esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea
## 2 Materials and Methods
### Statistical Analysis and Mapping  
This study conducted statistical analysis, including a correlation matrix, to compare heavy metal concentrations in five Western Mediterranean regions. The Pearson coefficient measured relationships between concentrations, and Principal Component Analysis (PCA) reduced data dimensionality, explaining variation in a new coordinate system. Both analyses utilized Statgraphics 19 software.  
To plot a map of wastewater discharges, we adopted a systematic approach involving weighted considerations based on discharges and population in each region.  
\[\frac{V\propto\left(r_{p}+f_{p}\right)}{200} \tag{2}\]  
where \(V\) represents volume in \(\text{hm}^{3}\), \(r_{p}\) is the resident population, \(f_{p}\) is the floating population, and 200 serves as the weighting factor.  
Wastewater volume data, measured in \(\text{hm}^{3}/\text{year}\), was sourced from regional public institutions, while resident and floating population figures were cross-referenced with national census data. Results from this process were utilized to generate a database and maps of heavy metals, with georeferencing done using ArcGIS 10.8.1 software. To determine pollutant distribution, the Average Nearest Neighbor (ANN) relationship [35] calculated distances between centroids and neighboring points, identifying values associated with the closest centroid.
## 3 Results
### Analysis of Ground Distortion Imaging  
During the imaging process of the dynamic scanning mirror, the payload's attitude angle changes periodically. When the angle between the optical axis of the camera and the satellite nadir gets larger, the ground imaging range, corresponding to CMOS pixels, gradually increases. It leads to the anisotropy of motion between pixels and the distortion of images projected onto the focal plane of the camera. In order to simulate this image, a unit pixel is set in every 30 pixels, which represents the pixel-number of the ground image. The arrows represent the direction of movement of the ground image during the imaging process. The distortion diagram of the ground pixels, at attitude angles of \(0^{\circ}\), \(30^{\circ}/-30^{\circ}\), and \(50^{\circ}/-50^{\circ}\), can be calculated by the curvature mapping function (15), as shown in the Figure 6.  
It can be seen that, when the attitude angle of satellite is \(0~{}^{\circ}\), the ground imaging range is about 300 pixels \(\times\) 250 pixels. It indicates that the ground imaging range is narrow, and there is almost no image distortion, as shown in Figure 6a. When the satellite attitude angle gradually increases to 30 \({}^{\circ}\) or 50 \({}^{\circ}\), the ground imaging range becomes 400 pixels \(\times\) 350 pixels or 700 pixels \(\times\) 450 pixels. It can be seen that, when the attitude angle increases, the ground imaging range becomes wider, and the image distortion deepens, as shown in Figure 6b,c.  
Figure 5: Ground platform test data. (**a**) Ground tracking velocity test curves, (**b**) The error of velocity image.
## 2 Satellite and Payloads  
The CSES is specially designed to monitor the ionospheric perturbations associated with seismic activities in the lithosphere. It carries eight types of scientific payloads, including 13 individual instruments to acquire the global geomagnetic field, electromagnetic field/waves, plasma parameters, and energetic particles data. The onboard configuration was introduced by [PERSON] et al. [19]. It also benefits space physics-related studies (e.g., space weather, ionospheric irregularities, and plasma environment). There are two working modes for payload operation: survey and burst mode. The former is operated along the whole orbit trajectory with lower sampling rates; the latter is only triggered above the global main seismic belts with a higher sampling rate. Here, we briefly introduce the scientific payloads as follows. Details of the scientific payloads are listed in Table 1.  
The geomagnetic field is detected by the high-precision magnetometer (HPM), which consists of two tri-axial fluxgate magnetometers (FAMs) [20] and one coupled dark-state magnetometer (CDSM) [21]. The FAMs provide the magnetic field vector data in the frequency range from DC (Direct Current) to 15 Hz, and the CDSM serves as a reference to the FGM by providing the scalar value of the total magnetic field. A tri-axial search coil magnetometer (SCM) detects the alternating magnetic field in the frequency range from \(\sim\)10 Hz to 20 kHz [22], providing data in the three detection frequency bands: ULF (Ultra-Low-Frequency, 10-200 Hz), ELF (Extremely-Low-Frequency, 200 Hz-2.2 kHz), and VLF (Very-Low-Frequency, 1.8 kHz-20 kHz). An electric field detector (EFD) measures the spatial electric field in the frequency band from DC to 3.5 MHz [23], providing data in the four detection frequency bands: ULF (DC-16 Hz), ELF (6 Hz-2.2 kHz), VLF (1.8 kHz-20 kHz), and HF (High-Frequency, 18 kHz-3.5 MHz). One advantage of the CSES satellite is that it provides high-resolution six-component electromagnetic field waveform data in ULF/ELF
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 4 Experiment Settings
### Evaluation Metric  
As the area around the North and South Pole is less than the equator area, a latitude-weighted root mean square error (RMSE) was employed as an evaluation metric. A latitude-weighted RMSE was also the evaluation metric in WeatherBench [1], the first benchmark for data-driven, medium-range climate prediction.  
\[RMSE=\frac{1}{N_{\text{forecasts}}}\sum_{i}^{N_{\text{forecasts}}}\sqrt{ \frac{1}{N_{\text{lat}}N_{\text{lon}}}\sum_{j}^{N_{\text{lon}}}\sum_{k}^{N_{ \text{lon}}}L(j)(f_{i,j,k}-t_{i,j,k})^{2}}, \tag{13}\]  
where \(f\) is the model forecast, and \(t\) is the ground truth of the data. \(N_{\text{forecasts}}\), \(N_{\text{lat}}\), and \(N_{\text{lon}}\) are the numbers for forecasting, latitude, and longitude, respectively. \(L(j)\) is the latitude weighting factor by the latitude (degree) at the \(j^{th}\) latitude index lat(\(j\)):  
\[L(j)=\frac{\cos(\text{lat}(j))}{\frac{1}{N_{\text{lat}}}\sum_{j}^{N_{\text{ lat}}}\cos(\text{lat}(j))} \tag{14}\]
## 1 Introduction
The auxiliary resonant snubber inverter (ARSI) has been proposed to eliminate the split capacitors, but the three-phase topology cannot utilize the conventional space-vector-pulse-width modulation (SVPWM) [15; 16]. Thus, they are more suitable for permanent magnet brushless DC motors than all types of motors. The single-phase topology is very attractive with only two auxiliary switches well fit to the conventional PWM. Meanwhile, the ZVT inverter using coupled magnetics has been proposed to eliminate the split capacitors [17; 18; 19]. However, these topologies need coupled inductors and a large number of auxiliary switches, which unfortunately increase the cost and difficulty of the circuit realization. The ZVT PWM converter has been synthesized and summarized in [20; 21]. Without adding the external resonant capacitors, the ZVT inverters can completely remove the dead-time effect. However, the external resonant capacitors are always required to reduce the voltage changing rate of the switches, so that the turn-off loss and electromagnetic interference (EMI) can be reduced. This will lead to output voltage distortion, which is caused by the finite commutation time. This kind of dead-time effect is quite different from that in the hard-switching inverters. Thus, this paper analyzes the dead-time effect of a typical example of ZVT PWM soft-switching inverters--ARSI. Then, a compensation method is proposed to remove the dead-time effect. Finally, the simulation and experiment are undertaken to verify the effectiveness of the proposed method.
## 1 Introduction
In the present review, the normal cavitating jet, i.e., the cavitating jet in water, was mainly discussed, and the cavitating jet in water was simply described as the cavitating jet. To simulate the cavitating jet numerically, it is important to understand the flow pattern of the cavitating jet. As is well known, numerical simulation of cavitating flow is not yet easy, due to the high Reynolds number and phase change phenomenon. Numerical investigation of three-dimensional cloud cavitation with special emphasis on collapse induced shock dynamics [44], simulation of cloud cavitation on propeller [45], and the hydrofoil [46] were carried out. In the area of numerical simulation of the cavitating jet, considering bubble dynamics, the numerical simulation of vortex cavitation in a three-dimensional submerged transitional jet near inception condition was carried out [47], and bubble growth in the shear layer of the cavitating jet was calculated [48]; residual stresses introduced by cavitating jet considering bubble growth and collapse were also simulated [49]. The cavitating flow in a venturi nozzle was also tried by a large eddy simulation of turbulence-cavitation interactions [50]. From the viewpoint of cloud-shedding of the cavitating jet, numerical analysis of cavitation cloud-shedding in a free submerged water jet was carried out [51; 52]. Figure 1: Typical aspects of cavitating jet: (**a**) cavitating jet in water; (**b**) cavitating jet in air. The interaction of cavitation bubbles and materials was also investigated numerically [53; 54; 55; 56]. In the case of the aggressive cavitating jet, the clouds shed periodically and form ring vortex cavitation on the impinging surface, and then collapse, producing severe impacts. Thus, the experimental investigation of the flow pattern would be assisted by numerical simulations for more precise investigations of the cavitation impacts produced by the cavitating jet. In the present review, in order to use the cavitating jet for practical applications, papers that investigated the flow pattern of the cavitating jet experimentally were reviewed. To enhance the aggressive intensity of the cavitating jet for these applications, the key factors of the cavitating jet were also summarized, and the estimation method of the aggressive intensity of the jet was discussed. In the present paper, the aggressive intensity of the cavitating jet means erosion rate measured by weight loss of the target metals and/or the peening intensity measured by the arc height of the metallic plate.
## 3 WebAssembly and IoT
### Why WebAssembly for IoT?
* **Support for Energy-Efficient Programming Paradigms*
* Wasm's support for languages such as Rust, which emphasizes zero-cost abstractions and fine-grained control over system resources, allows for energy-efficient programming paradigms. Developers can optimize their code to manage resources effectively and minimize energy consumption, which is crucial for power-constrained IoT devices.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 4 Discussion  
The increasing use of UAS imagery in science and science-related services demands a operational processing and reliable validation techniques in the commonly used photogrammetric workflows. This study introduces two optimizations to the conventional photogrammetric workflow: (1) a new optimization for the georeferencing workflow and (2) a novel technique aiming to evaluate the repeatability of photogrammetrically retrieved orthomosaics. The application of these methods demonstrated the possibility to acquire accurately referenced UAS orthomosaic time series with low-cost UAVs and RGB cameras for both forest and grassland environments. The reproducibility of orthomosaics was highly dependent on the vegetation structure of the survey area.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 4 Impedance Controller
### Design of the Impedance Controller  
For successful adhesion, the required contact time between the ADR system's tip and the debris must be ensured; thus, its adjustment is required. This adjustment was achieved by altering the ADR system's impedance. Therefore, an impedance controller with tunable gains was developed. Specifically, impedance control attempts to implement a dynamic relation between the ADR system's variables, such as tip position and contact force, rather than just controlling these variables alone [37].  
Figure 11: Schematic of the equivalent three-body CubeSat-ADR system.  
Subsequently, the controller needs to be informed, which is the wanted relation between the ADR system's variables during impact i.e., the desired system's behavior. The equation selected to describe this behavior is called _impedance filter_ and is shown in Equation (5), [34; 46]. It consists of three terms: one for the desired inertia \(m_{f}\) to be seen at the tip, one for the desired damping \(b_{f}\), i.e., the desired relationship between contact force and tip's velocity, and one for the desired stiffness \(k_{f}\), i.e., the desired relationship between contact force and tip's displacement [37].  
\[m_{f}(\ddot{x}_{c}-\ddot{x}_{s})+b_{f}(v_{c}-v_{s})+k_{f}(x_{c}-x_{s}-l_{m})=-F_ {i}. \tag{5}\]  
The desired contact time of the ADR system with the debris and, thus, the success of capturing directly, can be realized by tuning the mass, spring, and damper impedance parameters \(m_{f}\), \(b_{f}\), and \(k_{f}\), respectively. Parameter \(l_{m}\) in Equation (5) is the initial distance between \(m_{c}\) and \(m_{s}\).  
Substituting \(\ddot{x}_{s}\) of Equation (1) and \(\ddot{x}_{c}\) of Equation (3) into the impedance filter in Equation (5), and then, solving for the applied actuator force by the impedance controller \(F_{a}\) required to achieve the desired impedance behavior of Equation (5), yields  
\[F_{a}=\frac{m_{s}}{m_{f}}(\frac{m_{f}}{m_{c}}-1)F_{i}+\frac{m_{s}}{m_{f}}k_{f} (x_{s}-x_{c}+l_{m})+\frac{m_{s}}{m_{f}}b_{f}(v_{s}-v_{c})+\frac{m_{s}}{m_{c}} k_{s}(x_{c}-x_{e}-l_{s})+\frac{m_{s}}{m_{f}}b_{s}(v_{c}-v_{e}). \tag{6}\]  
The impedance parameter \(m_{f}\) is selected equal to \(m_{c}\) so that the actuator force \(F_{a}\) does not depend on the impact force \(F_{i}\)[34]. Then, the applied actuator force \(F_{a}\) is given by  
\[F_{a}=k_{p}(x_{s}-x_{c}+l_{m})+k_{d}(v_{s}-v_{c})+\frac{m_{s}}{m_{c}}k_{s}(x_{ c}-x_{e}-l_{s})+\frac{m_{s}}{m_{c}}b_{s}(v_{c}-v_{e}), \tag{7}\]  
where the controller's gains \(k_{d}\) and \(k_{p}\) are given by  
\[k_{p}=\frac{m_{s}}{m_{c}}k_{f} \tag{8}\]  
\[k_{d}=\frac{m_{s}}{m_{c}}b_{f}. \tag{9}\]  
To calculate the gains based on Equations (8) and (9), the impedance parameter \(k_{f}\) must be selected. Furthermore, choosing critical damping results in the impedance parameter \(b_{f}\).  
\[b_{f}=2\sqrt{m_{f}k_{f}}. \tag{10}\]  
The impedance control loop is shown as a block diagram in Figure 12.
## 2 Mobility Models in FANETs
### Random Mobility Models
#### 2.1.2 Random Waypoint  
The Random Waypoint (RWP) [39] mobility concept functions similarly to RW, but with a certain number of extensions. For a length of time, a mobile node is stationary. The node selects a random destination and speed when the timer runs out. A mobile node subsequently travels at the preset speed in the direction of the newly chosen destination. Whenever the mobile node reaches its destination, it takes a little break before proceeding with the operation. The presence of a halt interval and choosing a random destination rather than a direction distinguishes RWP from RW. The RWP model may be used in FANETs relaying network applications because of its past-time characteristics [40]. Figure 5 depicts an RWP mobility pattern for FANETs. The UAV movement pattern is centered in the simulation area's center. Despite the presence of pause times, they aid in smoothing unexpected changes in direction. A progressive increase or decrease in speed is necessary for UAVs.
## 3 Results and Discussion
### Suppression Mechanism of the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM Composite Powders  
According to the results of the explosion experiments, the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders present the considerable suppression properties on the methane explosion. The schematic diagram of the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders in the explosion process is illustrated in Figure 8. The suppression mechanism of the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders could be analyzed from two aspects, as follows.  
_Physical inhibition effect:_ The NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders have a good heat absorption performance, as shown in the Figure 5. When the composite powders were added into the explosion vessel, the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders were separated into the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) particles and red mud under the high pressure and temperature during the 9.5% premixed methane-air explosion. Then, the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) particles and the hydroxides (Al(OH)\({}_{3}\), Fe(OH)\({}_{3}\)) in the red mud decomposed with absorbing a lot of heat, leading to a good endothermic effect for explosion suppression.  
_Chemical inhibition effect:_ The mechanism of methane explosion is shown as the following chain reactions [33].  
\[\text{CH}_{4}+\text{O}_{2}\rightarrow\text{CH}_{3}+\text{HO}_{2}\]  
\[\text{CH}_{3}+\text{O}_{2}\rightarrow\text{CH}_{2}\text{O}\;+\;\text{HO}\]  
\[\text{HO}\;+\;\text{CH}_{4}\rightarrow\text{CH}_{3}+\text{H}_{2}\text{O}\]  
\[\text{CH}_{2}\text{O}\;+\;\text{HO}\rightarrow\text{CH}_{3}+\text{H}_{2}\text{O}\]  
\[\text{CH}_{2}\text{O}\;+\;\text{O}_{2}\rightarrow\text{HO}_{2}\;+\;\text{CHO}\]  
\[\text{CHO}\;+\;\text{O}_{2}\rightarrow\text{CO}\;+\;\text{HO}_{2}\]  
Figure 8: The schematic diagram of the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders in the explosion process.  
Figure 7: The images of explosion flame in different conditions: no powder, red mud, NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) and the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders with different content of NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\).  
\[\mathrm{HO_{2}}+\mathrm{CH_{4}}\rightarrow\mathrm{H_{2}O_{2}}+\mathrm{CH_{3}}\]  
\[\mathrm{HO_{2}}+\mathrm{CH_{2}O}\rightarrow\mathrm{H_{2}O_{2}}+\mathrm{CHO}\]  
During the methane explosion process, the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders decomposed and they generated a lot of gaseous free radicals, which can combine with the generated radicals by methane explosion. In addition, the red mud powders expose more pores on their surface due to the separation of the loaded NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) particles, which can adsorb and capture more free radicals that are generated by methane explosion. Both two reasons simultaneously contribute to the chemical inhibition effect of the NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)/RM composite powders.
## 2 Related Works
### Proof of Convergence  
**Theorem 1**.: _The function value in Equation (15) is non-increasing under the rules in Equations (20) and (23)._  
The updating rule for \(\mathbf{U}\) is the same as in the classical NMF. Thus \(O\) in Equation (15) is non-increasing under Equation (20). In the next, we will prove that \(O\) is non-creasing under Equation (23). The proof uses the auxiliary function [18] defined as follows.  
**Definition 1**.: \(G(v,v^{\prime})\) _is an auxiliary function for \(F(v)\) if_  
\[G(v,v^{\prime})\geq\mathrm{F}(v),\ G(v,v)=\mathrm{F}(v)\]  
_is satisfied._  
**Lemma 1**.: _If \(G\) is an auxiliary function for \(F\), then \(F\) is non-increasing under the updating rule_  
\[v^{(t+1)}=\mathrm{argmin}_{v}G(v,v^{(t)}) \tag{24}\]  
**Proof for Lemma 1.**  
\[\mathrm{F}(v^{(t+1)})\leq G(v^{(t+1)},v^{(t)})\leq G(v^{(t)},v^{(t)})= \mathrm{F}(v^{(t)})\]  
\(\square\)  
In this next section, we will show that the updating rule for \(\mathbf{V}\) in Equation (23) is exactly the rule in Equation (24) with a proper auxiliary function. We use \(\mathrm{F}_{ab}\) to denote the part of \(O\) that is only relevant to \(v_{ab}\).  
**Lemma 2**.: _Function_  
\[\begin{split} G(v,v^{(t)}_{ab})&=\mathrm{F}_{ab}(v^{(t)}_ {ab})+\mathrm{F}^{\prime}_{ab}(v^{(t)}_{ab})(v-v^{(t)}_{ab})\\ &+\frac{(\mathbf{U}^{T}\mathbf{U}\mathbf{V}+2\lambda\mathbf{VB}^{ +}+4\mu\mathbf{V}\mathbf{V}^{T}\mathbf{V}+\gamma\mathbf{RV})_{ab}}{v^{(t)}_{ab }}(v-v^{(t)}_{ab})^{2}\end{split} \tag{25}\]  
_is an auxiliary function for \(F_{ab}\)._  
Proof for Lemma 2.: Since \(G(v,v)=\mathrm{F}_{ab}(v^{(t)}_{ab})\) is evident, we only need show that \(G(v,v^{(t)}_{ab})\geq\mathrm{F}_{ab}(v)\). By comparing \(G(v,v^{(t)}_{ab})\) to Taylor series expansion of \(\mathrm{F}_{ab}(v)\), we get \(G(v,v^{(t)}_{ab})\geq\mathrm{F}_{ab}(v)\). Similar proof can be see in [7].  
Proof for Theorem 1.: By substituting \(G(v,v^{(t)}_{ab})\) in Equation (24) with Equation (25), we obtain the updating rule as below,  
\[\begin{split} v^{(t+1)}_{ab}&=v^{(t+1)}_{ab}-v^{(t )}_{ab}\frac{\mathrm{F}^{\prime}_{ab}(v^{(t)}_{ab})}{2(\mathbf{U}^{T}\mathbf{ U}\mathbf{V}+2\lambda\mathbf{VB}^{+}+4\mu\mathbf{V}\mathbf{V}^{T}\mathbf{V}+ \gamma\mathbf{RV})_{ab}}(v-v^{(t)}_{ab})^{2}\\ &=v^{(t)}_{ab}\frac{(\mathbf{U}^{T}\mathbf{X}+2\lambda\mathbf{VB}^ {-}+4\mu\mathbf{V})_{ab}}{(\mathbf{U}^{T}\mathbf{U}\mathbf{V}+2\lambda\mathbf{VB }^{+}+4\mu\mathbf{V}\mathbf{V}^{T}\mathbf{V}+\gamma\mathbf{RV})_{ab}}(v-v^{(t) }_{ab})^{2}\end{split} \tag{26}\]  
which is identical to Equation (23). Since \(G(v,v^{(t)}_{ab})\) is the auxiliary function of \(\mathrm{F}_{ab}(v)\), \(\mathrm{F}_{ab}(v)\) is non-increasing under this updating rule. So \(O\) in Equation (15) is non-increasing under Equation (23).
###### Abstract  
The treatment of many bacterial and fungal infections remains a problem due to increasing antibiotic resistance and biofilm formation by pathogens. In the present article, a methodology for the chemoselective synthesis of 2-(1H-indol-3-yl)-1H-benzo[_d_]imidazole derivatives is presented. We report on the antimicrobial activity of synthesized 2-(1H-indol-3-yl)-1H-benzo[_d_]imidazoles with significant activity against _Staphylococcus aureus_ ATCC 25923, _Staphylococcus aureus_ ATCC 43300 (MRSA), _Mycobacterium smegmatis_ (mc2)155/ATCC 700084), and _Candidida albicans_ ATCC 10231. High activity against staphylococci was shown by indolybenz[_d_]imidazoles **3 ao** and **3 aq** (minimum inhibitory concentration (MIC) < 1 ug/mL) and **3 aa** and **3 ad** (MIC 3.9-7.8 ug/mL). A low MIC was demonstrated by 2-(1H-indol-3-yl)-1-methyl-1H-benzo[_d_]imidazole **(3 ag)** against _M. smegmatis_ and against _C. albicans_ (3.9 ug/mL and 3.9 ug/mL, respectively). 2-(5-Bromo-1H-indol-3-yl)-6,7-dimethyl-1H-benzo[_d_]imidazole **(3 aa)** showed a low MIC of 3.9 ug/mL against C. _albicans_. Compounds **3 aa**, **3 ad**, **3 ao**, and **3 aq** exhibited excellent antibiofilm activity, inhibiting biofilm formation and killing cells in mature biofilms. Molecular docking analysis identified three potential interaction models for the investigated compounds, implicating (p)ppGpp synthetases/hydrolases, FtsZ proteins, or pyruvate kinases in their antibacterial action mechanism.  
azaheterocycle; indole; antibacterial activity; resistance; molecular docking