esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Influence of the Diffraction Effect
Then, within the upper and lower boundaries determined by the sensor pixels in Figure 5, the corresponding energy is given by the line integral to the diffraction pattern light intensity over the arc segment \(l(r)\); that is:  
\[E_{l}(r)=\int_{l}S(r)dl \tag{16}\]  
where \(l(r)\) is determined as follows:  
\[l(r)=2\pi arc\sin(\frac{p}{2r}) \tag{17}\]  
Given an arbitrary pixel \(i\), assuming that the pixel size is \(P\), and the distance between the pixel center and the Airy disk center is \(\Delta d\), for the diffraction pattern of light wavelength \(\lambda\), the energy in this pixel can be approximately calculated as follows:  
\[B_{\Delta d}^{i}(\lambda)=\int_{\Delta d-P/2}^{\Delta d+P/2}E_{l}(r)dr \tag{18}\]  
According to Equation (18), the actual spectrum corresponding to any pixel \(i\) can be obtained. Combined with the basic theory of FTS [1; 10; 19; 20; 21; 22], we can obtain the interference signal intensity corresponding to any pixel \(i\) as follows:  
\[I^{i}(x)=\frac{1}{2}\int\limits_{v_{r}^{i}}^{v_{l}^{i}}B^{i}(v)[1+cos(2\pi vx )]dv \tag{19}\]  
where \(x\) is the optical path difference. We use bandpass sampling theory to discretize interferogram sampling. We assume that the maximum optical path difference of the  
Figure 4: Relationship between the diffraction range of each wavelength and pixel. system is \(L\), and the optical path difference sampling step that satisfies the bandpass sampling theory is \(\Delta x\). Then, according to Equation (4), the maximum sampling step of the interferogram of the \(i\)th pixel of the sensor after considering the diffraction effect is:  
\[\Delta x_{\text{max}}^{i}=\frac{m_{\text{max}}^{i}}{2v_{l}^{i}} \tag{20}\]  
where \(m_{\text{max}}^{i}=\left\lfloor v_{l}^{i}/(v_{l}^{i}-v_{r}^{i})\right\rfloor\). It can be observed that for any pixel \(i\), the maximum sampling step of the corresponding interferogram is directly related to the maximum wavenumber and wavenumber bandwidth of the spectrum. In fact, optical systems always have diffraction effects. Therefore, the actual spectral bandwidth corresponding to a pixel is typically several times the ideal spectral bandwidth, and the difference between the maximum wavenumbers is usually small, which will lead to several times the difference in the interferogram sampling step. Through the simulation analysis in Section 5, it can be observed that the influence of several times the difference on the recovered spectrum is fatal. To ensure that there is no spectral aliasing in the recovered spectrum, it is necessary to comprehensively consider the diffraction effect of the optical system, and accurately determine the spectral wavenumber range corresponding to each pixel to eliminate the influence of the diffraction effect.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Glacier Surface Velocity Maps
#### 3.1.3 New-Zealand Southern Alps  
The largest glacier-covered area in New-Zealand is located in the Aoraki/Mount Cook peak in the Southern Alps mountain range in the South Island. The largest glacier in this area is the Tasman Glacier (95 km\({}^{2}\)) with a length of 26 km, and a 2-km width ice front that flows into the Tasman pro-glacial  
Figure 2: **(A) Glacier surface velocity mosaic of the Mont-Blanc massif (European Alps) in meters per year averaged over 2017–2018 from Sentinel-2 images. Colorbar is coded on a logarithmic scale and overlaid on a shaded relief version of the SRTM DEM; (B) Weighted standard deviation color coded from white (low variance) to dark red (high variance). Insets show a zoom on the tongue of Argentiere glacier for each map.**  
lake, south of Mt Cook. Glacier surface velocities of 250 m/year are observed in the ice fall close to the accumulation area while, the lowermost portion of Tasman is relatively slow with a speed of 60 m/year (Figure 3A). Two heavily crevassed glaciers with steep surface slopes are flowing on the northern flank of Mt Cook: Franz Josef and Fox glaciers with surface velocities reaching 500 m/year in some areas (Figure 3A). We also successfully mapped the tributaries flowing into Tasman, Hooker and Murchison glaciers with surface velocity ranging from 80-300 m/year.
## 3 Architecture Design and Implementation Workflow of the ABAQUS-MATLAB Cooperative Framework
### Python Scripting Implementation  
Upon receiving MATLAB system commands, the Python script executes key operations as depicted in Figure 4. It first reads the *.cae file and design variables from the MATLAB-generated *.xlsx file. The script traverses design domain elements to update material properties, generating a new *.cae file for ABAQUS solver execution. Subsequently, element IDs and their corresponding result data from the *.odb file are extracted and written into a new *.xlsx file. During the first iteration, the script additionally outputs the centroid coordinates of all elements. Reading *.xlsx files via the ABAQUS Scripting Interface requires the openpyxl library. The ABAQUS scripts in this study are written in Python 3.10, which does not include the openpyxl module by default, necessitating its separate download and installation for program execution.  
Specifically,.CAE/.ODB files are binary formats, whereas INP/FIL files are text-based. This study demonstrates that direct read/write operations on.CAE/.ODB files significantly enhance computational efficiency. For scenarios requiring dynamic refinement of local meshes or adjustment of load regions during optimization, the.CAE format enables native manipulation of mesh modules and load managers, offering superior flexibility compared to text-based workflows.
## 2 Study Area
### Local Geology and Geomorphology  
According to the map extracted from the Geological and Mining Cadastral Information System (GEOCADMIN), the geology present in the Laguna del Amor area is within the lower Chota formation (Figure 7). In addition, the province of Chota, located in the 17S Universal Transverse Mercator World Geodetic System (UTM WGS 84), in southern Peru, and in the center of the Cajamarca region, presents a complex geology due to its location near the convergent margin between the South American and Nazca plates [29].  
The geomorphological development of the 14f quadrangle of Chota is the result of degradational processes, presenting great unevenness; the structural geology is composed of very diverse phases, sectioning in the tectonic province of Cutervo, tectonic province of Chimu, tectonic province of Pacasmayo, and tectonic province of Santa Cruz. These, in their internal movement, modify the younger formations, folding them, rotating them, and faulting them. The lithost stratigraphy of the 14f quadrangle is composed of units originated in the Mesozoic: Pulluicana group (Ks-pu), Quilquinan group (ks-qui), Cajamarca formation (Ks-ca), and Celendin formation (Ks-ce); in the Cenozoic, there is only the Chota formation, with its three representative members: lower member (Pe-cho/i), middle member (Pe-cho/m), and upper member (Pe-cho/s) [30]. Among all these formations, the Celendin formation and the lower Chota formation stand out since they outcrop in greater proportion in the landslide study area of the Laguna del Amor zone (Figure 8). The geodynamic processes responsible for this high geological complexity are generally external geodynamic processes, including high fluvial precipitation, strong earthquakes, weathering, and the accumulation of large soil masses. On the other hand, anthropic processes can be summarized as follows: the destruction of the vegetation cover (Figure 4a,f,g), evidenced by the inclination of the trees, and the disturbance of the natural slope of the terrain (Figure 4d), which clearly exposes the Chota Formation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{5}{c}{**Station: Chota**} \\ \hline Department: & Cajamarca & Province: & Chota & District: & Chota \\ Latitude: & \(6^{\circ}32^{\prime}49.66^{\prime\prime}\) & Longitude: & \(78^{\circ}38^{\prime}55.07^{\prime\prime}\) & Altitude: & 2468 masl. \\ Type: & CO-Meteorological & Code: & 106034 & & \\ \hline \end{tabular}
\end{table}
Table 1: Chota weather station data.
## 3 Results
### Differences between the GPS Solutions  
As part of the analysis, we also compared solutions G1 and G2. This allowed us to assess the impact of differences between chamber and robot calibrations at the coordinate level. The direct comparison of Geo++ and University Bonn IGG products has already been carried out several times by others [21; 41], and the authors have shown a very good agreement in L1 patterns and some systematic differences in L2 patterns. This was confirmed in the work of [42], where the authors also compared these antenna calibrations, but they focused on the estimated position. A two-fold better agreement in the estimated height was derived for the L2 signals rather than the L1 signals. In another study [22], the authors analyzed the impact of using both calibrations for six antennas. They noticed a position agreement of 2 mm in the horizontal component and 5 mm in the height. Further research into the effects of using IGS-type mean models interchangeably with various individual calibrations for the EPN network has also been conducted. In the framework of the EPN reprocessing campaign [23], it has been shown that the mean differences in all affected EPN stations were below 2 mm for the horizontal component and 4 mm for the height.  
In the current study, the differences for the horizontal component in both approaches did not exceed 4 mm (Figure 10). Again, we received very consistent results between both positioning methods. The mean differences for DD were 0.3 mm (north), 0.6 mm (east) and 1.1 mm (up), and 0.4 mm, 0.4 mm and 1.1 mm for PPP, respectively. The standard deviations of these differences for both approaches were very similar: 1.1 mm, 1.4 mm and 5.7 for DD, and 1.1 mm, 1.6 mm and 5.9 mm for PPP. The differences between both approaches for the horizontal component were within the range of \(-\)1.9 mm to 2.5 mm, with standard deviations of 0.6 mm for the north and 0.8 for the east components. For the height, the largest differences were observed for NYA200 NOR, BADH00 DEU and PTBB00 DEU (Table A6, Appendix A), wherein it exceeded 10 mm. As one can see (Figure 9), the received differences were smaller than the differences obtained for Galileo (Section 3.1), which can only be considered significant for eight antennas. We also observed better agreement between the received coordinates for the newest calibrations, which demonstrates the growing compatibility between robot and chamber calibrations (Figure 11).
## 2 STATCOM Configuration and Operation
### Principle of Operation  
The construction of a binary transformer with various voltage source converter STATCOM is conducted to obtain the voltage output waveforms of an AC converter with minimum harmonic content. These VSCs have one DC-link capacitance, H-bridges, and transformers used at 1:2:4....2n\({}^{-1}\) turn ratios. The DC-link capacitance voltage is used to control the AC converter output voltage with the phase angle of the transmission system voltages as well as STATCOM output voltage. STATCOM output voltage is lower than the voltage of the system titular. Then, the reactive power is drawn from the transmission system. Meanwhile, STATCOM output voltage matches the system nominal voltage, which is greater than the reactive power injecting into the transmission system. The main advantage of these binary transformers with multilevel VSCs of a DC-Link capacitance voltages is their ability to accomplish the various voltage output waveforms, which are dissimilar to those of normal conventional voltage source converters.
## 5 Experiments and Results
### Detailed Result Comparison of Three Studies
#### 5.1.1 Results on PV Cell of RTC France in SDM  
For the SDM on the PV cell of RTC France, the best results extracted by different algorithms are shown in Table 3, in which the minimum RMSE is marked in bold. Smaller RMSE values suggest a better result of parameter identification. Based on Table 3, the DLMVO obtained the least RMSE (9.8602 \(\times\) 10\({}^{-4}\)), which was better than IJAYA, QMVO, CLPSO, and MVO. Otherwise, Rcr-JADE, IWOA, PGJAYA, and SATLBO algorithms could also get the minimum results, and MVO achieved the worst results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**SDM/DDM**} & \multicolumn{2}{c}{**SMM**} \\ \cline{2-5}  & \(\mathbf{U_{min}}\) & \(\mathbf{U_{max}}\) & \(\mathbf{U_{min}}\) & \(\mathbf{U_{max}}\) \\ \hline \(I_{ph}\) (A) & 0 & \(I_{ph}\) (A) & 0 & 2 \\ \(I_{d1}\), \(I_{d2}\) (\(\mu\)A) & 0 & \(I_{d1}\), \(I_{d2}\) (\(\mu\)A) & 0 & 50 \\ \(R_{s}\) (\(\Omega\)) & 0 & \(R_{s}\) (\(\Omega\)) & 0 & 2 \\ \(R_{sh}\) (\(\Omega\)) & 0 & \(R_{sh}\) (\(\Omega\)) & 0 & 2000 \\ \(n_{1}\) & 1 & \(n_{1}\) & 1 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter range for three cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter Setting** \\ \hline DLMVO & \(NP=50\) \\ MVO & \(NP=50\), \(p=6\), \(\mathrm{WEP}_{\mathrm{min}}=0.2\), \(\mathrm{WEP}_{\mathrm{max}}=1\) \\ QMVO & \(NP=50\) \\ Rcr-JADE & \(NP=50\), \(p=0.05\) \\ IWOA & \(NP=50\) \\ CLPSO & \(NP=40\), \(w\): 0.9–0.2, \(c=1.49445\), refreshing gap \(m=5\) \\ IJAYA & \(NP=20\) \\ PGJAYA & \(NP=20\) \\ SATLBO & \(NP=40\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting of DLMVO and each compared algorithm.  
To further verify the accuracy of DLMVO identification results, the best-estimated parameters extracted by the DLMVO algorithm were used to reconstruct the simulated data, and this was compared with the measured data. The results are shown in Figure 6. It can be seen from Figure 6 that there is a high consistency between the experimental data and the simulation data, which indirectly shows the accuracy of the identification parameters.
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 2 Related Work
### Datasets  
In order to verify the effectiveness of our deep convolutional network on different high-resolution remote sensing images, we used three high-resolution remote sensing datasets for the experiments. One was the Jilin-1 satellite high-resolution remote sensing dataset we produced, and the images were derived from Jilin-1 satellite remote sensing images; another was the Massachusetts building remote sensing dataset derived from aerial images; and the last set was the fusion of drone and satellite high-resolution large-scale remote sensing WHU datasets.
## 4 Experiments and Results
### Description of Experimental Data
The specific distribution of anomalous device data is shown in Figure 10. Because Allen-Bradley PLCs mainly use the EtherNet/IP industrial control protocol, their proportion is relatively high. Honeypot data refer to industrial control honeypots exposed on the public internet that support the EtherNet/IP protocol, which simulate the communication methods of industrial control devices to deceive attackers and capture attack traffic. The data categorized as \"Others\" refer to the network traffic data of industrial control devices from other brands. The software and hardware characteristics of these three types of devices exhibit different variations, which is beneficial for validating the methods proposed in this chapter. Figure 10: Abnormal device data distribution. Figure 9: Normal device data distribution. This section uses two representative evaluation metrics, Accuracy and F1-Score, to compare and analyze the effectiveness of the relevant methods. The definitions of Accuracy and F1-Score are as follows:  
\[ \begin{split} Accuracy&=\frac{TP+TN}{TP+FP+TN+FN}\\ FI-Score&=2*\frac{Precision*Recall}{Precision+Recall}\\ Precision&=\frac{TP}{TP+FP},Recall=\frac{TP}{TP+FN} \end{split}  \tag{9}\]  
where \(TP\),\(FP\),\(TN\), and \(FN\) represent true positive, false positive, true negative, and false negative data, respectively.
## 3 Control Design for Transition Phase
### Control Strategy for Soft Transition Design  
The three modes of transition phase design are shown in Figure 10, which includes the control logic and control allocation of the entire transition phase.  
The control strategy for transition phase is based on:  
1. For the height-pitch angle-pitch angle-rate loop, before the elevator stops controlling the pitch attitude, it starts the rotors in advance to replace the elevator to follow the commands related to altitude control. After the fixed-wing mode is switched to the switching mode for 10 s (or even shorter, 5 s), the UAV's flight attitude reaches a stable state, and then switches to the rotary-wing mode so that the elevator is no longer participates in attitude control, thus realizing a smooth transition of mode switching.
2. For the speed loop, during the switching mode, a deceleration signal is given (reducing from the cruise speed to the switching speed) to achieve the deceleration control of the transition phase.  
The advantage of the soft transition design is that the mode switching is divided into two steps: in the first step, the rotary wing takes over the height control, and then takes over the attitude angle and attitude angle rate control after it reaches a steady state, so that the abrupt change in flight attitude of the UAV is split into two parts, and the stability and safety of the switching process are improved at the cost of time and energy consumption.  
Figure 9: Structure of throttle control.  
The early start of the rotors, as well as the sudden change in height, speed, angle, and angle rate caused by mode switching, is a disturbance to the control law, which puts forward high requirements on the robustness of the UAV, as well as on the design of the control law and control strategy.
## 1 Introduction  
The International Commission on Non-Ionizing Radiation Protection (ICNIRP) is an independent scientific organization that offers regulations and recommendations about the health and environmental effects of non-ionizing radiation exposure [1]. The exposure limits recommended by international standards-setting bodies--ICNIRP [1], the Institute of Electrical and Electronics Engineers (IEEE) [2] and the European Committee for Electrotechnical Standardization (CENELEC) [3]--are based on short-term and immediate health effects of elevated tissue temperatures due to the absorption of energy during exposure to non-ionizing radiation. In 2002, the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) published a standard that specifies health limits of human exposure to radio frequency fields ranging from 3 kHz-300 GHz in order to avoid its adverse health effects [4]. These guidelines aim to minimise the heating effects caused by the absorption of radio frequency electromagnetic fields (RF-EMFs). Each of the standards indicate basic restrictions for the general public exposure, occupational exposure and the equipment and usage parameters.  
There are countless man-made sources of electromagnetic radiation (EMR), such as mobile phones [5; 6; 7; 8], radios and high-frequency (HF) welding machines. Aboard marine ships, there are numerous sources of EMR [9; 10; 11]; these include transmitters, such as radar and satellite for shipboard communication. Because of the extreme growth of the marine transportation industry, the number of marine vessel transmitters has greatly increased. Measuring the electric fields from marine ship transmitters is normally a complex and time-consuming process. Hence, there is a potential applicability of this work to a variety of marine vessels, which is significant, and could be quite useful as a reference for many professionals without direct access to the necessary equipment. Very high-frequency (VHF) radios [9], working in a frequency range from 156 MHz-176 MHz [10], and radars [11] seem to be more common in marine vessels that have been investigated in previous studies. The electromagnetic exposures aboard vessels could be affected by: (i) on-board radiation sources; (ii) conductive objects; and (iii) neighbouring radiation sources, such as other vessels and coastal transmitters. Electromagnetic fields can be reduced by properly designing the systems and equipment, such as choosing the correct material for vessels and finding the correct positioning of equipment on-board [12].  
The aim of this study was to measure and analyse the radio frequency radiation emitted from transmitters aboard a marine vessel, focused on areas normally occupied by crew members and passengers; and to observe whether the measured electric field (EF) levels comply with the ICNIRPand the ARPANSA limits. In this analysis, we used transmitters: VHF band fixed (156-157.42 MHz), MF/HF transceiver (1605 KHz-30 MHz), satellite communication (Sat-Com C) (6006) (1,626.5-1,646.5 MHz), shipborne Automatic Identification System (AIS) navigation (156-163 MHz), radar X-band (9410 MHz) and radar S-band (3,050 MHz).
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 9 Synchronization Analysis in Test 3  
An in-depth analysis of the synchronization between end nodes in the network is presented here. Test 3 of Section 7 is used as the reference, indeed the high generated traffic and the high packet collision rate on the channel tresses the synchronization procedure. The error is calculated between t\({}_{0}\) (Figure 1a) and the packet time of arrival measured by the gateway. With the \(T_{\text{b}}\) proposed in Test 3 the overall number of failed slots for all 24 nodes, is five, with a success rate of 99.6%. In this case, an overlap happens between adjacent slots because the tolerance interval is exceeded. As presented in Figure 14a, in point 1 and 2 two consecutive slot overlaps are shown, and both are generated from the same end node. This issue comes from end node reboot; indeed, when a firmware error is detected the MCU is restarted and the internal RTC is not configured. Therefore, the overall synchronization failures are three.  
Figure 13: Comparison between LoRaWAN and Slotted LoRaWAN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **Slotted LoRaWAN** & **LoRaWAN** \\ \hline Test duration [s] & 3524 & 9596 \\ Packets sent & 2894 & 6817 \\ N of end nodes & 24 & 24 \\ TX period [s] & 13 & 13 \\ TX success rate [\%] & 33 & 7 \\ Traffic generated (G) & 0.455 & 0.393 \\ Channel throughput (S) & 0.150 & 0.026 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between LoRaWAN and Slotted LoRaWAN.  
The previous tests are executed with a tolerance interval (\(T_{\text{b}}\)) calculated to fulfill the RTC drift in the worst case, we evaluated the S-LoRaWAN throughput in this condition to compare both protocols in a typical low power application. However, in Test 3 the \(T_{\text{b}}\) could be decreased ensuring a slot success rate over 90%; in Figure 14b and Table 4 is shown the relationship between \(T_{\text{b}}\) and the number of errors. For example, with a \(T_{\text{b}}\) of 100 ms the error rate is practically the same as Test 3, and with 50 ms the error rate is still acceptable. The tolerance interval length affects the channel throughput; indeed, reducing \(T_{\text{b}}\) decreases the protocol overhead, and allows more slots in a unit time.