esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Methods for Pallet Loading Problem
## 3 Methodology
### Evaluation Metrics
The cross entropy quantifies the difference between two probability distributions, and it can be calculated using the formula below [60; 61]. \[\text{Cross Entropy}=-\sum Likelihood_{Reference}(i)\times log_{2}Likelihood_{ Response}(i) \tag{8}\]  
Sensitivity by class, specificity by class, precision by class, F1 by class, balanced accuracy by class, and confusion entropy by class enable us to understand the performance of different classifiers with respect to the classes being analyzed [60]. Sensitivity, also called the true positive rate, captures the proportion of positive outcomes that are correctly identified as such [60; 62]. \(TP\) represents the prediction made when the model is able to correctly predict a positive outcome assigned to the correct class, whereas an \(FN\) occurs when a false prediction is assigned to the correct class [63]. \[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{9}\]  
Specificity, also called true negative rate, captures the proportion of negative outcomes that are correctly identified as such [60; 62]. In the case of \(TN\), the false prediction is assigned to the incorrect class, while \(FP\) represents the correct estimate assigned to the incorrect class [63]. \[\text{Specificity}=\frac{TN}{TN+FP} \tag{10}\]  
Precision, also called positive predicted value, represents the proportion of positives that corresponds to the presence of a certain condition [60; 62]. \[\text{Precision}=\frac{TP}{TP+FP} \tag{11}\]  
The F1-macro score is the harmonic mean of sensitivity and precision and is calculated according to the formula below [57; 60]. In the computation of the F1-macro score, the largest classes are assigned the same importance as small classes [57], which makes the F1-macro score a suitable indicator of the performance of a machine learning algorithm on an imbalanced dataset. A high F1-macro value means that the algorithm performs well on all classes under consideration, whereas a low F1-macro score is an indicator of poor prediction of the classes analyzed [57]. \[\text{F1}-\text{macro}=\frac{2}{|C|}\sum\frac{\text{Sensitivity}_{i}\times\text{ Precision}_{i}}{\text{Sensitivity}_{i}+\text{Precision}_{i}} \tag{12}\]  
The balanced accuracy represents, in essence, an average of recalls. It is computed according to the formula below [57]. \[\text{Balanced Accuracy}=\frac{\text{Sensitivity}+\text{Specificity}}{2}=\frac{ \left(\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\right)}{2} \tag{13}\]  
The confusion entropy indicates how well samples belonging to different classes have been separated from each other. It exploits the misclassification of information of confusion matrices in order to quantify the confusion level of the class distribution of misclassified samples [60]. \[\text{Balanced Accuracy}=\frac{1}{C}\times\sum\frac{p_{i}}{n_{i}}, \tag{5}\]0  
\[\text{Balanced Accuracy}=\frac{1}{C}\times\sum\frac{p_{i}}{n_{i}}, \tag{5}\]1  
where \(C_{ij}\) is the entry found at position (_i,j_) in the confusion matrix.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques
### Design and Classification
#### 2.2.1 What to Move  
By dynamically shifting attack surfaces, MTD techniques confuse attackers who rely on fixed system configurations to execute an attack. As a collection of resource attributes (shown in Table 2) in a system that could be used by an attacker to execute an attack, the attack surface can be exploited to confuse the attacker by dynamically changing these configurations. To facilitate the enumeration of joint attack surfaces, they can be classified hierarchically based on their level of existence. This classification leads to the first way of classifying MTD techniques, which includes the network layer, platform layer, runtime environment layer, software layer, and data layer [3].  
Various MTD techniques can disrupt the attacker's resource reconnaissance and vulnerability detection. By continuously shifting the attack surface, MTD techniques hinder an attackers' ability to locate and access target hosts, forcing them to continuously chase the target. This not only increases the attacker's cost but also eliminates their temporal advantage and information asymmetry advantage over defenders. The result is a more resilient defense mechanism that can adapt to ever-evolving threats.  
Research on MTD has primarily focused on the network layer for several key reasons. Firstly, designing defense mechanisms at the network layer is more in line with MTD's pursuit of affordable defense due to its small size, low resource consumption, and ease of operation. Secondly, narrowing the focus to a specific layer, such as the network layer, allows for more targeted study and development.  
Nevertheless, it is important to note that MTD has been in development for over a decade, and thus, the current top-heavy research situation does not fully represent the breadth and depth of MTD as a cyber defense mechanism.
## Determining the Best Sensing Coverage for 2-Dimensional Acoustic Target Tracking  
**[PERSON] * and [PERSON]**  
Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +98-411-3317709; Fax: +98-411-3303701  
_Received: 19 March 2009; in revised form: 15 April 2009 / Accepted: 30 April 2009 / Published: 8 May 2009_  
**Abstract:** Distributed acoustic target tracking is an important application area of wireless sensor networks. In this paper we use algebraic geometry to formally model 2-dimensional acoustic target tracking and then prove its best degree of required sensing coverage. We present the necessary conditions for three sensing coverage to accurately compute the spatio-temporal information of a target object. Simulations show that 3-coverage accurately locates a target object only in 53% of cases. Using 4-coverage, we present two different methods that yield correct answers in almost all cases and have time and memory usage complexity of _O(1)_. Analytic 4-coverage tracking is our first proposed method that solves a simultaneous equation system using the sensing information of four sensor nodes. Redundant answer fusion is our second proposed method that solves at least two sets of simultaneous equations of target tracking using the sensing information of two different sets of three sensor nodes, and fusing the results using a new customized formal majority voter. We prove that 4-coverage guarantees accurate 2-dimensional acoustic target tracking under ideal conditions.  
**Keywords:** Wireless sensor networks; quality of service; target tracking; geometric algebra; sensing coverage; accuracy
###### Abstract  
On lunar missions, efficient and safe transportation of human-robot systems is essential for the success of human exploration and scientific endeavors. Given the fact that transportation constructs bottlenecks for numerous typical lunar missions, it is appealing to investigate what function allocation strategies can generate optimal task implementation paths for robots with low-human workloads when the situation changes. Thus, this paper presents a novel approach to dynamic human-robot function allocation explicitly designed for team transportation in lunar missions. The proposed dynamic allocation framework aims to optimize human-robot collaboration by responding to existing and potential contingencies. First, a fitness concept model is designed to quantify the factors that motivate the functional adaptation of each agent in dynamic lunar mission scenarios. A hierarchical reinforcement learning (HRL) algorithm with two layers is then employed for decision-making and optimization of human-robot function allocation. Finally, the validity of the framework and algorithm proposed is validated by a series of human-robot function allocation experiments on a simulated environment that mimics lunar transportation scenarios, and is compared with the performance of other algorithms. In the future, path-planning algorithms can be incorporated into the proposed framework to improve the adaptability and efficiency of the human-robot function allocation in lunar missions.  
Keywords:fitness; human-robot function allocation; hierarchical reinforcement learning; unstructured environment; lunar mission +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Extracting Significant Travel Time Patterns and Computing the Statistics of These Patterns  
The flowchart, as shown in Figure 6, includes two processes of extracting significant travel time patterns and computing the statistics of these patterns via Hadoop MapReduce programming. Sections 2.2.1 and 2.2.2 give the details in the following.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Direction** & **Gantry ID** & **Distance (km)** & **Fe (TWD)** & **Interchange (Start)** & **Interchange (Stop)** & **North Latitude** & **East Longitude** \\ \hline \multirow{4}{*}{Northern} & 1 & 05F-000.1N & 4.1 & 4.9 & Shinding & Nangang System & 25.03497222 & 121.6248611 \\  & 2 & 05F-005.5N & 10.5 & 12.6 & Pinglin & Shihding & 24.9951556 & 121.6250972 \\  & 3 & 05F-028.7N & 15.7 & 18.8 & Touching & Pinglin & 24.84258389 & 121.7892861 \\  & 4 & 05F-030.9N & 6.5 & 7.8 & Yilan (Sicheng, Taifu) & Touching & 24.82370556 & 121.7862139 \\  & 5 & 05F-043.8N & 5.7 & 6.9 & Luedong & Yilan (Huangwei) & 24.710292222 & 121.789972 \\  & 6 & 05F-052.8N & 6.9 & 8.2 & Su-no & Luedong & 24.63272667 & 121.8071667 \\ \hline \multirow{4}{*}{Southern} & 1 & 05F-000.0S & 4.1 & 4.9 & Nangang System & Shihding & 25.03508889 & 121.6229306 \\  & 2 & 05F-005.5S & 10.5 & 12.6 & Shihding & Pinglin & 24.9964667 & 121.6250583 \\  & 3 & 05F-028.7S & 15.7 & 18.8 & Pinglin & Touching & 24.84256944 & 121.789667 \\  & 4 & 05F-030.9S & 6.5 & 7.8 & Touching & Yilan (Sicheng, Taifu) & 24.82370556 & 121.7862139 \\  & 5 & 05F-043.9S & 5.7 & 6.9 & Yilan (Huangwei) & Luedong & 24.71102778 & 121.7894778 \\  & 6 & 05F-049.4S & 6.9 & 8.2 & Luedong & Su-no & 24.64258056 & 121.7998500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Six gantries in both of the southern and northern directions on Taiwan National Freeway No. 5.  
Figure 6: The flowchart of extracting significant travel time patterns and computing the statistics of these patterns.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 3 Results
### Megadisaster Mapping  
EEO enables regional-scale flood and wildfire mapping using high resolution data. Indeed, widespread delineation mapping is fit-to-purpose for mega-fires and mega-floods, which are characterized by their intensity and size. The use of medium resolution sensors would be relevant to cover such large areas, but the challenge here was to map the whole event with high resolution data. A mega-disaster product has similar quality specifications to those covering smaller-scale events. This approach required high computing power to process big volumes of data, notably, Sentinel 1&2 images, which were very suitable data for large-scale mapping, by offering free and open, high-resolution data covering large areas, with a high revisit frequency. This last aspect was very important to not only cover the event, but also to monitor the extent over time, especially for mega-fires, which can be very dynamic, due to ever-changing meteorology, fire-event meteorology, duration, and uncontrollability. Three mega events were processed through EEO to test its scalability.  
Australia's 2019-2020 'Black Summer' mega-fire burnt more than 20 million hectares. A prototype of a mega-disaster map produced over this event is presented in the Figure 7. Processing details of this mega-fire mapping and of two massive flood events are reported in Table 4. Regarding the Australian event, the AOI size was 315,000 km\({}^{2}\) and was covered  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
* Automatic chain run from data pre-processing to results (fire/water/cloud vectors) on a standard workstation (processor: i9-9900K, RAM: 64 GB). The processing time does not include the manual refinement process.  
\end{table}
Table 3: EEO performances (automatic extraction compared with CEMS products).  
by two Sentinel-2 swaths. EEO ran on 51 Sentinel-2 tiles that intersected one strip. All the following processing steps were completed in 1 h and 9 min:  
* Download of the 51 \(\times\) 2 S2 tiles (pre and post-event);
* Burnt area detection at 20 m resolution;
* Cloud detection at 20 m resolution;
* Minimum Mapping Unit application (1 ha);
* Image stack and mosaicking for visualization and validation;
* Vectorization of the burnt areas and clouds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} Water surfaces (20 m) \\ Cloudy areas (20 m) \\ Post-event image mosaic \\ (100 m) \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Automatic chain run-time from data download to results (fire/water/cloud vectors & image mosaic(s)) on a standard workstation (processor: i9-9900K, RAM: 64 GB).  
\end{table}
Table 4: Processing details of EEO mega-disaster mapping.  
Figure 7: Prototype of mega-disaster map covering Australia’s 2019–2020 mega-fire.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 4 Transference of Programmed Quantum States Using Double Teleportation
### Generation of Quantum States as a Collaboration among Three Parties  
Following the discussion in the last section, by inserting explicitly the action of (12) on \(|\psi_{0}\rangle\):  
\[|\psi_{0}^{j}\rangle=\cos\omega_{j}(\cos\frac{\theta}{2}|0\rangle+e^{i\phi}\sin \frac{\theta}{2}|1\rangle)+\sin\omega_{j}(e^{i\phi}\sin\frac{\theta}{2}|0 \rangle-\cos\frac{\theta}{2}|1\rangle) \tag{15}\]  
with \(j=0,1\). We note that if \(e^{i\phi}\in\mathbf{R}\), it means such parameter takes one of the two possible values \(\phi_{p}=0,\pi\) or \(e^{i\phi_{p}}=(-1)^{p},p=0,1\). In such case, the last expression naturally states an orthogonal basis defined by the couple of vectors:  
\[|0\rangle_{\theta,p} = \cos\frac{\theta}{2}|0\rangle+(-1)^{p}\sin\frac{\theta}{2}|1\rangle \tag{16}\] \[|1\rangle_{\theta,p} = (-1)^{p}\sin\frac{\theta}{2}|0\rangle-\cos\frac{\theta}{2}|1\rangle \tag{17}\]  
they could be selected through the initial election of \(|\psi_{0}\rangle\) on the Bloch sphere meridian containing the states \(|0\rangle,|1\rangle\) and \(|+\rangle,|-\rangle\). It still leaves sufficient room to choose a quasi arbitrary state. Integrating those expressions in (11), we get:  
\[|\psi_{final}\rangle = e^{-i\phi_{m}}|b_{0}\rangle\big{[}(\sqrt{p_{0}}\beta_{0}\cos \omega_{0}e^{i\phi_{m}}-\sqrt{p_{1}}\beta_{1}\cos\omega_{1})|0\rangle_{\theta,p} \tag{18}\] \[+(\sqrt{p_{0}}\beta_{0}\sin\omega_{0}e^{i\phi_{m}}-\sqrt{p_{1}} \beta_{1}\sin\omega_{1})|1\rangle_{\theta,p}\big{]}\] \[+|b_{1}\rangle\big{[}(\sqrt{p_{0}}\beta_{1}\cos\omega_{0}e^{i\phi _{m}}+\sqrt{p_{1}}\beta_{0}\cos\omega_{1})|0\rangle_{\theta,p}\] \[+(\sqrt{p_{0}}\beta_{1}\sin\omega_{0}e^{i\phi_{m}}+\sqrt{p_{1}} \beta_{0}\sin\omega_{1})|1\rangle_{\theta,p}\big{]}\]  
For further applications, we will require certain coefficients of each pair \(|0\rangle_{\theta,p},|1\rangle_{\theta,p}\) in the previous expression become zero. It is only possible if \(e^{i\phi_{m}}\in\mathbf{R}\), meaning that \(\phi_{m}=0,\pi\). Then, \(e^{i\phi_{m}}=(-1)^{m},m=0,1\). In such case, the probabilities (13) and (14) for the measurements on the control state are:  
\[P_{0} = \beta_{0}^{2}p_{0}+\beta_{1}^{2}p_{1}+2\beta_{0}\beta_{1}\sqrt{p _{0}p_{1}}(-1)^{m}\cos\Delta^{-}\omega \tag{19}\] \[P_{1} = \beta_{1}^{2}p_{0}+\beta_{0}^{2}p_{1}-2\beta_{0}\beta_{1}\sqrt{p _{0}p_{1}}(-1)^{m}\cos\Delta^{-}\omega \tag{20}\]
###### Abstract  
The potential antibacterial activity of basil (_Ocinum basilicum_), chamomile (_Matricaria chamomilla_), origanum (_Thymus capitatus_), tea tree (_Melaleuca alternifolia_) and thymme (_Thymus vulgaris_) essential oils, was investigated against 29 Gram-positive bacterial strains isolated from wastewater treatment plants, clinical samples (n = 25) and American Type Culture Collection (ATCC) reference strains (n = 4). Wild bacterial strains included methicillin-resistant _Staphylococcus aureus_ (n = 16) and vancomycin-resistant _Enterococcus_ spp. (n = 9). The antimicrobial activity of the selected oils was studied using the broth macrodilution method. The Minimal Inhibitory Concentration (MIC) values for _S. aureus_ ranged from 0.06 to 0.5% (_v/v_) for origanum oil, 0.06 to 1% (_v/v_) for thymme oil, 0.12 to 1% (_v/v_) for tea tree oil, 0.25 to 4% (_v/v_) for basil oil and 2 to >4% (_v/v_) for chamomile oil. For enterococci the MIC values were significantly higher ranging from 0.25 to 1% (_v/v_), 0.5 to 2% (_v/v_), 1 to 4% (_v/v_), 4 to >4% (_v/v_) and >4% (_v/v_) for the above-mentioned oils, respectively. The main compounds of the tested essential oils were: estragole (_Ocinum basilicum_), bisabolol and trans-b-farnesene (_Matricaria chamomilla_), carvacrol and thymol (_Thymus capitatus_), terripen-4-ol and p-cymene (_Melaleuca alternifolia_), thymol, linalool, and p-cymene (_Thymus vulgaris_). Origanum essential oil yielded the best antimicrobial results followed by thyme, tea tree, and basil oil, while chamomile oil exhibited weak antibacterial properties.  
Keywords:essential oils; antimicrobial activity; MIC; multi-drug resistant; MRSA; VRE
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### Algorithm Implementation
#### 3.4.2 Data Preprocessing  
In reality, operation data are collected by many on-board sensors and trackside equipments. Considering the practical working environment, the sensors and actuators may fail to generate data due to line fault, equipment failure, communication quality, etc. This situation further leads to the problem of missing values. Missing values of the dataset reduce the fitting effect and increase the model bias, resulting in a poor performance of regression and prediction. For LSTM based algorithms, the effect of missing values is more serious. LSTM networks fail to run since null values cannot be calculated during the error propagation process. The missing values can be arbitrarily set to zero or other statistical values (such as the most frequent non-missing value). However, this treatment causes a great disturbance when handling time series problems. In this paper, similar to [PERSON] [43], a masking mechanism is proposed to solve the problem of missing values.  
In the masking mechanism, we predefine a mask value \(\phi\) and the missing values are directly set as \(\phi\). At each iteration \(t\), if one of the raw features is invalid, all the raw features, i.e., analog output\({}_{t}\), traction brake\({}_{t}\), train speed\({}_{t}\) and slope\({}_{t}\), are set to \(\phi\). In this paper, the mask value \(\phi\) is assigned as null. For a feature matrix \(\mathbf{X}=(\mathbf{x}_{-K_{pre}},\mathbf{x}_{-K_{pre-1}},\cdots,\mathbf{x}_{0})\), if there exists a missing element in \(\mathbf{x}_{k}\), the element equals \(\phi\) and the training process at the \(k\)th step will be skipped. Thus, the state vector \(\mathbf{c}_{k-1}\) is directly fed to the \((k+1)\)th time step. Note that the issue of continuous missing values can also be solved using this masking mechanism.
## 8 Possible Applications  
### Process Simulation  
The simulation is an important tool in both design and then subsequent maintenance of the system, especially if physical device is cumbersome, costly, or dangerous to use. The obvious method of simulating the engine temperature at specific rotational speed is to generate random numbers from a distribution specific for that temperature. The simplest method is the so-called acceptance-rejection method. In this method, a random number from distribution \(f(x)\in[a,b]\) is generated as follows (in general using any dense random number generator, but typically uniform is used):  
* generate random \(x\in[a,b]\),
* generate \(y\in[0,1]\),
* if \(y>f(x)\) then go to 1, otherwise the result is \(x\).  
We can easily obtain distribution for a specific parameter \(r\). Using the example from Section 7.1, with \(r\) = 50,000 we obtain a distribution as in Figure 9.  
If a random generation algorithm is used directly, the resulting process path would be highly irregular and perceived as not real. To avoid this, a simple filter can be used  
\[\hat{T}_{i+1}=\tau\cdot\tilde{T}+(1-\tau)\hat{T}_{i}, \tag{51}\]  
where \(\tilde{T}\) is the resulting new random number, \(\hat{T}_{i+1}\) is the new process value, and \(\hat{T}_{i}\) is the old process value. The result with parameter \(\tau=0.05\) can be seen in Figure 10.  
Figure 8: Reduction of the primeter data in order to avoid over-fitting. **Left**: reconstruction based on partial data, **Right**: resulting points.  
Figure 9: Probability for 50k rpm.
Sensors_**2012**, _12_, 189-214; doi:10.3390/s120100189  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks  
**[PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON]**  
School of Instrumentation Science and Opto-Electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (J.W.);  
[EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (R.F.) [EMAIL_ADDRESS] (N.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-8233-8089; Fax: +86-10-8233-9889.  
_Received: 3 November 2011; in revised form: 6 December 2011 / Accepted: 15 December 2011 / Published: 27 December 2011_  
**Abstract:** In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.  
**Keywords:** leak detection; leak point localization; hierarchical; pipeline monitoring; sensor networks