esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Vehicle Stability Controller
### Two-DOF Linear Reference Model  
The 2-DOF reference model is illustrated in Figure 3.  
Where \(F_{yf}\) is the lateral force of the front wheel; \(F_{yr}\) is the lateral force of the rear wheel; \(v\) is the lateral velocity of the vehicle; \(\mathbf{u}\) is the longitudinal velocity of the vehicle; \(\alpha_{f}\) is the sideslip angle of the front wheel; \(\alpha_{r}\) is the sideslip angle of the rear wheel; \(a\) is the distance from the front axle to the vehicle center-of-mass; b is the distance from the rear axle to the center-of-mass; \(\delta\) is the steering angle of the front wheel; \(\beta\) is the sideslip angle of center-of-mass; \(\omega_{r}\) Is the yaw rate of the vehicle.  
The dynamics equation of the 2-DOF model (Figure 3) is deduced as follows:  
\[\left\{\begin{array}{l}\overset{\bullet}{\beta}=\left(\frac{aC_{f}-bC_{r}}{ mm^{2}}-1\right)\omega_{r}+\frac{C_{f}+C_{r}}{mm}\beta-\frac{C_{f}}{mm}\delta\\ \overset{\bullet}{\omega_{r}}=\frac{aC_{f}+b^{2}C_{r}}{l_{z}u}\omega_{r}+ \frac{aC_{f}-bC_{r}}{l_{z}}\beta-\frac{aC_{f}}{l_{z}}\delta\end{array}\right., \tag{25}\]  
where Cf is the equivalent lateral stiffness of front axle tires; Cr is the equivalent lateral stiffness of rear axle tires; m is the whole vehicle mass; Iz is the rotary inertia of the vehicle around z-axis. The selection of tire lateral stiffness is based on the tire lateral characteristics. When the tire sideslip angle is small, the lateral force and the cornering angle are linear. It is considered that the ratio of cornering force and cornering angle is the lateral stiffness of vehicle tire under linear condition.  
Figure 3 shows the dynamics model when the 2-DOF linear reference model is the ideal condition. The real vehicle is a highly nonlinear body under machine-electricity-liquid coupling. During vehicle modeling, the uncertainty caused by model simplification during the driving process and the disturbance induced by variation in the external environment both will affect the design and development of control algorithms. For this reason, disturbance is integrated as a nonlinear uncertainty term. Thus, the 2-DOF model involving a nonlinear uncertainty term and additional yaw moment is shown below:  
\[\left[\begin{array}{c}\overset{\bullet}{\beta}\\ \overset{\bullet}{\omega_{r}}\end{array}\right]=\left[\begin{array}{cc}\frac {C_{f}+C_{r}}{mm}&\frac{aC_{f}-bC_{r}}{mm^{2}}-1\\ \frac{aC_{f}-bC_{r}}{l_{z}}&\frac{aC_{f}+b^{2}C_{r}}{l_{z}u}\end{array}\right] \bullet\left[\begin{array}{c}\beta\\ \overset{\bullet}{\omega_{r}}\end{array}\right]+\left[\begin{array}{c}\frac {C_{f}}{mm}&0\\ \frac{aC_{f}}{l_{z}}&\frac{1}{l_{z}}\end{array}\right]\bullet\left[\begin{array} []{c}\delta\\ \Delta M_{z}\end{array}\right]+\left[\begin{array}{c}D_{1}\\ D_{2}\end{array}\right], \tag{26}\]  
where \(D_{1}\) is the integrated nonlinear disturbance term from lateral movement; \(D_{2}\) is the integrated nonlinear disturbance term from yawing; \(\Delta M_{z}\) is the additional yaw moment; \(D_{1}\) and \(D_{2}\) are both bounded.
# Microseismic Location Error Due to Eikonal Traveltime Calculation
## 3 Numerical Results
### Heterogeneous Model  
In this example, we consider a laterally homogeneous velocity model varying smoothly with depth with a grid step of 20 m (Figure 6). We keep the acquisition setup and target event locations the same as in the above test (homogeneous medium). We again generate a synthetic dataset for a set of events at a fixed depth. Then, we locate these events using the diffraction stacking technique and traveltimes computed from the regular eikonal equation with FSM, the factored eikonal equation with FSM and using the neural network (PINN). Finally, we compare estimated locations with the true locations.  
The depth error in case of the regular FSM (Figure 7a) shows a similar pattern as in the homogeneous case: the error is maximal in the center of the acquisition setup and decreases for events closer to the edges of the target zone. The absolute value of the depth error exceeds 120 m, which is significantly larger than the 20 m grid step given the fact that the data is noise free synthetics. In contrast, when traveltimes are computed with the factored FSM, the location error does not exceed half of the grid step for the tested model (Figure 7c). We observe similar location accuracy when utilizing traveltimes computed with PINN. The depth error almost never exceeds the 20 m grid step, though all events now tend to be mislocated deeper into subsurface in contrast to factored FSM, where some of the events have negative depth errors. In addition, the error pictures for PINN are not symmetric any more, since training of the neural network involved stochastic optimization, which introduces random traveltime errors. In case of factored FSM, traveltime errors follow a clear trend and are not random, as shown on the Figure 8.  
Figure 6: P-wave velocity model.  
Figure 8: Comparison of traveltime errors obtained with FSM for the factored eikonal equation and using PINN for an event at the center of the model.  
Figure 7: Location error of diffraction stacking in meters using regular FSM **(a,b)**, factored FSM **(c,d)**, and physics-informed neural network (PINN) **(e,f)** in laterally homogeneous model.
## 3 Materials and Methods
### Remote Sensing Data  
To explore the associated environmental impacts and suggest a relevant adaptation action plan for a particular region, the satellite observational monthly data for nitrogen dioxide (NO\({}_{2}\)), carbon monoxide (CO), sulphur dioxide (SO\({}_{2}\)), formaldehyde (HCHO), methane (CH\({}_{4}\)), and aerosol optical depth (AOD) were acquired from July 2018 to April 2021. These data were obtained and processed using the cloud computing platform of Google Earth Engine (GEE) API [16].  
To quantify the concentration of air pollutants associated with the three major economic activities (industry, traffic, and brick kiln), three primary pollutants (NO\({}_{2}\), CO, SO\({}_{2}\)) and two secondary (HCHO, CH\({}_{4}\)) pollutants were selected [22; 36; 37]. Air pollution concentration data were obtained from the tropospheric monitoring instrument (TROPOMI) on-board Sentinel 5P [17]. The Level 3 data were produced at a spatial resolution of 0.01 [18]. The product contained high-quality pixels ('qa_value' \(\geq\) 0.75), while the poor-quality pixels were removed [10]. Furthermore, pixels with a cloud cover probability of more than 0.2 were removed from all datasets to avoid any misleading results. While meteorological conditions may vary the atmospheric concentration of the pollutants, monthly averaging and long time series may help to avoid severe bias from meteorological conditions. However, the influence of weather conditions was considered to be minimum when comparing sites in nearby locations. AOD provided a wide range of spatial information with near-daily global coverage at 0.01 spatial resolution [19]. A Level 2 product, MCD19A2-V6, of AOD at 550 nm was derived from the moderate resolution imaging spectroradiometer (MODIS). The product was derived by combining terra and aqua data using the multi-angle implementation of atmospheric correction (MAIAC) [20]. The quality tags layer \"AOD_QA\" was used to retain good quality pixels, while pixels labelled as 'Possibly Cloudy', 'Cloudy', and 'Cloud Shadow' were removed using the \"Cloud\" mask. All datasets were acquired by taking the average of the pixels acquired over the selected sites.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 2 Modeling, Data Pretreatment, and Missing Data Features
### Scan Data Pretreatment  
In our work, the scan-matching problem is transformed to an image registration problem. As a result, the scan data must be changed to an image. The raw data of one scan collected by the LRF sensor can be represented as  
\[Z=\{(\theta_{i},d_{i})\} \tag{3}\]  
where \((\theta_{i},d_{i})\) is a scan point detected on the direction angle of \(\theta_{i}\) with distance \(d_{i}\). One scan contains \(n\) scan points in total. If the scan point is invalid, distance \(d_{i}\) = 0. Invalid points usually exist in the directions where obstacles are out of the measurement range. When using a low-cost LRF sensor with limited detection range, such invalid points usually occur in the scenes of corridor, hall, etc.  
The image size has a great influence on the amount of computation. Fortunately, the effective detection range of a low-cost LRF sensor is limited. As a result, this paper chose the data within a certain distance from the robot (0 < \(d_{i}\leq d_{\text{max}}\)) as the data source for subsequent calculations.  
The corresponding position \((x_{i},y_{i})\) of scan point \((\theta_{i},d_{i})\) in the image is:  
\[\left\{\begin{array}{l}x_{i}=\mu d_{i}\cos\theta_{i}+w/2\\ y_{i}=\mu d_{i}\sin\theta_{i}+h/2\end{array}\right. \tag{4}\]  
where \(w\) and \(h\) are the width and height of the image and \(\mu\) is the factor for transforming actual distance to pixel distance. A binary image with values on corresponding positions \((x_{i},y_{i})\) set to 1 (stands for obstacles) and others set to 0 (stands for free) is generated. An example for data of the scan-image is shown in Figure 2.  
Figure 1: Robot modeling and different poses.
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 2 Toxicity of Organochlorine Pesticides  
A link has been established, based on studies, between the infection rate and the quantity of organochlorine pesticides in the dietary intake of children. Children with higher organochlorine intake have lower immunity to infections [12]. Some of these organochlorine compounds have also been reported to be carcinogenic. In a similar study, DDT was found to be very high among people suffering from cancer [8]. In fact, the International agency for research on cancer (IARC)--a body that deals with cancer related studies--has classified organochlorine pesticides such as dieldrin and aldrrin as human carcinogens based on the evidence from several investigations that linked them with cancer [13]. Exposure to high quantity of these two organochlorine pesticides was reported to cause kidney damage, detrimental effects on nervous system and could lead to convulsion. Even low dosage exposure, for an elongated period of time, could lead to involuntary movement of the muscles, vomiting, dizziness, headache, irritability and can also lead to convulsion [14]. Suppression of immune response and suppression of antibodies have been reported due to the exposure of the body to dioxins and other organochlorine pesticides [15]. [PERSON] et al. have further emphasized that organochlorine compounds suppresses both the immune and antibody response, as evidenced in the studies conducted with animals [16]. Due to high toxicity of organochlorine pesticides to living organisms and the environment, one of them (2,3,7,8-tetrachlorophenoxy-p-dioxin) has been described as the most toxic pesticides by World Health Organization (WHO) [17]. Organochlorine pesticides have been linked to endocrine disruption and cardiovascular diseases such as pectoris and hypertension [18; 19]. It was reported that about one billion people are suffering from hypertension worldwide, of which organochlorine in food was named as one of the causes [18]. It has equally been found that organochlorine pesticides can trigger reproductive problem and diabetes [19; 20]. Other health problems reportedly linked to organochlorine compounds are neuropsychological impairment, atherosclerosis, transient hepatotoxicity, porphyria, peripheral and chloracne [19]. The toxic effects have also been reported among other living things apart from humans. For instance, death of fish has been observed in the presence of high dose of pesticides. One of the studies carried out with endosulfan, showed that the chemical became lethal within four hours of administering about 26.3 ug/L of the solution to fishes [8]. In a similar report, pesticides contamination have been discovered to lower the population of Alaskan Fur seal (_Callorhinus ursinus_) [21].
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 4 Towards More Targeted Negative Sampling Strategies for Dynamic Link Prediction
### A Taxonomy of Negative Samples
: _Negative edge sampling_ (_edge-NS_) _takes a positive event \((u,v,t)\) and replaces the edge \((u,v)\) by another edge \((u^{\prime},v^{\prime})\in\mathcal{U}\times\mathcal{U}\)._  
_For a given cutoff time \(t_{split}\), we distinguish three types of negative edge samples:_  
**Historical Edge (HE)**: \[d^{(u^{\prime},v^{\prime})}_{\mathcal{H}}<t_{split};\]
**Overlap Edge (OE)**: \[b^{(u^{\prime},v^{\prime})}_{\mathcal{H}}<t_{split}\wedge d^{(u^{\prime},v^{ \prime})}_{\mathcal{H}}\geq t_{split};\]
**Inductive Edge (IE)**: \[b^{(u^{\prime},v^{\prime})}_{\mathcal{H}}\geq t_{split}.\]These strategies lend themselves to a straightforward visualization interpretation. For instance, in the Birth-Death diagrams (see for instance Figure 2), HE, OE, and IE correspond to sampling the negative edge, respectively, from the set of blue, orange, or green points. In the rest of this work, we will conduct experiments using the strategies HE, OE, and IE. These strategies enable us to compare the score of a positive event (an action that did happen) with the score of an event that occurred in the dataset but at a different time. Furthermore, we will closely investigate the HD, OD, and ID strategies. These are particularly relevant for evaluating many DLP tasks (e.g., recommendation), where we compare the score of an interaction from a given source (such as a user) to the true destination (an item or another user) with the score of a negative destination that is randomly sampled. The HD, OD, and ID strategies help us understand the impact of choosing a negative destination from different time intervals. This choice can significantly affect the results of an evaluation.
## 3 Instrument Characteristics and Tests
### Time bandwidth product
This is the consequence of sampling the low-pass filtered signal with the -3 dB cut-off frequency \(f_{\rm LP}\) = 400 Hz with \(f_{\rm ADC}\) = 2\(\cdot f_{\rm LP}\) = 800 Hz.
## 3 Results and Discussions  
The results obtained from the extensive simulation for the proposed EMBLR, well-known existing MEQSA-OLSRv2, and MP-OLSRv2 routing approaches with various device speed are presented in this section. The routing protocol's results are expressed in the performance metrics as mentioned above. In summary, this section presents a critical analysis of the obtained simulation results.
## 2 Results and Discussion
### Omission Experiment  
To develop a deeper understanding on the aroma contribution of each key aromaticity compound, an omission analysis was performed using 20 models in which only one odorant was omitted. The triangle test results of every single compound are displayed in Table 3. The models omitted 2,3-butanedione, dimethyl trisulfide, acetic acid, acetylpyrazine and 2-methoxyphenol were identified by all or most of assessors, which indicated the significance of these key aroma-active compounds in contributing to the complete aroma of Yongchuan douchi. In addition, 3-methylvaleric acid, 4-methylvaleric acid, malton, phenylacetic acid, \(\gamma\)-nonanolactone and eugenol were also confirmed as key aroma-active compounds in our douchi sample by omission experiments.
# Efficiency Improvement Strategy for Multiple Operating Points in Doubly Fed Magnetic Geared Motor
## 2 Operating Principle  
The speed of an output rotor is adjusted by the frequencies of the inner and outer windings, and it is defined as in Equation (1) [11]:  
\[\omega_{output\_MPs}=\frac{p_{inner}}{N_{MPs}}\omega_{inner}+\frac{p_{outer} }{N_{MPs}}\omega_{outer} \tag{1}\]  
where \(p_{inner}\) and \(p_{outer}\) are the number of pole pairs of the inner and outer windings, \(N_{MPs}\) is the number of modulating pieces, \(\omega_{inner}\) and \(\omega_{outer}\) are the mechanical angular velocities of the fundamental  
Figure 1: Topologies of the magnetic geared machine. (**a**) PM magnetic geared motor; (**b**) doubly fed magnetic geared motor (DFMGM).  
components of the magnetic flux produced by the inner and outer windings. In the DFMGM, the operating region can be divided into two subregions according to the winding in which the frequency is changed. Figure 2 shows examples of the speed-torque curve for the DFMGM with the different rated frequencies in the inner and outer windings under the condition that the inner winding frequency is increased in the low-speed region and the outer winding frequency is adjusted in the high-speed region. In Figure 2a-c, the inner and outer rated frequencies (\(f_{inner}\) and \(f_{outer}\)) are 180 Hz and 360 Hz (Type I), 270 Hz and 270 Hz (Type II), and 360 Hz and 180 Hz (Type III), respectively. The base speeds (\(\omega_{base\_l}\) and \(\omega_{base\_ll}\)) of each operating type are derived as follows:  
\[\textbf{{Type I}}\quad 2492\ \ rpm\big{(}\omega_{base\_l}\big{)}=831\ rpm \big{(}=\omega_{base\_l}\big{)}+1661\ rpm \tag{2}\]  
\[\textbf{{Type II}}\quad 2492\ rpm\big{(}\omega_{base\_II}\big{)}=1246\ rpm \big{(}=\omega_{base\_l}\big{)}+1246\ rpm \tag{3}\]  
\[\textbf{{Type III}}\quad 2492\ rpm\big{(}\omega_{base\_II}\big{)}=1661\ rpm \big{(}=\omega_{base\_l}\big{)}+831\ rpm \tag{4}\]  
In the low-speed region, while the frequency of the outer winding is fixed at 0 Hz corresponding to the DC operation, the frequency of the inner winding is increased until the base speed I, \(\omega_{base\_l}\). Then, the frequency of the outer winding is increased until the base speed II, \(\omega_{base\_l}\), while the frequency of the inner winding is fixed at its rated value. Meanwhile, to adjust the output torque, the current of inner winding, which plays a role as the armature part, is controlled, and the current of the outer winding, which plays a role as the field part, is fixed at its rated value. The design specifications are listed in Table 1.  
Figure 2: Division of the operating region according to the rated frequencies of the inner and outer windings. (**a**) Type I (\(f_{inner}\) 180 Hz and \(f_{outer}\) 360 Hz); (**b**) Type II (\(f_{inner}\) 270 Hz and \(f_{outer}\) 270 Hz); (**c**) Type III (\(f_{inner}\) 360 Hz and \(f_{outer}\) 180 Hz).
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 3 Results  
### User Interface  
The Aquasafe web-based platform is designed to be user-friendly and easy to navigate, making it accessible for users of all experience levels. The platform offers a wide range of features and functions to allow viewing and accessing the available information in a variety of formats including graphs, tables, and maps, as well as access historical data and forecast predictions for the coming days (Figure 4). It is arranged in four pages, namely: (a) map, (b) cages, (c) models, and (d) alerts. In the left panel, users can access information about each page.  
1. The page \"Map\" is also the platform's landing page (Figures 5 and 6). On the right side of the map, users can choose and view environmental and meteorological parameter layers. On the left side of the map, users can view and mark the relevant alerts. Additionally, the page provides users with tools to navigate through space and time.
2. The page \"Cages\" allows users to access information concerning their farms and cages. The users can view, edit, or delete cages or add new ones. Users have the ability to access detailed information about specific cages also by selecting them from the map. This level of customization allows users to better manage their operations and make informed decisions.  
Figure 3: Aquasafe logical architecture.  
3. The page \"Models\" includes the results of the biological model, which predicts fish growth and food intake, as well as forecasts for the next days (Figure 7). It is important to note that every manual user edit on the original data will be accordingly adapted to the model. Users can also view the results of the model and the forecasts for a specific cage by selecting it from the map. 4. The page \"Alerts\" allows users to view the notifications and mark them as \"read\" or delete them. By selecting a notification, the user can view the details and the specific cage in which it is referred to. The alerts also appear on the landing page and in the form of a list.  
Figure 4: Users can access water quality products in graph formats and view their time series and future forecasts.  
Figure 5: Aquasafe web-based platform landing page, showing relevant information about fish cages, model results, and alerts on the left panel and environmental and meteorological parameters on the right side of the map. On the map, the added cages appear as blue points labeled based on the user’s preference. In this example, the points have not been positioned on the cages for visualization purposes.
## 1 Introduction
Due to this limitation, studies on EC models so far have not been able to precisely predict the array performance while considering the flexibility of surrounding structures. In this study, we developed an EC-based model of a transducer array system that can consider the effects of the acoustic interaction and the vibration of the flexible surrounding structures. With the proposed model, it is possible to precisely predict the response of the transducer array, including the crosstalk phenomenon. Crosstalk is a phenomenon in which the acoustic pressure generated by the projector is transferred to adjacent hydrophones through the surrounding structures, and the transferred pressure generates noise signals in the hydrophones. The performance of the transducer array is deteriorated due to this acoustic interaction [31]. The proposed analysis procedure can be roughly divided into two parts: One is the construction of the impedance matrix using the EC model and the FE-BE coupling model [32; 33; 34; 35] and then using it to obtain the response of each transducer. To be more specific, the impedance matrix equation is derived based on the T-network EC model. The acoustic radiation impedance is calculated from the FE-BE coupling model. The FE-BE coupling model consists of the FE model of the surrounding structure, the BE model of the fluid between the array and the surrounding structure, and the BE model of the fluid outside the surrounding structure. The other part is the construction of an acoustic transfer matrix using the FE-BE coupling method and then the calculation of the sound pressure of the external acoustic field. With the proposed method, the array performance including the far-field directivity pattern and transmitting voltage response (TVR) can be estimated with less computational resources than the conventional FE analysis. Section 2 presents methods for constructing the impedance matrix equation and acoustic transfer matrix equation. In Section 3, a case of a cylindrical array with an enclosed dome is evaluated and compared to the result of multi-physics FEM to verify the accuracy and focus on a practical problem. We also evaluate the efficiency of the proposed method by comparing it with a conventional method based on Multiphysics FEM in terms of the reduction of DOFs. Conclusions are presented in Section 4.