esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison of Different Intelligent Algorithms in EEG Analysis
#### 4.5.1 Comparison between ICA and the Inverse WT in EEG Analysis  
ICA is a technique used to eliminate artifacts from the EEG, ensuring a cleaner and more reliable signal. It separates the mixed EEG in independent components, allowing the identification and elimination of unwanted noise and interference. Following the application of ICA, the EEG undergoes further preprocessing steps to enhance the quality of the signals [55; 56].  
The inverse WT reconstructed the signal employing an elliptic band-pass filter to attenuate any residual noise and interference, optimizing the removal of internal or external artifacts and noise from the EEG. The utilization of the inverse WT as a robust method played a pivotal role in enhancing the accuracy of our findings, ensuring the elimination of all non-relevant signals and preserving only the pertinent frequency bands. This filtering process selectively preserves the frequency bands of interest while efficiently removing any unwanted frequencies that might obscure the brain signals to analyze [28].  
The integration of the inverse WT in EEG analysis allowed us to obtain a refined and more representative signal of the targeted brain activity, enhancing the overall quality and reliability of the data analyzed in the study. By effectively filtering out unwanted components, we focused on identifying and extracting essential biomarkers related to specific brain functions or pathologies. Robust preprocessing steps were essential to achieve accurate and reliable results in our investigation, enhancing not only the scientific rigor of our study, but also paving the way for broader applications, ranging from clinical neurology to educational neuroscience.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 5 Experimentation and Results
\(i_{t}\) represents the travel time of the _Interest_ packet from the receiver node to the node that fulfills the request, while \(i_{r}\) denotes the travel time of the responding _Data_ packet. * Average Path Stretch (APS): The average path stretch measures the average increase in path length for each user request, \[APS=\sum_{i=1}^{I}\frac{path_{i,n_{u},n_{t}}}{Path_{i,n_{u},n_{s}}},\] (14) where \(I\) represents the total number of user requests. \(n_{u}\) denotes the receiver node that sends the request, \(n_{r}\) refers to the node that responds to the request, and \(n_{s}\) represents the source node that publishes the requested content. \(path_{i,n_{u},n_{t}}\) denotes the number of hops travelled by the \(i^{th}\) request, while \(Path_{i,n_{u},n_{t}}\) represents the shortest path from the receiver to the source. * Average Link Load (ALL): The average link load represents the average ratio of the total link load to the total number of links in the network, \[ALL=\frac{\sum_{I=1}^{I}\mathcal{L}_{I}}{L},\] (15) where \(L\) denotes the total number of links in the network, and \(\mathcal{L}_{I}\) represents the link load of the specific link \(I\). The caching performance of our proposed GNN-DDQN scheme was evaluated and compared with the state-of-the-art caching scheme MLP-DDQN. MLP-DDQN, which has been extensively studied in various research works [5; 6], was used as a baseline for comparison. We adapted the MLP-DQN framework to incorporate the DDQN technique to ensure a fair comparison. The MLP-DDQN agent consisted of four linear layers with dimensions of 1024, 512, 256, and \(C\). There are some differences between the state representations of the MLP-DDQN agent and our proposed GNN-DDQN approach. In the MLP-DDQN agent, the first component of the state representation includes the number of requests for each content item \(c_{i}\) passed through each node, covering all types of nodes (receivers, routers, and sources). This provides more general traffic-related information to assist the MLP agent in making predictions, as it lacks the ability to gather neighbouring information as in the GNN approach. Additionally, we compared our caching strategy with classical caching algorithms, including LCD, PROB_CACHE, LCE, and CL4M. These algorithms served as additional baselines to assess the performance of our proposed approach.
## 3 Evaluation of the Measured Values
### Comparisons of Samples 6-10
Due to a change in fiber deposition orientation, smaller weight losses were recorded for Sample 9 (with the fiber deposition strategy under the 90\({}^{\circ}\) angle with respect to the direction of wear in the even layer and perpendicular to the wear in the odd layer), with a material loss of 0.1074 g, Sample 10 (with fibers deposition strategy perpendicular to direction of wear in the even and the odd layers), with a material loss of 0.0988 g, and Sample 6 (with the fibers oriented under a 45\({}^{\circ}\) angle with respect to the direction of wear in even layers and the opposite strategy under a 45\({}^{\circ}\) angle in odd layers) with a material loss of 0.0933 g, and therefore, these samples showed the greatest resistance of the material to abrasive wear in terms of the fiber deposition strategy.
## 3 Proposed Stereoscopic Image SR Method with View Incorporation and CNN
### Estimation of View Difference Image with Modified CNN
#### 3.1.1 Image Patch Extraction and Representation  
The image patch extraction and representation operation extracts image patches from the view difference image and represents them as high-dimensional vectors by a number of bases. In our formulation, this is the same process as convolving the view difference image by a number of filters. These vectors obtained by convolution contain a number of feature maps that can be further mapped to finer feature vectors.  
Generally, the view difference image is defined by:  
\[I_{d}(x,y)=Z(x,y)-Y_{B}(x+d_{x}(x,y),y+d_{y}(x,y)) \tag{2}\]  
where \(\mathbf{Z}\) and \(\mathbf{Y_{B}}\) denote the observed FR left view image and the interpolated right view image, which is the FR; that is, respectively, \(\mathbf{Z}=\{Z(x,y)\}\) and \(\mathbf{Y_{B}}=\{Y_{B}(x,y)\}\). In addition, \(d_{x}(x,y)\) and \(d_{y}(x,y)\) are the horizontal and vertical components of the disparity at position \((x,y)\), and \(I_{d}=\{I_{d}(x,y)\}\) is the defined view difference image on the basis of the disparity information. Actually, for SR of the MR stereoscopic image, we cannot obtain the original FR stereoscopic image pair at the decoder side. Thus, the disparity map cannot be accurately identified. Similar to [25], the view difference image is directly calculated from the stereoscopic image pairs as:  
\[I_{d}(x,y)=Z(x,y)-Y_{B}(x,y) \tag{3}\]  
We take the initial view difference image, \(\mathbf{I_{d}}\), as the input of the modified CNN, and the convolutional operation in the first layer in the CNN is represented as:  
\[I_{d1}=W_{1}*I_{d}+B_{1} \tag{4}\]  
where \(\mathbf{{}^{*}}\) denotes the convolutional operation. Then, we apply the rectified linear unit (RELU) [27] to alleviate the overfitting problem after the convolutional operation:  
\[I_{d1}=\max(0,W_{1}*I_{d}+B_{1}) \tag{5}\]  
where \(W_{1}\) and \(B_{1}\) represent \(n_{1}\) filters of the support \(f_{1}\times f_{1}\times c_{1}\) and biases, which comprise an \(n_{1}\)-dimensional vector, respectively. Here, \(f_{1}\) represents the filter size, and \(c_{1}\) denotes the number of channels in the input image. Additionally, output \(\mathbf{I_{d1}}\) is composed of \(n_{1}\) feature maps of the input image.
# Comprehensive Effects of Atmosphere and Soil Drying on
## 3 Results
### Effect of Decoupling VPD and SWC on Vegetation Stomatal Behavior
The CRO and DBF sites showed a clear boundary around the 30 th percentile of SWC, where Gs, GPP, and NPP exhibited significant negative changes below this value and positive changes above it. The DBF sites were more significantly affected by  
Figure 3: The influence of Gs, GPP, and NPP on five PFTs (**(a–c)** CRO, **(d–f)** DBF, **(g–i)** ENF, **(j–l)** GRA, **(m–o)** SAV) when VPD and SWC are decoupled. The six colors represent different percentile ranges of soil water content (0–15%, 15–30%, 30–50%, 50–70%, 70–90%, and 90–100%). The interval of each point on VPD is 0.2 kpa. VPD, with the required SWC for positive changes increasing with higher VPD. In contrast, the CRO sites were more affected by VPD at extremely high SWC levels (90-100%), with all three indicators gradually increasing with increasing VPD (variability ranging from \(-\)10% to +20%), further confirming the conclusions drawn in Figure 3. The GRA and SAV showed similar variability distributions, but unlike the previous two vegetation types, they exhibited a clear boundary at the 60-70% percentile of SWC, with a large span of variability ranging from \(-\)40% to +40%. This phenomenon indicates that stomatal behavior and productivity in these two vegetation types are significantly affected by SWC and require higher SWC to maintain high stomatal conductance and productivity. However, these two vegetation types have a higher adaptability to atmospheric drought, as they exhibit only small negative changes in Gs, GPP, and NPP at higher SWC levels (70-100%) and under extremely high VPD conditions (90-100%). The change trend for ENF was the most unique, with the distribution of Gs and GPP for this vegetation type clearly affected by both VPD and SWC. Positive changes were observed when the SWC percentile was greater than 10%, but this change was restricted by VPD and only partially alleviated at higher SWC percentiles (80-100%). However, at moderate SWC percentiles (40-70%), the restriction by VPD was severe, and negative changes occurred only when the VPD percentile was greater than 20%, with Gs and GPP levels even lower than those at lower SWC levels (10-30%). In addition, the abnormal increase in NPP observed at the 30-50% percentile of SWC in Figure 3i is also reflected in Figure 4m.
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 2 Conventional Triaxial Test of Mudstone
### Conventional Triaxial Compression Test  
The conventional triaxial compression test results show that the deformation behavior of the mudstone rock samples under different confining pressures is similar, and the homogeneity is good (Figure 3). The samples also show brittle failure characteristics under low confining pressure and large plastic compression and lateral expansion under high confining pressure.  
When the confining pressure is low, the plastic deformation is small before the peak stress, and the plastic hardening is not obvious. After the peak stress, the stress begins to decrease, and the rock sample shows rapid brittle failure. With the increase in confining pressure, the peak stress also increases. Before the peak stress, the damage evolution is slow, plastic deformation is dominant, and the plastic hardening phenomenon is obvious (Table 1). After the peak stress, the damage evolution gradually dominates, and the stress shows a downward trend. Frictional sliding between the cracks intensifies, and plastic deformation continues to grow. Meanwhile, the internal cracks of the rock sample begin to penetrate, and the volume gradually expands at the macro scale, and the stress reaches the residual strength, which is carried by the friction force of the internal crack surface.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 2 Related Work  
### Natural Language Processing in Low-Resource Languages  
Natural Language Processing (NLP) for low-resource languages like Swahili presents significant challenges, primarily due to the lack of large, high-quality datasets. In contrast to languages such as English and Chinese, which benefit from vast digital resources and well-annotated corpora, Swahili remains underrepresented in NLP research [9]. This lack of resources makes it difficult to train models capable of understanding and generating accurate and contextually relevant responses in Swahili. Consequently, researchers have explored alternative approaches to maximize the utility of the limited Swahili data available. Early approaches often relied on traditional machine learning models like Naive Bayes, decision trees, and support vector machines. These methods are computationally lightweight and work reasonably well with small datasets but struggle with the complexity and nuances of natural language [10]. For example, these models have been applied to tasks such as spam detection in Swahili SMS, where researchers adapted the algorithms to handle mixed-language inputs, as Swahili speakers often switch between Swahili and English in communication [11]. While effective for basic tasks, these models fail to capture deeper linguistic patterns such as contextual relationships and idiomatic expressions. Deep learning methods, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have also been applied to Swahili NLP. These models are better suited for understanding the relationships between words and their sequences. For instance, CNNs have been used to extract key features from Swahili text, while LSTMs have focused on capturing sequential dependencies [12]. However, their performance remains constrained by the small size of available Swahili datasets. Without sufficient data, even advanced models struggle to generalize effectively, limiting their applicability to real-world scenarios [13].
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 2 Theories and Methods
### Modified Genetic Algorithm  
A modified genetic algorithm [39] that only contains the mutation and evolution operations for a chromosome encoding the value (0 or 1) of every grid node is used to implement the design. The hologram information is encoded into one chromosome containing \(N\times N\) genes, each of which is labeled by the value of a grid node. This algorithm has only mutation operations and no crossover operations. The mutation operation changes only one gene in the parent chromosome at each iteration. Offspring chromosomes are obtained after altering the gene. When calculating the offspring sound field, the sound field of the entire offspring chromosome is not calculated once, but only the influence of the changed gene on the sound field is calculated by using the Rayleigh-Sommerfeld theory, and then the influence is superimposed on the parent sound field. This superposition calculation method that reduces the amount of calculation significantly expedites an evolution operation by four orders of magnitude [39], which makes it more feasible in carrying out the design of a large-scale hologram.
# Structural Methodologies for Distributed Fault Detection and Isolation
## 3 MSO-Based Distributed Fault Detection and Isolation  
### Problem Formulation  
The objective in this work is to design a set of distributed diagnosers that together have the same diagnosability as a centralized diagnoser. This means that a distributed approach should detect any fault that is globally detectable and isolate any pair of faults that are globally isolable. In the ideal case, there are enough MSO sets in each subsystem to detect and isolate all of its faults, \(F_{i}\). In that case, no exchange of information is necessary between the different diagnosers. If the independence among diagnosers does not hold, the different subsystems need to share some sensor data with each other to be able to detect and isolate the faults.  
To address this problem in an efficient way, an integrated approach is derived to select a set of MSO sets for each subsystem that guarantee full diagnosability and minimum exchange of measurements among subsystems. The general idea is to augment each subsystem with additional measurements that are typically acquired from the (nearest) neighbors of the subsystem, such that all of the faults associated with the extended subsystem model are detectable and isolable. In the worst case, all of the measurements from another subsystem may have to be included to make the current subsystem diagnosable. When such a situation occurs, we say the two subsystems are merged and represented by a common diagnoser; therefore, the total number of independent distributed diagnosers may be less than \(k\).  
Let \(\mathcal{MSO}=\{MSO_{1},MSO_{2},\ldots,MSO_{r}\}\) denote the set of candidate MSO sets for the system \(S\). For each subsystem \(S_{i}\), the objective is to develop an algorithm to select a subset of \(\mathcal{MSO}\)s that guarantees maximal structural detectability and isolability for faults \(F_{i}\) associated with the subsystem, while using a minimum number of measurements from the other subsystems in the system to assure the equivalence of local and global diagnosability, i.e.,  
\[\begin{split}\forall S_{i};&\quad 1\leq i\leq k\\ Select&\quad MSO_{S_{i}}\subset\mathcal{MSO}\\ s.t.&\quad\min_{M_{o}\subseteq M}|M_{o}|\\ &\quad D_{i}(M_{i}\cup M_{o})=D_{i}(M),\\ &\quad I_{i}(M_{i}\cup M_{o})=I_{i}(M),\end{split} \tag{3}\]  
where \(M_{o}\) represents the set of measurement we need to communicate to the subsystem \(S_{i}\) and \(M_{o}\cup M_{i}\) represents the set of measurement subsystem \(S_{i}\) that will be used to diagnose all faults associated with it. \(M\) represents the set of all measurements in the system. For a given set of measurements, \(X\), \(D_{i}(X)\subseteq F_{i}\) represents the set of detectable faults, and \(I_{i}(X)\subseteq F_{i}\) represents the set of isolable faults in \(F_{i}\) and \(F_{i}\subseteq F\).
## 1 Introduction
### Background
#### 1.1.1 Scientific Papers Information Management  
Scientific papers information management is a challenging task and it can be divided into many different sub-tasks such as classification, clustering, labelling and summarization.  
There are many different research fields; among these, the biomedical field is one of those characterised by the highest number of articles published by topic and which has the highest number of citations and cross-references and for these reasons the document management of this kind of documents is a very challenging task. In this paper we present an experiment based on a corpus composed by a set of scientific papers related to DNA methods for food integrity analysis. Food safety and food quality have increasingly come to the forefront of consumer concerns, industry strategies and government policy initiatives.  
In this context DNA technologies represent a useful tool in food inspection and regulation.  
In fact, molecular markers are of extreme importance to traceability since they allow the outcome of a given item to be monitored at each stage in the food chain.  
The aims of the considered experiment have been to understand the current state-of-the-art of know-how and methodologies.  
Detection of fraud, especially in meat products, is important not only for economic problem but also for health, religious and ethical reasons.  
In recent decades, the main DNA methods are based on the detection of species-specific DNA sequences. Most of these methods rely on the polymerase chain reaction (PCR) technique for its specificity, sensitivity, simplicity and rapidity, allowing the identification of species of origin even in complex and processed foods.  
In our experiment (see Section 2.4) we aim to classify papers in relation to DNA methods and food analysed.  
In literature, many works have been provided about this topic, for instance in [19] authors provide a method for labelling hierarchical clusters of scientific articles; differently from our approach authors define a label for a cluster of documents and not for a single paper. Furthermore, the label is defined as a set of terms extracted from the cluster and not as a complete sentence.  
In [20] authors provide a large manually annotated corpus for scientific papers and a summarization method. The objective of this article is the summarization of the scientific papers; to achieve this aim the authors exploit both abstract text and references.  
In [21] authors provide a classification approach for biomedical literature; in [22] authors provide an approach based on feature selection for medical full text classification.  
In Table 1 the comparison between these approaches and SPUCL is shown.
###### Abstract  
The evolution of artificial intelligence (AI) has unveiled considerable prospects for delivering efficacious solutions in the medical domain. Nevertheless, existing legal frameworks and concerns regarding data privacy associated with medical information impose substantial constraints on implementing AI solutions in this domain. Federated learning is a paradigm that enables the training of machine learning models in a decentralized manner without transferring data to a central repository, allowing model development while preserving data privacy across medical and other industries. This study provided a comprehensive framework for applying federated learning to AI solutions in the medical domain. It advocates a sustainable learning ecosystem by overseeing federated learning servers and clients and evaluating performance by managing the federated learning lifecycle. To enhance its practical relevance, this framework includes a detailed process for continuous lifecycle management, involving model deployment, aggregation, testing, evaluation, versioning, and real-time monitoring through the FedOps platform, supporting a sustainable solution. In this study, the feasibility of the proposed methodology was verified using a post-transcatheter aortic valve replacement (TAVR) complication-prediction framework. The performance of the solution after transitioning to a federated learning approach was compared with that of an existing centralized solution. The findings indicated no statistically significant difference in performance between the two methodologies. This implies that federated learning can augment data usability and facilitate the integration of AI technologies into the medical domain, where the preservation of data privacy is critically important.  
federated learning; medical AI solutions; transcatheter aortic valve replacement (TAVR); complication prediction +
Footnote †: journal: Applied Sciences  
Article  
Medical Artificial Intelligence Applications: A Case Study on Post-Transcatheter Aortic Valve Replacement Complication Prediction Solution.
# Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
## 4 Experimental Setup and Result Analysis
### The Experimental Analysis
#### 4.2.8 Impact of Different Feature  
The influence of the gesture feature map on the recognition effect is obvious. The recognition effect has obvious differences between the range characteristic map and the range-doppler characteristic map. In this paper, the recognition results of a single feature map are obtained by analyzing different gesture features. As shown in Figure 20, the recognition effects of a single feature are almost the same. Therefore, it is necessary to extract the features between three-dimensional gesture images. By comparing the three-dimensional and single gesture feature maps, we can see the obvious difference in their recognition effect. Because the three-dimensional gesture feature is adopted in this paper, the range, doppler and angle feature parameters are fused. In contrast, the recognition effect of the fused gesture feature map is better and more accurate. This is because a single gesture feature cannot represent the whole gesture. After fusion, each feature will complement and extract all the contents of gesture features. Using a neural network algorithm for recognition training, the recognition effect is more accurate and higher. For the fused feature map, set different learning rates and the n observe the recognition efficiency.