esa-sceva/satcom-chunk-collection huggingface dataset:
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 4 Results and Discussion
### Class C Polling Results  
The random behavior in downlink slot allocation found on the Class B operation is not an issue in this case, so the results in Figure 12 show the exact duration in seconds of the polling period. Because there are no possible idle times between scheduled slots (as introduced by the Class B scheduling algorithm), and no restriction on minimum period to guarantee PDR, the calculated periods are not discrete and they show a lineal increase proportional to the network size.  
Figure 11: Impact of number of channels in polling delay for a multicast network of 100 nodes in Class B.  
Figure 10: Max latency for Class B unicast and multicast (eight channels) polling depending on network size.  
It can also be seen that, for network sizes over 10 devices, the impact of the SF on uplink messages is not relevant, because as there are more nodes to poll from the gateway, the limiting factors are the ToA and DC of the gateway.  
Channel-wise, the impact of varying \(n_{ch}\) in this multicast polling on Class C is shown in Figure 13. In this case, the impact of using more again greatly reduces latency, with the reduction being proportional to the one seen in Class B. As Class C is from the lower latencies achieved at the start, the multicast Class C polling solution with \(n_{ch}\) of 32 is the fastest data collection configuration tested, achieving a latency of 11 s, which can be considered fast enough for some critical IIoT applications, with sensors that have slow responses, such as temperature or humidity for instance. The ratio in latency between devices with different SFs based on the number of channels available is greater than in Class B polling, although it decreases when more channels are available.  
Figure 12: Period in seconds for Class C unicast and multicast (eight channels) polling, depending on network size.  
Figure 13: Impact of number of channels in polling delay for a multicast network of 100 nodes in a Class C.
## 5 Rotor Temperature Modeling and Prediction
### Spearman Correlation Analysis of Rotor Temperature Dependencies in PMSMs
Subsequently, [PERSON]'s correlation analysis was performed using Group B, and the results are shown in Figure 9.  
The results of the two Spearman analyses exhibit discrepancies. The differences are primarily reflected in three aspects: (1) reversal of correlation coefficient signs for voltage components (\(u\_d\), \(u\_q\)), (2) fluctuating strengths of correlations for stator temperature parameters, and (3) reduced correlation for coolant temperature. These discrepancies likely arise due to sensitivity to small sample sizes. In other words, a single outlier can significantly alter the rank of variables, and the presence of certain samples may disrupt overall monotonicity. Furthermore, the reduced experimental sample size may lead to insufficient coverage of the operational conditions space. For the design of PMSMs, the discrepancies between the two analyses provide new insights. The rotor temperature in PMSMs exhibits coupled characteristics, and the dominant heat transfer mechanisms may vary across different operating points. For scenarios such as low/high speed or heavy/light load, temperature prediction models can be constructed using combinations of physical parameters tailored to specific conditions. Figure 8: Spearman correlation between features and rotor temperature in Group A.
## 4 Discussions
### Guidelines for Producing 3 DP-WHOs  
A general observation from the investigations in this study is that these 3D-printed orthoses are characterized by intricate shapes, numerous open pockets, and reduced thickness, which enhance aesthetics and make them favored by users. At the same time, these features complicate the task of determining the optimal solution in terms of build orientation and process parameters. Both these investigations and the literature data show that balancing stiffness, surface quality, and reduced printing time remains a non-trivial challenge.  
Based on the results presented, several guidelines for producing customized 3D-printed wrist-hand orthoses can be formulated, correlating build orientation and process parameter settings with printing time and surface quality. There are two main solutions to design the orthoses, namely based on 3D scans: a case in which the orthosis is 3D-printed in its ready-to-use shape or by orthoses that can be printed flat, and a case in which they need thermoforming to fit the patient's hand. The first method requires design knowledge, while the second approach can be based on an online app to generate the flat model. The second approach is suitable for the MEX process but not for the DLP process. Flat orthoses can be manufactured in almost half the time of their ready-to-use counterparts.  
The problem of build orientation should be addressed only for orthoses 3D-printed in their ready-to-use shape, and the decision should take into account the specificity of the manufacturing process as well as the parameter settings. Here, the main recommendation is to 3D-print the orthoses at 100% infill density, and to test several orientations for finding the one which provides a balance between printing time and surface quality. Manual adjustments may be necessary to achieve the best results.  
Caution is required when minimizing the volume of the support structure (with the purpose of reducing material consumption and printing time) to avoid the orthosis' detachment from the platform. The findings in this study showed that a lower infill density can create gaps inside these thin prints (a design characteristic of these medical devices) with a negative impact on mechanical resistance. When adjusting the overhang angle in MEX-produced WHOs, careful consideration should be given to the design, thickness, and total volume of ventilation pockets.  
For the analyzed orthoses, the infill patterns proved not have any relevant impact on the printing time.  
For MEX-produced orthoses, decreasing the layer thickness from 0.2 mm to 0.1 mm does not significantly improve surface quality but doubles the printing time. Therefore, a lesser layer thickness is not necessary, as shown by our investigations. Orthoses are produced faster by DLP, but the range of materials available for this process is less extensive than for MEX. Additionally, the build space for DLP might be unsuitable for orthoses with large dimensions, such as those required for a male forearm.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 3 SARDIMM: The Proposed NMP Architecture for RDA  
As shown in Figure 4, the proposed SARDIMM performs FFT, reference signal multiplication, and IFFT, which are core operations in the RDA range and azimuth compression. In other words, all the operations except the RCMC and transposition of the RDA are processed near the memory, whereas the RCMC and transposition are performed by the host processor. The RDA range compression and azimuth compression involve performing an IFFT after the FFT. The SARDIMM applies DIF to the FFT and decimation in time (DIT) to the IFFT, allowing the FFT output to be used as input for the IFFT without bit-reverse ordering. The NMP module that accelerates the RDA is positioned in the buffer device, and the memory adopts dual-rank DIMMs, which are commonly used in general computer systems and each DRAM chip has a capacity of 8 GB.  
Figure 4: RDA flow diagram of the proposed SARDIMM for SAR image generation.  
Figure 3: Architecture of memory semiconductors: (**a**) commodity memory semiconductor; (**b**) processing-in-memory (PIM) memory semiconductor; (**c**) NMP memory semiconductor.  
The data type supported by the SARDIMM is the half-precision floating-point format (FP16), and the data path size is 64 bit. The data utilized for the RDA operations comprise complex numbers with real and imaginary parts. Since each datum consisting of real and imaginary parts is represented as FP16, one complex datum can be represented as 32 bit. The choice of FP16 over single-precision floating-point format (FP32) is driven by the need to perform computations efficiently within limited hardware resources. FP16 uses half the memory compared to FP32, which is crucial when dealing with large datasets or complex models. This reduced memory usage allows for more data to be loaded into memory, contributing to performance improvements. The SARDIMM conducts an FFT by employing a single butterfly structure. In a typical computer system, two data points are necessary for a single butterfly operation, which requires the loading of 32-bit data twice. However, with SARDIMM employing NMP, a single butterfly operation can be executed once by directly reading 64-bit data containing two 32-bit data points. Additionally, SARDIMM stores the result of each FFT stage in the static random-access memory (SRAM) within the NMP module, rather than in memory. The result of the previous stage is then read, and the next stage is performed. These features greatly reduce the number of memory accesses, thereby improving performance.
## 4 Discussion
### Alternator with SMC Core  
The alternators with SMC cores displayed intermediate performance in both damping and energy generation, with a 60% damping rate and Vrms and Vpeak values higher than those of the air core but lower than those of the iron core. The use of SMC as a core material presents an attractive compromise solution, as it adequately combines magnetic properties with manufacturing ease, allowing for complex shapes that can be optimal for specific alternator designs. The match in resonance frequency with the air core prototype suggests that SMC cores could be effectively used in similar applications, with the added advantage of greater energy generation.
## 3 Results and Discussion  
### Main Physico-Chemical Properties of the Sediments from the Three Studied Areas  
Table 1 presents the initial sediment characterization results for the three areas. Sediment and agronomic parameters results showed homogeneous sediment texture between the three zones (fine silt). Organic matter was in the high range \(>\)40 g kg\({}^{-1}\)[44], with a carbon/nitrogen (C/N) \(>>\) 10, probably due to the sediment matrix [29]. Moreover, the pH was alkaline in the three areas.  
For each area, total TE concentrations in sediments were compared to the sediment remediation criterion S1 (Table 1), a parameter for guiding the management of sediments according to their pollution (French Environmental code, article R214-1, Section 3.2.1.0) [45]. Given the total concentrations in each area, Zn, Cd, and Pb were above the S1 values, which confirmed the need to find a management option for these areas, taking into account the pollution. In addition, based on these total concentrations, the areas could be ranked from the least contaminated to the most contaminated, area 2 \(<\) area 1 \(<\) area 3, which confirmed TE heterogeneity of the sediment and the selection of the three areas.  
In the absence of reference for sediments, the extractable TE concentrations obtained with NH\({}_{4}\)NO\({}_{3}\) were compared to the reference values usually found in soils, following the standard ISO 19730:2008 (E) (As: 0.025 ug g\({}^{-1}\); Cd: 0.005 ug g\({}^{-1}\); Cr: 0.01 ug g\({}^{-1}\); Cu: 0.25 ug g\({}^{-1}\); Ni: 0.25 ug g\({}^{-1}\); Pb: 0.02 ug g\({}^{-1}\); Zn: 0.25 ug g\({}^{-1}\)) [42]. The extractable concentrations measured for Zn, Pb, Cd, and Cu in all sediment samples were higher than the baseline, suggesting that the solubility of these TE in the contaminated site is higher than that frequently measured in sediment (Table 1). The concentrations found for the other TE were lower than the reference values. Currently, regulation and threshold are based on total TE concentration in risk assessment and do not consider the available fraction; however, this is the fraction interacting with the environment and, therefore, the one with the most harmful effect [11]. In view of total and extractable concentrations, the elements requiring special attention were Zn, Pb, Cd, and Cu. Consequently, the following sections focused on these TE.
## 2 Study Area and Data Sources
### Data Sources
#### 2.2.2 Samples  
Since thin clouds partially obscured the October 2022 Sentinel-2 image, this study used GEE as the basis for visual interpretation based on the October 2022 Sentinel-2 image, with the March 2022 Sentinel-2 and Google Earth images as secondary references, to obtain five types of sample areas: apple orchards, croplands, artificial surfaces, water bodies, and other lands. An unbalanced number of samples will affect the accuracy of the classifier. Therefore, this paper uses the random point tool in ArcGIS 10.6 software; random samples were generated in the sample area, according to the area share of each site obtained from visual interpretation. A total of 1098 samples were generated, of which 770 were training samples and 328 were validation samples, as listed in Table 2.
## 2 Materials and Methods
### Joint Histograms, k-Means Clustering, and Segmentation  
Having established spatial correspondence between structures in the images via registration, the relationship of the tissue appearance between the modalities was investigated via bi- and tri-variate histograms for common areas and volumes, respectively. K-means clustering was employed in order to automatically over-segment these joint distributions into eight clusters. The k-means approach was chosen as it is an efficient method that minimizes within-cluster variances and finds clusters of comparable spatial extent in a feature space [27; 28]. A meaningful reduction in the automatically determined clusters into five classes was achieved via manual merging. This analysis was performed in MATLAB (release 2020a, The MathWorks, Inc., Natick, MA, USA).  
This segmentation captured the direct relationship between tissue appearances in various images, without taking account of the tissue's spatial arrangement, i.e., independent of anatomical features and their positions. This procedure has the advantage that general relationships for the whole image can be identified, that tissue properties at the pixel resolution are investigated and that the least complex segmentation approach is first investigated.
# Multipolar Plasmonic Resonances of Aluminum Nanoantenna Tuned by Graphene
## 3 Results and Discussion
In particular, it is found that the narrow absorption peak of the \(l=3\) mode is significantly enhanced when the inclined incident angle \(\theta\) is decreased form \(45^{\circ}\) to \(0^{\circ}\), which paves the way to practical applications. Figure 4b shows that the absorbance of the plasmonic fundamental mode (\(l=1\)) of G-AlNR is slightly decreased while the absorbance of the plasmonic quadrupole mode (\(l=2\)) and the plasmonic third-order mode (\(l=3\)) are slightly increased as \(t_{2}\) is increased from \(1\) to \(4\) nm. It is worth noting that there is no plasmon hybridization between the AlNR and the bottom monolayer graphene to bring the resonant positions of the multipolar plasmonic modes of G-ALNR changed, which is due to the monolayer graphene acting as a passive and lossy conductive surface with no plasmonic response from UV to visible range. Such behavior in our proposed G-AlNR structure is different from the plasmonic coupling in the conventional metallic plasmonic materials such as Ag and Al in the UV range. Finally, we explore the potential of the plasmonic high-order mode of the G-AlNR structure for UV sensing applications. In Figure 5a, the absorption spectra of the G-AlNR structure embedded in different surrounding media are calculated under TM polarization with inclined incidence angle (\(\theta\)) of \(30^{\circ}\). The geometrical parameters are the same as shown in Figure 1b. The spectral redshifts of two peaks (\(l=2\) and \(l=3\)) are observed when the refractive index of the surrounding media is changed from \(1.00\) to \(1.15\). The linearly fitted sensitivity \(S\) (\(S=\Delta\lambda/\Delta n\)) at UV range for the two peaks (\(l=2\) and \(l=3\)) are \(51\) and \(25\) nm/RIU, respectively, as shown in Figure 5b. Here, \(\Delta\lambda\) and \(\Delta n\) are the wavelength shift and refractive index change, respectively. We further quantify the figure of merit (_FOM_), which is defined as the sensitivity divided by _FWHM_[30; 31; 32; 33]. For the two peaks of \(l=2\) and \(l=3\), the two corresponding _FWHMs_ are \(27.5\) and \(8.5\) nm. Thus, the _FOMs_ of the two plasmonic modes (\(l=2\) and \(l=3\)) of our proposed G-AlNR in UV range are \(1.9\) and \(3\), respectively, providing the practical applications such as ultraviolet refractive-index sensing. Figure 4: **(a)** The absorption spectra of the G-AlNR and AlNR structures under TM light with different incident angle (\(\theta\)) from \(0^{\circ}\) to \(45^{\circ}\) with a step of \(15^{\circ}\). **(b)** The absorption spectra of the G-AlNR with various the vertical distance (\(t_{2}\)) between graphene and AlNR. The other parameters are the same as shown in Figure 1b.
## 3 Design and Implementation Results
### Interoperation between Two Planes  
Since the proposed dual plane architecture separates the data plane and the control plane, interworking between the two planes is required. In other words, signaling messages among messages received in the data plane have to be passed to the control plane, and the information acquired in the control plane should be referred by the data plane.  
To achieve this, we apply three interworking methods. First, the KNI of DPDK is used for exchanging messages between the two planes. Since the interfaces under the DPDK are only used in the data plane and are not assigned to the control plane, the control plane is not aware of the existence of the interfaces. As a result, it is not possible to pass signaling messages from the data plane to the control plane. For this reason, the DPDK proposed the KNI concept [33]. KNI assigns virtual interfaces in the control plane corresponding to data plane interfaces, and allows two planes to exchange messages each other. Second, we generate hugepage shared memory to allow data planes to refer the forwarding-related information. The control plane updates several tables in the Shared Memory, and the data plane uses the updated tables. Finally, we use Netlink [35]. Netlink is a Linux kernel interface for IPCs (Inter-Process Communication) between the kernel and a user-space application. Through Netlink, a user-space application can monitor changes of the kernel's information [36]. In the dual plane architecture, a change in the kernel's information should be referred to the forwarding decision in the data plane. The Sync Manager is made aware of the changes by Netlink and stores the changes in the Shared Memory.  
These interoperation methods are effective for performance tuning (i.e., assigning multiple cores to the data plane and one CPU cores to the control plane) because the control plane is independent of the data plane. In addition, these methods allow a physically distributed system which consists of one physical server including only a control plane and several physical servers including only a data plane.
# An Insightful Overview of the Wiener Filter for System Identification
## 4 Wiener Filter with Different Kinds of Regularization
The iterations continue until convergence. Let \(L=L_{1}L_{2}\) with \(L_{1}\geq L_{2}\), so that \(\mathbf{H}=\text{ivec}(\mathbf{h})\). In the same way, \(\widehat{\mathbf{H}}=\text{ivec}\Bigl{(}\widehat{\mathbf{h}}\Bigr{)}\). In many applications, \(\mathbf{H}\) is far to be a full-column rank matrix and this important information should be somehow exploited. In other words, \(\mathbf{H}\) is a low-rank system. Therefore, in the MSE criterion, it makes more sense to apply a regularization based on the rank of \(\widehat{\mathbf{H}}\), i.e., [26]  
\[\min_{\widehat{\mathbf{h}}}\Bigl{[}\mathcal{J}\left(\widehat{\mathbf{h}} \right)+\delta\text{rank}\Bigl{(}\widehat{\mathbf{H}}\Bigr{)}\Bigr{]}. \tag{27}\]  
Unfortunately, the matrix rank minimization in (27) is NP-hard, in general, due to the combinatorial nature of the function \(\text{rank}(\cdot)\)[26]. Interestingly, when the matrix \(\widehat{\mathbf{H}}\) is diagonal, the previous problem reduces to the optimization problem in (20). Consequently,the sparse problem is taken into account in (27). Using the fact that the convex envelop [27] of \(\text{rank}\left(\widehat{\mathbf{H}}\right)\) is \(\left\|\widehat{\mathbf{H}}\right\|_{*}\), (27) is roughly equivalent to the optimization problem [26]:  
\[\mathbf{\widehat{h}}_{\text{W},\delta}=\big{(}\mathbf{R}+\delta\mathbf{I}_{L }\big{)}^{-1}\mathbf{p}, \tag{19}\]0  
Some iterative algorithms exist to solve this problem, but they do not seem to work well for long impulse responses; furthermore, complexity may be prohibitive since an approximate SVD needs to be computed at each iteration. Recent insights can be found in [28] and the references therein.
## 2 Theoretical Background
### Hazard Management Structure for Sunken Ships  
Sunken ships pose a potential risk to the marine environment and safety and require scientific and systematic management. In 2013, the Korean government established Article 83(2) of the Marine Environment Management Act, which provides a legal basis for measures to prevent marine pollution accidents caused by sunken ships. These measures include (1) systematic management of information on sunken ships, (2) risk assessment of the potential for sunken ships to cause marine accidents, and (3) implementation of risk reduction measures for sunken ships.  
The Enforcement Regulations [31] for each of the above measures considered in the Marine Environment Management Act stipulate the following:  
* Article 47.2 of the Enforcement Rules of the Marine Environment Management Act (Risk Assessment, etc.).
* Article 47.3 of the Enforcement Rules of the Marine Environment Management Act (Implementation of Risk Reduction Measures).
* Article 47.4 of the Enforcement Rules of the Marine Environment Management Act (Calculation and Imposition of Costs for Implementation of Risk Reduction Measures).
* Article 47.5 of the Enforcement Rules of the Marine Environment Management Act (Management of Information on Sunken Ships).
* Sunken Ship Management Regulations (Decree of the Ministry of Oceans and Fisheries).  
The hazard management system for sunken ships in accordance with the aforementioned enforcement rules and administrative regulations is illustrated in Figure 1.  
Figure 1: Risk management framework for sunken ships.  
Sunken ship hazard assessment involves nine factors that comprehensively evaluate the potential hazards, including the likelihood of marine pollution owing to residual oil spills from the hull, the possibility of navigational obstacles, the potential destruction of marine ecosystems and natural environments, and the likelihood of obstacles in major fishing grounds. Table 1 lists each factor and its corresponding risk assessment index, which was calculated by adding the scores for each assessment item. Appendix B details the nine evaluation factors and their assigned evaluation scores.