esa-sceva/satcom-chunk-collection huggingface dataset:
_J. Sens. Actuator Netw._**2013**, \(2\), 288-315; doi:10.3390/jsan2020288  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan/  
_Article_  
**TinyCoAP: A Novel Constrained Application Protocol (CoAP) Implementation for Embedding RESTful Web Services in Wireless Sensor Networks Based on TinyOS**  
**[PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2,***  
* Wireless Network Group (WNG), Department of Telematics Engineering, Universitat Politecnica de Catalunya, C/Jordi Girona 1-3, Modul C3, Barcelona 08034, Spain
* i2 CAT Foundation, Gran Capita 2-4 (Nexus Building), 08034 Barcelona, Spain; E-Mails: [EMAIL_ADDRESS] (A.L.); [EMAIL_ADDRESS] (P.M.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-93-401-6013; Fax: +34-93-401-1058.  
_Received: 12 March 2013; in revised form: 16 April 2013 / Accepted: 26 April 2013 / Published: 14 May 2013_  
**Abstract:** In this paper we present the design and implementation of the Constrained Application Protocol (CoAP) for TinyOS, which we refer to as TinyCoAP. CoAP seeks to apply the same application transfer paradigm and basic features of HTTP to constrained networks, while maintaining a simple design and low overhead. The design constraints of Wireless Sensor Networks (WSNs) require special attention in the design process of the CoAP implementation. We argue that better performance and minimal resource consumption can be achieved developing a native library for the operating system embedded in the network. TinyOS already includes in its distribution an implementation of CoAP called CoapBlip. However, this is based on a library not originally designed to meet the requirements of TinyOS. We demonstrate the effectiveness of our approach by a comprehensive performance evaluation. In particular, we test and evaluate TinyCoAP and CoapBlip in a real scenario, as well as solutions based on HTTP. The evaluation is performed in terms of latency, memory occupation, and energy consumption. Furthermore, we evaluate the reliability of each solution by measuring the goodput obtained in a channel affected by Rayleigh fading. We also include a study on the effects that high workloads have on a server.  
CoAP; REST; WSNs; 6 LoWPAN; HTTP; TinyOS
## 3 Proposed Model
### Energy Model  
The energy consumption in a network was calculated using a first-order radio model, which considers both transmitter and receiver components. Energy usage depends on the transmission distance: the free space model is used for shorter distances and the multipath model for longer ones. The energy for transmitting an \(l\)-bit message combines the electronics' energy (\(E_{elec}\)) and the power amplifier's energy, which scales with distance and message size. The energy consumption of the transmitter and the receiver for transmitting an \(l\)-bit message can be calculated as follows:  
\[E_{T}=\left\{\begin{array}{ll}IE_{elec}+le_{fs}\times d^{2},&\quad d\leq d_{ th}\\ IE_{elec}+le_{mp}\;d^{4},&\quad d>d_{th}\end{array}\right. \tag{5}\]  
\[E_{R}=IE_{elec},\quad d_{th}=\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}} \tag{6}\]  
where \(E_{T}\) is the required energy for transmission and \(E_{R}\) is the required energy in the receiver. \(E_{elec}\) is the required energy per bit in both the transmitter and the receiver. The variable \(d\) is the distance between the transmitter and the receiver. The variable \(d_{th}\) is the distance threshold. The variables \(\epsilon_{fs}\) and \(\epsilon_{mp}\) are radio amplifier energy coefficients for the free space and multipath fading, respectively.  
We assumed that there are nodes \(N\) distributed uniformly in an \(M\) area. If there are optimum value of \(k\) clusters in LEACH protocol, there are on average \(N/k\) nodes per cluster (one CH and non-CH nodes). Each CH dissipates energy, receiving signals from the nodes, aggregating the signals, and transmitting the aggregate signal to the BS. Since there are many rounds within the steady phase, the energy consumption by a CH during any given round \(r\) within the steady phase can be calculated as sum of \(E_{T}\) and \(E_{R}\) as well as the required energy for data aggregation \(E_{DA}\) with data aggregation ratio.  
\[E_{CH}(r)=IE_{elec}\bigg{(}\frac{N}{k}-1\bigg{)}+IE_{DA}\frac{N}{k}+E_{T} \tag{7}\]  
where \(l\) is the number of bits in each message and represents the energy expended by the CH to transmit aggregated data to the BS. The \((N/k-1)\) factor quantifies the number of transmission events the CH undertakes per round. Each node transmission to the CH consumes \(E_{T}\), while \(lE_{DA}(N/k)\) represents the energy dissipated due to data aggregation performed by the CH [20]. The energy consumption of a normal member node sending data to its CH in round \(r\) is  
\[E_{non-CH}(r)=E_{T} \tag{8}\]Hence, the residual energy of node \(i\) in round \(r\) can be computed by  
\[E_{(i)res}(r)=\left\{\begin{array}{ll}E_{(i)res}(r-1)-E_{CH}(r),&i\in CHs\\ E_{(i)res}(r-1)-E_{non-CH}(r),&i\
otin CHs\end{array}\right. \tag{9}\]  
where \(E_{(i)res}(r-1)\) is the residual energy of node \(i\) in round \(r-1\).
## 3 Results
### Test of SF Algorithm with PTB Diagnostic ECG Database
#### 3.2.4 Test 4: PLI Linear Amplitude Change  
The test design of PLI linear amplitude change in Table 2 simulates 10 different slew rates of the PLI amplitude \(\Delta A_{PLI}=\pm\)(10, 20, 40, 100, 200 \(\upmu\)V/s), where positive and negative signs indicate tests with increasing or decreasing PLI amplitudes over 10 s (50, 100, 200, 500, 1000 \(\upmu\)V r.m.s.), respectively. Our preliminary observation of RMSE, MAXE, and \(SNR_{imp}\) led to the conclusion that there are not substantial differences between positive and negative sign tests; therefore, Figure 13 reports the statistical evaluation of all the performance metrics combined for \(\pm\Delta A_{PLI}\).  
Figure 13a shows that SF errors gradually increase while increasing the PLI slew rate. The maximal error among the 12 ECG leads is observed for leads II and V1-V3, with median values of RMSE = 1, 2, 4, 8, 14 \(\upmu\)V and MAXE = 5, 7, 12, 24, 42 \(\upmu\)V for \(\Delta A_{PLI}=\pm\)10, 20, 40, 100, 200 \(\upmu\)V/s, respectively. In such test conditions, the maximal error in any lead of any ECG record does not exceed 12, 14, 17, 32, 61 \(\upmu\)V, respectively.  
Considering that the amplitudes of the test sinusoids were designed with the same r.m.s. values over 10 s as Test 1, i.e., 50, 100, 200, 500, and 1000 \(\upmu\)V, the input SNR is the same as the distributions in Figure 9. The improvement in SNR is, however, lower than for Test 1 (by 20 dB), keeping the same coefficient \(SNR_{imp}\) = 39.8 dB for all amplitude slew rates  
Figure 12: SF algorithm performance for PLI constant (common-mode phase test), computed for 10 s with the PTB Diagnostic ECG database (lead II) and added PLI sinusoid with settings according to Table 2 (Test 3). The boxplots present MAXE, RMSE, and \(SNR_{imp}\) statistical distributions (median; box: 25–75%; whisker: min–max).  
and all 12 ECG leads, notably not impacted by lead-specific amplitudes and waveforms, as demonstrated by the ECG example in Figure 14. Here, SF sufficiently filters PLI in the worst-case scenario of \(\Delta A_{PLI}\) = 200 \(\upmu\)V/s so that residual errors and QRS and ST distortions are not visible in the typical ECG visualization plot with the 12 ECG leads. When present, SF error is permanent over the filtering period (keeping a similar trend as in Figure 8b, bottom plot), not deviating more than 50 \(\upmu\)V from the original ECG trace, as can be seen in the zoomed-in part of lead I (grid: 200 ms/div, 50 \(\upmu\)V/div).
# Optical Flow Estimation with Occlusion Detection
## 5 Experiments
### Experiments on Occlusion Detection  
In our first experiment we demonstrate the difference between our various occlusion detection strategies. Therefore, we depicted the results for some frames of the MPI-Sintel dataset (final version) in Figure 7. Through experiments, we find that \(M_{m}\) can generally detect occlusion areas well, but there are also a large number of false positive areas. \(M_{w}\) has high dependency on occlusion thresholds, but this strategy is the most reliable of the three methods in terms of F-measure. \(M_{e}\) is only the candidate areas for occlusion, but it cannot be used as occlusion detection strategy alone. Because of the good performance of \(M_{w}\), we also tried the strategy of \"\(M_{w}\)+regional consistency\", named as \(M_{w+}\). Due to the instability of \(M_{w}\), errors after consistency operation may be magnified. By integrating all the above strategies, we get a more robust occlusion detection algorithm \(M\). This finding is also reflected in the F-measure and AEE indicators of the entire MPI-Sintel dataset (final version). We also compared with the occlusion reasoning algorithm proposed by [PERSON] et al. [44] and mark it as \(M_{wang}\) in Table 1. The main constraint for occlusion reasoning in [44] is motion information and color information, which is essentially similar to our warped image-based strategy proposed in Section 3.2. Compared with [44], our method has achieved similar results, and our method does not depend on CNN, so it does not need a lot of training data and network parameters. From this point of view, our algorithm is competitive.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **F-Measure** & **AEE** \\ \hline \(M_{m}\) & 0.2919 & 3.8244 \\ \(M_{w}\) & 0.4534 & 3.7604 \\ \(M_{e}\) & 0.1167 & 4.1567 \\ \(M_{init}\) & 0.4483 & 3.7828 \\ \(M_{w+}\) & 0.4472 & 3.7760 \\ \(M\) & 0.4715 & 3.7440 \\ \hline \(M_{wang}\)[44] & 0.48 & 6.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: F-measure and AEE of different strategies.  
Figure 7: Occlusion detection on the MPI-Sintel dataset (final version). From top to bottom: overlaid input frames, ground truth, the results of matching-based strategy, the results of warped image-based strategy, the results of edge-based strategy, the results of initial fusion, the results of partial fusion and the results of final fusion. The F-measure is printed in the image.
## 2 The Node Deployment Strategy Based on 2-Fold Coverage  
In this section, we propose a series of calculation formulas for the coverage-based QoS metrics, including information integrity, validity, and redundancy, according to the phenomenon of the different coverage requirements of the monitoring target points in the real area. At the same time, we also determine the node deployment strategy based on 2-fold coverage.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Deep Learning in Image Processing with Copyright Detection
#### 2.1.2 Recent Advancements and State-of-the-Art Techniques  
Recent advancements in deep learning for image processing have seen significant strides in both classical and quantum-inspired approaches [34; 35]. Convolutional neural networks (CNNs) continue to dominate traditional image analysis tasks, while generative adversarial networks (GANs) push the boundaries of image synthesis and manipulation. The integration of quantum computing principles has led to the development of quantum convolutional neural networks (QCNNs) [36; 37], which combine classical CNN structures with quantum circuits to enhance performance. Quantum-enhanced feature extraction techniques are being explored to leverage quantum processing units for improved computational efficiency [20]. Transfer learning between classical and quantum models shows promise in accelerating classification tasks. As demonstrated by IBM Research, quantum kernels offer solutions to machine learning problems that challenge classical methods. Quantum neural networks (QNNs) are emerging as parameterized quantum computational models designed for quantum computers, showing potential in handling complex quantum data [38]. Adaptive layer-wise learning and error mitigation strategies are being developed to optimize these quantum models on near-term processors [39]. The field is increasingly focusing on hybrid quantum-classical-classical algorithms, aiming to synergize the strengths of both computing paradigms for advanced image processing applications.
# A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain
## 2 Background  
### Singular Value Decomposition (SVD)  
Singular value decomposition is a common transform used in numerical analysis. By SVD, a matrix could be decomposed into eigenvectors and eigenvalues [15]. For a matrix \(\mathbf{A}\in\Re^{m\times m}\), the SVD is defined as:  
\[\mathbf{A}\Rightarrow\mathbf{U}\mathbf{S}\mathbf{V}^{\mathrm{T}}, \tag{1}\]  
where \(\mathbf{U}\in\Re^{m\times r}\) and \(\mathbf{V}\in\Re^{m\times r}\) are known as left and right singular vectors, respectively. \(\mathbf{S}=\text{diag}(\lambda_{1},\lambda_{2},\cdots,\lambda_{r})\) is a diagonal matrix where \(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{r}\) are called the singular values of matrix \(\mathbf{A}\). The singular values have good stability. They can resist the slight disturbances in image processing. In addition, the SVD can be performed on an arbitrary matrix. Owing to these facts, the SVD transform has been widely utilized in robust watermarking schemes for copyright protection.
## Appendix B
### Hardware Parameters  
Processor (CPU)  
For VR development and operation, the Intel Core i7/i9 (12 th generation and above) and AMD Ryzen 7/9 (5000/7000 series) processors offer robust multi-threaded computing capabilities. These processors efficiently manage tasks like physical simulation, logical operations, and AI interactions in VR environments, ensuring smooth performance of complex VR applications.  
Memory (RAM)  
For optimal performance, configure at least 16 GB of memory. Although this demo does not involve a large-scale VR scene, future development of extensive VR environments, such as open-world games or expansive virtual exhibitions, would benefit from 32 GB of memory. This capacity ensures stable resource loading and efficient multitasking, minimizing scene loading delays and enhancing user interaction.  
Display Resolution  
The Meta Quest 3 VR headset offers a single-eye resolution of 1920 \(\times\) 1800, while the PICO 4 provides 2160 \(\times\) 2160. For development purposes, a monitor with a resolution of at least 2 K (2560 \(\times\) 1440) is essential to accurately view VR scene details, debug interfaces, and assess interaction logic.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### User Study
#### 3.5.2 Procedure
In ARG, we provided interactive MOP and AR maintenance guidance information. We recorded the time taken by every participant to complete the tasks, which was used to assess efficiency. After completing all the tasks, the participants were asked to fill out the System Usability Scale (SUS) questionnaire [42], which measured the overall system usability. The SUS questionnaire comprises a total of 10 questions. SUS questions 4 and 10 are utilized for assessing learnability, while the remaining 8 questions are employed for evaluating usability. The total SUS score (0-100) indicates the adjective ratings, where 38 is \"poor\", 52 is \"OK\", 73 is \"good\", and 85 is \"excellent\". The participants were also asked to fill out the Subjective Mental Effort Questionnaire (SMEQ) [43], which measured the mental effort required for each experimental condition, and the NASA-TLX [44], which evaluated the subjective feelings of the participants in the  
Figure 11: Avionics equipment installed in the equipment bay. different guidance groups. The score for the SMEQ can be rated: 2, \"Not at all hard to do\"; 12, \"Not very hard to do\"; 24, \"A bit hard to do\"; 36, \"Fairly hard to do\"; and 56, \"Rather hard to do\". We used the Shapiro-Wilk test and one-way analysis of variance (ANOVA) with Bonferroni correction to analyze the completion times for each task. If \(\mathrm{P_{SW}}\), the value of the Shapiro-Wilk test result between the TG and ARG groups, was greater than 0.05, it was indicated that the data followed a normal distribution. If \(\mathrm{P_{BF}}\), the result of Bonferroni correction, was less than 0.05, it was indicated that there was a statistically significant difference between the data for the groups. [PERSON] and Wilcoxon signed-rank tests were employed for indicating significant differences between the two groups on a discrete scale and for questionnaire data. If PWI, the value of the Wilcoxon signed-rank test result, was less than 0.05, it was indicated that there was a significant difference between the two groups. Finally, we analyzed the user feedback. 3. Review and interview (10-20 min)  
We videotaped all the participants performing the tasks. And, when the user study was complete, we asked them to go back and watch the videos and recall the overall experience. The interview included the following questions:  
* Do you have any general comments or questions about the study, task, or system? * What information are you most lacking in visual blind area assignments? * Do you think visual display of bolts is necessary? What about the bolt holes? * How do you feel during the operation of \"Assembly blind spot Perspective\"? * Do you think visual displays of aeronautical cable interfaces are necessary?
## 7 Rationale for ka-band selection
### Technical aspects
#### Minimizing penetration issues over some types of continental surfaces  
Flying AltiKa on an ENVISAT like orbit would allow the acquisition of data over ice sheets, and accurate surface topographies could then be generated: this could indeed be a secondary objective of an AltiKa mission as recalled in the paper introduction. In the Ka band, electromagnetic wave penetration effects are minimized, which is particularly important over continental ice surfaces. Also, the scattering coefficient can be assumed to be inversely proportional to the radar wavelength at a power 4. From Ku to Ka bands, the scattering coefficient will increase by a factor 55 : volume scattering will then be clearly dominant over surface scattering. Inversely, the radar wave extinction will also increase, leading to a penetration depth over the snow surface between 0.1 and 0.3 m (versus2 to 10 m in Ku-band). The altimetric observation and height restitution will thus correspond to a thin subsurface layer. The accuracy will then be considerably improved. Such a topic is discussed in [34].
## 6 Conclusions  
### Conclusion  
In this study, we proposed a method for detecting weed pixels in paddy fields using semantic segmentation based on forward-facing images captured by a camera mounted on an aquatic drone. To facilitate training of the NNs, we constructed a paddy field weed image dataset comprising images taken from an aquatic drone and binary GT masks labeled as either weed or background. A total of 200 images were collected and split into training (80%), validation (10%), and test (10%) sets. We trained two semantic segmentation models, UNet++ and DeepLabV3+, using ResNet-50 pretrained on ImageNet as the encoder. The bagged ensemble UNet++ achieved the best performance. The model achieved an IoU of 0.441, an mIoU of 0.706, and an accuracy of 0.971. Moreover, the prediction results showed no clear instances of incorrect or unreasonable inferences, indicating that the proposed method can accurately identify weed pixels. These findings suggest that the model can effectively capture weed features and support organic farming through precise weed detection. However, the lower accuracy compared to previous studies suggests the inherent difficulty of weed detection from a horizontal viewpoint, highlighting the need for future improvements.
## 4 Evaluation of Model Developments
### Evaluation of Urban Representation  
The evaluation of the single-layer urban scheme has been performed using both the IFS single-column model and ECLand. Urban parameters were first optimised with the IFS single-column model using a Gauss-Newton non-linear least square algorithm and observations of 2 m temperature from 8 European surface sites. The surface albedo, emissivity, volumetric heat content and thermal conductivity were simultaneously optimised for average urban roofs, roads and walls. Monthly comparisons with hourly output for independent (January) observations show model improvement when using the urban scheme at all sites except Warsaw (Figure 25). The Warsaw site is at an airport located outside of the city, which does not provide a realistic representation of an urban environment. When using the urban scheme, the largest temperature changes occur in summer at night with smaller changes in the daytime (figure not shown). These changes typically agree well with observation based studies, which also find urban heat island enhancements are larger at night than during daytime [93; 94].  
High-resolution global ECLand simulations (\(\sim\)1 km horizontal) show expected skin and 2 m temperature enhancements over both large and small urban conurbations (Figure 26). At coarser resolution the signal from smaller conurbations is lost (\(\sim\)9 km) and a small urban signal averaged over an unrealistically large area is simulated at even coarser resolutions (\(\sim\)30 km). It is therefore recommended that a reasonable land surface representation using the single-layer urban canopy model is only derived at resolutions of 9 km or higher. This development shows promising results to be considered as operationally viable for future cycles.  
Figure 25: Hourly observed 2 m temperature taken from 8 urban sites for January 2012 (black circles); also shown are single-column model results using the control model (SCM_43R3: red) and the optimised urban scheme (SCM-URB: blue). Numbers indicate RMSE values when compared to observations (adapted from [69]).
## 3 Task Planning Model and Solving Algorithm
### Optimization Solving Algorithm  
Satellite task planning is a complex optimization problem with multiple combinations and multiple objectives. In this paper, a hybrid algorithm with the GA and SA algorithms was developed to improve the optimization performance of the algorithms, and the advantages of each single algorithm was exploited.  
The combination of GA and SA improves the search ability from all aspects of the algorithm, so the choice of algorithm parameters is not necessarily very strict. When the hybrid algorithm adopts a single algorithm parameter, the optimization performance and robustness are greatly improved, especially for complex large-scale problems [20].  
The GA-SA hybrid algorithm is a two-layer parallel search structure. The hybrid algorithm performs GA and SA searches sequentially at each temperature.  
The initial SA solution is derived from the evolutionary GA result, and the solution obtained by SA's metropolis sampling process becomes the initial population for further evolution in the GA. At the spatial level, the GA provides a parallel search structure to transform the SA into a parallel SA algorithm, so the hybrid algorithm always performs group parallel optimization. Figure 4 is the flow diagram of the GA-SA hybrid algorithm designed in this paper.  
The following is the design process of the GA-SA hybrid algorithm:  
1. GA-SA hybrid algorithm coding rules  
The coding rules of the GA-SA algorithm designed in this paper adopt binary coding. The length of each individual in the population is the number of satellite clustering tasks in one orbital circle. The binary coded 1 represents the clustering task that is executed in this orbit. The 0 represents that a clustering task is not executed in this orbit (Figure 3).