esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Overall Discussion of Results  
Integrating the findings from the EDA, the spatial autocorrelation (Moran's I), and the clustering (DBSCAN) reveals that public transport in Greater Valparaiso exhibits notable disparities in waiting times. Three aspects, in particular, deserve attention:  
1. Mode of Transport as a Key Factor 2. Clusters with low waiting times (e.g., Cluster 7 featuring Funiculars and Cluster 2 featuring Metro Valparaiso) underscore the importance of high-frequency services and fixed routes, aligning with previous studies that emphasize the relationship between frequency and reliability [20]. In contrast, urban buses account for the longest delays (Cluster 8 at 60 min, Cluster 3 at 39.88 min), reinforcing findings from other research indicating congestion and operational variability as critical factors [22] 3. Taxi Colectivo displays dual behavior: They are efficient in some areas (Cluster 1) but can present systematic delays in others (Cluster 6), possibly due to insufficient supply or a lack of coordination with local demand.
2. Spatial Distribution and Inequalities 3. The high spatial autocorrelation (Moran's I = 0.5177, \(p<0.001\)) confirms that waiting times are not randomly distributed but cluster in specific locations, such as Santa Julia, Vina del Mar Oriente, and Miraflores, a pattern also hinted at in the heat map. These areas share distinctive urban and sociodemographic conditions: high density, limited infrastructure, and less access to efficient modes. This concentration of elevated waiting times is consistent with literature pointing to spatial factors in service availability [PERSON] et al. [12].
3. More Vulnerable User Groups 4. Individuals with lower incomes (unpaid family members) are hit hardest by prolonged waits (Cluster 4). This finding aligns with studies documenting inequalities in public transport access [22]. Even users with near-average waiting times (Cluster 0) face notable variability, reflecting service inconsistencies and underscoring the need for frequency adjustments.  
These conclusions call for prioritizing enhanced frequency and capacity of urban bus services, combined with bolstering more reliable modes (Metro Valparaiso, Trolleybuses, Funiculars). Real-time information, as proposed by other studies ([PERSON] et al. [24]), could alleviate negative perceptions of waiting without necessitating significant infrastructure investments.  
Collectively, these results suggest increasing bus frequency and capacity, in addition to reinforcing more reliable modes (Metro, Trolleybuses). Implementing real-time information systems ([PERSON] et al. [24]) may reduce the negative perception of waiting times without requiring major infrastructure work. Furthermore, updating data emerges as a primary challenge; the findings reflect conditions as of 2014, and newer methodologies (e.g., image recognition and AI techniques) could enable more current data collection and ongoing diagnostics for the transport system.
## 5 Applications in Control Theory
### Control of Chaos
#### 5.2.2 Adaptive and Predictive Control
They investigated both the control performance and the system's sensitivity to initial conditions, and compared them with that of the conventional reference model adaptive control by means of numerical simulations on Lozi (for \(a=1.8\) and \(b=0.4\)) and Henon maps. Simulation results showed that this new controller yields faster settling time and more robust tracking performance. For the same value of parameters of the Lozi map, Feng and Chen [117] have presented a new adaptive control algorithm. The basic idea behind their proposed approach is to represent the chaotic system as a Takagi-Sugeno fuzzy model (see Section 4.1.1), design a local linear adaptive controller in each local region, and then reassemble a global adaptive controller over the entire domain in such a way that the stability of the closed-loop adaptive control system is guaranteed.
# A Multi-Channel Data Simulator Based on the Time Unification System
## 5 Experimental Results
### Real-Time Mode Loopback Test  
The real-time mode loopback test is shown in Figure 10. The host computer sends the test data to the simulator through the 10 GbE interface. The simulator outputs the test data through the output interface and realizes data loopback using external wiring. The received data are transmitted to the host computer through the 10 GbE interface. The closed-loop delay of the simulator can be obtained by measuring the time difference between the sent  
Figure 8: The storage mode loopback test.  
Figure 9: The delay of the interface in sending the data and the time reference (1 clk = 10 ns).  
and received data packets. Meanwhile, the received data are stored in the SATA solid-state storage. The received data are also uploaded to the host computer via PCIe for comparison with the transmitted data in order to test the consistency between the transmitted and received data.  
The real-time mode loopback test uses the Wireshark network analysis tool on the host computer to analyze the transmitted UDP data packets. The simulator proposed in this study shows consistency among the received and transmitted data in real-time mode and can achieve an average closed-loop delay of 39.9 us. The histogram shows the delay time distribution of 1037 test data packets with a delay range of 30-94 us, and the delay time is generally distributed within 35-40 us, as shown in Figure 11. It is found that 920 bytes is the largest number of packets transmitted in the target system. We capture the packets on the Windows operating system, which is not a real-time operating system. Due to the scheduling of backend tasks, there are occasionally more delays in parsing UDP data packets.  
Table 1 compares the performance of the simulator proposed in this study with that of others. The simulators proposed by [PERSON] et al. [8] and [PERSON] et al. [9] can only output simulated data and cannot receive result data generated by the target system for closed-loop simulation. Additionally, they lack PPS input and cannot achieve time synchronization. The systems of [PERSON] et al. [11] and [PERSON] et al. [14] can generate simulated data based on the input data, but their computations are conducted on the FPGA, which only allows for simple data packaging and forwarding based on the data source and format, failing to achieve true data simulation. Moreover, since they do not have PPS input, they cannot send multiple data streams at a unified time, thus failing to achieve time synchronization.  
Figure 11: The histogram of the closed-loop delay test.  
Figure 10: The real-time mode loopback test.  
The high-speed data simulator proposed in this study can meet the synchronized data transmission requirements of time-synchronized systems. The simulator features high-speed data reception ports to receive output data from the target system. Furthermore, the 10 GbE Ethernet can transmit the simulated data generated by the host computer in real time, achieving closed-loop simulation.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 2 State of Art about Collective Perception for Connected and Autonomous Vehicle
### CP Overview  
CP allows vehicles to exchange collective perception message (CPM) with each other to obtain a more accurate and comprehensive perception of their surroundings, improving traffic safety and efficiency for CAVs [16]. The ETSIs defines the CPMs as a basic service protocol that contain information about perceived objects by CAVs. Specifically, ETSIs defines the message format and rules for CPMs generation [32; 33]. In this way, CPMs allows CAVs to share surrounding information (e.g., objects detected) detected by sensors, such as, cameras, RADAR, LIDAR, and others [34; 35]. A CPM contains data about the vehicle that generated the CPM, its onboard sensors (e.g., range and FoV), as well as data about the detected object (e.g., position, speed, distance, and size) [22; 27].  
In addition, CPM could be used to send information about tracked objects, where the CPM must be transmitted based on the following conditions: (i) The detected object must move at least 4 m from its last CPM transmission; (ii) The detected object must be moving by more than 0.5 m/s from its last CPMs transmission; (iii) The detected object must not be included in following CPMs. Figure 1 presents the general structure of a CPM. One standard intelligent transport system (ITS) protocol data unit (PDU) header and multiple containers constitute a CPM. The ITS header includes the information of the protocol version and the message type. Inside the CPM information, the parameters describing the objects identified within the CAV's FoV must be included.  
A CPM can be broadcast either by a CAV or by a road side unit (RSU). Some fields present in CPM are optional and describe the perception observed by the sensor FoV. The management container offers fundamental information about the transmitting entity, whether it is a CAV or an RSU. The sensor information contains the data for each sensor equipped on a vehicle. In this sense, the sensor container provides data, such as the range and horizontal and optional vertical opening angles. Furthermore, the free space addendum container may represent varying confidence levels for a given sensor's FoV. Finally, the perceived object container includes a detailed description of a detected object's dynamic state (i.e., speed, acceleration, and direction) and other properties related to the object's behavior.
# Research on Seeding Performance of Self-Propelled Industrial Hemp Seeder
## 4 Test and Verification of Seed Metering Device
### Multi-Factor Test
#### 4.5.3 The Field Test  
To obtain better seeding uniformity of self-propelled industrial hemp seeders under the premise of 57.6-62.4 kg/hm\({}^{2}\), the optimization parameter optimization module built in the software Design Expert 11.0 is used to solve the problem by extreme value theory. The optimization objective function and constraint conditions are shown in Formula (25). To facilitate the design of the working parameters and structural parameters of the metering wheel, when the operating speed of the machine is 4.7 km/h, the optimal parameters are the rotational speed of the metering wheel \(n\) = 90.0 r/min, the diameter of the hole \(d\) = 10.4 mm and the depth of the hole \(h\) = 6.4 mm, the variation coefficient of the uniformity of the metering is 3.66%. The coefficient of variation for consistency of seed quantity in each row is 0.78%.  
\[\left\{\begin{array}{l}\min Y_{1}(X_{1},X_{2},X_{3})\\ \min Y_{2}(X_{1},X_{2},X_{3})\\ s.t.\left\{\begin{array}{l}80.0\;\mathrm{r/min}\leq X_{1}\leq 100.0\;\mathrm{r/ min}\\ 5.5\;\mathrm{mm}\leq X_{2}\leq 7.5\;\mathrm{mm}\\ 10.0\;\mathrm{mm}\leq X_{3}\leq 12.0\;\mathrm{mm}\end{array}\right.\end{array}\right. \tag{25}\]  
In order to verify the reliability of the optimal parameter group and investigate the seeding performance of the self-propelled industrial hemp seeder, a field seeding test was carried out in the National Ramie Storage, Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences on 18 April 2021, as shown in Figure 15. According to the horizontal combination of the superior factors in the bench test, the rotation speed of the seed row wheel was adjusted to 90 r/min, the hole diameter was 10.4 mm, and the hole depth was 6.4 mm. The test unit ensured the straight line was forward, and the operating speed of the drill was set to 4.7 km/h to carry out the field test.  
The experiment took \"Long Ma 3\" as the seed row object, sowing 3 rows in each stroke with a row spacing of 150 mm. After seedling emergence, according to the distribution of industrial hemp in the field, 5 sections of each row were randomly tested. The verification results are shown in Table 6. The average variation coefficient of actual seeding uniformity is 6.19%, the average coefficient of variation for consistency of seed quantity in each row is 3.44%, and the coefficient is less than 8.0%, which meets the design requirements. The test results are consistent with the optimization results, which are credible.
# Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment
## 2 The Proposed Method
### Danger Index Based Artificial Potential Field (DIAPF) for a Dynamic Environment
\begin{cases}k_{ro}(v_{r}-f_{cm}v_{o}),&k_{o}>0\cap\alpha\in(-\frac{\pi}{2},\frac{\pi}{2})\cap f_{cm}>0,\\ k_{ro}(v_{r}+f_{cm}v_{o}),&k_{o}\leq 0\cap\beta\in(-\frac{\pi}{2},\frac{\pi}{2}) \cap f_{cm}>0,\\ 0,&\text{otherwise},\end{cases}  \tag{22}\]  
where \(k_{ro}\) is a proportionality constant, \(f_{cm}\) is the distance influence factor, \(\alpha\) is the angle between the \((v_{r}-f_{cm}v_{o})\) and the vector from the robot to the obstacle. \(\beta\) is the angle between the \((v_{r}+f_{cm}v_{o})\) and the vector from the obstacle to the robot. The novel danger index includes distance influence factor and speed influence factor. The robot can make smarter avoidance decisions according to the speed influence factor, i.e., the robot can move in front of the moving obstacle if the speed of the robot is not larger than \(\varepsilon\) times that of the dynamic obstacle, whereas the robot can move behind the moving obstacle for other situations. If the distance between the robot and the dynamic obstacle is very close, the distance influence factor will be large, such that the robot will move parallel with the dynamic obstacle, which is to ensure the safety of the path. If the distance between the robot and the dynamic obstacle is far, the distance factor will be small, such that the robot will not over-react to the obstacle motion, which can increase the efficiency of the robot motion. Furthermore, if the moving obstacle is outside of the local map, i.e., \(\rho_{cm}>\rho_{cmax}\), the danger index is 0, so the robot will not react to such a situation. The danger index combines the distance influence factor and the speed influence factor to optimize the planned path to avoid dynamic obstacles. The danger index based APF (DIAPF) function is defined as  
\[U^{\prime}(\phi)=U(\phi)+\frac{1}{2}DI^{2}, \tag{23}\]  
where \(U(\phi)\) is the value returned from the DAPF function. To summarize, the whole algorithm of our proposed DIAPF approach in the dynamical environment is shown in Figure 4.  
Figure 4: Danger Index Based Artificial Potential Field (DIAPF) based moving obstacle avoidance algorithm.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 4 Results
### Selected Set of Informative Features  
The features selected with the Boruta algorithm can provide insight into the way suicidal individuals communicate. As a result of the feature selection process, we extracted the list of features that have the most contribution to suicidal ideation detection. The feature selection was performed on each five-fold cross-validation split's training set. This step yielded five different sets of selected features. We identified the features that were selected in all splits by finding the intersection of five sets of extracted features. As a result of this step, we identified the 24 most informative TF-IDF features, as well as the 14 most informative LIWC features. We did not perform this process with NRC, since feature selection decreased the classification performance.  
From the informative TF-IDF features, we observed that suicidal posts scored higher in features pertaining to personal pronouns such as _myself_ and _my_. Suicidal texts also contain more death-related words represented by TF-IDF features _kill_, _die_, _suicide_, and _suicidal_, as well as words indicating intention and action under features _want_, _plan_, _do_, and _end_.  
A closer inspection of LIWC features and their average scores revealed that suicidal texts scored higher in the first-person singular pronouns category, which also reflects the trend discovered with TF-IDF features. Other noteworthy LIWC categories where suicidal texts scored higher include _want_, _death_, _friend_, _conflict_, _allnone_, and _authenticity_. However, they had lower scores under the _clout_ category. The interpretation of these results is discussed further in Section 5.  
Figure 5: F1 scores of models trained with different combinations of selected features: (**a**) logistic regression; (**b**) naïve Bayes; (**c**) random forest; (**d**) support vector machines.
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 5 Emission Characteristics
### Regulated Emissions
#### 5.1.2 Carbon Monoxide  
Carbon monoxide (CO) is one of the main gases emitted from vehicles. CO is formed because of incomplete oxidation of carbon in the fuel-rich zone due to an inappropriate air-fuel ratio [84]. Generally, CO emissions are affected by fuel type, combustion chamber design, air-fuel equivalent ratio, atomization parameters, injection timing start, injection pressure, engine load and speed [4]. Similar to HC, using biodiesel as alternative fuel can generally reduce CO emissions from internal combustion engines. The higher oxygen content in biodiesel may lead to lean combustion in the cylinder and effectively improve the combustion efficiency [4; 59]. Fewer carbon monoxide emissions are generated when biodiesel converts CO to CO\({}_{2}\), thus reducing the formation of CO [86; 89; 90; 94]. Besides, researchers pointed that CO emissions decrease as the proportion of biodiesel or engine load increases [56; 86; 89; 90; 95]. Research from [PERSON] et al. showed that CO emissions remained basically unchanged at low and medium loads as shown in Figure 10, but under high load conditions, the reduction in emissions after adding conola biodiesel reached about 32% [91]. [PERSON] et al. also found that the average values of CO (1.7%) and CO\({}_{2}\) (7.24%) produced by 100% chlorella protothecoides biodiesel were the lowest, being 28% and 4.2% lower than petroleum diesel [96]. This is because the oxygen concentration in the combustion chamber decreased as the load increases [56]. Although most of the studies on the emission effects of biodiesel have indicated that biodiesel can reduce CO emissions, even up to 90% in these literatures. However, [PERSON] et al. found that CO emissions increased with the increase of biodiesel blend ratio and the decrease of engine speed at lower engine loads. These experiments were performed on a four cylinder, four-stroke, common rail fuel injection diesel engine fueled with waste cooking oil biodiesel and its blends. They pointed out that the above results were mainly related to the high oxygen content and high viscosity of biodiesel. High oxygen content promoted the development of fuel-air ratio to a leaner mixture under low load, and high viscosity affected atomization, resulting in incomplete combustion and more CO emissions [97].  
Figure 9: Hydrocarbon (HC) emissions of diesel and biodiesel fuels at different speeds at full load [87].
## 3 Principle of the Damage Localisation of the Structure
### Localisation Assumptions  
The method of locating damage is based on four main assumptions.  
The first assumption is that the damage is detected \(\left(\exists\Delta f_{0j}<0\right)\). This condition is met if there is a RNFD in the system.  
A second assumption of localisation is that the RNFDs vary with the location of the damage. If the damage is in a region with a higher curvature of the related MS, the RNFD is greater than in a region of the node. The validity of this assumption is demonstrated in Figure 2, where for the same degree of damage, at positions (POS 1-4) with different degrees of curvature, there is a different degree of RNFDs according to the assumption. Moreover, tests have proved the assumption have been carried out in the past [25], therefore, this research is based on experimentally based concepts.  
The third assumption is that different RNFDs need to decrease evenly with damage. That means that the decreases should be linear or at least close to linear, so the ratio of decreases is unique for a specific location on the structure. As can be seen from Figure 2 (right), the linearity of the courses is not perfect, but for relatively small damage, this assumption is fulfilled sufficiently.  
The fourth assumption assumes the similarity of the MSs of the compared models. When comparing the reference MSs (no damage) and the examined MSs (relative damage \(\Delta d_{r}=5\%\)) by the MAC method, it was found that the MSs correlate at the level of approximately \(MAC>0.99\)[26].  
Figure 2: Depiction of the influence of damage on RNFDs regarding the position \(x\) of the damage within the MS. v\({}_{6}\) represents the 6 th MS as a function of the position on the beam with marked damage positions (**left**) and RNFDs of a 6 th natural frequency as functions of damage extent at the mentioned positions (**right**).
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 2 Thermal Analysis of Propeller Driver
### The Simulation Results and Discussion  
The overall temperature field of the driver is shown in Figure 3. It can be seen that the highest temperature in the driver system is 65.6 \({}^{\circ}\)C, and the lowest temperature is 42 \({}^{\circ}\)C. It can be seen from Figure 3 that the temperature of the driving device decreases from bottom to top. The temperature of the 4 th PCB layer is the highest, and the temperature of the capacitor is the lowest. The temperature of the 4 th PCB layer can be well transferred to the heat sink. Therefore, the temperature of the heat sink shown in the figure is almost the same as the temperature of the 4 th PCB layer.  
Figure 4 shows the surface temperature of the 1 st PCB layer. It has a maximum temperature of 59.5 \({}^{\circ}\)C and a minimum temperature of 52.8 \({}^{\circ}\)C, as shown in Figure 4. The maximum temperature appears on the power element U1, which lays on the top center of the 1 st PCB.  
Figure 5 shows the surface temperature of the 2 nd PCB layer. It shows that the temperatures there reach a maximum value of 62.4 \({}^{\circ}\)C and a minimum value of 57.7 \({}^{\circ}\)C. The power element U2 on the top of the 2 nd PCB has the largest temperature of 62.4 \({}^{\circ}\)C.  
Figure 6 shows the surface temperature of the 3 rd PCB layer. The highest temperature is 64.3 \({}^{\circ}\)C and the lowest temperature is 65.6 \({}^{\circ}\)C, as shown in Figure 6. The highest temperature appears on the power element U3, which lays on the top of the 3 rd PCB.  
Figure 7 shows the surface temperature of the 4 th PCB layer. As seen in Figure 7, the highest temperature on is 64.8 \({}^{\circ}\)C, and the lowest temperature is 64.1 \({}^{\circ}\)C.  
The temperature distributions of the heat sink and base are shown in Figure 8. The maximum temperature is 64.7 \({}^{\circ}\)C and the lowest is 62 \({}^{\circ}\)C. The temperature at the point where the base and the heat sink contact is shown in Figure 8. It can be seen that the high-temperature area is mainly located at the place where the driver and the base contact.  
Figure 3: The overall temperature distribution of the driver system.  
Figure 4: Temperature distribution of the 1 st PCB layer components.  
Figure 5: Temperature distribution of the 2 nd PCB layer components.  
Figure 6: The temperature distribution of the 3 rd PCB layer components.