esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 2 Literature Review  
The earth observation satellite scheduling problem has attracted intensive research attention over the last two decades. They mostly focused on the scheduling of imaging activities. [PERSON]. [5] proved that the scheduling of earth observation satellites is an NP-hard problem and proposed three simple heuristic approaches to solve the problem. [PERSON]. [6] proposed a branch and bound algorithm for onboard satellite scheduling, and [PERSON]. [7] used branch and price to solve the imaging scheduling problem. These exact algorithms can only deal with small-sized problems. [PERSON]. [8] considered a simplified version of the problem and formulated the problem as a MIP model. [PERSON]. [8] introduced several useful cuts to accelerate the solving process.  
Due to the complexity and problem scale, most studies investigate the application of heuristics and meta-heuristics to solve the problem, including genetic algorithm [9; 10; 11; 12], adaptive large neighborhood search [13], and other heuristics [14; 15; 16]. For a review of the planning and scheduling techniques for the EOS scheduling problem, readers could refer to [17].  
The satellite data transmission scheduling problem is somewhat less studied. [18] introduced the basic concept and characterisitcs of the satellite data transmission problem. [19] proposed to use a genetic algorithm to solve the problem, and [20] adopted an ant colony algorithm. [21] studied the case where satellites can communicate and transfer data with each other. [22] studied a multiobjective variation of EOSIS. The solution methods applied to this problem mainly focus on heuristics and metaheuristics, due to its NP-hardness nature.  
There are few studies on the EOS integrated scheduling where imaging and data transmission are considered simultaneously. [PERSON]. [23] proposed a two-phase genetic annealing method for the EOSIS problem, where a genetic algorithm and a simulated annealing algorithm are combined to solve the problem. However, general-purpose metaheuristics, without adaptation, are not able to perform well on such a complex problem. Dedicated heuristics are usually needed in order to achieve a satisfactory result.  
The ELSH we propose adopts several dedicated operators to improve algorithm performance. An efficient constraint handling mechanism is also incorporated to guarantee algorithm efficiency.
## 2 Literature Review
### Challenges and Considerations  
While AI-driven adaptive feedback and learning analytics offer numerous benefits, several challenges must be addressed to ensure their effective implementation. Concerns regarding data privacy and the ethical use of student information are paramount. Educators and policymakers must establish robust data governance frameworks to protect student data.  
Additionally, the successful adoption of these technologies requires educators to possess a certain level of data literacy [41]. Professional development programs are essential to equip teachers with the skills necessary to interpret learning analytics and implement AI-driven feedback effectively. Research by [42] highlighted the importance of ongoing support and training for educators in this domain.  
Moreover, the design of AI-driven tools should consider the diverse needs of students in mixed-ability classrooms [43]. Ensuring that these technologies are inclusive and accessible to all learners is crucial for promoting educational equity. To systematically synthesize prior research and highlight this study's contributions, Table 1 compares key studies on AI-driven feedback with our findings, focusing on improvements in conceptual mastery, engagement, and retention.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Focus**} & **Conceptual Mastery** & **Engagement** & **Retention** & **Comparison to Current** \\  & & **Improvement** & **Improvement** & **Improvement** & **Study** \\ \hline \multirow{3}{*}{[13]} & AI in STEM & Significant & & & Our study quantifies 28\% \\  & education & (quantitative not specified) & Not measured & Not measured & improvement, extends to mixed-ability settings. \\ \hline \multirow{3}{*}{[12]} & Personalized & Improved & Increased & Enhanced & We provide specific metrics \\  & AI feedback & (no \% specified) & (qualitative) & (qualitative) & We provide specific metrics (28\%, 35\%, 85\%) across diverse disciplines. \\ \hline \multirow{3}{*}{[34]} & Adaptive & \multirow{3}{*}{Not specified} & Significant & Reduced & Our 35\% engagement \\  & learning & & (metrics not detailed) & Reduced & increase and 85\% retention \\ \cline{1-1}  & case study & & (metries not detailed) & dropout rates & rate offer precise, scalable evidence. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of AI-Driven Feedback Studies.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Flood Map Validation and Quality Statistics  
The final step of the work is the validation and quality assessment of all the produced flood area maps (semi-automatic and manual). To do this task, we used the flood maps made by official authorities and published online as a benchmark. These maps are based on aerial photos taken a few days after the floods, field surveys, and remote sensing data. For the 2016 Po River basin flood, we used a flood map available from ARPA (Regional Environment Protection Agency) Piemonte geoportal [87], whereas for the 2015 Ebro flood, we used the maps of the Ebro River Basin Authority [53]. We could not apply this step to the Arahal study case because no official maps are available.  
The validation procedure is described in the following:  
1. Both the produced flooded area maps and the official reference maps were converted into a Boolean raster format (1--floaded or 0--not flooded)
2. The two rasters were crossed, and a raster map with four possible values was generated: True positive (TP), true negative (TN), false negative (FN), false positive (FP). TP corresponds to areas correctly classified as flooded, TN are the areas correctly classified as not flooded, FN indicates the undetected flooded areas, and FP are the not-flooded areas erroneously classified as inundated. To make a more-uniform comparison between different types of semi-automatic flood detection approaches, it is possible to use a permanent water mask to exclude river and lake areas from FR statistics.
3. We evaluated the efficacy of used datasets and methods by computing the flood-mapping ratio (Equation (5)) and the not-flood ratio (Equation (6)) represented as a percentage format. \[\text{FR}\left(\%\right)=\text{TP}/(\text{TP}+\text{FN})\] (5) \[\text{NFR}\left(\%\right)=\text{TN}/(\text{TN}+\text{FP})\] (6)
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Silver Nanoparticles and Silver Ions
#### 5.6.2 Dissipation of the Proton Motive Force  
The ability of submicromolar concentrations of silver ions to induce the collapse of the electrochemical proton gradient across the inner membrane of the bacterial cell envelope due to the massive proton leakage occurring through the Ag\({}^{+}\)-modified membrane has been proved by [PERSON] et al. and [PERSON] et al. [92; 93]. Hence, Ag\({}^{+}\) ions have a protonophore- and an uncoupler of oxidative phosphorylation-like effect. Dissipating the proton-motive force, Ag\({}^{+}\) ions cause a depletion of intracellular ATP levels. Translocation of newly synthesized precursors of envelope proteins across the inner membrane is powered by ATP hydrolysis. In turn, ATP is synthesized by oxidative phosphorylation using the energy stored in the form of the proton electrochemical gradient established across the inner membrane. Consequently, in the absence of the proton motive force and ATP, the precursor proteins are not translocated and accumulate in the cytoplasm. The accumulation of the envelope protein precursors in the cytoplasm of _E. coli_ cells treated with silver nanoparticles was interpreted by [PERSON] et al. as indicating dissipation of proton motive force and depletion of intracellular levels of ATP [93].
# New Generation of Electrochemical Sensors Based on Multi-Walled Carbon Nanotubes
## 2 MWCNT Synthesis Methods
The CVD method also allows greater control of selectivity/type, homogeneity and size of the nanotubes (a few tens to hundreds of micrometers) in comparison to the others, although these characteristics are strongly dependent on the nature and structure of the catalysts, and the operational conditions of the synthesis. All these aspects affect the density and speed of electron transfer through the nanostructures that, in turn, directly impact the performance of the electrochemical sensors.
## 5 Experimental Results
### Experimental Setup  
In all our experiments, Classification and Regression Trees (CART) are used as base classifiers for training all the classification models. Standard _Bagging_[71] is utilized to obtain the margin values of training instances. All the ensembles are implemented with 100 trees. Each data set has been randomly divided into two parts: training set and test set. In order to avoid the case that all the minority class instances are in the training set (or test set), and there are no samples of the smallest class in the test set (or training set), the percentage of the instances used for training and testing is set to 1:1, i.e., 50% original data is obtained via adopting random sampling without replacement to form a training set, and all the unselected instances compose a test set. All the reported results are mean values of a 10-time calculation. The range of sampling parameter \(a\) is set to 10-100.  
Figure 3: Flowchart of margin based imbalanced ensemble classification (ensemble size \(T=10\), range of resampling rate \(a\) 10–100%).
## 3 Results and Discussion
### Fracture Damage Analysis Based on Meso-Characteristic Parameters  
In this study, three parameters including air void content, connective void content and void number are used to quantitatively describe and evaluate the internal meso-structure of asphalt mixture specimens under F-T conditions and its influence. Air void content  
Figure 9: Tensile fracture mode classification based on the AE indirect parameters for asphalt mixture subjected to F-T conditions: (**a**) 0 cycle; (**b**) 3 cycles; (**c**) 6 cycles; (**d**) 9 cycles; (**e**) 12 cycles; (**f**) 15 cycle; (**g**) 18 cycles; (**h**) 21 cycles.  
in asphalt mixture reflects its compactness to some extent. The higher the density, the lower the air void content in bitumen mix, and vice versa. Connective void content is connected with each other between voids and the edge of the end branch of voids, which can be regarded as a void cluster. The void number parameter can reflect the formation and development of its internal damage to a certain extent.  
The internal structure of bitumen incorporating basalt fiber subjected to F-T conditions was scanned via CT scanning, varying with the interval of 0.6 mm along the vertical height of the specimen. Figure 10 compares the results of the air void content, connective void content and void number of the asphalt mixture in its middle section. For the air void content in Figure 10a, as the F-T condition continues, air void content for asphalt mixture incorporating basalt fiber becomes larger and larger, indicating that F-T treatment has an obvious impact on porosity for bitumen. By comparing the connective void content in bitumen subjected to different F-T conditions, from Figure 10b, the connective void content in the bitumen mix increases gradually as the F-T condition continues. As for the void number in Figure 10c, on the whole, the void number decreases first and then increases as the F-T condition continues.  
The rise in air void content could be attributed to the continuous generation of new voids in bitumen specimens. Based on CT image processing results, the F-T cycle causes new voids inside the asphalt mixture specimen, and the existing voids gradually expand and develop, which eventually leads to the continuous development of F-T damage in  
Figure 10: Distribution of internal meso-void characteristics of asphalt mixture under F-T conditions: (a) air void content; (b) connective void content; (c) void number.  
bitumen. The rise in connective void content is mainly attributed to the development of connected voids. The F-T cycle will lead to the continuous generation of new voids in the asphalt mixture, which will raise air void content. With the gradual development of internal damage in bitumen, when the F-T condition goes to a certain extent, the new voids inside the asphalt mixture increase significantly, the generation of new voids dominates, and void number increases sharply, which also shows that the F-T damage of bitumen is getting progressively worse.
# Construction of a Numerical Model for Flow Flash Evaporation with Non-Condensable Gas
## 2 Construction of the Numerical Model  
### Physical Model and Boundary Conditions  
A horizontal-flow flash chamber with three openings, namely, the fluid inlet, fluid outlet, and steam outlet, was studied, as shown in Figure 1a. The chamber length and width were 1 m and 1.4 m, respectively. A structured grid was used in the chamber, as shown in Figure 1b. Four grid sizes were independently tested, and the grid sizes were 3.0, 2.5, 2.0, and 1.5 mm, corresponding to 154,845, 224,000, 350,000, and 620,712 grids, respectively. A grid size of 2.0 mm was selected because of the computational time and clarity of the results.
# Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning
## 3 Methodology
### Deep Learning Models for Classification
#### 3.3.1 Convolution Neural Network  
Convolution neural network [44] consists of an independent filter used for image data, classification prediction problems, and regression prediction problems due to its deep structure, it is also called DCNN. The number of features depends on the number of filters and extracts the mel-spectrogram of raw data (wav file). Each convolution layer learns features from the mel-spectrogram and the remaining layers process the useful information from the learned feature. This network consists of the 24-layer architecture of DCNN given in Figure 6. We have used the following Algorithm 1 for DCNN.  
The transfer learning (TL) [45; 46] technique is used in ML and its sub-field DL. This method is designed for one task and can be reused as a starting point for a new related task. Pre-trained networks are used as a starting point in new research, as these  
Figure 6: Deep convolution neural network architecture.  
networks help us save vast computation and time resources required to design a network. There are two ways to use transfer learning. Firstly, by extracting features using a pre-trained network, and then train the network model. Secondly, by fine-tuning the pre-trained network by keeping the weights learned as an initial parameter. Fine-tuning is used when we are using an NN that has been designed and trained by someone else. It allows taking advantage without having to develop it from scratch. Therefore, we are relying on the second method.  
AlexNet is trained on millions of images from the ImageNet database [44]. It is trained in 1000 categories and is enriched with a wide range of feature representations. The standard size of this network is \(227\times 227\times 3\) (In image size 227 represents the number of frames, 227 represents the number of bands, and 3 represents the spectrum.) and consists of 25-layers shown in Figure 7. We convert the raw data to the mel-spectrogram because AlexNet is trained on the ImageNet dataset. The mel-spectrograms are resized according to AlexNet's standard input size, and inputs to the model. The standard AlexNet is trained on 1000 categories, whereas this work consists of 29 classes of the alphabet classification problem and 2 classes of the alphabet pronunciation classification problem. Therefore, to use pre-trained AlexNet, we have replaced 3 final layers of AlexNet named fully connected layer (Fc8), SoftMax layer, and classification layer (output layer). We have fine-tuned them according to our classification problems. After this, the network extracts features from the mel-spectrogram autonomously and learns from the dataset.  
We need to specify the output size of the fully connected layers according to the number of classes of our data. Other parameters are learned after their empirical analysis, and network training. The test dataset is compared to the training dataset to observe the performance of the network. We have used Algorithm 2 for the AlexNet pre-trained network model.
## 2 Research on UAV Post-Disaster Path Planning Problem
### Path Smoothing Algorithm Based on Cubic B-Spline Curves
#### 2.3.2 Path Smoothing Basis Functions and Control Points  
In Equation (15), \(V_{i-1},V_{i},V_{i+1},V_{i+2}\) represents a set of control points. In UAV path planning, it is necessary to determine the positional values of the desired points on the path, which are obtained by solving the inverse kinematics problem of the UAV motion. If planning a path based on known positional values, the control points need to be determined. Let \(P\) represent the positional values, and the following conditions are obtained:  
\[P_{i-1}(1)=P_{i}(0)=P_{i}=\frac{1}{6}(V_{i-1}+4V_{i}+V_{i+1})i=1,2,\cdots,m-1 \tag{17}\]  
In Equation (16), there are \(m+2\) unknowns but only \(m\) equations. Therefore, the following constraint conditions are added to the endpoints of the global path point set:  
\[\left\{\begin{array}{l}V_{1}=V_{0}\\ V_{m+1}=V_{m}\end{array}\right. \tag{18}\]  
A unique set of \(V_{i-1},V_{i},V_{i+1},V_{i+2}\) can be determined by given boundary conditions. The B-spline curve determined by Equation (15) is represented as follows:  
\[P_{i}(x)=\frac{1}{6}\Big{(}-x^{3}+3x^{2}-3x+1\Big{)}V_{i-1}+\frac{1}{6}\Big{(} -x^{3}+6x^{2}+4\Big{)}V_{i}+\frac{1}{6}\Big{(}-3x^{3}+3x^{2}-3x+1\Big{)}V_{i+1 }+\frac{1}{6}x^{3}V_{i+2} \tag{19}\]  
In Equation (18), controlled by four control points \(V_{i-1},V_{i},V_{i+1},V_{i+2}\), the coordinates of the control points are represented by \(\big{(}X_{i},Y_{i},Z_{i}\big{)}(i=1,2,\cdots,n)\). Among them, the parameter \(x\) takes all values uniformly from 0 to 1 with an interval of 0.01, which yields the \(i\) segment of the B-spline curve. The coordinates \(\big{(}X_{i},Y_{i},Z_{i}\big{)}\) of any point on the curve are represented as follows:\[\left\{\begin{array}{l}X_{t}=\sum\limits_{i=0}^{m}X_{i}F_{i,k}(t)\\ Y_{t}=\sum\limits_{i=0}^{m}Y_{i}F_{i,k}(t)\\ Z_{t}=\sum\limits_{i=0}^{m}Z_{i}F_{i,k}(t)\end{array}\right. \tag{20}\]  
Finally, based on Equations (18) and (19), the connecting points of the cubic B-spline curve can be obtained. To reduce computational complexity, redundant connecting points are removed, and the remaining connecting points are used to generate the smooth effect of the cubic B-spline curve, as shown in Figure 2.
## 2 Methods
### Sampling and Analysis  
Static VOC samples for pre-measuring tests of the self-prepared NTS and 100 \(\upmu\)m PDMS-SPME fibers were prepared by injecting BTEX (100 ppm) into 1000 mL glass bulbs. Simultaneously, VOC calibrations were performed to precisely quantify the BTEX concentration. All NTS were used to take static BTEX samples within the bulb in an oven heated to 27 \({}^{\circ}\)C. The VOC analysis of both calibrations and static samples of the NTS was also performed using the same GC-FID. After VOC sampling, an NTS was injected into the injection ports of the GC, which was used for VOCs desorption and further analysis. Desorption time and temperature at the injection port of the GC was 30 s and 250 \({}^{\circ}\)C, respectively. The temperature of the GC increased from 50 \({}^{\circ}\)C at increments of 15 \({}^{\circ}\)C/min, and the final temperature was held at 180 \({}^{\circ}\)C for 2 min. The FID detector was heated to 300 \({}^{\circ}\)C. The carrying gas flow rate was 1.2 mL/min for nitrogen, and the split off operation mode was selected. The SPME fibers were also analyzed using the same procedures as those for the NTS. Notably, after analysis, no VOC carryovers were available for sample extraction by the NTS or SPME, which was verified by earlier examinations [10; 11; 12; 13].
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions  
In our experiments, standard augmentations like random rotation and flipping are applied. We utilized a 12 layer Vision Transformer (ViT) [45] architecture for the pure transformer encoder model, with an input resolution of 8 \(\times\) 8 and a patch size of 16, unless specified. Four 2\(\times\) up-sampling was used in the cascaded up-sampler (CUP) for full resolution. The models were trained for 20k iterations using the Adam optimizer with a learning rate of 0.0001 and a batch size of 6. The training was performed on an Nvidia RTX2080 Ti GPU (Graphics Processing Unit, TechPowerUp, Yorkshire, NY, USA).  
We demonstrated our framework on different medical image datasets in distinct modalities, CT and skin image dataset. The CT segmentation dataset contains 100 axial CT scans from COVID patients, with radiologist segmentations of lung infections, and the HAM10000 dataset [59], Human Against Machine, contains 10,000 training images, a large collection of dermatoscopic images from multi-sources showing common pigmented skin lesions, including the ISIC (International Skin Imaging Collaboration) 2016 and 2018 skin lesion datasets. The COVID CT dataset contains 349 CT scans from 216 patient cases, with extracted metadata like age, gender, location, medical history, scan time, COVID severity, and radiology reports. It has more COVID positive cases than other datasets, providing greater image diversity.  
The HAM10000 dataset includes ISIC 2016 (900 images) and ISIC 2018 (1000 images) of dermatoscopic images from different populations and modalities, covering important diagnostic categories of pigmented skin lesions.  
The datasets were split into training (70%), validation (20%), and testing (10%) sets in a 7:2:1 ratio. All images were resized to 512 \(\times\) 512 pixels.  
The combined datasets include CT scans across age groups, as well as chest and eye images, reflecting a rigorous and diverse collection for model training and evaluation. Utilizing these diverse data spanning COVID CT, dermatology cases, and varied anatomies/modalities aimed to develop robust, generalizable segmentation models for clinical use.  
These datasets contain expert annotations and clinical severity ratings, using standard training schemes and hardware. This benchmarks performance on realistic data.