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
#### 3.3.6 Long-Term Planning and Deployment  
Long-term planning and deployment strategies for AVs in rural areas require a phased approach and a layered infrastructure strategy to address unique regional challenges. A phased deployment begins with demonstration projects and low-speed shuttles, allowing AV systems to adapt to rural complexities through iterative testing before transitioning to full-scale services [70,79]. The layered strategy categorizes roadways into AV-only roads, shared roads, and \"last-mile\" routes, optimizing compatibility and performance [73]. Infrastructure readiness, technological advancements, and user acceptance remain critical pillars for successful adoption. DriveOhio and the CASSI Project emphasized the importance of upgrading unpaved roads, improving road markings and signage, and enhancing telecommunications for reliable V2I communication [68,73]. Collaboration with local policymakers, as highlighted by the Drive MN project, is essential for addressing region-specific challenges, sharing findings with decision-makers, and maintaining standardized traffic engineering standards and pavement markings [43]. Community engagement is key to ensuring AV solutions meet local needs, particularly for the elderly and individuals with disabilities, fostering public trust. Government-private sector partnerships can secure funding and implement scalable, cost-effective solutions, including shared mobility services. Pilot projects enable comprehensive data collection to refine AV technology and create a foundation for future deployments [73,84]. Sustainable deployment examples, such as the National Park Service project, demonstrate how improved battery technology and weather-resilient designs can scale operations and integrate automated shuttles into existing transport systems to support rural mobility [73]. Simulation-based studies, like [PERSON]'s (2019) [14] research on Swedish municipalities, provide further insight into AV applications. By using a discrete-event simulation model, the study found that AV-based first- and last-mile feeder services could reduce driver costs, improve accessibility, and offer flexible transport during low-demand periods. Driverless shuttles capable of serving 100-150 passengers daily were identified as a viable solution for rural areas, with public transport authorities playing a key role in addressing local challenges and optimizing deployment.
# Investigation into the Effect of Atmospheric
###### Abstract  
The Global Positioning System (GPS) has been widely used in navigation, surveying, geophysical and geodynamic studies, machine guidance, etc. High-precision GPS applications such as geodetic surveying need millimeter and centimeter level accuracy. Since GPS signals are affected by atmospheric effects, methods of correcting or eliminating ionospheric and tropospheric bias are needed in GPS data processing. Relative positioning can be used to mitigate the atmospheric effect, but its efficiency depends on the baseline lengths. Air pollution is a serious problem globally, especially in developing countries that causes health problems to humans and damage to the ecosystem. Respirable suspended particles are coarse particles with a diameter of 10 micrometers or less, also known as PM\({}_{10}\). Moreover, fine particles with a diameter of 2.5 micrometers or less are known as PM\({}_{2.5}\). GPS signals travel through the atmosphere before arriving at receivers on the Earth's surface, and the research question posed in this paper is: are GPS signals affected by the increased concentration of the PM\({}_{2.5}\)/PM\({}_{10}\) particles? There is no standard model of the effect of PM\({}_{2.5}\)/PM\({}_{10}\) particles on GPS signals in GPS data processing, although an approximate generic model of non-gaseous atmospheric constituents (<1 mm) can be found in the literature. This paper investigates the effect of the concentration of PM\({}_{2.5}\)/PM\({}_{10}\) particles on GPS signals and validates the aforementioned approximate model with a carrier-to-noise ratio (CNR)-based empirical method. Both the approximate model and the empirical results show that the atmospheric PM\({}_{2.5}\)/PM\({}_{10}\) particles and their concentrations have a negligible effect on GPS signals and the effect is comparable with the noise level of GPS measurements.  
Global Positioning System (GPS); GPS signal propagation; atmospheric particulate matter (PM); PM\({}_{2.5}\); PM\({}_{10}\); air pollution +
Footnote †: journal: Sensors  
0000-0001-5000-0000-0000][PERSON].
# Comparative Test on the Bond Damage of Steel and GFRP Bars Reinforcing Soft Rock Slopes
## 3 Results  
### Bond Damage of the Bar Interface under Cyclic Load  
Figure 8 shows the test results of the relationship between the strain and load in the steel bar specimen at different depths and load. Figure 8 shows that: (1) the larger the anchorage depth was, the smaller the bar strain would be, that is, the bar strain at 65 cm away from the zero point was generally less than that at 15 cm, which was caused by load attenuation with depth; (2) in the case of similar loads, the strain of the bar body corresponding to the subsequent cyclic load was greater than that by the previous one, resulting in the strain of the bar body increasing with the increase in the number of cycles, a phenomenon indicating that the loading cycle might aggravate the bond damage of the bar body interface; (3) when the load reaches the maximum value of the previous load, the strain of the bar body was approximately equal to the previous strain. This last phenomenon indicated that the cyclic load level had little effect on the bond damage of the bar interface when the load is not greater than the previous load.  
Figure 9 shows a similar phenomenon to Figure 8 in the GFRP bar; (1) the strain at 80 cm away from the zero point is generally greater than that at 180 cm, which is caused by the attenuation of the axial force; (2) at the same depth and under the same load, the strain increases with the number of cycles, thereby indicating that cyclic load aggravates the bond  
Figure 8: Strain-load relationship curve of the steel bar at 15 cm and 65 cm away from the zero point [23].  
Figure 7: Test loading curve of the steel and GFRP bar. (**a**) Loading curve of the cyclic test (**b**) Loading curve of the constant load test and destruction test.  
damage at the bar interface; (3) when the cycle reaches the maximum value of the previous cycle, the strain is slightly larger than the previous cycle. In the actual loading process, due to the low accuracy of the jack oil gauge, it was difficult to accurately load to the exact value. When loading to the maximum, the specimen was loaded to a value slightly over the target value, and then part of the force was unloaded to reach the ideal value.  
In conclusion, both steel and GFRP bar bodies in soft rock are affected by cyclic load, which can aggravate the bond damage of the bar interface. The steel and GFRP bars in soft rock are both affected by the previous maximum load, which is the main influencing factor of the bond damage of the anchorage bar, compared with the number of times of cyclic loading.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 5 Rugged Design  
Across the wide bandwidth, all rods of the directors and reflectors serve as resonating elements with center voltages equal to zero. The simulated current distribution of QWL antenna is shown in Figure 7, where red indicates the areas of maximum current and blue denotes the areas of minimum current. Additionally, the voltage at the center of the two shorting arms in the driven element is also equal to zero across the operating band, as demonstrated in Figure 7. Since the voltages at the center points of the reflectors, directors, and shorting edges of the driven elements are all zero, a metallic structure can be used to connect all of these zero-voltage points, resulting in a rugged design. The fully designed QWL array is depicted in Figure 8[19, 27].  
Figure 6: The configurations of and results regarding the QWL antenna with the W-shaped reflector. (**a**) A front view of the driven element; (**b**) a top view and (**c**) the front view of the QWL antenna, where \(l_{\text{r1}}=173.7\), \(l_{\text{r2}}=118.5\), \(l_{\text{r3}}=130.5\), \(l_{\text{r4}}=105.0\), \(l_{\text{r5}}=158.4\), \(S_{\text{r1}}=20.0\), \(S_{\text{r2}}=20.0\), \(S_{\text{r3}}=20.0\), \(S_{\text{r4}}=20.0\), and \(S_{\text{r5}}=22.0\) (all in millimeters). (**d**) The measured and simulated \(\text{1S}_{\text{1}}\)\(\text{1}\) and gain results.  
Figure 7: (**a**) The QWL antenna; the simulated current distributions of the director and the reflector of the QWL antenna at (**b**) \(1.7\) GHz, (**c**) \(2.7\) GHz, and (**d**) \(3.5\) GHz.  
The QWL antenna is able to more effectively utilize its radiation structure to transmit or intercept electromagnetic waves. When enclosed in a minimal surrounding sphere, the maximum size of the QWL antenna is only 0.74 of that of the Yagi-Uda antenna, as shown in Figure 9. The antennae were compared at a low frequency range, because that is the frequency which determines antenna size.  
Figure 8: The full QWL antenna.  
Figure 9: (**a**) The QWL antenna (1.7–3.7 GHz) enclosed in a sphere. (**b**) The classic antenna (1.7 GHz) enclosed in a sphere.
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
## 3 Experimental Verification
### Simulation Experiments and Data Analysis  
#### 3.2.1 Experimental Scenario Description  
The basic urban road section with a 1000 m two-way four-lane between two adjacent intersections was utilized as the experimental scene (without considering the impact of intersection signal lights on traffic flow). Using SUMO as the experimental platform, the road speed limit was set to 60 km/h, the road capacity was set to 1200 pcu/h per lane, the expected speed of the CVs was 40 km/h, and the expected speed of the EVs was 60 km/h. The flow input obeyed uniformity, the flow distributions of the inner and outer lanes were uniform, and the speed distribution was divided according to the vehicle type. The ACC car-following model was adopted, the vehicle communication protocol adopted the LTE-V2X protocol, and the maximum communication delay was less than 1 s [18]. To avoid a local optimum in the simulation, the search step \(\Delta\theta\) for the traversal search method should be less than 1 s, and the simulation accuracy should be as high as possible. The parameter values in the control approach are reported in Table 3.
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 4 Results and Analysis
### Experimental Settings  
Two different models, namely an MLP model and a hybrid CNN-1D and LSTM model, were utilized in the applied experiments. The performance of these models was assessed using a variety of criteria, including recall, accuracy, F1 score, precision, and AUC (Area Under the Curve) score.  
The MLP model was trained for the binary classification task for 30 epochs, employing the ADAM optimizer. A batch size of 128 was used, along with the binary cross-entropy loss function and a learning rate of 0.001. Similarly, the hybrid CNN + LSTM model was trained for 30 epochs, utilizing the same learning rate and batch size, as well as the ADAM optimizer and binary cross-entropy loss function. To evaluate the performance of both models, metrics such as AUC, accuracy, precision, recall, and F1 score were employed.  
For the attack classification task, both the MLP model and hybrid CNN + LSTM model were trained with the same settings as the binary classification task. The MLP model utilized the categorical cross-entropy loss function, while the hybrid model used the binary cross-entropy loss function. The evaluation of the models once again involved metrics such as AUC, accuracy, precision, recall, and F1 score. Furthermore, for the all-class classification task, the MLP model was trained with the categorical cross-entropy loss function, and the hybrid CNN + LSTM model used the binary cross-entropy loss function. The training conditions remained comparable to the previous tasks, with both models trained using 30 epochs, a batch size of 128, and a learning rate of 0.001. The ADAM optimizer was employed for optimization, and the performance of the models was assessed using AUC, accuracy, precision, recall, and F1 score metrics.  
Figure 10: Architecture of the hybrid model.
## 4 Conclusions  
This manuscript describes a miniaturized EDFA developed and qualified for space operation, with a form factor compatible with the CubeSat platform. The optical power to size ratio is the biggest in the scientific literature and the satellite market, to the best of the authors' knowledge [10; 11], meaning that this is the world's smallest space-qualified high-power optical amplifier. NICT plans to test it in orbit as a key component of the CubeSOTA mission, currently in preparation [5]. Although this manuscript has focused on the high-power amplifier of the CubeSOTA terminal, NICT has also conceived its counterpart and it is currently designing and developing a low-noise amplifier to be integrated in the same case to support bidirectional communications.  
Conceptualization, A.C.-C.; investigation, A.C.-C., K.S. and P.V.T.; formal analysis, A.C.-C. and K.S.; validation, F.I.; writing--original draft preparation, A.C.-C.; writing--review and editing, A.C.-C. and P.V.T.; supervision, D.K.; project administration, T.F. and M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 5 Platform Preparation for the Field Test
### Preparation of the Sensor Node for the Field Test
This node installation flow requires minimum input from the operator, specifically:  
* trigger the deployment mode of the node by rubbing a magnet on its case. This is an intuitive and error-proof way to address the node under installation;
* read on a handheld device the quality of the current deployment position;
* tap a button on the handheld display to validate the deployment and send the data to the server. Because of the short time available to prepare the field test, we implemented this flow only partially. We mounted the need switches on the sensor nodes and changed the gateway protocol to provide the reception quality in the acknowledgments. The handheld device was implemented using a modified gateway that was displaying only the lowest reception level of the gateways in range on three LEDs. During normal operation, the sensor node periodically tests the on-board temperature transducer given its importance for the detection of the fire. The node can detect short-circuits of the transducer (decoded as installation requests, as described above) and open-circuits. The node notifies to the gateways both faults in order to propagate them to the server which warns the operators about the fault. Besides recognizing the extreme cases of short- and open-circuit, the selection of the temperature thresholds for alerts is very important. Low thresholds ensure timely alerts, but they also increase the sensor susceptibility to false alerts triggered by natural phenomena, such as sensor heating by direct exposure to sunlight. These considerations are especially important for fields with high solar exposure, as is the test field during summer time. After some testing, we decided that the fire alert threshold can be set to 60 \({}^{\circ}\)C and the threshold for warning of possible fire to a temperature increase rate of 1 \({}^{\circ}\)C/s. A normal mode helix antenna (NMHA) was soldered to the antenna pad on the right of the PCB, as shown in Figure 3b. The NMHA is made of about 12 turns on an internal diameter of \(\phi\) 10 mm with a \(\phi\) 1 mm copper wire and has about a 21.5 mm length. The number of turns and the length were experimentally tuned to maximize the RF radiation in deployment-like conditions. The sensor node package designed for the field test is shown in Figure 8. It holds the node PCB shown in Figure 8b with a half-AA battery soldered on top, an NTC transducer to the left and the NMHA antenna mounted axially to the right. The programming head on the left of the PCB was removed, and everything was sprayed with a protective resin against moisture and insects. The aperture on the left side of the package improves the thermal contact between the NTC and the ambient air. The node is mounted vertically on trees, at a height of 0.5 m, with the aperture pointing downwards for a good contact with the hot air rising from ground fires, which are notoriously difficult to detect using other techniques.
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 2 Materials and Methods
### Data Gathering
Well-adapted, high-quality seeds improve germination, growth uniformity, and yield potential across different climates [29]. Season--Denotes the planting or harvesting period (Spring, Summer, Autumn, Winter). Seasonal alignment improves crop health, yield forecasting, and risk management through informed scheduling [30]. Distance to Retailer--Measures proximity to markets. Longer distances discourage perishable crops due to increased transport risks and costs, guiding farmers in selecting suitable crop types [31]. Harvest Yield (%)--Represents the success rate of harvested crops, serving as a key performance indicator for farm practices under specific environmental conditions.
## 4 Results and Discussion
### Assessment of Potential Rockfall Sites
#### 4.1.4 Rockfall from Jijal to Dasu  
The 55 Km long section from Jijal to Dasu mainly comprises mafic to ultramafic complexes. This section witnessed the devastating 1974 Pattan earthquake, along with the major Main Mantle Thrust (MMT), Kamila Jal shear zone, and strike-slip Kamila faults (Figure 1) [61]. The incision of River Indus has caused more than 6000 m deep gorges in the Pattan and Dasu areas, where the major hazard-triggering factors are intense rainfall, complex geology, drainage, and thrust faults [62]. Rockfalls, toppling, and debris slides are the most common mountain hazards along this section. Rockfall activity often causes damage to vehicles on the route (Figure 6a). Rockfall data was collected from 17 stations from Pattan to Dasu. The most common unstable pattern along this section is wedge sliding and toppling, observed at all of the surveyed outcrops. (Figure 6h). Material from the upslope moves downslope in the form of falling, rolling, and bouncing; therefore, the development of talus slope is not common. Instead, small debris slides are a routine geohazard in this region, caused mainly by short, intense rainfall. Field records show that failed mass can reach 150 m in diameter, occasionally blocking the KKH. Slope ranges from 45\({}^{\circ}\) to 75\({}^{\circ}\) throughout the section, and cut slope height is 20 m to 220 m (Figure 6e,f). Road width is roughly constant at 7 m, but shoulder width is not more than 1 m without protection measures (Table 2). The Rocks are jointed and fractured with abundant suspended rockblocks. Block toppling and block sliding are common modes of failure from Jijal to Dasu. Three to four discontinuity sets were identified, depicting an acute degree of scattering due to intense shearing. The aperture of discontinuities is characterized as tight to moderate and spacing as close to wide. The filling of discontinuities varies from soft to hard material, indicating water circulation on the slope. This slope water has resulted in the moderate- to high-degree of weathering observed in the field.
## 4 Discussion
### DWI Examination  
To the best knowledge of the authors, in the specialist literature, there are no reports about the use of DWI technique in the diagnostics of FASD. There are, however, publications reporting results of diffusion tensor imaging (DTI), a technique developed from the DWI method. Microstructural anomalies have been observed in several brain areas, including, e.g., the temporal lobe, cingulate gyrus, corticospinal tracts, inferior frontal-occipital fasciculus, inferior and superior longitudinal fasciculus, and thalamus. The most frequently observed anomalies include a decreased value of Fractional Anisotropy (FA) and increased values of Mean Diffusivity (MD) as well as the Apparent Diffusion Coefficient of water (ADC) [40; 41; 42], although studies investigating young children and neonates present the opposite results [43]. In the corpus callosum, altered DTI parameters are found in the splenium, isthmus, and genu [23; 40; 43; 44; 45]. The present findings show statistically significantly increased values of DWI in the frontal lobes, basal ganglia, and in the whole brain and decreased ADC values in the whole brain as well. This deviation from the typical DWI and ADC values may have been indicative of impaired microstructure in the brain regions examined. It is also worth mentioning the so-called translucence effect, when a higher DWI signal was due to a higher water content. Then, there was no clearer ADC reduction. It is likely that children with DWI have a higher water content, and thus a lower maturity of myelin.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 5 Simulation and Experiment Results
### Simulation
#### 5.1.1 Case A  
In Case A, three UAVs realize formation transformation using Method 1 and Method 2*. The initial positions of the three UAVs are \(\mathbf{x}_{1}=[-1.2;3.9;6]\), \(\mathbf{x}_{2}=[1.2;2.9;6]\), and \(\mathbf{x}_{3}=[1;6;6]\). The positions of the target formation are \(\mathbf{t}_{1}=[0;0;6]\), \(\mathbf{t}_{2}=[-1;2;6]\), and \(\mathbf{t}_{3}=[1;2;6]\). The trajectories of UAVs are displayed in Figure 11 and the distance between the UAVs is given in Figure 12.  
It can be seen from Figure 11 that all UAVs arrived at their offline assigned targets using Method 1, despite the significant deviations from the planned straight-line trajectory. Using Method 2*, local TAPs are solved online such that UAV 1 and UAV 3 are assigned to different targets while avoiding collisions. The flight distances of the three UAVs are shown in Table 4. Compared with Method 1, the average flight distance and the arrival time of Method 2* are reduced up to 11% and 15%, respectively. Both methods can maintain the safe distance among UAVs during the formation transformation.  
Figure 11: Trajectories of three UAVs planned by Method 1 and Method 2*.  
Figure 10: Computational time comparison between Method 1, Method 2* and Method 3.  
Using Method 1, UAV 4 stops at a local minimum and is unable to arrive at the target, as shown in Figure 12(a). In Figure 12(b), local TAPs are triggered on and solved during collision-avoidance. All four UAVs have arrived at the target. The flight distance of the UAVs is shown in Table 5. It can be seen that using Method 2* increases the flight distance in this case, yet the number of UAVs that can arrive at the target position is increased and the arrival time is reduced up to 34%. The proposed dynamic task assignment and trajectory planning algorithm successfully helps UAVs escape from the local minimum point.