esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This possibility was also supported by the results of the comparison between area-volume relationships extracted from two datasets belonging to different areas of the Sahelian region [15]. The introduction of the new generation of high-resolution sensors (such as COSMO-SkyMed, TerraSAR-X and Sentinel-1) allows for an effective monitoring of small reservoirs. In fact, as discussed in [15], COSMO-SkyMed imagery has been successfully employed for the study of basins with extension of few thousand square meters. The data on water retention at reservoirs can be used also for the implementation and calibration of hydrological models, as suggested in [16]. One of the main limits of COSMO-SkyMed imagery is the significant cost required for acquiring a complete dataset. The recent launch of Sentinel-1, the new ESA SAR sensor, solves the problems related to the cost of images, since ESA proposes a free data distribution policy. The interpretation of the SAR images is still a limit for the diffusion of this technology, because the image characteristics depend on the geometrical and electromagnetic properties of the observed surfaces. The comprehension of the geometric distortions introduced by the side-looking acquisition mode [17] and of the non-linear electromagnetic scattering phenomena that contribute to the SAR signal formation requires the quantitative knowledge of the interactions between the transmitted electromagnetic field and the physical surfaces of the imaged scene. Therefore, several works in the past literature expressed the necessity of remote sensing processing chains devoted to produce results that could be easily interpreted by the potential end-users [18, 19, 20]. In this paper, we present an analysis of the potentiality of Sentinel-1 in water resource monitoring activities. The analysis is supported by a SAR raw signal simulator and is focused on applications concerning water-related problems in semi-arid regions. The work is organized as follows. In Section 2, we recall the main characteristics and results of the Water Resources Management in Semi-Arid Regions (WARM-SAR) project, which produced significant results for water resource management from high-resolution SAR data. Sections 3-5 deal with an analysis of the new opportunities offered by Sentinel-1: in Section 3, the main characteristics of the mission are recalled; Section 4 is devoted to introducing the SAR simulator used for the quantitative analysis of the Sentinel-1 performances; and, in Section 5, we present the results obtained by processing the simulated Sentinel-1 images. We provide specific results aimed at the production of DEM and related products, land cover maps and monitoring of water volumes. Implications of the use of this new sensor in the hydrological modeling of the study area are discussed throughout the examples. Conclusions are drawn at the end of the work.
## 5 Conclusions  
Despite the numerical progress and the advance in turbulence modeling, the prediction of complex flow structures can still be an issue. With the requirement of higher order numerics, grid resolutions and time steps, simulations often capture only a short period of time due to the high computational cost. As a result of highly unsteady and non-periodic flows, the evaluation of numerical results can be challenging and statistical approaches have proven themselves as powerful tools in the analysis of unsteady flows.  
After validation of the numerical setup, the unsteady flow of a critical operating point in a simplified steam turbine control valve is assessed. Using Spectral Proper Orthogonal Decomposition, strong temporal pressure oscillations previously observed in the experiment were isolated and corresponding flow fields were reconstructed. While the accessibility of the experiment does not allow a direct comparison of flow fields, several analogies to experimental data support the credibility of the numerical findings.  
For two dominant frequencies, strong forces are predicted by the CFD as a result of temporally excited pressure oscillations and vibrations can be triggered. Experiments support this thesis as an increased lateral vibration level is observed for the lateral pressure excitation.  
Figure 10: Histograms of the phase lag among the pressure sensors based on the SPOD analysis of the experimental data (\(M\) 1).  
With the SPOD analysis, the identification and isolation of the dominant structures from the chaotic and unsteady flow was enabled and couplings with the supersonic flow are observed. The method has shown its great capabilities of isolating dominant structure which helps the understanding of fundamental phenomena. For future numerical analyses of elastic valve configurations, SPOD is expected to enhance the understanding of coupling mechanisms of fluid-structure interactions, promoting measures to increase the operational stability of the valves.  
Conceptualization, all; methodology, C.W., M.L. and R.M.; software, C.W.; validation, C.W. and M.L.; formal analysis, C.W.; investigation, C.W.; resources, C.W.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, C.W. and M.L.; visualization, C.W.; supervision, M.L. and R.M.; project administration, R.M.; and funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), founding code MA 4922/7-1.  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors wish to gratefully acknowledge funding and support by the Deutsche Forschungsgemeinschaft (DFG) under the founding code MA 4922/7-1. The computations were performed on the Bull HPC-cluster at the Center for Information Services and High Performance Computing (ZIH) at TU Dresden.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 2 Materials and Methods
### Analysis of EIS Data  
Per set of experiments and investigated PEF parameter, N = 6 individual conductivity and permittivity curves were calculated using Equations (3)-(5) and referenced to the 'Ctrl' regions according to Equation (6), yielding N = 6 relative conductivity (\(\Delta\sigma_{rel}\)) and relative permittivity (\(\Delta\epsilon_{rel}\)) curves, respectively. Subsequently, a mean conductivity and permittivity curve and their standard deviation (SD) as well as the corresponding mean \(\Delta\sigma_{rel}\) curves and their SDs were calculated. The maximum relative conductivity (\(\Delta\sigma_{rel.max}\)) and the corresponding frequency (\(f_{\Delta\sigma_{rel.max}}\)) were read from the relative conductivity curves. Based on \(f_{\Delta\sigma_{rel.max}}\), the optimal decade (\(f_{opt10}\)) for analyzing PEF-mediated conductivity changes was determined by rounding the logarithm of \(f_{\Delta\sigma_{rel.max}}\) to the nearest integer and converting back to the linear scale. The same analysis was repeated for the permittivity data.  
The relative standard deviation (RSD) in percent was used to assess the precision of replicate conductivity and permittivity measurements within each sample group. It was calculated as follows:  
\[RSD\ (\%)=\frac{\textit{Standard deviation}}{\textit{Mean}}\times 100 \tag{9}\]
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 1 Introduction  
### Background  
The autonomous exploration of unstructured environments by mobile robots is witnessing increased interest, as it helps to accomplish diverse tasks such as search and rescue, surveying and data collection (e.g., [1] with unmanned ground vehicles and [2] with unmanned aerial vehicle), and surveillance. Performing these missions by autonomous mobile robots presents several advantages. It allows the avoidance of human intervention in hazardous area [3], as well as overcomes difficulties that may arise in the case of remotely controlled mobile robots such as a loss of connectivity or degraded latency. With robots being utilized in a wide spectrum of unstructured environments, the need for systems to build reliable representations of the environment is critical. Therefore, the estimation of the traversability of terrains is of great importance. In the so-called model-based approach, traversability is estimated based on mechanical analysis or simulation (e.g., [4; 5]). However, the approach requires detailed knowledge of the terrain properties and high computational complexity [6]; hence, it is not easily applicable to certain environments. The other approach, which is becoming more popular, is to rely on machine learning techniques based on many data samples.  
While supervised learning methods have proven to be efficient (e.g., [7; 8; 9]), there are several drawbacks to such methods. Manually labeling a large amount of data to train the classifier is tediousand subject to inconsistency. As a result, this approach is not flexible as it is unable to adapt to dynamic environments without more data annotation and training. As an alternative, self-supervised learning, using proprioceptive sensor information, such as inertial data, as label information to determine whether terrains is traversable, has been intensively investigated [10]. The self-supervised approach, also called the near-to-far strategy [11; 12], is promising for making mobile robots adaptive to a variety of environments.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 5 Conclusions  
This article introduced a methodology that combined multi-resolution remote sensing images to achieve accurate parcel extraction. Based on SAM, the proposed method enables large-scale and precise extraction of parcels in complex agricultural landscapes without the need for ground samples and local training.  
Initially, Sentinel images were utilized for extracting parcels at a 10 m resolution. To better focus on parcels and improve detection and stitching results, techniques such as cropland masking and overlap prediction were employed during the processing. The results demonstrated that our method achieved fast and accurate parcel extraction, performing no worse than existing deep learning models in plain areas (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). In hilly areas, our method even outperformed existing models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Meanwhile, we discarded the traditional way of using single-resolution data for parcel extraction. Instead, a method of using different-resolution data for parcel extraction for different farmland landscapes was proposed. By combining Sentinel-2 and GF-7 images, we further segmented the parcels by focusing on their histogram features and internal edge characteristics, ultimately achieving accurately delineated parcels in the three study areas. This mode of parcel extraction was more in line with human farming habits and land management patterns, and the results were more accurate and reliable, which can better provide data support for farmland management.  
Conceptualization, Y.D.; methodology, Y.D., H.W., Y.Z., X.D. and Q.L.; software, Y.D., Y.W., J.X. ([PERSON]), S.Y., S.G. and H.H.; validation, S.Z.; formal analysis, Y.D. and Y.S.; investigation, Y.D.; resources, H.W., Y.Z., X.D. and Q.L.; data curation, J.X. ([PERSON]); writing--original draft preparation, Y.D.; writing--review and editing, Y.D. and Y.Z.; visualization, H.W., Y.Z., X.D. and Q.L.; supervision, H.W., Y.Z., X.D. and Q.L.; project administration, H.W., Y.Z., X.D. and Q.L.; funding acquisition, X.D. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key R&D Program of China (2021 YFD1500103), the National Science Foundation of China (42071403 and 42371359), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070504).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 3 Evaluation
### Discussion  
In the proposed hybrid algorithm, independent component analysis is used to decompose the magnitude of HRTFs, factor analysis is used to select significant anthropometric parameters, and finally multiple linear regression is used to establish the prediction formula. With the proposed hybrid algorithm, individual HRTFs can be customized by measuring the anthropometric parameters. Furthermore, the performance is evaluated by the objective SD value and a subjective localization experiment.  
In order to further validate our proposed algorithm, we compare our method with other customization studies. Firstly, most studies extracted anthropometric parameters from photos, and some large-sized anthropometric parameters, such as head circumference and head height, were measured by a ruler. However, our work utilizes the anthropometric parameter extraction method based on a three-dimensional scanned head model, guaranteeing that the anthropometric parameter measurement had high accuracy. This is an important preliminary step for individual HRTF estimation based on anthropometric parameters. Secondly, our method outperforms the other customization studies regarding subjective localization performance. Our proposed method has the average angle error of 14.5\({}^{\circ}\) across six elevations, while [PERSON]'s method achieved the average angle error of 20.7\({}^{\circ}\)[15] and [PERSON] et al. achieved the average angle error of 24.5\({}^{\circ}\)[36] for various elevations in the median plane. Therefore, our method improves the median-plane localization performance by around 6.2\({}^{\circ}\) as compared to the existing method.
## 2 The Proposed Methods
### 1D-SENet Network  
SENet is a channel attention mechanism designed to enhance the feature extraction capabilities of convolutional neural networks. By analyzing and processing the relationships between feature channels, SENet selectively learns the most important feature information and determines the significance of each channel. This allows for the diagnostic model to adaptively adjust the weight of each channel, suppressing irrelevant features and amplifying those most relevant to the task at hand. Consequently, the model's diagnostic classification accuracy is significantly improved [35].  
The basic structure of SENet is shown in Figure 2. The SE block is the core component of SENet and can be embedded into various convolutional neural networks as a module. The SE block consists of two main stages: Squeeze and Excitation. The one-dimensional fault sample signal \(X\in R^{C^{\prime}\times L^{\prime}}\) undergoes a convolution operation to obtain \(U\in R^{C\times L}\)where \(C\) represents the number of channels and \(L\) represents the length of the sample signal. The specific calculation method is as follows:  
\[u_{i}=v_{i}\times X=\sum_{j=1}^{C^{\prime}}v_{i}^{k}\cdot x^{j} \tag{8}\]  
where \(u_{i}\) represents the \(i\)-th channel of the fault sample signal, \(v_{i}^{k}\) represents the \(i\)-th input of the \(k\)-th convolutional kernel, and \(x^{j}\) represents the \(j\)-th channel of \(X\).  
For one-dimensional signals, the Squeeze operation performs global average pooling on the features of each channel to obtain a \(1\times C\) vector, thereby capturing the overall characteristics and global information of each channel. This process is represented as follows:  
\[z_{c}=\frac{1}{L}{\sum_{i=1}^{L}u_{c}(i)} \tag{9}\]  
where \(z_{c}\) is the global average pooling result of the \(c\)-th channel, and \(u_{c}(i)\) is the value at position \(i\) of the \(c\)-th channel.  
The Excitation operation captures the dependencies between channels through two fully connected layers and a nonlinear activation function, adaptively adjusting the weights of the channels. This enhances important feature channels and suppresses unimportant ones, as detailed below:  
\[s=\sigma(W_{2}\delta(W_{1}z)) \tag{10}\]  
where \(s\) is the output channel weight vector with a length of \(C\), \(W_{1}\in R^{\frac{C}{\sigma}\times C}\) and \(W_{2}\in R^{C\times\frac{C}{\sigma}}\) are the weight parameters of the fully connected layers, \(r\) is the scaling factor, \(\delta\) is the ReLU activation function, and \(\sigma\) is the sigmoid activation function.  
Finally, through the Reweighting operation, the learned channel weights \(s\) are redistributed to each channel of the input sample signal, resulting in signal feature samples with weighted information, as follows:  
\[\hat{X}_{c}=X_{c}\cdot s_{c} \tag{11}\]  
where \(\hat{X}_{c}\) is the \(c\)-th channel of the one-dimensional sample signal with weighted information, \(X_{c}\) is the \(c\)-th channel of the input one-dimensional sample signal, and \(s_{c}\) is the weight of the \(c\)-th channel obtained through the Excitation operation.
# Multiple Facies Metamorphic Rocks: A Rare Example from the East Qinling Orogen, China
## 5 Discussion
### Interpretation of U-Pb Dating and Tectonic Setting  
The Kuamping Group in the Baishugang-Wuijianfang district was metamorphosed to the lower amphibolite-facies [41]. The peak metamorphism in the group has been recently dated at ca. 440 Ma [37; 38; 39; 42]. In addition, muscovite and biotite from the group yields a well-defined \({}^{40}\)Ar/\({}^{39}\)Ar plateau age of 365-383 Ma, interpreted as constraining the age for cooling [43].  
The rims of zircon from a metasedimentary rock from the Kuamping Group have Paleozoic ages of 418-386 Ma and low Th/U ratios of 0.04-0.11, interpreted as the metamorphic age of the host rocks. Similar \({}^{40}\)Ar/\({}^{39}\)Ar age of about 416 Ma was reported for amphibole at the Baishugang-Wuijianfang district [12]. As discussed above, the rims of the rutile from  
Figure 8: Pressure–temperature diagram. Reprinted with permission from ref. [40]. 2002 Wiley Online Library. The red circle represents an average value (630 \(\pm\) 30 \({}^{\circ}\)C) of estimated crystallisation temperature for rutile from the Baishugang–Wuijianfang district at a pressure of 7.0 kbar. Abbreviations of metamorphic facies: BS = blueschist-facies, GS = greenschist-facies, AM = amphibolite-facies, GR = granulite-facies, EC = eclogite-facies.  
the same sample yields a La-ICP-MS U-Pb metamorphic age of about 386 Ma (Figure 6A). Together with previous studies [9; 44], we therefore suggest that metamorphism in the area took place during the Devonian at about 418-386 Ma.  
On the basis of the comparison with zircon, LA-ICP-MS U-Pb analyses of rutile yield homogeneous age, constraining metamorphic event and supplying the upper limit of the regional metamorphism.
## 2 Background
### InterPlanetary File System  
The InterPlanetary File System (IPFS) is a peer-to-peer distributed file system that seeks to connect all computing devices with the same system of files [2]. The IPFS was born by looking to the data-sharing platforms of the past as Napster, large file distribution systems supporting over million of users.  
Content published to IPFS is public by design, and, currently, IPFS does not provide a built-in solution for storing private data. Anyway, it is possible to store and transfer private data over IPFS through encryption and to create private networks of nodes. IPFS cannot guarantee data availability that means it is necessary to continuously save the published content on an IPFS node to reach availability and thus the node should always be online.  
The main difference between a Centralized System and IPFS System architecture is shown in Figure 2. While in a centralized system architecture (Figure 2a), an uploader of content acts as a provider of content (maintaining it) and user nodes access the content. In an IPFS system architecture (Figure 2b), all user nodes act as a provider of content and so all the participants guarantee the maintenance.  
Since the Blockchain needs distributed consensus between all the network nodes in order to add a new block, it will be expensive to store any data into the Blockchain. It is more efficient to store transactional data on the Blockchain while using IPFS as a storage medium. Adopting this architecture is hinted at by several research papers found. Moreover, a commercial example of Blockchain combined with IPFS implementation is Filecoin, a decentralized storage network designed to encourage nodes to save third-party content in return for some reward [10].
# Influence of Micro-Textures on Cutting Insert Heat Dissipation
## 2 System Design and Implementation
### Experimental Device Description and Assembly  
The main components of the experimental device were manufactured using a Computer Numerical Control (CNC) machine and the small parts using conventional machining processes, being the selected materials identified in Table 1. The core in PET-G presents a complex geometry, for this reason, was produced using Fused Deposition Modeling (FDM), an extrusion-based 3D printing. All the components used to build the experimental device are laid out in Figure 5a and the main steps for its assembly are shown in Figure 5b-d.  
The location of the thermocouples for measuring the inlet (T1) and outlet (T2) temperature of the fluid are shown in Figure 5a. In Figure 5b is visible the insert seat with four thermocouples (T3-T6), to monitor the temperature on the cutting insert base, and two thermocouples (T7-T8), to monitor the main body temperature. The cutting insert assembly, the heat sources, and the 8 resistive electric elements are visible in Figure 5c. Two more thermocouples (T9-T10) are used to monitor the temperature in the insert-heat sources interface, located near the cutting insert tip. The complete assembly of the device with the volumetric pump is presented in Figure 5d. This last picture also shows the pipes connections from the volumetric pump to the inlet channel and from the outlet channel to the sink, as well as the thermocouples wires connected to the signal acquisition system of National Instruments(r), model NI 9213. Water is used as coolant fluid due to the large amount needed to perform experimental tests. The freshwater is pumped at a constant flow rate into the inlet channel of the device, where the temperature is measured by thermocouple T1. The water carries on to the upper surface of the cutting insert, where most of the heat transfer occurs. In the outlet channel, the water temperature is measured by thermocouple T2, and then it is expelled. A k-type thermocouple was selected for the temperature  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Input Power [W]** & **T1 [°C]** & **T2 [°C]** & **AT [°C]** & **Mass Flow Rate [g/s]** & **Heat Transfer Rate [W]** \\ \hline
100 & 15.22 & 16.39 & 1.17 & 15.6 & 76.41 \\
200 & 15.25 & 17.57 & 2.32 & 15.6 & 151.29 \\
300 & 15.27 & 18.73 & 3.46 & 15.6 & 226.09 \\
400 & 15.29 & 19.88 & 4.59 & 15.6 & 299.65 \\ \hline \end{tabular}
\end{table}
Table 2: Numerical thermal results for different input power levels.  
measurement at ten different points of the experimental device. These were manufactured by welding the hot junction tip in an inert atmosphere with suitable equipment, being later calibrated using the PREZYS T-25N equipment. The thermocouples were mounted into the device using suitable silicones and adhesive tapes, selected according to the restrictions and operating conditions.