esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 1 Introduction
### Optical Frequency Combs: A Background
#### 1.1.1 Technologies for Optical Frequency Comb Generation
During the last five years, there has been a remarkable progress in nonlinearly broadened EO combs [24; 25; 23]. This type of comb has been used as a WDM source and enabled complex modulation formats in single-core [26; 27] and multi-core fiber systems [28]. The latter results are remarkable because they illustrate that a comb source derived from a single seed  
Figure 2: **(a)** A common setup of an electro-optic (EO) comb. A radio-frequency (RF) oscillator with frequency \(f_{r}\) is used to phase- and intensity-modulate a continuous-wave laser, which creates an optical frequency comb with central frequency \(f_{c}\) and frequency spacing \(f_{r}\). The RF delay lines are adjusted so that the intensity modulator carves out the part of the light where the phase modulation is mostly linear, which enables a relatively flat comb. Several phase modulators can be used to increase the modulation depth without increasing the RF input power to the modulators, which increases the total number of comb lines. **(b)** Measured spectrum of an EO-comb. CW laser has sufficient OSNR performance to light multiple frequency channels simultaneously in multiple spatial modes.
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
###### Abstract  
This modeling study focused on the hydrological and water quality effects of controlled drainage (CD) when operated using a subsurface drainage system in an agricultural field in the Wielkopolska region. The DRAINMOD hydrologic model was well calibrated and validated in an experimental field. This model was used in the performance of CD and free drainage (FD) combinations (108 and 27, respectively) in a near-future climate change scenario. The objective was to understand the potential of CD on the groundwater table (GWT), drainage outflow, surface runoff, and nitrogen and phosphorus reduction under projected climate conditions in Poland during the 21 st century with shared socioeconomic pathway SSP370. The results indicated that the earliest start of CD practice is the most effective in increasing GWT. Compared to current climatic conditions, when applying CD on 1 March in the near future, with an initial GWT of 60 and 80 cm b.s.l. in wet years, drainage outflows will increase by 33% and 80% for the GFDL model, by 30% and 40% for the MPI model, and by 17% and 23% for the UKESM model. Comparing the surface runoff values obtained to current climate conditions, the MPI, GFDL, and UKESM models predict a significant increase in surface runoff in the near future, which is due to the predicted increase in precipitation. The annual NO\({}_{3}\)-N reduction was by 22, 19, and 15 kg per hecare for wet, normal, and dry years, respectively, in the near future. Among the climate scenarios, the UKESM model predicted higher NO\({}_{3}\)-N and PO\({}_{4}\) leaching values compared to the MPI and GFDL models.  
drainage water management; subsurface drainage; field hydrology; agriculture; water quality; water balance; modeling +
Footnote †: journal: Applied Sciences
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 2 Materials and Methods
### Firmness of Blackcurrants During Storage  
Fifty berries for each testing day of each treatment were randomly picked from storage conditions at day 0 (before storage), day 3, day 7, day 11, and day 15 during storage. The firmness of the blackcurrants from each treatment was measured using non-destructive testing with a FirmTech FT7 equipment (UP GmbH, Ilbenburen, Germany). Following the instrument manual, the flat measurement probe was used for blackcurrant fruits, and the test threshold parameter was 50-600 gm/mm. The firmness was expressed as gm/mm, indicating the force (grams) increases per unit of deformation (mm) on fruits.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 1 Introduction
### Contributions and Organization
We consider node distance, node speed, node direction, and remaining energy to form a cluster. * We design the DNN model to optimize the fitness function in both the routing and clustering process. By using DNN, the proposed DLSR protocol can optimize the weights of each parameter, such as remaining energy, distance, and hop count, which leads to choosing the best route against blackhole attacks; while in the DLC protocol, DNN is used to optimize the weights of each parameter, such as cosine similarity, cosine distance, and remaining energy, which leads to electing CH. * The simulation results show that the proposed DLSR with DLC protocol can establish a route that is much more resistant to blackhole attacks than the benchmark protocol. Moreover, as the clustering organizes nodes into groups, the proposed DLC protocol enables stronger connectivity and reduces control overhead. Additionally, in order to demonstrate the efficiency of the proposed protocol, we also compare it with different mobility models, namely, the reference point group mobility (RPGM) and random waypoint (RWP). The rest of the paper is organized as follows: Section 2 provides a brief background of blackhole attacks in VANETs. Section 3 explains the proposed system model that consists of the basic concept of the proposed routing and clustering protocol, the proposed DLC protocol, and the proposed DLSR protocol. Section 4 explains the proposed DL framework. Section 5 discusses the performance evaluation that consists of simulation environments and network parameters, performance metrics, and numerical results, and Section 6 concludes the paper.
## 6 Exploration of Findings
### Case No. 2: AHC-Based Feature Selection
#### 6.3.3 Support Vector Machines (SVC)  
Similarly, as clearly illustrated in Figure 14, the rapid achievement of optimal convergence after only 15 features strongly suggests that AHC-based feature selection has effectively identified the optimal subset. Conversely, Figure 15 indicates that RF-based feature selection requires a slightly larger number of features to compensate for the lack of informative features and to achieve comparable levels of accuracy and stability. Although the initial fitting time is high (indicated by the blue dashed line) due to the model's dif  
Figure 12: Performance analysis of the ANN with the AHC-based feature selection method.  
Figure 13: Performance analysis of the ANN with the RF-based feature selection method.  
ficulty in converging with a limited number of features, both algorithms show a steady decrease in fitting time as additional features are added. The average fitting time for the AHC-based algorithm is approximately 15.94 s, while the RF-based algorithm requires around 19.03 s. The AHC approach reaches its minimum fit time of approximately 2 s, consistently outperforming the RF-based instance. This behavior suggests that AHC-based feature selection may lead to a more stable and efficient training process.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 5 Limitations  
A major limitation of this study is the significant heterogeneity among the included studies, arising from several sources. First, the studies utilized different machine learning (ML) models, each trained on varying sets of clinical, demographic, and laboratory variables. This variability in feature selection and model design complicates direct comparisons and limits the generalizability of findings. Additionally, the datasets span a wide timeframe, from 1995 to recent years, during which significant advancements in laboratory techniques, data collection practices, and clinical management protocols have occurred. These temporal variations introduce inconsistencies that may affect the predictive performance of the models.  
Another limitation is the scarcity of studies evaluating the same ML model at specific follow-up intervals. This scarcity hindered the ability to perform comprehensive subgroup analyses or meta-analyses for all the included models, which could have provided a more robust assessment of their comparative effectiveness. Furthermore, variations in study design, sample sizes, and regional differences in patient populations further contribute to the heterogeneity, making it challenging to draw definitive conclusions.  
To address these challenges, we recommend future research prioritize standardizing datasets through collaborative multicenter studies. This involves establishing common data elements and uniform variable definitions, specifying consistent data collection timepoints relative to dialysis initiation, harmonizing laboratory techniques (e.g., using central laboratories or standardized assay protocols and units), employing prospective data collection with standardized electronic tools, and ensuring uniform outcome ascertainment. Such efforts, particularly leveraging prospective designs with modern, standardized laboratory techniques, could mitigate the temporal and methodological biases observed in this review and provide a stronger foundation for evaluating and refining the predictive capabilities of ML models in hemodialysis populations.  
Furthermore, our review primarily identified studies using \"classic\" machine learning algorithms. The absence of studies employing deep learning methods meeting our inclusion criteria suggests this may be an emerging area within hemodialysis mortality prediction that warrants future investigation as more relevant studies become available.
## 1 Introduction
Based on these analyses, we set our goals, which are to support (\(m\), \(k\))-firm broadcast and improve energy efficiency. To achieve these two goals, we propose a novel real-time network broadcast routing based on the (\(m\), \(k\))-firm real-time protocol, called the (\(m\), \(k\))-firm-based Real-time Broadcast Protocol (FRBP). The proposed scheme constructs a real-time broadcast tree by electing only some sensor nodes satisfying the real-time condition as intermediate transmission nodes to satisfy the first goal, the real-time constraint. Other sensor nodes, i.e., leaf nodes, only receive packets, but do not transmit them to other nodes for energy efficiency. Each sensor node can receive the data within the desired time deadline since each path (the source-intermediate node(s)-destination) over the tree that satisfies the real-time condition defined in SPEED [5]. The real-time broadcast tree also improves energy efficiency by avoiding network-wide flooding in which every sensor node has to transmit packets. To reduce energy consumption, the real-time broadcast tree is constructed in a way that includes as few intermediate nodes as possible. However, states of links and nodes can be changed anytime and anywhere due to the dynamic characteristics of WSNs. To deal with this unstable problem in the wireless link, the proposed scheme needs to include a recovery mechanism to deal with the state changes. For this, each intermediate node figures out local states such as node failure, link quality and real-time condition. Based on Distance-Based Priority (DBP) and local monitoring, the proposed scheme can avoid or recover real-time failures promptly by partial tree reconstruction. Moreover, the source node reconstructs the whole tree only in the worst case that local recovery schemes cannot guarantee the (\(m\), \(k\))-firm requirement. The performance of FRBP is evaluated by simulation scenarios in various conditions. Simulation results show that FRBP can satisfy the (\(m\), \(k\))-firm real-time requirement with reasonable energy consumption in various environments. The rest of this paper is organized as follows. In Section 2, we summarize and analyze the related work. The details of FRBP are explained and described in the next section. The performance evaluation through simulation results is described in Section 4. Finally, the conclusion and further work are given in Section 5.
## 5 Conclusions  
In this paper, we used queueing Petri net formalism as an analysis technique to prepare the SG of incoming requests in the early phases of system development. Performance engineering is a field of study that solves performance problems using simulators. This field suffers from a lack of guidelines and software solutions for client behavior modeling. Modeling a system and performance analysis in the early stages of system construction ensures the best performance. This approach is a complex modeling tool for a proper selection input stream to the modeled system.  
Various models of SG in miscellaneous classes of Petri nets may be considered. Our approach gradually evolves from CPN into QPN. Transition modes and firing weights, which are distinctive elements of QPN formalism, are beneficial. They can be used to model the probability of choosing appropriate binding of the transition; thus, manifold distributions of the tokens in the output stream can be generated in a comprehensive, logical manner. The model of SG presented in Section 3 is simple but flexible, able to produce the stream according to the designer's needs, as shown in Section 4.  
In this paper, the system client was taken into account. Its original design was performed using QPN models. Future research might confirm that the specific client behavior could be achieved by developing a model with a proposed SG.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes** & **Firing Weight \(w\)** & **Token Color** & **Mean Token Population for \(Stream\)** \\ \hline \(mode_{1}\) & 1.0 & \(u1r1\) & 9978.882 \\ \(mode_{2}\) & 2.0 & \(u1r2\) & 19,827.981 \\ \(mode_{3}\) & 3.0 & \(u2r1\) & 29,978.301 \\ \(mode_{4}\) & 4.0 & \(u2r2\) & 39,679.93 \\ \hline \(mode_{1}\) & 1.0 & \(u1r1\) & 14,195.611 \\ \(mode_{2}\) & 2.0 & \(u1r2\) & 28,625.108 \\ \(mode_{3}\) & 1.0 & \(u2r1\) & 14,308.058 \\ \(mode_{4}\) & 3.0 & \(u2r2\) & 42,688.806 \\ \hline \(mode_{1}\) & 1.0 & \(u1r1\) & 24,931.845 \\ \(mode_{2}\) & 1.0 & \(u1r2\) & 25,170.6 \\ \(mode_{3}\) & 0.0 & \(u2r1\) & 0.0 \\ \(mode_{4}\) & 2.0 & \(u2r2\) & 50,286.744 \\ \hline \(mode_{1}\) & 1.0 & \(u1r1\) & 20,154.555 \\ \(mode_{2}\) & 1.0 & \(u1r2\) & 19,958.521 \\ \(mode_{3}\) & 0.0 & \(u2r1\) & 0.0 \\ \(mode_{4}\) & 3.0 & \(u2r2\) & 60,068.627 \\ \hline \(mode_{1}\) & 0.0 & \(u1r1\) & 0.0 \\ \(mode_{2}\) & 1.0 & \(u1r2\) & 24,933.095 \\ \(mode_{3}\) & 0.0 & \(u2r1\) & 0.0 \\ \(mode_{4}\) & 3.0 & \(u2r2\) & 75,169.879 \\ \hline \(mode_{1}\) & 0.0 & \(u1r1\) & 0.0 \\ \(mode_{2}\) & 0.0 & \(u1r2\) & 0.0 \\ \(mode_{3}\) & 1.0 & \(u2r1\) & 24,894.468 \\ \(mode_{4}\) & 3.0 & \(u2r2\) & 75,242.374 \\ \hline \(mode_{1}\) & 0.0 & \(u1r1\) & 0.0 \\ \(mode_{2}\) & 0.0 & \(u1r2\) & 0.0 \\ \(mode_{3}\) & 0.0 & \(u2r1\) & 0.0 \\ \(mode_{4}\) & 1.0 & \(u2r2\) & 99,950.075 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Token colors (varied models).  
**Author Contributions:** Writing--original CPN model, D.R.; writing--original QPN model, T.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### Discussion of xLSTM Results
#### 4.2.4 Computational Efficiency and Scalability Analysis
Memory requirements increased by approximately 23% compared with standard LSTM implementations, primarily due to the storage of matrix memory states. However, this increase remains manageable for modern computing infrastructure, with the trained model requiring 228 MB of memory for the configuration used in this study. For deployment on edge devices at remote wind farms with limited computational resources, the model architecture can be adjusted by reducing the number of mLSTM blocks or head dimensions with minimal impact on performance. Regarding scalability to larger datasets and wind farms, the xLSTM model demonstrates favorable characteristics. When scaling from single-turbine to wind-farm-level predictions (an approximately 10\(\times\) increase in data volume), computational requirements increased by a scalability factor of 1.25, significantly lower than the quadratic scaling observed in Transformer-based architectures. The scalability factor represents the relative computational resource requirements when scaling to larger datasets. This linear scaling property makes xLSTM particularly suitable for distributed wind forecasting applications where models may need to process data from hundreds of turbines simultaneously. For large-scale operational deployment, the sLSTM components can be selectively pruned or quantized to reduce computational requirements in contexts where maximum accuracy is not critical. The experiments with 8-bit quantization showed only a 1.3% reduction in prediction accuracy while reducing inference time by 37%, suggesting promising pathways for further optimization in resource-constrained environments.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
### Data Description
#### 2.1.3 Fusion Dataset Description  
A fusion dataset was constructed by combining the MIAS mammographic dataset and the Breast Ultrasound Images Dataset. This fusion approach integrates two distinct imaging modalities, mammography and ultrasound, each offering complementary diagnostic information. While mammographic images provide high-resolution anatomical details and are widely used in breast cancer screening, ultrasound images are particularly effective in differentiating between cystic and solid lesions, especially in dense breast tissues.  
Combining these datasets improves the model's capacity to generalize across various clinical circumstances by allowing it to learn from a wider variety of visual aspects and lesion characteristics. Prior to fusion, both datasets were independently preprocessed to ensure consistency in image resolution, format, and labeling schemes. Only the benign and malignant classes were retained from both datasets to maintain a uniform binary classification framework. The fused dataset contains samples from both mammograms and ultrasound scans, ensuring a balanced representation of each imaging type. In addition to expanding the amount and diversity of training data, this integration makes use of multimodal learning's advantages, which have been demonstrated to greatly improve performance in medical picture analysis. The resultant dataset provides a strong basis for creating a computer-aided diagnostic system that is more dependable and flexible.  
Figure 5 displays sample images from the mammography dataset, divided into benign and malignant categories.  
Figure 4: Breast Ultrasound Images Dataset Description.  
Figure 5: Fusion Dataset Description.  
* Top Row (Benign): Three benign images show uniform textures with minimal irregularities, indicating non-cancerous conditions;
* Bottom Row (Malignant): Three malignant images exhibit distinct anomalies, suggesting potential malignancies.  
In order to have a coherent fusion dataset, MIAS (mammography) and BUSI (ultrasound) datasets consist of images matched according to identical class labels. Since there was such a marked imbalance in the classes, especially the overrepresentation of benign cases, balancing techniques were used. These were random undersampling of majority classes and oversampling of minority classes by augmenting the data by flipping, rotation, and contrast enhancement. This method enhanced the generalizability of the model and reduced bias in training. Combining the process of mammography and ultrasound images presented an issue of differences in the modalities in terms of texture, resolution, and contrast. In combination, all image sizes were uniformed to a standardized input (224 224) and standardized on an equalized intensity range. To ensure the clinical relevancy of the fused dataset, modality-specific preprocessing methods were employed to particularly retain diagnostic qualities specific to imaging modality, e.g., sharp boundary delineation in the case of mammograms and echotextural transforms of ultrasound.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 2 Related Works  
A great deal of work has been focused on the study of caching policies. In this section, we present some of the current work in caching research, mainly covering both non-cooperative and cooperative caching.
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 2 Design Method of Proposed Dual-Band Phase Shifter
### Analysis of Phase Shifting Range
#### 2.2.1 Phase Shifting Range at Low-Band Operation

Table 1: Calculated PSR and in-band PD with \(Z_{\rm Ho}\), \(Z_{\rm Hs}\), and \(C_{\rm v}\). Figure 5 depicts the PSR with and without compensation of for the \(\lambda/4\) shunt open stub. The proposed PS achieved a PSR of \(114^{\circ}\) at the \(f_{\rm L}\) band when the effect of the \(\lambda/4\) shunt open stub was compensated for. However, PSR was reduced to \(40^{\circ}\) in cases where compensation was absent.
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion
### LST Results  
The results of the LST analyses can be seen in Figures 5 and 6. The wetland area has temperatures below zero in the winter period and has its maximum temperature at the end of July for all classes. Compared with the average air temperatures of the central Anatolian region, the correlation is more than 0.9, which indicates that the LST of the wetland is substantially affected by the air temperature [21]. The Water class has the highest temperature in the leaf-off season, and lowest in the leaf-on season. The full LST results are presented in Figure A2.  
Figure 4: Seasonal comparison of the wetland classes.  
Figure 5: Land Surface Temperature (LST) results-classes comparison.
## 3 Wireless Sensor Networks
### WSN Applications
#### 3.3.3 Home Applications  
Home automation: as technology advances, smart sensor nodes and actuators can be incorporated into appliances, such as vacuum cleaners, micro-wave ovens, and refrigerators. These sensor nodes inside the devices can communicate with each other and with the external network via the Internet or satellite. They allow end users to control home devices locally and remotely more easily and can be used as alarms for disasters at homes.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis
### Generate Simulated Star Images  
Stars are selected from the SAO J2000 star catalog, and simulated star images generated using MATLAB 2024 are used to test the algorithm proposed in this paper. The star sensor's FOV for generating the simulated star images is set to 12\({}^{\circ}\)\(\times\) 12\({}^{\circ}\), with an image resolution of 1024 \(\times\) 1024, pixel size is set to 12 \(\upmu\)m, a focal length of 58.4563 mm, and a sensitive magnitude limit of 6.0 Mv. During the generation, right ascension and declination are uniformly traversed at 2\({}^{\circ}\) intervals, ultimately resulting in 16,200 simulated star images [15] that evenly cover the entire celestial sphere.