esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 High-Dynamic Atmospheric Turbulence Compensation Algorithm
### Zernike Polynomials Based Phase Reconstruction Method  
Collected from SHWS, the information of wavefront would be displayed in the coordinates of spots arrayed in every single aperture of the micro lens array shown in Figure 4.  
Figure 3: (**a**) Theoretical compensation ability of \(\frac{1}{4}\) MMDM for tilts aberrations. (**b**) Theoretical compensation ability of PDM for tilts aberrations. (**c**) Theoretical compensation ability of FSM for tilts aberrations.  
By computing the offsets of the spots, the disturbance of the wavefront can be acquired. Therefore, we adopt the mapping relationship between spots offset and wavefront slopes suggested by [30]. Assume there are totally \(N\) spots on the SHWS, and let \(\mathbf{slope}\) be the vector of partial derivatives of phase. Correspondingly, \(\mathbf{offset}\) represents the offset vector of spots. Both \(\mathbf{slope}\) and \(\mathbf{offset}\) contain \(2\times N\) elements. This process is expressed as  
\[\mathbf{slope}=\frac{2\pi}{\lambda f}\times\mathbf{offset} \tag{1}\]  
where \(\lambda\) stands for wavelength and \(f\) indicates focus length of micro lens array on SHWS. Furthermore, following the modal algorithm presented in [31], the coefficients of assigned Zernike terms are fitted by a least-mean-square method, yielding  
\[\mathbf{A}=\left(Z^{T}Z\right)^{-1}Z^{T}\mathbf{slope} \tag{2}\]  
where, \(\mathbf{A}\) denotes the fitted coefficients vector and \(Z\) is the wavefront restoration matrix.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 2 System Model
### Hybrid Beamforming and Pilot Placement
#### 2.1.3 Singular Value Decomposition  
Singular value decomposition (SVD) in hybrid beamforming serves as a pivotal technique employed to enhance the performance of m-MIMO systems within the realm of modern networks. SVD, as a fundamental matrix factorization approach, dissects a given matrix into three distinct matrices, thereby simplifying the complexity of mathematical processes and improving the efficiency of beamforming algorithms. Within the domain of hybrid beamforming, the significance of SVD becomes pronounced in its important role concerning the accurate estimation of the CSI matrix and the computation of beamforming weights [16; 18].The SVD of a matrix is represented as follows:  
\[H=U\Sigma V^{T} \tag{5}\]  
where \(H\) is an \((m\times n)\) matrix describing the relationship between the transmit and receive antennas, \(U\) is an \((m\times m)\) orthogonal matrix (left singular vectors derived from the singular value decomposition (SVD) of the channel matrix \(H\)), \(\Sigma\) is an \((m\times n)\) diagonal matrix with non-negative real numbers on the diagonal called the singular values of \(H\), and \(V^{T}\) is an \((n\times n)\) orthogonal matrix (right singular vectors), where \(V^{T}\) denotes the transpose of \(V\). Mathematically, SVD can be expressed as follows:  
\[H=\sum_{i=1}^{\min\ (m,n)}\sigma_{i}u_{i}v_{i}^{T} \tag{6}\]where \(\sigma_{i}\) are the singular values, \(u_{i}\) and \(v_{i}\) are the corresponding left and right singular vectors, and min \((m,n)\) denotes the minimum of the number of rows and columns of \(H\). This decomposition helps in understanding the channel's behavior and designing efficient transmission strategies by exploiting the channel's spatial characteristics, such as optimizing the transmission strategies to exploit spatial multiplexing gains and mitigate interference and reducing the complexity of solving the HB matrix weights.
## 1 Introduction
### Rationale  
The goal of this research is to find relatively simple, semi-analytical (spectral signature-based) phyocyanin and chlorophyll-a reflectance algorithms that are adaptable to a variety of satellite imagers (i.e., portable). A main objective is to maximize the utility of multiple current and near-future satellite imaging systems to counter the frequent cloud cover occurring over many small inland water bodies to estimate BGA/PC values. We conducted a case study using Harsha Lake, a 2000-acre (8.9 km\({}^{2}\)) drinking water reservoir in southwestern Ohio experiencing HABs. We began this research with a comparison of real aircraft and simulated satellite data imager/algorithm combinations against chlorophyll-a (Chl-a) coincident water surface observations [6]. We focus on BGA/PC estimation in this paper.
## 2 Materials and Methods
### Image Evaluation  
Both the H&E slides and eFuCM images were completely anonymized and blindly examined by 2 liver pathologists (one with eFuCM image reading experience and the other one without any experience), with a clearance period of three months between both techniques (Figure 2a).  
Each pathologist recorded their findings in a preformed questionnaire. The presence of portal tracts and central veins was noted as well as the identification of arteries and bile ducts. The presence/absence of steatosis, lobular and portal inflammation, fibrous septa and cirrhosis was recorded using a binary system (being 0 absent and 1 present). When present, the degree inflammation was evaluated in a 3-tiered system (1 = mild, 2 = moderate and 3 = severe), differentiating between portal and lobular inflammation. When steatosis was present, it was graded in a semi-quantitative system according to the NASH Clinical Research Network System (1 = 5-33%; 2 = 33-66% and 3 > 66%) [21]. Although it was not the aim of the study, if a tumor was present it was noted. Finally, a pattern approach to diagnosis approximation was performed for acute hepatitis, chronic hepatitis, steatosis, cirrhosis, tumor or normal/minimal changes.  
The conventional histological diagnoses of both pathologists were statistically compared. Their discordances were then assessed to reach a consensus diagnosis. Finally, the eFuCM diagnoses from each pathologist were compared with the consensus of the conventional H&E diagnosis (Figure 2b).  
Figure 1: Sample acquisition and preparation for eFuCM visualization. After core biopsy acquisition, a fast-staining process was performed for further eFuCM scanning.
# A Gamified AI-Driven System for Depression Monitoring and Management
###### Abstract  
Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments.  
Keywords:depression; gamified intervention; AI for mental health; IoT-based monitoring; metacognitive self-regulation; e-mental health; digital mental health +
Footnote †: journal: Applied sciences
## 4 Discussion  
The supervised learning method was conducted on the ground objects classification based on feature fusion of multi-source remote sensing images. The property of supervised classification relying on labeling limits the application of artificial intelligence in forestry supervision. The self-supervised learning method attracted extensive attention because of no human annotation [9; 10; 11]. PCL combines the clustering algorithm with contrast learning and obtains good performance, but cannot be used directly with hyperspectral and multispectral data [29]. ContrastNet demonstrated strong self-supervised learning with processing ordinary images, but its single data source greatly reduces the features of the sample [30]. Contrastively, in the M-SSL model, the HJ-1A and Sentinel-2 remote sensing image data were fused to enhance the features of the samples. The M-SSL model takes advantage of the combination of AAE and VAE. The generator uses the generative learning mode, and the discriminator uses the heavy loss function, which obtains the ability of extracting discriminating features. The following conclusions were drawn from the experiments:  
1. The self-supervised learning model proposed in this paper is similar to traditional deep learning methods, and features are extracted and fused from the two data sources. HSI and MSI provide implicit augmentation with spectral and structural information differences. Using HSI and MSI as multi-modal features for network training provides more desirable presentation than other methods.
2. By taking the advantages of generative learning and contrastive learning to conduct joint learning, the M-SSL model extracted two types of features from multi-source datasets in the pretext task, and fine-tuned parameters only in downstream task. The sharing features of multi-source tree species images learned from pretext task training bring robustness and stability to downstream tasks.
3. Observed from the compared results, it is proven that the feature learning based on M-AAE or M-VAE can better integrate the discriminate features and remove some redundant features. The pixel from the same tree species with positive samples provides more abundant information to make the result of tree species classification more accurate.  
Although M-SSL uses different particle sizes as negative samples to reduce calculation of negative label samples, the learning process of M-SSL is based on MOCO model to make itself still complex. In the next step, we can try other methods, such as data distillation DINO or Barlow Twins, to reduce the computational complexity. In addition, the spatial resolution of datasets is 10 m, in the future, we further explore the self-supervised methods for tree species classification with higher spatial resolution datasets.
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 3 Applications of Non-Mechanical Beam Steering with PGs
### Steering of Monostatic Coherent Doppler Lidar for Wind Sensing
To quantify this impact, the PG was switched onto (Figure 13a) and then off of (Figure 13b) the spinning disc sandpaper target. When steering off target, the steered beam was directed at a static laboratory environment which generates no Doppler signal. In this state, residual light can remain on target due to imperfect polarization of the transmit beam and/or imperfect polarization control of the LC cells. For this device and system, such residual light was approximately 0.2% of transmitted power. Figure 12 shows a measured \(\sim\)30 dB decrease in CNR when steering off target, essentially showing no measurable signal from residual light (i.e., side lobes) hitting the target. This is due largely to the opposite polarization state of the residual light relative to the steered beam, which does not efficiently couple back through the PG steerer and cannot generate a coherent signal. This inherent polarization discrimination against stray light is a significant benefit of PG steering in polarization sensitive applications. Figure 12: Schematic representation of the coherent lidar breadboard used to evaluate PG beam steering contributions to phase noise and insertion loss in a Doppler detection application. Modified from [PERSON] et al.
## 3 Results and Discussion
### pH Effect on Sorption Affinity of Organic Pollutants on MPs
Additionally, the obtained results imply a slightly higher affinity of naphthalene and fluorene toward the standard polyethylenes (both PEp and PEg) and PE_PCPs_2 compared to other types of MPs, which may be a consequence of the larger specific surface areas of PEp, PEg and PE isolated from cosmetics in comparison to the active surface area of the other selected MPs. The obtained results shown in Figure S6 imply that the adsorption affinity of the selected PAHs toward the MPs increased with hydrophobicity, and the difference in the binding percentage on MPs was less significant. Thus, naphthalene, which was less hydrophobic, had the lowest affinity towards the MPs. The percentage of naphthalene adsorption was about 20% for PET, while for PEp, PEg, PE_PCPs_1, PE_PCPs_2 and PP it was about 85%, 95%, 65%, 95% and 75%, respectively. With the increase in the hydrophobicity of the tested PAHs, an increase in the binding percentage was determined. In all four types of PE and PP, it was in the range of 95-99%, while for PET it was 65%. The lowest adsorption affinity of PAHs was shown with PLA, where the binding percentages for naphthalene, fluorene, fluoranthene and pyrene were 30%, 50%, 60% and 75%, respectively. Since the change in pH value had the highest impact on the adsorption behavior of CPs towards MPs, in order to better understand the effect of pH on sorption kinetics with MPs, the obtained experimental data were fitted with three kinetic models: the pseudo-first-order, the pseudo-second-order and the Elovich models. Furthermore, to better understand the influence of pH value on the adsorption mechanisms of the selected CPs on MPs, adsorption isotherm experiments were conducted.
## 5 Data Processing
### GNSS Data Processing  
The GNSS raw data were post-processed by RTKLib open source software in the carrier-phase differential mode with respect to the Leica Viva GNSS receiver mentioned in Section 5.1 as base station. Table 2 presents the configuration of main parameters used in RTKLib for the GNSS trajectory processing. The ratio of epochs with a fixed solution to total epochs of the GNSS trajectory solution is 99.17% and the mean standard deviation along \(x\), \(y\) and \(z\) axes amounts to 8.4, 5.5 and 9.6 mm, respectively.
## 3 Methodology
### ConvLSTM  
ConvLSTM is an extension of LSTM, which uses convolutional layers to process spatiotemporal data [23]. It applies convolutions on the input data before passing it through the LSTM cells, allowing it to capture spatial and temporal dependencies within the input sequence [25]. As shown in Figure 3, the structure of ConvLSTM is similar to LSTM that contains forget gate (\(f_{t}\)), input gate (\(i_{t}\)), and output gate (\(O_{t}\)), but with convolutional layers instead of fully connected layers, which enables ConvLSTM to capture underlying spatial features [26]. The transmission relationship between the gates is expressed using the following equation:  
\[i_{t}=\sigma(W_{xi}*\chi_{t}+W_{hi}*H_{t-1}+W_{ci}\circ C_{t-1}+b_{i}) \tag{2}\]  
\[f_{t}=\sigma\Big{(}W_{xf}*\chi_{t}+W_{hf}*H_{t-1}+W_{cf}\circ C_{t-1}+b_{f} \Big{)} \tag{3}\]  
\[C_{t}=f_{t}\circ C_{t-1}+i_{t}\circ\tanh(W_{xc}*\chi_{t}+W_{hc}*H_{t-1}+b_{c}) \tag{4}\]  
\[o_{t}=\sigma(W_{xo}*\chi_{t}+W_{ho}*H_{t-1}+W_{co}\circ C_{t}+b_{o}) \tag{5}\]  
\[H_{t}=o_{t}\circ\tanh(C_{t}) \tag{6}\]  
where \(\circ\) denotes the Hadamard product; \(*\) denotes the convolution operator; \(\sigma\) is the sigmoid activation function, which is given by:  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{7}\]
## 1 Introduction  
The use of 3D technologies and modelling in archaeology has experienced a rapid increase during the two last decades. Different topographic methodologies have been used over the last years for the survey of archaeological structures, including Terrestrial Laser Scanning (TLS) [1], aerial survey with Laser (LiDAR) and the use of photogrammetry from cameras; however, drones [2] help us to take high-resolution images with an accurate topographic positioning of each image [3]. They also make it possible to program specific flights with the desired conditions around the element to be modeled, making the pre- and post-processing work much more efficient, fast and accurate, thus offering relevant time and cost efficiency.  
The creation of 3D models from the drone images of entire archaeological sites, particular buildings, monuments, statues or relevant artistic ornaments has been used to preserve and disseminate cultural heritage and scientific information, or simply to capture the modern condition of archaeological objects for further virtual restorations [4]. Among many other applications is the generation of 3D models as digital twins of damaged or destroyed sites based on preserved archaeological materials and historical descriptions [4; 5; 6; 7]. This paper presents a study on this research line applied to archaeoseismology with the purpose of illustrating, depicting and quantifying some of the most significant, preserved deformations caused by an earthquake in the late fourth century AD in the ancient Roman City of _Baelo Claudia_ in South Spain [8; 9].  
The Roman city of _Baelo Claudia_ is one of the most renowned archaeoseismological sites in the Mediterranean, since its present remains preserve the impact of two different earthquakes during Roman times (first and fourth centuries CE; [9]) and the subsequent tsunami destruction that followed the last event [10; 11]. The site has been the subject of deep archaeoseismological analyses, including geoarchaeological research, 2D GPR and ERT geophysical prospects [12; 13; 14; 15], and 2D detailed mapping of damage [14; 16], as well as complementary geological and paleoographical analyses [11; 12; 14; 17]. To date, all the compiled 2D information has followed the archaeoseismological classification and routines proposed by [PERSON] et al. [18], presently synthetized and updated in the Springer Encyclopedia on Cultural Heritage [19; 20]. This paper is focused on converting from a 2D rendering to a 3D digital format three emblematic, seismically damaged structures in the Roman site, including (a) the folded E-W _Decumanns maximus_ of the city; (b) the tilted and cracked northern bastion of the city walls; and (c) a large marble dish from the baths surfied away by the tsunami backwash. This work represents a first step towards the enactment (development) of 3D modelling from drone imagery of the entire city to highlight archaeoseismic damage, but also the valorization of 3D modelling in the geological analysis of both particular geoarchaeological elements and the changing landscapes around key archaeological sites.
# Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning
## 6 Conclusions  
A manufacturing scheduling method is presented based on hierarchical CTPNs and RL. CTPNs model the system and implement the scheduling. Since the manufacturing scheduling is NP-hard, in the search for an optimal or near-optimal solution a single scheduling agent employs the Q-learning algorithm. The proposed method is an alternative hybrid approach in the context of the complementarity of methods used to handle the scheduling. CPNs can efficiently model the complexity and dynamic behavior of FMS, and evaluate its performance, whereas an intelligent agent with learning capabilities searches for an optimal scheduling solution by using RL. The method does not depend on state space exploration for the scheduling; however, state space analysis can be used to identify and verify system properties. While PNs have been combined with AI techniques in the literature, the potential of the method lies in the exploration, development, and evaluation of the agent's learning capabilities with respect to the manufacturing scheduling objectives in a CTPN-based modeled system. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed method has been tested under different scheduling objectives. It has been compared to existing methods in order to illustrate its validity and applied to known job shop benchmark examples. Besides simulation results, state space results have supported the validation of the method. State space results have indicated a deadlock-free system model.  
The optimal scheduling solution was based on the criteria set in the policy table, i.e., in the model of the environment. The number of state-action pairs, as well as the agent training period number of cycles, influences the state space size as well. State space explosion is a main limitation in PN-based scheduling. Similarly, the large number of environmental states poses limitations in the accuracy of the employed RL method. These parameters can change depending on the case studied.  
Future research could explore a distributed scheduling method, in which each resource is linked to its own agent in order to further control the CTPN model execution. Under this scheme, a system state could be defined by the remaining operations for each resource and their precedence relationships, whereas actions could be defined by the set of enabled operations for the resources. The Q-learning would be implemented by each agent in order to obtain an optimal or near optimal solution.  
Figure 15: Top page of the implemented CTPN model for the scheduling of five batches of 28 orders each with order size of two line items per order. The color tokens show the order sequencing from top to bottom, with the list of tote pick locations and compartment pick locations, as well as the cumulative workload schedule.  
**Author Contributions:** [PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] analyzed the data and prepared the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 1 Introduction
Furthermore, 2D materials demonstrate superior structural stability compared to zero-dimensional materials, affording them substantial advantages and broad applications in electrocatalysis [66, 67, 68, 69, 70]. An emergent subclass of 2D materials, 2D MOFs with a sheet-like morphology [71, 72, 73, 74, 75, 76] have displayed enormous potential in the realm of electrocatalysis [77, 78, 79, 80, 81]. 2D MOFs incorporate the benefits of 2D materials and MOFs, including larger specific surface areas facilitating more active site exposure, adjustable surface structures via ligand modification, and nanoscale thickness that promotes electron and mass transfer rates. Additionally, 2D MOF derivatives [82, 83, 84, 85, 86, 87], which can be transformed into metal/carbon materials through high-temperature carbonization [88] (typically between 700 and 1000 \({}^{\circ}\)C), possess excellent electrocatalytic properties owing to their enhanced porosity, optimal exposure of metal sites, and heteroatom doping. This review aims to provide insight into the recent advancements of 2D MOFs and their derivatives within the realm of electrocatalysis. Initially, we present a comprehensive summary of the preparation strategies adopted for 2D MOFs and their derivatives. Following this, we delve into the detailed exploration of their applications in the sphere of electrocatalytic water splitting. Finally, we discuss the future development prospects of these promising materials in the field of electrocatalysis.