esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 6 Conclusions  
This paper illustrates a prototype low-power FPGA-based wireless optical communication system for building-to-building communication utilizing the advantages of license-free FSO technology. LOS communication at a distance of 12 m and data rate of 115 kbps via OOK and 230 kbps via higher-level IM was successfully implemented for various types of data, including text and audio files. A 650 nm 5 mW laser module was used for transmission. A measurement of the system quality was analyzed using a figure of merit that is purely a function of the received data rate and total power used. It was confirmed and proven that by lowering power consumption and increasing the received data rate, an optimum figure of merit and higher system performance can be achieved. Higher data rates can be obtained using the proposed system with advanced light sources as well as extended distance records for much further range outdoor applications. Adaptive algorithms can also be developed for more modulation technique optimization, and the system can be enhanced using artificial intelligence models that would help pre-analyze the system conditions and channel estimation models to reinforce its performance. Using versatile FPGA boards as a base design platform is a robust election in altering the specifications of the system to meet new technological challenges and further modification techniques.  
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**Author Contributions:** R.A.: Methodology, Investigation, System Analysis and Implementation, Writing--Original Draft, and Visualization. H.E.: Software Installment and Simulation Analysis. H.H.A.: Investigation, Supervision, Writing Review, Editing, and Validation of Implementation. S.M.E.: Conceptualization of the FPGA part of the project, Supervision, Writing Review, and Editing. M.A.: Conceptualization of the FSO part of the project, supervision, Writing Review, and Editing. All authors have read and agreed to the published version of the manuscript.  
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**Funding:** This research received no external funding.  
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**Institutional Review Board Statement:** Not applicable.  
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**Indromed Consent Statement:** Not applicable.  
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**Data Availability Statement:** Not applicable.  
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**Conflicts of Interest:** The authors declare that there are no conflict of interest.
## 3 Results and Discussion
### Effect of Oil Viscosity on LSW/EW Performance
Application of standalone LSW/EW is not recommended for reservoirs with an oil viscosity higher than 150 cp. It is better to apply hybrid EOR approaches such as thermal/LSW flooding. By applying thermal methods, oil viscosity decreases which improves the oil recovery in combination with the oil detachment by the LSW/EW interactions.
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 4 Experiments
### Evaluation of Domain-Invariant Feature Representation in Cross-Domain Scenarios  
In cross-domain scenario problems, domain adaptation aims to solve the issue that a model trained on one domain cannot generalize to another domain due to domain-shift issues. This paper follows [67]'s idea and proposes a domain-adaptation method, at the feature layer, to extract domain-invariant feature representations to reduce domain gaps. This section describes the experiments of the proposed domain-invariant feature-extracting method, on GCC and three real-world datasets. The GCC dataset is presented as the source domain and the remaining three real-world datasets are defined as the target domain. The results are shown in Figure 7.  
We propose a new method for extracting domain-invariant features in cross-domain scenarios. First of all, we trained the crowd count models, which each consist of a feature extractor and density estimator. Then, we predicted the density maps, based on the previous phase, and generated pseudo-labels for the real-world datasets. Finally, the domain discriminator was trained, in adversarial mode, with a real-world dataset and GCC dataset. The discriminator was unable to distinguish whether the input images are from the GCC or real-world domain, and simultaneously, the domain-invariant feature extraction layer was reversed. In this section, we verify the efficacy of our proposed domain-invariant feature method. As shown in Figure 7, the methods we provide in this paper can be adapted for medium-sized crowd scenarios, as well as extremely large and empty scenarios. Therefore, we use CSRNet as the backbone of this module and test it on four different domains for satisfactory verification. Table 2 shows the results of the baseline without any adaptation, SFCN (state-of-the-art) [10], and our proposed cross-domain feature-extracting method. It is clear that our proposed method can improve performance in different real-world domains.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 2 Network Slicing in 5G  
Slicing of the network should be done in an end-to-end (E2E) manner in order to provide the dedicated services to specific applications over a common physical infrastructure. There are some technical challenges for the development of network slicing over 5G network that need to be solved. These challenges include efficient management of resources, mobility prediction, security, and end-to-end orchestration. In Sections 5 and 6, we discuss how ML techniques can be utilized to manage the resources intelligently and predict the mobility. Many research projects, such as 5 GEx [8], 5 GNORMA [9], 5G-MEDIA [10], and 5G-XHaul [11], have provided the study of NS enabling technologies using SDN and NFV.  
There is a need for optimization and new architecture to cope with new challenges. The NGMN defines the concept of NS in three layers [12]:  
* Service instance layer.
* Network slice instance layer.
* Resource layer.
## 4 Experiments
### Analysis of Model Generalization and Real-Time Application Feasibility  
The generalization capability of change detection models is manifested in their adaptability to heterogeneous scenarios, unknown ground objects, and variable data characteristics. Although no out-of-distribution (OOD) testing has been performed, the consistent performance of MDNet across three heterogeneous datasets has confirmed its generalization potential. (i) In terms of cross-scenario adaptability, MDNet maintained top-tier or second-tier performance across high-density building changes (WHU-CD), urban-rural construction growth (LEVIR-CD), and low-contrast farmland changes (CLCD), as shown in Tables 2-4. This advantage is attributed to the DPEM's adaptive suppression of pseudo-changes such as illumination variations and seasonal shifts. (ii) For scale robustness, the collaborative mechanism of the EMF and CASAM captures small-target details via deformable convolutions and enhances structural consistency in large regions through cross-layer interactions, enabling adaptation to 0.2-2 m resolution and scenarios with coexisting large and small targets. (iii) Transformer's global modeling reduces reliance on local textures, effectively avoiding misclassification of similar textures in CLCD farmland scenarios, as shown in Figure 9, and mitigating overfitting to specific spatial patterns.  
At the real-time application level, existing efficiency metrics and architectural design provide feasibility support. (i) In terms of computational load, its 26.43 G FLOPs are significantly lower than those of SNUNet (54.83 G) and WS-Net++ (40.59 G), as shown in Table 5. The adaptive convolutions of the EMF and adjacent-layer aggregation design of the CASAM effectively control redundant computations. (ii) With a parameter size of 36.62 M--only 4.0% of WS-Net++'s parameter count--its shared-parameter backbone and modular fusion reduce redundancy, making it suitable for memory-constrained devices. (iii) The modular architecture supports flexible adjustments, providing customization capabilities for real-time scenarios.  
In summary, MDNet achieves cross-scenario generalization through structural design and gains advantages in computational efficiency and deployment flexibility. Its adaptability stems from the architecture itself rather than scenario-specific tuning, confirming its practical application value.
## 5 Throughput Performance Using IEEE802.11 ac Signals
(Binary phase shift keying: BPSK. Quadrature phase shift keying: QPSK. Quadrature amplitude modification: QAM. Minimum Received power: Rmin. Transmission rate:TR. Signal to Noise Power Ratio: SNR)  
Figure 12: Transmission rate (TR) vs. distance between base station (BS) and user terminal (UT) for different \(N_{T}\). the TR without calibration decreased from 600 to 360 Mbps. In contrast, the TR with calibration was almost identical to the ideal TR (without error). Hence, the calibration technique is essential for IBF. Figure 14 shows the average throughput when the control signals in Table 2 are considered. The results with and without CSI feedback and calibration are given for a data size of 40,000 bytes. When calibration was adopted, the throughput was higher than that with CSI feedback, regardless of the transmit distance; thus, the IBF effect was ideally obtained. However, the throughput without calibration was lower than that with CSI feedback when \(d\) was greater than 13 m. Therefore, it is essential to apply IBF with the calibration technique for massive MIMO transmission. Figure 15 shows the throughput improvement produced by the IBF with calibration \(R_{\mathrm{w/cal. }}\) for various data sizes. As can be seen from this figure, a large \(d\) and relatively small data size produce a higher throughput improvement for CSI feedback (\(R_{\mathrm{w/cal.}}/R_{\mathrm{Exp. }}\); the latter is the throughput produced by explicit beamforming with CSI feedback). \(R_{\mathrm{w/cal. }}\) is superior to that without calibration \(R_{\mathrm{w/o\ cal. }}\) for larger data sizes. We can observe from Figure 15 that the throughput attained by IBF with calibration (\(R_{\mathrm{w/cal. }}\)) is twice that produced by explicit beamforming (with CSI feedback, \(R_{\mathrm{Exp. }}\)) and 1.8 times that given by the IBF without calibration (\(R_{\mathrm{w/o\ cal.}}\)). Figure 14: Throughput vs. distance between base station (BS) and user terminal (UT) with and without calibration, and with channel state information (CSI) feedback. Figure 13: Transmission rate (TR) vs. distance between base station (BS) and user terminal (UT), with and without calibration and for ideal case without error.
## 4 Experiments and Result
### Training Process of RL Agent and Results of Experiments  
The digital twin was built based on a simulation model in the FlexSim software, as described in Section 4.2, consisting of objects included in the buffer area and the M1 production cell (the first area after the inflow of semiproducts--see the gray area in Figure 7). Training experiments were conducted for such an area. The simulation model was supplemented with Interface DT FlexSim-DRL Agent modules (see Section 4.4). The training and test experiments were performed on a PC with an AMD Ryzen 5 3600 Six-Core Processor @ 3.60 GHz, and Windows 10 64-bit operating system. The simulation software, the DRL agent, and the required interfaces were installed on this platform. In the first stage, the DRL agent was trained using a DT through continuous trial and error. At each step, the agent received the reward value from the DT and assigned an action. Therefore, the agent's actions influenced the operating sequence of the M1 production cell, and the results obtained there were subject to evaluation. Seven versions of the semiproduct were selected, the process times (\(t_{ij}\)) of which depended on the preceding version at the considered workcell. The processing times ranged from 90 to 210 [s]. The distributions of the arrival rate and versions in the input stream to the buffer are given in the description of each experiment. The initial parameter settings for training PPO and A2C are listed in Table 1. The values of the initial DRL parameters of the algorithms were the default values introduced in the used libraries [57], set by the authors. The models were trained multiple times to achieve convergence in the training process and the highest possible reward values. Several parameters were changed, such as the total number of samples to train on, the number of steps to run for each environment per update, and the discount factor gamma. The results presented in the following subsections include only selected significant values from a series of experiments conducted on the models. In the next stage, the trained models controlled the operation of the DT in experiments to verify their effectiveness. At this stage, the same hardware platform was used to run subsequent replications of the simulation. The value of the reward function (6), corresponding to the efficiency of the production cell, was used as an the evaluation metric.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **PPO** & **A2C** \\ \hline _n\_steps_ & 1024 & 5 \\ _learning\_rate_ & 0.0003 & 0.0007 \\ _gamma_ & 0.99 & 0.99 \\ _total\_timesteps_ & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PPO and A2C initial parameter settings.  
Figure 9: State space, action space, and reward function definition in FlexSim.
## 3 Methods and Results
### Sar
#### 3.2.5 Stereo-Radargrammetry  
The stereo-radargrammetric method for DEM generation requires the collection of a stereo pair of images acquired from different incidence angles. Ultra-fine and fine-quad RS-2 images were acquired with different incidence angles to extract the DEMs of sea-ice and iceberg topography. Features common to both images are matched to estimate the stereo parallax, which can be used to calculate an elevation model of the area; therefore, this method is sensitive to dynamic environments such as sea ice. PCI Geomatica (CATALYST)  
Figure 16: Ice-island topography extracted using shape-from-shading technique (**a**); difference of DEMs (in m) generated using a single-pass TDM InSAR pair (**b**).  
Orthoengine was used to generate the DEMs of the RS-2 data of sea ice around areas near Meighen Island and in Baffin Bay with the technique described in [32].  
Stereo pairs of images were tested in OrthoEngine, with varying parameters with or without applying speckle filters for separate test cases. Tests of stereo images show that a DEM of sea ice and large icebergs can be extracted; however, image matching quality is low for rubble fields, the smooth surfaces of multiyear (MY) ice and young level ice. Dynamic features, such as ice islands in Baffin Bay, can be displaced during the time difference for stereo data acquisition.  
For the Baffin Bay data, RS-2 fine-quad FQ11 and FQ26 (Figure 17a,b) datasets were acquired with one day of time difference, and ice islands were not expected to move during that time period. After analyzing the DEMs extracted from the fine-quad data (Figure 17c), a number of icebergs could be found, which corresponded with the images.
## 4 Case Studies
### Case 2: RTCVD System
#### 4.2.1 Experimental Results Under Variation of the External Disturbances Factor \(\sigma_{0}\)  
After 50 s online, the environment outside the RTCVD system changes. When the radiation coefficient of the quartz chamber decreases, the heat of the furnace body spreads slower, and the temperature of the system undergoes a sudden increase, and after a certain period, the temperature of the system remains stable. The distribution of predicted and corresponding actual outputs is shown in Figure 11. The predicted output of the model is very close to the actual output. Figure 12 shows the actual and predicted outputs of sensors \(S_{5}\) and \(S_{11}\). The experimental results show that the proposed model achieves better accuracy than the comparison spatiotemporal model.  
In addition, the prediction and relative errors are shown in Figures 13 and 14, and the comparison between the TNAE and RLNE is shown in Figures 15 and 16. It can be seen that the proposed method has significant advantages in modeling.  
Figure 11: Measurement output and prediction output under external disturbances in RTCVD.  
Figure 12: Model prediction results of sensors \(S_{5}\) and \(S_{11}\) under external disturbances.  
Figure 14: Relative error of different models under external disturbances.  
Figure 13: Prediction error of different models under external disturbances.  
Figure 15: TNAE comparison of different methods under external disturbance in Case 2.  
2.2 Experimental Results Under Variation of the Internal Disturbances Factor \(\omega_{\mathrm{r}}\)  
Similarly, after 50 s online, the environment within the RTCVD system changes. When the wafer density \(\omega_{r}\) within the system changes the decrease in \(\omega_{r}\) causes the temperature of the wafers to decrease, and the temperature of the system plummets and remains stable after decreasing to a certain level. The prediction output of the 3D-OMSVR-SGD online model was built by varying the parameter \(\omega_{r}\) during the online process and is shown in Figure 17. The actual and predicted outputs of sensors \(S_{5}\) and \(S_{11}\) are shown in Figure 18, and the online prediction error is shown in Figure 19. The relative errors are shown in Figure 20. It can be seen that the proposed 3D-OMSVR-SGD online modeling method can follow the system changes well.  
The TNAE and RLNE are in Figures 21 and 22. The RMSE of the two methods is presented in Table 2. From the simulation results, it can be seen that the 3D-OMSVR-SGD model outperforms the ST-LS-SVM model when either the internal interference or the external interference is varied.  
Figure 16: RLNE comparison of different methods under external disturbance in Case 2.  
Figure 17: Measurement output and prediction output under internal disturbances in RTCVD.  
Figure 19: Prediction error of different models under internal disturbance.  
Figure 18: Model prediction results of sensors \(S_{5}\) and \(S_{11}\) under internal disturbances.
## 5 Summary
### Commercial Sensor Nodes  
Table 4 depicts the construction details and total active power of the most important commercial sensor nodes, _i.e_., motes. This will allow a better understanding of the improvement and impact of using FPGA-based nodes or sensor nodes coupled with FPGA coprocessors. The parameters from Table 4 are based on [11].  
Typical platforms of commercial nodes are usually based on 8-bit (particularly Mica, Mica2 Dot and Mica2) and 16-bit microcontrollers (MSP430) running at low frequency, with simple peripherals such as ADCs in order to connect external sensors, a small battery and a 802.15.4/ZigBee transceiver. As depicted in Table 4, the total active power ranges from 27 mW to 89 mW.
## 4 Experimental Results and Discussion
### The Variance of Relaxation Index  
Table 6 shows the average Relaxation Index values of the children with and without developmental delays identifying the 30 graphic symbol sets with various numbers of components. Table 6 demonstrates that the average Relaxation Index values for children with developmental delays identifying single- and multiple-component symbols were 55.56 (\(SD\) = 8.23) and 61.88 (\(SD\) = 9.06), respectively. The overall Relaxation Index for these participants was 58.53 (\(SD\) = 8.97). By contrast, the average Relaxation Index values for children without developmental delays identifying single- and multiple-component symbols were 60.07 (\(SD\) = 10.98) and 59.37 (\(SD\) = 9.67), respectively. The overall Relaxation Index for these participants was 59.68 (\(SD\) = 10.11). In addition, the overall Relaxation Index values for the total population identifying single- and multiple-component symbols were 58.37 (\(SD\) = 10.10) and 60.11 (\(SD\) = 9.67), respectively. The research team conducted a two-way ANOVA to examine the effects of developmental status and the number of components on the Relaxation Index during graphic symbols identification. The results revealed a nonsignificant interaction between developmental status and the number of components (\(F\) = 1.451, \(p\) > 0.05), indicating no interaction  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Group** & \(N\) & \(M\) & \(SD\) & \(F\) & \(\eta^{2}\) \\ \hline DD—single-symbol & 17 & 60.22 & 14.66 & & \\ multiple-symbol & & 50.00 & 7.29 & & \\ \hline Peer—single-symbol & 34 & 50.33 & 11.13 & & \\ multiple-symbol & 34 & 45.16 & 7.76 & & \\ \hline DD (All) & 17 & 55.41 & 12.59 & 5.845 * & 0.111 \\ \hline Peer (All) & 34 & 47.44 & 9.60 & & \\ \hline Single-symbol (All) & 51 & 54.04 & 13.19 & 6.386 * & 0.120 \\ \hline Multiple-symbol (All) & 51 & 46.59 & 7.82 & & \\ \hline \hline \end{tabular} \({}^{*}p\) < 0.05.  
\end{table}
Table 5: Attention Index of children with and without developmental delays (DD) during PCS identification.  
between these two variables. Subsequently, this study separately examined the main effects of these two variables. However, no significant differences were observed in a one-way ANOVA between developmental status and Relaxation Index (\(F\) = 0.118, \(p\)\(>\) 0.05); no significant differences were observed in a one-way ANOVA between a number of components and Relaxation Index (\(F\) = 0.931, \(p\)\(>\) 0.05). Therefore, no variance of Relaxation Index was yielded by these two variables during graphic symbols identification.