esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Methodology and Data Collection
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Indices**} & \multicolumn{1}{c}{**Measurement Units**} \\ \hline Guide vane inclination & \% \\ Separation losses & \% \\ Fuel consumption & L h\({}^{-1}\) \\ Throughput & t h\({}^{-1}\) \\ Broken grain & \% \\ Sieve average losses & \% \\ Engine load & \% \\ Feed flow & kg s\({}^{-1}\) \\ Fuel consumed per ton & L t\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluated combine parameters. crop residue and grain falling from the combine. After the drop, the collected material is processed through the \"Bushel Plus air separator\", which separates the grain from the chaff and straw. Cleaned grain is then weighed using a digital scale. Weighed grain mass, header width, and field yield are entered into the mobile application \"SmartDrop\", which calculates grain loss in kg/ha or percentage from grain yield. The experiment was conducted on the same day in the winter wheat field. The selected field area was 32 ha, and was divided into alternating strips harvested by Combine A and Combine B. Each strip corresponded to a different guide vane inclination setting ranging from 10-35\({}^{\circ}\), as shown in Figure 9. GPS coordinates and combine telemetry data were used to map and assign guide vane zones to specific field passes for each machine. Figure 8: Determination of actual grain loss using the “Bushel Plus SmartPan” system: 1. Drop pan mounted under combine; 2. Sample cleaning and grain separation using Bushel Plus air separator; 3. Grain loss measurement using a digital scale. Figure 9: Geo-referenced harvest map of the experimental field: (**a**) Combine A harvesting zones with 6 different guide vane angle settings; (**b**) Combine B harvesting zones with 6 different guide vane angle settings. 1.—10\({}^{\circ}\), 2.—15\({}^{\circ}\), 3.—20\({}^{\circ}\), 4.—25\({}^{\circ}\), 5.—30\({}^{\circ}\), 6.—35\({}^{\circ}\), guide vane. This experimental framework--featuring dual-machine comparisons, GPS-linked telemetry, and physical grain loss verification--enabled detailed quantification of harvesting performance under varying guide vane configurations. The collected data was analyzed to evaluate separation losses, throughput, fuel efficiency, and grain quality.
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 5 Experimental Results
### Estimating Doppler-Shift of Iridium Satellite Signal in Weak Signal Environment
The discussion above gives the detail of the Iridium weak signal Doppler-shift estimation by the new QSA-IDE algorithm and shows that QSA-IDE algorithm can effectively estimate Doppler-shift in weak signal environment. In the collected 30 min IRIDIUM NEXT data, most Iridium signal powers are close to the power of the Iridium signal in Figure 5a, even the power is lower for it. The Doppler-shift curves of the ring alert signal and primer message signal corresponding to the collected 30 min data obtained by QSA-IDE algorithm and the results of positioning using the Iridium satellite SOPs in weak signal environment are shown in the next section.
## Material and Methods
### Assessment of Vulnerability  
The parametric method DRASTIC (indexation of parameters) was used to assess the natural vulnerability to contamination, dedicated to porous water-bearing media. The most important  
Figure 3: Annual recharge rate taken from numerical groundwater flow modeling: (**a**) 2003; (**b**) 2011; (**c**) average in 1999–2013. assumptions of this method include the advective migration of conservative pollutants from the ground surface through the vadose zone. DRASTIC is a method often used to assess groundwater vulnerability to a wide range of potential contaminants. It is a standard tool for assessing groundwater vulnerability to contamination, and is applied in many countries [2; 10; 34; 35; 36; 37; 38; 39; 40]. Calculations of the DRASTIC index (IPZ) are conducted according to the following formula [1]:  
\[\text{IPZ}=\text{D}_{\text{R}}\;\text{D}_{\text{w}}+\text{R}_{\text{R}}\text{ R}_{\text{w}}+\text{A}_{\text{R}}\text{A}_{\text{w}}+\text{S}_{\text{R}} \text{S}_{\text{w}}+\text{T}_{\text{R}}\text{T}_{\text{w}}+\text{I}_{\text{R}} \text{I}_{\text{w}}+\text{C}_{\text{R}}\text{C}_{\text{w}}, \tag{1}\]  
where R--parameter rating (Table 2); W--parameter weight (Table 2); D--depth to groundwater; R--net recharge; A--aquifer media; S--soil media; T--topography; I--impact of vadose zone; C--hydraulic conductivity of the aquifer. An advantage of this method is that the used parameters comprehensively characterize the analyzed multi-aquifer system by providing information about its both typical hydrogeological elements (groundwater table depth, recharge rate, aquifer lithology, hydraulic conductivity) and environmental elements that include terrain topography and the type of soil profile and its parent lithology of the vadose zone. Because the circulation conditions are very well known in the study area, the analysis was based on various, highly detailed data (Table 2). They comprised the analysis of sections of research and hydrogeological boreholes, geophysical surveys, maps, including those compiled within the framework of the Protection plan of Kampinos National Park [41]. To calculate the index of vulnerability (IPZ index), a high-resolution (10 \(\times\) 10 m) digital terrain model was also applied. A separate issue was the analysis of spatial variability of hydraulic conductivity, whose values were determined based on grain-size analyses, pumping tests, and direct studies using the PARAMEX [26] and BAT tests [29]. For the calculations of groundwater vulnerability to contamination, the original weight values were taken ([1]; Table 2). The assessment of groundwater vulnerability to contamination was performed for three variants of the depth to groundwater table and infiltration recharge: variant A--average groundwater levels from the period of 1999-2013, treated as long-term average values; variant B--the lowest long-term levels from 2003; and variant C--the highest long-term levels from 2011. The vulnerability calculations were performed using GIS software. Continuous spatial data (i.e., soil types, aquifer media etc.) were discretized using ArcGIS 10.3 and the Raster calculator tool.
## 3 Proposed System
### Action Classification with SVM Multiclass Classifier  
After computing the features from video, an appropriate classifier has to be used for classification of actions. In supervised learning, discriminative models are more effective than generative models [36]. Support vector machine (SVM) is one of the most successful classifiers in discriminative models category. It has shown better performance than some old-style classifiers such as backpropagation neural networks, Naive Bayes, and k-nearest neighbors (KNNs) in many classification problems [37]. SVM was first proposed in [38], and, originally, it was developed for binary classification but later on extended to the multiclass classification problem. There are two main approaches for multiclass SVM. The first approach considers all classes of data directly into one optimization formulation, while the second approach constructs and combines binary classifiers in some manners to build a multiclass classifier. The second approach is computationally less expensive and easy to implement. Many algorithms have been derived for multiclass classification using this approach, such as one-against-all [39], one-against-one [40], Directed Acyclic Graph-Support Vector Machine (DAG-SVM) [41], Error-Correcting Output Codes Support Vector Machine (ECOC-SVM) [42], and Support Vector Machines with Binary Tree Architecture (SVM-BTA) [43]. Among these, one-against-all [39] and one-against-one [40] are two commonly used methods for multiclass classification. The one-against-all needs N SVM binary classifiers for an N class classification problem, while one-against-one method needs \(\frac{N(N-1)}{2}\) binary classifiers for an N number of classes, each trained from samples of two corresponding classes. As compared to one-against-all method, one-against-one is better in terms of accuracy for many classification problems [44].  
We used multiclass SVM classifier implementation in [45] for multiview action recognition, which uses the one-against-one method with radial basis function (RBF) kernel. Moreover, to estimate the best parameters for classifier, we conducted grid search to know the best value for parameter \(\gamma\) and C. Here, \(\gamma\) represents the width of the RBF kernel and C represents the weight of error penalty. The appropriate set of (C, \(\gamma\)) increases the overall accuracy of the SVM classifier [46].
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Mobility Metrics
#### 3.2.3 Link Expiration Time  
The high-speed of vehicles in VANETs is the source of fast topology changing, which leads to frequent link breakage. The protocol should select the vehicle which gives the longest connection time. To calculate the link expiration time (\(LET\)), the proposed scheme uses the formula proposed in [44]. \(LET\) is considered as the period of time during which two vehicles stay connected. Let \((x_{i},y_{i})\) and \((x_{j},y_{j})\) be the coordinates of two vehicles \(i\) and \(j\) which are moving in directions \(\theta_{i}\), \(\theta_{j}\) where\((0\leq\theta_{i},\ \theta_{j}<2\pi)\), having speeds \(v_{i}\) and \(v_{j}\) respectively and let \(r\) be the transmission range. \(LET\) is estimated using Equation (3):  
\[LET_{ij}=\ \frac{-\left(ab+cd\right)+\sqrt{\left(a^{2}+c^{2}\right)r^{2}- \left(ad-bc\right)^{2}}}{a^{2}+c^{2}} \tag{3}\]  
where \(a=\ v_{i}\cos\theta_{i}-v_{j}\cos\theta_{j}\), \(b=x_{i}-x_{j}\), \(c=v_{i}\sin\theta_{i}-\ v_{j}\sin\theta_{j}\) and \(d=\ y_{i}-y_{j}\).
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 1 Introduction
These protocols are considered the most significant approach for solving the routing issues in FANETs [16]. Topology-based routing primarily aims to provide an optimal path between UAVs by reducing the control overhead. Thus, this article aims to present a state-of-the art review, based on the simulation analysis among different topology-based routing protocols. This study will help in choosing efficient routing protocols for FANET deployment. Our work is based on the investigation of existing topology-based routing protocols. We do not aim to design new routing protocols. We also do not aim to provide mathematical analysis in this article. The rest of this paper is arranged as follows. In Section 2, we present an extensive review of the existing topology-based routing protocols, followed by simulation environments and performance evaluation using the OPNET simulator in Section 3. The simulation results and analysis are provided in Section 4. Finally, we present concluding comments in Section 5.
## 2 Problem Statement
### Stopping Policy Determines the Option Value  
The option value (the fair price of an American Option) depends on the stopping time. In other words, the AO's value is the expected value of discounted payoffs at stopping times given all possible sets of prices over time. The discount factor \(P\) formally depends on \(r\), \(\tau\), \(N\) and \(T\), as expressed in Equation (2) (\(P(r,\tau;N,T)\)). However, since \(T\) and \(N\) are fixed for each specific option valuation, \(P\) is simply is written as \(P(r,\tau)\). Let \(P(r,\tau)\) be the discount factor, where \(r\) is the risk-free interest rate, defined as  
\[P(r,\tau)=\exp\biggl{(}-r\cdot\frac{\tau T}{N}\biggr{)} \tag{2}\]  
\(T\) is expiry time (in year) and \(N\) is the number of exercise periods. The value of the American Option is  
\[Q(x_{0})=\max_{\tau\in T}P(r,\tau)\mathbb{E}[H(X_{\tau})|s_{0}=(0,x_{0})] \tag{3}\]  
To illustrate this concept, consider a specific example with an initial asset price of \(x_{0}=USD25\). Suppose we have an American Put option with a strike price of \(K=USD40\) and a maturity time of \(T=1\) and \(N=5\). Assume that the underlying asset's price over time follows the sequence \(\{23,26,42,33,34\}\) in one possible realization of the stochastic process. If the stopping time is chosen as \(\tau=2\), the payoff for this specific path is given by \(H(x_{2})=\max(40-26,0)=14\). The present value for this specific path (single path), denoted as \(\hat{Q}(x_{0})\), is calculated as  
\[\hat{Q}(x_{0})=\exp\biggl{(}-r\cdot\frac{\tau T}{N}\biggr{)}H(x_{2})=\exp \biggl{(}-0.06\cdot\frac{2}{5}\biggr{)}\times 14\]  
Here, \(\hat{Q}(x_{0})\) represents the value of the option for this particular realization of the asset's price path, whereas \(Q(x_{0})\) in the general case represents the expected value across all possible paths.
# Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model
## 3 Experiment
### Field Experiment  
In order to evaluate the model performance under an extremely high colored noise background in the field, we conducted a field experiment to detect the subway stop information in the Beijing Yuandadu Park, lasting 2 h (Figure 2). This park is surrounded by heavy traffic lines with many passengers and is close to the roads and subway stations.  
The magnetometer we used is FGS-03, which is a low noise 3-axis fluxgate sensor. The sensor noise is less than 6 \(\mathrm{pT}/\sqrt{\mathrm{Hz}}\), and the output sensitivity is 0.1 \(\mathrm{mV}/\mathrm{nT}\). We chose the sample rate of 128 Hz for more information. A total of 120 min of three-component continuous magnetic records were obtained from the experiment.  
We acquired the probability of magnetic anomaly detection by our model using a 300 s-long sliding window on the data with 30% overlap per step. Figure 6 shows the magnetic records from the field experiment and the probability of MAD, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline _F-Score_ & [\(-\)**10**, \(-\)**7.5**) & [\(-\)**7.5**, \(-\)**5**) & [\(-\)**5**, \(-\)**2.5**) & [\(-\)**2.5**, 0**) & [\(0\), 2.5**) & [\(2.5\), 5) & [\(5\), 7.5) & [\(7.5\), 10] \\ \hline \(\alpha=0.0\) & 0.249 The probability is almost higher than 0.5 or 0, shown in Figure 6, which indicates that the model can output direct magnetic anomaly detection results in the case of urban background noise. After checking the train schedules (Table A1), it was determined that 38 trains passed through the subway station near the magnetometer during the data recording period. The occurrence of these trains is consistent with the peak of the probability plot. There are only two false alarms with a probability of just reaching 50%, which can be avoided by increasing the screening threshold. Further analysis showed that when the time interval of two trains is small, it is very difficult to distinguish only by the peak. The eight blue solid icons show the following case: trains in both directions enter the station almost simultaneously. Figure 7 further demonstrates this case. Figure 7a is an enlargement of minutes 20 to 25, showing the two trains being detected almost simultaneously. The two peaks (red circles) show that the model's predictions are still distinguishable. Figure 7b shows a correct detection for minutes 85 to 90. Although there are still a few false alarms, they are easily filtered. Overall, the model completed the magnetic anomaly detection.  
Figure 6: The data from the field experiment and the probability of MAD. The blue hollow icon represents a correct detection, the blue solid icon represents two trains, and the red hollow icon represents a false alarm.  
Figure 7: Two examples from the field experiment in Figure 6. The blue hollow icon represents a correct detection, the blue solid icon represents two trains, and the blue dotted line represents a false alarm. (**a**) minutes 20–25, (**b**) minutes 85–90.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries  
A _univariate time series_\(X\) of length \(T\) is a sequence \(X=((x_{1},t_{1}),(x_{2},t_{2}),\ldots,(x_{T},t_{T}))\) of pairs where each \(x_{i}\in\mathbb{R}\) is a data point and each \(t_{i}\in\mathbb{R}\), with \(0\leq t_{1}<t_{2}<\cdots t_{T}\), is the point in time when \(x_{i}\) is observed.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 4 Numerical Results
### CS with Added Measurement Noise  
For our numerical analysis, we use addition of shot noise, as well as thermal noise. Typically, shot noise is of Poisson distribution. Poisson noise is applied and cannot be added, as it is signal dependent. Hence, for each element in \(y\), Poisson noise is applied by generating a Poisson distribution with \(\lambda=\) the value of that specific element in \(y\). Here, \(\lambda\) is the expected value of the Poisson distribution. We apply Poisson noise to different simulated star fields, over 100 simulations, and obtain the average % error over all the Monte Carlo simulations. We use \(A\) as a binomial random matrix. The expected value of the product of the two independent probability distributions are given by  
\[E[\lambda]=E[y] =E[A]E[x] \tag{21}\] \[=p\bigg{(}\frac{1}{2}(x_{h}-x_{l})\bigg{)} \tag{22}\]  
where \(\lambda\) is the expected value and variance of the Poisson distribution for each element in \(y\), and \(p\) is the expected value of the binomial distribution in \(A\) with the number of trials = 1. Hence, \(E[\lambda]\) refers to the expected value of the variance of the Poisson distribution over all samples of  
Figures 12 and 13 show that for high-magnification pixels in the time-series light curve (as shown in Figure 5), the Poisson noise error is lowest at those time samples, as compared to the low-magnification time samples. In addition, we show that by decreasing \(p\) of the Binomial matrix, \(A\), we can decrease the noise error by an average of 4.6% over all time samples for \(\mu_{0}=0.1\) and 4.27% for \(\mu_{0}=0.01\).
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 4 Cluster-Based Channel Allocation Method
### Computational Complexity  
Our base module is composed of two parts. In the first part, an interference relationship graph is partitioned into clusters by the Louvain algorithm. In the second part, a channel is allocated to each WBAN in a cluster by the Welsh Powell graph coloring method. The time complexity of the Louvain algorithm is known to be \(O(|E|)\)[28], where \(E\) is the set of edges in a graph. Therefore, the computational complexity of the first part in our base module is \(O(|E|)\). The computational complexity of the Welsh Powell graph coloring method is \(O(|N|^{2})\), where \(N\) is the set of vertices in a graph. However, in the second part of our base module, a graph coloring is applied not to the graph \(G=(N,E,W)\) but to the subgraph formed by the WBANs in a cluster. The set of clusters made by the first part of our base module is denoted as \(C\). If we assume that WBANs are uniformly distributed in an area, the average number of WBANs in a cluster is expected to be \(m=|N|/|C|\). Therefore, the computational complexity of the second part in the base module is \(O(m^{2}|C|)=O(|N|^{2}/|C|)\). Therefore, compared with the method that does not group the coexisting WBANs, our base module reduces the computational complexity from \(O(|N|^{2})\) to \(O(|N|^{2}/|C|)\).  
Instead of partitioning an interference relationship graph, our adaptive module exploits the cluster information made by the base module. However, the adaptive module rearranges the cluster membership of each WBAN by comparing the current location of a WBAN and the center point of each cluster. Therefore, at the time slot \(t\), the time complexity of updating the cluster information becomes \(O(|N(t)||C(t-1)|)\). Therefore, comparedwith the base module, the adaptive module reduces the time complexity for constructing a set of clusters from \(O(|E(t)|)\) to \(O(|N(t)||C(t-1)|)\).
## 2 Materials and Methods
### Data Editing and Analysis  
The data were collected via the SoSci Survey (2019) and afterwards exported to \"Microsoft Excel 16.5\" (2019), where data checking, editing and coding took place. Improvements were made with regard to the logic of the answers, e.g., in the case of incorrect occupational classifications.  
Named drugs were grouped into categories. While the pain medications were divided into their active ingredients (arcoxia, ASS 500, systemic diclofenac, local diclofenac, ibuprofen, metamizol, naproxen, paracetamol, tapendadol, tilidin, tramadol), other medications were grouped under \"further\". \"Further\" includes immunosuppression (further subdivided into systemic cortisone, cortisone injections, MTX and other individual treatments), muscle relaxants (further subdivided into ortoton, tizinidine, tolerisone and others with individual mentions), other injections (except cortisone injections) as well as other medication (adjuvants, antidepressants, antihyperstensives, anticoagulation, hormones, minerals, vitamins and others).  
If the recorded number of medicines taken did not correspond to the single mention of medicines, the number was corrected. The answers to the option of an open statement regarding the frequency of taking medication (in addition to monthly, weekly and daily) were divided into categories. The answers were subsequently allocated under \"as needed\", \"rarely\" and \"others\" (information that could not be clearly classified).  
After the improvements, the data were transferred and analysed descriptively in IBM Statistics SPSS 26 (2019). The median and the interquartile range were calculated for the non-normally distributed data which were evaluated using the Kologorov-Smirnov test. Significant differences were determined by using the Chi2 test. The significances in the results section were marked, for example, as: * at \(p=0.02\). The individual therapies with their subcategories were not tested for significance due to their low frequency (\(\leq\)9) in the present paper. The \(p\)-values were subjected to a Bonferroni-Holm correction. The significance level was set at 5%.  
Research manuscripts reporting large datasets that are deposited in a publicly available database should specify where the data have been deposited and provide the relevant accession numbers. If the accession numbers have not yet been obtained at the time of submission, please state that they will be provided during review. They must be provided prior to publication.  
Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code.