esa-sceva/satcom-chunk-collection huggingface dataset:
# In the phase of testing
## 4 Implementation and Results
### Data Preparation and Implementation
The confusion matrix helps to calculate four important metrics which are:  
* Accuracy: is a popular machine learning model validation method used in evaluating classification problems and it is the rate of correct predictions over all test cases. For binary classification, as in our study, accuracy can also be calculated through pros and cons. However, when the class distribution is imbalanced, overall accuracy in machine learning classification models can be misleading, and correctly predicting the minority class is critical. In this case, the majority class may be correctly predicted, resulting in a high accuracy score, while the minority class is misclassified. This creates the false impression that the model is performing well when in fact it is not. * Precision: It is called the Precision of Positive Observations because it represents the ratio of expected correct positive observations to expected total positive observations. It aids in measuring the model's ability to classify positive samples. In terms of precision when calculating the precision of a model, we should take into account both positive and negative samples that are classified. A machine learning model's precision is affected by both negative and positive samples. * Recall (sensitivity): It is the ratio of correctly expected positive feedback to all actual observations (not just the positive). It allows us to determine how many positive samples the ML model correctly classified. When calculating a model's recall, we only need all positive samples and ignore all negative samples. The recall is concerned with correctly categorizing all positive samples. * F1-Score: It is the weighted average of precision and recall. Therefore, this result considers bogus pros and false cons and is better than the accuracy criterion when the distribution is unequal for the categories. It is also useful when false positives and false negatives have the same cost to the system. Otherwise, each criterion (precision and recall) is best viewed separately. The area under the ROC (AUC) measures the area underneath the entire ROC curve based on the true positive rate (TPR) and false positive rate (FPR), where TPR is equal to the value of sensitivity or recall, and FPR is equal to 1 minus the specificity value.
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 2 Background
### Design Goals  
The proposed work should satisfy security and privacy preservation through the following goals:  
* Authentication: There are two forms of authentication: mutual authentication and message authentication. Mutual authentication demands the ability of two entities to identify each other at a specified session. Message authentication confirms the integrity of the messages and proves that they are generated from authorised vehicles and have not been unmodified through the transmission.
* Nonrepudiation: This property applies to a case in which the recipient is willing to show to a third party that the sender cannot dispute responsibility for the messages' generation. It prevents the attacker from forging messages with other identities. In a particular scenario of VANET low density, we aim to achieve essential properties.  
Figure 1: Our C-ITS PKI High-Level Architecture: (1) RCA is a government entity, and it is responsible for managing all subordinate CAs. (2) \(LTCA\) is responsible for entity registration and issuing certificates. (3) RA is responsible for retrieving misbehaving entities’ credentials. (4) \(PCA\) is responsible for pseudonym issuance. Moreover, \(RSU\)s are responsible for vehicles entering/leaving the mix-zones. There are two types of communication: (1) through a shared key that is distributed by \(RSU\)s inside the mix-zone [8]. (2) Anonymous communication, which is performed outside the mix-zone.  
* Unlinkability: The certificate and information in the messages have to be unlinkable for privacy protection, even if identical vehicles change certificates or the exact vehicle sends new messages with new data, so that an adversary can never discover shared properties in several messages and then link them to a specific vehicle and trace its location. The change time to time in each communication and in the mix-zone on pseudo-identity rather than real identity. In addition, certificates in the communication have a relation with the pseudo-identity.
## 3 Simulation-Based System Architecture
### Requests Queue Scheduler
#### 3.4.3. Implications of Delayed Last Legs of RP on Agents' Behaviour Models  
While the vehicle agent behaviour model serves each RP leg as an independent request, the customer agent behaviour model has the possibility of dealing with several legs of RP and PT, consistently with the batch dispatching approach and with the processing of intermodal trips with delayed last legs of RP (PT + RP or RP + PT + RP). The states corresponding to the customer agent behaviour model appear in the previous Figure 3 as well as the related functionalities. The batch dispatching approach requires the requests to be queued as they arrive and leaves them unanswered for some time, entering them in a \"PENDING\" state, until a batch dispatch serves them. The possibility of delayed last legs forces the customer agent to be able to take into account the eventualities of the transfers between both modes of transport (although possible, the system's parameters make them very unusual). Thus, it may happen that after ending its PT leg, a client has to wait to be picked up by an RP vehicle and then the customer agent enters into a \"WAITING AT STOP FOR LAST LEG\" state. The client may stay in this state for an undetermined number of cycles, being almost always picked-up by an RP vehicle. However, if a cycle comes in which the time window requirements of the client can no longer be satisfied, then the client will enter into a \"LEFT AT STOP\" state, making that no RP vehicle may be considered for performing that infeasible last leg of RP. These two states appear previously highlighted in green in Figure 3.  
Figure 4: Time-representation of requests scheduling with delayed last-leg requests.
## 5 Applications and Challenges
### Challenges
#### 5.2.2 Modelling and Control Challenges  
1. Dielectric elastomer modelling complexity Dielectric elastomers exhibit complex dynamic nonlinear behaviours, such as hysteresis, rate correlation, temperature, and load effects, leading to multifaceted challenges in the focus control of tuneable lenses driven by them. Regarding static performance, the lens's focal length change is more linear at low voltage, whereas it is significantly nonlinear and accelerated at high voltage. Regarding dynamic response, the hysteresis effect causes a delay in focal length adjustment, which impacts control accuracy and response speed [106; 107]. This nonlinear behaviour reduces the imaging quality of high-precision imaging systems and also results in blurring during high-speed imaging. Meanwhile, the nonlinear modelling of dielectric elastomers is influenced by multiple factors such as stress, temperature, and electric field. It is closely related to its initial state, deformation history, and material type. Although considerable research and progress have been made in modelling to predict the nonlinear properties of dielectric elastomers, the models still face numerous challenges in terms of accuracy, long-term performance, and generalisability [108].
2. Control system challenges Current DETLs are primarily controlled using open-loop systems, largely due to the difficulties of obtaining accurate feedback signals for focal length. Developing an effective feedback control scheme presents a core challenge for achieving precise focus adjustment of DETL. In the case of liquid lenses, changes in hydraulic pressure can be employed as the feedback signal. In contrast, displacement zoom-based DETL determines the lens position in real time through sensors and achieves closed-loop focus control by integrating with a dynamic model [109]. The accuracy of the DETL control system is significantly affected by variations in material parameters over time. In the pre-stretched state, the dielectric elastomer's elastic modulus and dielectric constant gradually change due to stress relaxation, leading to the failure of the original control parameters. This, in turn, affects the driving effect and focus control accuracy. If the system cannot be adjusted promptly, it may result in issues such as reducing focal length adjustment accuracy. Furthermore, in the face of vibrations and other complex environments [110], designing the control system to enhance the imaging stability of the lens, combined with the lens model, and utilising the control algorithm to improve the lens's response time remains a current challenge [111; 112].
# BIM and GIS Applications in Bridge Projects: A Critical Review
###### Abstract  
In recent years, interest in BIM and GIS applications in civil engineering has been growing. For bridge engineering, BIM/GIS applications such as simulation, visualization, and secondary development have been used to assist practitioners in managing bridge construction and decision-making, including selection of bridge location maintenance decisions. In situ 3D modelling of existing bridges with detailed images from UAV camera has allowed engineers to conduct remote condition assessments of bridges and decide on required maintenance actions. Several studies have investigated the applications of BIM/GIS technology on bridge projects. However, there has been limited focus on reviewing the outcomes of these studies to identify the limitations of BIM and GIS applications on bridge projects. Therefore, the aim of this study was to review the research on BIM/GIS technology applications in bridge projects over the last decade. Using a systematic review process, a total of 90 publications that met the inclusion criteria were reviewed in this study. The ruiber identified the state-of-the-art methods of BIM and GIS applications, respectively, at the planning and design, construction, and operation and maintenance phases of bridge projects. However, the findings point to segregated application of BIM and GIS at all phases of bridge projects. The findings of this study will contribute to guiding practitioners in selecting appropriate BIM and GIS technologies for different aspects of bridge projects. Building Information Modelling (BIM); Geographical Information System (GIS); Bridge Information Modelling (BrIM); bridge; application
## 1 Introduction
The core goal of the present work is the production of the digital atlas for tracking climatic variations of basic hydrometeorological parameters in the western part of the Russian Arctic (60-75\({}^{\circ}\) N, 30-85\({}^{\circ}\) E) over 1950-2021 based on the MERRA-2 atmospheric reanalysis dataset. The article describes the main hydrometeorological parameters forming the atlas: surface air temperature, total precipitation, wind speed at the Earth's surface, soil temperature, soil moisture content, air humidity, and snow cover thickness. The methodology of the map compilations is described in detail. For each of the parameters, we present its brief characteristics, methods of measurement, and provide examples of the different map types. Comprehensive assessment of regional changes in climatic parameters was previously performed for the Russian Arctic, e.g., for the Barents Sea [13]. The most significant results of studying the inter-annual variability of certain climatic parameters directly affecting the smooth railway operation are thoroughly discussed. In conclusion, we agree in favor of atlas expansion that will ensure the theoretical basis for sustainable development and operation of the Russian railways in the Arctic zone. Thus, the novelty of the research is in building of a specialized digital climate atlas for the needs of the Russian Railways in the western part of the Russian Arctic and in the subsequent analysis of change of basic meteorological parameters which may impact operability of the existing railway network and its development in future. Building of the atlas required development of original algorithms of geospatial data processing and their further representation, as well as the maps compiled in a GIS environment based on the MERRA-2 atmospheric reanalysis for the past 40 years. Comprehensive analysis of climatic changes included 7 hydrometeorological parameters presented in different characteristics in 459 maps.
## 2 Results and Discussion  
### Drying Kinetics  
Figure 1 shows changes with time of the moisture ratio (MR) of leaf samples dehydrated by VMD at three magnetron powers (240, 360 and 480 W, Figure 1a), CD at temperatures in the range of 50 to 70 \({}^{\circ}\)C (Figure 1b), and combined (CPD-VMD) drying consisting of CD at 60 \({}^{\circ}\)C and VMD at a magnetron power of 480 W (Figure 1c). The drying times, together with the maximum temperatures, the final moisture content and the constants of the Page model are listed in Table 1.  
Figure 1: _Cont._  
The Page model can be successfully used to describe the drying kinetics of the true lavender leaves dehydrated by the CD, VMD and CPD-VMD methods, characterized by high values of the determination coefficient (R\({}^{2}\) > 0.99) and low RMSE values (<0.05). A good adaptation of the applied Page model for description of the drying kinetics can be found in many earlier publications of dill leaves, chanterelle and oyster mushrooms [23; 24; 25].  
In the case of CD increasing the drying air temperature from 50 to 70 \({}^{\circ}\)C decreased the time of drying from 245 to 135 min, respectively. In VMD drying, radical reductions in the total drying time have been observed: the time was shortened from 32 to 14 min with a power change from 240 to 480 W. This radical reduction in the total drying time of VMD compared to CD is a result of the conventional water diffusion occurring, according to [PERSON]'s law, that is supported by a pressure diffusion mechanism of the Darcy type [26]. Combined CPD and VMFD using 480 W, shortened the drying time of leaves almost 18-fold compared to CD at 50 \({}^{\circ}\)C. The use of CD and 480 W power caused a drop in the material temperature during VMD by 4 \({}^{\circ}\)C for leaves and 2 \({}^{\circ}\)C for flowers in reference to VMD 480 W. This condition is caused by the molecular distribution of water particles inside the dried CD and the distribution of water particles has an effect on the generation of heat energy production under microwave radiation during VMD [21; 27; 28]. Energy consumption during the CD of plant materials is much lower than in VMD [29; 30]. In industrial conditions, the best solution is a combined drying process consisting of CPD and VMFD. The CD is very effective at the beginning of the drying process (the largest loss of water occurs during that phase) and VMD at the final stage of drying (removal of water strongly bound to the cellular structure of the material being dried) [18; 27; 28]. The final choice of recommended drying process should be related to the aspects of the dried material (volatile composition and sensory attributes) [27; 31].  
Figure 1: (**a**) Drying kinetics of true lavender leaves samples processed using VMD at magnetron powers 240, 360 and 480 W; (**b**) Drying kinetics of true lavender leaves samples processed using CD at temperatures of 50, 60 and 70 \({}^{\circ}\)C; (**c**) Drying kinetics of true lavender leaves samples processed using VMFD at 480 W after CPD at temperature 60 \({}^{\circ}\)C.
## 5 Human-Assisted Within-Show Diarization
### Analysis  
In order to further improve our approach, we analysed the correlation between the benefit of human active correction (in terms of DER reduction or number of questions asked) and the characteristics of the processed audio files (number of speakers, duration of the file...). Based on the Pearson correlation coefficient, no strong correlation has been found (all less than 0.4).  
The question generation module is inspired by the idea that closer nodes to the HAC's threshold reflect a lower confidence from the automatic system and thus should be questioned. In order to confirm this hypothesis, the benefits of successive questions for each show, has been checked in an ordinal way. The total DER improvement and CQR (number of corrections over the number of questions) are calculated based on the question order. For both stopping criteria, the first questions lead to larger DER reductions. This confirms that following the _delta_ ranking as the sorting method for questioning nodes is a reasonable choice.  
The proposed stopping criteria have been compared according to their average CQR per ranked question. Our observation shows that when using the _2c criterion_, successive questions keep contributing to the DER reduction while, for the _All_ criterion, the CQR reduces. This demonstrates the expected behaviour where the _All_ criterion leads to more questions and thus a finer correction, while the _2c_ criterion limits the number of questions to assure higher benefits per question. The choice between these two methods can be guided by the available human resources.
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 6 Impact of Mixed Traffic Flows on Traffic Signal Control
Based on provided data TSC system have better insight into the traffic state, and gathered information is used for optimizations of signal programs. Therefore, the TSC system can also inform drivers about the current traffic state, recommending the driver's optimal speed profile and/or route. Currently, it is up to the driver if he will follow the recommendations. CAVs are also mobile sensors but provide even more information to TSC systems since CAVs are usually equipped with more sensors than CVs. Moreover, CAVs can be controlled by the TSC system, where the TSC system informs CAVs about signal phase timing and sequence. With such information, a particular CAV can alter its speed and route to reduce travel time and emissions. With a sufficient penetration rate, CAVs can affect the speed of other vehicles in traffic flow. However, a heterogeneous traffic flow made up of conventional HDVs, CVs, and CAVs will exist for a while before the CAVs are fully deployed, which could cause uncertainty in the existing transportation system. How much and in which way will the existing transportation system be affected is currently unknown. Furthermore, it is necessary to assess the connection between CVs and CAVs penetration rates and potential improvements in road capacity. A tabular overview of referenced papers regarding the impact of mixed traffic flows on TSC is summarized in the Table 2.
## 5 Experimental Process and Results Analysis  
### Calibration Experiment of Circle Center Detection  
The system hardware of the calibration experiment is mainly composed of a camera, lens, calibration plate, disc, and data-processing platform. A camera with a SONY IMX586 photosensitive component as the main camera was used in this study. The resolution of this camera is 8000 \(\times\) 6000 pixels. When the shooting distance of the camera relative to the target is 60 cm, the shooting vision of the camera is 50 cm\(\times\) 20 cm, the resolution is 1824 \(\times\) 4000, and the focal length is 4.76 mm. A computer with an i7-4750 HQ CPU and 10 GBRAM was used to process the experimental data.  
The camera was used to capture the image as shown in Figure 8. Firstly, the Zhang You Zheng calibration algorithm was used to calibrate the internal and external parameters of the camera and then the simulated flange end face was detected based on the least squares method. The vertical direction and rotation of the object remain constant, and the radial distortion was [0.2815, \(-\)1.5715, 0] and tangential distortion was [0, 0, 0]. The experimental results of the target end face center are shown in Figure 9.  
On this basis, a calibrated camera is used to measure the end image of the target object. Figure 10 shows the absolute error diagram obtained using the above algorithm to locate and detect the center of the simulated flange target end face, which includes the absolute errors of the X-axis direction deviation distance and the Y-axis direction deviation distance relative to the standard image. After 81 experimental processes, a few maximum errors are removed and absolute error of X coordinate is \(\mathrm{e_{x}}\) = 0.97726 mm, and the absolute error of Y coordinate is \(\mathrm{e_{y}}\) = 1.09105 mm. In Figure 10, the \(X\)-axis represents the serial numbers of the 81 groups of circle centers. The two dot-line graphs represent the absolute error of the X and Y coordinates of the center of the circle, respectively, and each has a horizontal dashed line representing the respective error mean. Valid points are indicated by 'o', and error points are indicated by '+'. The accuracy of center positioning detection error meets the mechanical installation tolerance within the accuracy range of 3 mm. The experimental results show that this method can effectively improve the accuracy, efficiency and stability of the automatic docking system, improve the assembly consistency, and meet the requirements of automatic docking of a small crane tube.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 3 Materials and Methods
### Proposed Methodology
#### 3.2.5 Post-Processing  
Since images from satellites have low spatial resolution, the proposed methodology explores two post-processing techniques to overcome problems of apertures and discontinuities in crops with similar features.  
Post-Processing Based on Morphological Operations (PMO)  
This technique analyzes the map classified by a sliding window of 3 \(\times\) 3. If the prominent class has more than six pixels in the window and it is different from the central pixel's class, the central pixel adopts the label of the most predominant class; otherwise, the label value of the central pixel is retained. This technique allows to improve the overall results by reducing small misclassified zones. An example of this technique for the first three iterations is depicted in Figure 7; nevertheless, it is repeated until the entire image is analyzed.  
Post-Processing Based on Iterative Label Refinement (PILR)  
This technique is a biologically inspired method. It uses a set of color classifiers that convert visual color information from the scene into segmentation labels [56]. This technique is used to remove jagged edges, small holes, and unconnected regions that are common in map classification. The scheme of this technique is illustrated in Figure 8. It performs the linear operation given by Equation (2) with a Gaussian filter of 3 \(\times\) 3 window size, as follows:  
\[P^{r+1}=w(\alpha P^{0}+(1-\alpha)(P^{r}*G_{\theta})), \tag{2}\]  
where \(P^{0}\) is the label map with a size of \(R\times C\times O\), with \(R\) as the number of image rows, and \(C\) as the number of image columns. \(P^{r+1}\) y \(P^{r}\) are the label maps in iterations \(r+1\) and \(r\). \(G_{\theta}\) is a Gaussian window. \(\alpha\) establishes the influence of the initial classification map and  
Figure 6: Example of crop identification based on patch classification.  
methodology [57; 58]. Experiments are done by running the software on an Intel Core i7 processor with 16 GB of RAM memory.