esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### Information and Energy Transfer
Because of underlay spectrum sharing, the transmit power of relay \(R\) is restricted not only by the harvested energy but also by the interference threshold of PU, which is written as  
\[P_{R}\leq\frac{E}{1-t}=\eta\rho(|h_{1}|^{2}P_{1}+|h_{2}|^{2}P_{2} +\sigma_{u,r}^{2})\frac{t}{1-t} \tag{13}\] \[|t_{r}|^{2}P_{R}\leq Q \tag{14}\]  
Equation (13) can be rewritten in a simpler form as  
\[P_{R}\leq\min\biggl{(}\eta\rho(\gamma_{\Sigma}+\sigma_{u,r}^{2})\frac{t}{1-t },\frac{Q}{|t_{r}|^{2}}\biggr{)}. \tag{15}\]  
The signal received at source node \(S_{i}\) is  
\[y_{S_{i}}=h_{i}\sqrt{P_{R}}x_{R}+\tau_{u,i}+n_{i} \tag{16}\]  
where \(\tau_{u,i}\sim\mathcal{CN}(0,\sigma_{u,j}^{2})\) is the interference caused by the primary network, \(n_{i}\sim\mathcal{CN}(0,\sigma^{2})\) is white noise at source node \(S_{i}\). Source node \(S_{i}\) decodes \(x_{R}\) and then uses self-cancellation to decode the intended information. For example, source node \(S_{1}\) decodes \(x_{2}\): \(x_{2}=x_{R}\oplus x_{1}\). According to Equation (16) and [10], the rate region of the BC phase is obtained as  
\[C_{BC}=\left\{(R_{1},R_{2}): \begin{Bmatrix}R_{1}\leq(1-t)\cdot C(Y_{r2})\\ R_{2}\leq(1-t)\cdot C(Y_{r1})\end{Bmatrix} \right\} \tag{17}\]  
where \(Y_{r1}\) and \(Y_{r2}\) are the SINRs from the relay to \(S_{1}\) and \(S_{2}\), respectively, and are written as  
\[Y_{r1}=\frac{|h_{1}|^{2}P_{R}}{\sigma_{u,i}^{2}+\sigma_{1}^{2}} \tag{18}\] \[Y_{r2}=\frac{|h_{2}|^{2}P_{R}}{\sigma_{u,i}^{2}+\sigma_{2}^{2}} \tag{19}\]
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 3 Proposed Location Verification Scheme
### Location Validation  
The location validation is implemented by comparing the similarity between the current channel parameters and the previous ones with preset thresholds, based on a binary hypothesis test. If we denote the difference between the current channel parameters (i.e., \(\phi,\alpha,\theta\)) at successive time as \(\Theta_{\zeta}\) for \(\zeta\in\{\phi,\alpha,\theta\}\), then we have  
\[\Theta_{\zeta}=\sum_{l=1}^{L}\big{|}\xi_{l,XB}(t)-\xi_{l,AB}(t-1)\big{|}^{2}, \zeta\in\{\phi,\alpha,\theta\}. \tag{44}\]  
Let \(\eta_{\phi}\), \(\eta_{\theta}\) and \(\eta_{\alpha}\) be preset thresholds for AAoA, EAoA and path gain, respectively. Next, we design a simple binary hypothesis testing for location validation as  
\[\begin{cases}H_{0}:\Theta_{\zeta}\leq\eta_{\zeta},&\zeta\in\{\phi,\alpha, \theta\},\\ H_{1}:\Theta_{\zeta}>\eta_{\zeta},&\zeta\in\{\phi,\alpha,\theta\},\end{cases} \tag{45}\]  
Under \(H_{0}\), the current transmitter is [PERSON]. In contrast, \(H_{1}\) means that the current transmitter is [PERSON].  
The fundamental principle for the proposed scheme in this paper is that location validation decision is implemented by comparing the similarities between current estimated channel features at time \(t\) and previous ones at time \(t-1\) with the corresponding preset thresholds, based on the framework of hypothesis testing theory. Channel statistics and the temporal values for AAoA, EAoA and path gain at time \(t-1\), and correlation coefficients can be modeled as side information that is known at both the transmitter ([PERSON]) and the legitimate receiver ([PERSON]), but not the adversary ([PERSON]). The legitimate transmitter ([PERSON]) and intended legitimate receiver ([PERSON]) cooperate to secure communication transmission by means of the knowledge of this side information; this is beneficial for both partners. It should be stressed that the validation and efficiency of this idea were already studied from an information-theoretical point-of-view in [30], and the work [30] also provided the right guidance to the appropriate code design in practice.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### Automated Measurements: The Radiometric Calibration Test Site (RadCaTS)
#### 2.2.2 RadCaTS: Part of a Global Radiometric Calibration Network  
The RadCaTS is one of the four original sites in the Radiometric Calibration Network Working Group (RadCalNet), which was established by the Committee on Earth Observing Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Infrared and Visible Optical Sensors (IVOS) [11]. There are currently five instrumented sites located in the USA, France, Namibia, and China, with new sites currently under construction and evaluation [59]. TOA reflectance, bottom-of-atmosphere (BOA) reflectance (or the surface BRF, as denoted in this work), and various atmospheric parameters from all sites are all freely available to registered users at the RadCalNet web portal (www.radcalnet.org, accessed on 1 January 2024). RadCalNet data are currently being used to validate the calibration of a variety of spaceborne and airborne sensors [15; 47; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70]. The web portal also contains documentation for those interested in developing a RadCalNet site, information for the users of RadCalNet data, and historical intercomparison reports from a RadCalNet user workshop. Additionally, the portal hosts the RadCalNet Forum, which allows users to interact directly with the RadCalNet Working Group, including site owners and data providers (forum.radcalnet.org, accessed on 1 January 2024). Notices regarding site status, data availability, and data quality are provided by site operators on a regular basis in order to keep users informed of data outages or quality issues.
## 4 Conclusions and Future Work  
This work proposes a novel method for piezo technology systems as a digital data pipeline for future research on various artificial intelligence (AI) or machine learning (ML) modeling applications. COMSOL Multiphysics was used to create a structure for constructing a digital data pipeline for a smart sensor. Multiple piezo sensors that measure the voltage resulting from a change in the location and mass of a system's load pressure are incorporated into the model structure that we are proposing.  
Seven mesh studies with different mesh parameters have been conducted to ensure mesh quality and guarantee accurate findings. The mesh parameters of studies 6 and 7 converge to the same result with an average error of 1.1% and may thus be utilized to validate the research results. The Von Mises stress distribution throughout the structure has been evaluated by repositioning a 40-kg load. Results indicate that sensors' voltage values increase as the load site moves away from the structure's fixed edge. The highest voltage values have been observed for sensors L2 and R2, which are closest to the load location.  
In this article, we demonstrated the practicality of a system based on piezoelectric pressure sensors that measure the voltages of mechanical loads at various magnitudes and positions (speeds). The primary contribution is a data pipeline for digital environments that will eventually accelerate AI/ML modeling and enhance data cleaning/filtering. Our suggested system may deliver millions of data readings in a relatively short period (days, depending on processing power) and at a cheap cost compared to the time (months, years) and expense of collecting the same data from a real-world environment. The design of the structure, the piezoelectric material, and the dimensions of the piezoelectric sensor can be adjusted and tailored based on the requirements. The collected data set can be customized to expedite data gathering processes for various on-road assessments, such as speed/weight detection, traffic safety tire-asphalt contact friction warning system, road stress points, heavy load traffic flow, etc. A prototype of the suggested design is currently being developed, and its specifics will be detailed in future work.  
Conceptualization, B.M. and S.V.; Methodology, B.M. and M.H.; Project administration, B.M.; Software, B.M., M.H., A.A., N.F., M.A., A.P., A.L. and H.I.T.; Supervision, B.M. and S.V.; Validation, B.M. and S.V.; Writing--original draft, B.M., M.H., A.A., M.A. and S.V.; Writing--review & editing, B.M. and S.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 4 System Model and Route Selection  
This section presents the system model of our testbed, our proposed route selection mechanism, and the end-to-end delay measurement.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 4 Experimental Results
### Experiment 2  
For a certain VMR value of an image before filtering, the corresponding standard deviation \(\sigma_{d}\) of the image after filtering by PMD and WJD is shown in Table 2. The \(\sigma_{d}\) of images after filtering by the PMD are much higher than those of filtering by WJD. We can come to the conclusion that WJD is less sensitive to noise than PMD model. That is to say, anisotropic diffusion shows better performance than isotropic diffusion in terms of edge preservation and de-noising.  
In this experiment, \(\lambda(I_{g}(t))=\mathrm{d}(\exp(-t/K^{2}/2))/\mathrm{d}t\), \(K\) is set to be 2, 4, 6, 8, and 10, respectively, and the iterative time is set to be 15. Table 3 indicates that when iterative time is kept unchanged for  
\begin{table}
\begin{tabular}{c c c} \hline \(\sigma_{d}\) & \(\sigma_{r}\) & MR (1/VMR) \\ \hline
4 & 35.0 \(\pm\) 1.7 & 0.555 \(\pm\) 0.038 \\
8 & 27.3 \(\pm\) 1.3 & 0.483 \(\pm\) 0.027 \\
12 & 25.0 \(\pm\) 1.5 & 0.447 \(\pm\) 0.025 \\
16 & 22.8 \(\pm\) 1.3 & 0.457 \(\pm\) 0.017 \\
20 & 21.5 \(\pm\) 1.4 & 0.471 \(\pm\) 0.016 \\
22 & 21.1 \(\pm\) 1.6 & 0.477 \(\pm\) 0.021 \\
24 & 20.9 \(\pm\) 1.0 & 0.483 \(\pm\) 0.022 \\ \hline \end{tabular}
\end{table}
Table 1: The MR (1/VMR) (MR, an intermediate variable; Mean to Variance Ratio, VMR) value and \(\sigma_{r}\) value that corresponding to the \(\sigma_{d}\) in the Bilateral Filter (BF).  
Figure 3: The relationship of \(\sigma_{d}\), \(\sigma_{r}\) and MR (1/VMR) (MR, an intermediate variable; Mean to Variance Ratio, VMR): (**a**) the relationship between \(\sigma_{d}\) and \(\sigma_{r}\); and (**b**) the relationship between \(\sigma_{d}\) and MR (1/VMR).  
15, the value of MR rises as \(K\) decreases. Therefore, we can conclude that the high \(\varphi_{i}\) leads to high degree of noise.  
From the observation of Figure 4e,f,h,i, the WJD model results in more continuous edge than the PMD.  
Figure 4: Visual effect between images filtered by Perona–Malik diffusion (PMD) model diffusion and Weickert J Diffusion (WJD) model diffusion: (**a**) original image; (**b**) one segment of (**a**); (**c**) another segment of (**a**); (**d**) filtered image by PMD; (**e**) one segment of (**d**); (**f**) another segment of (**d**); (**g**) filtered image by WJD; (**h**) one segment of (**g**); and (**i**) another segment of (**g**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(K\) & **10** & **8** & **6** & **4** & **2** \\ \hline MR (1/VMR) & 0.548 \(\pm\) 0.011 & 0.585 \(\pm\) 0.015 & 0.629 \(\pm\) 0.020 & 0.670 \(\pm\) 0.022 & 0.699 \(\pm\) 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MR (1/VMR) value of PMD for different values of \(K\), which corresponds to \(\varphi_{i}\).
# Querying on Federated Sensor Networks
## 4 Results  
### Simulation Environment  
We evaluated querying protocols on an FSN topology designed according to the north campus buildings of University at Buffalo (UB), State University of New York. We name our FSN topology _UB topology_, which is demonstrated in Figure 3. In this figure, each set of buildings is representing an island that is shown by blue circles. We assume that a WSN is deployed in each building, and WSN nodes in each building are assigned to the corresponding IG that is assumed to be deployed on the building roof. We use the Network Routing API [9,10] of ns-2 [11] for our simulations. Table 2 lists our simulation parameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Querying Protocols & Rumor R. [5] DQT [6] GHT [7] D. Diffusion [8] L-FSN \\ Island size by gateway(\# of islands) & 1[1] 2[3] 3[3] 4[2] 6[1] 9[2] \\ Path policy & The shortest path selection \\ Area & 1500 meter \(\times\) 2000 meter \\ Sensor node range & 100 meter \\ Gateway range & 400 meter \\ Simulation time & 300 second \\ Number of buildings & 48 \\ Number of nodes in each building & 9 \\ Number of gateways on each building & 1 \\ Number of repeater gateways & 5 \\ Event start time & 50.th second \\ Query start time & 100.th second \\ Preamble packet length & 271 bytes \\ Packet length & 36 bytes \\ Low-Power-Listening power for one second & 0.263 milliWatt \\ Transmit power for 1 byte & 59.73 milliWatt \\ Receive power for 1 byte & 44.73 milliWatt \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for the layered and flat Federated Sensor Network models.  
Figure 3: FSN topology in University at Buffalo, State University of New York.
## 2 Visual Coding Fundaments
The main idea is to reduce the end-to-end delay exploiting multiple paths and increase the error resilience of the communication, since packet losses will not interrupt the stream (just temporally reducing the perceived quality of the transmission). Codecs that do not generate substreams in such way are referred as single description codecs. * _DVC_: Distributed Video Coding may significantly benefit video transmission in wireless multimedia sensor networks. In this technique, the encoders are less complex than the decoders. In other words, less energy is expected to be consumed in source nodes when using codecs based on distributed video coding, when compared with codecs based on other usual techniques. The complexity is shifted to the destination (sink), which is expected to be resource-full. The main idea of codecs based on distributed video coding is to exploit the source statistics at the decoder. For that, such coding technique takes advantage from the fact that there is a high degree of spatiotemporal information in data retrieved by visual sensors [14]. DVC is based on [PERSON] and [PERSON]'s [15] and [PERSON]'s [16] works, but many variations of these original investigations can be found in the literature [17,18]. In the last few years, the research community has wondered about the proper codecs to be used in WMSN applications [3,5,6]. Initially, it has to be considered that WMSNs typically have very specialized applications, such as fire monitoring, wildlife observation and battlefield surveillance, just to cite a few. The desired quality of received data by the sink also varies according to the application, but it is reasonable to conceive that most monitoring applications will not need such good images as videoconferences, for example. The energy and processing constraints of such networks also influence the required characteristics from multimedia codecs. Nevertheless, we believe that the employed multimedia coding techniques in WMSN applications should combine high compression efficiency, low complexity and error resilience, saving energy in source node and through the entire path(s) toward the sink. The next section describes some issues of cross-layer design in wireless sensor networks. In Section 4, such issues are exploited by many multimedia-based cross-layer optimization solutions.
## 4 Results
### Results and Comparison
#### 4.4.2 Deep Globe Data  
Road extraction in Deep Globe data is obviously challenging due to the large intra-class variances and small inter-class distinctions. The DPBFN is effectively applied, as illustrated in Figure 8, with focused details in Figure 9 using yellow rectangles. Two images with ambiguous road-class distinctions were deliberately chosen. The test images, found in both figures and maps, predominantly encompass rural terrains where the roads are narrow, and their texture resembles that of cultivated land.  
The statistical analysis of Table 5 shows that the overall accuracy of the DPBFN is higher than that of the other methods. Our method shows that the classification accuracy of the two shown images is the highest, with an average OA of 96.57% and an average IoU of 96.11%. U-Net holds the last classification rank, with an average OA of 92.62% and an  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IOU (\%)** \\ \hline \multirow{6}{*}{Massachusetts} & U-Net & 97.26 & 76.91 & 74.00 & 74.66 & 59.57 \\  & DeepLabv3+ & 97.34 & 80.01 & 70.46 & 74.94 & 59.92 \\  & RoadFormer [23] & - & 80.70 & 77.60 & 79.2 & 65.50 \\ \cline{1-1}  & Swin-UNet & 98.16 & 79.03 & 76.84 & 77.92 & 65.48 \\ \cline{1-1}  & TransFuse & 98.09 & 76.46 & 79.38 & 77.89 & 65.76 \\ \cline{1-1}  & DPBFN & 98.26 & 80.79 & 79.38 & 80.08 & 66.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Various compared results on the Massachusetts road test set.  
Figure 8: Different results outputting of five methods from the Deep Globe dataset.  
Figure 9: Local amplification results for Figure 8.  
average IoU of 91.52%. The rest of the network performance is between the two, the second models are TransFuse and Swin-UNet, the average OA is 95.89% and 95.33%, respectively, and the average IOU is 95.39% and 95.35%, respectively. Although the DPBFN has achieved better results, the fracture and mis-extraction of rural road extraction results will be more obvious than in urban roads.  
In the Table 6, it can be seen more clearly that the DPBFN has better extraction accuracy when facing the bifurcation road, and the phenomenon of mis-extraction and fracture of the bifurcation road is clearly less than that of other network models. And in the figure-the overall extraction comparison chart, the extraction accuracy of the DPBFN is also higher, and the data performance in the table is also better. The OA and IOU reach their highest at 95.25% and 72.08%, respectively, which is significantly improved compared with other models.
## Materials and Methods  
### Database Description  
An experimental dataset of HSC slender beams which fails in shear has been selected for constructing the applied hybrid AI models. The data were gathered from 33 intensive published types of research from between 1957 and 2013 [4; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77]. Total observations of the dataset are 250, which are based on rectangular HSC slender RC beams. In general, the data were selected based on geometric and material characteristics appearance. Furthermore, the followings precise standards were considered strictly during the selection of the esteemed database:  
1. The selection of beams was based on those that were longitudinally reinforced with pre-stressed and fewer steel rebars and lack of shear stirrups.
2. Shear failure was the primary benchmark for the specimens which were uniformly loaded along with one or two weights.
3. Range of shear span (_a_) was observed between 399-2745, where the data are skewed right (Skewness = 1.85) and leptokurtic (Kurtosis = 3.73). However, in the case of _a_g/_d_, the calculated value was found to be between 0.010811-0.176056, where data were characterised by leptokurtic (kurtosis = 2.02) but considerably symmetric skewness (0.33).
4. The majority (83.6%) of observations contained the range of effective depth of beam (_d_) from 133 to 300 mm; besides, 65.2% of data lie between 200 and 300 mm.
5. Acceptance of the dimension of the shear span was \(\geq\) 2.5 times the _a_/_d_ (effective depth), where the majority (84%) of the data falls in the 2.5-4 range of _a_/_d_. Moreover, _a_g varies from 9.5 to 25.4 mm, where 92.0% of the dataset contains the maximum size of aggregate between 9.5 and 19 mm.
6. Most of the data (92%) contain the aggregate size between 9.5 and 19 mm; in case of compressive strength of concrete (\(f_{\rm c}^{\rm r}\)), 90% of the data lie at \(\leq\) 100 MPa of \(f_{\rm c}^{\rm r}\), and 4% out of 100 MPa. Among 90%, the majority (56%) were between 40-60 MPa; in case of the percentage of tension reinforcement (\(\rho\)), the majority of the data (91.2%) are up to 4%.  
It is worth mentioning that there are some limitations in the data which have not been considered, such as rebar diameter, the concrete tensile strength, the initial shrinkage/thermal strains at early ages, which might have an impact on shear strength of HCS slender RC beams due to being absent in the database; hence, these have been excluded from this study.
## 2 Related Work
### Multi-Frame Feature Modeling and Single Frame  
Single-scale STFT features are insufficient to fully capture the detailed information of both high- and low-frequency components in audio signals. Reference [22] proposed a fusion approach that aggregates features from multiple scale branches using skip connections and feature concatenation mechanisms, effectively enhancing the integration of multimodal information and significantly improving audio denoising performance. Although multi-scale STFT features help improve the model's ability to represent spectral details, as mentioned in Section 2.2, most existing models are trained using multi-frame inputs. Multi-frame inputs, however, lead to increased computational demands during inference, limiting the applicability of these models in low-latency or resource-constrained scenarios. To reduce computational complexity, the FPSEN model [5] attempted a pseudo-single-frame strategy to alleviate computational load. While this method partially reduces the computational burden, the lack of contextual information hinders the model's ability to accurately capture noise characteristics in complex acoustic environments, thereby limiting denoising performance. Although such single-frame models have inherent performance limitations, they provide valuable insights for research on ultra-low-complexity denoising models. Building upon this, the present work proposes a character-level concatenation method for single-frame feature computation, effectively compensating for the absence of contextual information, enhancing the detailed representation of single-frame features, and further improving model performance.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 6 Numerical Results
### Obstacle Avoidance in 50-Node Scenario and 100-Node Scenario
#### 6.3.2 Comparison and Discussion  
Considering the general trend, the NN-based approach shows better performance than the OANN-based approach. Table 3 shows the total travelled distance by the AUV with the NN-based approach and OANN-based approach under the 50-node scenario in Figure 2 and the 100-node scenario in Figure 7.  
From Table 3, we can make the following observations. Under the 50-node scenario, the NN-based approach and OANN-based approach achieve almost the same performance with just a slight difference. (the difference between their achieved path lengths is 138 m, that is, 2.13% less than the OANN-based approach). Similarly, under the 100-node scenario, the NN-based approach and OANN-based approach achieve almost the same performance with just a slight difference (the difference between their achieved path lengths is 10 m, that is, 0.11% less than the OANN-based approach). We expect the difference in the 100-node scenario to be much less than the difference in the 50-node scenario because increasing the number of nodes increases the alternative destination for a node much more. In this case, the probability of preferring a node with the next ID can decrease considerably.  
Figure 13: Achieved path planning solution for visiting the 100 nodes by AUV with OANN under limitations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Iteration** & **Achieved Length** \\ \hline NN with 50 nodes & 6358 \\ \hline OANN with 50 nodes & 6492 \\ \hline NN with 100 nodes & 9045 \\ \hline OANN with 100 nodes & 9055 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total distance (in terms of meters) with NN-based approach and OANN-based approach under 50-node and 100-node scenarios.