esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Measurement Standard for Surface Roughness on Involute Gears
## 4 Physical Measurement Standard (PMS) Representing Roughness on Involutes
### Design of the PMS
In this manner, it is possible to perform roughness measurements on the PMS both on classical profilometers and gear measurement systems. The gear piece consists of a shank that fits into the gear shaft and ensures the precise positioning of the gear piece and the actual embodied involute roughness profile. It has an edge for the steel ball to lie in. When performing measurements on profilometers, one has to account for their limited vertical measuring range. This can be optimised by tilting the gear piece so that the necessary vertical lift of the probe is minimised. The embodied involute determines the used base radius \(r_{\text{b}}\) and the gear piece has to be manufactured accordingly. The involute itself is marked with two approximately 5 \(\upmu\)m deep notches. These are used to facilitate orientation on the profile. The positions of these markers, also referred to as fiducials (in roll length/arc length and the Cartesian coordinate system), are known so that profile measurements can be aligned with these fixed points in the coordinate systems. The primary profile after the form removal, as displayed in Figure 9, clearly shows those fiducials in the root and tip part of the profile. Manufacturing the shape of the involute by EDM creates a profile with the heights following a homogeneous and normal distribution (see Figure 10). Although not periodic, this ensures that the roughness evaluation is relatively independent of the exact position of the evaluation subprofile, reducing the overall variance of ro  
Figure 9: Primary profile of PMS, i.e., profile after form removal and before filtration; physical markers (fiducials) for relocalisation at root and tip of profile (green).
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
###### Abstract  
In order to increase channel usage efficiency, unlicensed users within a Cognitive Radio Network (CRN) are permitted to utilize channels that are not currently occupied by licensed users. However, ensuring communication between users in a CRN remains a challenge. To overcome this issue, a variety of channel hopping protocols have been developed. Time-invariant channel hopping protocols are vulnerable to attacks, so several channel hopping protocols that are resistant to jamming attacks have been proposed. In the majority of existing anti-jamming protocols, users create their channel hopping sequence using a channel hopping matrix, with the rendezvous probability between two users being determined by the structure of their respective channel hopping matrices. The channel hopping matrices designed by existing methods still have room for improvement. To overcome the difficulty of guaranteeing communication between any pair of users, while also providing protection against jamming attacks and minimizing the time to rendezvous (TTR) in a CRN, this paper presents the Anti-jamming Low-Latency channel hopping (ALL) protocol. This protocol allows a sender to adjust their channel hopping matrix structure to match that of the receiver, thereby improving the chances of successful rendezvous between users. Based on the simulation results, the ALL protocol performs better than the recently proposed practical solution, OLAA, by up to 33% in network throughput and 30% in TTR. On average, ALL outperforms OLAA by 25% in network throughput and 20% in TTR.  
c pacs: 12.30.Jc, 12.30.Jc, 12.30.Jc, 12.30.Jc, 12.30.Jc, 12.30.Jc, 12.30.Jc  
**Citation:** [PERSON]; [PERSON]; [PERSON], [PERSON], [PERSON] Antj-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks. _Electromies_**2023**, 12, 1811. [[https://doi.org/10.3390/electronics12081811](https://doi.org/10.3390/electronics12081811)]([https://doi.org/10.3390/electronics12081811](https://doi.org/10.3390/electronics12081811))
# Dynamic Planning of Mobile Service Teams' Mission Subject to Orders Uncertainty Constraints
In this context, the purpose of our research was to develop an ordered-fuzzy-numbers-driven declarative model, enabling to define the DRP subject to fuzzy maintenance time and transportation time constraints, the solution to which provides the possible dynamic rerouting scenarios. Unlike most of the problems discussed in the literature which focus on the search for solutions that optimize the path traveled or the cumulative cost of the mission carried out, in our approach, an answer to the following question was sought: can the newly reported delivery requests or the performance date correction of the already requested ones be accepted or not? The present study is a continuation of our previous work that explored methods of the fast prototyping of solutions to the problems related to the routing and scheduling of tasks typically performed in batch flow production systems [22; 23; 24; 25; 26; 27; 28]. Its main contribution are threefold:  
* Outbound mobile teams-driven maintenance services require taking into account disruptions occurring in road traffic (e.g., congestion-restricted delivery time) and the uncertainty of the delivery (e.g., unpacking and storage) operations or maintenance (e.g., repair or condition monitoring) services as well as changing the ordered dates of the service/delivery performance. * Formulation of the DRP implementing the algebra of ordered fuzzy numbers allows one to plan mobile teams' operation, taking into account the uncertainty of their travel time and the time of conducted repairs. * In opposition to standard fuzzy numbers, the support of the fuzzy number as a result of algebraic operations performed on ordered fuzzy numbers domain does not expand, which determines its dominance on the currently used computer simulation methods, the proposed algebraic approach allows for online vehicle rerouting and/or rescheduling forced by disturbances caused by ad hoc changes in the orders performed. The structure of the paper is organized as follows. Section 2 includes the review of the literature. Section 3 provides preliminaries briefly referring to some known concepts from ordered fuzzy numbers theory and constraint programming techniques. The problem statement and the methodology used for its solution are described in Sections 4 and 5, respectively. Computational results are then reported and analyzed in Section 6, while conclusions and future directions of work are considered in Section 7.
## 3 Results
### Predicting Long-Term SIF and GPP Using the Optimized Model  
We further predicted the long-term SIF and GPP from 2003 to 2017 using the optimized SCOPE model. The GPP and SIF series simulated by the SCOPE model against flux tower GPP and satellite-based SIF data at Park Falls are illustrated in Figures 9 and 10. The seasonal trajectories of GPP and SIF simulated based on the optimized parameters were compared against those based on the default parameters. The simulated daily GPP based on the optimized parameters was more consistent with the flux tower GPP compared with that based on the default parameters. Similarly, the simulated SIF (757 nm, 771 nm, and 740 nm) based on the optimized parameters were also more consistent with the OCO-2 and GOME-2 SIF observations compared with that based on the default parameters. Figure 10 shows the scatter plots of predicted GPP against flux tower GPP from 2003 to 2017. The optimized model had improved performance in predicting GPP than the model with the default parameters at both hourly and daily timescales. We also found that the model with the optimized parameters had higher performance (lower RMSE and higher slope) in predicting SIF at GOME-2 SIF wavelength (740 nm) over the period 2007-2017 (Figure 11).  
Figure 8: Seasonal trajectories of GPP and SIF simulated by the SCOPE model at the Willow Creek site. The black lines and blue lines represent the modeled GPP based on the default parameters and modeled GPP based on parameters optimized with both SIF and GPP, respectively. The magenta symbols stand for flux tower GPP (**a**) and OCO-2 SIF observations (**b**,**c**).  
Figure 10: Comparison of flux tower GPP (GPP\({}_{\text{EC}}\)) against GPP simulated by the SCOPE model at Park Falls using (**a**) simulated hourly GPP with default parameters; (**b**) simulated hourly GPP with optimized parameters; (**c**) simulated daily GPP with default parameters; (**d**) simulated daily GPP with optimized parameters.  
Figure 9: Seasonal trajectories of SIF and GPP simulated by the SCOPE model at Park Falls. The black lines and blue lines represent the modeled values with the default and optimized parameters, respectively. The magenta symbols stand for flux tower GPP (**a**), OCO-2 SIF (**b**,**c**), and GOME-2 SIF (**d**).
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 3 Background on Communication Protocols
### Source Tree Adaptive Routing--Least Overhead Routing Approach (STAR-LORA)
There are two unique time frames within the active period, known as the contention access period (CAP) and the contention-free period (CFP). Devices attempting to send data frames in the CAP use a procedure known as slotted CSMA/CA to acquire access to the wireless channel. The CFP, on the other hand, is used to transport data frames with assured quality of service since it is split into guaranteed time slots (GTSs). A coordinator, for example, might provide a GTS, particularly for applications that require real-time updates or a large amount of bandwidth. [PERSON]'s research on the emission of electric dipoles at the boundary surface dividing two media aroused interest in the use of electromagnetic waves to transport information between antennas immersed in a conducting medium. [PERSON] and [PERSON] investigated ELF transmissions between antennas submerged in a conducting half-space medium. The study was based on the assumption that a lateral wave with three components is the primary mode of communication between submerged antennas. These components are as follows: (1) a wave that travels along the sea surface; (2) a wave that travels directly from the  
Figure 2: Superframe design based on IEEE 802.15.4.  
transmitting dipole to the water's surface; and (3) a wave that travels perpendicular to the water's surface from the water's surface to the receiving dipole. With this notion, direct path attenuation was projected to be substantially greater than lateral wave attenuation. Waves reflected from the ocean floor were not taken into account because they would result in significantly more attenuation than the straight path. While very low frequency (ELF) transmission was considered, the displacement current commonly associated with a medium losing conductivity was disregarded. Figure 3 represents the communication model of the UWSN with various hardware and network assemblies.
## 6 Conclusions  
In conclusion, we present a spiking neural network (SNN) implementation of the Canny edge detector as its neuromorphic analogue by introducing algorithms for spike based computation in the five steps of the conventional algorithm with the conductance-based Hodgkin-Huxley neuron as the building block. Edge detection examples are presented for RGB and infrared images with a variety of objects. A quantitative comparison of the edge maps from the SNN-based Canny detector, conventional Canny detector and a Sobel detector using the \(F_{1}\)-score as a metric, shows that the neuromorphic implementation of the Canny edge detector achieves better performance. The SNN architecture of the Canny edge detector also offers promise for image processing and object recognition applications in the infrared domain. The SNN Canny edge detector is also evaluated with medical images, and the edge maps compare well with the edges generated with the conventional Canny detector. Future work will focus on the implementation of the algorithm on an FPGA or on a neuromorphic chip for hardware acceleration and testing in an infrared object detection task potentially with edge maps as features together with a pre-processing layer to remove any distortions, enhance contrast, remove blur, etc., and a spiking neuron layer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Canny** & **SNN Canny** & **Canny** & **SNN Canny** \\ \hline  & **PR** & **PR** & **\(F_{1}\)-Score** & **\(F_{1}\)-Score** \\ \hline
35,010 & 15.7 & 17.5 & 0.009 & 0.018 \\
3096 & 7.3 & 11.6 & 0.002 & 0.013 \\
67,079 & 12.7 & 17.4 & 0.014 & 0.034 \\
23,080 & 12.6 & 13.8 & 0.014 & 0.023 \\
68,077 & 11.1 & 10.7 & 0.015 & 0.016 \\
231,015 & 15.5 & 14.2 & 0.015 & 0.022 \\
216,041 & 13.4 & 9.9 & 0.088 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of performance ratio, PR, and \(F_{1}\)-score for selected images.  
Figure 6: A comparison of edge detection by Sobel, Canny, and SNN Canny edge detectors for medical images from CT emphysema dataset.  
as a final layer to introduce a machine learning component. An extension of the SNN architecture of the Canny edge detector with additional processing layers for object detection in LiDAR point clouds would be another interesting new direction of research [39].  
This work was internally funded by Riverside Research's Open Innovation Center.  
Not applicable.
## 2 Materials and Methods
### Methods
#### 2.2.3 Ridge Regression  
Collinearity among the independent variables during multiple linear regression can result in least-squares estimation errors [38]. The present study overcame this challenge by using ridge regression, which effectively considers collinearity to identify more reliable coefficients compared to those by estimates of least-squares through the introduction of a ridge parameter [7]. The present study used five-fold cross validation to select the ridge parameter. The explanatory variables included the annual-scale potential drivers of WUE, including the MAT, MAP, Rg, SWC, VPD, Ca, and LAI. The dependent variable was the annual WUE.
## 4 Conclusions  
The hypothesis raised by ISO 11561 states that the thermal resistance of a sliced sample after 25 years and the 25-year average thermal resistance were degraded by time. It also specifies that the anticipated results may differ by approximately 10% compared to the original thickness sample. Based on this hypothesis, we compared the long-term ageing test data with the actual elapsed time. When the minimum period to consider the remodeling of an old building was assumed to be 3600 days (10 years), the prediction of ageing by the slice-accelerated test method appeared to have exhibited similar results as the performance degradation by the actual ageing. The experimental results are summarized as follows:  
* On conducting the accelerated laboratory test suggested in ISO 11561 using extruded insulation material (special grade), the material exhibited a stable state after approximately 120 days and underwent 38-41% degradation in thermal performance compared to the initial value. This result was similar to the rate of change of 39-42% after approximately 5000 days compared to the initial value of the 50 mm specimen analyzed in the previous study [16].
* When the thermal performance of the 50 mm specimen after 25 years was predicted using the scaling technique, the thermal resistance and 25-year-average thermal resistance were calculated as 1.542 m\({}^{2}\cdot\)K/W and 1.704 m\({}^{2}\cdot\)K/W, respectively. Furthermore, the rate of change of the thermal resistance after 25 years and the 25-year average thermal resistance compared to the initial value ranged from 37 to 41% and from 30 to 38%, respectively.
* While the ageing analysis of the two methods revealed a slight difference in the transition point, the difference was less than 10% after approximately 3600 days (10 years) owing to stabilization. Because the slice-accelerated test includes a method to predict the ageing of insulation materials after 25 years, the ageing results of the extruded insulation material using the two test methods were assumed to be similar.  
Conceptualization, H.-J.C. and J.-S.K.; methodology, H.-J.C.; software, H.A.; validation, G.-S.C. and J.-S.K.; formal analysis, H.-J.C.; investigation, H.-J.C.; resources, H.-J.C.; data curation, H.A.; writing--original draft preparation, H.-J.C.; writing--review and editing, J.-H.H.  
Figure 9: Comparison of measurement results on converting them to results for a thickness of 50 mm.  
J.-S.K., H.A.; visualization, H.-J.C., H.A.; supervision, J.-H.H.; project administration, J.-S.K.; funding acquisition, G.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Agency for Infrastructure Technology Advancement (KAIA), grant number 21 AUDP-B151639-03.  
The authors declare no conflict of interest.
## 3 Method
### Retriever Consensus Confidence Fusion (RCCF)
#### 3.3.2 Confidence-Aware Fusion  
Based on the consensus measurements established above, RCCF implements an adaptive fusion mechanism that adjusts retriever weights according to their agreement levels. The core principle is to trust retrieval results more when retrievers show high consensus and adopt more conservative strategies when consensus is low.  
**Adaptive Weight Calculation:** For each retriever \(i\), its adaptive weight for document \(d\) is  
\[w_{i}(d)=w_{i}^{\text{base}}\times(1+\beta\cdot C(d)\cdot I_{i}(d)) \tag{7}\]  
where \(w_{i}^{\text{base}}\) is the base weight for retriever \(i\), \(\beta\) is the confidence amplification factor, and \(I_{i}(d)\) is an indicator function that equals 1 when retriever \(i\) returns document \(d\); it is 0 otherwise.  
**RCCF Fusion Formula:** The final score for document \(d\) is computed as follows:  
\[\text{Score}_{\text{RCCF}}(d)=\frac{\sum_{i=1}^{n}w_{i}(d)\times s_{\text{ norm},i}(d)\times I_{i}(d)}{\sum_{i=1}^{n}w_{i}(d)\times I_{i}(d)}\times(1+ \gamma\cdot C(d)) \tag{8}\]  
where \(\gamma\) is the confidence reward factor that directly boosts scores of high-consensus documents.
## 2 Methodology
### Feature Compression  
Directly classifying the crops with higher dimensional features above is cumbersome, which involves complicated computations and large amount of memory to store the features, and these enormous features would suffer from the great difficulty of the dimensionality disaster. Therefore, to make full use of the wealth of multiple temporal PolSAR data, the dimension reduction in resulting features is indispensable and crucial. In the past few years, the methods of auto-encoder and sparse auto-encoder have attracted more and more attention, which were commonly used to perform the compression of high-dimension data [17; 55; 56; 57]. Therefore, the sparse auto-encoder with non-negativity constraint was proposed to further improve the sparsity of auto-encoder.
## 5 Experiments
### Results  
Experimental results are presented in Tables 11-19. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Device/** & **Attack** & **Algorithm** & **Accuracy** & **TP** & **FP** & **FN** & **TN** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{8}{*}{Router/} & RF & 0.999333 & 5490 & 4 & 1 & 2005 & 0.999272 & 0.999818 & 0.999545 & 0.999691 \\  & DT & 0.9992 & 5472 & 5 & 1 & 2022 & 0.999087 & 0.999817 & 0.999452 & 0.999982 \\  & kNN & 0.998933 & 5455 & 3 & 5 & 2037 & 0.99945 & 0.999084 & 0.999267 & 0.999836 \\  & XGBOOST & 0.9992 & 5417 & 2 & 4 & 2077 & 0.999631 & 0.999262 & 0.999446 & 0.999285 \\  & SVM & 0.9976 & 5394 & 9 & 9 & 2088 & 0.998334 & 0.998334 & 0.998334 & 0.999444 \\ \hline \multirow{8}{*}{Router/} & RF & 0.999344 & 10196 & 5 & 3 & 1996 & 0.99951 & 0.999706 & 0.999608 & 0.999488 \\  & DT & 0.999672 & 10171 & 1 & 3 & 2025 & 0.999902 & 0.999705 & 0.999803 & 0.999932 \\  & kNN & 0.999426 & 10146 & 3 & 4 & 2047 & 0.999704 & 0.999606 & 0.999655 & 0.999835 \\  & XGBOOST & 0.999426 & 10120 & 2 & 5 & 2073 & 0.999802 & 0.999506 & 0.999654 & 0.999844 \\  & SVM & 0.998279 & 10137 & 7 & 9 & 10137 & 0.998301 & 0.997736 & 0.998019 & 0.998421 \\ \hline \multirow{8}{*}{Router/} & RF & 0.999771 & 19767 & 2 & 3 & 2018 & 0.999899 & 0.999848 & 0.999874 & 0.999853 \\  & DT & 0.999725 & 19746 & 3 & 3 & 2038 & 0.999848 & 0.999848 & 0.999848 & 0.999995 \\ \cline{1-1}  & kNN & 0.999679 & 19716 & 2 & 5 & 2067 & 0.999899 & 0.999746 & 0.999823 & 0.999973 \\ \cline{1-1}  & XGBOOST & 0.999771 & 19666 & 1 & 4 & 2119 & 0.999949 & 0.999797 & 0.999873 & 0.999794 \\ \cline{1-1}  & SVM & 0.99899 & 19665 & 4 & 18 & 19665 & 0.99990 & 0.999518 & 0.9997502 & 0.999452 \\ \hline \multirow{8}{*}{DNS} & RF & 0.999298 & 9351 & 3 & 5 & 2041 & 0.999679 & 0.999466 & 0.999572 & 0.999457 \\ \cline{1-1}  & DT & 0.999474 & 9301 & 4 & 2 & 2093 & 0.99957 & 0.999785 & 0.999678 & 0.999974 \\ \cline{1-1}  & kNN & 0.999737 & 9285 & 2 & 1 & 2112 & 0.999785 & 0.999892 & 0.999838 & 0.999859 \\ \cline{1-1}  & XGBOOST & 0.999386 & 9243 & 5 & 2 & 2150 & 0.999549 & 0.999784 & 0.999621 & 0.999482 \\ \cline{1-1}  & SVM & 0.99895 & 9302 & 7 & 10 & 9302 & 0.998109 & 0.99542 & 0.996763 & 0.998561 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Classification results (router—Dark Nexus).
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 4 Experiments and Results
### Experimental Data Sets and Setting
** Several widely used image quality indices were used to compare the performance and reconstruction quality, including erreur relative global adimensionnelle de synthese (ERGAS), the mean spectral angle mapper (SAM), mean peak signal-to-noise ratio (mPSNR), mean square error (MSE), and mean relative absolute error (MRAE) [11].
# Advanced Numerical Analysis of Transport Packaging
## 2 Materials and Methods  
### Workflow of the Study  
This study analysed the impact of the selected corrugated cardboard material on the decrease in the compressive load bearing capacity of packaging after a cycle of standard transportation loads. Thirteen types of corrugated cardboard materials and three packaging designs were considered, as shown in Figure 1. A variety of corrugated cardboards with different material parameters were selected (the first \"For\" loop for the (i) index in Figure 1). Section 2.2 discusses the material testing of the cardboards in detail, including sample preparation, test procedures, and the results obtained.  
The packages chosen were typical flap boxes, commonly known as American boxes, with various dimensions: (I) \(250\times 250\times 150\) mm, (II) \(300\times 200\times 250\) mm and (III) \(300\times 200\times 450\) mm (the second \"For\" loop for (j) index in Figure 1).  
The compressive load bearing capacity of the packaging was determined through computer simulations using Finite Element Analysis (FEA), which was used in previous studies by the research group; see [39]. The adopted modelling approach is detailed in [30]. In the current study, the initial static compressive load bearing capacity, \(BCT_{0}^{ij}\), as shown in Figure 1, was first calculated (bottom left). Next, dynamic computations were performed for each packaging, taking into account standard transport loads in accordance with ISO 13355 [24]. The examined packaging, showing permanent deformations due to the applied material model with plasticity and dynamic transport loads, was subsequently subjected to static FEA to calculate \(BCT^{ij}\), as shown in Figure 1 (bottom right). The proposed procedure will be presented in more detail in Section 2.3. Finally, Equation (1) was applied to determine \(\Delta BCT^{ij}\), interpreted as the percentage decrease in the compressive load bearing capacity of the packaging after the transportation load cycle:  
\[\Delta BCT^{ij}=100\cdot\frac{\left|BCT_{0}^{ij}-BCT^{ij}\right|}{BCT_{0}^{ ij}}. \tag{1}\]