esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.7 Costs and Economic Feasibility  
The economic feasibility of AV deployment in rural areas hinges on overcoming high infrastructure and operational costs while leveraging opportunities for enhanced mobility and cost-efficient transport options. As highlighted by field tests, initial investments include upgrading road surfaces, signage, charging facilities, and cellular networks to support V2X communication, along with procuring AV shuttles, creating HD maps, and equipping vehicles for weather resilience [72]. Recurring expenses, such as maintenance of AV software, hardware, sensors, and retroreflective road markings, further add to the financial burden [73; 74]. Operational costs remain high due to manual interventions and the need for expanded charging infrastructure, limiting scalability. Rural residents also express concerns about the affordability of infrastructure development and vehicle ownership [72]. Shared mobility services and government-private sector collaborations are vital to enhance scalability and affordability. The ADS for Rural America project highlighted that, despite initial costs, AV deployment could deliver long-term economic benefits by enhancing rural mobility, supporting local economies, improving community connectivity, and reducing reliance on human-driven vehicles [80]. Other benefits, such as improved visitor experiences, reduced traffic congestion in sensitive areas, and better accessibility, could gradually offset costs [73]. Simulation-based studies offer valuable insights into achieving cost efficiency. [PERSON] et al. (2021) [29] evaluated autonomous demand-responsive transport (DRT) in Germany's Bremerhaven region using the MATSim simulation framework. Pooling trips and optimizing fleet sizes reduced operational expenses, vehicle kilometers traveled, and environmental impact, with fleet sizes cut by over 80% in some scenarios, demonstrating substantial cost savings in sparsely populated areas. [PERSON] et al. (2020) [18] explored replacing low-utilization rural train lines in Switzerland with autonomous mobility-on-demand (AMoD) services. Simulations showed that AMoD reduced travel times and operational costs on three out of four train lines, offering higher service levels compared to trains. However, on the Tosstal line, high passenger demand reduced cost-effectiveness, with human-driven on-demand services outperforming AVs in some cases. Overall, optimizing AV technology to minimize operational costs while ensuring reliable service is crucial for sustainable rural deployment. Simulation studies and field trials demonstrate that AV-based transport systems can improve rural mobility and cost efficiency, but strategic planning and strong collaboration between stakeholders are essential to address economic challenges and realize long-term benefits.
## 3 The Proposed System
### Convolutional Autoencoders Unit
#### 3.4.1 Convolutional Autoencoders  
RED30 consists of an encoder with 15 convolutional layers, a decoder with 15 deconvolutional layers and element-wise sum layers for skip connections. All of the convolutional layers and deconvolutional layers use ReLU as activation functions. The architecture of the convolutional autoencoder is shown in Figure 2.  
The encoder extracts the feature of the image data and converts it into a latent vector. Then, the decoder converts the latent vector into a restored image data [17]. In these processes, the defects in the image data are removed. Difference between the input image data and the restored image data are used to detect defects.
## 3 Results
### Engine Performance
#### 3.1.2 Fuel Consumption  
Since the brake torque is almost null at an engine load of 0 Nm, the BSFC cannot be calculated. Therefore, the indicated specific fuel consumption (ISFC) is calculated using the indicated torque calculated from the IMEP. At 40 Nm, the BSFC is calculated using the brake torque measured by the engine dynamometer. Figure 5 shows that the fuel consumption rate increases as the blend ratio of palm oil biodiesel increases. In general, the density of palm oil biodiesel is higher than that of diesel, which increases the mass per unit volume, and the lower heating value of palm oil biodiesel increases fuel consumption because it requires more fuel to produce the same output. The fuel consumption of pilot injection timing 15 \({}^{\circ}\)CA at an engine load of 0 Nm increased by 19% from 202 g/kW\(\cdot\)h of PD0 to 248 g/kW\(\cdot\)h of PD100 and increased from 214 g/kW\(\cdot\)h of PD0 to 250 g/kW\(\cdot\)h of PD100 at 20 \({}^{\circ}\)CA. At an engine load of 40 Nm, the pilot injection timing of 15 \({}^{\circ}\)CA increased by 12%, from 308 g/kW\(\cdot\)h of PD10 to 350 g/kW\(\cdot\)h of PD100. When the pilot injection timing is 20 \({}^{\circ}\)CA, the fuel consumption increased by 18% from 281 g/kW\(\cdot\)h to 341 g/kW\(\cdot\)h. In the case of an engine load of 0 Nm (a), the pilot injection timing of 15 \({}^{\circ}\)CA was burned with the main injection of a part of the pilot injection, the maximum combustion pressure increased, and the IMEP was higher. The position at which the maximum combustion pressure occurred was the same at both injection timings. Therefore, the ISFC at the pilot injection timing of 15 \({}^{\circ}\)CA was low. However, at an engine load of 40 Nm (b), the fuel consumption of 15 \({}^{\circ}\)CA at the pilot injection timing was as high as 8.8% for PD0, 8.3% for PD10, 6.2% for PD20 and 4.6% for PD30. As shown in Figure 4d,e, the maximum combustion pressure was generated at a position farther from the top dead center (TDC) during the expansion stroke period than the pilot injection timing 15 \({}^{\circ}\)CA, so that the efficiency for translation to power decreased. Furthermore, the faster injection timing indicates a longer the time to mix the fuel and the air, so the combustion efficiency increased. Therefore, as shown in Figure 4f, the small difference in combustion pressure and heat release rate in the PD100 can reduce the fuel consumption difference by 2.4%.
###### Abstract  
By using the two-step activated swelling method, monodisperse porous poly(styrene-divinylbenzene) (P(S-DVB)) microparticles were successfully synthesized. The influence of porogens, swelling temperatures and crosslinking agents on the porosity of porous microparticles was carefully investigated. Porous P(S-DVB) microparticles were used as a packing material for high performance liquid chromatography (HPLC). Several benzene analogues were effectively separated in a stainless-steel column as short as 75 mm due to the high specific surface area of the porous microparticles. Porous P(S-DVB) microparticles were further sulfonated and subsequently modified with diazoresin (DR) via electrostatic self-assembly and UV (ultraviolet) radiation. After treatment with UV light, the ionic bonding between sulfonated P(S-DVB) and DR was converted into covalent bonding through a unique photochemistry reaction of DR. Depending on the chemical structure of DR and mobile phase composition, the DR-modified P(S-DVB) stationary phase performed different separation mechanisms, including reversed phase (RP) and hydrophilic interactions. Therefore, baseline separations of benzene analogues and organic acids were achieved by using the DR-modified P(S-DVB) particles as packing materials in HPLC. According to the \(\pi\)-\(\pi\) interactional difference between carbon rings of fullerenes and benzene rings of DR, C\({}_{60}\) and C\({}_{70}\) were also well separated in the HPLC column packed with DR-modified P(S-DVB) particles.  
polystyrene; seeded polymerization; macroporous structures; diazoresin; high performance liquid chromatography +
Footnote †: journal: materials
## 1 Introduction
Despite having alternative navigation sensors such as Inertial Measurement Units (IMUs) and Distance Measurement Instruments (DMIs) [4] that help to bridge gaps in the navigation solution [5] and increase accuracy, IMUs experience drift in their measurement of pitch, roll and yaw. DMIs are survey grade odometers and can limit this drift by confirming when the system is stationary [6]; however, the quality of the GNSS position calculation is the main source of errors in a mobile survey [7]. In that study, the authors demonstrated that in areas of high satellite visibility, the planimetric accuracy of the resulting point cloud from an MMS survey with no ground control was approximately 0.30 m. One of the main recommendations of that study for areas with obstructions present was that the operators schedule their survey to coincide with times of high GNSS visibility [7]. There is therefore a clear need to provide a method for predicting GNSS shadows for mobile surveys. This paper will demonstrate how a GIS can provide a novel approach for predicting GNSS visibility over large areas at multiple times, thereby facilitating mobile surveys. The use of readily available software and data will negate the need for time-consuming surveys to create high detail city models and also remove the requirement for computationally intensive simulations or orbital calculations to locate satellite positions. This approach to GNSS survey planning differs from previous approaches through the following innovations:  
1. Combining readily available 2D vector data with low-density 3D data to create the Digital Surface Model (DSM). 2. Reversing output from GNSS shadow prediction tools to calculate pseudo-satellite positions. 3. Applying a GIS viewshed with distant observer points in a different coordinate system. Figure 1: GNSS Survey Planning (**a**) GNSS signal during topographic surveys can be obstructed by surrounding objects (**b**) specifying portions of the horizon in Trimble Planning where obstructions are present to simulate buildings or other vertical structures.
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 2 Datasets
### WHU-RS19 Dataset  
The WHU-RS19 dataset was extracted from Google Earth satellite imagery. The spatial resolution of these satellite images is up to 0.5 m, and the spectral bands are red, green, and blue. There are 19 scene categories, including airport, beach, bridge, commercial, desert, farmland, football, forest, industrial, meadow, mountain, park, parking, pond, port, railway station, residential, river, and viaduct. There are about 50 images corresponding to each category, and the entire dataset contains a total of 1005 images. The original image size is \(600\times 600\) pixels. Because the resolution, scale,  
Figure 1: Example images of UC Merced dataset: (**a**) agriculture; (**b**) airplane; (**c**) baseball diamond; (**d**) beach; (**e**) buildings; (**f**) chaparral; (**g**) dense residential; (**h**) forest; (**i**) freeway; (**j**) golf course; (**k**) harbor; (**l**) intersection; (**m**) medium residential; (**n**) mobile home park; (**o**) overpass; (**p**) parking lot; (**q**) river; (**r**) runway; (**s**) sparse residential; (**t**) storage tanks; (**u**) tennis court.  
Figure 2: Example images of RSSCN7 dataset: (**a**) grass; (**b**) field; (**c**) industry; (**d**) river lake; (**e**) forest; (**f**) resident; (**g**) parking.  
direction, and brightness of the imagery vary greatly, processing this dataset is somewhat challenging. These data are also widely used in evaluating various scene classification methods. Figure 3 shows some samples from the dataset. The WHU-RS19 dataset images were converted into \(224\times 224\) pixel size for transfer learning.
## 2 Network Model
### Latencies Modeling  
For modeling of flows latencies in the packet-switched network, we applied the latencies model that we presented in [27]. In general, the model accounts for the main sources of latencies in the network [8], which are  
* propagation in links,
* storing and forwarding of frames in switches,
* transmission times of bursts of frames, and
* queuing of frames at output ports of switches.  
The first three sources of latency are constant (static) and can be estimated easily. In particular, the propagation delay equals the link length divided by the propagation speed (\(2\times 10^{5}\) km/s). The delay of a burst transmission in a link is equal to the burst size (see Table 1) multiplied by the transmission time of the frame, which in turn equals the frame size (1542 bytes) divided by the link bit-rate. As in [8], the store-and-forward delay is assumed to be equal to 5 \(\upmu\)s.  
To model the non-deterministic (dynamic) latency produced by burst queuing in switches, we applied a reliable estimation of latencies. In particular, we estimated the latencies that may occur in the worst possible case. This worst case corresponds to the queuing delays produced by the bursts of frames that belong to other flows than the flow considered, denoted as Y, that might be selected for transmission at the switch output link before the burst of flow Y [8]. To this end, we divided queuing delays into the following two elements:  
* delay produced by the flows of either higher or equal priority (\(t^{HEP}\)), and
* delay produced by lower priority flows (\(t^{LP}\)).  
We assume that delay \(t^{HEP}\) is produced by the queued-up bursts that belong to all other flows of either equal or higher priority than the priority of flow Y. Concurrently, delay \(t^{LP}\) is produced by the largest burst that belongs to a lower-priority flow. We assume that the interfering flows may arrive from different switch input ports, but they go through the same output port as flow Y.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Direction** & **Type of Flow** & **Flow Bit-Rate (Gbit/s)** & **Burst Size** \\ \hline Uplink & Fronthaul & 9.632 & 52 \\  & Midhaul & 1.111 & 6 \\  & Backhaul & 1.111 & 6 \\ \hline Downlink & Fronthaul & 11.113 & 60 \\  & Midhaul & 1.111 & 6 \\  & Backhaul & 1.111 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bit-rate and burst size (number of frames) of traffic flows assuming functional split options 7.2 (in fronthaul) and 2 (in midhaul).
## 3 Algorithm and Strategies of Data Processing
### Absolute PCO and PCV Separated from Relative Observation
\begin{split}\
abla\Delta\phi_{1,2}^{t_{B}}-\
abla\Delta\phi_{1,2 }^{t_{A}}&=\Delta b^{t_{B}}\mathbf{r_{2}}-\Delta b^{t_{A}}\mathbf{r_{2} }-\Delta a^{t_{B}}\mathbf{r_{1}}+\
abla\Delta a^{t_{A}}\mathbf{r_{1}}-\Delta PCV_{1}( az^{t_{B}},el^{t_{B}})\\ &+\Delta PCV_{1}(az^{t_{A}},el^{t_{A}})+\Delta PCV_{2}(az^{t_{B}},el ^{t_{B}})-\
abla\Delta PCV_{2}(az^{t_{A}},el^{t_{A}})+\
abla\Delta\varepsilon_ {1,2}^{t_{B}}\\ &-\
abla\Delta\varepsilon_{1,2}^{t_{A}}\end{split}  \tag{4}\]  
Note that, the DD ambiguities in Equation (3) can be easily fixed, e.g., using well known LAMBDA [20], and be eliminated as constant values in Equation (4). As discussed above, the multipath errors were verified to be sharply reduced when the time difference of DD is performed. Therefore the \(\
abla\Delta MP_{1,2}^{t_{B}}-\
abla\Delta MP_{1,2}^{t_{A}}\) term has also been removed from Equation (5). In a very short time interval \(t_{B}-t_{A}\), a slight change of the elevation and azimuth from static reference station to satellite leads to a negligible PCO and PCV increment, \(\Delta a^{t_{B}}\mathbf{r_{1}}-\Delta a^{t_{A}}\mathbf{r_{1}}+\Delta PCV_{1}(az^{t_{B}},el^{t_{B}})-\Delta PCV_{1}(az^{t_{A}},el^{t_{A}})<1\) mm, in Equation (4). On the contrary, the PCO and PCV of the test antenna with the robot help of rapidly moving through a large range of motions, _i.e., \(z_{test}^{t_{A}}\
eq az_{test}^{t_{B}},\ el_{test}^{t_{A}}\
eq el_{test}^{t_{B}}\)_, cannot be disregarded directly and still remain in Equation (4). Now, taking these factors [20] into account, Equation (4) can be simply rewritten as:  
\[ \begin{split}\
abla\Delta\phi_{1,2}^{t_{B}}-\
abla\Delta\theta_{1,2 }^{t_{A}}&=(\Delta b^{t_{B}}-\Delta b^{t_{A}})\mathbf{r_{test}}+ \Delta PCV_{test}(az^{t_{B}},el^{t_{B}})-\Delta PCV_{test}(az^{t_{A}},el^{t_{A}} )+\
abla\Delta\varepsilon_{1,2}^{t_{B}}\\ &-\
abla\Delta\varepsilon_{1,2}^{t_{A}}\end{split}  \tag{5}\]  
After the short time difference of consecutive epochs, the PCO and PCV terms of the reference antenna are safely removed. Finally, the PCO and PCV of the test antenna are successfully transferred from the sense of \"relative\" to \"absolute\". For a convenient implementation, the PCO of the antenna is firstly estimated in a least squares sense without PCVs, and then when PCO has been adjusted and removed as constant in Equation (5), the PCVs of the test antenna are therefore accessed using a lower degree and orders of surface spherical harmonics to fit the TD residuals in Equation (5).
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## 4 Results and Discussion
### Scenario 1: Cases without Local Minima Problem
#### 4.1.3 Case 3: An Obstacle on a Direct Path  
In this case, the obstacle is intentionally placed in the robot's trajectory toward the target. The robot is able to reroute itself as soon as it senses the presence of the obstacle. For a short while, it continues its journey on the boundary of the safe zone defined by the obstacle. As soon as it gets away from the repellent force of the obstacle, it redefines its course towards the goal point, as shown in Figure 5. Due to the generation of the random particles around the robot, its behavior is not fixed, and can change from trial to trial. We noticed that on some occasions, the robot bypassed the obstacle from the left side.  
Figure 4: Trajectory of the robot when three obstacles are encountered.  
In other methods, such as potential field, the robot gets trapped in a local minimum because the existence of the attractive and the repulsive forces on the same line nullify each other's effect. Consequently, the robot gets stuck and stays at one place indefinitely, yet the proposed algorithm effectively solves the problem by avoiding the local minima and causing the robot to reach the target.
# An Efficient Algorithm for the Separable Nonlinear Least Squares Problem
###### Abstract  
The nonlinear least squares problem \(min_{y,z}\|A(y)z+b(y)\|\), where \(A(y)\) is a full-rank \((N+\ell)\times N\) matrix, \(y\in\mathbf{R}^{n}\), \(z\in\mathbf{R}^{N}\) and \(b(y)\in\mathbf{R}^{N+\ell}\) with \(\ell\geq n\), can be solved by first solving a reduced problem \(min_{y}\|f(y)\|\) to find the optimal value \(y^{*}\) of \(y\), and then solving the resulting linear least squares problem \(min_{z}\|A(y^{*})z+b(y^{*})\|\) to find the optimal value \(z^{*}\) of \(z\). We have previously justified the use of the reduced function \(f(y)=C^{T}(y)b(y)\), where \(C(y)\) is a matrix whose columns form an orthonormal basis for the nullspace of \(A^{T}(y)\), and presented a quadratically convergent Gauss-Newton type method for solving \(min_{y}\|C^{T}(y)b(y)\|\) based on the use of QR factorization. In this note, we show how LU factorization can replace the QR factorization in those computations, halving the associated computational cost while also providing opportunities to exploit sparsity and thus further enhance computational efficiency.  
Keywords:separable equations; nonlinear least squares; full-rank matrices; QR factorization; over-determined systems; Gauss-Newton method; least squares solutions; LU factorization; quadratic convergence Msc: 65H10 +
Footnote †: journal:
## 4 Summary and Conclusions  
The aim of our research was to develop a numerical model of microflows occurring in the culture chambers of the microfluidic device of our construction, that is dedicated to high-throughput drug screening. The model describing incompressible fluid flow is based on the lattice-Boltzmann equation with an external body force term approximated by the HSD scheme and the BGK approximation of the collision operator. The solution of the model was performed in the open-source Microflow 3D solver for the full 3D culture chamber geometry on the Cartesian D3Q19 lattice, comprising over 24.5 million computational nodes. The model and code were validated using the analytical solution of NSE for 3D duct flow and using experimental data of volumetric flow rates of culture medium in CC. The mean errors were 1.0% and 6.1% respectively, in comparison to the NSE solution and experimental data. However, we should be aware that the deviation of simulation results from the reference values was influenced to a large degree by the experimental data deviation (SD = 3.8%). The obtained accuracy of the model is sufficient to conduct engineering calculations, and we used our model to perform an analysis of the inlet/outlet effects of flow in CC.  
The biochips' cell culture chambers' construction promotes stable and homogeneous laminar plug-flow-like conditions within their workspace area, along the whole CC. The plug-flow-like velocity profile is responsible for the even distribution of species across the CC, while the parabolic profile developed across the CC (along the shortest edge of the CC) allows the development of the linear gradients of species during fluid reciprocating flow.  
Figure 10: Velocity profiles at the inlet of CC: (**a**) plot over line 1 placed in the middle of the height of CC, (**b**) plot over lines 2–6 placed in the middle of the height of CC, and (**c**) CC cross-section across the XY plane placed in the middle of the height of CC.  
The fluid reciprocating flow is invoked by alternately tilting the microsystem several times, and is prone to human operation errors, e.g., inclination angle. This can cause discrepancies in the volumetric flow rates of culture media. However, as the channel surface-to-volume ratio is high, the flow perturbations at the inlet/outlet of the CC are rapidly suppressed and do not influence species' distributions within the working space of the CC, making test procedures robust and repeatable. If the lengths of the inlet and outlet sections are set to 10 mm, the change in fluid velocity along the axis of the chamber, within its entire length, does not exceed 10%.  
The conclusions drawn from the simulation results are consistent with the observations made previously during the practical tests of the device [20]. In a further article, we intend to present an in-depth analysis of research results on the formation of active substance concentration profiles in CC and their stability over time.
## 4 Feature Parameter Extraction
### Transmission Coefficient  
The transmission coefficient is defined as T = \(At/Ai\), where \(At\) is the maximum value of the transmitted wave spectrum and \(Ai\) is the maximum amplitude of the direct wave spectrum. Figure 9 shows a 3D map with the depth as the \(X\)-axis, angle as the \(Y\)-axis, and transmission coefficient as the \(Z\)-axis.  
Figure 9 clearly shows that the transmission coefficients gradually increase with decreasing depth. The contour line is curved upward at 90\({}^{\circ}\) and nearly parallel to the \(Y\)-axis when the depths are lower than 0.4 mm. Figure 8 also shows that angle has less influence on the transmission coefficient when the oblique crack depth is small.  
Figure 8: Three-dimensional map of the angle–depth–root mean square.  
Figure 9: 3D map of the depth-angle-transmission coefficient.