esa-sceva/satcom-chunk-collection huggingface dataset:
# Speeding up Statistical Tolerance Analysis to Real Time
## 4 Case Study: Overconstraint Door Hinge Assembly  
The overconstraint door hinge assembly is shown in Figure 2, following [27; 28]. It should be noted in advance that this supposedly'simple' assembly (the assumption of a one-dimensional chain with purely linear dependencies seems obvious) is, however, significantly influenced by interactions between the occurring deviations (due to the overconstraint design), and thus is non-trivial.  
The hinge consists of two main parts (the outer and inner hinge plates) connected by a pin. The hinge enables the opening and closing of a cover plate. Due to manufacturing deviations of the dimensions \(M_{1}\) to \(M_{7}\), a variation of the vertical position of the cover results. These deviations are limited by the tolerances specified in Table 2. The tolerances follow a Gaussian or a uniform distribution. In order to ensure a sufficient sealing gap, the standard deviation of the gap \(p\) should not exceed the limit of 0.1 mm. Hence, the functionally relevant key characteristic of the assembly is the vertical displacement of the inner hinge plate with respect to the outer hinge plate, which directly influences the width \(p\) of the gap for the gasket.  
Figure 2: Overconstraint door hinge assembly.  
First, the mathematical relation between the functional key characteristic \(p\) and the deviating dimensions \(M_{1}\) to \(M_{7}\) is required. The overconstraint design of the assembly already comes into play here: Due to the design of the hinge and the resulting interactions of the deviations, two assembly scenarios are possible (see Figure 3). The assembly scenarios differ in how the plates' surfaces are in contact. In scenario #1, the plates are in contact in the upper slot, whereas in scenario #2, there is contact in the lower slot. Thus, the distances \(A_{1}\) (upper slot) and \(A_{2}\) (lower slot) can be determined from \(M_{1}\) to \(M_{7}\).  
\[A_{1}=\left(M_{6}+\frac{M_{7}}{2}\right)-\left(M_{3}-\frac{M_{4}}{2}\right) \tag{2}\]  
\[A_{2}=M_{5}-\left(M_{1}-\frac{M_{2}}{2}\right). \tag{3}\]  
If \(A_{1}\leq A_{2}\) the hinge will be assembled according to scenario #1. In this case, the resulting key characteristic \(p\) is the determined value of the distance \(A_{1}\) and vice versa. This results in a piecewise-defined mathematical function of the gap p.  
\[p=\begin{cases}A_{1}\:if\:A_{1}\leq A_{2}\\ A_{2}\:if\:A_{1}>A_{2}\end{cases} \tag{4}\]
# An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving
## 2 Proposed Method
### Adaptive Parallel Detection Subnet
#### 2.2.1 Adaptive Context Expansion (ACE)  
As we know, small objects need extra information to help detection. FPN assigns small RoIs to finer-resolution level to add extra detailed RoI feature. Suppose a RoI has width \(w\) and height \(h\) (on the input image of the network); the level \(k\) of RoI is determined by its area size \(S\):  
\[S=wh, \tag{9}\]  
\[k=\lfloor k_{0}+\log_{2}(\sqrt{S}/S_{0})\rfloor. \tag{10}\]Similarly, in ACE, an extra context feature is embedded for adding surrounding information for small RoIs. We define two context expanding criteria, which guide the calculation of context region expansion ratio for each RoI.  
_Vertical expansion criterion_ leverages the hierarchical structure of pyramid network to calculate the vertical context expansion ratio \(R_{v}\):  
\[R_{v}=\alpha k, \tag{11}\]  
where \(\alpha\) denotes the vertical context enlarge coefficient. Since context expending of RoI is positively correlated with the level of RoI, \(\alpha\) is always positive. In Figure 3, the small \(RoI_{b}\) and the large \(RoI_{c}\) belong to different pyramid levels, hence their \(R_{v}\) are clearly distinct from each other, that is a larger one for \(RoI_{b}\) and a smaller one for \(RoI_{c}\).  
_Horizontal expansion criterion_ focuses on the calculation of refined expansion ratios between different objects on the same pyramid level. Although RoIs within the same scale range have the same \(R_{v}\), their varying sizes lead to different requirements. We take this factor into account to provide more precise horizontal context expansion ratio \(R_{h}\):  
\[R_{h}=\beta(\log_{2}(\sqrt{S}/S_{0})+k_{0}-k), \tag{12}\]  
where \(\beta\) denotes the horizontal context enlarge coefficient. \(\log_{2}(\sqrt{S}/S_{0})+k_{0}-k\) is the residual part of level vector, ranging \([0,1]\), which indicates the size difference among the RoIs of the same pyramid level. In Figure 3, for \(RoI_{a}\) and \(RoI_{d}\) of the same pyramid level, the smaller its area is, the more context information it needs. Thus, they have slightly different \(R_{h}\), with a larger one for \(RoI_{a}\) and a smaller one for \(RoI_{d}\).  
Above all, the final context expansion ratio \(R\), which considers the two independent criteria of each RoI, is:  
\[R=R_{v}+R_{h}+\gamma, \tag{13}\]  
where \(\gamma\) is used to adjust magnitude of extension ratio among different dataset resulting from factors, such as background complexity, objects density, etc.  
Next, the scale-up operation \(\Theta\) based on the center point of each RoI is used to get the context region ceRoI:  
\[ceRoI=\Theta(RoI), \tag{14}\]  
Figure 3: The structure of Adaptive Context Expansion (ACE).  
where the ceRoI has the width of \(w\times R\) and height of \(h\times R\) with the same center as the original RoI.
## 3 Results and Discussion
### Electronic Nose Detection
#### 3.1.2 Signal Filtering Wave  
During data collection, some of the gathered data may exhibit abnormalities due to environmental interference or other factors. Utilizing such abnormal data can have unpredictable effects on the entire system. To ensure system stability and mitigate the impact of environmental interference, it is essential to filter the raw data. Common filtering methods include arithmetic average filtering, Savitzky-Golay (SG) filtering, and recursive evaluation filtering. SG filtering is a linear filtering technique widely employed for smoothing data streams and reducing noise. This method is based on local polynomial least squares fitting in the time domain. The primary advantage of SG filtering is its ability to eliminate noise while preserving the shape and width of the signal. Compared to other filtering techniques, SG filtering offers distinct advantages, effectively minimizing noise while maintaining the trend and shape of the data. It allows for flexible adjustment of the polynomial fitting order, thereby accommodating various types of data with enhanced computational speed and reduced storage requirements. Consequently, SG filtering is utilized to process the raw oyster data, followed by feature extraction.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 2 SmartBAN Specifications
### Physical Layer
#### 2.1.2 Packet Format, Modulation and Coding Scheme  
Figure 1 presents the packet format in the physical layer. The physical layer protocol data unit (PPDU) had a two-octet preamble--10101010101010--used for frequency synchronization, timing synchronization and automatic gain control. The physical layer convergence protocol (PLCP) header consisted of the packet length, the PHY scheme and other components. It was encoded by the (36, 22) shortened BCH code as an error-correcting code (ECC) and CRC-4-ITU as an error-detecting code. The physical layer service data unit (PSDU) was either an encoded or uncoded MAC protocol data unit (MPDU). The MPDU was encoded by CRC-8 (-CCITT) and CRC-16 (-CCITT) as an error-detecting code.  
In the SmartBAN PHY, Gaussian frequency shift keying (GSFK) with a bandwidth-bit period product BT = 0.5 and modulation index h = 0.5 was utilized as a modulation scheme. However, as an error-control scheme in the SmartBAN PHY, a scheme of repeatedly transmitting PPDUs and a scheme encoding the MPDU by using the (127, 113) BCH code as an ECC may be used. In the method of repeated transmission, it is possible to set the number of repetitions \(N_{R}\) = 2, 4. For (127, 113) BCH encoding, the following generator polynomial is used:  
\[g(x)=x^{14}+x^{9}+x^{8}+x^{6}+x^{5}+x^{4}+x^{2}+x+1 \tag{1}\]  
Figure 1: Packet format at the physical layer (PHY).  
Then, the (36, 22) shortened BCH code used in the PLCP header is generated based on the (127, 113) BCH code. It is also possible to use these schemes in combination. Table 1 summarizes the throughput in the PHY.
## 3 Future Work
### Prosumer Communities
#### 3.1.3 Prosumer Community/Coalition Formation  
This section outlines the need for further research on the formation of prosumer communities. Although this area has progressed, we believe there is room for improvement to enhance the proposals or come up with novel and innovative approaches.  
One promising direction of research could be to plan and implement new methodologies in the formation of PCGs. The concept of goal-oriented virtual prosumer communities had been introduced [10], but novel, smart techniques and strategies could be designed to optimize the formation, growth, and management of prosumer communities [11].  
The objectives of these new techniques could be to make way for a more robust formation of sustainable PCG's that encourage active participation among prosumers for a more sustainable energy sharing.  
Trust and local reputation of prosumers can be used to study prosumer behavior and form cohesive prosumer groups [81]. One interesting direction of research would be to assess the degree of effectiveness of trust and reputation in building sustainable PCGs. Further, a comparative study of trust based and profile based systems could be undertaken to understand the superiority of these approaches.  
Due to high implementation and maintenance costs of centralized schemes for energy exchanges in prosumer communities, another future work would be to design decentralized energy exchange strategies using other data-based approaches. One recommendation is through agent-based modeling approach using Reinforcement Learning [17]. Since agents can intrinsically self-improve over time with newly acquired data, this approach could be an effective decentralized strategy for decision management and control in smart grids [51].  
In evaluating these new methodologies, actual data extracted from energy markets and aggregators' records could be used for testing to mimic closer to real-life scenarios [45]. Doing so will yield more realistic results. Additionally, research efforts could be directed towards understanding switching of prosumers from one PCG to another.  
Another interesting future research area is to explore the different communities that exist in the energy market. We mentioned about other communities that are closely related to PCGs used across the literature such as EPCs, ICESs and CECs. These communities, including microgrids and virtual power plants and other emerging energy communities, could be compared and contrasted to gain a deeper understanding of the strengths, weaknesses, and effectiveness of each towards developing a sustainable energy exchange.
# Evaluating the Efficacy of Segment Anything Model for
## 2 Study Area and Data Used  
We carefully handpicked study areas within the United States of America (USA), strategically covering the southeast, northwest, and central regions, thus ensuring an exhaustive representation of the USA's primary agricultural field types and various forms of UGSs (Figure 1). In our research, we employed remote sensing imagery of varying resolutions. This study site selection was also based on the availability of multi-sensor and multiresolution datasets for the ARSI and UGS types. Thus, we managed to ensure that our resolution spectrum encompassed 0.5 m, 1 m, 3 m, 10 m, 20 m, and 30 m per pixel resolution images acquired from various aerial and spaceborne platforms. The availability of very high-resolution images also allowed for visual accuracy assessment of the segmentation process. For the 10 m images, we used Sentinel-2 Multispectral Imager (MSI) data, while for 30 m resolution images, we used data from Landsat 8 Optical Imager (OLI). While testing the impact of spatial resolutions on the segmentation results, it is important to keep the spectral bands constant across the images. Therefore, to ensure the incorporation of relevant spectral information in the used RGB bands and avoid combinations of spectral images of different bands affecting the test results, we employ the 10 m resolution bands for further resampling to the 20 m data. Meanwhile, the 0.5 m and 1 m images were obtained from the USGS archive of very high spatial resolution aerial images. Our 3 m resolution PlanetScope remote sensing images were procured from Planet's official website ([[https://www.planet.com/](https://www.planet.com/)]([https://www.planet.com/](https://www.planet.com/)), accessed on 16 December 2023), which offers global high-resolution imagery at 3 m resolution.
## 3 Experiments and Results  
### Experimental Environment  
This paper's overarching aim was to evaluate the performance of the CNN-transformer-BiGRU for the task of fault detection in water pumps in IRASs. To ensure the accuracy and reliability of the experimental data and render them reproducible, this work was carried out in an environment with the specific hardware and software parameters listed in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value or Item** \\ \hline Operating system & Windows 11 \\ Processor model & 11 th Gen Intel(R) Core(TM) i9-11900K @ 3.5 GHz \\ Memory capacity & 64 GB \\ Graphics card Model & NVIDIA GeForce RTX 3080 Ti \\ Video Memory capacity & 12 GB \\ Python & 3.8.3.final.0 \\ Pytorch & 2.3.0 + cu118 \\ CUDA version & 12.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental environment’s parameters.  
Figure 4: Flowchart for the model fault detection process used in this paper.  
To train the model faster, we chose to use an NVIDIA GeForce RTX 3080 Ti graphics card with a dedicated 12 GB GPU memory. It supports Cuda parallel computing, which can significantly improve the computational speed of operations, such as convolutional calculations and matrix multiplications [32]. The experimental algorithm model was developed using Python, and the network framework is Pytorch.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 4 Simulation Results
Figure 6 shows energy consumption by amount of data based on energy efficiency in Table 4. In terms of energy consumption, GIFT-COFB and ASCON consume less energy than AES-GCM (optimized), but SpoC and Spook consume more energy. Considering that both GIFT-COFB and ASCON show better performance than AES-GCM (optimized) in the performance simulation, when GIFT-COFB or ASCON is applied to the proposed D2D communication, both speed and energy efficiency of the proposed D2D communication are better than for AES-GCM (optimized)-based D2D communication. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AEAD Cipher** & **Area (LUTs)** & **Power (mW)** & **Throughput (Mbps)** & **Energy (nJ/bit)** \\ \hline AES-GCM (Optimized) & 1532 & 35.9 & 31.2 & 1.15 \\ ASCON & 1808 & 33.6 & 39.0 & 0.86 \\ SpoC & 1344 & 34.7 & 28.8 & 1.20 \\ Spook & 7082 & 125.9 & 88.3 & 1.43 \\ GIFT-COFB & 2695 & 36.6 & 120.8 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The hardware implementation result of AEAD ciphers. Figure 5: The processing time of the proposed D2D communication system.
## 2 The LIGOM Model
#### LICOM's Parallel Scheme  
The ocean is discretized into two-dimensional grid points with staggered latitude and longitude coordinates in the horizontal direction. The latitudinal direction is divided into \(imt\_global\) grid points, and the longitudinal direction is divided into \(jmt\_global\) grid points. The vertical direction is divided into km layers with the ocean depth as the coordinate. Thus, the ocean is decomposed into a three-dimensional structure. On this basis, the ocean grid points are organized into grid blocks. Each grid block contains \(BLCKX\times BLCKY\times km\) grid points. There is barotropic-mode communication between grid blocks, and there are mutual pressure and vertical velocity calculations between layers. As shown in Figure 2, the ocean area is decomposed into \(\left\lceil imt\_global/BLCKX\right\rceil\times\left\lceil jmt\_global/BLCKY\right\rceil\) grid blocks. In order to achieve a higher efficiency with the difference method, LICOM uses MPI to parallelize and speed up the calculation. \(NBLOCKS\_CLINIC\) grid blocks are allocated to each process and seouentially calculated inside one grid block.  
There is an independent array for each grid block. The size of the array decreases as the degree of parallelism increases. Since differential computation requires boundary values of adjacent grid blocks, frequent boundary communication is necessary. The array for each grid block is updated according to the array boundaries from two-dimensional logically adjacent processes. Therefore, the array of each grid block stores the boundary data of the adjacent grid block, in addition to its own data. The boundary data from other grid blocks are called the ghost boundary, while its own boundary data are called the real boundary. LICOM employs an adjustable ghost boundary strategy. The size of the ghost boundary depends on a specific issue. The blue squares in Figure 2 show the situation when the ghost equals 2. Setting the ghost to 2 instead of 1 will effectively eliminate the communication-calculation ratio, since communication is only needed for every two calculation steps.  
Figure 3 illustrates the calculation procedure of a grid point in one iteration step. First, the two stripes of the ghost boundaries of each of the grid blocks are updated. The real boundary in grid blocks 1, c, and d is transferred to b and a of grid block 2. Meanwhile, the real boundary in grid blocks 2, c, and d is transferred to b and a of grid block 1. Then, the real boundaries are updated. In grid block 1, the grid points are updated from left to right until b. Meanwhile, in grid block 2, the grid points are updated from right to left until b. Finally, both grid blocks 1 and 2 update to c.  
Figure 2: Ocean grid.
## 2 Simulation Model
### Integration of Degraded Models  
As mentioned earlier, this model can be used in parallel with a fuel cell model to achieve online estimation in a simulation model. It can also be applied to fuel cell power control to achieve degradation control of the fuel cell. By using this model for real-time monitoring and estimation, the degradation status of the fuel cell can be detected promptly, and corresponding control strategies can be implemented to mitigate the degradation rate, improve the fuel cell's lifespan, and enhance its performance stability. This control approach can help optimize the operation of the fuel cell system, improving its reliability and efficiency.  
In the first scenario, integrating this model into the energy management strategy allows for optimal energy control. In this case, the energy management strategy can receive feedback on the degree of fuel cell degradation, which can be reflected in hydrogen consumption and power output. The fuel cell voltage degradation can be applied to either the voltage or power.  
In the second scenario, this model can be directly applied to the fuel cell power demand as a power degradation factor, guiding the operation of the fuel cell.  
\[P_{fc}=P_{fc,ref}(1-\delta) \tag{16}\]  
In this case, due to the influence of the power degradation factor on the fuel cell's power output, the energy management strategy is unable to distinguish whether the insufficient power output is caused by the degradation of the fuel cell itself or by other factors such as load demand or system faults. As a result, the energy management strategy may incorrectly assess the fuel cell's state and hydrogen consumption.  
To validate the accuracy of the degradation model proposed in the study, actual vehicle operational data was used for simulation and analysis. The fuel cell operating current during real-world driving cycles was collected and used as input for conducting durability tests on an actual operating bench. The actual operating current of the fuel cell is shown in Figure 6. After 5323 h of cyclic operation, polarization curve measurements were performed to evaluate the actual degradation rate of the fuel cell. To verify the accuracy of the proposed degradation model, a fuel cell degradation model and a fuel cell system model were built using SIMULINK, and the experimental data were used as input. The results are shown in Figure 7. From the figure, it can be observed that the simulation model can effectively represent the initial characteristics of the fuel cell. The model also follows the experimental values for the polarization curve of the fuel cell after durability testing, with an average error of 0.51%. The maximum error, reaching 1.5%, occurs at a high current density of 1.52 A/cm\({}^{2}\).  
After completing the 5323 h durability test, the fuel cell's power degradation is determined to be 2.3%. Based on calculations, it is estimated that it would take 23,143 h for the fuel cell system to degrade by 10%, which aligns with the target set by DOE2020.  
Figure 6: Actual operating current of fuel cells.
## 4 Discussion
### Why Do General Dental Practitioners Need to Be Aware of Oral Melanocytic Nevi?  
According to the literature, the majority of patients with OMN are unaware of the existence of such lesions [12; 13], while a few lesions may present with pain and tenderness. [PERSON] et al. [13] also indicate that general dental practitioners (GDPs) may play a role in the identification of OMN. GDPs may easily identify lesions that are gradually enlarging, especially in patients who keep up follow-up appointments or those who regularly go for routine check-ups. In the present series, in contrast to the observation by [PERSON] et al. [12], all patients were aware of the lesions when they sought treatment. However, all the lesions were asymptomatic.  
With respect to the lesions included in the present series, the majority were clinically diagnosed as nevi. However, in contrast, [PERSON] et al. [12] have reported that smaller numbers of their cases were diagnosed as malignancies including melanoma. In addition, melanaotic nevi may also carry a clinical impression of reactive lesions such as irritation fibroma and papilloma [12]. Therefore, it is important to remember that all OMN that present in patients may not carry an indolent clinical impression and, therefore, incisional biopsies of larger lesions are recommended to avoid overtreatment.  
With respect to the configuration of the OMN, the majority were described as well circumscribed, flat or slightly raised lesions [9; 11; 13; 14]. In addition, junctional or compound nevi have a higher chance of being pigmented, while intramucosal lesions could be non-pigmented [14]. The majority of non-pigmented nevi have been described to have a raised configuration. It is important to consider the configuration to narrow down the clinical differential diagnosis. For example, if the lesion is slightly raised, it can be differentiated from racial pigmentation, melanotic macules or amalgam tattoos, all of which are flat lesions. However, with the slightly raised configuration, it is important to remember the other raised lesions that should be considered in the differential diagnosis as melanoma [14].