and \(v_{j}\) at time \(t\); \(\Delta v_{ij}(t)\) represents the relative speed between the two vehicles; \(a_{ij}(t)\) indicates their relative acceleration; and \(\alpha\), \(\beta\), and \(\gamma\) are coefficients that regulate the influence of these factors.  
Ultimately, the complete mathematical expression for modeling dynamic multi-level relationship graphs is obtained as follows:  
Figure 4: Modeling spatial and temporal interaction of vehicle behavior.  
\[\begin{array}{ll}G(t)&=\left(V,E(t)\right)=\left(V,\sum_{l=1}^{L}\theta_{l}\cdot E ^{l}(t)\right)\\ &=\left(V,\sum_{l=1}^{L}\theta_{l}\cdot\left\{\left(v_{i},v_{j},\frac{1}{1+exp \left(-\left(ad_{ij}(t)+\beta\Delta v_{ij}(t)+\gamma\Delta a_{ij}(t)\right) \right)}\right)\right\}\right)\end{array} \tag{7}\]
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 6 Discussion
#### Emerging Design Trends in Lightweight Block Ciphers  
Tweakable ciphers introduce a third input called a tweak, in addition to the plaintext and key. The tweak acts like a dynamic, nonce-based modifier that allows the same key to produce different ciphertexts for different contexts. This design is especially valuable for authenticated encryption, format-preserving encryption, and secure multi-user environments. In lightweight settings, tweakable ciphers such as FlexAEAD and NDN provide strong protection against related-key attacks and allow for efficient domain separation without rekeying, which is highly useful in constrained IoTs devices that perform multiple roles (e.g., sensing, communication, logging).  
Involutive ciphers, such as IVLBC, INLEC, and LELBC, are designed so that the encryption and decryption processes use the exact same logic. This symmetry allows for significant resource savings in hardware, as the same circuitry can be reused for both operations. Involutive structures also simplify secure implementation, reduce code complexity, and can enhance resistance to certain implementation attacks, such as fault injection. For example, symmetric round functions can minimize timing variations and improve energy efficiency, crucial for low-power IoTs environments such as wearables and RFID tags.  
Hybrid ciphers combine elements of different architectural paradigms typically SPN, ARX, and Feistel to achieve a balance between security and efficiency. By leveraging the strengths of multiple structures, hybrid designs such as GFRX, SFN, and FeW can mitigate the individual limitations of traditional models (e.g., the slow diffusion of Feistel or the S-box dependency of SPN) while maintaining compactness and scalability. These designs are particularly promising in contexts where performance and flexibility must be tailored to diverse hardware and software constraints.  
These trends point to a new generation of lightweight ciphers that are not just minimal in area or power, but also adaptive, secure, and easy to implement across platforms. As IoTs systems increasingly demand multifunctional security primitives with low integration overhead, tweakable and involutive structures--especially when embedded into hybrid designs--offer a compelling blueprint for future cryptographic standards.
## 5 Experiments  
### Datasets  
The data utilized in this paper are provided by the TADPOLE challenge [8], which is designed to identify the data, features and approaches that are most predictive of the future progression of subjects at risk of AD. The TADPOLE dataset comprises 1737 patient samples sourced from the ADNI database [31], encompassing a total of 12,741 recorded time points. The time point of the first data collection for each sample is called the baseline, and the subsequent time points are named according to the interval with baseline. Each sample contains a different number of time points, and there is missing time-series data in the dataset.  
Although the TADPOLE challenge provides researchers with a wide range of biomarkers to choose from, this paper, similar to previous studies [6; 13], only selects 22 variables among them:  
* Cognitive tests: Rey's Auditory Verbal Learning Test (RAVLT)_forgetting, ADAS-Cog13, RAVLT_learning, ADAS-Cog11, RAVLT_perc_forgetting, Functional Activities Questionnaire (FAQ), Mini-Mental State Examination (MMSE), Clinical dementia rating sum of boxes (CDRSB), Montreal cognitive assessment (MOCA), RAVLT_immediate.
* PET measures: Fluorodeoxyglucose (FDG), AV45.
* MRI measures: Hippocamp, WholeBrain, Fusiform, Intracranial Volume (ICV), Ventricles, Entorhinal, MidTemp.
* CSF measures: Beta-amyloid (CSF), Total tau, Phosphorylated tau.  
We also take the disease clinical diagnosis into consideration. In the dataset, disease diagnosis is divided into AD, CN, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and significant memory concern (SMC). Consistent with prior studies [6,7,13], the CN and SMC groups are consolidated into a unified CN group, whereas the EMCI and LMCI groups are amalgamated to form the MCI group. As a result, the dataset is categorized into three distinct groups: CN, MCI, and AD.