esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Open Research Issues
This diversified nature of data creates significant challenges for QoS WMSN routing protocols. * _Multiple Base Stations and Sinks_: Most WMSN applications involve a single base station and sink but some applications require multiple base stations and sinks, e.g., military and disaster recovery applications. In this situation, WMSNs should be capable to handle the mixed QoS levels related with multiple base stations or sinks [52]. There are several techniques available in the literature to handle such different kinds of issues to improve the QoS. However, the issue still exists that need to be resolved for QoS provisioning. In addition to the challenges for QoS, we also focus on some of the important directions and research issues that need to be highlighted. Mobility is one of the major threats for provisioning QoS because some of the sensor network models are based on the assumption that sinks are static, but this assumption cannot be accurate for all types of scenarios. For example, battlefield scenarios consist of mixed types of sensor nodes; static and mobile. Therefore, in this situation, sink and sensor nodes should be provided with mobility support. Furthermore, the network topology also keeps on fluctuating dynamically. It is more important to address the mobility and dynamicity of WMSNs should be also considered before designing the QoS routing protocols. The placement of heterogeneous multimedia sensor nodes is another research area for QoS provisioning [53]. Thus, secure data routing is a significant aspect that needs to be considered for WMSNs. In all of these conditions, WMSNs are highly challenging for designing QoS routing protocols. Based on our detailed survey, we have compared the characteristics of existing QoS routing protocols in Table 2.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure  
Since switches are the building blocks of a local area network, as the first line of action in securing a network, the network hardware must be physically secured. Furthermore, the running configuration on these switches needs to be protected through the use of strong passwords. Any attempt to change the access privilege level or the password must trigger an alarm.  
The network administrator must install the cryptographic key for each switch. Only the legitimate switches that are configured by the system administrator will take part in the secure multicast process outlined in our proposal. Any other switches that try to join the multicast group will be rejected. This may include switches that are plugged into the network by end-users to expand their connectivity, which may not support the PrECast infrastructure. The administrator must also set appropriate time-out values mentioned in Section 5.1  
These are the only activities that require the administrator's involvement. All other processes mentioned in the PrECast protocol are automatic and do not need any involvement from the administrator. Since the inter-switch communication needs to be protected in a corporate environment from eavesdropping, the administrator needs to install certificates and private keys irrespective of the PrECast architecture. Thus, the processes that involve the administrator in the PrECast protocol do not requires an extra overhead.  
Figure 2: Traditional Content Addressable Memory (CAM) table and a modified CAM table.
## 2 Materials and Methods
### Fabrication of Vertically Stacked SiNW Photodetector  
Figure 3 illustrates the fabrication process of the top-down SiNW photodetector. The photodetector is fabricated on a p-type (100) silicon-on-insulator (SOI) wafer with a diameter of 100 mm, a 2 \(\upmu\)m-thick buried oxide (BOX) layer, and a 400 \(\upmu\)m-thick Si handle layer. The SOI wafers containing top silicon device layers with thicknesses of 1 \(\upmu\)m, 3 \(\upmu\)m, and 5 \(\upmu\)m were used to create photodetectors with varying numbers of SiNW layers. This is because the potential number of SiNW layers is determined by the thickness of the silicon device layer on the SOI. Firstly, the electrode area on the SOI device layer, excluding an area of 200 \(\upmu\)m by 200 \(\upmu\)m, is patterned using AZ4330 photoresist (PR). Metal electrodes, i.e., titanium (Ti) with a thickness of 400 A and aluminum (Al) with a thickness of 2000 A, are deposited onto the open area, with the Ti layer deposited beneath the Al layer for improved adhesion with the Si device layer. Subsequently, the electrode layer of the photodetector is fabricated through a lift-off process. To form the SiNW array on the silicon device layer with the electrodes, the SS03A9 PR is spin-coated at 4500 RPM. The coated PR is then exposed to a wavelength of 405 nm (H line). The pattern width of the SiNW is 0.5 \(\upmu\)m, the pitch is 1 \(\upmu\)m, and the length is 10 \(\upmu\)m.  
The nanowires are fabricated using the modified deep-reactive-ion etching (DRIE) method proposed in our previous work [27]. The DRIE process involves repeated cycles of isotropic silicon etching and passivation layer deposition, which enables the implementation of structures with a high-aspect ratio. Since the SiNW pattern width is 0.5 \(\upmu\)m, the target for scallop dimension during isotropic silicon etching is set to 0.25 \(\upmu\)m. Consequently, scallops are formed simultaneously on both sides of the pattern during the isotropic etching, enabling the stacking of horizontal nanowires. The SiNW array can be stacked to increase its density by adjusting the number of cycles of the DRIE process. The proposed fabrication method enables an increase in the SiNW density within a finite dimension.  
Figure 2: Schematic of the silicon nanowire and its corresponding energy band diagram. **(a)** The operation model of the SiNW photodetector. **(b)** The energy band diagram shows that, when the nanowire is in the dark state, (1) holes are trapped at the surface of the nanowire. When the nanowire is illuminated, (2) electron-hole pairs are generated first, and photo-excited electrons are created in the conduction band (CB). (3) The excited electrons and the trapped holes recombine with each other at the surface. (4) Then, the remaining holes at the core of the nanowire contribute to the photo-generated current.  
Following the formation of the SiNW, the PR on the surface is removed through O2 plasma ashing. Rapid thermal annealing (RTA) is then performed to reduce the contact resistance between the metal electrodes and the Si layer, thereby maximizing the gating effect induced by the photons.
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 4 Data Acquisition and Analysis
### H/V Measurements  
With the aim of retrieving the information about the stratigraphy of the landslide and nearby areas, ambient noise measurements were taken in 26 different sites in the study area. The location of these sites was selected in order to obtain information along cross-sections of the main landslide where the town was built and considering the distribution of diverse bodies constituting the landslide (Figure 3). In any case, it was not always possible to measure at the planned sites due to access limitations (no permissions were given, in some cases, by the property owners for performing the measurements). Distance between sites varies from several dozens to about 100 m in the more distant sites (Figure 3). When field work was designed, an attempt was made to ensure that the distance between points did not exceed 50 m, but when all the planned sites were established, access to some places were not possible, so these initial estimations had to vary to the final positions shown in Figure 3. Other measurements were taken along the upper parts of the slope, outside of the landslide, to obtain information about the distribution (thickness) of materials in stable areas.  
The measurements were performed through Guralp CMG-6 TD broadband stations. The recording duration was approximately 60 min, which was enough to ensure the statistical stabilization of the signal. The sampling frequency was set to 100 Hz, and the data saved in Guralp Compress Format (GCF). Geopsy software (www.geopsy.com) were used for the H/V analysis of data. First, the data were baseline corrected; then, the signals were each divided into windows of 40 s length and analyzed separately. The time windows were tapered using a Hanning function to avoid the leakage phenomena, and the corresponding spectra were smoothed using a triangular moving window with frequency dependent half-width (5% of central frequency).  
Figure 4: (a) S-wave profile obtained by joint-inversion of H/V and dispersion curves at the borehole site (site A2). On left panel, the minimum misfit model (black) is shown together with the models comprising inside the minimum misfit + 10% (dark gray) and all the tested models (light gray). On the right panels, the mean value plus standard deviation bars of the experimental H/V curve (right bottom panel) and dispersion curve (right top panel) are shown in black together with the ellipticity/slowness curves associated with the best-fitting model (darkest gray), minimum misfit + 10% (dark gray) and all the other tested models (light gray). The minimum misfit value is shown above the right panels. (b) Comparison of the first 60 m of the best-fitting model with, (c) the stratigraphic column obtained from the borehole drill.  
The reliability of the obtained H/V curves has been checked through the guidelines established in the SESAME project [59].
# A Low-Cost Contactless Overhead Micrometer Surface Scanner
## 1 Introduction
### Visual Registration of Images  
The problem of computational image registration dates back at least four decades of study. Methods in the literature are usually called \"local\" if they use point features correspondence or \"global\" if they use overall image similarity [10; 11]. When combining images in a mosaic, these images are required to have some lateral \"overlap\" and the registration task is called stitching. Due to restricted overlap, stitching is more accurate when local methods are used.  
Given point correspondences across two images of a planar surface, robust registration solutions have been found and, by now, are textbook material [12; 13]. When many images are registered sequentially in a mosaic, the error is accumulated, distorting the result. A solution is to employ a \"global alignment\" or \"bundle adjustment\" step, which either obtains a more accurate solution or, at least, distributes error so that the shape of the scanned area is retained. Although this improves the result, the error from the registration of many images manifests as local, noticeable distortions, often called \"seams\". For a few images, these distortions are small and well-treated by methods that reduce their visual prominence. However, when the number of images is increased by two orders of magnitude, we observed these distortions to become significant and noticeable at close and macroscopic inspection.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.1 Approximate Entropy (ApE)  
The ApE parameter can be regarded as the entropy that estimates the variation and instability in a signal [8]. It is calculated as given in Equation (3) [53]. The value of ApE quantifies the regularity in the signal.  
\[\text{ApE}=\ln\left(\frac{S_{l}(k)}{C_{l+1}(k)}\right) \tag{3}\]  
where \(k\) represents the coefficient of similarity and \(S_{l}(k)\) and \(C_{l+1}(k)\) represent patterns with a mean length of \(l\) and \(l+1\), respectively. The parameter \(l\) was set to 2 for the calculation of ApE in our study.
# Application of Machine Learning Techniques to
## 3 Methodology
### Climate Zoning
#### 3.1.2 Principal Component Analysis (PCA)  
The benefits associated with the PCA are as follows--(1) identification of spatially coherent variations that can improve the signals, where the leading components represent the maximum variance in the patterns; (2) explanation of the covariance between the variables at various places; and (3) reduction of dimensionality, thereby providing resources to describe the variability in large spatially dimensional datasets with a reduced number of principal components [60]. PCA can present all the data in a smaller matrix and attempts to retain as much information about the data as possible. We used the dimensions from 138 APHRODITE grid point datasets for PCA. The aim was to project the data onto different orthogonal axes known as principal components. A linear transformation is performed using a symmetric covariance matrix, developed from the dataset, into the principal orthogonal components. The direction of principal components is represented by eigenvectors and the eigenvalues represent the magnitude of the stretch of the axis. This gives the direction (eigenvector) and magnitude (eigenvalue) of the spread of the dataset. The leading principal component, which presents the maximum spread of the dataset, is used for further analysis. Several principal components were selected for use in hierarchical clustering using the scree plot, which shows the variance explained in percentage by each of the principal components. Each principal component has component scores for every station, based on the eigenvectors and eigenvalues. These component scores depict the climate change pattern at the respective grid point/station and can be treated as metrological parameters that are stochastically independent of each other [61]. This analysis has been performed using the PCA function of scikit-learn [53].
## 5 Results and Discussion  
### Validation of Classification Accuracy of GlobeLand30  
Since GlobeLand30 data are used as the reference data, it is highly necessary to firstly evaluate the classification accuracy for ensuring the reliability. The statistics of primary land cover types are selected, including the crop and forest area from the Anhui Statistical Yearbook [56] and the wetland area from the second China wetland survey [57]. The fractional error is used to validate the accuracy of GlobeLand30, which represents a ratio of the absolute value of the differences between the actual area and estimated area and the actual area.  
As shown in Table 5, in addition to the \"woodland\" in Wanbei and \"wetland\" in Wanbei and Wannan, all the other fractional errors are less than 30%. Furthermore, \"woodland\" is taken as a study case to compare the MCD12Q1 and GlobeLand30. The \"woodland\" area derived from the five classification schemes of MCD12Q1 is, respectively, 42.75 km\({}^{2}\) of IGBP, 53.50 km\({}^{2}\) of UMD, 8.75 km\({}^{2}\) of LAI/FPAR, 52.75 km\({}^{2}\) of NPP and 57.00 km\({}^{2}\) of PFT in Wanbei, while it is 106.25 km\({}^{2}\) of GlobeLand30 and is much closer to the yearbook statistics. Therefore, the GlobeLand30 have higher classification accuracy and can be used as the reference data for evaluating the consistency of MCD12Q1.
## 4 Experiment 2: Amplitude Dependence of Vibration-Weight Illusion
### Analysis  
The data were analyzed in the same manner as in Experiment 1. The grip forces measured by the load cell were submitted to a moving-average low-pass filter with a window size of 0.1 s to remove the effect of the vibration stimulus. The means of the grip forces before and after the vibration were calculated from the grip forces during the 0.5 s before the vibration and during 0.5 s from 1 s after the commencement of the vibration, respectively. For each stimulus, the mean and standard error of the change in the grip force among the participants were calculated, and two-tailed \(t\)-tests were conducted to confirm whether the changes were significantly different from zero. The ranks of the perceived heaviness were compared using the Wilcoxon signed-rank test with a Bonferroni correction, with the correction factor being 3 (\({}_{3}\)C\({}_{2}\)). In the last step, the relationship between the change in the grip force and perceived heaviness was examined using [PERSON]'s rank correlation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Amplitude** & **Acceleration** & **Frequency** & **Voltage** \\ \hline
156 μm & 5.5 m/s\({}^{2}\) & 30 Hz & 2.44 V \\
177 μm & 6.4 m/s\({}^{2}\) & 30 Hz & 2.85 V \\
203 μm & 7.2 m/s\({}^{2}\) & 30 Hz & 3.25 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accelerations, amplitudes, and input voltages of the vibratory stimuli used in Experiment 2.
## 5 Anomalous Hall Effect in Thin Films  
In the above sections, we only focus on the AHE in bulk crystals. However, in microelectronic applications, thin films are used most of the time. When discussing thin films from a theory point of view, one has to distinguish between two cases: On the one hand, the film can be above a certain thickness threshold and behave as the bulk system. On the other hand, the film can be of very small thickness. In this case, the bulk picture is not longer valid and extrinsic effects are more pronounced. The thickness threshold is a material dependent value, e.g., \(\approx\)80 nm in Co\({}_{2}\)MnGa [70].  
When the film thickness is below the threshold value, surface effects get stronger compared to the bulk properties. This can lead to a larger extrinsic contribution to the AHE due to enhanced scattering at the surfaces. Depending on the sign of the extrinsic part with respect to the intrinsic one, this can either enhance or decrease the intrinsic AHE [27; 71; 72]. For very thin films, it can even be possible that the confinement to leads a changed band structure where the Weyl points are annihilated and an inverted band gap is created [73; 74; 75], leading to a 2D QAHE system.
# SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark +
## 5 Experiments
### Sketch vs. Other References
The proposed approach remains robust even in challenging scenarios, such as when multiple similar objects are present, there are appearance changes, rapid motion occurs, or objects are partially occluded and move outside the frame. In Figure 10, we compare our method with ReferFormer and SgMg, two state-of-the-art language-based referring VOS approaches. In the first video, ReferFormer and SgMg lose track of the target object due to the presence of multiple similar objects, while our model consistently tracks and segments the referenced duck. In the more challenging second video, ReferFormer and SgMg fail to identify the target object altogether. In contrast, our sketch-VOS method achieves precise segmentation, leveraging the fine-grained and distinctive information embedded in the sketch input. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Youtube-VOS**} & \multicolumn{3}{c}{**DAVIS17**} & \multicolumn{3}{c}{**DAVIS16**} \\  & & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) & \(\mathcal{J\&F}\) & \(\mathcal{J}\) & \(\mathcal{F}\) & \(\mathcal{J\&F}\) & \(\mathcal{J\) \\ \hline \multirow{8}{*}{Text} & VOSwL [14] & - & - & - & 39.3 & 37.3 & 41.3 & 84.1 & 82.8 & 85.4 \\  & URVOS [15] & 46.5 & 44.2 & 48.8 & 51.7 & 47.3 & 56.0 & - & - & - \\  & HINet [29] & - & - & - & 52.0 & - & - & 84.8 & 84.4 & 85.3 \\  & YOFO [30] & 48.6 & 47.5 & 50.0 & 55.4 & 50.1 & 58.7 & - & - & - \\  & MLRL [31] & 49.7 & 48.4 & 51.0 & 57.9 & 53.9 & 62.0 & - & - & - \\  & LBDT [32] & 49.4 & 48.2 & 50.6 & 54.1 & - & - & - & - & - \\  & MTTR [33] & 55.3 & 54.0 & 56.6 & - & - & - & - & - & - \\  & ReferFormer [34] & 64.9 & 62.8 & 67.0 & 61.1 & 58.1 & 64.1 & - & - & - \\  & SgMg [35] & 65.7 & 63.9 & 67.4 & 63.3 & 60.6 & 66.0 & - & - & - \\  & LoSh [36] & 67.2 & 65.4 & 69.0 & 64.3 & 61.8 & 66.8 & - & - & - \\ \hline \multirow{2}{*}{Mask} & STM [44] & 74.7 & 72.8 & 76.6 & 69.5 & 67.0 & 72.0 & - & - & - \\  & STCN [45] & 79.6 & 77.1 & 82.1 & 74.4 & 71.5 & 77.2 & - & - & - \\ \hline \multirow{2}{*}{Sketch} & Vanilla Baseline [25] & 75.4 & 73.4 & 77.5 & 70.2 & 66.9 & 73.4 & 81.6 & 80.2 & 83.1 \\  & Ours & 77.3 & 74.9 & 79.9 & 72.4 & 69.2 & 75.5 & 82.3 & 80.9 & 83.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with other reference methods on Youtube-VOS, DAVIS17, and DAVIS16 datasets. Figure 8: Visualized sketch queries and corresponding feature maps weighted by attention on the Sketch-YouTube-VOS validation set.
## 3 Results and Discussion
### Reproducibility and Precision  
A typical error is to confuse the decimal digits and value digits. Figure 10 is identical of Table 12 from ref. [2]:  
Contrary to the general belief, not the decimal digits but the value digits determine the precision. The bottleneck is the minimum value digits: the end results cannot be more precise than the minimum value digits. Giving more decimal and value digits gives the false impression of obtaining and handling more precise data. A fair comparison of models is simply impossible if the fitted equations are given in different precision.
## 3 System Model
### Incentive Model
By representing he longest allowable training time of the platform in each global round as \(t_{a}\), the local iteration rounds of the strategic edge servers are denoted as \(r_{i}=\lfloor\frac{t_{a}}{T_{i}^{p}}\rfloor=\{1,2,\cdots,\overline{\theta}\}\), where \(\overline{\theta}\) is the maximum local training round that the edge servers can execute in each global epoch. Since edge server type is private information, they can choose a contract item designed for other types. Hence, by determining the contract item \(\phi_{j}\) and the corresponding training strategy as \(r_{j}\), an edge server's total utilities are the difference between the reward, \(p_{j}\), and the costs, \(c_{j}s_{j}\), multiplied by \(r_{j}\):  
\[U_{j}\big{(}c_{j},\phi_{j},r_{j}\big{)}=r_{j}(p_{j}-c_{j}s_{j}) \tag{4}\]  
**The platform's utility**: We denote the platform's total costs as our joint optimization objectives, which is equivalent to the sum of the global model's accuracy loss and the total payments for edge servers. According to [35; 36], the upper bound of model accuracy loss after \(T\) training rounds is \(O(1/\sqrt{DT}+1/T)\), where \(D\) is the total amount of data contributed by all edge servers in each global epoch (\(D=\sum_{j\in\mathcal{J}}r_{j}|j_{j}s_{j}\)). In the case where FEL can converge in finite rounds, i.e., a fixed \(T\), minimizing the upper bound of accuracy loss is mathematically equivalent to minimizing \(1/\sqrt{\sum_{j\in\mathcal{J}}r_{j}|j_{j}s_{j}}\)[30]. Then, given that all edge servers choose their corresponding contract items and determine their local training rounds, the total rewards are \(\sum_{j\in\mathcal{J}}r_{j}|j_{j}p_{j}\). It is clear that the model accuracy loss decreases with the increase in the edge servers' total training data size, while the total payments increase as the data size increases. Therefore, we expect to achieve the best trade-off between accuracy loss and expenditure cost to minimize the total cost of the platform:  
\[U_{S}=\frac{1}{\sqrt{\sum_{j\in\mathcal{J}}r_{j}|j_{j}s_{j}}}+\xi\sum_{j\in \mathcal{J}}r_{j}|j_{j}, \tag{5}\]  
where \(\xi\) is a weight coefficient that balances the amount of accuracy loss and expenditure costs. A smaller \(\xi\) indicates that the platform places more emphasis on improving model performance and less on reducing expenditure costs.