esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 4 Numerical Simulation, Experimental Results and Discussions
### Numerical Simulation and Discussions  
The experimental quadcopter parameters and controller gains are used for the simulation. The controllers are implemented based on Matlab/Simulink with the sampling time is chosen identically as the operating frequency of the controllers in the experimental system. The initial vehicle altitude and vertical velocity are all zero, i.e., \(z(0)=0\) and \(\dot{z}(0)=0\). The vehicle is armed on a horizontal level surface with the attitude stabilization controller enabled.  
We present two test scenarios to evaluate the effectiveness of our proposed algorithm, namely the tracking of (1) a step altitude reference; and (2) a moving altitude reference. For comparison, we also simulated the performance of a conventional PID controller for both scenarios.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 5 Results and Discussion
### Strain According to Resolution  
Tensile tests were conducted on the 3 \(\times\) 5 circular grid array using STS 304 material. The video resolution during the tensile test was 1920 \(\times\) 1080 (1080p), which was then changed to 1280 \(\times\) 720p (720p) and 854 \(\times\) 480p (480p) to analyze the impact of resolution on strain calculation. Strain was calculated for images at each resolution, and the stress-strain curves were compared, as shown in Figure 9. The graph shows that, while calculating strain over the gauge length is not significantly affected by resolution, the accuracy of local strain calculations decreases as resolution decreases. Experiments confirmed that, when the material elongation is 0.7, the image pixel size for the circle must be at least 10 \(\times\) 10 pixels. To verify the proposed strain prediction algorithm, tensile test data for the predicted material was measured using a 3D DIC device; the calculated stress-strain curve is shown in Figure 9. At this time, (a) represents the strain calculation result for the gauge length, and (b) represents the result for the local strain. The strain for the gauge length is similar to the DIC results regardless of the resolution. However, for local strain, it was confirmed that, the higher the resolution, the more accurate the calculation. Therefore, it was confirmed that resolution has a significant impact on calculating local strain, and a minimum resolution of 1080p is required.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **VGG16** & **VGG19** & **ResNet50** & **MobileNet** \\ \hline R\({}^{2}\) & 0.978 & 0.974 & 0.9614 & 0.904 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AI training coefficient of determination.  
Figure 9: Comparison of DIC and Engineering strain prediction algorithm: (a) gauge length, (b) local strain.
## 4 Modeling and Results
### Metrics
#### 4.2.1 Accuracy  
One of the most well-known and basic metrics for binary and multiclass classifications is accuracy. It offers a direct measure of the proportion of correctly classified samples relative to the total number of inputs, serving as a primary indicator of the overall model performance.  
This metric is easy to interpret and compute. The accuracy score reflects the percentage of the inputs that the model correctly classifies. So, a higher value suggests a better performance. The model's accuracy can be obtained using the following formula:  
\[Accuracy=\frac{Correct\ Predictions}{Total\ Predictions} \tag{7}\]  
The _accuracy_ is typically employed in tasks where the dataset is balanced (meaning that each class is represented equally) and where the cost of the misclassification is uniform across the categories. However, this metric can be deceptive in situations involving class imbalance, and our situation is one of those. In such cases, a model might achieve a high degree of recognition _accuracy_ simply by favoring the majority class, thereby masking a poor performance in underrepresented categories. Consequently, we do not solely rely on this metric; it is primarily used here for comparison with the previous SOTA solution.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 4 Localization Methods
### Fingerprinting  
Fingerprinting is a popular method of localization because of its good accuracy compared to other methods [36]. Traditional fingerprinting is based on RSSI but can also be based on static magnetic field measurements. This technique consists of two phases: a first offline phase of scene analysis and sampling, and a second online phase of pattern matching. In the offline phase, the signals from the different access points are acquired and subsequently stored in a database with the coordinates of the client device. This information can be deterministic or probabilistic. For example, in the online tracking phase,  
Figure 10: Intersection of hyperbolas that identify the position of the target.  
the user's estimated position is obtained by matching the positions stored in the fingerprint database (Figure 11). Such systems may provide an average accuracy of 0.6 m [37]. Any change in the environment, such as removing or adding furniture, changes the \"fingerprint\" corresponding to each location; hence, the fingerprint database needs to be updated.
## 4 Conclusions  
In this study, thermodynamic analysis for a seawater-cooled and marine diesel engine exhaust-driven organic and inorganic RC has been carried out. The cycle efficiencies for a simple RC and an RC with a recuperator for the marine engine have been investigated. The systems' cycle efficiencies, exergy efficiencies, net power, and irreversibility have been computed versus turbine inlet temperature by keeping the condensing temperature constant. The net power and irreversibility of the system have been calculated at full load for marine diesel engines.  
The cycle and exergy efficiencies of RC are highly dependent on the working fluid.  
For dry fluids, the most efficient fluid at low turbine inlet temperatures is n-hexane, while at high turbine inlet temperatures it is toluene.  
For isentropic fluids, the most efficient fluid at low turbine inlet temperatures is R123, while at high turbine inlet temperatures it is R141b. The results demonstrate that when the turbine inlet temperature in the RC system with a recuperator is above 200 \({}^{\circ}\)C, the dry fluid with the highest efficiency is toluene. When it is below 200 \({}^{\circ}\)C, dry fluid with the highest efficiency is n-hexane. Although toluene and n-hexane have an ODP of 0 and a low GWP of 3, they have higher flammability. R123, which features higher toxicity, and R141b have a high GWP.  
For wet fluids, R718 is one of the most suitable working fluids at low and high turbine inlet temperatures for marine diesel engine exhaust heat onboard ships with a safety group classification of A1, ODP of 0, and GWP100 of 0 at high temperatures among compared fluids. For a turbine inlet temperature of 300 \({}^{\circ}\)C, R718 has a cycle efficiency of 33%, exergy efficiency of 71%, net power of 171.544 kW, and irreversibility of 67.773 kW at a whole load of marine diesel engine. R718 is environmentally friendly, abundant in nature, and can be used safely onboard ships.  
This study contributes to significant improvements in fuel efficiency and reductions in greenhouse gas emissions, leading to more sustainable and cost-effective maritime operations.  
Investigation, H.K.; Formal analysis, C.E.; Methodology, H.K. and C.E.; Project administration, H.K.; Resources, C.E.; Validation, C.E.; Writing--original draft, H.K.; Writing--review and editing, C.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
Figure 12: Net power and irreversibility for isentropic fluids.
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
###### Abstract  
This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. The baseline fitness improvement (BFI) measure quantifies the potential value gained by applying feature selection. The BFI measure can be used to compare the performance of feature selection algorithms across datasets by measuring the change in classifier performance as a result of feature selection, with respect to the baseline where all features are included. Empirical results are presented to show that there is performance complementarity for a suite of feature selection algorithms on a variety of real world datasets. The BFI measure is a normalised performance metric that can be used to correlate problem characteristics with feature selection algorithm performance, across multiple datasets. This ability paves the way towards describing the performance space of the per-instance algorithm selection problem for feature selection algorithms.  
feature selection; baseline fitness improvement; performance analysis Article
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 3 The Technologies for Bridging the Service-Delivery Divide
### Multi-Access Edge Computing  
In the vein of the previous discussion on the shifting to distributed networking paradigm, also the Multi-access Edge Computing (MEC) is increasingly catching the eye, since it allows to improve the delivery of services in several respects. First of all, the resources of MEC servers can be provided, through virtualization, to limited-resources consumers based on the most appropriate service model (Infrastructure as a Service--IaaS, Platform as a Service--PaaS, Software as a Service--SaaS) [20]. Then, the proximity of the MEC to users enables various benefits, including low latency and context-awareness, which allows us to customize the service delivery to the needs of the consumers. The authors of [21] cite the efficacy of MEC in providing support to communication, computing, and storage, thus improving the Quality of Service (QoS) provided to users. This can be considered a plus in bridging the digital gap as poor QoS is seen as an impediment to the effective delivery of bandwidth-intensive services.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 5 Data Analysis, Results and Discussion
### Results of the Jammer Detection and Its Localisation  
In order to obtain the best jammer positioning lateration performance of down to 10 m for a jammer closer than 50 m approaching at 30 km/h (a decrease of the C/N\({}_{0}\) in Figure 8), the proportionality parameter \(\alpha\) (consider Equation (7)) was typically 1.0, while \(\overline{\beta}\) was around 30 for the surveying receivers. For the consumer grade (Ublox, M8N), \(\alpha\) was typically over 1.0, whereas \(\overline{\beta}\) was less than 20.  
Forming a distance/cost function for each drive from the sum of the differences of true distances and the best estimated distance gave an insight into the influence of selection of the satellites' signals that were used for averaging C/N\({}_{0}\), both for azimuth and elevation. The sensitivity to the selection of satellites can be seen in Figure 9. Moreover, the influence of shifting the minimum of the C/N\({}_{0}\) pattern with respect to the moment of the closest point of the receiver and jammer is also given (see Figure 9). Interestingly, applying the non-jammed L2-frequency observations (1227.60 MHz) C/N\({}_{0}\) generally did  
Figure 7: Skylplot of visible satellites for 16 July 2019: (**a**) at the beginning of the experiment 08:20:00 UTC and (**b**) at the end of the experiment 09:58:00 UTC [48].  
not give significantly worse results than on L1, since the effect on C/N\({}_{0}\) within L2 occurs due to the processing reasons.  
However, jammer localisation is only of side importance for the surveyor, with estimated distances, followed by alterations from pairs of receivers. A surveyor may assume from the disturbance that it may decay due to its moving in or it may last until the jammer is turned off.
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 2 Materials and Methods  
### Study Area  
Tuscany is located in central Italy and covers about 23,000 square kilometers. The climate ranges from the Mediterranean dry climate along the coastline to the temperate humid and wet climate in the inland and northern areas of the region. Tuscany is mainly hilly (about 67%) and mountainous (about 25%), and it also includes some plains (about 8%). The cultivated areas represent about 39% of the region, mainly characterized by arable land, vineyards, and olive groves.
# Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data
## 2 Materials and Methods
### Description of Metrics Derived from LV Mesh  
Once a final 4D mesh of the left-ventric is created, a series of metrics that characterize its regional kinematics are systematically extracted based on the Lagrangian-definition of linear or engineering strain [28; 29; 30; 31] in both the circumferential (Equation (4)) or longitudinal frame (Equation (5)):  
\[\varepsilon_{C}(t,z)=\frac{perimeter_{t-perimeter_{t-0}}}{perimeter_{t-0}} \tag{4}\]  
\[\varepsilon_{L}(t,\theta)=\frac{length_{t-length_{t-0}}}{length_{t-0}} \tag{5}\]  
where each metric is a function of time t (i.e., a given position within the cardiac cycle), and circumferential and longitudinal strain curves are a function of both position z (i.e., location along the \(z\)-axis) and angle \(\theta\) (i.e., rotation from the positive \(x\)-axis), respectively. Furthermore, additional metrics can be derived from each curve including the early/late systolic strain rates and early/late diastolic strain rates, providing insight into how the heart is moving between end-diastolic and peak-systolic states.  
To assess the robustness and practical use of the machine learning-based predictions of wall kinematics, these metrics were computed at select locations in the circumferential (e.g., basal, mid-LV, and apical) and longitudinal (e.g., anterior, posterior, anterior free-wall and septum, and posterior free-wall and septum) frames. Derived metrics from both the ground-truth and machine learning-predicted boundaries were compared using paired \(t\)-tests with [PERSON]'s multiple comparisons corrections. We note that the distribution of several metrics showed some minor deviations from normality (e.g., slight skewness). Adjusted Shapiro-Wilk tests suggested non-normality for roughly 10% of the cross-validation samples. However, the central limit theorem ensures that even when deviations exist from normality among the individual observations, the sampling distribution of the test statistic converges to a normal distribution with larger sample sizes. The recommended sample size threshold for assuming normality via the central limit theorem is 30 [32]. As the sample sizes used for our \(t\)-tests were roughly 600, it is safe to assume normality of the computed test statistics.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Communication System** \\ **Availability model/years** \\ \end{tabular} & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \begin{tabular}{c} **Combined results of** \\ **improved corrective and** \\ **preventative maintenance** \\ \end{tabular} & 100 & 92.23 & 90.36 & 89.9 & 89.79 & 89.76 & 89.75 & 89.75 & 89.75 & 89.75 & 89.75 \\ \hline \begin{tabular}{c} **Preventative maintenance** \\ **was improved by 50\%** \\ \end{tabular} & 100 & 92.23 & 87.7 & 85.04 & 83.47 & 82.54 & 81.98 & 81.65 & 81.45 & 81.33 & 81.26 \\ \hline \
\begin{tabular}{c} **Corrective maintenance** \\ **doubled** \\ \end{tabular} & 100 & 88.41 & 85.97 & 85.43 & 85.31 & 85.28 & 85.27 & 85.27 & 85.27 & 85.27 \\ \hline
\begin{tabular}{c} **Current** \\ \end{tabular} & 100 & 88.41 & 82.04 & 78.5 & 76.5 & 75.37 & 74.73 & 74.36 & 74.15 & 74.03 & 73.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation results of single-shaft underground mine communication system reliability without standby system (included preventive and corrective maintenance analysis).
## 3 Results and Discussion
### Transport and Retention Behavior of \(ZnO\)-NPs without SRHA  
To investigate the transport behavior of \(n\)ZnO in the packed column, experiments were conducted in the general soil environment conditions of NaCl (0.1-10 mM) and at pH 9. Figure 3a,b, as well as Figure 4a,b, show the breakthrough curves and retention profiles of the two types of \(n\)ZnO in the absence of SRHA. In the breakthrough curves of both type of \(n\)ZnO (Figure 3a,b), no breakthrough was observed over the whole ionic strength range (0.1-10 mM). Because no breakthrough was observed with either type of \(n\)ZnO, they were considered to have been deposited in the soil media, which corresponded to the results of mass balance (Table S2).  
Because no breakthrough was observed with either type of \(n\)ZnO from the column, it was difficult to compare the apparent transport behaviors. Accordingly, the distribution of the \(n\)ZnO retained in quartz sand was investigated by confirming the \(n\)ZnO retention profiles. As described, all the \(n\)ZnO were retained on the column according to the mass balance confirmation (Table S2). According to the retention profiles (Figure 4) of the two types of \(n\)ZnO, no difference in the deposition amount was observed as a function of change in ionic strength (0.1-10 mM) between the two types of \(n\)ZnO, and the deposition of both type of \(n\)ZnO was confirmed mainly near the column inlet. The similar graph pattern (i.e., large amount of deposition at the column inlet) was also observed from many previous studies [20; 56; 57; 58; 59; 60; 25], and they attributed this trend to the straining caused by the concurrent aggregation among \(n\)ZnO.  
Figure 3: Breakthrough curves of _n_ZnO-1 (open symbol) and _n_ZnO-2 (solid symbol) in the absence (**a,b**) and presence (**c–f**) of suspended SRHA at difference ionic strengths in NaCl solution (0.1–10 mM). The inset of (**a,b**) is the enlarged image of the BTC profiles for clarity; note that the BTC percent is \(\leq\)2% for all cases, which is considered negligible. C/C\({}_{0}\) represents normalized concentration; here, C and C\({}_{0}\) represent the concentration of NPs passing through the column at certain pore volume and of injected NPs suspension, respectively. The pore volume is defined as t/VQ; here, t, V, and Q represent time, column void volume, and volumetric flow rate.  
Figure 4: Retention profiles of \(n\)ZnO-1 (open symbol) and \(n\)ZnO-2 (solid symbol) in the absence (**a**,**b**) and presence (**c**–**f**) of suspended SRHA and at difference ionic strengths in NaCl (0.1–10 mM). Here, the dimensionless depth represents the normalized column depth (i.e., column depth at certain position divided by total length of the column). The x-axis represents the normalized concentration of NPs deposited at a certain depth per unit sand mass; \(N_{c}\), \(N_{\text{tc}}\), and \(\text{g}_{\text{sand}}\) represent the number of the deposited NPs, the number of total NPs injected to the column, and the sand weight at a certain depth, respectively.
## 4 Model Training and Testing
### Training Results and Analysis
#### 4.2.2 SimAM Module Validation  
Currently, a CBAM attention mechanism is widely applied to the model of YOLOv5 [25]; to obtain the effect of a simAM module on the model, YOLOv5s, YOLOv5s+CBAM, and improved YOLOv5s were trained to compare the accuracy, recall, and average precision, and the results are shown in Table 3.  
Although the CBAM attention module also improves the performance of the network, it does not improve as much as the simAM attention module, and unlike the simAM module, the CBAM module is parameter-free, which prolongs the training time of the model and can easily lead to overfitting.
###### Abstract
VIs have been known to help in grazing management decisions in the past [10; 11], making them a viable option for low-input, large-scale beef enterprises. Pairing VIs with LW and LWC data could help to determine thresholds linked to animal performance, such as LW loss in larger operations with low inputs where this is not measured. Machine learning techniques have been used in the industry to predict meat characteristics and calving time [12; 13], and have the potential to predict production outcomes such as the LWC by utilising ancillary variables such as VIs and rainfall in extensive production systems. To the author's best knowledge, there are no studies which incorporate the use of VIs to predict changes in the growth of breeding cows under commercial conditions in Northern Australia. An investigation into this area would be of benefit in increasing the productivity of breeding animals under extensive conditions. Due to the global availability of freely accessible data, such as rainfall and VI from satellites, predictive modelling can nowcast the growth of cows in the coming months and could have benefits for management interventions such as the stocking rate, provision of supplementation, early warning indicators for poor performance of animals, and predictions of mortality. The hypothesis of the present study is that there is a relationship between VIs and cattle LW and LWC. The objectives of the present study are, firstly, to assess the relationship between VIs and cattle LW and LWC under extensive grazing conditions of northern Australia. Secondly, the study aims to utilise machine learning to develop a model which could predict the LWC of breeding cows using VIs and freely available data such as rainfall.