esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 4 Algorithms and Implementation
### d-Separation  
Currently, MBTK contains a very simple but inefficient algorithm for computing d-separation. The algorithm is essentially a straightforward implementation of the definition of the d-separation criterion [2; 19]. It is very simple because it enumerates all possible undirected paths between two nodes and then checks each node on each path to see if it 'blocks' the path, following the conditions described in Section 3.1. However, at the same time, this simplicity is detrimental. Its inefficiency is largely caused by the full enumeration of paths, and the number of paths between two nodes can be exponential in the size of the graph [19]. The algorithm is defined as follows:  
Algorithm 1 shows the pseudocode for computing the d-separation criterion as it is currently implemented in MBTK. The procedure DSeparation receives two nodes \(X\) and \(Y\) as arguments, along with a conditioning set \(\mathbf{Z}\), and returns true or false indicating whether \(X\) and \(Y\) are independent or not given the nodes in \(\mathbf{Z}\). As stated earlier, the MBD algorithms only truly need the binary information about the conditional independence of two variables given a conditioning set, therefore the DSeparation procedure above is sufficient for their operation. The procedure FindAllUndertectedPaths was not specified in detail in Algorithm 1 because it is a generic graph operation.  
```
1:procedureDSeparation(\(X,Y\): Node, \(\mathbf{Z}\): List[Node])
2:paths = FindAllUndertectedPaths(\(X,Y\))
3:forall\(path\in\)pathsdo
4:if\(\
eg\)IsPathBlockedByNodes(\(path,\mathbf{Z}\))then
5:return FALSE
6:return TRUE
7:
8:procedureIsPathBlockedByNodes(\(path,\mathbf{Z}\): List[Node])
9:forall\(node\in\)path[\(1\): \(-1\)]do\(\triangleright\) skip first and last nodes
10:isCollider = IsNodeCollider(\(path,node\))
11:hasConditionedDescendants = \(\exists\mathbf{m}\in\)node.descendants such that \(n\in\mathbf{Z}\)
12:ifisCollider \(\wedge\)node \(\
otin\mathbf{Z}\wedge\
eg\)hasConditionedDescendants then
13:return TRUE
14:if\(\
eg\)isCollider \(\wedge\)\(n\in\mathbf{Z}\)then
15:return TRUE
16:return FALSE
17:
18:procedureIsNodeCollider(\(path\): List[Node], \(node\): Node)
19:\(edgeInboundFromPrev\) = \(node\in\)node.previous.descendants
20:\(edgeInboundFromNext\) = \(node\in\)node.next.descendants
21:return\(edgeInboundFromPrev\wedge edgeInboundFromNext\)
22:
23:procedureFindAllUndertectedPaths(\(start,end\): Node) \(\triangleright\) unspecified for brevity
```  
**Algorithm 1** Simple algorithm for computing d-separation  
For context, [PERSON] and [PERSON] [19] present a much more efficient algorithm informally called Reached (the name of the procedure in their pseudocode). The Reached algorithm is linear in the size of the graph, but it is far more complex. Because the Bayesian networks chosen for the experiment are quite small, implementing Reached was deemed unnecessary at the time.
## 3 Application
### Ecosystem Level  
PRI obtained from MODIS using different reference bands was generally used for estimating ecosystem RUE and GPP [20]. Tower or tripod-mounted spectroradiometers were also used to study ecosystem carbon uptake at long timescales [77; 123]. Forty relationships in 14 articles linked PRI with RUE based on eddy covariance in various kinds of ecosystems (e.g., Mediterranean forests, temperate deciduous forests, mountain grassland, evergreen tropical rainforest and boreal and coniferous forests; Table 1). Median \(R^{2}\) for the correlations between RUE and PRI ranged from 0.12 to 0.59 (Figure 8a), with few non-significant relationships during severe drought years [25]. The correlation between PRI and RUE (\(R^{2}\) = 0.12, \(p\) < 0.001) for a tropical evergreen forest [77] was lower than that for broadleaf (0.59 of median \(R^{2}\)) and coniferous forests (0.39 of median \(R^{2}\)), indicating a site-specific difference for the PRI-RUE relationships. PRI was significantly correlated with net CO\({}_{2}\) uptake for Mediterranean forests (\(R^{2}\) was 0.28 [123] and 0.38 [28]; Figure 8b) and grassland ecosystems (\(R^{2}\) ranged between 0.13 and 0.81 [136; 153]; Figure 8b).
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Ultrasound  
Ultrasound technology is popular in medicine for creating images of the internal body structure. It operates at frequencies above those audible to the human ear. For localization, commonly, the ToF measurements together with the sound velocity are used for estimating the distance between the transmitting and receiving nodes. Although it can obtain an accuracy of sub-centimeters, it has limited maximum range (approximately 10 cm) in still air conditions [102], and it cannot penetrate walls. It also suffers from interference resulting from the reflected ultrasound signals from different materials, such as metal [21]. The directional transmission pattern of the ultrasonic pulse is known to be hemispherical, and some orientation information about the receiver can be estimated based on its location and the ultrasonic pulse detected [121].  
The ultrasound signals can be also used in combination with RF, as in the \"Cricket\" system [32]. More specifically, its beacons transmit their IDs and location information through the RF signal. The listeners utilize both the RF and ultrasound pulses for estimating their distance from the beacons. The \"Cricket\" achieved an accuracy of a few centimeters over ranges of up to ten meters. Similarly, the distributed object locating system for physical-space internetworking (DOLPHIN) [122] utilizes the ultrasound pulse, and the receiving nodes are localized in a distributed manner by using only a few manually configured reference nodes. It is based on a hop-by-hop locating mechanism with LoS; thus, it requires at least three nodes for determining the rest of the nodes' locations in the network, even if the nodes cannot receive ultrasound from the reference nodes directly. The technologies used include the RF for time synchronization and ultrasonic pulses for the TDoA measurements. By the time that an ultrasonic pulse is detected by the receiving nodes, the internal counter stops, and they compute their distance from the transmitting nodes. \"DOLPHIN\" achieved an accuracy of approximately 15 cm, which was degraded as the hop count between the reference and receiving nodes increased. This principle was also utilized by the \"Bat\" system [31].
## 4 Experiments
### Experiments on Registration Effectiveness
#### 4.3.2 Evaluation Metrics of Registration Effectiveness  
In [41], two metrics--the rotation error \(\mathbf{\theta_{r}}\) and the translation error \(\mathbf{\theta_{r}}\)--are utilized for a quantitative evaluation of the registration's performance. The error between the real matrix \(\mathbf{R_{A}}\) and the ground truth rotation matrix \(\mathbf{R_{GT}}\) is represented by \(\mathbf{\theta_{r}}\). The inaccuracy between the translation vector \(\mathbf{t_{A}}\) and the ground truth vector \(\mathbf{t_{GT}}\) is represented by \(\mathbf{\theta_{t}}\). Here is how they are defined.  
\[\theta_{r}=cos^{-1}\left(\frac{trace\left(R_{A}R_{GT}^{-1}\right)-1}{2}\right) \ast\frac{180}{\pi} \tag{34}\]  
\[\theta_{t}=\frac{\|t_{A}-t_{GT}\|}{d_{res}} \tag{35}\]  
where \(\mathbf{R_{GT}}\) and \(\mathbf{t_{GT}}\) are known in advance time and \(\mathbf{d_{res}}\) is 1 mr. It should be mentioned how \(\mathbf{R_{A}}\) and \(\mathbf{t_{A}}\) can be obtained. A coarse transformation matrix \(\mathbf{Rt_{C}}\) between a source point cloud and a target point cloud can be estimated following coarse registration, and it is presented as formula (36). A transformation matrix called \(\mathbf{Rt_{F}^{i}}\) will be produced throughout each iteration of the fine registration procedure. Therefore, each iterative transformation matrix can be multiplied to generate a fine transformation matrix, as indicated by formula (37). Ultimately, the final transformation matrix \(\mathbf{Rt_{A}}\) is obtained by multiplying \(\mathbf{Rt_{F}}\) by \(\mathbf{Rt_{C}}\), as expressed in formula (38).  
\[Rt_{C}=\begin{bmatrix}R_{c}&t_{C}\\ 0&1\end{bmatrix} \tag{36}\]  
\[Rt_{F}=Rt_{F}^{n}Rt_{F}^{n-1}\dots\ Rt_{F}^{1}=\begin{bmatrix}R_{F}&t_{F}\\ 0&1\end{bmatrix} \tag{37}\]  
\[Rt_{A}=Rt_{F}Rt_{C}=\begin{bmatrix}R_{F}&t_{F}\\ 0&1\end{bmatrix}\begin{bmatrix}R_{c}&t_{C}\\ 0&1\end{bmatrix}=\begin{bmatrix}R_{F}R_{C}&R_{F}t_{C}+t_{F}\\ 0&1\end{bmatrix} \tag{38}\]  
Figure 9: The datasets used in the experiments.  
where \(\mathbf{R}\mathbf{t}_{A}\) includes a rotate matrix \(\mathbf{R}_{A}\) and a translator vector \(\mathbf{t}_{A}\), \(\mathbf{R}_{e}\) and \(\mathbf{t}_{C}\) stand for the rotation matrix and translation vector of \(\mathbf{R}\mathbf{t}_{C}\), respectively, and \(\mathbf{R}_{F}\) and \(\mathbf{t}_{F}\) stand for the rotation matrix and translation vector of \(\mathbf{R}\mathbf{t}_{F}\), respectively.
# Key Concepts, Weakness and Benchmark on Hash Table Data Structures
## 1 Introduction
### Methodology  
The work for this paper was planned as a survey or perspective, not as an actual research project, that is, no novelty was primarily intended. However, since the evaluation of empirical performance is an interesting matter, it was decided to focus the survey on the development of benchmark cases and the empirical evaluation of algorithms instead of focus on a theoretical comparison or review.  
The first target of the study was finding the influence of the range-hashing function. As the default hash in Java and C++ for integer numbers is the identity function (a perfect hash function in terms of \"good uniformity\"), using integers as keys leaves an open window to look for the influence of the range-hashing function and, eventually, the influence of _qualified_ collisions in the hash tables.  
In fact, that influence was found, but the preliminary results also show something quite unexpected: the measured times were not constant with the data structure size. That is, the empirical measurements does not show the theoretical complexity \(\mathcal{O}(1)\). That finding introduces a new target in the survey and its methodology because the goal from that moment was to confirm that fact by providing enough evidence.  
Such evidence is based on:  
1. A reliable measure of elapsed times. As any single operation on the standard mapping structures takes a very short time, it was decided to measure the time for millions of such operations. That way, measured times have a magnitude order of seconds. Since the absolute error in time measure should not exceed a few milliseconds, the relative error in the total time is very low.
2. Including checking operations in all benchmark cases. That is, benchmarks were designed as if they were unit test cases. That way, it is sure that they do what they are supposed to do. In addition, those checks avoid compiler optimizations (some compilers could avoid the evaluation of an expression in the source code if they detect that there is no further dependency on the resulting value). Benchmark cases are described in the next section.
3. Providing a wide variety of benchmark cases. That variety includes: using different programming languages, using standard and no standard implementations and using different value types as keys. In addition, of course, benchmark cases themselves are different. All of them share a similar design: they all build the data structure by inserting (a lot of) elements and they all execute a loop looking for keys. However, they do these general steps in different ways.
4. Evaluating other data structures that should be upper or lower bounds of performance. For instance, a binary tree could provide a lower bound since it has \(\mathcal{O}(\log N)\) complexity, while an array could be an upper bound since it does not need doing any hash.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 5 Methodology
### Performance Metrics  
Three key performance metrics will be monitored to evaluate the impact of the attacks: packet delivery ratio (PDR), end-to-end delay (E2E), and throughput.
## 4 Conclusions  
In this work, a set of quartz and carbonate depressants were studied in fluorite flotation. Of all the depressants studied, the combination of QT with WD shows the best depressant efficiency, with values of 20% CaCO\({}_{3}\) + MgO\({}_{3}\) and 3.7% SiO\({}_{2}\) in concentrate at pH 9.5 and column equipment. This fact led to having the best results in terms of fluorite grade, reaching maximums of 74% in a single stage for column flotation. The use of QT + CMC and QT + PS depressant mixtures present similar depressant efficiency than the use of QT alone, so its use is ruled out due to the economic costs of using two depressants reagents. Additionally, it should be noted that the use of QT + SHMP is not a good option, since it presents the worst depressant efficiency, even worse than the use of QT alone, obtaining values of 28% CaCO\({}_{3}\) + MgO\({}_{3}\) and 4.6% SiO\({}_{2}\) in concentrate stream at pH 9.5 and column equipment.  
Floration of fluorite in the column (pneumatic cell) has shown better results than in the mechanical cell for all depressants studied. When the feed has a content of 25.98% in particles smaller than 36 \(\upmu\)m, considered slime particles, column flotation shows better results than in cell. This fact confirms what other authors say about the presence of slime in feed. It would be interesting to remove this smaller fraction of 36 \(\upmu\)m from the feed and see how the column flotation behaves versus cell flotation.  
Finally, it is demonstrated that the optimum pH for the flotation of fluorite is around 9.5. Values above this pH make the oleic acid also collect quartz. Meanwhile, pH values below 8 promote carbonate flotation.  
It should be noted that the scale in which the tests are completed at do not provide conclusive evidence for larger scale performance, whereas it is a good starting point for scaling the process.  
**Author Contributions:** [PERSON] carried out the experiments and wrote the original draft of manuscript; [PERSON] was especially involved experimental design and in application of software for data analysis; [PERSON] and [PERSON] were involved in the supervision of experiments, preparation of equipment and material, and reagents support; [PERSON] and [PERSON] provided ideas, contributed to the data analysis, and supervised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the company MINERA DE ORGIVA, S.L., Spain.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
In molecular imprinting [1], a highly cross-linked polymer is synthesized in the presence of a template compound that can be either a small molecule or a much larger species such as a microorganism [2]. If this template does not interact with the polymerization reaction, it leaves behind adapted cavities in the material. In the case of small molecules these are usually distributed within the entire polymer bulk, whereas for larger analytes soft-lithographic techniques are preferred that lead to a surface pattern. The resulting materials are highly suitable to selectively re-incorporate the analyte by binding it reversibly via non-covalent interactions. Combining them with e.g. mass-sensitive devices, such as quartz crystal microbalances (QCM) or surface acoustic wave resonators (SAW), leads to highly effective sensor systems. Within this article we will cover both some aspects of mass-sensitive measuring technique as well as the capacity of imprinted materials as sensor layer for analytes ranging from small molecules to micro organisms [3]. Xylene isomers, engine oil degradation, viruses and erythrocyte ghosts will serve as examples.  
As previously mentioned, the imprinting techniques for small molecules and micro organisms differ from each other. Choosing a molecular template usually requires adding it to the entire reaction mixture, where it is incorporated into the bulk of the forming polymeric material and removed by evaporation or washing after hardening. Consequently, the interaction sites are distributed within the entire bulk of the material. Especially for small molecules this is very advantageous, as the amount of interaction sites is appreciably high. In contrast to this, micro organism imprinting usually is done exclusively on the surface of the respective polymer material. One reason is that the analyte-layer interaction then is not diffusion-hindered. The lower amount of interaction sites compared with bulk imprints, however, is no limitation, because already the interaction with a single analyte can lead to a detectable frequency shift on a mass-sensitive device.  
Within this paper we will show the versatility and diversity of imprinting techniques with the examples of detecting analytes at both dimension scales mentioned, namely xylene detection and engine oil degradation sensing for small molecules as well as erythrocyte and human rhinovirus sensing on the other side.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work  
An efficient routing mechanism in VANETs high mobile environment is necessary for proper data dissemination. Numerous routing protocols have been proposed in the past [38; 39] and they are mainly classified as topology-based, position-based, reactive-based, proactive-based, cluster-based, and opportunistic-based. This work compares the performance of OLSR and DSDV proactive protocols and AODV reactive protocol on a realistic VANETs environment. Furthermore, considering the path loss due to obstacles (such as vehicles, buildings, road-curvature, etc.) in the path of signal transmission from one vehicle to another has to be addressed. Loss of data packet depicts network channel characteristics, and propagation models are implemented to estimate the path loss. Thus, path loss and fading models are analysed along with proper routing protocol selection.  
The WAVE includes IEEE 802.11p and IEEE 1609.1\(\sim\)4 standards. The IEEE standard implements CSMA/CA with QoS and reliable communication. Thus, as an interfacing protocol for the scenario, like IEEE 802.11p and 1609.x provides efficient short-range communication. A flow diagram of simulation is shown in Figure 2.  
Figure 2: Flow diagram of simulation.
# A Deep Reinforcement Learning Approach for Active SLAM
## 2 Problem Formulation
### Traditional Approach
\[Q^{*}(s,a)=\mathcal{R}(s,a)+\gamma\sum_{s^{\prime}\in\mathcal{S}}\mathcal{T}(s,a, s^{\prime})\max_{a}Q^{*}(s^{\prime},a) \tag{6}\]  
```
Parameters: \(\alpha\in(0,1]\), \(\gamma\in(0,1]\) Initialize Q-table with arbitrary Q-values for episode \(\gets 1\) to max episodes do  Perceive \(s_{t}\) while\(s_{t}\) not terminal do  Select \(a_{t}\leftarrow\pi(s_{t})\)  Take \(a_{t}\), get \(r_{t}\) and perceive \(s_{t+1}\) if\(s_{t+1}\) is terminal then\(Q_{t}\gets r_{t}\) else\(Q_{t}\gets r_{t}+\gamma\max_{a}Q(s_{t+1},a)\) endif \(Q(s_{t},a_{t})\leftarrow(1-\alpha)Q(s_{t},a_{t})+\alpha Q_{t}\) \(s_{t}\gets s_{t+1}\) endwhile endfor
```  
**Algorithm 1** Q-learning (for deterministic environment). Furthermore, active SLAM can be formulated as a multi-objective optimization problem [7] where the cost function \(\mathcal{J}\) must contain the cost of performing a free-collision trajectory (\(\mathcal{F}\)) and a metric of the covariance matrix (\(\mathcal{U}\)), which represents the uncertainties in the estimates of the robot's pose and the map. As shown hereafter, both terms should be scaled by task-dependent coefficients, and can be considered in an \(n\)-step horizon,  
\[\mathcal{J}=\sum_{n}\beta_{n}\mathcal{F}_{n}+\sum_{n}\alpha_{n}\mathcal{U}_{n} \tag{7}\]  
Such formulation can be straightforward generalized to RL problem, just by designing the reward function as follows,  
\[\mathcal{R}_{aug}=\mathcal{R}_{\mathcal{F}}+\mathcal{R}_{\mathcal{U}} \tag{8}\]  
where the first term refers to a fully extrinsic reward that accounts for the collision-free trajectories (i.e., navigation), and the second one is an intrinsic reward, that can be defined inversely proportional to a metric of the covariance matrix \(f(\Sigma)\), e.g., the aforementioned utility functions. This definition would motivate the robot not only to navigate the environment but also to explore it, in terms of improving the estimate of the map's representation and the robot's localization on it.
## 7 Conclusions  
In this paper, the breakdown of MOST is investigated experimentally, and its side effects on wake modeling through numerical simulations are also investigated. New similarity functions based on a field measurement in a wind farm are proposed and applied in a turbulence model to eliminate the side effects of the breakdown of MOST on wake modeling. Wake simulations of two types of turbines under different stability conditions are carried out and compared with measurements from a LiDAR and cup anemometers. The main findings are as follows. (1) Field measurements show that MOST overestimates velocity gradient for \(\zeta>0.1\) in the full range of \(R_{f}\). The proposed similarity functions can limit the velocity gradient as the stability increases. (2) Due to the breakdown of MOST for \(\zeta>0.1\), the test models based on MOST overestimate wake effects in both wake deficits and the added turbulence under stable conditions. (3) The new similarity functions constrain velocity gradient  
Figure 19: Same as Figure 18 but for boxplots of the root-mean-squared error (RSME) of wake deficits.  
Figure 20: RSME of the lateral added turbulence in wakes of Danwin 180 kW wind turbines under the stable condition at hub height; \(\sigma_{u}\) and \(\sigma_{u0}\) represent the standard derivations of wind speed in wakes and in the atmosphere, respectively. All turbulence models are combined with AD based on thrust coefficient.  
for \(\zeta>0.1\) as compared with MOST, and introducing the new similarity functions into the Laan model improves the wake prediction under stable conditions. (4) By distributing the blade force through the rotor, momentum effects of the rotor to the atmospheric boundary layer are simulated in a more detail way than the uniformly distributed blade force applied based on the thrust coefficient. This enables the proposed model to capture the double-bell near-wake shape.  
Data curation, X.H.; Formal analysis, X.H.; Methodology, X.H.; Project administration, D.L.; Software, X.H. and L.L.; Supervision, D.L.; Validation, X.H. and F.X.; Writing--original draft, X.H.; Writing--review & editing, C.X. and W.S.  
This research was funded by the Joint Funds of the National Natural Science Foundation of China, grant number U1865101; Jiangsu provincial science and Technology Department, grant number BZ2018007; the Ministry of Science and Technology of China, grant number 2014 DFG62530; and the Danish Energy Agency, grant number 64013-0405.  
The authors declare no conflict of interest.  
The following abbreviations are used in this paper:  
\begin{tabular}{l l} AD & Actuator Disk \\ AGL & Above Ground Level \\ AL & Actuator Line \\ BEM & Blade Element Theory \\ CFD & Computational Fluid Dynamics \\ LES & Large Eddy Simulation \\ LiDAR & Light Detection and Ranging \\ MOST & Monin-Obukhov Similarity Theory \\ OpenFOAM & Open-Source Field Operation And Manipulation \\ RANS & Reynolds-averaged Navier-Stokes Equations \\ TKE & Turbulence Kinetic Energy \\ WD & Wind Direction \\ \end{tabular}
## 4 Results
### Performance Analysis: Resolution vs. Frame Rate
#### 4.1.3 Functional Capability Comparison  
In addition to frame rate analysis, it is important to evaluate the functional capabilities of each system, especially in the context of XR interaction. Table 4 summarizes the feature comparison between Unity Cloth and the GPU-PBD implementations.