esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Localization Using the Compact Maps
#### 5.3.1 Localization in the Quorum V Environment  
Figure 16 shows the average localization error (cm) obtained when FS (first row), HOG (second row), and _gist_ (third row) are used, respectively, as the descriptor. Figure 17 presents the computational time (s). In the case of HOG, the effect of homomorphic filtering adds a constant time of 0.02 s per test image. Regarding the number of clusters, \(n_{c}=872\) is considered since this value provides the case in which the localization is solved without compacting the map. This value is used as a reference to know the relative utility of the compacted map.  
Figure 15: Clusters obtained in the COLD environments through the use of Spectral clustering and _gist_ description. (**a**) Freiburg and (**b**) Saarbrücken environment.  
The FS descriptor is not good for localization since the best choice (correlation distance) presents errors between 650 cm and 800 cm depending on the number of clusters and the size of the descriptor. HOG clearly improves the localization task. Except for the case \(k_{2}=2\), the average localization error decreases as the number of clusters increases, and these values go from 500 cm when \(n_{c}\) is low and achieve values under 100 cm (when \(n_{c}\) is high). As for the _gist_ descriptor, it also produces relatively good results, but they are not as good as those obtained through the use of HOG. The localization task achieves the best results when the correlation distance is used.  
Regarding the computation time, with the FS descriptor, as the number of clusters increases, the computational time required for the localization task increases substantially. With HOG, the time is much lower than FS, and it keeps constant independently of the number of clusters. This means that the time to calculate the descriptor is higher than the time to compare it with the map. The computation time required for _gist_ is also worse than HOG. The time required by _gist_ is around twice the time with HOG.  
Figure 16: Results of the localization process with FS, HOG, and _gist_ used to describe the representatives of the clusters and the test images: average localization error (cm) vs. number of clusters. Quorum V environment.  
In general, as the number of clusters increases, the computation time required for the localization task also increases, and the average localization error decreases. This is an expected behaviour due to the fact that a high number of clusters means that the map is less compact and the information stays in representatives of the clusters whose distance to the test image is lower. Hence, the more clusters, the more comparisons with representatives must be carried out. This leads to a higher computation time and lower average localization error distance. Thus, a balance between these behaviours must be achieved. Therefore, in order to solve the localization in an environment whose properties are similar to the Quorum V environment (grid-distributed data), the optimal values are reached through the use of a HOG descriptor with \(k_{2}=[32,64]\) and correlation distance.
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 3 Energy-Efficient Clustering Multi-Hop Routing Protocol
### Qualitative Comparison of Clustering Protocols  
In this subsection, we provide a qualitative comparison of the clustering protocols for UWSNs with different aspects.  
The clustering protocols can take into account many metrics which describe the network topology, such as distance to the sink or other nodes, number of neighbors, path loss, location of the node, and residual energy. Even though each protocol uses different metrics to select the cluster head, they mostly focus on the residual energy of the node and the distance between the node to the sink. To increase network reliability, some clustering protocols select an assisted node which can assume the role of the sink [9; 12; 18] or the control node [10]. The assisted node can receive data from the sensor node or control the network topology. As a result, the energy consumption of the nodes may be reduced by conserving the node's energy. However, some clustering protocols are only presented and evaluated in two-dimensional (2D) deployment, whereas in the network scenarios of UWSNs, the deployment is three-dimensional (3D). To investigate the clustering protocol with an assisted node in 3D, our proposed EECMR protocol groups the sensor nodes with regard to the residual energy, cluster head duration, and distance to the sink. Among the clustering protocols, only EECMR considers the duration of the cluster head and cluster relay or assisted node in 3D, whereas the others do not consider the duration of the cluster head. A qualitative comparison of the clustering protocols is presented in Table 2.
## 2 Problem Statement  
Air Traffic Management (ATM) has traditionally been anchored on centralized systems for the sake of safety and efficiency. However, the advent of technological progress has begun to reshape discussions, putting the feasibility of a distributed ATM ecosystem into focus. Both centralized and distributed models come with their sets of merits and shortcomings. In both approaches, ground stations play a key role in ATM operations. The Air Traffic Management (ATM) ecosystem has three components: Advancement of Air Traffic Services (ATS), Air Traffic Flow Management (ATFM), and Airspace Management (ASM) [45]. Figure 1 shows the ATM ecosystem and the focus of this study.  
The ATS refers to the flight information, air traffic control, and alerting services that prevent collisions between aircraft [46]. The ATFM optimizes the air traffic flow and manages its capacities when demand increases in routes and airdromes, and the ASM is concerned with the allocation of airspaces to aircraft with the aim of reducing the risk of their conflicts [47]. The study presented in this paper focuses on improving the ASM system, and our main objective is to design and develop a reliable and efficient algorithm for airspace allocation and management.
## 2 Existing Technologies and the Position of the Present Research
### Technologies for Avoiding Contention in Multi-Access to the Transmission Medium
#### 2.2.2 CSMA/CA with Ack and CSMA/CA with RTS/CTS  
CSMA/CA with Ack is a protocol in which the access point (AP) that has received a data transmission frame always acknowledges the receipt of the frame by sending an Ack to the sending terminal. Sending of this Ack message lets other terminals know that the channel has been cleared and is now available. However, in an ad-hoc mode broadcast communication, no Ack message is returned. Therefore, no retransmission can be made.  
CSMA/CA with RTS/CTS has been devised to solve the problem of hidden terminals in a LAN using CSMA/CA. The AP uses a data transmission request message, called RTS (request to send), and a transmission permission message, called CTS (clear to send), to give or not to give each terminal permission to send packets. However, in an environment in which messages are transmitted in multiple hops, nodes that cannot directly communicate with the AP cannot receive RTS/CTS messages from the AP.  
DACAP [10] is a version of CSMA/CA with RTS/CTS designed for underwater communication. This protocol was proposed by [PERSON]. It utilizes a timer whose value is adapted to underwater propagation delay and low transmission speed to avoid a collision. However, being a version of RTS/CTS, it is not usable in a multi-hop environment. There are other studies on RTS/CTS-based technologies designed for underwater use, but none of them assume use in a multi-hop environment [11]. These protocols are adopted in access point mode communication. They cannot be used in ad-hoc mode communication because there is no terminal that controls message transmission.
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 1 Introduction
### Vehicle Recognition  
Tracking is the process of recognizing a previously detected vehicle at any following frame. Template-based object matching is the method mostly used when you have two objects of the same nature, but we need a fast scale-invariant method. Therefore, we use the speeded-up robust features (SURF) algorithm for the template matching. The SURF algorithm is faster and more accurate than template-based methods and proves to be more robust as regards certain transformations. This method includes three steps: extraction of points of interest, calculation of descriptors, and matching of points of interest (Figure 10).  
The SURF method uses the fast-Hessian algorithm for the detection of points of interest and an approximation of the Haar features to calculate the descriptors (Figure 11) [14]. They make it possible to estimate the local orientation of the gradient and therefore to provide the invariance concerning the rotation. The responses of the Haar features are calculated in x and y in a circular window whose radius depends on the scale factor of the point of interest detected. These specific responses contribute to the formation of the characteristic vector corresponding to the key point. Regarding the matching of descriptors (Figure 12), i.e., the search for the best similarity between the descriptors of two images, the criterion used is the same as that used in most algorithms, the Euclidean distance.  
Figure 10: Steps of the SURF algorithm.  
Figure 9: Feedback Adaboost system.  
We have applied the algorithm to a video sequence containing vehicle images. The training database consisted of previously detected vehicles and the test image is the newly detected vehicle. In our detected vehicle database, we already have a template of the vehicle. Therefore, there is no need to crop the vehicle image or subtract the background. We need to minimize the computational time, as the detected vehicle database will increase with time. This causes the recognition system to become slower and slower over time, because of the number of detected vehicles stored in the database.  
Therefore, our system will complete the recognition process after 30 s minimum, as there is generally not much change in the camera view for vehicles going in the same direction. We will maintain the assumption that all vehicles will follow our host for a minimum of 30 s to be considered as a potential follower. Also, we have decided to separate the recognition system from the detection system. In doing this, we are able to recognize all previous detected vehicles without interfering with the tracking process. Figure 13 shows the combination of the detection and recognition phases, without them interfering with one another.  
Figure 11: SURF descriptors.  
Figure 12: SURF matching descriptors.  
Figure 13: Detection and recognition phases combined.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 2 Related Work
### Change Detection  
An intuitive and very common option to analyse the impact of natural disasters in image data are change detection algorithms. The literature about change detection is vast [57; 58; 59] and not primarily concerned with natural disasters, although there recently have been efforts to encourage such experiments [43]. A limited number of studies have applied change detection to identify regions whose state significantly differs before and after a given event [33; 53].  
In their simplest form, change detection algorithms aim to classify pixels into changed and unchanged classes. Most change detection algorithms belong to one of the following groups: (1) image differencing with automatic thresholding [60], (2) change vector analysis [61], and (3) direct change prediction [45; 53; 62]. The earliest approaches simply form a difference image and turn it into a (unitless) measure of per-pixel change, followed by thresholding or clustering [57; 58]. Another family of methods, named change vector analysis (CVA), describes the temporal evolution of each pixel with a high-dimensional vector that encodes the change \"direction\", so that changes are not only identified but also characterised to some degree [63]. Object-based approaches were also proposed to increase robustness, by analysing image \"objects\" (often simply small, homogeneous segments) instead of individual pixels [58]. Finally, the problem can also be formulated as semantic co-segmentation, where a label is assigned directly to each pair of corresponding (before/after) pixels, without explicitly image differencing or thresholding [59]. This last formulation is especially suited for data-driven algorithms like convolutional neural networks (CNNs), which are nowadays one of the most powerful tools for image analysis [46].  
Unsupervised change detection algorithms do not require training data to be annotated by experts, which is often expensive and slow, and at least conceptually should generalise well across different geographical regions, since they do not suffer from training data biases [58; 64]. Thus, they may seem particularly well suited to study natural disasters. On the downside, the price to pay for not fitting to the specific task is that these methods cannot discriminate different types of changes that have comparable visual saliency.  
At least one representative method from each of the three groups described above has also been selected as baselines in our experiments, these are described in more detail in Section 4.3.
# Development of an Organic Culture Medium for Autotrophic Production of _Chlorella vulgaris_ Biomass
## 3 Results and Discussion
### Screening of Organic Substrates for Autotrophic Production of C. vulgaris
The most expensive media was Nutriverde with 58.13 \(\upmu\)kg  
Figure 1: _Chlorella vulgaris_ growth for 13 days using different organic substrates as culture media and synthetic medium as control. Presented results are the mean and standard deviation of three individual biological replicates. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Treatment** & \begin{tabular}{c} **Biomass Productivity** \\ **(g L\({}^{-1}\) d\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Specific Growth** \\ **Rate (d\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Reagent Costs** \\ **(g/kg Biomass** \\ \end{tabular} &
\begin{tabular}{c} **Nutrient Consumption (\%)** \\ **Nitrate** \\ \end{tabular} \\ \hline Synthetic & \(0.037\pm 0.001\) a & \(0.123\pm 0.001\) a & \(1.32\) & \(89.6\pm 2.3\) & - \\ Bioscape & \(0.033\pm 0.005\) a & \(0.111\pm 0.011\) a & \(1.56\) & \(14.3\pm 0.0\) & \(99.2\pm 0.2\) \\ Nutrimais & \(0.017\pm 0.002\) b & \(0.075\pm 0.004\) b & \(14.23\) & \(57.4\pm 16.3\) & \(99.1\pm 0.2\) \\ Nutriverde & \(0.009\pm 0.002\) c & \(0.036\pm 0.008\) c & \(58.13\) & \(17.1\pm 4\) & \(91.0\pm 0.2\) \\ EcoMix4 & \(0.038\pm 0.003\) a & \(0.122\pm 0.009\) a & \(21.24\) & \(43.1\pm 4.1\) & \(98.8\pm 0.2\) \\ \hline \hline \end{tabular}  
* Different letters in the same column represent statistically significant differences among treatments (\(p<0.05\)).  
\end{table} 
Table 2: Growth indicators, reagent costs applied to each medium and nutrient consumption of nitrate and ammonia of _Chlorella vulgaris_ grown during 13 days in 2 L reactors using different organic substrates and synthetic medium as a control. The results represent the mean and standard deviation of three individual biological replicates. there has been a demand for products labelled as organic by the consumers who refuse to include \"non-organic\" products in their diet, independently of the price. Hence, the low supply and high demand in this field, enables higher consumer prices and gives the companies the opportunity to increase the sales volume. Results show that all cultures grown using the different OS consumed preferentially ammonia over nitrates, being ammonia almost fully consumed (except for Nutriverde), while nitrates were consumed at a much slower rate. [PERSON] _et al._[20] reported that N is preferably assimilated in the following order: NH\({}_{4}\)\({}^{+}\) > NO\({}_{3}\)\({}^{-}\) > NO\({}_{2}\)\({}^{-}\) > CH\({}_{4}\)N\({}_{2}\)O. The assimilation of nitrates requires transport through the cell membrane and reduction to ammonia, which demands energy, via consumption of carbon and protons. The assimilation of ammonia at high pH causes an imbalanced reaction that lowers the pH, according Reaction (4) [21; 22]. For this reason, it is important to avoid the addition of excess ammonia. \[N\!H_{4}^{+}\!\leftrightarrows N\!H_{3}+H^{+}\!\leftrightarrows org-N+H^{+} \tag{4}\]
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 5 Discussion  
This article focused on designing and implementing a user-centered interactive visualization system, SINDVis. The system abandons the traditional C/S architecture and satisfies many kinds of user needs in web-oriented form. The users of the SIN can be broadly divided into three categories based on their sources and characteristics. Firstly, there are people who are interested in SIN but know little about it. The second includes users involved in the design and construction of SIN. The third is that of users involved in the management and maintenance of SIN. Due to the difference of background knowledge and expertise of different users, we proposed designing the main view of the system as the entity domain view GeoView and the topology domain view TopolView together to meet the different needs of many users. In the theory and method analysis, combined with the characteristics of SIN, we designed the SINDVis architecture and elaborated on the various functions of GeoView and TopolView. For the view realization, we emphatically discussed the TopolView realization method,including improving the FDA layout and proposing a FDA layout method, as well as proposing and analyzing the FAT and P&Z methods.  
For the entity domain, GeoView, we showed the process of dynamic networking of SIN from the perspective of the entity domain. This included the operation and networking of different levels of the information system. By combining this with the architecture of SIN, finally, users can realize the initial understanding of the SIN. To determine the advantages and disadvantages of the STK, SaVi, and SINDVis, we performed a comparative analysis of four aspects: architecture, service type, interactivity, and scope. The details is shown in Table 3.  
For the topology domain, TopolView, we showed the process of dynamic networking of SIN from the perspective of the topology domain. The included the increase of nodes and edges, the establishment of the connection, and the real-time operation of the network. Meanwhile, we focused on the realization of a real-time interactive view function for users.  
The development process of the visualization system was a continuous iterative process. The user-oriented visualization system was based on the analysis of the user's initial requirements, and through the design of its basic functions, the design and realization of the initial SINDVis was completed. Based on the initial version, through users' continuous use, combined with their feedback, suggestions, and comments, we will improve the system. Through continuous modifications, trials, feedback, new requirements and recommendations, we will develop useful SINDVis and products. Therefore, the most important factor is the development of the user-centered SINDVis, and only the users can know what their purpose is based on the SINDVis. In addition, timely and effective communication between developers and users is the precondition and foundation to ensure the continuous optimization of the SINDVis.
###### Abstract
The research presented in this paper introduces the design of processes within a real case study in the higher education context, offering the following original contributions. Process Modeling for Enhanced Understanding: this study develops a detailed process model for a real case study in the higher education sector, enabling a comprehensive understanding of the organization and the structure of processes. This approach aims to achieve more precise and actionable insights during the analysis phase, thereby supporting decision making and strategic planning. Effective Communication of Process Information: the process model serves as a tool to effectively communicate information about the organization of processes to external stakeholders, such as accreditation bodies, governmental agencies, and other partners. This transparency fosters better collaboration and alignment between internal and external actors. Support for Resource Allocation and Continuous Improvement: by identifying key critical issues, potential risks, and inefficiencies, the process model provides a robust foundation for optimizing resource allocation and supporting continuous improvement initiatives. Furthermore, it facilitates the reengineering of processes to enhance their efficiency and adaptability to changing institutional needs and external requirements. This research not only identifies critical inefficiencies in the teaching assignment processes at the University of Basilicata but also presents several significant contributions. Theoretically, it contributes to the body of knowledge on Business Process Reengineering (BPR) in the higher education context, emphasizing the integration of simulation methodologies. Practically, the study provides a tailored framework for higher education institutions to enhance administrative efficiency while adhering to regulatory compliance, thus serving as a blueprint for similar institutions facing bureaucratic challenges. Overall, this research highlights the importance of process modeling as a strategic instrument for improving organizational efficiency, ensuring alignment with quality standards and fostering a culture of continuous improvement in higher education institutions. The paper is organized as follows. Section 2 presents an overview of the literature; the Business Process Reengineering process is described in Section 3. The case study studied is presented in Section 4, while Section 5 provides the numerical results of the simulations conducted. Finally, conclusions and future research paths are drawn in Section 6.
## 1 Introduction
It is able to match patterns in the presence of global or partial extensions, compressions and deformations, thus solving the problem of the similarity metrics and the classification of dynamic patterns [15]. The dynamic time warping algorithm can effectively solve the limitations of the Euclidean distance calculation method in the expansion and bending of the time axis. Based on the idea of dynamic programming, the DTW algorithm finds the shortest path between two pieces of data, that is, the total distance (similarity) between the two pieces of data. With the advantages of a fast computation, a high positive recognition rate and robustness, the DTW algorithm is widely used to solve pattern recognition problems in many fields. For example, good results have been achieved in speech recognition [16], dynamic gesture recognition [17], fault diagnosis [18; 19], problem classification [20], gene matching [21] and reservoir identification [22]. This paper analyzes the characteristics of the surface measurement parameters under different gas kick types, on the basis of the flow model, coupled with the wellbore and the formations established by previous authors. AI algorithms are used to analyze the surface measurement parameters during the overflow process and the parameters computed from the physical model, to identify the type of gas kick by means of the combination of the physical model and AI methods. Compared with the existing methods, the method proposed in this paper does not need to wait for a lag time, and there is no need to adjust the wellhead backpressure, and the fine outlet flow measurement is not required, which is not only suitable for managed pressure drilling, but also for conventional drilling.