esa-sceva/satcom-chunk-collection huggingface dataset:
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 5 Conclusions  
This paper proposes an efficient software architecture for a fusion system that processes data from multiple LiDAR sensors. Through an experiment it was confirmed that when the LiDAR sensor was processed through a client, the response time increased linearly, and therefore it is generally difficult to process multiple LiDAR sensors. Multiple clients were implemented to process data from multiple LiDAR sensors, and a round-robin scheduler was implemented to run multiprocessing. In the experiment, the response time decreased as the number of LiDAR sensors increased. It was confirmed that the response time could be shortened by preferentially processing the client whose receive buffer is full while other clients are receiving data. The server process was implemented as a priority-based multithread to improve efficiency. When the receive buffer is full, the collection thread is prioritized through an interrupt. The server process confirmed that the response time increased in proportion to the number of LiDAR sensors. In terms of the overall system, it was confirmed that the response time converges at a specific time as the number of LiDAR sensors increased. We can predict that if the point cloud increases, response time also increases because of the increasing of processing time.  
The results of this study can be utilized in autonomous driving systems where the number of installed sensors is high. In the future, system optimization can be achieved through analyses of various scheduling techniques. We will also design a software architecture that applies an object recognition algorithm for use in the field of autonomous driving.  
Conceptualization, M.J.; methodology, M.J. and D.-Y.K.; software, M.J.; validation, M.J., D.-Y.K. and S.K.; formal analysis, M.J., D.-Y.K. and S.K.; investigation, M.J.; resources, M.J.; data curation, M.J. and D.-Y.K.; writing--original draft preparation, M.J.; writing--review and editing, D.-Y.K. and S.K.; visualization, M.J.; supervision, D.-Y.K. and S.K.; project administration, M.J., D.-Y.K. and S.K.; funding acquisition, M.J. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant No. 2018R1D1A1B07041296); this work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (RS-2022-00167197, Development of Intelligent 5G/6G Infrastructure Technology for The Smart City); this work was funded by BK21 FOUR (Fostering Outstanding Universities for Research) (no. 5199990914048); and this work was supported by the Soonchunhyang University Research Fund.  
Not applicable.  
The authors declare no conflict of interest.
# Performance Analysis of Direct Torque Controllers in Five-Phase Electrical Drives
## 4 Results and Discussion
### Steady-State Operation  
First, the performance of the system in steady-state operation at 500 rpm is analyzed in Figures 9 and 10, where different load torques are applied (1 N\(\cdot\)m in Figure 9 and 2.75 N\(\cdot\)m in Figure 10). The reference and measured values are colored red and blue, respectively. The speed and electrical torque responses are shown in the upper rows, where it is appreciated that the controller works well and the mechanical speed is successfully maintained in the reference value. Note that the electrical torque is mathematically estimated using the machine model, which produces some estimation errors. The reference and estimated stator flux in the regulated \(\alpha\)-\(\beta\) plane are then shown in the second row, where it can be observed that the estimated stator flux values coincide with their references. Lastly, the measured stator currents are depicted in the last two rows, where it is appreciated that the \(\alpha\)-\(\beta\) stator current vector describes a circular trajectory, with nearly null \(x\)-\(y\) stator current components. Therefore, the control goals are met using the DTC controller in steady-state operation because the results obtained can be extended to different reference speed and load torques.  
Figure 9: Experimental steady-state operation test where the reference speed is settled at 500 rpm and a load torque of 1 N\(\cdot\)m is applied. Upper row: speed and torque responses. Second row: stator flux waveforms. Third row: current trajectories of the stator in the \(\alpha\)–\(\beta\) and \(x\)–\(y\) planes. Last row: stator phase currents.
## 5 Conclusions  
The frame crossbar design of the alternative SkidCar system was evaluated and optimized, in the present paper. The load on the frame wheels detected by MSC Adams will cause strain on the frame arms, which is evaluated by the mechanical stress and limit states. The loading of the auxiliary wheels of the alternative SkidCar frame was determined by experimental measurement and used for the validation of the model in MSC Adams. The strength analysis was evaluated from the perspective of mechanical stress and limit states and is in satisfactory condition.  
Subsequently, the design modifications were investigated and led to the following core results:  
* Weight reduction of the frame;
* A more uniform distribution of stress in the area of concentration;
* Increase the stiffness of the entire frame;
* Improve the stability of the frame. Based on the results, it can be concluded that:
* According to the [35] standard applied, all proposed optimization solutions are compliant in terms of strength--the design-modified frame using the original material, the steel of higher strength and the aluminium alloy;
* Significant weight savings have been recorded;
* The functionality and compatibility of the frame with the experimental car have not been limited or affected. Recommendations for further research:
* The subject of further examination can be the evaluation of the fatigue and durability of the frame, detailed modeling and evaluation of the notches and welds of the structure;
* It would also be advisable to design a frame for composite material;
* The created model can be further developed, for example, by using a different type of SC wheel, considering the flexible behavior of the material or changing parameters for the use of the alternative SkidCar system on different cars;
* It would also be possible to build up a model of a car with an alternative SkidCar system that overcomes road irregularities, and feed this force impulse into the FEM analyses. Furthermore, it would be appropriate to deal with the dynamic analyses of the system.  
The optimized frame will help improve the accuracy of the vehicle stability measurements during driving tests, thereby increasing the safety of road vehicles in a facilitated manner and enabling improvements to car autonomous driving systems. The use of an optimized alternative SkidCar frame is a means of experimental verification of the research findings in the field of vehicle driving stability.  
Conceptualization, P.J. and J.B.; methodology, J.B.; software, P.J.; validation, P.J. and J.B.; formal analysis, P.J. and J.B.; investigation, P.J.; resources, P.J.; data curation, J.B.; writing--original draft preparation, P.J.; writing--review and editing, P.J. and B.S.S.T.; visualization, P.J.; supervision, [PERSON]; project administration, P.J.; funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### Key Update  
The key update procedure will be invoked by \(OBU_{i}\) when the key lifetime of the TV is approaching expiration. The steps of this procedure are described as follows.  
1. \(OBU_{i}\) randomly chooses \(r_{i}\) to compute the messages \(M_{1}=PSK_{old}\oplus r_{i}\), \(M_{2}=PSK_{old}\)\(MSG_{KU}\), and \(M_{3}=h(r_{i}||MSU_{KU})\).
2. \(OBU_{i}\to LE_{j}\): A key update request (i.e., \(M_{1}\),\(M_{2}\),\(M_{3}\)) is sent to \(LE_{j}\) by \(OBU_{i}\).
3. \(LE_{j}\) retrieves \(r_{i}\) and \(MSG_{KU}\) using the current \(PSK\) (i.e., \(PSK_{old}\)). The key update request will be rejected if \(h(r_{i}||MSG_{KU})\) does not match \(M_{3}\). Otherwise, \(LE_{j}\) chooses a random number \(r_{j}\) and computes \(M_{4}=r_{j}\oplus h(r_{i})\), \(M_{5}=PSK_{new}\oplus r_{j}\), and \(M_{6}=h(r_{j}||PSK_{new})\), where \(PSK\) is produced via the hash-chain method. Therefore, the new \(PSK\) cannot be inferred by other OBUs using the current \(PSK\). Finally, \(LE_{j}\) computes \(SK_{ij}=h(r_{i}||r_{j}||PSK_{new})\).
4. \(LE_{j}\to OBU_{i}\): \(LE_{j}\) returns the reply messages (i.e., \(M_{4}\),\(M_{5}\), and \(M_{6}\)) to \(OBU_{i}\).
5. Upon receiving the reply messages, \(OBU_{i}\) computes \(h(r_{i})\) to retrieve \(r_{j}=M_{4}\oplus h(r_{i})\), and obtains \(PSK_{new}=M_{5}\oplus r_{j}\). Next, \(OBU_{i}\) checks whether \(M_{6}=h(r_{j}||PSK_{new})\) and \(PSK_{old}=h(PSK_{new})\). If this condition holds, \(OBU_{i}\) renews the \(PSK\) and computes \(SK_{ij}=h(r_{i}||r_{j}||PSK_{new})\). Otherwise, \(OBU_{i}\) terminates the process.
6. \(OBU_{i}\to LE_{j}\): \(OBU_{i}\) sends the message \(SK_{ij}\oplus h(r_{j})\) to \(LE_{j}\).  
* \(LE_{j}\) retrieves \(h(r_{j})\) using \(SK_{ij}\). Then, it checks whether the retrieved hash value is equal to the pre-computed hash value using the chosen \(r_{j}\). In this way, a replay attack from an illegal OBU is avoided. Now, this session key can be used to communicate securely between two trusted vehicles.
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 2 Materials and Methods  
### Study of Spectral Characteristics of Feed Mix Components  
The physical phenomenon serving as a basis for the offered method consists in measuring the image reflection ability of a forage mix surface layer.  
At the preliminary laboratory study stage, it was necessary to determine spectral characteristics of components of feeding mixes, as exemplified by a cattle feed ration. This type of ration can contain natural-origin forages (grass green mass, corn silage, alfalfa haylage) and concentrated mixed fodder (consisting of grain mash, corn, and barley as rape meal and sunflower meal).  
A Specim IQ (Specim, Oulu, Finland) hyperspectral camera was used in the laboratory study. Here are the camera specifications: \"VNIR 400-1000 nm (CMOS)\" matrix, Wavelength band 400-1000 nm, Spectral resolution FWHM 7 nm, Spectral bands 204, Spatial Sampling 512 pix.  
According to the camera manufacturer's recommendations, during the image acquisition process, a calibration plate was placed in the capture area of the lens to ensure high-quality spectral image data and maximum performance.  
There are three ways of using the calibration plate:  
* The WR panel is included and defined from the recorded data;
* WR panel is recorded before the actual data recording;
* Preset WR settings to match the most common recording environment's illuminations.  
For good statistics, it was necessary to take the spectral characteristics of a single image and collect reliable data, using a setup with a hyperspectral camera (Figure 1).  
The images were recorded in a laboratory with high natural light intensity during the lunchtime hours on a clear day.  
Corn silage with 70% moisture content and alfalfa haylarge with 50% moisture content were taken as green fodder samples. Pellets intended for high-yielding dairy animals were used as compound feed.
# Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process
###### Abstract  
Urbanization is a challenge faced by most countries worldwide and leads to several problems. Due to rapid communication capabilities, conforming the megacities into Ubiquitous cities (U-cities) seems to be a potential solution to mitigate the problems caused by urbanization. Extensive reliance and dependencies of U-cities on information and communication technologies (ICTs) bring forth a new set of risks and vulnerabilities to these megacities. This research investigates the vulnerabilities of ICTs against man-made and natural hazards in a systematic way using the Analytic Hierarchy Process. The study identifies the vulnerabilities of different ICTs in U-cities and helps in improving the system's resistivity against various hazards. The task is performed by evaluating the level of disruption on the different technologies and areas under the identified man-made and natural hazards. The research provides an insight into the working mechanisms of involved ICTs. It also helps to manage U-cities with more secure and sustainable services. The research identified that the new ICTs-based hazards have emerged and have become among the most influential hazards. The research has concluded that the vulnerabilities of U-cities are significantly different from that of conventional cities and need further studies to develop further understandings. The research recommends similar vulnerability studies for regional areas as well.  
urbanization; ubiquitous cities; risk assessment; vulnerabilities; information and communication technologies (ICTs); urban management; urban hazard management; smart cities +
Footnote †: journal: Future Internet
# FLINT: Flows for the Internet of Things
## 5 Evaluation
### Levels of Interoperability
#### 5.1.1 Syntactic Interoperability  
Processing tasks that involve local information do not require any adaptation to the network. The SCHC router, for example, must store information about the fragmentation state of a device. If the device does not acknowledge a fragment in time, the SCHC router will re-transmit the fragment or discard the packet. These actions depend on the packet's content and do not involve any other adapters. Other processing tasks, however, do require information from another adapter in the chain.  
Passing information between adapters requires a common messaging pattern, often referred to as syntactic interoperability. FLINT uses a message scheme that adapters must adhere to. The message scheme contains fields to carry information along and may include:  
* _Device information._ Information such as the device MAC address and the original data packet are stored in this field. Any additional information can be added to the device-custom-ctrl field.
* _Adapter information._ The adapter-ctrl field contains information about the previous adapter in the chain. Information about the chain is also stored in this field. Every I/O adapter first consults the _Mapper_ adapter to generate the routing scheme that is added to this field.
* _Input information._ The input-ctrl field contains information about the adapter that first received the packet. Custom information about the network the adapter is connected to, can be added to the input-custom-ctrl field.
* _Output information._ Additional information about the device is stored in the output-ctrl field. The TD of every device contains the uuid of each device and is added to the message by the _Mapper_ adapter. Other adapters in the chain must be able to uniquely identify devices, regardless of their input adapter. The uuid provides an abstraction for devices that have multiple network interfaces.  
Every _sink_ serializes these messages using the above grammar in order to provide _syntactical interoperability_.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Notation and Variable  
To formulate the service provisioning problem in MEC, we first introduce some notations. Let \(\mathcal{N}=\{1,2,3,...,N\}\) denote the set of MEC nodes, \(l\) denote the core cloud, \(\mathcal{S}=\{1,2,3,...,S\}\) denote the set of services, and \(\mathcal{U}=\{1,2,3,...,U\}\)denote of the set of users. Each MEC node \(n\in\mathcal{N}\) has a storage capacity \(R_{n}\), and a CPU of computation capacity \(F_{n}\). Each service \(s\in\mathcal{S}\) is represented by a tuple of five parameters as \(<\)_img\({}_{s}\)_, \(d_{s}\)_, \(w_{s}\)_, \(T_{s}^{min}\), \(T_{s}^{max}\)\(>\), where \(img_{s}\) is the size of service image containing libraries and databases associated with service \(s\), \(d_{s}\) is the input data size for service \(s\) per request, \(w_{s}\) is the required amount of processing for service \(s\) per request, \(T_{s}^{min}\) and \(T_{s}^{max}\) are two latency thresholds associated with service \(s\) to evaluate the utility function. For the sake of simplicity, we assume that at a time slot, each user \(u\in\mathcal{U}\) performs only one request for a service. We will refer to a user request as a user. Let \(n_{u}\in\mathcal{N}\) denote the connected MEC node (i.e., directly covering) of user \(u\), and \(s_{u}\in\mathcal{S}\) denote the service requested by user \(u\).  
The main decision variables of the service provisioning problem are the following two sets of optimization variables. We define the service placement profile as \(\mathbf{x}=\{x_{sn}|s\in\mathcal{S},n\in\mathcal{N}\cup\{l\}\}\), in which \(x_{sn}=1\) indicates that service \(s\) is placed at node \(n\), and \(x_{sn}=0\), otherwise. Similarly, the request scheduling profile is defined as \(\mathbf{y}=\{y_{un}|u\in\mathcal{U},n\in\mathcal{N}\cup\{l\}\}\), in which \(y_{un}=1\) indicates that user \(u\) is scheduled to node \(n\), and \(y_{un}=0\), otherwise. Here, \(x_{sl}=1,\forall s\in\mathcal{S}\) since we assume that the core cloud hosts all services.
# A Unified Denoising Framework for Restoring the LiDAR Point Cloud Geometry of Reflective Targets
###### Abstract  
LiDAR point clouds of reflective targets often contain significant noise, which severely impacts the feature extraction accuracy and performance of object detection algorithms. These challenges present substantial obstacles to point cloud processing and its applications. In this paper, we propose a Unified Denoising Framework (UDF) aimed at removing noise and restoring the geometry of reflective targets. The proposed method consists of three steps: veiling effect denoising using an improved pass-through filter, range anomalies correction through M-estimator Sample Consensus (MSAC) plane fitting and ray projection, and blooming effect denoising based on an adaptive error ellipse. The parameters of the error ellipse are automatically determined using the divergence angle of the laser beam, blooming factors, and the normal vector along the boundary of the point cloud. The proposed method was validated on a self-constructed traffic sign point cloud dataset. The experimental results showed that the method achieved a mean square error (MSE) of 0.15 cm\({}^{2}\), a mean city-block distance (MCD) of 0.05 cm, and relative height and width errors of 1.92% and 1.91%, respectively. Compared to five representative algorithms, the proposed method demonstrated superior performance in both denoising accuracy and the restoration of target geometric features.  
LiDAR point cloud; reflective targets; veiling effect; blooming effect; alpha shape; adaptive error ellipse +
Footnote †: journal: Applied sciences  
1  
Academic Editor: [PERSON]
# An Improved Image Filtering Algorithm for Mixed Noise
## 3 Experiment
### Parameter Setting  
In our proposed algorithm, both the improved wavelet threshold algorithm and the improved NLM algorithm involve the setting of parameters. After adding different noise levels to different test images and comparing the experimental results repeatedly, the following parameter setting can, at present, ensure the best denoising effect.  
For the improved wavelet threshold algorithm, after trying and verifying different wavelet bases and different scale values, it was finally found that when the wavelet base is _sym15_ and scale value \(n=4\), the image denoising can achieve the best effect. For the high-frequency components obtained by the wavelet decomposition of each layer, the proposed threshold function and threshold are used for denoising, with the following parameter settings: \(\rho=0.3\), \(\alpha=21\), \(n=1.9\), and \(m=3.09\). When the reconstructed image1 is decomposed by \(1\)-scale wavelet, the wavelet base is still _sym15_, and then the low-frequency components obtained by decomposition are processed by the improved NLM algorithm. In order to achieve the best filtering effect and balance the relationship with running time, the radius of the search block is set to \(7\) and the radius of the search window is set to \(9\). When calculating the Euclidean distance function, the times of accumulating the Gaussian kernels with different radius is set to \(7\), i.e., the parameter \(r=7\), the variance of the Gaussian kernel function \(\sigma=300\), and when calculating the distance weight function, the smoothing coefficient \(h\) is set to \(0.12\) and the power value \(n\) is set to \(2\). Meanwhile, the high-frequency components continue to be processed through the new wavelet threshold function; \(\rho=0.3\), \(\alpha=21\), \(n=1.9\), and \(m=1.13\) is the best setting.
## 5 Results and Analysis Using Learning Analytics Techniques
### Principal Component Analysis (PCA) and Correlation's Circle  
Considering all the student profiles obtained in our sample, biplot diagrams of principal components for MI and SRLAS were built. In Figure 3a,b, the principal planes containing the largest amount of possible information with only two axes are presented for the MI and SRLAS dimensions, respectively. These axes are named PC1 and PC2 for convenience. In these plots, each dot represents the profile of a given student. Each dimension is represented with an arrow, and the angle between any two arrows corresponds to the degree of correlation between these two dimensions. Therefore, a small angle corresponds to a strong correlation between these two dimensions, a 90 deg angle implies zero correlation, and a 180 deg angle indicates total anti-correlation. Moreover, points close to a specific arrow represent students with preponderance in the corresponding dimension. Large arrows or far points from the center indicate dimensions or students best represented in this plot, respectively.  
In Figure 3a, it is observed that _LogMath, Lin, BodKin_, and _Intra_ are quite correlated, while the remaining dimensions are less correlated with them, with _Mus_ being almost independent of _Spa_ and _Inter_. Overall, the PCA biplots show relative independence among the MI dimensions, which was one of [PERSON]'s hypotheses when he proposed his multiple intelligences' scheme [37].  
Regarding the SRLAS biplot (Figure 3b), they form two distinct groups. On one hand, _Anx_ and _ExtMot_ are highly correlated, while the remaining dimensions are correlated among them, but not with _Anx_ and _ExtMot_. The numerical values of the correlations indicate that _IntMot_ and _SelfReg_ present the highest correlation within the SRLAS dimensions.