esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Ablation Study
#### 4.6.1 Patch-Wise Diffusion and Uncertainty-Guided Module Ablation  
The updated ablation study in Table 4 illustrates the individual and combined effects of the Patch-wise Diffusion (PWM) and Uncertainty-Guided (UC) modules in our proposed model. The baseline model (B), which uses a diffusion model with Unet, achieves a PSNR of 24.029 and an SSIM of 0.782. Adding the patch-wise diffusion model (PWM) to the baseline significantly boosts performance, with PSNR rising to 27.431 and SSIM to 0.894, indicating an improvement in detail retention. Integrating only the uncertainty-guided module (UC) with the baseline results in moderate gains, with PSNR reaching 26.737 and  
Figure 4: Qualitative comparison of different low-light image enhancement methods on other datasets. SCI [75], Zero-DCE++ [50], KinD++ [69], GSAD [28], FourierDiff [76], and our proposed method on the VV, NPE, MEF, LIME, and DICM datasets.  
SSIM 0.835, showing the benefit of uncertainty handling. The full model, which combines both PWM and UC, attains the highest performance, with a PSNR of 27.923 and SSIM of 0.882. These results highlight that both PWM and UC contribute distinct improvements to the model, and their combination leads to optimal enhancement, effectively balancing image clarity and structural integrity in low-light conditions.
# End-Effectors Developed for Citrus and Other Spherical Crops
## 2 Materials and Methods  
### Overall Design of the End-Effector  
By analyzing the picking of citrus by human hands, the movement of human hands can be divided into two types: stable grasping by fitting the inner side of the fingers to the surface of citrus and cutting of fruit stems by a blade. Considering that the task of picking can be completed, and the structure needs to be simple and easy to control, it was decided to design the end mechanism as a three-finger structure to realize the two grasping methods. The gripping process is when the end-effector reaches the designated position, and the motor starts and drives the fingers to perform the gripping action through the mechanical structure. When the gripping force reaches the rated value, the finger stops moving and completes the gripping action; then, the base starts to drive the citrus to rotate so that the blade between the two fingers completes the cutting of the citrus fruit stalk and separates the citrus from the fruit stalk.  
As shown in Figure 1, the designed three-finger clamping end-effector consists of; 1. a connecting chuck, 2. fixed bracket, 3. clamping fingertip, 4. moving linkage, 5. stepping motor, 6. blade-fixed frame, 7. cutting blade, 8. clamping finger. The end-effector can be fixed at the end of the robot arm to complete the action of clamping the citrus fruit and cutting the fruit stalk to realize the picking operation of different kinds of citrus fruit.  
The end-effector of the picking robot holds the citrus fruit with its clamping fingers. To adapt to the different sizes of citrus fruits, the clamping fingers are driven by the stepper motor to maximize the opening of the clamping fingers before grabbing the fruits. When the end-effector reaches the picking point, the gripping finger slides down under the positive drive of the stepper motor, thus driving the gripping finger to close the movement until the gripping finger makes adaptive contact with the target fruit to form a certain envelope on the surface of the citrus, generating a large friction force with a small gripping force to reliably grab the citrus fruit without causing damage to the citrus fruit. After the gripping fingers grip the citrus fruit, the end-effector rotates as a whole so that the cutting blade fixed between the gripping fingers cutting the citrus fruit's stalk.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 4 Experiments Demonstrating Opportunities and Challenges in Hybrid RF-Acoustic Positioning  
To analyze the approach from Section 3 for realistic positioning scenarios, experiments were performed in a true-to-life test environment. In this section, we first describe the test environment and hardware used in the bistatic measurement setup. Consequently, three types of measurements were conducted. In the first two, the interdistance between the microphone-speaker pair and the transmitting and backscatter antenna is increased. In the latter, the angle between the transmitting and backscatter antenna is changed. This paper limits itself to ranging capabilities when acoustic and RF LOS is assured. With median errors below 10 cm for most distances, these experiments show the potential of low-power hybrid RF-acoustic ranging for 3D positioning.
## 2 Electrochemical Properties of Graphene, Graphene Oxide (GO), and Reduced Graphene Oxide (rGO)
The high biocompatibility of graphene-based electrodes for different types of molecules especially glucose due to its high surface interaction area, electrical conductivity, and high electron transfer rate, makes a great advantage to develop more biosensors which can detect various types of molecules, as shown in Figure 4.  
Considering the graphene-based electrodes biosensors, some aspects of electrochemical properties influencing the detection limit of the target need to be studied. Different compositions, synthesis methods, and synthesis batches of the graphene-based electrodes lead to different electrochemical properties such as selectivity, detection limit, and sensitivity of the biosensors. While the orientation of graphene and its diversity and bioreactors could be directly linked to the selectivity and sensitivity of the biosensors, the electrochemical sensing performance could be affected by the redox states of the electrode, and the type of the electrode compositions that leads to different interaction and detection limits of the molecule. Taking into account all of these properties and limitations, graphene-based electrodes for electrochemical biosensors (such as non-enzymatic glucose biosensors) are highly stable/sensitive due to their significant response time [72, 73, 74, 75, 76, 77, 78]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Graphene** & **GO** & **rGO** \\ \hline Electron mobility @ room & \(\sim\)200,000–250,000 cm\({}^{2}\) & 0.1–10 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) & 2–200 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) \\ temperature & V\({}^{-1}\) s\({}^{-1}\) & \\ Surface area & 2630 m\({}^{2}\) g\({}^{-1}\)[38] & 736.6 m\({}^{2}\) g\({}^{-1}\)[52] & 466–758 m\({}^{2}\) g\({}^{-1}\)[53, 54] \\ Thermal conductivity & \(\sim\)5000 W m\({}^{-1}\) K\({}^{-1}\)[38, 54] & 0.5–18 W m\({}^{-1}\) K\({}^{-1}\)[55, 56] & 1390–2275 W m\({}^{-1}\) K\({}^{-1}\) \\ Carbon-carbon bond length & 0.142 nm [54, 55, 56, 57, 58, 59] & N/A & N/A \\ Specific capacitance (depends on & 550 F g\({}^{-1}\) & 215–255 F g\({}^{-1}\) & 210–425 F g\({}^{-1}\) \\ cyclic voltammetry) & [60] & [61] & [60, 62, 63] \\ Electrical conductivity (depends & \(\sim\) \(6\times 10^{5}\) S m\({}^{-1}\) & \(5.7\times 10^{-6}\) S m\({}^{-1}\) & 102–105 S m\({}^{-1}\) \\ on reduction technique) & [53, 64] & [62] & [65–67] \\ Sheet resistance & 200 \(\Omega\) sq\({}^{-1}\)[68] & \(\sim\)10\({}^{10}\)–10\({}^{12}\)\(\Omega\) sq\({}^{-1}\)[51] & \(\sim\)10\({}^{2}\)–10\({}^{6}\)\(\Omega\) s\({}^{-1}\)[51, 67] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Graphene, graphene oxide (GO), and reduced graphene oxide (rGO) properties in general. Figure 4: Schematic diagram of graphene-based biosensors for different biomolecules.
## 2 Modeling Methodology
### Approaches to Capture Soil Behavior during Liquefaction
#### 2.1.2 Effective Stress Analysis  
Effective stress analysis can identify the liquefaction behavior at a desired position in real time by integrating the three steps of the total stress analysis into one. Although the equations and calculations for this analysis are much more complicated and require more time than that of the total stress analysis, highly reliable results can be expected when an appropriate model is used because this approach is more accurate and straightforward. Therefore, this method requires a liquefaction model that can accurately implement the liquefaction behavior of the target soil in the event of an earthquake. In this study, effective stress analysis was applied and the dynamic behavior of the soil-pile-structure system during liquefaction was evaluated using the Finn model, a built-in liquefaction model in FLAC3D. The applicability of the Finn model was verified through comparisons with various field observations and model tests in several studies conducted by [PERSON] & [PERSON] (2004) [19], [PERSON] et al. (2011) [20], [PERSON] et al. (2011) [21], and [PERSON] & [PERSON] (2012) [22]. Moreover, by incorporating the Finn model into Mohr-Coulomb criteria, it demonstrated good compatibility for different types of analysis such as dry ([PERSON] & [PERSON], 2019 [23]) and liquefiable conditions.
## 3 Network Model and Problem Description
### Problem Description  
We assume a typical scenario that all sensor nodes generate packets randomly with a constant rate and the packets should be delivered to the sink before their own deadlines. Take the source node \(s\) in Figure 1 as an example. Our object is to find a delay-constrained energy-optimal route towards the mobile sink \(M\) for data packets generated from \(s\). The end-to-end delay constraint is \(D_{s}\). As mentioned before, we propose to first relay the packets to a node in the destination region via the shortest path. That node is called _destination node_, denoted by \(N_{D}\). Then, the sink will pick up the cached data when it arrives within the time limit. The problem can be formulated as follows:  
\[\text{minimize}\ \sum_{e\in Path(s,N_{D})}ETX(e), \tag{2}\]  
Subject to  
\[\left\{\begin{array}{l}N_{D}\in\Omega(P),\\ I_{M\to N_{D}}\leq D_{s}.\end{array}\right. \tag{3}\]  
Object function (2) minimizes the energy cost taking for transferring a packet from the source node \(s\) to the destination node \(N_{D}\). The cost is defined as the total number of transmissions and can be estimated by the accumulated ETX (Expected Transmission Count) of all links (termed \(e\)) along the path from \(s\) to \(N_{D}\) (termed \(Path(s,N_{D})\)). Equation (3) indicates that \(N_{D}\) is located in the destination region, which means \(N_{D}\) must be an RN, and the time for the sink \(M\) to reach it is less than \(D_{s}\). We can see that the destination region varies as the sink moves.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 7 Numerical Results
### Validation of the Analytical Model  
To validate the analytical results derived from the proposed Markov chain, we developed a discrete event simulator of P2P-LiFi (LOS and NLOS modes), P2P-WiFi, C-LiFi and C-WiFi (with maximum and reduced radio coverage) systems to compare both resultsin different settings. The simulation results are obtained based on Algorithm 2, while the analytical results are obtained using Algorithm 1.  
Figure 12 shows the system performance in terms of the average download times in a P2P network with LiFi communication capabilities in LOS conditions for different values of the upload rate (\(\mu\)) and download rate (\(c\)). From these results, we can clearly see a good fit between the analytical and simulation results, which validates the mathematical model. Also, we can observe that the download rate has a major impact on the system's performance according to the abundance state, in which all leechers can download at the maximum rate, and the conversion rate is described as \(\tau=cx\). The upload rate also has an impact, but is not as significant as the download rate. This is a very important result, since it can provide concrete guidelines to develop specific hardware that focuses on maximizing the download rate. Similarly, in Figures 13 and 14, we can see a very good fit between the simulation and analytical results. We can see from the results in Figure 13 that in a centralized network, the download rate also has a higher impact than the upload rate. However, the upload rate in a P2P network is much more important than in a centralized system, since in the P2P system, the upload rate is used to share the file among nodes, while in the centralized system, the server is the only node uploading the file. As such, the average file download time is lower in a P2P network, compared to the centralized system.  
For the P2P network with WiFi capabilities, we show in Figure 14 the case where the coverage area is reduced in such a way as to form clusters of nodes, reducing the interference with other systems, as shown in Figure 7. In this case, the nodes outside the coverage area of the servers have to wait for the nodes closest to the servers to download the file to connect to them and start their own download. This entails higher download times, as shown in these results. The rationale for reducing the coverage area is to also reduce interference with other systems, since the WiFi channels are usually very congested due to the high number of mobile nodes connected to public sites. However, since the available bandwidth in WiFi systems is much lower than in LiFi systems, this WiFi clustering architecture proves to be very inefficient compared to LiFi communications.  
Figure 12: Average file download time for P2P-LiFi (LOS propagation mode) for different values of the upload and download bandwidths, \(c\) and \(\mu\), for 24 nodes in the system.
# Continuous-Variable Quantum Key Distribution Robust Against Polarization-Dependent Loss
## 3 The PDL-Involved CVQKD System
### Polarization-Dependent Loss in Communication  
In most of birefringent crystals inside, the polarized light absorptions of two orthogonal polarizations are the same. Nevertheless, some of the crystals will have different absorbing ability due to their dichroism. Dichroism is a phenomenon associated with polarization states of input light, which is generated within a crystal. And then it produces the PDL that interfere with the system's performance. For the single-mode optical fiber containing SiO\({}_{2}\), the effect of PDL is very weak. But in optical devices, such as multiplexers, splitters, etc., it should not be ignored since it may make optical signals undergo different losses in different polarization directions. PDL describes the maximum and minimum value of the transmission of light through a device or communication system, in the presence of polarization. It can be expressed as the ratio of power, i.e., \(10\log(\gamma_{max}/\gamma_{min}(dB)\), in consequence, where \(\gamma\) is the optical power taken over by the entire polarization state. The output light power will vary between the maximum and minimum values due to the influence of different polarization states. Thus, PDL could be viewed as polarization selective fading. The impact on system performance is the increased signal distortion [21; 32]. Figure 2a shows that PDL causes the destruction of orthogonality of the PDM-based system, where two polarizations lose their orthogonality after transmission with the different loss in their polarization direction.  
As the signal polarization is not limited to the optical fiber network, the insertion loss of the device varies with the polarization state. This effect increases uncontrollably along the transmission link and has an impact on transmission quality. PDL of individual devices can cause large power fluctuations in the system, thus raising the error rate and causing network failure. PDL may be the main source of pulse distortion and diffusion. The PDL-distributed model is shown in Figure 2b. There are some PDL emulators (PDLE), and the amplified spontaneous emission (ASE) noise depolarized by PDL is loaded, both of which are distributed along a link [33]. Usually, several polarization controllers are inserted before the PDLE to obtain the PDL penalty at a special polarization.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion  
### Simulation Setup  
A QoS-linked privileged content-caching strategy is implemented using ndnSIM [30]. ndnSIM is a module of the NS-3 [41] simulator, which implements the NDN architecture for simulation purposes. ndnSIM executes as a network-layer protocol over any of the link-layer protocols (point-to-point, CSMA, etc). ndnSIM implements NDN components, i.e., PIT, FIB, CS, in a modular fashion, with interfaces for implementing new user-defined algorithms. The modular structure of ndnSIM facilitates modifications for NDN components with no/minimal effect on other modules [30].  
The ndnSIM is executed on a personal computer with an Intel i5 processor having 3.80 GHz clock frequency, 64 GB RAM, and Ubuntu 18.04 operating system. The total number of nodes in the NDN network is \(|M|=500\) and \(|M|=1000\). For a given value of \(|M|\), \((0.1)|M|\) are content producers and \((0.35)|M|\) are QoS nodes as the default setting, if not mentioned otherwise, \((0.25)|M|\) are consumer nodes and \((0.3)|M|\) are intermediate NDN nodes. The 6 LoWPAN protocol, along with the IEEE 802.15.4 standard, is employed for simulating a low-power IoT network scenario. The memory value of the content store is set to 10 MB for intermediate NDN nodes, with a default of 100 MB for QoS nodes if not specified otherwise. A total of 20% of the interest packets generated are for priority content [28]. The simulation scenario is executed ten times for each memory size value of the content store. The average of the outputs is plotted as a result. The results of QLPCC are compared with EQPR [28], PRR [29], probability cache, and LFU and LFF schemes, and a subsequent analysis is provided in Sections 5.2-5.5. The network settings are provided in Table 5.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Comparison with State of the Art
#### 4.3.2 Experiments on UAV-VeID  
On the UAV-VeID dataset, the methods of comparison include [1, 22, 30, 31, 32, 35, 39, 40, 41, 42, 30, 43, 44]. Table 1 compares our proposed DMANet to other methods in the UAV-VeID dataset. For vehicle ReID, an efficient multiresolution network (EMRN) [32], which can implicitly learn collaborative multiresolution features via a unitary deep network, is proposed. Compared with DMANet, Rank-1 and Rank-5 are 13.16% and 8.7% higher than EMRN. DMANet can make better view decisions by mixing and integrating the feature information of different views. In contrast, EMN lacks fine processing and dynamic selection mechanisms in the view of decision making. DMANet achieves better results than EMRN in vehicle ReID tasks. In order to build a model for vehicle re-identification, graph interactive transformer (GiT) blocks are layered. In this model, graphs extract reliable global features, and transformers extract distinctive local features inside patches. Compared with DMANet, Rank-1 is 4.15% higher than GiT. DMANet can better model and handle changes in the vehicle's perspective, integrated channel and spatial feature information from different perspectives. However, it relies more on the transfer mode of the graph structure and cannot fully capture and utilize the critical features brought by the change of perspective. Table 1 shows that, compared with the baseline, DMANet contributes 5.69%, 3.98%, and 3.53% of the Rank-1, Rank-5, and Rank-10 improvement to the four subsets of UAV-VeID. In the end, by fusing attention mechanisms, DMANet can provide more flexibility so that the model can adaptively select and adjust different attention mechanisms according to the needs of specific tasks. This flexibility can help the model category and scale, better adapt to different perspectives such as diversity, and improve the generalization ability and adaptability of the model.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 4 Multipath Data Scheduling
### Data Scheduling
For a multipath connection, the state value of each sub-path is initially set to the same value, and the RTT and packet loss rate of each sub-path are statistically counted to calculate the path state value, which is then used to distinguish the priority of each path. As shown in Figure 3, the first data packet of each chunk is assigned to the sub-path with the highest state value, and subsequent data packets of the same chunk are assigned to the same path. The proposed algorithm integrates the impact of RTT and packet loss rate, thereby improving the sensitivity to path congestion. Figure 3: The principle of the multipath data scheduling.
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
###### Abstract  
Fabric inspection is critical in fabric manufacturing. Automatic detection of fabric defects in the textile industry has always been an important research field. Previously, manual visual inspection was commonly used; however, there were drawbacks such as high labor costs, slow detection speed, and high error rates. Recently, many defect detection methods based on deep learning have been proposed. However, problems need to be solved in the existing methods, such as detection accuracy and interference of complex background textures. In this paper, we propose an efficient segmentation algorithm that combines traditional operators with deep learning networks to alleviate the existing problems. Specifically, we introduce a Gabor filter into the model, which provides the unique advantage of extracting low-level texture features to solve the problem of texture interference and enable the algorithm to converge quickly in the early stages of training. Furthermore, we design a U-shaped architecture that is not completely symmetrical, making model training easier. Meanwhile, multi-stage result fusion is proposed for precise location of defects. The design of this framework significantly improves the detection accuracy and effectively breaks through the limitations of transformer-based models. Experimental results show that on a dataset with one class, a small amount of data, and complex sample background texture, our method achieved 90.03% and 33.70% in ACC and IoU, respectively, which is almost 10% higher than other previous state of the art models. Experimental results based on three different fabric datasets consistently show that the proposed model has excellent performance and great application potential in the industrial field.  
defect detection; fabric defect segmentation; Swin UNet; neural network Article  
A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter