esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 3 Materials and Methods  
This project comprises four activities outlined in Figure 1, illustrating the work breakdown structure (WBS) for developing a system to map gaps in sugarcane fields using RGB drone imagery. Below is a concise overview of each activity:  
1. The initial activity involves capturing fields images, and following image capture, generating orthomosaics from the acquired images.
2. The second activity involves segmenting samples from the orthomosaic to train the convolutional neural network and generate models for mapping field gaps.
3. In the third phase, the optimal model will be utilized to identify field gaps within the orthomosaics.
4. Lastly, the fourth activity entails producing the mapping of field gaps, accompanied by a comprehensive report detailing the location of these gaps.  
Figure 1: Work breakdown structure (WBS) of the mapping gaps.
# Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning
## 4 Method  
### State Representation  
To accurately capture the key factors in traffic signal control, this paper defines a set of state features, including the queue length of vehicles on the incoming lanes at the current intersection, the number of moving vehicles, and the vehicle distribution along road segments. The vehicle distribution is divided into four sections for each incoming lane, with each section measuring 100 m, and the number of vehicles in each section is calculated. This detailed design reflects the distribution of vehicles in different segments, providing the model with fine-grained traffic information. Additionally, the state representation includes the current phase and its duration, which are crucial for deciding the next action.
## 5 Monitoring Decomposition  
Monitoring decomposition is important for carbon accounting. The extent of decomposition can be followed by measuring the volume and composition of the biogas formed by decomposition. Each of the following chemical phases of decomposition is characterized by its unique composition of biogas.  
**Phase I:** Aerobic decomposition. This phase produces only CO2 and water, and is characterized by decreasing oxygen levels and slower decreasing N2 levels as the CO2 begins to displace the N2. Measuring the O2 concentration as it asymptotically approaches zero will be the best indication that the pits are in Phase I.  
**Phase II:** Anaerobic, non-methanogenic, acid-forming phase. This phase produces CO2 and H2. CO2 and H2 concentrations reach a maximum in Phase II. The presence of H2 in the biogas is the best indication that the pits are in Phase II. Decreasing the pH of any leachate (the water phase) would be another indication that the pits are in Phase II.  
**Phase III:** Anaerobic, methanogenic, unsteady transition phase. CO2 and H2 are consumed in the CO2 reduction reaction. CO2 and H2 levels decrease. The H2 level decreases to zero. Methane formation begins, but the ratio of CO2/CH4 in this phase is well above one.  
**Phase IV:** Anaerobic, steady-state methanogenic phase. The biogas reaches a steady-state composition with a CH4/CO2 ratio of about 1:1. It is not quite 1:1 due to the fact that the carbon concentration of the actual biomass deviates from the ideal carbohydrate simple formula of CH2O, which is due to the presence of other elements (N, P, etc.) and the fact that proteins, lipids, and lignin deviate from the CH2O formula.  
A depiction of the characteristic composition of the biogas in these phases is shown in Figure 5. Note that, since it will take time to move through Phases I and II before any methane is generated, there is ample time to install gas vent wells after the pits are sealed with the clay roofs, if necessary. Since the biomass will occupy the space where the clay has been removed, there will be adequate clay to cap the pits with sloped clay roofs, which will shed rainwater away and keep away any water infiltration from neighboring surface water.  
For municipal waste, it can take months to move to Phase III. It is expected to take much longer for woody biomass, based on its inherent slower decomposition due to cross-linking, and thus the dense packing of cellulose and hemi-cellulose [51].  
The pit temperature is another important variable to monitor. Decomposition reactions are exothermic, which leads to a rise in the pit temperature, and an increasing temperature accelerates the reactions. Temperature probes with multi-point junctions at varying heights can be used to measure the difference between the temperature within the pits and the probes placed nearby, but outside, the pits. The absolute temperature is not a good indicator, since it varies seasonally. It is the temperature difference between the pit temperature and the probes outside of the pits that is the most sensitive indicator.
###### Abstract  
This study numerically investigates the limitations of structural hot-spot stress (SHSS) methods and proposes a guideline for the calculation of hot-spot stresses, which can be used for the better evaluation of fatigue-related problems. Four different SHSS evaluation methods have been applied to the rib-to-deck (RD) welded joint in orthotropic steel deck (OSD). These methods are used to calculate SHSS at this critical joint utilizing finite element analyses (FEA) based software Siemens NX.12. The limitations and the accuracy of these methods have been observed under different element types and meshing techniques. Moreover, the effect of the nodal-averaging feature is being studied. Two types of governing stresses are produced by the application of Eurocode fatigue load model-4. Essentially, the bending in deck-plate produces highly non-linear stress at the deck-toe, and the membrane effect in rib-plate generates linear stress at the rib-toe. Guidelines are proposed considering different parameters on these two stress states by applying SHSS evaluation methods. In comparison to other SHSS approaches, the International Institute of Welding (IIW) quadratic stress extrapolation (QSE) method shows better results for solid single-element, and the American Society of Mechanical Engineers (ASME) through thickness stress linearization (TTSL) method stands out in solid cubic-mesh technique. In general, shell elements have more consistent SHSS results as compared to solid elements for both stress states.  
structural hot-spot stress; rib-to-deck joint; orthotropic steel deck; finite element analysis; surface stress extrapolation; bending stress; through thickness stress linearization
# Experimental Investigation into the Number of Phases in Debris Flows
## 4 Laboratory Experiments
### Escape Test of Fluid  
An escape test of fluid is similar to a slump one [40; 74; 75], and, to a lesser extent, to a dam-break one [26; 39; 76]. It was intended to investigate the differentiated responses of potential fluid and solid phases to the sudden release of the slurry.  
The escape test was run by abruptly lifting cylindrical containers holding the slurry on a gently tilted smooth surface with an inclination of 5\({}^{\circ}\), which corresponds roughly to the surface gradients of debris-flow fans, that is, the deposition angles of debris flows [20; 39; 71; 77], and can assist the relative motion of fluid and solids (if present). The diameters of the two containers used were 10 (for eight slurries with \(d_{\rm max}\) = 2 and 10 mm) and 30 cm (for four slurries with \(d_{\rm max}\) = 30 mm), and their respective heights were 4.3 and 11.5 cm, which, on the inclined plane, produced storage volumes (\(V\)) of 300 and 7170 cm\({}^{3}\), respectively.  
The experimental flow propagation on the tilting plane was observed and recorded. After full stoppage of runout, we photographed experimental deposits (to take orthophotos), capturing their three-dimensional geometry with a 3-D laser scanner (Leica@Scan Station C10) (Leica, Wetzlar, Germany), collected and weighed the fluid (effluents) escaping out of main deposits (if there was any), and determined the grain-size distribution of sediments constituting the effluents with the laser diffraction particle size analyzer described above.  
The uniformity of runout is characterized by coefficient of runout (\(C_{\rm r}\)). \(C_{\rm r}\) is defined as the ratio of the surface area including the convex sides (\(A_{\rm s}\)) to the inundated area [7; 34; 54; 76; 77; 78; 79; 80] of deposits (\(A_{\rm i}\)),  
\[C_{\rm r}=\frac{A\,\rm s}{A\,\rm i} \tag{1}\]  
Additionally, _Solidity_ (\(S\)) was used to describe the 2-D geometry of the experimental deposits and was defined as \(A_{\rm i}\) divided by the area of the minimum circumscribed polygon of the deposits. As the irregularities at the boundary of a deposit decrease, \(S\) increases to a maximum value of 1.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 3 Results
### Quality Evaluation of PPP-B2b Corrections
#### 3.2.1 PPP-B2b Orbit Corrections
For the GPS, the RMSEs are 0.093, 0.386, and 0.278 m in the R/A/C directions for the PPP-B2b real-time orbit, and 0.034, 0.052, and 0.034 m for the CNES real-time orbit. Furthermore, the accuracy of the PPP-B2b orbit in the R direction is within 0.1 m, which is slightly lower than that of the CNES real-time orbit.
###### Abstract  
This paper presents a novel design of a wideband circular polarization 2 \(\times\) 2 microstrip antenna array working at Ka-band frequencies, from 27.5 to 31 GHz. This module is highly integrable with new silicon beamformer chips, creating a unit cell that can be part of a large electronically steerable antenna for compact, ultra-low-profile, Satcom-on-the-move (SOTM) platforms. A multi-layer structure fabricated in standard printed circuit board (PCB) technology with high-yield substrates has been used. The radiating elements consist of double-stacked circular patches housed in a cavity and fed by H-shaped aperture coupling. It achieves a bandwidth of 16.5 % with a wide beam-width of 95\({}^{\circ}\) in the desired band, which is necessary for wide scanning angles in a large phased array. In the 2 \(\times\) 2 unit cell, the antenna elements are distributed by means of a sequential rotation technique where the separation between two of them is 5.3 mm in the XY-plane. Broadside beam-widths ranging from 53.4\({}^{\circ}\) at 27.5 GHz to 42.1\({}^{\circ}\) at 31 GHz are achieved, with boresight directivities from 10.7 to 12.9 dBi, respectively, in both the RHCP and LHCP polarization. Moreover, mutual coupling levels below \(-\)20 dB and an axial ratio less than 3 dB in the whole band guarantee a good circular polarization purity.  
active electronically scanned arrays (AESA); Ka-band; printed circuit board (PCB); Satcom-on-the-move (SOTM); silicon beamformer 2021106121[[https://doi.org/10.3390/electronics10141621](https://doi.org/10.3390/electronics10141621)]([https://doi.org/10.3390/electronics10141621](https://doi.org/10.3390/electronics10141621))
## 2 Theoretical Background
### Bathtub Curve  
The operational life of a piece of equipment has three important characterizable phases, namely, the infant-mortality phase, useful-life phase, and wear-out phase [18]. A visual representation of the failure rate of a component over time is given by a bathtub curve, as illustrated in Figure 2.  
Figure 1: Graphical relationship between failure and fault for (**a**) for discrete variables and (**b**) continuous variables [19].  
Figure 2: Bathtub-curve failure rate versus time [18].  
There are three phases of the bathtub curve, which is so-named due to its characteristic bathtub shape [21]. The first phase represents early failures due to poor design and installation. Second-level failures occur in the useful-life period and at random times [18]. The last phase is wear-out failures, where the failure rate rises rapidly due to equipment deterioration [18].
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 4 Proposed Approach
### Initial Locations of the RPs
Once all sensor nodes know the destination RPs, the MS starts to accomplish data gathering by going through all RPs. When the MS becomes close to each destination RP, it sends a polling message with the ID of the corresponding RP. In this case, the corresponding sensor nodes prepare and transmit their data to the MS when it reaches the corresponding RP. If a node is within the MS communication range, it sends its data directly. Otherwise, it forwards the data to the nearest node of the corresponding RP. This procedure is repeated until all RPs are visited. If the RPs are kept fixed during the entire operation of the network, it could pose energy holes around the fixed RPs location and, thus, the MS will no longer be able to receive data from sensor nodes with two or more hops around the RPs. Therefore, in this paper, a dynamic approach for updating the RPs locations every predefined number of rounds is proposed. For this purpose, we assume the MS keeps track of the following parameters during each round:  
1. The current locations of the RPs. 2. The ID of the corresponding RP for each sensor. 3. The distance between each sensor and the corresponding RP. 4. The energy level of each sensor. 5. The number of surrounding sensor nodes for each sensor node. Based on this information, the locations of the RPs are appropriately updated to extend the network lifetime and reduce the overall energy consumption as well. To perform the dynamic data collection for each new updated RPs' selection, the MS updates the ID of the corresponding RP of each sensor and broadcasts new RIP packets to inform the sensors with the new ID of the destination RP.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 7 Conclusions  
This paper has discussed the importance of smart cities applications and the WSN as a core network in smart cities. Due to the limitations of the sensing devices, it is exposed to different security attacks. The paper has discussed one of the critical attacks on WSNs--sinkhole attack. Therefore, the paper has proposed two intrusion detection and prevention algorithms for the sinkhole attack. The first algorithm is based on multipath routing, MBIDS, where a control message has to be sent with the data message as well as through a separate route. The second algorithm is based on a threshold that is checked for each cluster head. Those two proposed intrusion detection algorithms are compared to three recent algorithms, which are ABC, S-LEACH, and MS-LEACH. Through an extensive set of experiments, the results show that TBIDS and MBIDS overperform all of the three algorithms using different criteria. It is worth mentioning that TBIDS results are very light and promising, and TBIDS could be implemented on a small-scale and large-scale network. The possible extension of this paper could be in three directions: (1) Investigating different methodologies to identify the best threshold value for TBIDS algorithm; (2) distributed intrusion detection systems where cluster heads can cooperate to detect the sinkhole node and isolate it; (3) reducing the overhead in MBIDS and lookup operation in the routing table at the sink node.  
In addition, the future direction of smart cities is to have sensors that are smart enough to perform data analysis to its neighbors' behavior and discover any intrusion process gaining on. It is believed that smart cities will be designed based on security as the first approach, and its planning will be focused on its potential risk. For instance, 5G technology is extending the connectivity in smart cities, and at the same time, it opens an opportunity for attackers to step in. Therefore, a holistic security approach has to come to the play during the design of the smart city. It is also expected that artificial intelligence (AI) and big data (BD) analytics will be the \"city brain\" of smart cities where every \"thing\" could be represented as a neuron in the huge smart city brain. Therefore, the potential research directions in smart cities could be in sensing technologies, security, AI, and BD. Certainly, communication will be a major factor in smart cities.  
This research received no external funding.
# A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study
## 2 Predictive Maintenance  
### Predictive Maintenance by Data-Driven Models  
Once the monitoring strategy is chosen, the next step is to select the failure detection and prediction model. The available models are classified based on the diagnostic strategy [(41)].  
1. Data-driven models: These typically apply machine learning (ML) or deep learning (DL) algorithms. An example is given in [(42)], where the authors compared the prediction of the ship speed loss due to fouling by two completely different methods: the ISO 19030 standard [(43; 44)] procedure and a data-driven digital twin. The results clearly showed the data-driven model had better prediction capability than the ISO 19030 standard. In general, the main advantage of data-driven models is a better determination of the time between overhauls (i.e., the time between two maintenance actions).
2. Physics-based models: In this case, the outputs from the real asset are compared to those given by a physical model that can be developed to represent the system under maintenance. An example is given in [(45)], where the authors proposed an adaptive extended Kalman filter for estimating the fault magnitude in an autonomous surface vehicle. In this case, a digital twin was developed to implement all the mathematical modeling equations which represent the autonomous vessel's physics. The main advantage of this approach is independence from the amount of collected data, differently from the data-driven model, but a substantial limiting drawback is the mathematical approximation, which can reduce the method's reliability.
3. Knowledge-based models: These try to mimic an expert's reasoning; the main advantage is that complex physical models are not needed. An example is given in [(36)], where the authors analyzed the deterioration of a marine engine lubrication system using a dynamic Bayesian network (dBN). The model was realized by using both the real data provided by the shipping company and the experts' background knowledge. The latter was used to assess the cost of the risk model of the dBN (condition monitoring cost, maintenance expenses, repair expenses and failure expenses).  
Although all the aforementioned models are valid for diagnosis, the authors focus on online monitoring by data-driven models.  
Data-driven models are linked to the current technology development, especially in the maritime sector regarding propulsive systems. In fact, the objectives that must be reached by the 2030 Agenda and Paris Agreement 2050 determine a high interest for investigating new fuels for marine propulsion, such as hydrogen fuel cells [46] and ammonia engines [47]. For that reason, physical models or experts' knowledge are often not available. Instead, data-driven models do not require the implementation of physical laws, which gives high flexibility to the detection and prediction algorithms, such that they can be applied to different engines without deep changes to the algorithmic structure.
## 2 Materials and Methods
### Statistical Analyses  
Continuous variables are expressed as mean \(\pm\) standard deviation (SD) or medians and IQRs. Qualitative variables are reported as count and percentage. Characteristics of patients who were adherent vs. those who were partially adherent or non-adherent were compared using the chi-square test of independence or [PERSON]'s exact test (when the expected number of events in the cell is less than 5) for categorical variables and two independent samples t-test (normally distributed data) or Mann-Whitney test (non-normally distributed data) for continuous variables. Ordinal variable was compared using Cochran Mantel-Haenszel Modified Ridit Scores (Row Mean Scores' statistic). Continuous outcome (change in peak VO2 in CPET) was compared between groups using analysis of variance adjusted for atrial fibrillation, HF, and baseline level of the outcome measure. NYHA class will be analyzed as an ordinal variable using ordinal logistic regression, including terms of baseline NYHA class. A general linear model was applied (PROC GLM in SAS 9.4 software), in which the independent variables, apart from the main adherence effect, were: HF, atrial fibrillation, and covariance in the form of the baseline measurement. The dependent variable was the difference between the tested measurement after 9 weeks and the baseline measurement.  
Kaplan-Meier plots were created to illustrate survival experience, and differences were evaluated by the log-rank test. All time-to-event outcomes were compared between groups using Cox proportional hazards regression. Hazard ratios and their corresponding 95% CI adjusted for atrial fibrillation/futter and etiology of HF. All tests were two-sided, and a \(p\)-value < 0.05 was considered statistically significant. All analyses were conducted using SAS software version 9.4.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 2 Materials and Methods
### Experimental Apparatus  
Experiments to certify the self-calibration method for the vertex distance of the elliptical paraboloid array were undertaken several times with the help of an XL80 laser interferometer with a resolution of 0.001 um and system accuracy of \(\pm 0.5\) ppm ( Renishaw, Gloucestershire, UK), and a Brown & Sharpe Chameleon 9159 bridge-type coordinate measuring machine (CMM) with a technical target of U12.3 + 2.8 L/1000 (Hexagon, Stockholm, Sweden). The experimental apparatus of the self-calibration system is shown in Figure 4. The optical slope sensor mounted on the quill of the CMM moves along the direction of the elliptical paraboloid array, whose direction is parallel to the X direction of the CMM. The function of the laser interferometer is to monitor the actual displacement of the quill for accurate calibration. It should be noted that the apparatus of the calibration experiment is also suitable for the measurement experiment, which is the reason the method is called self-calibration.  
Figure 4: Experimental apparatus of the calibration system.  
Figure 3: Schematic diagram of the optical slope sensor.