esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the rapid development of remote tower technology has made it crucial to accurately assess the situational awareness (SA) levels of remote tower controllers. Such an assessment is significant for controller training and remote tower system design. This study employed the SART scale to compare controllers' SA scores in traditional and remote tower environments. Results revealed significant differences, especially in attention demand and situational understanding. Subsequently, a quantitative analysis of controllers' perception, understanding, and decision-making abilities was conducted, integrating subjective and objective data. Eye-tracking, heart rate, working memory scales, and communication-coordination scales showed significant results. Experienced controllers had better psychological safety skills, while trainees were more likely to increase vigilance. Moreover, a series of sensitive SA indicators were identified. An evaluation index system was established using the entropy weight method. By calculating the Euclidean distance, Gray relational degree, and comprehensive proximity coefficient, the SA levels of controllers were comprehensively evaluated. The top five important indicators were average blink rate, scan length, average fixation duration, fixation duration, and average pupil diameter. These findings support enhancing air traffic control safety and refining SA assessment for remote tower controllers.  
controllers; situation awareness; remote tower; EWM-TOPSIS-Gray analysis model; comprehensive evaluation Article
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 5 Conclusions, Extensions, and Open Questions  
In this work, we have shown algorithms for clustering that use a suitable combination of high-cost and low-cost comparison (ordinal) operations on the underline distance (dissimilarity) function of the data points. Some of the results are based on a popular graph-based Single-Linkage algorithm at its enhanced version Single-Linkage++ which has provable optimal performance in \(\gamma\)-pertubation-stable instances [1; 2; 3]. Our results apply to \(k\)-medoids clustering, and use comparisons between groups (sum) of distances according to (1). Though this is a rather rich model, and some of these queries may be difficult to realize in practice, some of the results can be extended to simpler queries as we explain below (in fact, the simpler Single-Linkage algorithm uses very simple queries, and the two algorithms differ only in the edge-removal step--Phase 2). The second set of results we provide are algorithms that make use of a rather limited number of exact _same-cluster queries_, in addition to the comparison high-low cost operations of our model. For example, for instances where the smallest optimal cluster has size \(n_{1}=\Omega(\frac{n}{k})\), there is an algorithm using \(O(k\log k)\) same-cluster queries, \(O(k\log^{2}n)\) high-cost operations, and \(O(kn\log n)\) low-cost operations (Theorem 5 and Theorem 6 provide general trade-offs and also deal with the case \(n_{1}\) not being known, respectively).  
In the remainder of this section, we shall discuss further (possible) extensions of our results, including different _query models_ and different _error models_ that can be considered. We conclude with a discussion on interesting _open questions_ and their relation to the above aspects.
## 1 Introduction
This paper proposes a power-aware ant colony optimization (ACO)-based autonomous secure routing protocol for FSNs. The ACO routing protocol is based on a probability function to calculate values by giving the maximum weight to energy and lower weights to link quality and delay parameters. The novel routing protocol helps in maintaining power consumption with secure transfer and privacy preservation, especially in network scenarios that involve massive mobile and malicious wireless nodes. The main contributions of this paper are as follows:  
* A power-aware biologically inspired secure self-organized routing protocol (P-BIOSARP) for FSNs in mobile surveillance systems is proposed. * P-BIOSARP, a biologically inspired self-organized routing protocol (BIOSARP), encryption-enabled E-BIOSARP, and secure real-time routing protocol with load distribution (SRTLD) routing protocols were implemented in network simulator 2 (NS-2). * Experiments were conducted and results were generated to show the variation in the total number of nodes and mobile nodes with different packet rates, multiple mobile routing, and mobile source nodes, and in terms of security, malicious nodes were injected. * The results prove that P-BIOSARP performs far better in terms of energy consumption, delivery ratio, and data packet overhead, even with a high number of flying mobile nodes and with malicious nodes. The rest of the paper is organized as follows. The next section reviews the most relevant published literature. Section 3 explains the method and process flow of P_BIOSARP. Section 4 describes the implementation of the proposed and other routing protocols. Section 5 presents and provides a description of the experimental results. Section 6 discusses the findings. The concluding remarks and the future work are given in the last section, Section 7.
## 4 Formative Study
### Results  
This section presents the main results from the formative study, coded with R# in order to facilitate referencing later in the paper.  
R1. Physical characteristics of objects  
Participants often mentioned specific characteristics of physical objects that might be relevant to convey in a TUI, \"Be able to feel the texture and shapes of certain parts of a monument.\" [P9], \"feel the smell of the lamp in the chapel\" [P11]. Geometric shapes, textures, volume, density, smell are some of the characteristics mentioned by participants.  
R2. Object for system input  
Several users suggested the use objects as a way to directly send input to the system: \"use the object to hit the tower's bell and reproduce the sound\" [P7], \"control to see a cut of the floor\" [P13]. Participants suggested various kinds of input such as using the tangible to \"hit\" another object, to change location, to use it as a key to activate content, as a controller to change music.  
R3. Output modality associated with object's manipulation  
Participants also mentioned several examples of what they expected to see or hear as the result of the manipulation of the tangible. Interestingly, audio was an often mentioned output modality: \"see the chapel's organ and be able to press the keys to reproduce the different sound of the tubes\" [P11], \"...book is accompanied by ambient sound of the respective eras\" [P4], \"the experience can be acompanied with audio of languages of other eras such as archaic Portuguese, latin, etc.\" [P4]. Video and 3D models were also often mentioned, but animations, text, images were all also suggested.  
R4. Object as replica  
Tangibles were often associated with existing physical objects as a substitute to the experience of holding or manipulating the original physical object: \"Materialize objects of religious art... because they represent something very important but cannot be available to the public\" [P4], \"Use the 'tightrope walker' of the physics department and we control it equilibrium position\" [P10].  
R5. Deconstructing objects  
Participants also often described situations where it would be useful to be able to open up and deconstruct an object: \"Open the computer to know what is inside and understand how the connected components work\" [P2], \"... can be opened to explore the interior of old buildings...\" [P3], \"Open a computer and use objects as components\" [P5], \"Disassemble a construction model by its parts to see inside\" [P13].  
R6. Magical interactions  
Some of the ideas offered by participants referred interactions that went beyond simple physical manipulation, but instead represented magical interactions that are impossible in our physical world: \"With the architectural model, I could choose a specific apartment so that clients could go inside the space\" [P1].  
R7. Fixed objects  
Although most ideas referred to the manipulation of small objects, a few ideas referred to fixed/large objects existing in the environment: \"Climb a few of the steps to see the area of the tower's bell\" [P7].
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
###### Abstract  
The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.  
sentiment analysis; stock market prediction; machine learning; data mining; classification; microblogging; twitter; stocktwits +
Footnote †: journal:  
This paper provides a strategy for predicting stock movements using ML methods and SA. SA is accomplished by the use of publicly accessible information from microblogging networks such as Twitter and StockTwits, which give crucial insights into people's emotions. According to a widespread hypothesis, when public perception towards a firm is good, stock prices tend to rise, and the other way round [6]. However, when additional economic aspects are included, this idea is not always validated.  
Microsoft stock is employed as an example, and we evaluate price fluctuations based on financial and sentiment data. From 16 July 2020 to 31 October 2020, 90,000 tweets from Twitter and 7440 tweets from StockTwits were gathered. During the same time-period, financial data were also collected from Finance Yahoo. TextBlob and Valence Aware Dictionary and sEntiment Reasoner (VADER) were used to perform SA on these tweets. Furthermore, seven ML models were incorporated, including K-nearest neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP), as well as two performance evaluation metrics, F-score and Area Under Curve (AUC). When utilizing data from Twitter with VADER as a sentiment analyzer, the results show that SVM has the greatest F-score of 76.3% and an AUC value of 67%.  
The rest of the paper is organized as follows. Section 2 gives context and an assessment of the state of the art. Our methodology is described in depth in Section 3, and the assessment of outcomes is discussed in Section 4. The study concludes with Section 5, which discusses accomplishments, limitations/challenges, and future work.
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 10 Discussion  
Suppose a regulator of medical devices asked manufacturers of medical devices using software seeking pre-market approval (PMA) two questions:  
1. What fraction of the software state space have been tested?What evidence is provided that the untested state space will be safe and effective?  
Because even diligent testing can only cover a tiny fraction of software state space, the answer to the first question is \"minuscule\". Software errors that cause medical device recalls invariably occur in the untested state space--otherwise the error would have been detected and fixed. Because manufacturers can give no evidence for untested state space, regulators do not ask the second question.  
A manufacturer using the design principles advocated in this paper could answer thus:  
* For the software performing medical function that must be safe and effective, we have formally verified that every possible execution will conform to its safety and effectiveness specification. Here are the verification artifacts as evidence.
* Our design conforms to an architecture that compels components to only interact through well-defined interfaces. By this (and separation kernel enforcement), we ensure that errors in only tested software cannot compromise our formally verified software.
* The only tested software either performs functions that do not affect safety and effectiveness, or are used in such a way that failures are evident so their risk can be mitigated acceptably.  
By using these design principles, the extra effort of formal verification can be focused on the small fraction of software that is truly safety-critical, minimizing the cost of formal verification while maximizing its effect.
## 4 Rice Monitoring Results and Discussion
### Four- and Three-Component Decomposition  
The four- and three-component decomposition results for the FCP and DCP modes are shown in Figure 11 respectively, where \(P_{s}\), \(P_{d}\), \(P_{v}\), and \(P_{c}\) show surface, double-bounce, volume, and helix scattering respectively. The results are normalized to the maximum value of both modes. In Figure 11a, the double-bounce and surface scattering contributions of four-component decomposition demonstrate a similar evolutionary trend as the backscattering coefficients of co- and cross-polarization in Figure 6. This similarity proves that co- and cross-polarization indicate even and odd bounce scattering mechanisms respectively, as mentioned in Section 4.1. Figure 11a also reveals a stronger volume scattering component than other scattering mechanisms at stages 4 and 5 as well as its evolutionary trend during rice growth. Relatively low helix scattering is also shown, as expected, from vegetation.  
When we compare the results of both modes, we see that both volume scattering components result in a similar evolutionary trend and value. In contrast, the surface and double-bounce scattering of the DCP mode show lower power than the FCP mode. This error might be attributed to dominant \(\alpha\) values because surface and double-bounce scattering are a function of dominant \(\alpha\) values, shown in (12). Since we found the dominant \(\alpha\) difference between the FCP and DCP modes in Figure 9, this error obviously affects the power level.  
To investigate the relative contribution of the scattering component, we present the rate of each scattering mechanism on a triangle plot, which is a similar analysis to [33], shown in Figure 12a,b for the FCP and DCP modes respectively. Note that we exclude the helix scattering contribution from the FCP mode for comparison between both modes. Figure 12a shows similar results to \(H/\bar{\alpha}\) display, where the triangle plot for the FCP mode loosely falls into four groups: germination, tillering, stem elongation, and a mix of booting and ripening stages. Therefore, booting and ripening stages still cannot be clearly separated. Moreover, we notice that double-bounce scattering gradually increases as rice grows (from 7% to 36%), while vice versa, the surface scattering decreases in the FCP mode. This situation can also be seen in the DCP mode, where double-bounce scattering increases from 7.8% to 20%, as depicted in Figure 12b. However, as indicated above, the DCP mode shows a relatively stronger volume scattering component (lower surface and double-bounce scattering contribution) in Figure 12b. Therefore, three-component decomposition for the compact mode should be improved to better approximate the FP mode results.  
Figure 11: Four-/three-component decomposition results. (**a**) Four-component decomposition results for the FCP mode; (**b**) Three-component decomposition results for the DCP mode.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 3 Proposed AIER Mechanism
### AIER Mechanism  
Assuming that there are no link fabrication attacks and no migration of OpenFlow switches [40], the proposed AIER mechanism adds an ANN model in the SDN controller. First, the AIER mechanism collects a set of training data in which each record consists of feature data and label data. Next, the training data are used to train the ANN model, iteratively. The routing algorithm obtains learning abilities after model training has been completed. Thus, the AIER mechanism not only can predict the corresponding output based on the new data but also can select a suitable path to avoid congestion. Figure 7 shows the pseudo code of the proposed AIER mechanism, which includes the following three stages.
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 3 Material and Methods
### Fuzzy Controller Implementation in SUIPERSYS_HCDC Program and E. coli Cultivation Test
#### 3.4.2 Bioreactor Experimental Set-Up  
The experimental set-up for the cultivations is shown in Figure 5. Cultivations were conducted in a 5 L stirred tank bioreactor (1), developed in house and supervised by SUIPERSYS_HCDC program (2). pH was controlled by adding NH\({}_{4}\)OH (30%) or H\({}_{3}\)PO\({}_{4}\) (3) (pHmeter GLI PRO--Milwaukee, WI, USA) (4). Temperature was measured by a thermistor (PT100 Camtec mit Exata) (5) and controlled by a cooling jacket (6) coupled with a thermostatic bath (7). DOC was monitored by a dissolved oxygen sensor (8) (Mettler Inpro 6800--Columbus, OH, USA) and controlled by a hybrid system composed by a PID controller and a fuzzy controller. The PID controller set agitation speed between 200 and 950 rpm (9). Gas flow rate was controlled by two flowmeters (10) (GFC AALBORG--Orangeburg, NY, USA). Conductivity and permittivity were monitored by a biomass sensor (HAMILTON--Reno, NV, USA) (11). The exhaustion gas composition was measured by a gas analyzer (12) (902P Quantek instruments--Grafton, MA, USA). Medium feed was introduced in the bioreactor using a peristaltic pump (13) (MASTERFLEX--Radnor, PA, USA). Monitoring and control of all instruments were made via compact field point (14) (cFP-2020 National Instruments--Austin, TX, USA) [21; 23; 47; 52].
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 2 Methods
### Experiment Scheme and Data  
In this paper, CLM4.0 and CLM4.5 performed offline simulations for the years between 1952 and 2002 using the satellite phenology option, where the years 19521-982 were used for the spin-up. Land cover was held constant at values for the year 2000. The Qian dataset [26], which combines NCEP-NCAR reanalysis data and observation-based analyses of monthly precipitation, surface air temperature, and surface downward solar radiation, was used as the atmospheric forcing data. This dataset was extensively applied to the improvement and evaluation of the land surface model [16; 17; 27; 28; 29; 30; 31; 32]. The spatial resolution of the model is 1.25\({}^{\circ}\) in longitude by 0.9375\({}^{\circ}\) in latitude. The vegetation reanalysis data (Ec and Et) were provided by NOAA [22]. The reanalysis data for Es and latent heat flux were provided by CFSR [23] and FLUXNET data [20; 21] respectively. The distribution of PFT based on remote sensing data (see Figure 1) and the leaf area index (LAI) and stem area index (SAI) for vegetation based on satellite phenology data that takes monthly variation into account (see Figure 2) and were provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). It is worth mentioning that the unit of Ec and Et provided by NOAA and Es provided by CFSR are in W/m\({}^{2}\). For the convenience of comparing, latent heat flux and its components are presented in terms of energy (W/m\({}^{2}\)) instead of water flux (mm) in this article.  
Figure 1: Global plant functional types (PFT) distributions from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, categorized as follows: (1) no-vegetation areas, (2) temperate evergreeniferous forests, (3) northern evergreen coniferous forests, (4) boreal deciduous forests, (5) tropical evergreen broad-leaved forests, (6) temperate evergreen broad-leaved forests, (7) tropical deciduous forests, (8) temperate deciduous broad-leaved forests, (9) deciduous broad-leaved forests, (10) temperate evergreen broad-leaved shrubs, (11) temperate deciduous shrubs, (12) north deciduous broad-leaved shrubs, (13) boreal grassland, (14) C3 grassland, (15) C4 grassland, (16) C3 dry land crops, and (17) C3 irrigation crops.
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 5 Discussion  
The suitability map generated for installing PV systems was statistically analyzed by considering the administrative areas (Figure 1), as presented in Table 5. Among the five administrative areas, the southern area showed the highest mean score for suitability, at 78.72 (between fair and good conditions), followed by the center (65.68), Altai-Uliastai (65.01), eastern (57.83), and western areas (55.31). The suitability score's standard deviation in the southern area was also lower than that in the other areas (8.99).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Weight** & **CR** \\ \hline GHI & 0.448 & 0.444 & 0.307 & 0.473 & 0.516 & 0.390 & 0.448 & 0.43 \\ \hline Temperature & 0.064 & 0.063 & 0.152 & 0.059 & 0.046 & 0.063 & 0.058 & 0.07 \\ \hline Elevation & 0.071 & 0.020 & 0.049 & 0.018 & 0.068 & 0.088 & 0.061 & 0.06 \\ \hline Slope & 0.091 & 0.102 & 0.267 & 0.096 & 0.097 & 0.104 & 0.074 & 0.12 \\ \hline Aspect & 0.089 & 0.139 & 0.073 & 0.101 & 0.102 & 0.148 & 0.124 & 0.11 \\ \hline Distance from main road & 0.113. & 0.098 & 0.054 & 0.091 & 0.068 & 0.098 & 0.112 & 0.09 \\ \hline Distance from major power line & 0.124 & 0.135 & 0.098 & 0.162 & 0.102 & 0.109 & 0.124 & 0.12 \\ \hline \end{tabular}
\end{table}
Table 4: Pairwise comparison matrix, the weights allocated to each criterion, and the consistency ratio (CR).  
Figure 11: Suitability map for installing PV systems in Mongolia.  
Approximately 80% of Mongolia's population is concentrated in the central area [30; 31], which consumes most of Mongolia's electricity, as shown in Figure 12. The central area's power systems import approximately 300 million kWh of electricity annually from Russia, mostly during peak periods [22]. The present results show that the mean suitability value for PV systems in the central area is 65.68, demonstrating a level of installation suitability conditions rated between low and fair, with a standard deviation of 14.42. Considering its population and power consumption, the central area should therefore be prioritized for the installation of PV power plants.  
The southern area, which has the highest mean suitability score, includes the Gobi steppe, and is home to Mongolia's largest gold and copper mine, the Oyu Tolgoi mine. Approximately 1.1 billion kWh is annually imported from China for operations at this mine, suggesting that installing PV systems in the southern area could supply supplementary power to this mine.
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 1 Introduction
To meet the same level of performance even on a small dataset, and to classify pneumonia from normal chest X-rays, we conducted this study to propose a novel approach of transfer learning using pretrained architectures trained on ImageNet. We used five different pretrained models and analyzed their performances. Finally, the combination of all five models was taken, forming a large ensemble architecture and achieving promising results. Our main contribution in this paper is to provide a novel ensemble approach based on the transfer learning of five different neural network architectures.