esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Traditionally, the use of advanced technology in remote areas has depended on being able to fly in highly skilled specialists to install and configure the solutions on site. The consequences can be serious when technology fails because of the lack of specialists on site. To get the full picture, it must be said that there are also drawbacks in the solutions presented; similar to the heat exchanger example, the Internet can also be fragile in remote locations. If it fails, part of the overall solution fails too. However, backups and local buffer solutions can be established to bridge such difficulties, and in the setup presented, the solutions would run independently, collecting sensor and log data locally, waiting for the Internet to restart and synchronize the relevant data and states.
## 1 Introduction
[PERSON] presented a framework to link the stochastic variation and uncertainty distribution for the production availability [23]. [PERSON] proposed a probabilistic analysis approach based on finite element analysis and a vehicle load model to determine the uncertainty of fatigue crack growth [24]. Many works use probabilistic models to represent the impact of uncertainty on the system over time. However, uncertainty is random and cannot be accurately represented by a probability model. The fuzzy Markov method proposed in this paper can avoid the work of selecting a probability model, conduct a specific analysis for the uncertainty problem, and provide new insights and guidance for how to understand the impact of uncertainty on the system. The different fuzzy Markov chains have been proposed for the decision-making of uncertain problems by some scholars. [PERSON] [25] used fuzzy theory and Markov chain to evaluate the structural integrity of transportation bridges. [PERSON] [26] and [PERSON] [27] predicted the state of the controller by combining fuzzy theory and Markov model. [PERSON] [28] proposed a fuzzy Markov chain single-step transition matrix to evaluate the inherent rules of stock historical fluctuation. These fuzzy Markovmethods are based on a given fuzzy range [29] or dozens of sets of cases [25] for target prediction, which can be well used in medical treatment [30; 31], transportation [25], power station energy [32], etc. The fuzzy Markov in this paper is based on the previous method, adding dynamic iteration when failure of the system occurs. It can follow the system state in real time, is not limited to a fixed fuzzy range and any sets of cases, and can classify the uncertainties of different failure modes in different states of the system. An example of a subsea wellhead connector is used to illustrate the method in this paper. This present work is aimed at developing a new model for the availability, reliability and uncertainty analysis of engineering systems, which can be used to sort out the impacts of different failure modes and uncertainty problems on the system. The paper takes the subsea wellhead connector as a case study. The rest of the paper is organized as follows: Section 2 describes in detail the analysis techniques of risk analysis methods followed by a full presentation of our proposed fuzzy Markov methodology. Section 3 presents the case study. In Section 4, a detailed discussion of reliability, availability, uncertainty and impact analysis was performed based on the established model and results. Finally, conclusions are given in Section 5.
## 3 Fixed Wireless Access
### Lessons Learned  
As previously mentioned, the FWA application gives a number of challenges to offer adequate cell site connectivity. For instance, compared with the conventional macro deployments, it could demand not only more cells deployment, but also more cell site connections. This puts considerable pressure on the network backhaul/fronthaul. It is remarkable that the required cell site density varies and significantly depends on the utilized frequency band, as well as the propagation scenario. However, each cell's demands depend on the adopted RAN decomposition scheme between the central unit (CU) and DU [8; 27]. Section 8 presents a comprehensive overview of 5G RAN FSOsn and a number of industry groups that have contributed significantly to its implementations and definitions.  
In general, the transport network requirements depend on the RAN deployment, as well as the employed interfaces. Moreover, to accomplish the peak rates and capacities required for residential access, FWA deployment demands support for MU-MIMO and BF using advanced array antennas. Consequently, this defines the RAN split interface capacities for different scenarios [8; 27; 27]. The potential FWA transport solutions that can be employed for different RAN deployment scenarios and use cases are discussed in Section 6. In the following section, we discuss some FWA design considerations and the associated challenges and proffer viable solutions that can help in the system design to improve the network performance.
# Design and Construction of a Low-Cost Test Bench for Testing Agricultural Spray Nozzles
## 2 Materials and Methods
### Software User Interface Design  
The software user interface, running on a Windows 10 PC operating system, was designed to allow for managing the spray trials (i.e., starting and stopping spraying, nozzle speed, flow rate and pressure monitoring, positioning of the platform in a given location along the rails, stopping the experiment in case of safety problems) (Figure 4). It was built with Delphi Community Edition Integrated Development Environment (IDE) (Embarcadero Technologies Inc., Austin, TX, USA) using object-oriented Pascal language and the FireMonkey cross-platform graphic user interface (GUI) framework, developed also by Embarcadero Technologies Inc. All the commands were sent to the hardware layer through a user datagram protocol Ethernet packet at the different IP addresses assigned to each device, using a specific UDP port.  
The application is composed by three main tabs. The \"Connection\" tab allows users to check the communication with each system device. Usually, just after the application start, a system check is performed automatically. In the tab there is a log box, in which all messages from/to the application and hardware devices are printed. In this way, it is possible to identify any system fault. In the \"Experiment Setup\" tab, it is possible to set up every aspect of the desired experiment, mainly the moving platform speed, some start delay for the platform motor and hydraulic electro valve, the system sampling time, to move the platform to the home position, and so on. Some options can be useful for testing purposes.  
After setting up all the experiment parameters, using the \"Experiment\" tab it is possible to start the test; in this tab, it is possible to monitor different run-time graphs about the sensor data and the mobile platform speed profile. A log box in this tab is used to show the experiment progress until the end. The application also implements a data logger function that can save all the data and the experiment parameters in a text file using the standard comma separated value (CSV) format with time-stamp.
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 2 Microgrid Operation Structure
### Model of Distributed Generations
#### 2.1.3 Micro Gas Turbine  
MTs are small, efficient distributed generation units, typically with a single-unit power output ranging from 25 kW to 500 kW. The operational efficiency of an MT is shown as follows:  
\[\eta_{\text{MT}}=0.0753\left[\frac{P_{\text{MT}}(t)}{65}\right]^{3}-0.3095\left[ \frac{P_{\text{MT}}(t)}{65}\right]^{2}+0.4174\left[\frac{P_{\text{MT}}(t)}{65} \right]+0.1068 \tag{3}\]  
where \(\eta_{\text{MT}}\) is the operational efficiency of the MT, and \(P_{\text{MT}}(t)\) is the active output power of the MT.  
Additionally, three components are included in the cost model of an MT: the environmental treatment cost model, the fuel cost model, and the maintenance cost model.  
\[C_{\text{MT}}=C_{\text{MT}o}+C_{\text{MT}m}+C_{\text{MT}p} \tag{4}\]  
where \(C_{\text{MT}}\) is the total operational cost of the MT, \(C_{\text{MT}O}\) is the fuel cost, \(C_{\text{MT}m}\) is the maintenance cost, and \(C_{\text{MT}p}\) is the environmental treatment cost.
It is considered as a crucial material's parameter characterizing its mechanical strength and, in turn, also the quality of setting, as well. Microhardness testing is based on the evaluation of resistance of material against plastic/elastic deformation [18]. Conventionally, the hardness test is based on the measurement of the diagonal's dimensions of the residual plastic impression created during the indentation process, where the hard indenter is pressed into the sample's surface [18]. Vickers diamond indenter, four-sided pyramid, is used in most studies [16]. This approach relies on the visual observation of residual indentation and also on the assumption that only limited elastic recovery of the residual indent takes place. Generally, the measurement of the actual size of the indent becomes difficult with the decrease of its dimensions and increase of surface roughness, which can lead to inaccurate or incorrect conclusions after the evaluation [18]. On the contrary, the advanced approach of hardness testing, also called depth sensing indentation, based on the continuous recording of the force-displacement data during the whole test can be employed. This approach also allows study of material properties through exploration of the longitudinal cross-section of the specimen. To our knowledge, no study on the effects of thickness of root canal wall on setting of MTA has been reported in literature so far. The purpose of this study was to examine the role of root canal wall thickness on microhardness and elastic modulus of MTA along the whole apical plug after setting in simulated root canal environment. The null hypothesis tested was that there are no differences in the microhardness and reduced elastic modulus of MTA within entire specimens.
## Results and Discussion
### Total Phenolic Content and Antioxidant Capacity
#### 3.1.2 Wounding Effect in Pineapple Shell and Core  
As the main objective of the current work was to evaluate the use of mid-infrared spectroscopy to study the impact of abiotic stresses on the amount of bioactive compounds, wounded samples without thermal treatment, control samples, were analyzed for total phenolic compounds and antioxidant capacity using conventional methods. As shown in Table 1, the control samples after 8h (Ctr_8) and 24 h (Ctr_24) of storage time presented significantly higher (\(p<0.05\)) levels of phenolic compounds than the initial control samples (Ctr_0). This indicates that storage time after cutting improves the synthesis of phenolic compounds. The same behavior was observed for antioxidant capacity, determined by the three tested methods (DPPH, FRAP, and ABTS). The enhancement of bioactive compounds in fresh and treated fruits by applying abiotic stresses (wounding, heat shock, UV irradiation, among others) has been reported previously by [PERSON] and [PERSON] (2009). The wound-induced increase of phenolic compounds has been suggested to be due to phenylpropanol pathway activation [13].
# Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters
## 2 Materials and Methods  
### Study Area  
This study comprised the three catchments (b1 Sill with 853 km\({}^{2}\), b2 Drava with 669 km\({}^{2}\), and b3 Isel catchment with 1197 km\({}^{2}\)) in the inner Alps in Austria, which represent the uppermost parts of their basins (Figure 1). The catchments share similar geomorphological features, such as a substantial elevation difference (approx. 2800 m), very steep slopes (26\({}^{\circ}\)), and a similar soil mix (orthic podsols, dystric cambisols, and lithosols), while other features (river origin, slope aspect, land cover) differ (see Table 3 for details). The 34 km long b1 Sill River flows mainly northwards (41% of the slopes facing NW, N or NE), and the catchment is covered by 31 km\({}^{2}\) (3.6%) of glacial ice. The 48 km long b2 Drava River originates from a spring (in Toblacher Feld, South Tyrol) and flows mainly easterly (37% of the slopes facing NE, E or SE). The 52 km long b3 Isel River rises at the glacier gate and flows mainly southerly. While evergreen forests dominate in the b1 Sill and b2 Drava catchments (46% of the b2 Drava catchment), rock cover predominates in the Isel catchment (40% bare rock, 26% forest).  
The climate in the inner Alps is temperate dry to temperate humid [97]. The climate is influenced by the Atlantic Ocean and the Mediterranean Sea and, in combination with the high altitudes and specific land formations, produces several characteristic phenomena. Precipitation is enhanced along the Alpine foothills (northern and southern parts), while it is lower in the interior of the mountain range with shielding effect in the inner valleys [98]. Much of the topographic impact on precipitation is associated with slope and shielding rather than elevation [16]. Snow is another important driver of alpine hydrology. The contribution of snowmelt in the upper Alpine region is estimated at 45% of annual runoff, increasing to 60% in summer and during snowmelt [99]. Spatial variability of snow cover and permafrost is also common [99], mainly caused by westerly winds and different local climatic conditions on the northern and southern slopes of the mountains. As such, the permafrost on the northern slopes can occur several hundred meters lower [100]. The generous river runoff, ensured by high precipitation and the presence of perennial snow and ice, has three underlying influences: orographically led precipitation patterns, lower evapotranspiration than precipitation due to colder air temperatures, and temporal delay of runoff due to snow and ice formation ([PERSON], 2017).
## 5 Results and Discussion
### Scenario 2: PV-Battery System (\(S=[s_{1}\ \ 0\ \ s_{3}]\))  
In this scenario, the energy of the PVs and battery units was used to satisfy the user's load at various \(LPSP^{max}\) values. The PV-battery results are summarized in Table 4. Based on the final rankings, the EESA and TLBO schemes performed equally in terms of TAC minimization for all \(LPSP^{max}\) values. The EDE and SSA schemes were placed into the second and third categories based on their achieved TACs. The achieved PV-battery system's configurations of EESA at four \(LPSP^{max}\) values were given as:  
\(S=[199\ \ 0\ \ 3150]\) at \(LPSP^{max}=0\%\),  
\(S=[194\ \ 0\ \ 2898]\) at \(LPSP^{max}=0.5\%\),  
\(S=[191\ \ 0\ \ 2746]\) at \(LPSP^{max}=1\%\), and  
\(S=[178\ \ 0\ \ 2090]\) at \(LPSP^{max}=3\%\).  
In Table 4, the lowest TAC was achieved by TLBO and EESA. Here, the TAC achieved was $104,640 at \(LPSP^{max}=0\%\) with configuration \(S=[199\ \ 0\ \ 3150]\). As \(LPSP^{max}\) values were increased from 0%, EESA resulted in minimized TACs along with reduced system's components. For instance, at \(LPSP^{max}=3\%\), the system's configuration was \(S=[178\ \ 0\ \ 2090]\) with a reduced TAC of $71,790.  
The hourly PVs' power produced and energy storage level of the PV-battery system obtained by EESA during a year at various \(LPSP^{max}\) values are plotted in Figure 8. In Figure 8(a), the highest energy generation was achieved by \(s_{1}=199\) and \(s_{1}=194\) PVs at \(LPSP^{max}\) values of 0% and 0.5%, respectively. The lowest energy was produced by the PW-battery system with configuration \(S=[178\ \ 0\ \ 2090]\) at \(LPSP^{max}=3\%\). Here, the TAC value achieved was also lowest, i.e., $71,790. The expected amount of energy stored in battery units at four \(LPSP^{max}\) values is plotted in Figure 8(b). The system was very reliable at \(LPSP^{max}=0\%\) because it contained the highest storage capacity with \(N^{b}=3150\). As the  
Figure 7: Breakdown of the total annual cost values of the PV-WT-battery system at various \(LPSP^{max}\).  
\(LPSP^{max}\) values were increased from 0%, a decline in the number of battery units was observed along with TACs. From Figure 7(b), it is also evident that LOL occurred at time slots where battery units reached their minimum capacity limit at higher \(LPSP^{max}\) values.  
The breakdown of TACs for four \(LPSP^{max}\) values is given in Table 5 and also plotted in Figure 9. In this work, the maintenance cost of PVs is ignored. Table 5 shows that a major portion of TAC was caused by the number of battery units \(N^{b}\). At \(LPSP^{max}=3\%\), the TAC was $71,790 with configuration \(S=[178\ \ 0\ 2090]\). Here, the sub-costs: \(\xi^{pv}=\$870\), \(\xi^{but}=\$62,760\), and \(\xi^{ic}=\$260\) resulted in a TAC of $71,790. Figure 7(b),c shows similar pie charts because the average values were rounded to the nearest integer. The detailed breakdown of costs is given in Table 5.  
The next section discusses the last scenario of the WT-battery system at various \(LPSP^{max}\) values. TACs achieved by the evolutionary algorithms are reported.
# Track Line Recognition Based on Morphological Thinning Algorithm +
## 2 Related Work
The commonly used track recognition system based on an edge detection operator [29] cannot adapt to a complex road environment that includes different light scenes and multiple turnout track routes, so real-time performance cannot meet specific requirements. An algorithm based on deep learning also requires high computing power equipment, which is not available for freight trains, and is costly. Beginning with the autopilot technology of freight trains, we study track line detection based on shallow features and propose a track line extraction algorithm based on morphological thinning theory. Our algorithm is suitable for complex road conditions and changeable weather scenes, and ensures the continuity of track lines.
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 4 Experiment and Results  
### The Experimental Environment and Dataset  
In this study, all training and testing were conducted on a single computer equipped with an NVIDIA GeForce RTX 3090 graphics card (NVIDIA, Santa Clara, CA, USA). The machine was running Ubuntu 20.04, using CUDA version 11.3, and the deep learning framework PyTorch 1.11.0 [37]. Training was performed on both the original YOLOv7-tiny model and an improved version of the YOLOv7-tiny model, as well as our teacher model, YOLOv7. The training images were set to a resolution of 640 \(\times\) 640 pixels. The batch size was configured at 16, with a total of 300 training iterations. The Adam optimizer was selected, starting with an initial learning rate of 0.001 [38].  
The original dataset utilized in this study exhibits two types of defects: insulator damage and insulator flashover, as depicted in Figure 14. This dataset includes insulator defects of varying sizes, enhancing its realism. To improve the model's generalizability, images from the original dataset were augmented through random rotations, flips, and brightness and contrast enhancements. The expanded dataset contains 1631 distinct images of  
Figure 13: The process of obtaining the OD-YOLOV7-tiny network model.  
insulator defects. These 1631 images were standardized to a resolution of 640 \(\times\) 640 pixels, annotated in the format of the VOC2007 dataset, and split in a 7:2:1 ratio into training, testing, and validation sets, containing 1142, 326, and 123 images, respectively.
# Cable-Driven Exoskeleton for Ankle Rehabilitation in Children with Cerebral Palsy
## 2 Exoskeleton Development and Control Methodology
### Novel Ankle CDPR Module
#### 2.2.2 Electronic Architecture  
The ankle module is controlled by a single-board computer running a Linux-based operating system with a PREEMPT-RT real-time patch, ensuring deterministic execution for time-sensitive control tasks. Communication with the actuation hardware is established via USB serial interface, connecting the computer to two motor drivers (ODrive Robotics, Sunnyvale, CA, USA)--one dedicated to the front motors and the other to the rear.  
Each driver independently controls two brushless dual-shaft motors (D374, 150 KV, ODrive Robotics, USA). These motors are equipped with AMT10 incremental encoders (CUI Devices, USA), which provide high-resolution measurements of shaft rotation, enabling accurate estimation of cable lengths.  
The entire system is powered by a 48 V DC power supply (model HRPG-450-48, Mean Well Enterprises Co., Taiwan), which delivers sufficient current for simultaneous multi-motor operation under dynamic loading conditions.  
An overview of the electronic architecture corresponding to a single motor driver is presented in Figure 8. It should be noted that the complete system integrates two such drivers operating in parallel to control all four motors.
## 4 Proposed Orchestrator
### Solution Considerations
#### 4.2.1 Multi-Provider Network Slice Configuration  
A multi-provider NSI slice is instantiated following the procedure illustrated in Figure 6.  
Figure 6 shows the flow of requests, definitions, modifications, as well as the establishment of slices. It appears that the initial negotiation occurs between the equipment of users and the operators' offers of various slices within a given geographical area. The offer of slices from predefined templates does not inhibit the requests for custom slices, which is one of the main functions of the Orchestrator [28; 85; 92; 97]. This function of mapping and evaluating the best slice is performed by the _Multi-Provider Network Slices_, as described in Section 4.1.4.  
Hence, after the perception of the slice by the end-user suggested by the _Multi-Provider Network Slices_, the _Mobility Manager_ module requests the _Intelligent Service Leader_ module, which MNO complies with the requested SLA requirements, then initiating the provisioning of the slice, whereby the computational and network resources are verified and allocated for establishing the requested slice.  
The proposed orchestrator solution can be deployed in the Core and Edge scenario, assuming scalability according to available computational (pod) resources. The scalability threshold will be marked according to the resources mapped in the VIM (OpenStack or Kubernetes NFVI). This part is not inherent in the proposed solution, which only makes use of resources from the consumption of APIs from the Multiple Providers block (as shown in Figure 6).  
The multi-provider slice provisioning follows the ETSI MANO framework and occurs through cooperation between the _United Connectivity Resource Manager_ and the _United Distributed Cloud Mediator_..  
After mapping the resources, the _Service Manager_ module triggers the defined templates, which is a common resource used mainly in the provisioning of vertical applications and/or modifies the template to accommodate the allocation of resources, providing the negotiated SLA requirements. Issues related to allocation time, besides the compliance with QoS metrics, are maintained by the _Slice Life-Cycle Manager_ module, which implements the ETSI MANO specifications. The whole part of _softwarization_ is implemented from the treatment of data flows in the _SDN Controller_, in addition to the instantiation of the network functions (VNFs) necessary for the services that the slice will serve.  
The operation described above occurs in parallel in multi-providers, multi-operators, multi-domains, and under the management and orchestration of 5G-Horizontal. The initial tests have shown good performance in meeting the latency and jitter requirements. However, it is outside the scope of this article.