esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Recent developments in wireless communication have presented a new networking paradigm, Internet of Things (IoT) [1]. IoT is a network of interconnected devices including sensors, health-care devices, home appliances, autonomous cars, and many others. IoT devices are connected to the internet and are uniquely addressable. IoT promises machine-to-machine (M2M) and human-to-machine (H2M) communications any-time, anyplace, with anyone, using any network (or service) [2]. However, maintaining continuous connectivity and the enormous number of IoT devices present challenges to a radio network. The demands of high bandwidth and spectrum resources for IoT applications lead to the spectrum scarcity [3]. Traditional wireless spectrum standards rely on the static spectrum allocation policies where specific frequency bands are assigned to a specific licensed service and its users. Unlicensed users are not authorized to access the licensed bands, resulting in the underutilized bands. Such policies cause unbalanced utilization of spectrum and degrade the spectral efficiency. Thus, static spectrum allocation policies are insufficient to address the high demands of spectrum resources required for the wireless access of large number of IoT sensor devices [4]. Spectrum allocation flexibility and spectrum utilization efficiency, required for IoT, can be achieved with different proposedtechnologies such as long term evolution (LTE) wireless local area networks (WLAN) aggregation (LWA), non-orthogonal multiple access (NOMA), operations in millimeter-wave band, LTE over unlicensed band (LTE-U), ultra-dense 5G small cells, and software-defined cognitive radio network (SD-CRN) [5].
## 4 The Prediction Model When Signal Approaches the Ground Station (Section B)
### Setting Up the Prediction Model Testbed  
We performed our experiment on a building with a reinforced concrete shear wall structure. Figure 8 depicts the setup of the receiving and sending antennas, and Table 2 is an example of the antenna setup features.  
Previous measurements showed high dependency on construction material in determining the primary propagation mode and the amount of attenuation caused by the obstacle. Metal-based construction buildings generated the highest average signal attenuation, 35-40 dB. Concrete caused 25-35 dB and wood 10-25 dB. The transmission was the main propagation mode for wooden and concrete structures.  
Figure 8: The receiving and the transmitting antennas.  
Figure 7: Building shadowing loss.  
propagation by diffraction for metal, which increased from the corners towards the center of the building shadow. The attenuation due to diffraction increased from 5.0 dB to 10.0 dB.  
To better understand the meaning of the following formulas, Figure 9 describes the context of the elements used in the formulas presented in this section. The free-space loss formula is as follows:  
\[L_{fs}=32.45+20\cdot log(d_{(m)})+20\cdot log(f_{(GHz)}) \tag{1}\]  
where \(d\) is the distance defined in Figure 2 and \(f\) is the carrier frequency (GHz). Transmitted power is \(Pt\) (dBm) and received power is \(Pr\) (dBm). The losses through the building are stated as \(L_{E}\), and sending antenna gain and receiving antenna gain in the transmission path are denoted as \(G_{T}\) and \(G_{R}\), respectively. Then we obtain Equation (2):  
\[P_{t(dBm)}+\left(G_{t}-L_{fs}-L_{E}+G_{r}\right)=P_{r(dBm)} \tag{2}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Transmitting Antenna** & **Omni-Directional Vertical Polarization Antenna** \\ \hline Receiving antenna & Horn antenna with vertical polarization \\ Transmitter & Agilent E8267D signal generator \\ RF power amplifier & The output power in the test is set to 33 dBm \\ Receiver & Agilent N9030A signal analyzer \\ \hline \hline \end{tabular}
\end{table}
Table 2: The antenna setup features.  
Figure 9: _Cont._  
Figure 9: (**a**) Experiment results for 2.4 GHz; (**b**): Experiment results for 10 GHz; (**c**) Experiment results for 23 GHz; (**d**): Experiment results for 48 GHz; (**e**) Experiment results for 72 GHz.  
Then, the losses through the building are inferred from Equation (3):  
\[L_{E}=\left(P_{t(dBm)}-P_{r(dBm)}\right)-L_{fs}+\left(G_{t}+G_{r}\right) \tag{3}\]  
The omni-directional antenna used in the experiment is like a half-wave dipole antenna, the gain of which is 2.15 dB, and the normalized directivity function of the electric field of the half-wave dipole antenna is presented by Equation (4):  
\[F(\theta)=sin(\theta) \tag{4}\]  
From Equation (4), we obtain Equation (5):  
\[G_{t}+G_{r}=4.3+10\cdot log\left(\left[F(\theta)\right]^{4}\right)=4.3+10\cdot log \left(sin^{4}\theta\right) \tag{5}\]  
The horn antenna is the receiving antenna in the building scenario, so the receiving antenna gain is appropriately considered.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Electrochemical Etching
The synergy between these techniques could lead to hybrid photodetectors with enhanced responsivity and light-trapping capabilities across a broader spectrum [42]. Material modifications play a crucial role in optimizing the performance of advanced photodetectors. Just as the addition of plasticizers, like glycerol, has been shown to influence the molecular mobility and morphology of poly(vinyl alcohol) (PVA) films, surface modifications in black silicon significantly affect its light-trapping capabilitiesand overall efficiency in photodetectors. Recent studies on PVA films demonstrate how plasticization can decrease crystallinity while increasing the mobility of the polymer chains, therefore, leading to enhanced mechanical properties and flexibility. Similarly, surface treatments of black silicon aim to fine-tune the material's structural and optical properties, improving its performance in light detection applications [43].
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 1 Introduction
Unlike traditional methods, SS3 DNet-AF eliminates intermediate proposals by employing an attention-based fusion (AF) mechanism that seamlessly integrates image features with geometric information. The AF layers enhance the network's focus on the most relevant aspects of input images, enabling accurate 3D reconstructions across diverse object categories. The proposed SS3 DNet-AF method was evaluated on a subset of the ShapeNet dataset, comprising 13 object categories of synthetic 3D models. Experimental results demonstrate the network's competitive accuracy and efficiency in addressing the computational limitations of traditional approaches. However, the proposed single-stage design encounters challenges in handling variations in lighting conditions, such as underexposure and overexposure, where object details become unclear, as well as occluded objects. Nevertheless, these limitations are less critical for most 3D  
Figure 1: Examples from the ShapeNet dataset: 2D images of an airplane and a vehicle alongside their corresponding 3D models viewed from multiple angles. applications, where objects are typically fully visible, making the method suitable for a wide range of practical scenarios. Key contributions of this work include the following:  
1. An improved single-stage framework for 3D reconstruction, eliminating the need for intermediate proposal generation and reducing computational complexity while maintaining competitive performance. 2. The development of an attention-based fusion (AF) mechanism that effectively integrates image features and geometric information, enhancing the reconstruction of 3D models from single-view images. The remainder of this paper is structured as follows: Section 2 reviews related work, summarizing key advancements and challenges. Section 3 presents the proposed methodology in detail. Section 4, Experimentation and Results, covers the dataset overview, experimental setup, an ablation study examining the impact of the initial geometry representation, and an analysis of the attention mechanism's effectiveness by comparing single-stage and two-stage fusion layers. Finally, Section 5 concludes the study.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Real Data Collection from LEO Satellites  
In the majority of cases, software-defined radios (SDR) with passive antennas in receiver modes are used on-board vehicles and aircrafts (UAV) to collect raw data from different LEO satellites, such as Iridium Next, Orbcom, Globalstar and in the future, OneWeb, Starlinks, etc. [155; 156; 94; 157]. The goal is to identify the signal of interest (Tones) with channel frequency and develop or design an adapted receiver to track the frequency, phase and extract Doppler shift-Doppler rate for positioning and localization. In the following, in Figure 36, one can observe the electronic bench installation on-board the aircraft with LEO L-band (1626.27-1626.43 MHz) and VHF/UHF antennas (400.1 MHz) for Iridium Next and Orbcomm data collections, respectively [94]. The optimal sampling rate can be obtained by maximizing the SNR optimization function. That means, at the moment, for each system, a new design and parameter identification for tone detection and tracking are necessary [94].  
In Figure 36, one can observe that a differential positioning and localization mode is possible to achieve in addition to the absolute mode by using LEO satellite signals of opportunity. The huge potential of the present and future constellations, such as described in Table 7, demonstrate the feasibility and the reliability of these constellations when the localization and target tracking would be considered in a denied GNSS environment [94]. Until now, no open public dataset regarding LEO SoOP-based positioning systems has been released; meanwhile, considerable efforts and original results were achieved by the authors of [155; 156; 94; 157].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **No. of SV** & **Orbital** & **Inclination** & **Altitude** & **Period** & **Frequency** & **Coordinate** & **Time** & **System** & **Coding** \\  & **(2015)** & **Plane** & **(Degree)** & **(Km)** & & & **(MHz)** & **Frame** & **System** & **Coding** \\ \hline \hline \multirow{3}{*}{
\begin{tabular}{c} GPA \\ (USA) \\ \end{tabular} } & \multirow{3}{*}{28} & \multirow{3}{*}{6} & \multirow{3}{*}{55} & \multirow{3}{*}{20,200} & \multirow{3}{*}{11 h 56} & \multicolumn{3}{c}{L1:1575.42} & \multirow{3}{*}{WGS-84} & \multirow{3}{*}{GPST} & \multirow{3}{*}{CDMA} \\  & & & & & & & & L5:1176.45 & & \\ \cline{1-1} \cline{5-10} \cline{5-10} \cline{5-10} \cline{5-10
## 3 Results
### Geomorphons-Based Geomorphic Elements Mapping for Describing Subaqueous Bedforms  
Geomorphons requires that the lookup distance has a relatively large size. [PERSON] and [PERSON] [21] suggested using 50 cells, as a larger distance provided no added benefits in their study. We also used a lookup distance containing 50 cells (equal to 50 m) in this study. The function of flatness is more important, and the slot is denoted by 0 for basically horizontal surface with the difference between the nadir and zenith angle smaller than flatness. When the area of flatness is large, a greater number of slots are denoted by 0, which means that a larger area is then defined as being a flat element, as shown in Figure 7. The figures show that the flat element area is too large when flatness is greater than 4\({}^{\circ}\).  
In the case of geomorphons, ridge and valley elements were also used to represent crest and trough bedforms: their scales were determined by flatness, and they increased in accordance with decrements in flatness (Figure 8). Similarly, the size of the pit element was determined by flatness, and smaller degrees of flatness created a greater surface with low roughness into a pit element within troughs of bedforms, as indicated in Figure 8, where the scale of the pit element is enlarged in both the longitudinal and transverse profiles and the decrements in flatness relate to those in cross-sections 1, 2, 4, and 5. Geomorphons use flat element to indicate relatively flat areas under suitable flatness degrees, and a flatness degree of 2\({}^{\circ}\) can be used to filter relatively flat bedforms using flat elements. Cross-section 6 in Figure 8 shows that when flatness was 0\({}^{\circ}\), the flat bed could be redefined as a slope element without a ridge and a valley, and Cross-section 3 shows that relatively flat bedforms were redefined as slope elements coupled with a ridge and a valley (Figure 8). It thus seems feasible to use geomorphons to distinguish flat beds from relatively flat bedforms by combining geomorphons-based element maps when flatness is equal to zero and relatively smaller.  
Figure 7: Geomorphons-based distribution of geomorphic elements with different flatness degrees for the sample data of (**a**) Chizhou Reach of the Yangtze River and (**b**) the Yangtze Estuary.
# A Real-Time Automated System for Dual-Aperture Common-Path Interferometer Phase-Shifting
## 1 Introduction
The computational process for the phase-difference calculation is performed in real time at the image plane. The proposed system is a reliable alternative to improve the accuracy of the phase difference calculation, incorporate low-cost elements, and use a homemade ruling translation system.
## Direct Electron Acceleration with Radially Polarized Laser Beams  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2,*}\), [PERSON] \({}^{3}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{3,*}\)**  
\({}^{1}\) Department of Physics and Centre for Photonics Research, University of Ottawa, 150 Louis Pasteur, Ottawa (Ontario) K1N 6N5, Canada; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) INRS Energie Materiaux Telecommunications, Universite du Quebec, 1650 boulevard Lionel-Boulet, Varennes (Quebec) J3X 1S2, Canada; E-Mails: [EMAIL_ADDRESS] (S.F.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS](N.T.); [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (J.-C.K.)  
\({}^{3}\) Centre d'optique, photonique et laser, Universite Laval, 2375 de la Terrasse, Quebec (Quebec) G1V 0A6, Canada; E-Mails: [EMAIL_ADDRESS] (V.M.); [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (P.-L.F.)  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (C.V.); [EMAIL_ADDRESS] (S.P.); [EMAIL_ADDRESS] (M.P.); Tel.: +1-613-562-5757 (C.V.); +1-514-228-6900 (S.P.); +1-418-656-2753 (M.P.); Fax: +1-613-562-5190 (C.V.); +1-450-929-8102 (S.P.); +1-418-656-2623 (M.P.).  
_Received: 3 December 2012; in revised form: 15 January 2013 / Accepted: 16 January 2013 / Published: 30 January 2013_  
**Abstract:** In the past years, there has been a growing interest in innovative applications of radially polarized laser beams. Among them, the particular field of laser-driven electron acceleration has received much attention. Recent developments in high-power infrared laser sources at the INRS Advanced Laser Light Source (Varennes, Qc, Canada) allowed the experimental observation of a quasi-monoenergetic 23-keV electron beam produced by a radially polarized laser pulse tightly focused into a low density gas. Theoretical analyses suggest that the production of collimated attosecond electron pulses is within reach of the actual technology. Such an ultrashort electron pulse source would be a unique tool for fundamental and applied research. In this paper, we propose an overview of this emerging topic and expose some of the challenges to meet in the future.
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 4 Evaluation and Analysis
### Evaluation of the Proposed DNN-Based Power Allocation  
In this subsection, DBPA is evaluated in terms of the loss rate and average BER by numerical results. As the model aims to obtain the minimum average BER by changing the power allocation ratio, the output of the model is compared to the minimum point of the average BER for learning and will be used as a power allocation ratio for NU.  
Figure 7: BER comparisons. (**a**) BER comparison. (**b**) BER comparison (Transmit SNR 40 dB). (**c**) BER comparison (Transmit SNR 45 dB). (**d**) BER comparison (Transmit SNR 50 dB).  
In Figure 8, the loss of learning is shown along epochs. To create datasets, the locations of FUs, NUs, and LEDs are set as listed in Table 4, and the other simulation parameters are the same as listed in Table 3. The number of epochs for learning was 10,000 and the batch size was 64. We can see that the loss reduces as learning progresses. Figure 9 shows the average BER comparisons between power allocation algorithms with the proposed DNN model. The numerical results based on (30) are compared among power allocation algorithms, including the proposed algorithm. The channel gains are decided by varying the locations of nodes. As the locations of nodes are randomly chosen, the channel gains are randomly decided. According to the simulation results, the range of \(p_{n}\) was from 0.002 to 0.482. The users' BERs are evaluated by power allocation algorithms such as fixed power allocations, fractional transmit power control (FTPC) [28; 29; 30], finite blocklength (FBL)-based power allocation [31], and DBPA. The average BER performances are decided by channel gains and power allocation algorithms. They show that DBPA has the lowest BER performance among all algorithms.  
\begin{table}
\begin{tabular}{l c} \hline LED axis (x,y,z) & 5 m, 5 m, 2.5 m \\ \hline NUs’ axes (x,y,z) & (4.5–5.5) m, (4.5–5.5) m, 1.5 m \\ \hline FUs’ axes (x,y,z) & (3.8–4.5, 5.5–6.2) m, (3.8–4.5, 5.5–6.2) m, 1.5 m \\ \hline \end{tabular}
\end{table}
Table 4: Users’ positions.  
Figure 8: Loss for learning.
## 4 Results and Discussion
### Recognition of Different Spectral Data
Nevertheless, a very important problem is that the recognition effect of PLSDA was achieved every time, based on the optimal parameters obtained by the cross-validation after the division of the samples, that is, the optimal parameters were almost different in each the division of the samples. Therefore, the recognition rate of PLSDA in the table is not steady, and the parameters will be discussed further in Section 4.3.
## 2 DRFM Systems
### Digital Radio Frequency Memory  
The digital equivalent of a tape recorder for radar signals, DRFM systems are generally composed of the following parts: receiver, transmitter, signal processing, digital memory, and the ECM control system. While the antenna and/or array can vary in design significantly, the remainder of the RF front-end is generally a variation of the super-heterodyne receiver [18]. The range of frequencies covered, with the high end being 18 to 40 GHz in radar jammers, requires an external up-conversion and down conversion stage. The frequency translation from IF to baseband is usually performed digitally in modern systems [19].  
Once on the FPGA, the detection and Pulse Descriptor Word (PDW) encoder determine the presence of a signal and encode a PDW. Depending on the signal detected, this could trigger a recording to the DRFM. At this point, the ECM control system takes over, generating an ECM based on the recorder waveform by adjusting the time delay and Doppler shift before re-transmitting.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 5 Discussion
### The Ability to Complete the Task Objectives  
As described in Section 4, regarding the implementation process of various system functions, including recording safety management information, recording inspection information, recording sampling check information, and accident responsibility tracing, it can be ensured that the design of the system architecture fully considers the characteristics of various tasks in the supervision work, and users can use the system to achieve their task objectives. In addition, we also simply tested a network channel whose nodes and configuration are consistent with our description in Section 4.1. Within 180 s, the network can successfully complete 800 transactions with 100% availability; however, more reliable performance test data need to be obtained in the actual application of the system, in view of the complexity of special equipment supervision network.
## 6 Conclusions  
This study demonstrates the effectiveness of integrating multi-source remote sensing data with deep learning techniques for high-precision land cover classification in the CAT region. By leveraging the complementary strengths of high-resolution optical imagery from PlanetScope, multispectral data from Sentinel-2, and SAR data from Sentinel-1, we achieved superior classification accuracy compared to models using single data sources. The Fusion + Sentinel-1 dataset emerged as the most effective, yielding the highest overall accuracy and _F1 score_. This confirms the hypothesis that data fusion can significantly enhance the classification performance by providing comprehensive spectral and structural information. The classification results revealed that 50.3% of the study area is covered by natural vegetation, with forests and savannas each occupying over 20%, while Cerradao covers only 8.4%. Cerradao, a forest-like vegetation type within the Cerrado biome, is notoriously difficult to distinguish from adjacent tropical forests using remote sensing due to its structural similarities. Despite its limited spatial extent, Cerradao plays a critical ecological role in the Amazon-Cerrado transition zone and is experiencing rapid loss. Preserving the Cerrado biome--particularly the Cerradao forest formation--is essential for biodiversity conservation, ecosystem resilience, and maintaining ecological connectivity in this transitional landscape. Our analysis revealed significant fragmentation of natural vegetation, primarily driven by extensive agricultural expansion in the southern regions. While forest areas exhibited high aggregation and connectivity, Cerradao was characterized by more dispersed and irregularly shaped patches. This study highlights the potential of data fusion and deep learning approaches to enhance land cover classification in ecotonal regions, providing valuable insights for biodiversity conservation and sustainable land-use planning in the CAT region.  
Conceptualization, C.L., A.H. and P.d.C.B.; methodology, C.L., A.H. and P.d.C.B.; software, C.L.; validation, C.L., B.S.M., B.H.M.J. and P.d.C.B.; formal analysis, C.L.; investigation, C.L.; resources, B.S.M., B.H.M.J. and P.d.C.B.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L., A.H., B.S.M., B.H.M.J., M.D. and P.d.C.B.;visualization, C.L.; supervision, A.H., M.D. and P.d.C.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
Figure A4: Confusion matrix heat-map for Optical Fusion model.  
Figure A5: Confusion matrix heat-map for Sentinel-2 model.  
Figure A6: Confusion matrix heat-map for PlanetScope model.  
Figure A7: Confusion matrix heat-map for Sentinel-1 model.