esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence, Machine Learning, and Deep Learning in Digital Pathology  
In the context of this review, it is crucial to make the terminology used around this recent topic clear to the reader. Understanding the distinctions and overlaps of these technical terms is essential for recognizing their roles in digital pathology. Artificial intelliggence encompasses all the applications aiming to create software (or machines) capable of performing tasks that typically require human intelligence. For example, in digital pathology, artificial intelligence is used to develop systems that can assist in computational histopathology analysis and AI-assisted diagnostics [15; 29]. Machine learning is a subset of artificial intelligence, and it mainly focuses on the development of algorithms that enable computers to improve their performance on a specific task by learning directly from data. In digital pathology, machine learning algorithms are trained on large datasets of medical images and have been used to implement pattern recognition tasks and to predict disease states [30; 31]. Deep learning refers to a specific subset of machine learning algorithms that make use of neural networks (typically organized into many layers) to model complex patterns in data (see Figure 1). Recently, in digital pathology, deep learning approaches have been shown to be effective for analyzing complex histopathological images and, in some specific applications, for providing accurate clinical-related insights [8; 19; 32]. In brief, both machine learning and deep learning are subsets of artificial intelligence, deep learning models represent a specific type of machine learning algorithms, and all machine learning approaches are part of the broader artificial intelligence systems. In conclusion, while artificial intelligence provides the overall goal of creating intelligent systems, machine learning offers methods for data-driven learning, and deep learning provides advanced techniques for the analysis of high-dimensional data. To date, all these technologies have played a crucial role in driving fundamental innovations in human digital pathology.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 4 Experimental Evaluation
### Results
In such conditions, the performance of CNP and HTAPF gets worse and the greedy approach proves more robust as it does not rely much on communication. Significant differences are observed when \(S\geq-100\), where the greedy approach outperforms both CNP and HTAPF (see Figure 6, bottom-left, and Table 2). In case of failures, all approaches show a similar drop in performance, due to failed robots blocking corridors or doorways, and no algorithm is more robust than the others (see Figure 7, bottom-left, and Table 3). Finally, the lab environment confirms the robustness of HTAPF both with respect to limited communication and robot failures. In the former case (Figure 6 bottom-right and Table 2), HTAPF always outperforms the greedy approach and is significantly better than CNP when \(S=-80\). In the latter case, HTAPF is significantly better than both approaches but for the maximum failure rate, where the advantage is marginal (Figure 7 bottom-right and Table 3). It is worth noting that the performance of HTAPF is rather constant despite the complexity of the environment and the introduced limitations and failures, which suggests that the algorithm properly exploits the available robots, flexibly deploying them to the areas that mostly need work to be performed, quickly adapting to variations in the service demands. Figure 7: Number of accomplished tasks by the different approaches (greedy, CNP, and HTAPF) across the different environments, obtained with unlimited communication and variable robot failures rate.
###### Abstract  
Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi's \(\alpha\) entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase-amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase-amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.  
EEG data; phase-amplitude interactions; cross-frequency interactions; transfer entropy; kernel methods; Renyi's entropy; connectivity analysis +
Footnote †: journal: Applied Sciences
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 2 Related Research
### Combination of DNNs and Filters  
A favorable property of multi-channel filters is that they introduce little distortion to the enhanced speech. Thus, many studies in multi-channel speech enhancement combine DNNs with multi-channel filters to obtain speech with higher quality and fewer distortions.  
In [8], a two-stage approach was proposed for multi-channel speech enhancement. First, the RI components of different channels are concatenated as input features for the first DNN, which estimates the RI components of the target speech. The predicted speech is then used to compute signal statistics for filters. Many kinds of filters have been tested, such as MVDR, MPDR, multi-channel Wiener filter (MCWF), etc. Finally, the predicted speech and the result of the filter are used as extra features to train the second DNN. This model was modified into a multi-stage one in [9] and a multi-frame MCWF (MFMCWF) is used to provide the filtering result.  
The framework proposed in [8] has been migrated to single-channel scenarios. In [22], a similar model is proposed for single-channel speech dereverberation and speaker separation. Since the filters used in [8] are originally designed for multi-channel speech signals, the model proposed in [22] uses a linear-prediction module to estimate a dereverberation filter based on the first DNN's output. The predicted filter then provides the dereverberated signal as extra features to help train the second DNN.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model
### Transformer Components for TGAN-AD
#### 4.3.2 Transformer-Based Discriminator  
As shown in Figure 4(b), a sample, from the training data or generated data, is input into the Transformer encoder to obtain the hidden representation of the sample; then the sample and its representation are sent to the decoder. The class distribution of the sample is output by the Transformer decoder.  
The goal of the discriminator is to accurately distinguish the generated fake data from the training data so that the model can be well trained by the _mini-max_ game.
## 3 Results
### Differentially Expressed Unigenes in the Interactive Pathways of Geldistisoside Biosynthesis  
Previous research has demonstrated that the MEP and MVA pathways are involved in plant growth, development, and stress responses. Moreover, these pathways supply precursor molecules and are essential for the synthesis of key plant hormones, such as ABA, GA, and BR [34]. KEGG pathway analysis revealed significant enrichment of ten biosynthetic pathways in association with saponin synthesis. In the TBB pathway, the intermediates (DMAPP and GPP) were catabolized as enzymatic substrates for ZTB, MTB, and IAB by five enzymes: IPT, TRIT1, TPS14, TPS10, and TPS-Cin (Tables S5 and S6).  
In addition, during the oxidative cyclization of squalene, the 2,3-oxidasequalene produced was cyclized by another kind of cyclizing enzyme, CAS1, to generate sterol, which enters the sterol pathway (STB), and then the STB leads to the brassinolide pathway (BRB). These ten branch pathways and the gleditsioside synthesis pathways share the same precursor substances, IPP, DMAPP, GPP, and FPP (Figure 4A). Through the differential analysis of ten interactive pathways, a total of 700 unigenes were detected, among which ZTB (n = 161) and CTB (n = 141) each had more than 100 unigenes. Additionally, CL12162.Contig5 (CAS1), CL13571.Contig19 (SPS/IPT), Unigene10127 (TPT), and CL333.Contig3 (TPS14) were highly expressed in the interactive pathway, and these genes were gradually down-regulated over time (Figure 4B). They might act as negative regulators in gleditsioside synthesis.  
Figure 4: Interactive pathways of gleditsioside biosynthesis and gene expression profile of the key enzymes in _G. sinensis_. (**A**) Linking gleditsioside biosynthesis with other interactive pathways. Blue box represents the primary gleditsioside biosynthesis process; blue oval: interactive pathways; orange square framework: hormones synthesized by interactive pathways; GGPP: geranylgeranyl diphosphate; SP: solanesyl pyrophosphate; PHPP: phytyl diphosphate. Solid arrows indicate direct transformation, and dashed arrows indicate indirect or complicated connections. (**B**) Gene expression profiles of the pathways. Both the circle size and the color scale represent the count of the genes.
## 3 Material and Methods  
### Dataset Construction  
We chose Nanjing for our case study. Map data including buildings, road networks, and satellite images are available online from mapping services such as OpenStreetMap. However, Google street view image is not available in mainland China due to business restrictions [38]. Therefore, we collected geometric data in shapefiles, street view images from Baidu Street View (BSV), and satellite images from Baidu satellite view. The geometric data include building footprints and area of interest (AOI), which is the boundary of each plot (Figure 1). We take each plot and the buildings in the plot as a sample according to the AOI. The attributes of a building include height and area. Figure 2 shows 200 out of 5803 plots, where the color transparency of the buildings refers to the value of height.  
Static street view images and satellite images can be requested from BSV and Baidu satellites via its public API. One can retrieve an image with the HTTP request where the FOV, heading, pitch, and panorama ID, which are expressed in degrees, need to be supplied. We set the FOV value as 90. A developer key is required. An image can be retrieved by simply pasting a request URL into a web browser or by sending API requests in a batch using any programming language. The heading value is related to the angle of looking at the panorama from the observing point. We set the observing points along the AOI boundary. We retrieve 4 SVIs from 4 heading values which are determined by the orientation of the boundary line where the point is located at. We set 2 angles to look at the corresponding plot and the other 2 to look at the plot on the other side of the street because we want to see more of the buildings along the boundary. We take the AOI boundary box location as a reference for requesting satellite image tiles, which is followed by merging  
Figure 1: Visualization of map data including the area of interest, building footprints, and road networks.  
Figure 2: Examples of plot slices including AOI and buildings.  
the tiles into one image. Figure 3 shows examples of satellite images, master plan images, and SVIs.
## 4 Solution Method
### Hybrid Genetic Algorithm
#### 4.2.2 Genetic Operators  
In this section we designed a 2-point crossover operation and multi-point mutation operation to improve the current population and explore solution space. The 2-point crossover operation is described in detail as follows:  
**Step 1:**: select randomly two individuals (P1 and P2) with crossover probability \(p_{c}\);
**Step 2:**: select randomly two crossover genes (g1 and g2) ranging from 0 to \(n_{\min}\), and \(n_{\min}\) is the number of customers with less customers between the two individuals;
**Step 3:**: add the same genes into set S0 (5, 0, and 6) sequentially between g1 and g2 for individual P1, and add the same genes into set S1 (6, 0, and 5) sequentially between g1 and g2 for individual P2;
**Step 4:**: for individual P1, add the genes into set S2 (4 and 2) which is different with P2 between g1 and g2, and for individual P2, add the genes into set S3 (3 and 1) which is different with P1 between g1 and g2;
**Step 5:**: reinsert S0 and S3 into P2 sequentially between g1 and g2, and then get an offspring individual O2; and reinsert S1 and S2 into P1 between g1 and g2, and then get an offspring individual O1.  
An example is given to illustrate this operation in Figure 2.  
In the mutation operation, we designed multi-point mutation operations to generate new offspring individuals. The procedure is as follows:  
**Step 1:**: select an individual randomly with mutation probability \(p_{m}\);
**Step 2:**: select a gene (such as g1 = 6) randomly on the individual and revise it into another gene (such as g2 = 5) randomly;
**Step 3:**: check if there is the same gene as g2 on other locations, if yes, then revise the gene into g1 = 6; and
**Step 4:**: repeat step 2-step 3 m times;  
An example is given to illustrate this operation in Figure 3.  
Figure 3: Multi-point mutation operation.  
Figure 2: 2-point crossover operation.
# A Regional Multi-Agent Air Monitoring Platform
## 2 Related Works
A new version, extended with an artificial intelligence algorithm, of a previously proposed platform for air quality monitoring based on carbon dioxide concentration measurements is presented in [20]. In [21], the authors claim that a comparative analysis in the USA, conducted with monitors using two methods (Federal Equivalent Method and Federal Reference Method), shows that there is a problem with the accuracy and reliability of data when measuring fine particulate matter concentrations. Comparability between the two types of monitors can have significant implications for maintaining compliance with National Ambient Air Quality Standards (NAAQS). Furthermore, this study examined the effectiveness of the performance of both monitors used to conduct PM2.5 measurements collected from 276 monitoring stations. In the paper [22], various methods such as support vector regressions, gradient boosting decision trees, neural networks, and others are presented to analyze the impact of air quality index in Xi'an city, China, starting from 1 October 2022. Comparative tests were also conducted. In [23], it is argued that traditional air monitoring methods are often limited by spatial coverage and accessibility. Therefore, the authors propose using methods whose mobility and advanced sensing capabilities overcome these limitations. In [24], a multi-scale fusion model is proposed to study both single-step and multistep prediction. In [25], the results of developing an adaptive hardware-software platform are summarized, following the IoT paradigm, demonstrating a high level of accuracy in predicting carbon dioxide trends by analyzing only a limited window of recent data. Air quality is influenced by multidimensional factors, including location, weather, and uncertain variables. Currently, many scientists use big data for their analyses thanks to advances in this field and the availability of a numerous environmental monitoring and sensor networks. In [1], various big data and machine learning-based techniques are explored for air quality prediction. In addition, on the paper highlights some of the challenges and future research needs.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 5 Conclusions  
This paper presented an integrated communication and computation framework for UAV swarms, combining cloud-edge-end collaboration with a CF-mMIMO architecture. A robust CF-mMIMO protocol with synchronization and delay compensation was implemented to ensure reliable wireless connectivity in dynamic swarm environments. The proposed system achieved a 30% reduction in onboard computation while maintaining detection accuracy after migration. Experiments demonstrated that the proposed system significantly improves computational efficiency, communication reliability, and end-to-end latency.  
While this study validates the feasibility of CF-mMIMO-based offloading for UAV swarms, future work will focus on scaling to larger swarm sizes via decentralized coordination and adaptive task partitioning. Integrating technologies such as reconfigurable intelligent surfaces (RIS) and non-terrestrial networks (NTNs) may further enhance communication robustness [47]. In addition, AI-driven control using federated or reinforcement learning could enable more autonomous, adaptive behavior. These directions also raise new challenges in latency control, convergence, and reliability under resource constraints. Furthermore, hardware-level enhancements--such as terminal miniaturization, aerodynamic integration, and systematic EMI shielding--will be pursued to reduce onboard load and improve real-world deployability in complex electromagnetic environments.  
[PERSON] was the main author. [PERSON], [PERSON] and [PERSON] provided the methodology, software, and constructive feedback on every part of this manuscript. [PERSON] was our supervisor. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program under Grant 2020 YFB1806608, and in part by the Special Fund for Key Basic Research in Jiangsu Province No. BK20243015.  
The datasets generated and analyzed during the current study are not publicly available due to institutional policy and hardware privacy constraints. Requests to access the data can be directed to the first author at [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.
# SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
## 2 Related Works  
In agriculture 4.0, certain technologies play a crucial role. Existing research covers a range of advancements from ground equipment [19] to UAV imaging [20], significantly promoting the development of precision agriculture. Additionally, after obtaining image data, some studies utilize cloud-edge architecture for task offloading [21; 22], which enables real-time analysis of the data. In such cases, farmers and researchers can manage crops better, thereby improving production efficiency and yield. However, existing research has not yet addressed the challenges of agricultural image acquisition and storage in remote areas or extreme environments. In these scenarios, field imaging is essential for accurate monitoring and management of agricultural production, ensuring data integrity and avoiding decision-making errors due to harsh environmental conditions [23].
## 1 Introduction
Current techniques also cannot handle multiple structures distributed at different depths next to each other in the same image. This paper presents a new method named the pixel-by-pixel scan matrix method that uses deep learning to de-blur and estimate depth information of the absorbing structures in a turbid medium. Consequently, with de-blurred two-dimensional (2D) images at different angles as the projection images, the 3D de-blurred absorbing structures and the cross-sectional images could be reconstructed using the filtered-back-projection method. It also allows for restoring the \"clear\" image of the light-absorbing structure, so that only one convolutional neural network needs to be used for depth estimation and explicit image reconstruction. If the result of the proposed method could be achieved, the 2D de-blurred image and its pixel depth information can lead to reconstructing a 3D view of absorbing structures with a limited acquisition angle, even with only a single 2D image.
## 4 SDR Implementation of FM-ACSK System Prototype
### Master Chaos Generator  
In accordance with the description of the master chaos generator in Section 3.1, the generator in the transmitter's ACSK modulator is built. To be able to run this ACSK modulator with SDR, the Simulink model should run in discrete time. Therefore, a continuous-time model with differential equations must be converted into a discrete-time model with difference equations. The equations for the continuous-time \"State-space\" unit were:  
\[\dot{x}_{c} =A_{c}x+B_{c}u=u\] \[y =C_{c}x+D_{c}u=x\, \tag{8}\]  
where \(u\) is the input, \(x\) is the state, \(y\) is the output. As \"state-space\" units are integrators \(A_{c}=0\), \(B_{c}=1\), \(C_{c}=1\), \(D_{c}=0\). The respective \"Discrete state-space\" blocks are described by equations:  
\[\begin{split} x(n+1)&=Ax(n)+Bu(n)\\ y(n)&=Cx(n)+Du(n)\,\end{split} \tag{9}\]  
where parameters \(A\), \(B\), \(C\), \(D\) can be determined using discretization technique [42]:  
\[A=e^{A_{c}T},\quad B=\int_{0}^{T}e^{A_{T}}B_{c}d\tau,\quad C=C_{c},\quad D=D_{c }\, \tag{10}\]  
w  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Carrier Frequency** & \begin{tabular}{c} **SDR Baseband** \\ **Sample Rate** \\ \end{tabular} & \begin{tabular}{c} **ACSK Sample** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Samples Per Bit** \\ \end{tabular} & \
\begin{tabular}{c} **Data Rate** \\ \end{tabular} &
\begin{tabular}{c} **FM Frequency** \\ **Deviation** \\ \end{tabular} \\ \hline
867 MHz & 240 kHz & 10 kHz & 1000 & 9.9 bit/s & 10 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of an FM-ACSK system prototype.  
Figure 6: Baseband spectrum of the FM-ACSK at 10 kHz FM deviation.  
where \(T\) is the relative sample time which depends on the dynamics of the continuous system and it must be less than 0.5. After setting \(T=0.05\), we get \(A=1\), \(B=T=0.05\), \(d=1\), \(D=0\). Considering that value of \(T\) is 10 times smaller than the critical value (0.5), the experimental evaluation has shown that discretized system behaved exactly as a continuous one.  
Implemented in Simulink master chaos generator's scheme is depicted in Figure 7.