esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Neural Model for Machine Translation of Agglutinative Language
###### Abstract  
Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word sequence and the linguistic feature sequences. Compared with the standard NMT model, we utilize an additional encoder to incorporate the linguistic features of lemma, part-of-speech (POS) tag, and morphological tag by extending the input embedding layer of the encoder. Moreover, we use a serial combination method to integrate the conditional information from the encoders with the outputs of the decoder, which aims to enhance the neural model to learn a high-quality context representation of the source sentence. Experimental results show that our approach is effective for the agglutinative language translation, which achieves the highest improvements of +2.4 BLEU points on Turkish-English translation task and +0.6 BLEU points on Uyghur-Chinese translation task.  
artificial intelligence; neural machine translation; agglutinative language translation; complex morphology; linguistic knowledge +
Footnote †: journal:  
_Future Internet_
## 6 Conclusions  
Knowledge about degraded and reclaimed areas was collected at many spatial scales and with miscellaneous methods, as this is an important issue for environmental management. The vegetation growth data help to establish the monitoring reclamation treatments and functional changes in the forest ecosystem. Based on the broad references of satellite images and LiDAR data used in environmental monitoring, quantifying the forest structure for post-mining areas is possible by using these technologies. The results of this study can serve as a reference for the development of image-based and semi-intelligent geoinformation techniques.  
Fusing satellite images with LiDAR data for land use/land cover classification and deriving the spatial parameters of vegetation related to the process of monitoring progress in forest ecosystem dynamics and reclamation success, will help significantly develop comprehensive monitoring of the dynamics of novel forest ecosystem formation and forest succession mapping. Acquiring 3D information about biomass and carbon storage is important for post-mining forested and reclaimed areas.  
The remote sensing method allows one to define the dynamics of changes occurring, as well as the direction of the expansion of vegetation. The integration of remote sensing data facilitates the definition of the spatial characteristics of vegetation to determine monitoring changes according to the reclamation process. The application of the methods of image classification and airborne laser scanning point cloud processing combined with GIS analyses provides wide possibilities in terms of automating the cyclic monitoring of LULC changes and biomass dynamics.  
Conceptualization, M.S., J.L.-C. and M.P.; methodology, M.S.; software, M.S.; validation, M.S.; formal analysis, M.S.; visualization, M.S.; writing--original draft preparation, M.S., J.L.-C. and M.P.; supervision, M.S., J.L.-C. and M.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by project no. 2019/03/X/NZ9/00424, MINIATURA-3, National Science Centre, Poland.  
Not applicable.  
This to the Planet team for the account and imagery from Planet's Education and Research (E&R) Program; Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. [[https://api.planet.com](https://api.planet.com)]([https://api.planet.com](https://api.planet.com)) (accessed on 7 July 2021).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
Background: Non-specific low back pain (LBP) is a widespread condition with significant impacts on physical activity, muscle strength, psychological well-being, and economic status. Traditional physiotherapy shows variable efficacy, prompting growing interest in AI-assisted physiotherapy for its potential to offer personalized feedback and multidisciplinary care integration. Objective: This systematic review and meta-analysis aimed to evaluate AI-assisted physiotherapy's effectiveness in reducing pain intensity and functional impairment and improving mental health compared to usual physiotherapy. Method: A comprehensive search strategy was employed across Embase, MEDLINE, Cochrane Library, and Web of Science databases from inception to 30 May 2024. Comparative studies were identified and screened using PICOS criteria. Data extraction involved detailed study characteristics and outcomes, with methodological quality assessed via the Cochrane Risk of Bias tool. Meta-analyses using random-effects models calculated standardized mean differences (SMDs). Results: Eight studies met the inclusion criteria. Compared to usual physiotherapy, AI-assisted physiotherapy did not demonstrate any statistically significant differences in outcomes across the aspects studied, including pain intensity (SMD = \(-\)0.2711, 95% CI: \(-\)0.5109 to \(-\)0.0313, \(p\) = 0.267), functional impairment (SMD = \(-\)0.2508, 95% CI: \(-\)0.5574 to 0.0559, \(p\) = 0.1089), and mental health (SMD = \(-\)0.0328, 95% CI: \(-\)0.1972 to 0.1316, \(p\) = 0.6956). These findings indicate that AI-assisted physiotherapy had no demonstrable additional effect compared to usual physiotherapy for patients with LBP. Sensitivity analyses were conducted to address inter-study heterogeneity, confirming the robustness of these results. Conclusions: While AI-assisted physiotherapy shows potential in managing LBP by providing personalized treatment and feedback, the current evidence does not demonstrate significant advantages over usual physiotherapy. Further large-scale, long-term, and methodologically rigorous randomized controlled trials are necessary to validate these findings, assess their clinical relevance, and explore broader public health applications.  
low back pain; AI-assisted physiotherapy; meta-analysis; pain management; functional impairment +
Footnote †: journal: Applied sciences
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 4 Human Activity Signatures by Using a Distributed MIMO FMCW Radar System
### Measurement Setup  
To realize the proposed distributed \(2\times 2\) MIMO radar configuration, we have used an FMCW mm-Wave radar operating at 24 GHz to capture the micro-Doppler information of a human body moving in the \(xy\)-plane. The MIMO FMCW radar system operating in the TDMA mode was configured according to the parameters listed in Table 2. For the Ancortek SDR-KIT 2400T2R4 radar system, we deployed RF cables with different lengths to avoid interchannel interference [47]. Owing to the TDMA mode of operation, the PRF of the \(2\times 2\) MIMO FMCW radar system is equal to \(1/2\) of the inverse of the chirp interval \(T_{sw}\); i.e., \(\text{PRF}=1/(2T_{sw})\). In TDMA mode, the transmitter antennas \(A_{1}^{T_{x}}\) and \(A_{2}^{T_{x}}\) of the radar subsystems Radar\({}_{1}\) and Radar\({}_{2}\), respectively, transmit the chirp waveform \(c_{i}(t^{\prime})\) alternately and periodically in their respective time slot according to (2).  
For the proposed distributed \(2\times 2\) MIMO configuration, the indoor environment remains exactly the same as described in Section 3. The activities of a person are observed in a laboratory cluttered with fixed objects such as electronics, chairs, tables, and other  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Symbols** & **Values** \\ \hline RF cable lengths & \((L_{1}^{T_{x}},L_{1}^{R_{x}}L_{2}^{T_{x}},L_{2}^{R_{x}})\) & \((0.3,0.3,7,7)\) m \\ Carrier frequency & \(f_{c}\) & 24.125 GHz \\ Radar’s bandwidth & BW & 250 MHz \\ Sweep time & \(T_{sw}\) & 500 μs \\ Pulse repetition freq. & PRF & 1 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters of the \(2\times 2\) MIMO experimental setup.  
miscellaneous items, as shown in Figure 6. This Figure 6 also illustrates the actual experimental setup, where the placement of antennas shows a close resemblance to the proposed \(2\times 2\) distributed MIMO radar configuration depicted in Figure 7. The boresights of the two radar subsystems are orthogonal to each other, which enables the \(2\times 2\) MIMO radar system to capture the radial velocity distribution \(p_{ii}(v,t)\) using (14) regardless the direction of activity. For practical reasons, we have marked the \(2\times 2\) MIMO radar's operation region according to the field of view (FOV) of the two radar subsystems (Radar\({}_{1}\) and Radar\({}_{2}\)) as shown in Figure 6.
## 5 RIS Implementation Structures
### Metasurface-Based Implementation  
A metasurface is made up of a large number of deeply subwavelength resonating structures, which are closely spaced. The very small size and large number of these tightly  
Figure 3: (**a**) Reconfigurable Intelligent Surface (Reflectarray) (**b**) Reflectarray RIS implementations: A 48-element reflect array-based RIS. Each element is a traditional antenna connected to a phase shifter.  
packed atoms give a massive number of degrees of freedom to control the electromagnetic waves, as shown in Figure 4[19]. Earlier designs of metasurfaces have been based on static preset methods of meta-atoms. As a result, the elements cannot be changed after fabrication. However, this is good enough for optical applications to create custom lenses where the reconfigurability could be accomplished by combining electrical components tuned in two ways: thermally, electrically, or mechanically. In particular, when the antenna size and antenna spacing decrease and the applied amplitude/phase profile becomes roughly continuous along the surface. This allows for a significant degree of versatility in wave-front manipulation of the incident. For instance, in a different direction, each tile may represent the incident wave-front. The increased versatility associated with metasurface-based RIS results in even better results in the literature; however, only real-world experiments could say whether realistic performance gains will result from increased sophistication.
# Aiding Dictionary Learning Through Multi-Parametric Sparse Representation
## 2 The Dictionary Learning Problem
### The Cosparse Model  
While the sparse representation is interested in finding the non-zero coefficients corresponding to a low-dimensional subspace where signal \(\mathbf{x}\) lies, the cosparse representation is interested in finding its complementary subspace, the subspace orthogonal to \(\mathbf{x}\). The sparse model can be seen as a synthesis process, focused on constructing \(\mathbf{x}\) based on the dictionary, and the cosparse model as its analysis counter-part, where the focus is on the original signal \(\mathbf{y}\) and its null subspace.  
As depicted in Figure 0(b), in the cosparse model [17], we apply the analysis dictionary \(\mathbf{\Omega}\in\mathbb{R}^{n\times m}\) to the signal \(\mathbf{y}\) and obtain the sparse vector  
\[\mathbf{\gamma}=\mathbf{\Omega}\mathbf{y}. \tag{5}\]  
The atoms are now the rows of dictionary \(\mathbf{\Omega}\), of which only a few are used in the multiplication with \(\mathbf{y}\). Unlike the sparse model, here we are interested in the atoms that are not used by \(\mathbf{y}\), the atoms that are orthogonal to it and create the zero entries in \(\mathbf{\gamma}\).  
Assume that signal \(\mathbf{y}\) is the noisy version of the original, unknown, signal \(\mathbf{z}\in\mathbb{R}^{m}\) that lies in a lower-dimensional subspace  
\[\mathbf{y}=\mathbf{z}+\mathbf{v}, \tag{6}\]  
where \(\mathbf{v}\) is white additive noise. Let \(\mathbf{\Omega}\in\mathbb{R}^{n\times m}\) be the given cosparse dictionary. Then, recovering \(\mathbf{z}\) means finding the low-dimensional subspace to which it belongs and implies finding the set of atoms \(\Lambda\) from \(\mathbf{\Omega}\) orthogonal to \(\mathbf{z}\). In the literature, \(\Lambda\) is sometimes called the cosupport of \(\mathbf{z}\).  
The \(\ell_{0}\) optimization problem becomes  
\[\min_{\mathbf{z},\Lambda}\quad\|\mathbf{y}-\mathbf{z}\|^{2}\quad\text{s.t.}\quad\mathbf{\Omega }_{\Lambda}\mathbf{z}=0,\quad\text{rank}(\mathbf{\Omega}_{\Lambda})=m-s, \tag{7}\]  
where we are interested in minimizing the distance between \(\mathbf{y}\) and \(\mathbf{z}\) such that the cosupport defines an orthogonal \(m-s\) subspace in which \(\mathbf{z}\) lies. Thus, applying the cosparse dictionary leads to an \(s\)-sparse signal as in Figure 0(b).  
For our purposes, we relax the equality constraints of (7) and move to its convex form  
\[\min_{\mathbf{z},\Lambda}\quad\|\mathbf{y}-\mathbf{z}\|^{2}\quad\text{s.t.}\quad\|\mathbf{ \Omega}_{\Lambda}\mathbf{z}\|_{1}\leq\theta,\quad\text{rank}(\mathbf{\Omega}_{\Lambda })\leq m-s, \tag{8}\]  
where we induce sparsity using the \(\ell_{1}\)-norm just like we did in (2). Focusing on sparsity instead of approximation, we can rewrite (8) as  
\[\min_{\mathbf{z},\Lambda}\quad\|\mathbf{\Omega}_{\Lambda}\mathbf{z}\|_{1}\quad\text{s.t.} \quad\|\mathbf{y}-\mathbf{z}\|^{2}\leq\varepsilon,\quad\text{rank}(\mathbf{\Omega}_{ \Lambda})\leq m-s. \tag{9}\]
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 3 Design and Principle of Path Programming
### Workflow of Multi-Vehicle Path Programming
#### 3.3.4 Improved Path Solution  
This section covers general path programming, and path complementation for failed tasks is discussed in the next section. As the path could be improved and converged into the optimal solution, this critical method was applied as the core of this study.  
In this phase, the 2-opt rules were followed to swap the node lines in the in-route and cross-route movement paths, and the solution gradually converged into the optimal solution. At this point, the distance summation of all paths was minimized to ensure that the assigned distance of each robot car did not exceed the maximum distance limit. The flow chart is shown in Figure 11. First, the flow of the improved TS was executed to improve the cross-route. Then, the single path of each group that was not improved by TS was improved. Until each group was improved, the flow returned to the step involving the improved TS for cross-route path programing.  
When this phase proceeded to the second generation, part of the cross-route improvements could identify a solution that had not been improved and that could be identical to the one obtained in the first generation. If this solution was used to calculate the in-route, it could result in a useless local optimal solution without any further improvements. To avoid this situation, it should be determined whether this solution was improved before proceeding to the second generation. If not, the suboptimal solution from the cross-route path exchange should be implemented for improvement to end the local optimal solution loop and to obtain the global optimal solution.
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 2 Related Works
### Reflection Removal  
Reflection removal treats reflection as a type of noise and aims at approximating the light emitted by the background scenes. The traditional methods [14; 21; 22; 23] set the best segmentation boundary as the target to seek for the dominant transmission layer and the non-dominant reflection layer through the gradient, color, and other fields of pixel-level separation. Recent methods [10; 11; 12; 13; 16; 17] mainly adopt the deep learning (DL) framework to solve this problem. Specifically, all the DL-based methods can be further divided into the following two categories:  
(1) Multi-stage methods: obtaining the most relevant predicted information from an image, and then combining it with other leading information as the prior, thus forming a multi-stage network. For example, [PERSON] et al. [19] proposed a two-stage cascaded network for edge prediction and image reconstruction. To be specific, they designed a network to estimate the transmission edge first and then used this predicted edge to estimate and reconstruct the reflection removal image. Similarly, [PERSON] et al. [11] proposed a concurrent model to predict the edge details of the background. They further proposed a cooperative model [12] to utilize the edge information better. [PERSON] et al. [13] proposed a simple yet effective reflection-free cue for robust reflection removal via subtracting the ambient image from the corresponding flash image in raw data space. Recently, [PERSON] et al. [16] introduced three auxiliary techniques into their architecture. Unlike other reflection removal methods, they used the classifier network to determine whether the mixture image contained reflection.  
(2) One-stage methods: refection removal does not use the complicated network but focuses more on directly adding the prior input or constructing loss functions based on the relationships between layers. For example, [PERSON] and [PERSON] [14] indicated that the transmission is smoother than the reflection; thus, they used the difference to achieve reflection removal with the distribution. [PERSON] et al. [10] proposed a network with the perceptual loss to improve the qualities of the generative image in content, then used an exclusion loss to separate transmission and reflection from the gradient. [PERSON] et al. [17] proposed a cascaded network that iteratively refines the estimates of transmission and reflection layers to boost the prediction quality to each other. The above-proposed methods successfully remove reflection from both synthetic and real-world images.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 1 Introduction
[25] established an STK-based spatial posture simulation system. These visual simulation scenarios, in combination with STK, focus on analyzing the motion and coverage of satellites. When the sensor is operating under the EOIR module, [PERSON] et al. [26] proposed a simulation method for simulating the infrared imaging of space-based infrared system to point source targets and analyzing their characteristics. [PERSON] et al. [27] used it to generate sequence images and proposed an algorithm for detecting spatial targets in and out of the field of view of the sequence frame images. [PERSON] et al. [28] used it to compare the simulation results with their own starfield simulator, and mentioned performance problems of STK during target group simulations; however, they did not solve the performance issue of this module in simulating image output which is very important for real-time visual simulation [29; 30; 31; 32]. The current system has the following main issues: the simulation performance does not match the actual needs, there are limited options for star catalogs, and it is difficult to construct complex scenes. Thus, we construct a space-based infrared point target simulation system to improve performance and practicality. We decompose the EOIR simulation flow and build a lookup table to improve the background simulation efficiency; use external MSX catalogs to generate stellar backgrounds, improving the scalability of the system; and use COM mode to connect STK to adapt to complex simulation needs.
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 2 Synthetic Strategies of Mn-Zn Ferrites
### Hydrothermal Method  
The hydrothermal process is one of the most promising synthesis methods which exploits the solubility of inorganic materials in water at elevated temperatures and pressures, with a resulting production of high-quality nanoparticles with controlled size and size distribution.  
The first pioneering article on the synthesis of Mn-Zn ferrites was published in 1999 using the hydrothermal method, to apply the advances in research on this soft material, which is used mostly in electronics, to the biomedical field [36]. In more recent contributions, monophase Mn-Zn ferrite NPs, with a mean particle size of 7 nm, is used in the presence of citric acid as chelator. Interestingly, the capping agent was not only useful to disperse the nanoparticles by avoiding the aggregation between the cores, but its role was fundamental in increasing the crystal purity of the final nanoparticles, achieving, in addition, a control on the particle growth [37]. Similar evidence was observed by using another capping agent, the oleic acid, in the hydrothermal process. In this case, a significant reduction of the Mn\({}_{0.8}\)Zn\({}_{0.2}\)Fe\({}_{2}\)O\({}_{4}\) crystal size in the presence of the surfactant was observed [38].
## Results
### Atmospheric Investigations
A summary of all the identified anomalies in the atmosphere is reported in Table 2. An \"X\" in the table reports any day for which the surface air temperature, aerosol, SO\({}_{2}\), surface total energy latent heat flux, or total precipitation parameter exceeded the positive threshold of 2 standard deviations of the 40-year background (1980-2021). For surface specific humidity, we extracted the anomalous days when the parameter was below the negative threshold of 2 standard deviations. This choice relied on the fact that the lithosphere, atmosphere, and ionosphere coupling mechanism proposed by [PERSON] and [PERSON] [40] predicts an anomalous decrease in humidity due to the chemical bonds of water vapour molecules in the air with the ionised charges produced by the release of radon or the positive holes emitted from the fault according to [PERSON] [41]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & \multicolumn{4}{c}{**Atmospheric Parameter**} \\ \hline  & **Surface Air Temperature** & **Surface Specific Humidity** & **Aerosol** & **SO\({}_{2}\)** & **Surface Total Energy** & **Total Precipitation** \\ \hline
4 July 2022 & \(\times\) & & & & \\ \hline
7 July 2022 & & & & & \(\times\) \\
8 July 2022 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the atmospheric anomalies identified inside the square area of 3\({}^{\circ}\) sides, centred on the ML3.3 Guidonia 1 January 2023 earthquake. The parameter which is anomalous on a specific day is marked with a “X” in the table. Figure 6: SO\({}_{2}\) maps on 17 July, 23 October and 1 November 2022. The epicentre of Guidonia 1 January 2023 earthquake is shown with a white star. Finally, we compared the volcanic activity in nearby regions in the same period to determine if the atmospheric anomalies could be caused by wind transport of volcanic gases and ash above the research area. Figure 7 compares the SO\({}_{2}\) time series we obtained in the research area with the SO\({}_{2}\) measured by the Italian National Institute of Geophysics and Volcanology (INGV) for three volcanoes (Etna, Stromboli, and Vulcano) and provided in the official weekly or monthly active volcano reports. Considering that the data from the Etna volcano are already normalised, we decided to normalise the other data also, including our time series. Notably, the SO\({}_{2}\) anomalies identified in July 2022 followed some Vulcano degassing. Consequently, they could be due to volcanic activity. On the other hand, the SO\({}_{2}\) anomalies of October and November 2022 could be excluded as not being induced by volcanic activity, as some emissions from the calderas of these volcanos did not precede, but followed these anomalies. We propose that the emissions recorded in October and November 2022 could be from a tectonic source in the Tyrrhenian Sea subduction slab.