esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Design for Laser Diode Scanner Headlamp with
## 2 Conceptual Design of Scanning Optics with Efficiently Distributed Optical Power  
Scanning devices, such as MEMS mirrors and galvanometers, which are frequently used in conventional scanning optics, are driven by sinusoidal functions for high-speed scanning. This reduces the surrounding scan speed compared with the center speed and results in a light distribution that does not satisfy the standard specifications of the headlamp. When scanning the center part, the speed of the mirror scanning device is maximized; therefore, the optical power is relatively low. When scanning the edge area, the speed of the mirror scanning device is minimal; as such, the optical power is relatively high. To overcome these shortcomings, a method to implement a sharp peak when scanning the edge to minimize the residence time of light in the surrounding environment has been considered; however, vibrations occurred owing to the large external force applied to  
Figure 2: Examples of scanning method. (**a**,**b**) Examples of optical path utilizing Lissajous pattern, in which both axes have a resonance mode.  
the system. Therefore, precise and complex control methods are required to apply this method [12].  
Herein, we propose an optical system that can increase the amount of light in the center through simple sinusoidal scanning without applying additional control methods by adding a prism designed to refract light in the appropriate direction, as shown in Figure 3.  
When considering the design of the optical system, first, allow the light passing through the prism to pass through the outside of the focusing lens in the low-angle region where the scanning mirror is fast, and allow light to pass through the prism when scanning the large-angle region where the scanning mirror is slow. This allows light to focus on the phosphor center through the focusing lens. In addition, because the pixelated phosphor has a partition structure [14], when light enters the phosphor, it must be allowed to enter at a minimum angle. In addition, to ease alignment, and reduce the manufacturing cost, the prism surface is designed as a flat surface, and light focused only through the focusing lens.
## 3 An Implementation of the Proposed Architecture
### Implementation of the Proposed Supporting Measures.  
To implement the concept of the virtual smart classroom, which is one of the proposed supporting measures, a model house was made. As shown in Figure 16, the model house is designed based on the real smart classroom; application development and technical support related activities can be performed with the model house while real smart classrooms are occupied frequently.  
Figure 15: A facial recognition door access control procedure for smart classroom.  
Figure 17 shows that the model house was demonstrated in a showcase of the International Conference on Smart Campus and Exhibition in November 2018 (iCSC&E 2018) [85]. The model house is playing an important part in smart classroom application development. With the model house, most smart classroom applications can be developed and tested without going to a real smart classroom. In this showcase, Raspberry Pi is used to re-implement most functions of the MCU Smart Classroom. Although smart classroom functions are complicated combinations of various kinds of device control actions. It takes less than two weeks to implement all the functions that are planned to be demonstrated in iCSC&E 2018. As a result, Raspberry Pi has been proven as a very productive tool for developing context-aware smart classroom applications, and a good model house can boost  
Figure 16: The concept of virtual smart classroom implemented by a physical model house.  
Figure 17: Smart classroom model house demonstration in International Conference on Smart Campus and Exhibition 2018. Most smart classroom functions are re-implemented with Raspberry Pi, Python, and PHP: (**a**). Lights can be controlled separately with a mobile phone and control panel. A camera toward the whiteboard is live streaming real-time classroom situation; (**b**) context-aware functions are also implemented, fans on the model house are triggered by CO\({}_{2}\) levels. The RFID reader is used to control the door lock of the model house.  
the process of developing smart classroom applications, which validates the concept of the virtual smart classroom in the supporting measures of the proposed architecture.  
A campus-wide view of the smart classrooms based on the proposed architecture is illustrated in Figure 18. As the proposed supporting measures, all smart classrooms, including the virtual smart classroom, use identical private network configurations. Devices in layer 1, which are responsible for the same functions, are having identical configurations too. Therefore, devices configured and tested in the virtual smart classroom can be replacement devices for any smart classroom in this architecture. As mentioned in Section 2.3, the only network configuration for device control network differs from classroom to classroom is the external IP address of the NAT gateway for each classroom.
## 3 Results and Discussion
### Other Compounds  
Table 8 shows some analytes of different nature, scarcely described in the literature as volatile aromas of _Citrullus Lanatus_. Among the identified molecules, there are some furans, a phenolic derivative (2,4-dimethyl phenol), and some more complex analytes with different functional groups, such as 3-hydroxy-2,2,4-trimethyl ester of 2-methyl propanoic acid.  
Some authors [1, 32] associate 2-pentylfuran with the radical peroxidation of fatty acids, a mechanism already mentioned above. Another compound, 3,4-dimethyl-2,5-furandione, has been described as a bioactive molecule naturally produced by various plants, with efficient insecticidal and biofumigant activity [52]. This analyte was identified in modest quantities in the sample aged for 30 days and decreased by approximately 50% after 24 months of storage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **WDF\_t0** & **WDF\_t1** & **WDF\_t2** & \\ \hline
**Analyte** & **Aroma** & **LRI** & **Area (\(\times\)10\({}^{\text{g}}\))** & **Area (\(\times\)10\({}^{\text{g}}\))** & **Area (\(\times\)10\({}^{\text{g}}\))** & **Ref** \\ \hline
2-Methylfuran & Ethereal, vinegar, chocolate & 604 & 1.83  & 2.26  & - & - \\ \hline
3-Methylfuran & - & 615 & 2.05  & 1.44  & - & - \\ \hline
2-Ethylfuran & Sweet, malty, earthy & 706 & 3.49  & 4.18  & - & [23] \\ \hline Dimethyl disulfide & Cabbage, vegetable, onion & 757 & - & - & 12.8 \(\pm\) 0.2 & - \\ \hline Furfural & Sweet, brown, woody, bread & 829 & - & - & 8.98 \(\pm\) 0.08 & [29] \\ \hline Methional & Tomato, potatoes, yeast & 887 & 7.81 \(\pm\) 0.06 & - & - & - \\ \hline
2,4-Dimethylphenol & Dark, roast, smoked & 905 & 4.89  & 6.94 \(\pm\) 0.07 & - & - \\ \hline
2-Pentyfuran & Green, waxy, caramel & 947 & 76.1 \(\pm\) 0.3 & 29.5 \(\pm\) 0.2 & 4.96  & [6,10,32] \\ \hline
2-(2-Pentenyl)furan & - & 953 & 23.3 \(\pm\) 0.2 & - & - & [29] \\ \hline
3,4-Dimethyl-2,5-furandione & - & 978 & - & 29.7 \(\pm\) 0.1 & 16.9 \(\pm\) 0.1 & - \\ \hline
5-Ethyldihydro-2(3H)-furanone &
\begin{tabular}{c} Creamy, fatty, fruity, coconut \\ \end{tabular} & 996 & - & - & 15.9 \(\pm\) 0.2 & - \\ \hline
5-Hydroxy-2-Methyl-3-esenoic acid & - & 1193 & 4.38  & 17.7 \(\pm\) 0.1 & - & - \\ \hline
5-Heptyldihydro-2(3H)-furanone & - & 1196 & 2.16  & 3.43  & - & - \\ \hline
2-Methyl propanoic acid, & & & & & \\
3-Hydroxy-2,2,4-trimethylpentyl & - & 1202 & 5.87 \(\pm\) 0.06 & 25.4 \(\pm\) 0.3 & 2.14  & - \\ \hline \hline \end{tabular}
\end{table}
Table 8: Composition of the VOC fraction of Gavina® watermelon pomace WDF identified using HS-SPME-GC-MS analysis: other compounds.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Physical Processes
#### 4.1.1 Physical Separation  
Physical separation permits the removal of heavy metal/metalloid particles from contaminated soils at a large scale [64]. This technique exploits the differences in certain physical characteristics between metal particles and soil, such as size, density, magnetism, and hydrophobic surface properties [118]. Physical separation does not alter the chemical properties of soil, such as pH, elemental composition, and OMC [63].  
Depending on the particle properties, physical separation can be done by [118]:  
* Screening: the particle separation occurs on the basis of particle size, by passing (or not) through screen holes.
* Hydrodynamic separation: this operation exploits the different settling velocities of particles in a water flow; as an alternative, the different effects of centrifugal force can be applied.
* Froth flotation: the method exploits the differences in hydrophobic properties to separate metal-bearing particles from the soil matrix through air bubbles injected in a soil slurry.
* Magnetic separation: separates particles based on their different magnetic properties.
* Soil washing: the contaminates are desorbed and extracted from soil with an extractant solution.  
[PERSON] et al. [63] applied physical techniques to separate arsenic, copper, mercury, lead, and antimony from a brownfield affected by pyrite ash disposal. The used methods were high-intensity magnetic separation and hydrodynamic centrifugal separation. Magnetic separation was tested both under wet conditions, this is to say that the soil was mixed with water to form a slurry, and under dry conditions, with dried soil. The tests were carried out by changing the soil granulometry. They found that dry magnetic separation was more appropriate for large particle sizes (>250 \(\upmu\)m), while the wet process was suitable for all particle sizes, except for clay soils (particle size lower than 63 \(\upmu\)m). Hydrodynamic centrifugal separation was efficient for a wide fraction of sand (63-500 \(\upmu\)m).  
[PERSON] et al. [64] found that the concentrations of heavy metals such as Pb, Cd, and Zn in soil increase with decreasing particle size and when soil has a high content of organic matter or clays. The use of mechanical mixing in soil washing promotes physical contact between contaminated soil particles and the washing liquid. To increase the efficiency of heavy metals removal, ultrasonic mechanical soil washing was also studied [65].  
The advantages of physical methods are their high efficiency, simplicity, and rapidity of application, but they have a high cost, and they can change some soil properties; for example, soil texture or particle size, causing a deterioration in soil fertility [66].
## 3 Test Results and Discussion
### Results of the Single Impact Test
#### 3.1.4 Dynamic Failure Pattern of Specimens under a Single Impact  
The strain rate and dynamic strength of the specimen are the main factors affecting the dynamic failure pattern after loading. Figure 7 shows that sandstone specimens present different failure patterns at different impact velocities. The law of the failure pattern is as follows. Internal damage occurs under the action of a small incident amplitude, but it does not cause macroracks, and only the decrease in longitudinal wave velocity can be obtained from the wave velocity test. With the gradual increase in the incident wave amplitude, cracks develop from the inside to the outside of the specimen, as shown in Figure 7b. At this time, the cracks appear sporadically on the end or side, which are not interconnected. When the incident wave amplitude is further increased, these sporadic cracks will continue to develop and form visible penetrating cracks, as shown in Figure 7c. When the incident wave amplitude is large enough, these penetrating cracks continue to expand, and finally the specimen fractures. The specimen has multiple fracture surfaces, and with the increase in incident wave amplitude, the fracture surface gradually increases, then finally crushing failure occurs. From the perspective of the fracture surface, the failure pattern is mostly splitting failure, namely the tensile stress failure caused by the Poisson effect.  
Figure 7: The failure pattern of sandstone under a single impact.
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 5 Results
### Stem Diameter and Tree Height  
The DBHs determined with the method of the least squares cylinder fitting are derived from the QSM generation (Section 5.4). The comparison of manually measured DBHs with automatically determined DBHs of the TLS data of plot 1 shows a good agreement with a RMSE of 1.1 cm for the cylinder fitting. The DBHs of the MLS data exhibit an underdetermination (cylinder fit RMSE of 3.7 cm) (Figure 8a). A comparison of the DBHs determined with circle fitting and cylinder fitting is presented in Appendix B. Table 3 shows the proportion (\(DBH_{MLS}\)/\(DBH_{TLS}\)) of the DBHs derived from the QSM with cylinder fitting.  
Figure 8: (a) DBH determination using the MLS and TLS data in comparison with the manually measured diameters; (b) Tree height estimation of the MLS and TLS data.  
Figure 7: Three horizontal point cloud sections of the tree detection algorithm overlaid with the stem profiles of plot 1 (height interval 15 cm). Point clusters (blue points) detected as ‘tree’ are shown with a green bounding box and rejected point clusters with a red bounding box. (a) leaf-off TLS; (b) leaf-off MLS.  
The results and the RMSE indicate that the determined tree height based on the MLS data is similar to those from the TLS data and slightly underneath the TLS height estimates (Figure 8b).
# Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems
## 5 Experiments
### Experiments on the NSL-KDD Dataset
#### 5.1.2 NSL-KDD Binary--Modified Distribution  
The second binary classification experiment considered using the same 123 features of the data preparation stage but slightly changing the training and testing subsets distribution. The idea behind this was that, maybe, the neural network could not learn from the training subset prepared by the authors. This experiment aims to see if a different distribution of records in the training and the testing subsets will give better results.  
To obtain the new subsets, we combined the two subsets together into one dataset, shuffling the data and then splitting them again so that 80% of the records are used for the training of the network, and 20% of the records are used for testing. The training subset contained 118,813 records and the testing subset 29,704 records. The architecture of the neural network was the same as for the first experiment, and again, Early Stopping was used. The confusion matrix for this experiment can be seen in Table 7. It can be seen that the number of false negatives in this experiment is equal to 32, which is significantly lower than in the \"NSL-KDD binary--full features\" experiment. The number of false positives is 52, which is also lower when compared to the previous experiment.
# Networked Roadside Perception Units for Autonomous Driving
## 1 Introduction
However, we focus on RSU-based cooperative perception in this paper because of the following reasons. Considering the above scenario wherein vehicles pass through the intersection at maximum speed, object detection coverage at the intersection must be near 100%. Thus, RSU-based cooperative perception is suitable for such a task due to the better visibility from the sensors mounted in the infrastructure environment. Moreover, the availability of the infrastructure sensors is higher than relying on the sensors mounted on vehicles, especially in the deployment phase's low penetration ratio. Considering this background, we explore the two concepts introduced in Figure 1: (1) Roadside perception unit (RSPU) system and (2) networked RSPUs. On the left side of the figure, we consider a fully autonomous vehicle with cooperative ITS functions approaching an intersection. The intersection has mixed traffic with legacy vehicles and VRUs, where some of them are invisible from vehicle sensors. The RSPU system consists of sensors that detect all the objects at the intersection and RSUs that share object information via V2X communication. The right side of the figure shows networked RSPUs, which is the inter-connection of RSPUs broadly deployed at intersections. Our study has two contributions. First, we designed and implemented an RSPU system by integrating two open-source autonomous driving and cooperative ITS software. Our software implementation is also an open-source release so as to accelerate the development of RSPUs. To our knowledge, this is the first implementation of cooperative perception that combines cooperative ITS and autonomous driving software. We also evaluated the implementation in a field test. Secondly, we developed a scheme to disseminate cooperative perception messages over broader areas by interconnecting the RSPUs through high-speed roadside networks. We also developed a priority algorithm to deliver the messages with a high packet delivery ratio (PDR) and a low delay by their respective safety contributions. The proposed scheme efficiently transfers messages in large road traffic conditions created in Tokyo and Paris's real maps in the simulation. The rest of the paper is organized as follows. Section 2 overviews the state of the art on cooperative perception systems for autonomous vehicles. Section 3 explores the system design and implementation of our roadside perception unit (RSPU) system for autonomous driving, called AutoC2X. In Section 4, we propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network. Section 5 describes the evaluation of the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation. Finally, the paper is concluded in Section 6, which summarizes the study and discusses future scope of work.
## 3 Evaluation Results and Discussion  
### Data Retrieved from Central Database of the Smart System  
In the spreadsheet-based central database, the data are stored in 41 columns (see Table 4 and Appendix A). Data entered by actors directly on the platform and data transferred from the smart box are stored mainly in the 41 columns, while some additional information can be displayed and visualized on the platform. It is important to test the performance of the smart system by considering how correctly the recorded values and information are displayed in each of the 41 columns. This enables us to understand the performance of the smart system and identify the columns where incorrect information might be displayed. In general, during this test, about 104 records of product lines (with specific lot numbers) were retrieved from the SLS database. This in turn enabled us to identify functional features of the SLS linked to errors in displayed values and to figure out what further improvement would be needed to upgrade the system.  
Figure 5: (**a**) Smart Box assembly: (1) smart box protected with metal case; (2) GPRS/GSM antenna; (3) Global Positioning System (GPS) antenna; (4) temperature and humidity measuring sensor probe; (5) power cable. (**b**) PowerTM 9500 scanner with its components.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Column No.**} & **Column Header as** & **Description of Parameters under Each Column Header** \\  & **Implemented in Database** & **Description of Parameters under Each Column Header** \\ \hline
1 & title & Name given to the biomass registered with a unique lot number \\ \hline
2 & status & Status of the registered biomass along logistics chain (e.g., delivered or not) \\ \hline
3 & providerID & Label code assigned to the provider (i.e., farmer or trader) of the biomass \\ \hline
4 & transporterID & Label code assigned to the transport company delivering the biomass \\ \hline
5 & consumerID & Label code assigned to the end user that ordered (purchased) the biomass \\ \hline
6 & shipmentID & Number to identify the delivery route of the biomass with the indicated \\  & & lot number \\ \hline
# Conditional Dependencies and Position Fixing
## 1 Introduction  
Dealing with imprecise data requires models that include knowledge in the framework. To meet this requirement, one should engage fuzzy systems and the mathematical theory of evidence (MTE), which deliver platforms for processing uncertainty [1; 2; 3]. The models require methods for obtaining formal evaluations of the uncertainty. In nautical science, the knowledge refers to the distribution of the random variable instances, which are usually perceived of as governed by Gaussian dispersion patterns. Knowing the magnitude of the standard deviations enables the introduction of a rough assessment observation in terms of good, bad, or sufficient, which is an approach that is popular among specialists. For those who are involved in implementing computer procedures, a variety of measurement qualities are perceived of as uncertainty and should be included in the processing scheme. The author of [4] discuss this concept, proposing a model of accepted nautical knowledge thanks to evidence proximity exploration and the engagement of the principles of fuzzy systems. Based on the available navigation systems, the author defined the standard deviation adequate membership functions and input them into the processing. In the proposed approach, analyses of the available raw recorded instances are performed to extract an even wider range of useful parameters. The author proposes the scope and methods of their evaluation. Thanks to this model, the prior estimations are of secondary meaning and do not directly affect the final solutions. However, the former evaluations might be unavailable; thus, the suggested approach appears as a universal one whenever random data are involved. Moreover, the evidence engaged in the problem should be normalized, and the decisiveness in terms of the effect of the solution should be ranked. Items with lower uncertainty have the most substantial influence in this respect. This paper contains a section that is devoted to defining the hierarchy among the evidence within a pool of data.  
The proposed approach engages fuzzy sets and the combination mechanism available in the MTE [4]. I suggest and describe the necessary conversions and transformations of the traditional histograms. In this respect, the paper is an extension of the previous one delivered by the author of [5]. I describe uncertain data handling, histogram transformation, and their applications for solving the object identification problem. The expansion aims at the conditional dependency excavation from the recorded instances of distorted variables. I use the position fixing problem as an example to implement the presented idea.