esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The paper described the sample preparation and hysteresis measurements of SLM-processed ring samples. The powder employed for manufacturing contained approximately 94.7% Fe, 4% Si and 1.3% Cr. Quasi-static measurements revealed comparable DC magnetic properties to commercial and other additively manufactured soft ferromagnetic materials with DC hysteresis losses of 0.95 W/kg (1 T, 50 Hz) and 2.81 W/kg (1.5 T, 50 Hz). Despite beneficial grain orientation, the sample in the study showed lower saturation magnetization when compared to other commercial electrical steels, most likely due to the pinning effect of intragranular porosities on the large recrystallized grains.  
AC measurements demonstrated a considerable increase in measured iron losses, which were confirmed as predominantly classical eddy current losses. The losses were also shown to intensify in deep saturation of the material: increasing from 8.17 (1 T) to 83.7 W/kg (1.5 T) for the ring sample after the annealing, suggesting the nonlinear saturation core losses component at magnetization in deep saturation. Both empirical and FEM results confirm considerable eddy currents generated in the printed bulk toroidal sample, which increase dramatically at high material saturation. Geometrical manipulation was proposed as a promising method for classical eddy current losses reduction, however potentially resulting in a poor filling factor and thus lower magnetic saturation of the magnetic material. This would result in:  
Substantially increased iron and copper losses if the operation point of the magnetic material is shifted into the nonlinear part of the magnetization curve to compensate for the reduced filling factor; and  
Operating the magnetic material below its optimal knee point, resulting in reduced flux density in the ferromagnetic component.  
Future research of the project will involve the optimization of the material filling factor and structure for minimal iron losses and maximum flux density conducted in the material with considerations to its mechanical properties.  
Conceptualization: A.K.; Methodology: A.R.; Validation, D.L.; Investigation, H.T.; Resources, H.T.; Writing--original draft preparation, H.T.; Writing--review and editing, H.T., A.K., A.B., Supervision, A.K., A.B. A.R., D.L.; Project Administration, T.V.; Funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by Estonian Ministry of Education and Research (Grant PSG-137) and Government of Russian Federation (Grant 08-08). The APC was funded by Estonian Ministry of Education and Research, grant PSG-137.  
Preparation of micrograph samples, SEM and EDX analyses were supported by the projects IUT-19-28 and TK-141.  
The authors declare no conflict of interest.
## 5 Discussion
### Quantifying Urban Interstices: Methodological Innovation and Governance Fragmentation  
We respond directly to the call by [PERSON] and [PERSON] (2018) in \"_Mind the Gaps! A Research Agenda for Urban Interstices_\". The publication emphasizes the critical but understudied role of urban interstices in contemporary urbanization processes [3]. This study employs quantitative methodologies, specifically mobile signaling data and origin-destination (OD) flow analysis, to measure spatial connectivity and functional integration in a fragmented governance environment.  
Unlike most prior interstice studies that rely on qualitative or conceptual approaches [4; 21; 36; 59], this research quantitatively captures population mobility and spatial interactions, thereby enhancing methodological rigor and comparability across cases.  
Secondly, this study highlights the unique characteristics of urban interstices under Chinese distinctive administrative governance systems and state-led urbanization contexts, contrasting with Western models that are primarily driven by market mechanisms. In China, this phenomenon stems from the country's hierarchical administrative structures and political tournament system, shaping interstitial urban spaces that are distinctly different from those in Western contexts [56; 60; 61; 62]. The development of Chinese urban interstices is heavily mediated by administrative boundaries and local governance priorities. This form of administrative fragmentation results in governance mismatches, where local governments, incentivized by economic competition and political evaluations, strategically align with more economically dynamic metropolitan cores rather than their nominal administrative superiors [63; 64]. Thus, this study significantly enriches the global theories of urban interstices by incorporating the critical dimension of governance fragmentation and its interplay with metropolitan spillovers.  
Furthermore, the analysis shows that urban interstices are not passive spillover zones, but active agents of regional restructuring. Mobility is concentrated along infrastructure rich corridors and key transit nodes, notably the city center and Baohua Town, while other areas exhibit weaker connections. Together, these insights contribute to a more nuanced understanding of urban interstices under fragmented governance while offering a foundation for future studies on cross-jurisdictional integration, comparative urban governance, and interstitial dynamics over time.
## 3 Model and Methods
### Statistical-Based Bathymetric Method  
Initially, [PERSON] et al. [7] proposed the CLRM, and then [PERSON] [11] showed that reliable bathymetric information can still be extracted from fused RGB images based on the CLRM; the DBMA-RGB method proposed by [PERSON] et al. [10] also demonstrated the feasibility of applying the model on RGB images. The mathematical formula of the model is expressed as follows.  
\[H=m_{0}W+m_{1} \tag{2}\]  
\[W=\frac{ln(qR_{n}(Bamd_{1}))}{ln(qR_{n}(Bamd_{2}))} \tag{3}\]  
where \(H\) represents water depth, while \(m_{0}\) and \(m_{1}\) are two unknown constants, \(W\) denotes the water depth index, and \(q\) is a constant that ensures the logarithmic expression is mean ingful, typically set to 10,000. \(R_{rs}(Band_{1})\) and \(R_{rs}(Band_{2})\) refer to the surface reflectance data of two bands in the imagery. By selecting appropriate spectral combinations, water depth can be effectively modeled. Currently, a commonly used combination in the field of bathymetry involves using the blue band for \(Band_{1}\) and the green band for \(Band_{2}\).  
This linear model is the most widely used statistical model in bathymetric inversion, but [PERSON] et al. suggested that the results of this linear model are unsatisfactory in the actual bathymetric inversion process, especially in the inversion of shallow water areas, and thus proposed the QPRM instead of the traditional linear model for bathymetric inversion [25]. Its mathematical formula is expressed as follows:  
\[H=m_{0}W^{2}+m_{1}W+m_{2} \tag{4}\]  
where \(m_{0}\), \(m_{1}\), and \(m_{2}\) are all constants to be determined by fitting. Specifically, in this research, water depth imagery at a spatial resolution of 10 m was first obtained based on the water depth mapping method introduced in Section 3.3, using this water depth data as control data. Next, the water depth index in 10 m \(W_{10}\) was calculated through Formula (3) for the 10-m spatial resolution imagery obtained from the resampled GF-1 fused data. The two datasets were then paired to construct a training dataset. To improve the quality of the dataset, anomalous data pairs in the GF-1 data that may be affected by clouds or cloud shadows were manually removed through visual interpretation. After that, a set of coefficients, \(m_{0}\), \(m_{1}\), and \(m_{2}\), at the 10-m spatial resolution can be fitted by QPRM. Finally, the water depth index in 2 m \(W_{2}\) was calculated for the 2-m GF fused data, and based on the scale-invariant effect, the fitted coefficients obtained earlier were directly applied in the QPRM, achieving high-resolution water depth mapping at a spatial resolution of 2 m.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 3 Modeling the Arrival Processes
### Correlated Arrivals  
We also consider for the experimental evaluation in this paper a versatile process able to generate correlated arrivals in the different slots. Specifically, we use the occupancy process of an M/G/\(\infty\) queuing system as our model for non-memoryless arrivals.  
The M/G/\(\infty\) process [38], denoted as \(X\), is a stationary version of the occupancy process of an M/G/\(\infty\) queuing system. In this queuing system, users arrive according to a Poisson process, occupy a server for a random time with a generic distribution \(S\) with finite mean and leave the system.  
In its discrete version--which means that the service times \(S\) of users are iid and take discrete values--if the initial number of users in the system \(X_{0}\sim\mathsf{Poisson}(\lambda\operatorname{\mathbf{E}}\left[S\right])\), and the users' service times have the same distribution as the residual life \(\widehat{S}\) of the random variable \(S\), i.e.,  
\[\operatorname{\mathbf{P}}\left(\widehat{S}=k\right)=\frac{\operatorname{ \mathbf{P}}\left(S\geq k\right)}{\operatorname{\mathbf{E}}\left(S\right)}, \tag{8}\]  
then \(X\) is strictly stationary, ergodic and satisfies the following properties:  
* The marginal distribution is \(\sim\mathsf{Poisson}(\lambda\operatorname{\mathbf{E}}\left[S\right])\).
* The covariance function is \(r_{k}=\operatorname{\mathbf{P}}\left(\widehat{S}>k\right)\), \(k\geq 0\).  
When the covariance function is summable, namely, \(\sum_{k=0}^{\infty}r_{k}<\infty\), the process is termed as short range dependent (SRD); in contrast, if the covariance is not summable, \(\sum_{k=0}^{\infty}r_{k}=\infty\), the process is termed as long-range dependent (LRD). In particular, the M/G/\(\infty\) process exhibits LRD when \(S\) has infinite variance, as in the case of some heavy-tailed distributions. The latter are the discrete probability distribution functions satisfying \(\operatorname{\mathbf{P}}\left(S=k\right)\sim k^{-\alpha}\), asymptotically as \(k\to\infty\). The (discrete) Pareto distribution is a clear example of a heavy-tailed distribution.  
In this work, we choose \(S\)[39] as a discrete heavy-tailed distribution with two parameters, \(m\) and \(\alpha\), that allow to model simultaneously the short-term correlation behavior (by means for example of the one-lag correlation coefficient, \(r_{1}\)) and the long-term correlation behavior (by means of the Hurst [40] parameter, \(H\)).  
For this choice of \(S\), the correlation function can be computed and is given by  
\[r_{k}=\begin{cases}1-\frac{(\alpha-1)k}{m\alpha}&\forall k\in(0,m],\\ \frac{1}{\alpha}(\frac{m}{k})^{\alpha-1}&\forall k\geq m.\end{cases} \tag{9}\]So, if \(\alpha\in(1,2)\), then \(H=\frac{3-\alpha}{2}\in(0.5,1)\), and \(\sum_{k=0}^{\infty}r_{k}=\infty\). Hence, in this case, this correlation structure gives rise to an LRD process. In this work, we use the efficient generator of this version of the M/G/\(\infty\) process described in [41].
# Physical Activity Programmes in the Treatment of Addictions: A Systematic Review
## 3 Results  
### Selection of Studies and Data Extraction Process  
After the search, the title, abstract and keywords of each article were reviewed, with the aim of selecting those that could be relevant and discarding those that did not meet the inclusion criteria.  
The searches were conducted, and the inclusion criteria were applied, obtaining a total result of 50 articles. The first search was performed in the Web of Science (WOS), where 21 articles were found; the second search was performed in the Scopus database, gathering a sum of 22 articles; and the third search was conducted in PubMed, obtaining 7 articles.  
A total of 12 duplicate articles were discarded, obtaining a final result of 38 articles for full-text review. After the application of the eligibility criteria, 28 articles were excluded. Thus, 10 articles were ultimately selected. The reason for discarding the 28 articles excluded from the systematic review was that, although they initially met the inclusion criteria, after reading them thoroughly, some of them were intervention designs (\(n=6\)), reviews (\(n=3\)), systematic reviews (\(n=1\)) or descriptive studies (\(n=3\)), whereas others did not apply physical activity programmes in their treatment (\(n=12\)), and others did not focus on substance addiction but other types of addiction (\(n=3\)). With the aim of reducing the selection bias, each article was reviewed independently by two of the researchers ([PERSON] and [PERSON]), who decided whether each document fit the established criteria. If these researchers did not reach a consensus on the inclusion of an article, a third researcher ([PERSON]) mediated the decision.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
## 5 Discussions  
In this manuscript, we mainly focus on the specific error in MIMO array radar, i.e., the waveform coupling. Besides the specific error, there are some general errors that are common to all the array radars, including the channel gain and phase error, array element position error, carrier error, imaging model error, noise, etc. Since both array element position error and carrier error will eventually lead to channel phase error, they can be dealt with as the channel phase error. For MIMO array imaging, the channel phase errors usually have a great impact. A large phase error may lead to imaging failure. The noise usually has a slight influence, and will reduce the signal-to-noise ratio of the image to  
Figure 8: Imaging result of electromagnetic calculation data via MIMO-LI-Net.  
Figure 7: Imaging results of electromagnetic calculation data via comparison methods: (**a**) matched filtering method; (**b**) 1D-SR method; (**c**) MB-SL0 method; (**d**) CNN method.  
some extent. The mutual coupling among waveforms will enhance the range sidelobes and degrade the image quality eventually. The imaging model error may result in a deformed image, reduce the signal-to-noise ratio, and so on. In this work, we propose a new method to deal with the error of waveform coupling. For the random general errors, some error correction methods will be necessary. There have been many studies focused on correcting the various errors, such as the method in [2]. One possible approach to combine the existing error correction methods with the proposed imaging network is embedding the correction methods into the 'echo transformation module' in Figure 2. In this way, the correction methods can be directly applied with little change. Moreover, another potential approach is described in [27; 28; 29], by combining the step of error correction with the network in the 'feature imaging module', in which the network structure needs to be changed significantly.  
In this work, we train the proposed network via simulated data. There may be some degradations for various real scenarios in practice. In order to improve its performance on real scenarios, one strategy is to build a more accurate simulated system model and produce more accurate simulated training data. In addition, it is also important, for the deep-learning based method, to collect measured data as much as possible. However, often, it is very difficult to obtain enough measured data in practice. Transfer learning, as one of the most important techniques in deep learning, can transfer knowledge from the simulated data to the real data, and then good performance on the real test data can be obtained by fine-tuning the pre-trained network (which is trained via a large number of simulated data) via only a small number of real training data. Therefore, it is possible to expand the proposed network trained via simulated data to different scenarios in real microwave imaging.
###### Abstract  
The introduction of sixth-generation mobile communication technology (6G) poses new requirements for the capacity, rate, latency, and reliability of communication systems. As a vital component of 6G technology, unmanned aerial vehicle (UAV) communications also face various challenges, such as noise interference and limited hardware resources. To meet the high demands of 6G, advanced channel coding techniques need to be adopted. Polar codes, due to their theoretically achievable Shannon limit performance, have potential applications in UAV communication systems. Constructing reliable polar decoding schemes is currently a research hotspot in the field of communications. The Belief Propagation List (BPL) decoding algorithm for polar codes can effectively enhance the accuracy of polar code BP decoding. However, existing BPL decoding algorithms for polar codes face issues such as high hardware resource consumption and unsatisfactory decoding accuracy. Addressing the aforementioned issues, this paper proposes a BPL decoding algorithm for polar codes based on information geometry. An information geometry framework is constructed, where the soft information output by the BP decoder is treated as points on a statistical manifold, and their geometric properties are calculated. By introducing the concept of the soft information centroid and a path selection criterion based on the soft information centroid, combined with geometric distance as a weight, the decoding performance is improved, and hardware overhead is reduced. Simulation results show that under the conditions of a maximum of 60 iterations and 5 decoders, the proposed algorithm reduces the bit error rate by 16.2-74.9% compared to the classic BPL algorithm, providing strong technical support for the application of polar codes in scenarios such as UAV communications.  
U
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 4 Experiment and Analysis  
### Dataset  
We evaluate our method using the DCASE 2020 challenge Task2 dataset [13]. The dataset consists of the ToyADMOS dataset [37] and the MIMI dataset [38], including normal and anomalous sounds of six machine types: ToyCar, ToyConveyor, Valve, Pump, Fan, and Slider. Except for ToyConveyor, which includes six machine IDs, the other types contain seven machine IDs. Here, the machine ID is used to identify different machines with the same machine type. The collected sound signals include the running sound of the target mechanical equipment and the background noise of the real factory. Each sound sample is around 10 s long in the dataset. They are sampled at 16 kHz. The training set only contains the running sound of the machine in a normal state, and the test set consists of normal sounds and various anomalous-state running sounds.
## 5 Simulations  
A numerical model can calculate the power required for traction while the vehicle is moving on the road. This traction power is directly proportional to the force exerted by the electric motor. For vehicle model calculations, the following assumptions hold:  
* The vehicle moves only in the longitudinal direction.
* The system is considered as ideally rigid; no vibration or damping effects are accounted for.
* The tire radius is assumed to be constant.  
A backward vehicle model was built and tested using MATLAB/Simulink(r). The Figure 6 shows the general block diagram of the model, starting from the speed profile given by the driving cycle, which reaches the block where the required mechanical power is calculated. Next, the model is complemented by calculating significant contributions that oppose the vehicle's movement and transform quantities through the gearbox's ratios and the differential. Next, electrical to mechanical conversion takes place in the electric motor, whose efficiency is accounted for. Finally, the induction machine demands variable current profiles according to the traction request in the electrical domain; the way to find the energy consumption is done by solving all equations with respect to time. The time is the time it takes to perform the driving cycle.  
In this context, using a backward model works under the hypothesis that the vehicle can reproduce all the speed points in the input profile. The idea should hold true when simulating standardized driving cycles, as in this case. One benefit of this approach is that the simulation takes a significantly lower amount of resources when compared to forward models [26].
# Complex Wavefront Shaping through a Multi-Core Fiber
## 1 Introduction  
Wavefront shaping has emerged as a powerful tool due to the progress of spatial light modulators (SLMs) enabling program control of light. Specifically, structured illumination facilitates microscopic resolution improvement beyond the diffraction limit [1; 2]. Digital optical phase conjugation (DOPC), which back-projects the conjugated phase of the measured light field for focusing through a scattering medium, enables imaging through deep tissues [3; 4] or intact bones [5; 6] and the monitoring of micromotors [7]. Advances in wavefront shaping through step-index multi-mode fibers [8; 9; 10] enables deep brain imaging with minimum invasiveness [11; 12], providing higher degree of freedom for optical manipulation of microparticles [9] or biological cells [13]. Introduced in [13], a dynamic modulated double-lobe mode profile through a few-mode fiber leads to the rotation of biological cells in a dual-beam trap. However, the decoupled mode from the multi-mode fiber depends on the polarization, fiber length and bending radius [14], which requires complex calibration procedure [8]. The multi-core fiber (MCF) is a bundle of single-mode fiber cores with a diameter around several hundred micrometers. Individually modulating phase of cores transforms the MCF to a phased-array for precise wavefront shaping in the region of interest (ROI) close to the diffraction limit. Recently, it is investigated that the performance of a precisely-twisted MCF is not affected by fiber bending [15], which shows the potential of MCFs becoming a more flexible waveguide beyond multi-mode fibers. Nevertheless, DOPC is required to compensate for the optical path difference between cores which can lead to phase distortion when light propagating through an MCF [16; 17; 18]. Focusing scanning employing simple wavefront shaping through MCFs is thus achieved [19; 20; 21; 22], paving the way to lensless endoscopy. Besides, dynamic wavefront control in an MCF-baseddual-beam trap enables optical manipulation of the cell-orientation. However, the discrete distribution and lower number of cores induce strong spatial sampling when the modulated light is coupled into the MCF, leading to spatial aliasing in the fiber output. Especially for complex wavefront shaping through MCFs, the spatial aliasing can cause strong distortions in the output light field.  
We propose a novel phase retrieval algorithm named Core-Gerchberg-Saxton (Core-GS) which is specialized for complex wavefront shaping through an MCF. The experimentally captured core distribution map is implemented for simulating the spatial core sampling on the MCF facet, overcoming the limitations from the pixelation of MCFs. Complex light fields can thus be formed at the distal far-field of MCFs with high fidelity. Closed-loop control with on-the-fly experimental feedback enables precise intensity control of the wavefront shaping. Benefiting from the rather small diameter of the MCF, our approach can open new perspectives for MCFs becoming a vital waveguide in nanotechnologies and biophotonics, for instance, lab-on-a-chip applications.