esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Multiple Tasking Sensors  
For the purpose of increasing estimation accuracy and reliability, multiple tasking sensors may be scheduled to track the target with detection uncertainties. In [5], target localization strategies based on a communication protocol between the clusterhead and cluster members are presented. In these approaches, a subset of sensor nodes is selected for querying detailed target information. Although energy and communication bandwidth are conserved in a certain amount, the processing burden all falls on the clusterhead, which may drain its power quickly. [PERSON], _et al._[11] focuses on tracking error and energy management involved in tracking the target. In this approach, the sensor nodes collectively monitor and track the movement of the target, which involves detecting, clustering and localization of target.  
In [12], an information-driven approach in ad hoc sensor networks is proposed to determine the tasking sensors in a \"sensor collaboration\" by dynamically optimizing the information utility of data for a given cost of communication and computation. In [13], a distributed estimation method is proposed based on mobile agent (MA) computing paradigm and generic sequential Bayesian filtering for the target state estimation at each time step. Nonetheless, the MA migration planning problem needs to be handled in order to achieve the desired tracking accuracy. The tracking schemes in [14, 15] combine the mechanisms of the tree-based and cluster-based schemes and propose information-based target tracking methods. However, the proposed sensor systems still have to deal with complexity issues. Authors in [16, 17, 18, 19, 20, 21] propose multi-sensor scheduling schemes for maneuvering target tracking in sensor networks, while not considering the motion information of the target. [PERSON], _et al._[22] presents an integrated approach to dynamically routing measurements and models in a sensor network and examines the problem of tracking objects within a region wherein the responsibility for combining measurements and updating a posterior state distribution is assigned to a single sensor at any given time step. The proposed approach is able to substantially reduce the communications cost incurred in tracking an object.  
However, this strength comes along with the additional complexity of transmitting the representation of the state distribution between the tasking sensors. Comprehensive surveys of design challenges and recently proposed target tracking algorithms can be found in [1].  
Note that most of these design approaches are dynamic clustering protocols in a flat network. In contrast, the method in [5] is built upon a static cluster-based network topology. Thus, reference [5] may provide a good way to benchmark the performance of the proposed tracking scheme.
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 4 Nitrate Removal Technologies in Waterbodies
### Separation-Based Technologies  
Separation methods for the treatment of natural waters contaminated by nitrate (ion exchange, reverse osmosis, adsorption, electrodialysis, electrocoagulation and capacitive deionization) have a long history, and some of them are currently the most widely used. Although, depending on the initial concentration, these techniques provide excellent results, some of them are expensive due to energy costs and do not completely solve the problem, as they do not transform the nitrate into a harmless compound, but generate a concentrated brine, without any economic value and which must be treated or stored appropriately.
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 3 Hygro-Thermal Simulations and Discussion
### Hygro-Thermal Behavior of a Brick Masonry Wall
#### 3.2.3 Time-Dependent Analysis with Variable External Boundary Conditions (TD2)
Again, the results obtained with the new \(R_{IF}\) values did not show significant differences. Therefore, it was concluded that the impact of the hydraulic resistance is not significant for the boundary conditions considered in this problem. Nonetheless, it must be recalled that the influence of the interface may become non-negligible for higher moisture levels. Figure 7: Time-dependent analysis with variable external boundary conditions (TD2). Temperature evolution for different points of the studied three-wythe brick masonry wall: (**a**) joints with natural hydraulic lime mortar (LM); (**b**) joints with cement mortar (CM). Independently of the studied case, the response of the wall stabilizes (dynamic equilibrium) after a couple of cycles. It is noted that external/internal phase delay entails temperature and relative humidity gradients higher than the ones expected if the shift was not considered, and maximum and minimum values were occurring simultaneously at one and the other sides of the wall. In particular, the maximum thermal gradient obtained for the average temperatures between the two faces is \(\Delta T=13.0\)\({}^{\circ}\)C for the LM case and \(\Delta T=12.4\)\({}^{\circ}\)C for the CM case. In turn, the maximum moisture gradient obtained for the average relative humidity values between the two faces is \(\Delta\varphi=0.18\) for the LMJ case and \(\Delta\varphi=0.17\) for the CM case. Considering these aspects, a time-dependent analysis with variable boundary conditions may produce more severe scenarios and is, therefore, recommended for a better approximation of real environmental actions. Considering the results described in the previous paragraphs, it is evident that the hygro-thermal behavior of brick masonry walls depends, to a great extent, on the type of mortar. When considering the wall with natural hydraulic lime mortar (LM), the temperature and relative humidity trends showed comparable values for the groups of studied points, independent of their location on brick or mortar joints. Therefore, this type of hygro-thermal problem could be simulated for the LM case by means of a macro-modeling approach with an equivalent masonry material or a simplified 1D model with hardly any loss of information. Conversely, the wall with cement mortar (CM) showed some differences in temperature and relative humidity depending on the material where  
Figure 8: Time-dependent analysis with variable external boundary conditions (TD2). Evolution of the relative humidity for different points of the studied three-wythe brick masonry wall: **(a)** joints with natural hydraulic lime mortar (LM); **(b)** joints with cement mortar (CM). the studied point was located. Thus, for the CM case, the macro-modeling approach or the reduction to a 1D problem would entail a certain loss of detail and accuracy.
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Feature-Selection Stability Measure  
One of the first measures used to assess stability was based on the intersection of feature sets, such as the Jaccard Index [44]. These measures allow for the evaluation of similarity between feature sets with different cardinalities, but they do not account for the total number of features and, therefore, lack a correction for chance. This correction was first introduced by [PERSON] [45], but it applied only to sets with the same cardinality. [PERSON] later extended this approach [46] to accommodate sets with different cardinalities. More recently, [PERSON] and [PERSON] [47] proposed a new feature-selection stability measure based on the frequency of selected features, which is calculated based on the \(Z\) matrix.  
\[Z=\begin{bmatrix}z_{11}&z_{12}&z_{13}&\dots&z_{1n}\\ z_{21}&z_{22}&z_{23}&\dots&z_{2n}\\ &\dots\dots\dots\dots&\\ z_{k1}&z_{k2}&z_{k3}&\dots&z_{kn}\end{bmatrix} \tag{8}\]  
Each row \(Z_{k}\) of \(Z\) corresponds to a feature-selection subset, while the columns represent individual features. The selection frequency of a feature \(f\) is calculated as  
\[p_{f}=\frac{1}{k}\sum_{i=1}^{k}z_{if}\]  
and the unbiased sample variance of the selection of the \(f\)-th feature is given by:  
\[s_{f}^{2}=\frac{k}{k-1}p_{f}(1-p_{f}).\]  
The average number of selected features over \(k\) feature sets, denoted by \(\bar{n}\), is:  
\[\bar{n}=\frac{1}{k}\sum_{i=1}^{k}\sum_{f=1}^{n}z_{if}.\]  
Additionally, we define the normalized average number of selected features \(p_{\bar{n}}\) as:\[p_{n}=\frac{\bar{n}}{n}.\]  
Finally, the Nogueira feature-stability measure \(\phi(Z)\) is defined as follows:  
\[\phi(Z)=1-\frac{\frac{1}{n}\sum_{f=1}^{n}s_{f}^{2}}{p_{n}(1-p_{n})} \tag{9}\]  
As demonstrated by [PERSON] and [PERSON] [47], this measure satisfies all five desired properties, while other measures fail to meet some of them. For this reason, we chose to utilize this measure in our research.  
1. Fully defined. It should also measure stability for subsets with different cardinalities.
2. Correction for chance. Subsets of features can share some of the features even if selected randomly.
3. Bounds. The stability \(\phi(Z)\) (9) should be upper/lower bounded by constants not dependent on the overall number of features or the number of features selected.
4. Maximum stability. A measure should achieve its maximum if and only if all feature sets in \(Z\) are identical.
5. Strict monotonicity. The stability estimator \(\phi(Z)\) (9) should be a strictly decreasing function of the sample variances \(s_{f}^{2}\) of the variables \(Z_{f}\) (column in the matrix \(Z\)).
## 5 Results
### Historical Series
#### 5.1.2 Vertical Ionospheric Sounding  
The typical pattern of the summer behavior of the ionospheric F2 characteristics and of their variability was evaluated for different Spanish Antarctic surveys, and the differences of the above patterns from survey to survey were related to the different solar activity [54]. Such a pattern at mid-high latitudes of the ASJI was also compared with the summer behavior at other mid-latitude stations and it was noticed a larger post-sunset electron density enhancement at the ASJI than that recorded in other regions. The significant decrease of the foF2 and their strong uplift of the F2 layer as observed by the significant increase in the virtual height, h'F2, for the geomagnetically disturbed period of 18 February 2005, was also investigated and explained in terms of a response of the ionosphere to the geomagnetic storms [55, 56]. In addition, the data recorded with the AIS VIS, especially MUF(3000)F2, have been correlated with data extracted from the HF radio link established between the ASJI and Spain [57]. This HF radio link was used as multi-hop Oblique Ionospheric Sounder, OIS. In [57], [PERSON] _et al._ have shown that the frequencies with largest availability of the HF radio link (FLA) correlate reasonably well with the MUF(3000)F2 recorded by VIS placed at the receiver site (40.8\({}^{\circ}\)N, 0.5\({}^{\circ}\)E) (see Figure 10, left) and that frequencies of OIS delivered at the receiver being larger than the MUF(3000)F2 recorded by VIS suffer a significant drop of power. The left plot of Figure 9 also shows the maximum usable frequency obtained by the Rec 533 model (MUF Rec 533) which is defined as the highest frequency for which an ionospheric communication path is predicted on 50% of the days of the month [58]. The above studies obtained with the ionospheric sensors installed at the remote region of the ASJI for surveys July 2006 have been extended for three consecutive surveys (October 2009, November 2010, and December 2011) with different solar activity levels observing the effect of increasing the solar activity.  
Other results obtained by the team show significant correlation of the MUF(3000)F2 recorded by VIS with signal to noise ratio of the HF radio link established between the ASJI and Spain (SNR) as can be shown in Figure 10, right. This fact jointly with the aforementioned results presented in [57] motivates us to future development of models for predicting the best frequencies for establishing the HF radio link between the ASJI and Spain. Moreover, we found additional correlation between observations by the OIS and by VIS at the receiving site. Thanks to the upgrade of the OIS with more accurate clocks and better GPS for synchronization, it was possible to analyze the time of flight and the Doppler shift and to notice that the Doppler shift suffered for the radio link between ASJI and Spain correlates with the vertical drift velocity of the ionospheric F region over the receiving site in Spain (see Figure 11). [PERSON] _et al._[59] show details on the measurements of the F-region vertical drift velocity and the results of its daily pattern.
## 1 Introduction
### Solutions for Idle Listening
#### 1.1.1 Duty Cycling  
Duty cycling, also known as'sleep schedule', has been a crucial design consideration to prolong the lifetime of a wireless sensor network (WSN) [6; 7]. It mitigates the energy consumption due to idle listening and overhearing in the sensor nodes. The radio is kept in a sleep mode and is switched to active mode from time to time via its internal synchronization clock to transmit or receive data. The major challenge of the duty cycling is to wake-up the nodes at the exact time when packets are sent to it, i.e., being received. If the sleep period is scheduled to be longer than the active period, more energy is saved, but there is an increased probability of packet missing during sleep time and network latency due to the increased waiting period. There is also increased overhead due to synchronization packets used within the nodes to schedule the wake-up time in duty cycling, hence increasing the energy wastage. Several techniques [8] have been proposed to address this challenge such as the use of spatial scheduling [9] or cognitive radio [10].
# A Performance Evaluation for Software Defined Networks with P4
###### Abstract  
The exponential growth in the number of devices connected via the internet has led to the need to achieve granular programmability for increased performance, resilience, reduced latency, and jitter. Software Defined Networking (SDN) and Programming Protocol independent Packet Processing (P4) are designed to introduce programmability into the control and data plane of networks, respectively. Despite their individual potential and capabilities, the performance of combining SDN and P4 remains underexplored. This study presents a comprehensive evaluation of SDN with data plane programmability using P4 (_SDN+P4_) against traditional SDN with Open vSwitch (_SDN+Ov5_), aimed at answering the hypothesis that combining SDN and P4 strengthens the control and data plane programmability and offers improved management and adaptability, which would provide a platform with faster packet processing with reduced jitter, loss, and processing overhead. Mininet was employed to emulate three distinct topologies: multi-path, grid, and transit-stub. Various traffic types were transmitted to assess performance metrics across the three topologies. Our results demonstrate that _SDN+P4_ outperform _SDN+Ov5_ significantly due to parallel processing, flexible parsing, and reduced overhead. The evaluation demonstrates the potential of _SDN+P4_ to provide a more resilient and stringent service with improved network performance for the future internet and its heterogeneity of applications.  
Keywords:Software Defined Networking (SDN); Programming Protocol Independent Packet Processing (P4); ONOS; Mininet; internet topologies; performance evaluation; _SDN+P4 Article_
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 1 Introduction
### Research Purpose  
The aim of this research was to analyze the results of land cover classification using multi-wavelength LiDAR and compare the vegetation features from active multi-wavelength LiDAR and passive multispectral images. The geospatial object-based image analysis (GEOBIA) was used to achieve land cover classification. The multi-wavelength LiDAR was from Optech Titan using 532 nm (green), 1064 nm (NIR), and 1550 nm (MIR), whereas the multispectral image is from Worldview-3 (WV-3) satellite multispectral images. This study adopted the object-based image classification approach, a proposed scheme including feature extraction, image segmentation, and object-based classification. The correctness and completeness were generated by manual interpretation and verified by comparing LiDAR and multispectral images. The analysis compared (1) the results from single-wavelength LiDAR and multi-wavelength LiDAR; (2) spectral and geometrical features from multi-wavelength LiDAR system; and (3) LiDAR-derived and image-derived vegetation index.
# Biochar Affects Heavy Metal Uptake in Plants through Interactions in the Rhizosphere
## 3 Results
### Effects on Soil  
Biochar amendments did not affect soil pH (Table 5), except for the highly polluted soil where it caused a statistically significant increase of 0.1-0.2 units. In this soil, the pH also increased slightly in the rhizosphere compared to bulk soil (0.1 unit). For the two other soils, rhizosphere soil was slightly more acidic than bulk soil (0.15-0.3 units). The biochar amendment significantly decreased the concentrations of NH\({}_{4}\)NO\({}_{3}\)-extractable Cu, Pb and Zn in the highly polluted soil, but no such decrease was seen when comparing metal concentrations in CH\({}_{3}\)COOH-extracts (Table 6). In the weakly polluted soil, biochar reduced NH\({}_{4}\)NO\({}_{3}\)-extractable Cu, and Cd in rhizosphere soil. Rhizosphere soil frequently contained more Cu, Pb and Zn than bulk soil, and biochar tended to counteract this increase. For Cd, the tendency was opposite, and we observed lower extractable concentrations in the rhizosphere compared to bulk soil. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & \multirow{2}{*}{**Treatment**} & \multicolumn{4}{c}{**TF**} \\ \cline{3-6}  & & **Cd** & **Cu** & **Pb** & **Zn** \\ \hline \multirow{2}{*}{Unpolluted soil} & Control & 0.41a * & 0.28a & 0.74a & 0.68a \\  & Biochar & 0.37a & 0.25a & 0.52a & 0.54a \\ \multirow{2}{*}{Weakly polluted soil} & Control & 0.31a & 0.11a & 0.33a & 0.97a \\  & Biochar & 0.27a & 0.06b & 0.25a & 0.97a \\ \multirow{2}{*}{Highly polluted soil} & Control & 0.31a & 0.29a & 0.44b & 1.88b \\  & Biochar & 0.28a & 0.03b & 0.19b & 0.54b \\ \hline \hline \end{tabular}  
* Mean values (\(n\) = 3) followed by the same letter are not significantly different (Tukey’s test, \(p\) < 0.05).  
\end{table} 
Table 4: Translocation factor (TF) calculated for barley grown in three differently polluted soils amended or not with biochar (2% by weight). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Soil**} & \multirow{2}{*}{**Sampling**} & \multicolumn{2}{c}{**Control**} & \multicolumn{3}{c}{**Biochar**} \\ \hline \hline \multirow{4}{*}{Unpolluted soil} & \multirow{4}{*}{Bulk soil} & \multirow{4}{*}{6.16} & ab & 0.11 & 6.36 & a & 0.14 \\  & Rhizosphere soil & & 5.99 & b & 0.03 & 6.06 & b & 0.05 \\ \multirow{2}{*}{Weakly polluted soil} & Bulk soil & 7.88 & a & 0.03 & 7.93 & a & 0.12 \\  & Rhizosphere soil & & 0.04 & 7.68 & b & 0.20 \\ \multirow{2}{*}{Highly polluted soil} & Bulk soil & 7.52 & a & 0.03 & 7.63 & a & 0.03 \\  & Rhizosphere soil & & 0.35 & b & 0.08 & 7.53 & a & 0.04 \\ \hline \hline \end{tabular}  
* Within a soil, values (means and SEM, \(n\) = 3) followed by the same letter are not significantly different (Tukey’s test, \(p\) < 0.05).  
\end{table} 
Table 5: Soil pH(d-water) measured in rhizosphere soil and bulk soil after 4 weeks growth of barley on three differently polluted soils.
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 2 Modeling, Data Pretreatment, and Missing Data Features
### The Missing Data Features  
The raw laser data collected by LRF sensors usually contain missing data caused by out of range or unreliable measurements. Such missing data occurs more often for a low-cost LRF sensor because of the low detection range. To the best of our knowledge, almost all previous related works ignored or threw away the missing data because they assumed the laser sensor was perfect or the missing data were meaningless.  
However, such missing data is meaningful. For a robot with a low-cost LRF sensor running in normal indoor environments without floor glasses, the fail detection on certain direction angles usually indicates an out of measurement range in that direction. In fact, some navigation applications interpolate such missing data to the maximum distance value to make the running of the robot more robust in practice.  
In this paper, we put forward a new approach for making use of such missing data with (\(d_{i}=0\)) or \(d_{i}>d_{\text{max}}\), called missing data feature. For raw scan data, the missing data feature is extracted as  
\[f_{mis}=\{(\beta_{1},\varphi_{1}),(\beta_{2},\varphi_{2}),...,(\beta_{m}, \varphi_{m})\} \tag{5}\]  
An angle range of continuous missing data is represented by \((\beta_{m},\varphi_{m})\), in which \(\beta_{m}\) is the center angle and \(\varphi_{m}\) denotes the size of the angle range. In practice, only the angle ranges with large \(\varphi_{m}\) are considered to avoid noises. The size of the feature is expressed as  
\[\|f_{mis}\|=\sum_{m}\varphi_{m} \tag{6}\]  
Figure 3 shows the expression example of a missing data feature, as well as its appearance in scan data.  
Figure 3: The expression of a missing data feature (left) and a feature in a scan scene (right).  
Figure 2: (a) Simulation of scan in environment, (b) raw scan data, and (c) scan image.  
For two different and adjacent robot poses, if the size of the missing data features \(f_{m1}\) and \(f_{m2}\) are big enough, there could be a quick way to estimate the rotation angle \(\alpha\), by minimizing the following matching error:  
\[e=\underset{\alpha}{\text{argmin}}(\frac{\|f_{m1}-rotate(f_{m2},\alpha)\|}{\|f_ {m1}\|+\|f_{m2}\|}) \tag{7}\]  
If the matching error \(e\) is smaller than a certain threshold, \(\alpha\) could be regarded as the rough rotation angle between two poses. It is important to note that the missing data features should be big and stable enough for this processing. Fortunately, this is not uncommon indoors, especially when using a low-cost LRF sensor.