esa-sceva/satcom-chunk-collection huggingface dataset:
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 3 Results
### Fruit Quality Characteristics  
Concerning sensorial quality traits, soluble solid content (expressed as \({}^{\circ}\)Brix) did not differ with respect to the cultivation method, whereas weight and titratable acidity (TA) were significantly lower in organically grown fruit (Figure 3a). Anthocyanins are the main compounds responsible for berry colour. Cyanidin-3-sophroside was the most abundant anthocyanin in raspberry fruit, followed by cyanidin-3-glucose (Figure 3b). Organic raspberries showed significantly higher concentration of all anthocyanins with the exception of cyanidin-3-glucuronoside. The higher concentration of anthocyanins partially influenced berry colour, despite no significant differences were observed in any of the CIE coordinates (L \(\times\) C \(\times\) h). Canonical correlation analysis (CCA) was performed to investigate possible correlations between the bacterial genera harboured on fruit and the quality parameters (Figure 4). _Gluconobacter_ positively correlated with total anthocyanin content, _Methylobacterium_ and _Sphingomonas_ with titratable acidity, whereas _Brevibacillus_ and _Rosenbergiella_ genera with soluble solid content.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cultivation Method** & **Bacterium** & **IAA Production** & **Acetoin Production** & **NH4\({}^{*}\) Production** & **Siderphones** & **ACC Deaminase Activity** \\ \hline \multirow{4}{*}{IPM} & _Rosenbergiella_ spp. & – & – & – & – & – \\ \cline{2-6}  & _Enterobacter_ strain 2 & – & + & – & + & – \\ \cline{2-6}  & _Enterobacter_ strain 3 & – & – & + & + & – \\ \hline \multirow{4}{*}{ORG} & _Pantone_ agglomerans_ strain 2 & ++ & – & + & – & – \\ \cline{2-6}  & _Cronobacter_ spp. & – & – & + & – & – \\ \cline{2-6}  & _Klebsiella oxytocin_ & – & – & + & + & – \\ \cline{2-6}  & _Tatumella punctata_ & – & – & + & – & – \\ \cline{2-6}  & _Tatumella terra_ & ++ & – & + & – & – \\ \hline \hline \end{tabular}  
* Qualitative rating of compound production/activity: – = not detected, + = detectable, ++ = high.  
\end{table}
Table 2: Functional traits of bacteria strains isolated uniquely in organic or IPM berries.  
Figure 3: Main quality parameters (**a**), anthocyanins content and color values (**b**) of raspberry fruit ‘Enrosadira’. Bars represent the mean \(\pm\) SE. Different letters indicate significant differences between cultivation methods according to Student’s \(t\)-test at \(p\)-value \(<\) 0.05.
# Vulnerability Assessment of Sensor Systems
## 2 Background and Related Work
### Current State of the Research Field
#### 2.3.2 Security Evaluation and Vulnerability Assessment, Especially in the Common Criteria Domain
This approach is general and does not comply with Common Criteria. These concepts may be used to develop the vulnerability assessment ontology. Another paper [41] discussed cyber security ontology related to common sources of information of IT products, assets, attacks, threats, and, first and foremost, vulnerabilities, including [42; 43; 44; 25; 45; 46; 47] and others. These commonly used knowledge bases are for cybersecurity management purposes. This ontology can also be useful to organize access to these sources during the Common Criteria vulnerability assessment process, which supports searching these sources for potential vulnerabilities for the evaluated IT products. The NIST document [48] presents a vulnerability ontology specification. The ontology includes the minimum information needed to properly inform the vulnerability management process and to facilitate the sharing of vulnerability information. It is used by vulnerability data feeds and for vulnerability scoring [49]. This approach is used in the method discussed in this paper. Another previous work [50] discussed a high-level ontology involving different sources of cyber security information. It is focused on the needs of security operation centres (SOC). The solved problem is very similar to the CC vulnerability assessment, although the CC process is focused on the vulnerability of a specific IT product, e.g., a sensor. There is neither a method nor a tool, especially ontology-based, to support the vulnerability assessment activities according to the CC standard, e.g., the search for public information sources for vulnerabilities, evidence-based vulnerabilities, structured vulnerability analyses, and penetration test management. This was a motivation for the author to support the vulnerability assessment process of IT products, especially specific ones, such as sensors.
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 2 Knowledge Graph Key Technologies
### Knowledge Extraction
#### 2.1.2 Relationship Extraction  
Relationship extraction refers to the extraction of possible relationships from two or more entities and is the subsequent step of entity extraction.  
Supervised learning-based relationship extraction is essentially a manual annotation of data and classification of relationships [32; 33]. In this case, a vector representation of words and positions in the input sentence based on the deep learning method is used to optimize the entities and relations in the same model using a joint extraction method [34; 35]. This joint extraction can avoid the final extraction performance degradation caused by the accumulation and propagation of errors in each sub-stage of the pipeline method extraction [36; 37].  
Figure 1 below shows the joint model of entity extraction and relationship extraction [38]. The model consists of three layers: a word embedding layer, a word sequence-based LSTM-RNN layer, and a dependent subtree-based LSTM-RNN layer. Entity recognition is performed at the sequence layer, and relationship classification is implemented at the dependency layer. The embedding and sequence layers are shared by the entity recognition and relationship classification tasks. In addition, the shared parameters are jointly influenced by both entity and relationship labels.  
Weakly supervised learning extraction methods can use a small corpus for model learning, mainly including the APCNs model [39], GHE-LPC [40], and CNN-RL model [41] for remotely supervised methods. However, all of these methods encounter the problem of semantic drift due to noise.
_Applied_  
_sciences_  
Appendix A Comparison of Pain Perception between Clear Aligners and Fixed Appliances: A Systematic Review and Meta-Analysis  
**[PERSON] 1,2**, **[PERSON] 1,2,*(r)**, [PERSON] 2, [PERSON] 3, **[PERSON] 2 and [PERSON] 1,2**  
Orthodontics Department, CRU, CiEM, Egas Moniz-Cooperativa de Ensino Superior, 2829-511 Almada, Portugal; [EMAIL_ADDRESS] (D.P.); [EMAIL_ADDRESS] (A.S.D.)  
Clinical Research Unit (CRU), Centro de Investigacao Interdisciplinar Egas Moniz (CiiEM), Instituto Universitario Egas Moniz, 2829-511 Almada, Portugal; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (J.M.)  
Quantitative Methods for Health Research Unit (MQIS), CiiEM, Egas Moniz--Cooperativa de Ensino Superior, 2829-511 Almada, Portugal; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
Received: 23 May 2020; Accepted: 19 June 2020; Published: 22 June 2020  
Appendix A Featured Application: Clear aligners are associated with significantly less pain than fixed appliances during the first seven days of orthodontic treatment.  
**Abstract:** We aimed to compare the pain discomfort levels between clear aligners and fixed appliances at multiple time points. Four electronic databases (Pubmed, Medline, CENTRAL and Scholar) were searched up to May 2020. There were no year or language restrictions. Randomized clinical trials and case-control studies comparing pain perception through pain visual analog scale (VAS) in patients treated with clear aligners and with fixed appliances were included. Risk of bias within and across studies was assessed using Cochrane tool and Newcastle-Ottawa Scale (NOS) approach. Random-effects meta-analysis were conducted. VAS score and analgesic consumption were collected. Random-effects meta-analyses were used to synthesize available data. Following the review protocol, five articles met the inclusion criteria and were included, with a total of 273 participants (177 females, 96 males). Overall, clear aligners were associated with significantly less pain than fixed appliances during the first seven days of orthodontic treatment. Patients treated with clear aligners experience less pain discomfort than those treated with fixed appliances and consume less analgesics, with SORT A recommendation.  
**Keywords:** clear aligners; fixed appliances; pain perception; VAS (visual analog scale) scale; systematic review; meta-analysis
## 4 Performance Evaluation
### Simulation Environment
#### 4.1.2 Training Stage  
The construction of each tree in the random forest was achieved by selecting a random subset of samples from the training data \((D)\). Each tree in the RFR was trained independently by sampling from the data on a random basis. During the training process, samples and features were selected with replacements, which means that some samples or features may have been repeated during the training of each tree in the forest. In the training stage, the construction of each tree followed the procedure outlined below:  
* Count samples in the randomly chosen sub-space \(C_{n}(U,E)\).  
Figure 5: Some samples of the dataset.  
* The goal of decision tree training is to reduce the entropy \(S\) and maximize information gain.
* For each selected feature, compute the feature's information gain and evaluate the difference. For instance, for the encounter feature, compute the entropy and information gain.
* The coefficient of variation is employed to set a threshold for further branching. If a node contains fewer than a specific number of samples, the partitioning process stops, and the node is designated as a leaf node.  
In this simulation, the mean square error (MSE) was used as a measure of classification error, where the term \"samples\" refers to the number of data samples represented by the node, and \"value\" denotes the average value of the data that the node represents. For this particular simulation, the minimum number of samples required in a node was set to one, which implies that a node could contain only one sample.
## 6 Results and Discussions
### 69-Bus System
#### 6.1.1 Results without Reducing the Search Space
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **PSO [46]** & **FGA [47]** & **HM [48]** & **DE [30]** & **IHA-PLI [27]** & **PODESCA** \\ \hline \multirow{4}{*}{SCs locations} & 46 & 241 & 59 & 100 & 8 & 600 & 57 & 150 & 21 & 350 & 20 & 250 \\  & 47 & 365 & 61 & 700 & 58 & 150 & 58 & 50 & 61 & 1350 & 61 & 1150 \\  & 50 & 1015 & 64 & 800 & 60 & 1050 & 59 & 100 & - & - & - & - \\  & - & - & - & - & - & - & 60 & 150 & - & - & - & - \\  & - & - & - & - & - & - & 61 & 1000 & - & - & - & - \\ \hline Total No. of \multirow{2}{*}{locations} & 46 & 241 & 59 & 100 & 8 & 600 & 57 & 150 & 21 & 350 & 20 & 250 \\  & 47 & 365 & 61 & 700 & 58 & 150 & 58 & 50 & 61 & 1350 & 61 & 1150 \\  & 50 & 1015 & 64 & 800 & 60 & 1050 & 59 & 100 & - & - & - & - \\  & - & - & - & - & - & - & 61 & 1000 & - & - & - & - \\ \hline Total No. of \multirow{2}{*}{locations} & \multirow{2}{*}{3 locations} & \multirow{2}{*}{3 locations} & \multirow{2}{*}{3 locations} & \multirow{2}{*}{3 locations} & \multirow{2}{*}{5 locations} & \multirow{2}{*}{2 locations} & \multirow{2}{*}{2 locations} & \multirow{2}{*}{2 locations} \\  & & & & & & & & & & \\ \hline Total kVAr & 1621 & 1600 & 1800 & 1450 & 1700 & 1400 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal locations and sizes of shunt capacitors (SCs) by PODESCA comparing to other methods for the 69-bus system.
## 4 Numerical Results
### Simulation Setup and Common Parameters  
In all the simulations, the channels between source, relays and destinations have independent complex Gaussian distribution. Some common parameter values for simulation are shown in Table 2. In all simulations, the channel gain, \(h\), is modeled as [40]:  
\[h=\Phi K_{o}\left(\frac{d_{o}}{d}\right)^{\beta} \tag{11}\]  
where \(K_{o}\) is a constant that depends on the antenna characteristic and average channel attenuation, \(d_{o}\) is the reference distance for the antenna far field, \(d\) is the distance between the transmitter and receiver, \(\beta\)  
\begin{table}
\begin{tabular}{|l|l|} \hline
**Parameters** & **Values** \\ \hline \(p_{l}^{max}\) & 1 Watts \\ \(P_{s}\) & 10 Watts \\ \(\gamma\) & 0.2 \\ \(\Delta_{t}\) & 20 \\ \(\eta_{t}\) & 10 \\ \(\rho_{s}\) & 0.5 \\ \(I_{Ter}\) & 5000 \\ \hline \end{tabular}
\end{table}
Table 2: Common Parameter Values.  
Figure 3: EDA thresholding.  
is the path loss constant and \(\Phi\) is the Rayleigh random variable. Since this formula is not valid in the near field, in all the simulation results, we assume that \(d\) is greater than \(d_{o}\). In all the results, \(d_{o}=20\)m, \(K_{o}=50\) and \(\beta=3\). The PU's protected distance \(R_{m}\) is set to 10m. The secondary and primary users are uniformly distributed. All the simulations are performed using Monte Carlo runs.  
For each PU, there is a PU protection area, wherein the strengths of the cognitive radio signals must be constrained. We define as \(R\), the radius of the protected circular area for each individual PU. Given a distance, \(d_{m}\), between the SU base station and the \(m\)th PU and the radius, \(R_{m}\), of the protected circular area of the \(m\)th PU, the channel from the source to the \(m\)th PU in the \(k\)th SU band is given as:  
\[g_{m,k}^{s}=\frac{\tilde{g}_{m,k}^{s}}{\left(d_{m}-R_{m}\right)^{\beta}} \tag{12}\]  
where \(\tilde{g}_{m,k}^{s}\) is the small scale fading and \(\beta\) is the path loss exponent. For simplicity, throughout this paper and in simulation results, we assume that \(R_{1}=R_{2}=\cdots=R_{M}\).  
We compare the results of EDA and MEDA with the standard continuous genetic algorithm (GA) [38]. The EDA, MEDA and GA use IGS-CSSP and IGS-CSRA for constraint satisfaction. We will use the the word \"non-green communication\" when we set \(w_{1}\) = 1 and \(w_{2}\) = 0. In all the simulation results, we set the (M)EDA/GA parameters as \((\Delta_{t},\rho_{s},I_{Ter})=(20,0.5,1000)\), where \(\Delta_{t},\rho_{s},I_{Ter}\) are the population, selection probability and maximum iteration, respectively.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Experimental Setup  
In our computational experiments, we applied our optimization algorithm to a set of 396 benchmark instances. For both the OEU and the taxi data set, we generated two different demand forecasts by using their respective \(N_{ij}\) and \(D_{ij}\) matrix entries as the revenue forecasts \(p_{ij}\) between each pair of sub-districts. As previously noted, the cost for including each sub-district in the area of operation corresponds to its size in square meters. For each of these four demand forecasts, we then generated nine different _original_ instances with varying budgets. Since we wanted to investigate different expansion strategies for ELOOP, we chose budget values \(B=\beta\cdot H\), where \(H\) is the size of the current AoO. The budget factor \(\beta\) is chosen from \(\{1,1.1,1.25,1.33,1.5,1.75,2,2.5,3\}\). In comparison, the entirety of Vienna has an area of approximately \(9.55\cdot H\).  
For each of these 36 original instances, we then created ten further _bootstrapped_ instances for stability analysis by sampling 10% of the original data set's trips, yielding a total of 396 instances (original and bootstrapped).  
We implemented our optimization algorithm in Java and used the CPLEX Studio 12.6.2 framework as our MILP solver. All benchmark runs were performed on a single core of an Intel(r) Xeon(r) CPU E3-1220 V2 with 3.10 GHz (Intel Corporation, Santa Clara, CA, USA).
## 4 Conclusions  
The rapid growth of cellular-based communication systems has been undergoing viable advancement in terms of multimedia service deployment and mobile subscriptions. The advancements are also being accompanied by the global evolution of other higher broadband cellular communication technologies and deployment infrastructures. In this contribution, a hybrid Random Forest and Particle Swarm Optimization (RF-PS) method was proposed for efficient path loss modeling, leveraging measured path loss data. The proposed RF-PS model exhibits optimal performance in the real-time prognostic analysis of measured path loss data acquired over operational 4G long term evolution (LTE) networks in Nigeria. The proposed RF-PS optimization method showed better prediction performance with lower RMSE values; 3.51-4.33 dB, 2.55-2.98 dB, 3.23-3.54 dB, and 3.11-5.22 dB for locations 1, 2, 3, and 4, respectively. For standard PS and RBF-PS optimization methods, the RMSEs are quite higher; 3.51-4.33 dB, 2.55-2.98 dB, 3.23-3.54 dB, and 3.11-5.22 dB for locations 1, 2, 3, and 4, respectively. These results clearly demonstrate the robustness and superiority of the proposed RF-PS method for path loss prediction over the existing techniques. Future work would focus on the optimization of the projected RF-PS  
Figure 34: Measured path loss and the proposed hybrid RF-PS model validation plot using another site in location 3.  
Figure 35: Measured path loss and the proposed hybrid RF-PS model prediction validation plot using another site in location 4.  
model to improve its path loss prediction accuracy for application in emerging wireless communication systems.  
The manuscript was written through the contributions of all authors. [PERSON], S.A. and J.I. were responsible for the conceptualization of the topic; article gathering and sorting were carried out by [PERSON], S.A. and J.I.; manuscript writing and original drafting and formal analysis were carried out by [PERSON], S.A., J.I., A.L.I., C.-T.L. and C.-C.L.; writing of reviews and editing were carried out by [PERSON], A.L.I., C.-C.L. and C.-T.L.; [PERSON] led the overall research activity. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Council, Taiwan, R.O.C., under contract no.: MOST 110-2410-H-165-001-MY2.  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.  
The work of [PERSON] is supported by the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
The authors declare no conflict of interest related to this work.
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 3 Literature Review and State-of-the-Art
### Qualitative Results
#### 3.3.3 How?--Architecture for an AR-Powered Digital Twin  
This section summarizes our findings regarding different frameworks and architectural approaches to implement AR and DT. Several publications present respective work but on different levels of detail. Another noticeable aspect within literature is that no best practice framework or architecture exists yet. Many authors introduce proprietary architectures. However, some common themes can be identified.  
Some authors present generic, holistic frameworks for IoT architectures or platforms, including AR and DTs [6, 34]. Other authors are more focused on specific architectures for enabling the visualization of DT data with AR [35, 37, 46]. The central theme within these specific setups is that they pivot towards mainly service-oriented, layered architectures. Naturally, these architectures' fundamental parts are a physical layer, representing the physical hardware related to the use case, and a visual layer to augment the physical hardware with additional data [32, 55]. The DT itself is part of these architectures as a layer including several processes, such as a control process to remotely control the physical layer via the visual layer and an augmentation process to realize the AR visualization [15, 55]. Some authors separately define a DT layer including all the necessary components (databases, simulation models, computing units, knowledge ontologies) and an interaction layer enabling the interaction of users with the DT via web services [5, 46]. As these layers are often labeled differently, and the identified literature tends to introduce proprietary architectures, we propose a minimal, common architecture for AR-powered DTs, as in Figure 5.  
The underlying _Physical Layer_ includes any hardware related to the use case under consideration. This physical layer is reflected in a _Digital Twin_. The DT is connected to the physical layer via synchronization processes that transport all necessary data from the physical to the DT and a control process that allows the DT to interact with its physical twin. The information and functionalities contained in the DT are accessed through applications or services located within the _Application Layer_. Within the application layer, any service can retrieve data from the DT by actively querying it. However, the applications can also be  
Figure 5: Derived layered architecture for AR-DT combination.  
connected to the DT via a data provisioning process directly pushing new information from the DT to the application layer. The _Augmented Reality_, finally, allows the representation of the DT on the physical layer. It augments the physical layer with the information retrieved from the services within the application layer. This layered, service-based architecture allows a bi-directional, synchronous, and remote interaction of the user with the physical layer powered and contextualized by AR and DT, and vice versa.
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 3 Methods
### Validation of Planet Water Fraction Maps  
A key component of assessing the utility of Planet imagery for water mapping is determining whether surface water area can be reliably and automatically extracted from CubeSat imagery. We validated our Planet imagery water fraction maps by comparing them to lake shorelines delineated manually from one WorldView-2 and two WorldView-3 panchromatic images acquired on 30 August and 8 September 2016 (Table 1). WorldView-2 and WorldView-3 offer high radiometric stability and finer spatial resolution (0.5 m, 0.3 m respectively) than RapidEye and PlanetScope imagery, and have been previously demonstrated to yield high-quality mapping of surface water [38; 39]. We delineated 48 lakes from the September 8 image and 45 lakes from the 30 August image, and compared them to our water fraction classification of one 8 September RapidEye image (Figure 1 inset c) and three 2 September PlanetScope images (Figure 1 inset b), respectively. We compared 100% water area, >75% water area and >50% fractionated water area with the manually delineated WorldView area (Figures 4 and 5).  
Geolocation error is also thought to affect CubeSat imagery due to the small size of the satellites and possible orbital drift. To assess image geolocation accuracy, we reprojected the manually delineated WorldView lakes to WGS84 UTM Zone 6N to correspond with Planet's gridding system. We then calculated the centroids of both the manually delineated WorldView lakes and RapidEye/PlanetScope-derived lakes and compared the centroids of the WorldView lakes to the centroids of the RapidEye/PlanetScope lake boundaries outlining >75% water area.  
Figure 4: RapidEye (**top row**) and PlanetScope (**bottom row**) lake area vs. manually delineated WorldView lake area. Column 1 shows 100% water area, column 2 shows >75% water area and column 3 shows >50% fractionated area.