esa-sceva/satcom-chunk-collection huggingface dataset:
# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 3 Materials and Methods
### Evaluation Metrics  
In this paper, to evaluate the performance of our proposed method and other methods, we use several metrics of overall accuracy (OA), Intersection over Union (IoU), Mean Intersection over Union (MIoU), and F1-score. These are the most widely used measurements in both road extraction and other segmentation tasks [28]. They are defined by:  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
Figure 1: Examples of the original images and coresponding ground truth. The white represents the road pixels and the black represents the non-road pixels.  
Figure 2: Samples of data augmentation adopted in this paper. (**a**) is the original image; (**b**) is the result after shifting; (**c**) is the result after flipping.  
\[IoU=\frac{TP}{TP+FP+FN} \tag{2}\]  
\[F1\text{-}score=\frac{2\times precision\times recall}{precision+recall} \tag{3}\]  
in which,  
\[precision=\frac{TP}{TP+FP},recall=\frac{TP}{TP+FN} \tag{4}\]  
where \(TP\), \(FP\), \(TN\) and \(FN\) are the true positive, false positive, true negative and false negative, respectively.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 2 Remote Sensing for Agriculture  
### Plant Spectral Reflectance Properties  
Plants interact with sunlight--the full spectrum of sun-emitted electromagnetic radiation--differently depending on the wavelength observed. In this sense, incident solar radiation can follow three pathways: it can be transmitted, reflected, or absorbed. The electromagnetic radiation that is reflected by plants contains information about their biophysical composition and physiological status and can be measured using satellite sensors, such as those placed in the ESA Sentinel-2. Focusing on the visible domain, the chlorophyll pigments present in green leaves strongly absorbs in the visible (VIS) region of the spectrum (400-700 nm), especially in the blue and red wavelengths, where energy is captured for photosynthesis. Meanwhile in the near-infrared (NIR), from approximately 700 to 1300 nm, leaves exhibit high reflectance values and transmission, mostly related to leaf structural properties and biomass, and absorb less radiation in this spectral region [10; 11]. Furthermore, plant canopy structure and leaf area are also fundamental traits connected to canopy reflectance patterns [12; 13] and are key parameters for monitoring growth. Regarding the shortwave infrared region (SWIR), from approximately 1300 to 2500 nm, the absorption of radiation is largely dominated by water, followed by other biochemical components present in leaves. Concerning agriculture, in situations where crops interact with any given aspect of their environment (seasonal climatic variations, meteorological extreme events, pests, soil properties, etc.) or as crops grow and pass through different phenological stages, the interactions between plants and light reflectance translate into changes in plant signal patterns that can be interpreted using satellite data. The use of remote sensing instruments to monitor electromagnetic radiation reflectance changes in crops is well demonstrated in the scientific literature [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. For example, chlorophyll content plays a direct role in photosynthesis and it is responsive to a range of stresses, and hence is often used as a general plant health indicator [25] and interpreted along with the signals related to biomass and water content. These are examples of general potential uses of plant spectral reflectance properties for vegetation monitoring. However, often it is the temporal and spatial properties of satellites that determine if their use is appropriate for agriculture. This is a consequence of the characteristics of crops, which grow more quickly and frequently in smaller and more closely managed parcels of land compared to natural vegetation.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.8 REMChlor  
REMChlor is designed to simulate the transient effects of remediating contamination sources and plumes [403; 404]. This model utilizes a power function relationship between source mass and source depletion, providing users with the flexibility to simulate partial source remediation. Users can choose to simulate three processes: complete or partial plume remediation, natural attenuation, and source decay. Applications of REMChlor can be found in studies by Tyre [405] and Henderson et al. [406]. Although this tool remains available to users, it is no longer updated or supported. A more recent development is the incorporation of diffusion through the matrix into this analytical tool, resulting in the REMChlor-MD model, which was employed for the attenuation of perfluoro-octane sulfonate. The model effectively replicated field data for concentration, mass release, and total mass. Furthermore, when used to analyse long-term transient effects over 40 years of groundwater transport, the REMChlor-MD model demonstrated that the majority of the measured contaminant mass leaving the source areas accumulates in downgradient zones with low permeability [407].
# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 3 Experiments
### Assessment of Augmented Image Quality  
In this study, image augmentation techniques were applied to ships and aircraft. Diverse background settings were utilized to select representative images for the training process. Below in Figure 7, several instances of the augmented samples are presented as examples.  
From Table 1, it can be seen that for the FID evaluation metric, all categories have relatively high FID values. It is important to take into account that FID values could be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target** & **FID** & **MMD** & **PSNR** & **SSIM** \\ \hline Aircraft & 125.94 & 0.2479 & 19.6539 & 0.5994 \\ Ship & 123.50 & 0.2358 & 16.0296 & 0.5956 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computed FID, MMD, PSNR, and SSIM metrics for augmented images.  
Figure 7: Augmented images.  
compared against varying benchmarks when analyzing side-scan sonar images. The FID values for the \"Ship\" and \"Aircraft\" categories are similar, indicating a high generation quality for both categories. For the MMD evaluation metric, all categories have MMD values around 0.2, suggesting a significant statistical similarity between the produced images and their authentic equivalents. The \"Aircraft\" category has the highest PSNR value, indicating better generation quality for this category. For the SSIM metric, both categories have relatively low SSIM values, revealing a notable variance in structural characteristics between the produced and authentic images. Analyzing these assessment criteria reveals that the underwater target augmentation technique presented in this study exhibits varying effectiveness across different targets, successfully generating high-quality augmented images.
## 2 System Model and Formulation
### Simplified System Frequency Response Model Incorporating UFLS  
As mentioned before, the dynamic model used to adequately predict the behaviors of a power system are usually very complex, which makes it difficult to obtain a simple analytical model regarding the response of the system frequency and to design the UFLS schemes. In the presented literature, the full scale model is commonly reduced to a simplified equivalent single-machine model in the analysis of the system dynamic frequency behaviors and the UFLS scheme design. This assumption can be accepted for small receiving-end power systems after islanding.  
The simplified SFR model incorporating UFLS schemes for a small power system is shown in Figure 6. The simplified SFR model is a single-mass model. The inertias of the different machines are joined in one equivalent inertia. The overall characteristics of the loads due to their frequency dependency is modeled by a single damping factor \(D_{L}\), which is the system load frequency damping factor. Governors and prime movers are demonstrated by a short-term first-order model approximation. Generally, the equivalent inertia \(He\) and the equivalent gain \(Ke\) of the simplified SFR model can be calculated from the individual generator and governor-prime mover as:  
\[H_{e}=\sum_{i}\frac{H_{i}S_{i}}{S} \tag{1}\]  
\[K_{e}=\sum_{i}\frac{K_{i}S_{i}}{S} \tag{2}\]  
where \(H_{i}\) is the inertia constant of the \(i^{\text{th}}\) generator with a power base \(S_{i}\) and \(K_{i}\) is the gain of the \(i^{\text{th}}\) governor-prime mover with a power base \(S_{i}\). \(S\) is the system power base.  
Figure 5: Western Electricity Coordinating Council (WECC) composite load model.  
\(T\) is the equivalent time constant of the governor-prime mover and \(\Delta P_{\mathit{shed}}\) is the amount of load shedding. However, \(D_{L}\) and \(T\) are obtained with difficultly because of the variety of load dynamic behaviors and the different governors-prime movers installed in the different generators. The model and method used to extract the accurate parameters of the simplified SFR model will be proposed in the next section.
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### Ossia--Cota  
Ossia's solution focuses on the safety of users. It uses directed beams that transfer energy to charge devices equipped with dedicated circuits. The transmitter uses the beacon received from the receiver to find the best path to the target. The beacon is reflected by obstacles such as walls or furniture, but it is absorbed by human or animal bodies. Thanks to this, the best path is chosen in a way that avoids contact with living beings. In the press release, the company states that Cota works similarly to WiFi and can power devices at distances of up to 9 m [19]. During the 2017 CES, GuRu presented a solution called the Cota Forever Battery. This device is equipped with an RF energy harvester and is supposed to replace traditional battery packs.  
Ossia's products are the next ones that use beamforming technology and require a dedicated transmitter to provide enough power for the RF energy harvester.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 4 Proposed Scheme
### Network Architecture  
The network architecture of DOW-PR protocol is composed of sink nodes, relay nodes and anchored node as shown in Figure 2.  
Sink stations are situated at the sea roof and consists of radio and acoustic modem in order to communicate with each other through radio link and with the sensor networks through acoustic signals. These nodes are centralized stations, which can receive and transmit signals to the external networks. Anchored nodes are fixed at the seabed and their task is to collect data from the environment. Anchored nodes are fixed with the tether [34] and movable with water current or any other disturbance in the environment. Relay nodes are deployed at different depths, which forward the received data. Sink nodes can communicate within water through acoustic links and communicate with the external network through radio links. Basically, sink nodes are the centralized stations. Since sink nodes can communicate with each other, the data packet received by any sink nodes will be considered a successful delivery to the destination. Typical applications of this network include monitoring of underwater plates in tectonics or environmental monitoring [35].
###### Abstract  
Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management.  
public transportation; passenger flow prediction; point-of-interest data; extreme gradient boosting algorithm +
Footnote †: journal: Applied sciences  
Article
## 4 Methodology
### Quantized Question Embedding  
Knowledge concepts describe the main characteristics of a question, but regarding them as completely equivalent overlooks the subtle variation between different questions that share the same knowledge concept. In real-world data, the number of questions is often much larger than the number of knowledge concepts, thereby resulting in a data sparsity problem--in other words, directly tokenizing and embedding the questions can lead to overparameterization and overfitting issues.  
To address this, we employed quantized question embedding to tackle this problem. As shown in Figure 2, a codebook was created to store the quantized question embedding features, where the number of features in the codebook was much smaller than the number of questions. For each question \(q_{t}\), we first used an embedding layer to obtain its embedding feature \(z_{t}\), Then, the model searches for the most similar feature \(x_{t}\) in the codebook and sums it up with the knowledge concept embedding, which serves as the input to the encoder and decoder. Intuitively, the features stored in the codebook can be seen as several cluster centers of the questions' variations. Instead of directly using the embedding features of the questions, these quantized cluster center features help to avoid overparameterization and overfitting.  
Figure 1: Overall architecture of the proposed network. The model consists of stacked encoder and decoder layers. It incorporates the knowledge concept and quantized question embeddings for question data, which are then processed through multiband attention and feed-forward layers in the encoder to capture question interactions. The resulting output is combined with response data and fed into the decoder, which learns the relationship between the question and knowledge state. The decoder’s output undergoes linear and Softmax layers to predict the learner’s response.  
The transfer process from \(z_{t}\) to \(x_{t}\) is nondifferentiable. Consequentially, we introduced the stop gradient [36] operation as follows:  
\[sg(x)=\left\{\begin{array}{ll}x&(in\;forward\;propagation)\\ 0&(in\;backward\;propagation).\end{array}\right. \tag{6}\]  
During forward propagation, the value inside the stop gradient operation remains unchanged. During backward propagation, the gradient of the stop gradient operation is set to zero, indicating that this calculation has no gradient. With this operation, we can incorporate the output of the quantized question embedding module into the network as follows:  
\[Oupt_{\text{QQE}}(x_{t},z_{t})=z_{t}+sg(x_{t}-z_{t}), \tag{7}\]  
where it forward propagates \(x_{t}\) to the next layer of the framework and backpropagates the gradient of \(z_{t}\) to the previous layer.
# Plant Disease Detection Using Deep Convolutional Neural Network
## 5 Conclusions  
A novel DCNN model was proposed to detect plant leaf diseases from leaf images in this research. The proposed 14-DCNN model was designed and trained to detect 42 leaf diseases in 16 plants through leaf images. The data augmentation and hyperparameter optimization techniques were also used to enhance the performance of the 14-DCNN in this research. Three augmentation techniques were used to enhance the dataset size to 147,500 images. The augmentation techniques are NST, DCGAN, and BIM. The individual class size, including original and augmented images, of the dataset, was 2500 images. The  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **AlexNet** & **Inception-v3-Net** & **ResNet-50** & **VGG16 Net** & **14-DCNN** \\ \hline No. of Parameters & 44,752,739 & 24,937,283 & 26,722,211 & 39,443,043 & 17,928,571 \\ \hline Model Size (MB) & 133 & 92 & 98 & 128 & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Complexity comparison of proposed 14-DCNN and existing model.  
Figure 7: Performance comparison of proposed 14-DCNN and existing model.  
14-DCNN comprises five Conv and five max-pooling layers. The random search with the coarse-to-fine technique was used to optimize the value of the hyperparameter for training the proposed 14-DCNN model. Training of the most successful 14-DCNN model was completed with the training and validation dataset of 139,000 images and optimized hyperparameter values. The proposed 14-DCNN model achieved a classification accuracy of 99.9655%, a precision value of 99.7999%, a recall value of 99.7966%, and an F1 score of 99.7968% on the training dataset. The optimized hyperparameter values and the data augmentation techniques had a considerable influence on the results of the proposed DCNN model. Compared with standard transfer learning techniques, the proposed 14-DCNN model has higher classification performance. An extension of this research will be adding new classes of plant diseases and an increasing number of training images in the dataset and modifying the architecture of the DCNN model using more convolutional and other layers. In the future, we plan to estimate the possibility of plant disease and analyze the severity using the deep learning technique. Moreover, we will extend disease detection from plant leaves to other parts of the plants, such as flowers, fruits, and stems.  
Conceptualization, J.A.P. and V.D.K.; writing--original draft preparation, J.A.P. and V.D.K.; writing--review, designing, analysis and editing, J.A.P., V.D.K., O.G., M.H., M.A. and K.K.; supervision, V.D.K.; funding acquisition, M.H. All the authors contributed in writing, reviewing, and structuring of the work. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number 203, COFUND-ICT-AGRI-FOOD-MERIAVINO-1, within PNCDI III.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Comparative Evaluation
#### 4.2.1 Efficiency (Makespan)  
Traditional methods (PCP-only and PIP-only) struggled to optimize makespan under dynamic job conditions due to their static nature. The DRL-based methods showed better adaptability, with DRLCC achieving the lowest makespan on average.  
As shown in Table 4, while all models experienced an increased makespan with higher job loads, the DRLCC framework consistently maintained the lowest total completion time, demonstrating better adaptability and decision-making under system pressure. Notably, traditional methods (PCP and PIP) scaled poorly due to their static conflict resolution mechanisms. DRL-only and DRL + PIP showed moderate degradation, whereas the proposed hybrid maintained performance with minimal drop-off. Figure 9 shows total completion time (mean \(\pm\) SD), where bars represent the average; error bars show one standard deviation. DRLCC delivers the shortest completion time in every scenario, outperforming pure DRL, DRL + inheritance, fixed priority ceiling, and Priority Inheritance baselines.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **4 \(\times\) 80** & **4 \(\times\) 100** & **5 \(\times\) 100** & **5 \(\times\) 120** & **6 \(\times\) 100** & **6 \(\times\) 120** & **\% Improvement** \\ \hline DRLCC (Ours) & 14.42 \(\pm\) 0.14 & 14.98 \(\pm\) 0.14 & 13.03 \(\pm\) 0.04 & 13.29 \(\pm\) 0.12 & 12.68 \(\pm\) 0.04 & 12.94 \(\pm\) 0.13 & – \\ \hline DRL\_Inheritance & 15.06 \(\pm\) 0.13 & 15.79 \(\pm\) 0.15 & 14.33 \(\pm\) 0.13 & 14.70 \(\pm\) 0.19 & 13.81 \(\pm\) 0.05 & 14.10 \(\pm\) 0.06 & 6.21\% \\ \hline DRL & 15.20 \(\pm\) 0.28 & 15.22 \(\pm\) 0.11 & 13.16 \(\pm\) 0.07 & 13.46 \(\pm\) 0.09 & 12.66 \(\pm\) 0.06 & 12.97 \(\pm\) 0.09 & 1.51\% \\ \hline Priority Ceiling & 19.81 \(\pm\) 0.27 & 19.38 \(\pm\) 0.27 & 17.92 \(\pm\) 0.21 & 17.99 \(\pm\) 0.23 & 15.46 \(\pm\) 0.19 & 17.09 \(\pm\) 0.23 & 24.27\% \\ \hline Priority Inheritance & 17.83 \(\pm\) 0.31 & 19.52 \(\pm\) 0.11 & 13.82 \(\pm\) 0.11 & 14.34 \(\pm\) 0.15 & 12.78 \(\pm\) 0.10 & 13.12 \(\pm\) 0.09 & 9.60\% \\ \hline EDF & 59.55 \(\pm\) 0.29 & 73.95 \(\pm\) 0.46 & 68.69 \(\pm\) 0.64 & 82.61 \(\pm\) 0.15 & 65.19 \(\pm\) 0.42 & 78.27 \(\pm\) 0.52 & 80.75\% \\ \hline EDF\_SRP & 53.13 \(\pm\) 0.22 & 65.74 \(\pm\) 0.23 & 65.43 \(\pm\) 0.21 & 77.81 \(\pm\) 0.15 & 55.74 \(\pm\) 0.19 & 66.00 \(\pm\) 0.19 & 78.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of mean values of total completion time of all models.
## 4 Analysis and Results
### Results Based on Confusion Matrix
This could potentially lead to missed opportunities for telecom companies to take retention action. The area under the Receiver Operating Characteristic (ROC) curve is a metric widely used to assess the overall performance of binary classification models. The results of AUC-ROC for all algorithms tested in this study are shown in Figure 6. Random Forest had the highest score of 0.95, followed closely by Naive Bayes, with a score of 0.88. These two models were the most effective at predicting customer churn in the telecom industry in this study. Logistic Regression also performed well, with an AUC score of 0.84, indicating that it is a reliable algorithm for predicting customer churn. KNN and Decision Tree had lower AUC scores of 0.81 and 0.8, respectively, suggesting that they may be less accurate in distinguishing between the customers at risk of churn and customers who will not churn. See Figure 6.  
In summary, from the analysis of the Confusion Matrix and AUC-ROC curve, Random Forest was the most effective model for automatically detecting customer churn risk for telecom companies. The performance of the five models will be further evaluated in the next section.