esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 2 State of the Art Control Strategies for District Heating (DH) and District Cooling (DC) Systems
### Demand-Side Management (DSM) in DHC System
Findings indicate that the achieved decline in the peak load of a distribution network supplying 62 buildings is equal to about 24% during a typical winter day. An integrated demand-supply co-optimization methodology is presented in [PERSON] et al. (2019) [88] and applied to the DH system of Gothenburg (Sweden). The space heating demand in buildings is modeled with one thermal zone and the dispatch of heat production units and internal temperature set point variation in the buildings (only upwards) is obtained by mixed-integer linear programming in GAMS with hourly time resolution. The aim is to investigate the flexibility potential of DR exploiting the thermal mass of buildings for a DH network whose characteristics and limitations have been here neglected. All suggested scenarios manage to share the peaks achieving a reduction in on-off switching of the peak units up to 80%. In the context of the H2020 project OPTi [33], some specifications for the implementation of automated DR in DHC system are provided in [89], whereas in the H2020 project E2 District [90], the authors in [PERSON] et al. (2019) [91] applied behavior DR to the demo case of the CIT Bishopstown campus (Ireland). A couple of behavioral parameters such as action-regulation theory, high-performance cycle and planned behavior theory have been embedded in their methodology, and yielded results show that the proposed model can increase the energy-saving up to 4.5%. Cost-saving and emission reduction considering thermal comfort during DR programs have been investigated in [PERSON] et al. (2020) [92] where also dedicated experimental tests using a thermal manikin to assess the effect of draught from cold windows have been performed. The MPC algorithm exploits a dynamic DH price model and included constraints to restrict the temperature of cold windows. Sector-coupling between electrical and thermal grids is also possible by exploiting the presence of HPs and TES systems at the customer sited-station level and allowing their participation in more common electrical DR programs. In [PERSON] and [PERSON] (2017) [93], a 4 GDH application with ULTDH and a booster HP for DHW production is analyzed. Peak shaving and energy-cost saving are the main goals of the suggested MPC strategy that has been implemented in the form of non-convex quadratic programming (QP) and solved with the optimization engine CPLEX. Similarly, in a 5 GDHC application, an ANN-MPC algorithm has been developed in [94]. The binary version of PSO has been applied to minimize the user energy bill and the state change with different time-of-use (TOU) DR scenarios exploiting a composite ANN model of the integrated HP and TES systems. The results show that up to 14.2% of electricity consumption has been shifted from peak to off-peak hours.
# An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
## 4 Numerical Simulation Analysis
It is also shown in the results that the proposed algorithm requires a smaller number of multiplications and additions than the OMP precoding [15] and the SDRAltMin precoding [16] for the same number of RF chains and operating conditions, which makes effective for mmWave systems. This means that the proposed algorithm requires a lesser amount of energy to operate the systems and also results in reduced hardware complexity. On the other hand, the conventional OMP hybrid precoding scheme requires a greater number of complex multiplications and additions, which makes it unsuitable for mmWave communications systems hardware. Figure 5: Comparison of BER under different SNR levels of the proposed algorithm with other state-of-the-art algorithms with \(N_{\mathrm{RF}}^{\mathrm{t}}=N_{\mathrm{RF}}^{\mathrm{r}}=4\) and \(N_{s}=1\). Figure 8 shows the energy efficiency analysis under different number of RF chains for different precoding schemes. As it can be seen, the energy efficiency of all the precoding schemes decreases with an increasing number of RF chains. Moreover, the proposed BSAMIBOMP precoding algorithm shows better energy efficiency than the existing OMP hybrid precoding [15] and SDRAltMin [16] precoding under the same number of RF chains and operating conditions, which makes it more effective for mmWave systems. Therefore, the proposed algorithm has a close performance to Fully-Digital precoding and an overall better performance than other competing alternatives. Figure 6: Comparison of BER under different SNR levels of the proposed algorithm with other state-of-the-art algorithms with \(N_{\rm RF}^{\rm t}=N_{\rm RF}^{\rm r}=4\) and \(N_{s}=3\). Figure 7: Computational complexity comparison of the proposed algorithm with existing algorithms.
# Optimization of Mode-Switching Quality of Hybrid Tractor Equipped with HMCVT
###### Abstract  
During the mode-switching process of a hybrid tractor equipped with a hydraulic-mechanical continuously variable transmission (HMCVT) device, the separation and combination of the clutch will cause transient shocks, affecting the smoothness and driving comfort of the entire vehicle. This article conducts simulation and experimental research on the impact problem when switching from pure electric drive mode to hybrid-power speed coupling mode. Firstly, establish a system dynamics model in SimulationX 3.5 software and build a hardware-in-the-loop (HIL) experimental platform. Secondly, a strategy of \"clutch oil pressure fuzzy control + motor torque compensation\" is proposed to solve the problem of the slow dynamic response of the engine. Finally, the orthogonal experiment range analysis method and variance analysis method are used to optimize the quality of mode switching, with six clutch-switching time sequences as experimental factors. The simulation results show that adopting the strategy of \"clutch oil pressure fuzzy control + motor torque compensation\" and optimizing the clutch-switching timing can effectively reduce the amplitude of output shaft speed reduction, dynamic load coefficient, and impact, and shorten the switching time. The comparison between the HIL test results and the simulation results shows that there is a certain difference between the two, but the overall trend is consistent, which verifies the effectiveness of the proposed control strategy and method.  
hybrid tractor; hydraulic-mechanical continuously variable transmission; orthogonal test; mode-switching quality optimization
# Opportunistically Exploiting Internet of Things for Wireless Sensor Network Routing in Smart Cities
## 6 Conclusions  
Our society is continually adapting and embracing the new paradigms of IoTs and Smart City as they come along with not only new requirements and challenges, but have new opportunities to embrace and exploit as well. Moreover, there is a need to integrate existing systems with new paradigms that utilize new platforms and frameworks.  
In this paper, we have introduced and detailed an IT Platform that exploits IoT devices to bypass existing traditional multihop routing in WSN. This leads to an overall improvement in the performance of WSN with respect to energy consumption, network lifetime, and reception rate without creating any significant load on the sensors. The IT Platform enables WSN to save its communication cost and use its energy mainly in sensing, while giving reward opportunities to the IoT device owners. Currently, our cities have Smart devices, but are not fully integrated with each other and do not exploit the full potential of IoTs and Smart Cities. Thus, IT Platform can be used in such scenarios to improve the efficiency of a deployed WSN using the ever growing number of IoT devices in the city.  
In the future, we plan to use our framework alongside other energy saving recent MAC protocols like TMAC. We will evaluate the effect of using IoT devices if TMAC protocol is used while overcoming the synchronization challenges for mobile IoT. Moreover, we can also extend our work by improving the algorithms for specific application domains and by evaluating our approach with multiple routing protocols. Additional efforts are required to fine tune the frame size and slot size according to the network density and network traffic, which can yield more energy savings.  
All authors contributed together to realize this work and its hard to specify. Conceptualization and methodology by [PERSON], [PERSON] and A.M.K. Software and original draft preparation by S.H. Validation, writing, reviewing, editing, supervision, by [PERSON], [PERSON] and A.M.K. This research work is funded by University of Sharjah, Big Data Minning & Multimedia Research Group. The authors are thankful to the University of Sharjah for its support. Special thanks to [PERSON] and [PERSON] for their valuable discussion and comments. The authors declare no conflict of interest.  
Figure 10: Improvement in Reception rate of WSN when IT Platform is used.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### Authentication and Blockchain Nodes  
The system classifies blockchain nodes into three categories based on their computational capabilities:  
* Miner Nodes: These high-performance nodes generate new blocks and verify transactions. They handle resource-intensive tasks and serve as gateways for transactions and light nodes [7; 8; 9].
* Transaction Nodes: Operating on lightweight systems like Alpine Linux, these nodes relay transactions to miner nodes for verification and processing. While they do not participate in mining, they play a crucial role in maintaining network efficiency [7; 8; 9].
* Light Nodes: These simple IoT devices with minimal computational power are primarily used for sensing tasks. Light nodes rely on miner nodes for data aggregation and fusion, ensuring efficient data processing [42].  
The proposed architecture employs a Proof-of-Stake (PoS) consensus algorithm on the Ethereum blockchain to optimize performance and sustainability. The Proof-of-Stake (PoS) consensus algorithm significantly reduces computational overhead and energy consumption compared with the Proof-of-Work (PoW) algorithm, making it ideal for real-time Industrial Internet of Things (IIoT) environments [43]. Algorithm 1 provides a simplified outline of how the Blockchain Management Module interacts with the controller to secure IIoT data and ensure its traceability through the blockchain. This modular design ensures that the system remains secure, scalable, and efficient, addressing the unique challenges of IIoT environments while leveraging the strengths of blockchain technology.  
```
moduleBlockchainManagement(Controller): # Iterate over each data batch received from the hubs for each data_batch in received_data_batches: # Step 1: Calculate SHA256 hash of the data data_hash = SHA256(data_batch) # Step 2: Generate a transaction transaction = { 'data_hash': data_hash, 'device_id': data_batch.device_id, 'database_id': data_batch.database_id } # Step 3: Append the transaction to the blockchain append_to_blockchain(transaction) # End of data batch processing end module
```  
**Algorithm 1**Pseudocode for Blockchain Management Module in the Blockchain-Integrated IIoT System
## 3 Measurement  
We fabricated and tested eight-element linear arrays operating at 3.5 GHz and 26 GHz. Figure 14a illustrates that the 3.5 GHz HT array is fed via an HT network. The amplitude and phase alterations of radio frequency channels provide distinct control over each excitation of the 26 GHz HT array, as demonstrated in Figure 14b. The S and VSWR parameters of the two antennas are analyzed using a vector network analyzer (VNA), as depicted in Figure 15. Despite frequency variations caused by manufacturing accuracy and measurement errors, the \(\mid\!S_{11}\!\mid\) of both antennas can fall below \(-20\) dB at the operating frequency, as shown in Figure 15a,b. The VSWR results displayed in Figure 15c,d agree to the S parameter well, and the VSWR values are all lower than 1.5 at the operating frequency. However, a very thick tin was used during welding because the thickness of the 3.5 GHz HT array is half of the distance between the SMA connector's core and the ground. Nevertheless, this means that under stress, the SMA and the antenna are virtually connected, which could cause the antenna to have multiple frequency points in the test but not influence its good performance at 3.5 GHz. For the 26 GHz antenna, we employed a GPPO-JHD1 connector but the VNA with an SMA connector; thus, we chose a transfer radio line with length of 10 cm to connect them in this measurement, and this line was not applied in simulations, so the loss of the line makes the measured \(\mid\!S_{11}\!\mid\) and VSWR perform better than the simulations.  
Figure 14: Physical and actual test scenarios: (**a**) 3.5 GHz HT; (**b**) 26 GHz.  
Figure 15: Measurements and simulations of the two eight-element arrays: (**a**) 3.5 GHz \(\mid\!S_{11}\!\mid\); (**b**) 26 GHz \(\mid\!S_{11}\!\mid\); (**c**) 3.5 GHz VSWR; (**d**) 26 GHz VSWR.  
Figure 16a displays the normalized far-field pattern of the HT antenna operating at 3.5 GHz. It is clear that the simulated and experimental results agree well. The simulation and actual beam scanning test results are compared for the HT antenna operating at 26 GHz, as Figure 16b illustrates. The dashed line is the simulation result; the test line is the actual line. The simulation and the measurement results are in good agreement. The measured results of the two antennas further prove advantages regarding the simplicity, null-filling effect, and beam scanning range of the ONF beam depending on the HT approach.  
The comparison with works of recent years is shown in Table 2. We judge the complexity of these methods from aspects of whether requiring special sampling, iterations or structures. It can be seen that HT method proposed here has advantages in simplicity, null filling effect and beam scanning range.
# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
## 2 Related Work
Unlike previous research that mainly focuses on the communication optimization and resource allocation of UAVs, this paper proposes a deep reinforcement learning-based UAV cluster-assisted task-offloading solution, which provides stable and efficient computational offloading services for ground users in the disaster area by jointly optimizing UAV flight trajectories and task-offloading policies.
## 4 Experiments
### Pressure Ulcer Segmentation  
#### 4.3.1 Datasets  
Dongguk University Ilsan Hospital pressure ulcer images (DGU): Pressure ulcers are caused by constant pressure on the skin, so it is important to observe the size and depth of the ulcer. For pressure ulcer segmentation, the experiment was conducted using pressure ulcer images collected at Dongguk University Ilsan Hospital [95]. The dataset includes 101 images taken with a digital camera, and the data were labeled by a clinical specialist.  
Medetec Wound Database (MWD): The MWD is an open dataset that includes various chronic wound types such as malignant wounds and surgical wound infections [96]. The dataset contains 492 images, and it was used for the transfer learning of \(T_{sim}\) for pressure ulcer segmentation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Experiment** & \multicolumn{3}{c}{**Metrics**} \\  & **Acc (\%)** & **Precision (\%)** & **Sensitivity (\%)** \\ \hline Baseline & 75.9 & 52.9 & 58.6 \\ \(T_{sim}\) & 74.2 & 56.0 & **61.6** \\ \(T_{ssl}\) & 73.4 & 55.1 & 43.2 \\ \(T_{rol}\) & **77.7** & **58.7** & 55.9 \\ \(T_{multi}\) & 20.4 & 12.0 & 14.4 \\ \hline Ensemble & **86.0** & **77.3** & **75.7** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transfer learning results for skin lesion classification.  
Advancing the Zenith of Healthcare wound dataset (AZH): For additional training data, this study used the AZH dataset [97], which includes 1109 images of foot ulcers collected over two years from 889 patients during several clinical visits. Annotation of the images was performed by a wound treatment specialist at a wound clinic. Because the AZH images look similar to pressure ulcer images, as shown in Figure 9, the AZH dataset was used for the \(T_{sim}\) experiment.
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 4 Experiments
### Visual Comparison  
Due to its rich texture details, we chose the [PERSON] image to facilitate a comprehensive visual comparison. The outcomes obtained from various competing denoising methods are presented in Figure 4. For closer scrutiny, we zoomed in on specific regions of the image. It is notable that, in regions exhibiting intricate texture details, methods such as FFDNet, DnCNN, and BM3D showed signs of excessive smoothing, leading to the loss of image details. Particularly in [PERSON]'s eye region, our method distinctly outlined her lower eyelashes, a feature smoothed out by alternative methods. Our proposed approach adeptly mitigates visual artifacts while retaining sharp edges and intricate details. The comparative analysis with competing methods underscores the superior visual fidelity achieved by our proposed methodology.  
The distribution of actual noise in real-world applications is often unknown, leading to a significant degradation in the performance of state-of-the-art denoising methods when applied to real-world noisy images. To further illustrate the effectiveness of our proposed method, experiments were conducted on real-world noisy images, and the denoised results were compared with those obtained using FFDNet [27], SwinIR [32], and Restormer [19]. An image of a staircase alongside its denoised versions obtained through different methods is showcased in Figure 5. As depicted in Figure 5, most methods struggled to accurately restore details present in the noisy image, such as the cracks on the wall, whereas our method effectively restored these details. In summary, our approach consistently achieves superior visual fidelity, whether encountering synthetic or real noise scenarios.  
Figure 4: Visual denoising results of various methods on Lena image.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DnCNN** & **N2V** & **CBDNet** & **R2R** & **RIDNet** & **AP-BSN** & **MM-BSN** & **Ours** \\ \hline PSNR & 23.66 & 27.68 & 33.28 & 34.78 & **38.70** & 35.97 & 37.37 & 38.49 \\ SSIM & 0.583 & 0.668 & 0.868 & 0.844 & 0.950 & 0.925 & 0.936 & **0.963** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative comparison of real-world denoising on images in SIDD benchmark dataset. We compared DBSNet with other denoising methods in terms of PSNR and SSIM.
## 3 Numerical Simulation Verification
### Comparative Analysis between Theoretical and Numerical Models  
The radial displacements of 12 points within \(r\) = 5 mm-60 mm at different angles were calculated by FLAC3D, referring to Figure 5a for the specific positions of these 12 points. The curves of \(u_{r}\) and \(r\) were obtained, as shown in Figure 8. As seen in Figure 8, \(u_{r}\) gradually decreased as \(r\) increased, and the decreasing rate was smaller. When \(r\) equals \(6R\), the radial displacement was 0.79 mm (taking the point in the direction of \(\theta=0^{\circ}\)), accounting for 13.3% of the radial displacement at \(r=R\), which was also close to 10.2% of the theoretical model in Equation (24), which proved the accuracy of the model in this study.  
Figure 7: FLAC3D model of bolted rock joints at compression-shear side.  
We can infer from Equation (24) that \(u_{r}\) would be generated when the rock joint was constrained by the bolt under shear load. Points with the same value of \(u_{r}\) were found and connected to a curve to obtain the influence range diagram of the bolts. In this section, we used FLAC\({}^{\text{3D}}\) to calculate the value of \(u_{r}\), and the point where the radial displacement \(u_{r}\) was equal to 0.55 mm was extracted (marked with a red dotted line in Figure 9a) and imported into Origin to draw the influence range diagram of the bolt at the compression-shear joints, as shown in Figure 9b. The bolt was located at the origin of coordinates. Compared with the influence range diagram (Figure 6) derived by the model established in this study, the shapes of the two were basically the same, both an ellipse, which also further verified the correctness of the model established in this study.  
It should be pointed out that the elastic solution of the radial displacement distribution of the bolted joint was derived. However, the rock mass may be in a plastic state after an  
Figure 9: Action range diagram of numerical simulation bolt. (**a**) FLAC3D simulation diagram. (**b**) Contour map.  
external disturbance and the stress state will be different in practical engineering. At the same time, this paper only considers the effect of a single bolt on the rock mass, and the bolt is perpendicular to the joint, which is special. However, the research results are indeed helpful for a preliminary understanding of the deformation characteristics of the bolted joint. Therefore, the authors will discuss the deformation characteristics of the bolted joint in the plastic state and explore the influence of different numbers and sizes of bolts on the bolted joint in future research.
# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology
### Sensor Fusion  
By simulating depth information for the camera, we can investigate the influence of four different sensors for the overall detection score: radar, camera, simulated depth camera, and lidar. In addition to the different sensors, consecutive time steps of radar and lidar sensors are concatenated to increase the data density. While the nuScenes data set allows to concatenate up to 10 lidar sweeps on the official score board, we limit our network to use the past 3 radar and lidar sweep data. While using more sweeps may be beneficial for the overall detection score through the higher data density for static objects, more sweeps add significant inaccuracies for the position estimate of moving vehicles, which are of greater interest for a practical use case.  
As discussed in our main conclusions from the state of the art in Section 3, we fuse the different sensor modalities in an early fusion scheme. In particular, we fuse lidar and camera data by projecting the lidar data into the image space, where the lidar points serve as a mask to associate the color of the camera image with the 3D points.  
To implement the simulated depth camera, we first apply the IP-Basic algorithm to the lidar input point cloud to approximate the depth of the neighborhood area of the lidar points to generate a more dense point cloud. The second step is the same as in the lidar and camera fusion, where the newly created point cloud serves as a mask to create the dense depth color image.  
The radar, lidar, and simulated depth camera data all originate from a continuous 3D space. The data are then fused together in a discrete voxel representation before they are processed with the network presented in Section 3.2. The first layers of the network compress the input data to discrete voxel features. The maximum number of points per voxel is limited to 40 for computational efficiency. As the radar data are much sparser than lidar data, it is preferred in the otherwise random downsampling process to make sure that the radar data contributes to the fusion result and its data density is not further reduced.  
After the initial fusion step, the data are processed in the RadarVoxelFusionNet in the same fashion, independent of which data type was used. This modularity is used to compare the detection result of different sensor configurations.