esa-sceva/satcom-chunk-collection huggingface dataset:
# Radioactivity in Soils of Kosovo and Radiological Implications
## 1 Introduction  
Radiation is found everywhere in the earth's environment, and there is evidence that it was so from the beginning of time and will continue to be present. Therefore, all human beings are exposed to natural radiation sources and human-made sources daily. Natural background radiation comes from cosmic, terrestrial, and internal radiation. The most important source of man-made radiation exposure to the public is medical procedures, such as diagnostic X-rays, nuclear medicine, and radiation therapy. Smaller contributions to man-made radiation sources include nuclear weapons tests, nuclear reactor accidents, and nuclear power plants. The artificial radionuclide \({}^{137}\)Cs were spread to the soil of Kosovo due to fallout after the Chornobyl nuclear power accident on 26 April 1986 [1,2]. Furthermore, during the Kosovo War in 1999, the North Atlantic Treaty Organization (NATO) used depleted uranium (DU). Hence, some regions of Kosovo were radiologically contaminated [3, 4, 5, 6].  
Humans are exposed to background radiation from cosmic radiation and the gamma rays released in soils, building materials, water, food, and air. Some regions have been identified as high background radiation areas (HBRAs), and possible harmful effects on public health were detected [7]. Therefore, it is crucial to install an adequate system for health protection. In recent years, these conditions have attracted more attention from researchers, and much more researchers have been conducted on them [8, 9, 10, 11, 12, 13, 14]. Radioactivity in soil directly impacts fruits and vegetables [15, 16, 17].  
The present study is carried out as there is no study on determining radioactivity levels in soil samples taken from all regions of Kosovo. In this context, the primary purpose of this study is to detect natural and artificial radioelements \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs in soil samples. For the assessment of radiological hazards arising from natural radioactivity, radiium equivalent activity (Ra\({}_{\rm eq}\)), gamma index (I\({}_{\
u}\)), external hazard index (H\({}_{\rm ex}\)), absorbed dose rate in the air (D), annual gonadal dose rate (AGDE), the annual effective dose rate(AEDE), and excess lifetime cancer risk (ELCR) were calculated. The results were compared with internationally recommended values.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.8 Monitoring  
Monitoring and identification of moving objects and differentiation between normal and abnormal events/states for the purpose of surveillance are popular applications of wireless sensor networks [71, 72]. For example, the design of intelligent transportation system using sensor network has been proposed in [73] to detect the regions with vulnerable or dangerous drivers. A multi-objective sensor network model has been proposed in [74] for water sensor network design to monitor the water distribution system of municipalities. The proposed model focused on minimizing the volume of water from potential contamination, minimizing the expected time of detection and maximizing the probability of contamination detection. Monitoring of oceanic turbulence is the key to take preemptive measures for the safe transportation of mass and energy in the ocean and for the safety of the inhabitants along the costal cities [75]. Airfoil shear probes are the instruments to monitor and measure the turbulence in the ocean. A multi-objective optimization algorithm has been proposed in [76] to obtain the critical design parameters of the probe so as to enhance its sensitivity.
# Space-Efficient, Fast and Exact Routing in Time-Dependent Road Networks +
## 2 Materials and Methods
### Preliminaries
#### 2.1.1 Contraction Hierarchies  
_Contraction Hierarchies_ (CH) [6] is a speed-up technique exploiting the inherent hierarchy in road networks. It was initially developed for networks with scalar edge weights. Nodes are heuristically ranked by their importance. Nodes with higher rank should cover more shortest paths. During preprocessing, all nodes are _contracted_ in order of ascending importance. Contracting a node \(v\) means removing it from the network but preservingall shortest distances among remaining higher ranked nodes. For this, _shortcut arcs_ are inserted between the neighbors of \(v\) if a shortest path goes through \(v\). A shortcut is only necessary if it represents the only remaining shortest path between its endpoints. This can be checked with a local Dijkstra search (called _witness search_) between the endpoints. The result of the preprocessing is called an _augmented graph_.  
Queries can be answered by performing a bidirectional Dijkstra search on the augmented graph. The forward search starts at \(s\) and relaxes only forward arcs to higher ranked nodes. The backward search starts at \(t\) and traverses arcs in reverse direction and also only searches to higher ranked nodes. The construction of the augmented graph guarantees that the searches will meet and find a path that has the same length as shortest paths in the original graph. The higher ranked nodes reachable from a node are referred to as the node's _CH search space_.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 3 Materials and Methods
### Feature Extraction
#### 3.3.2 Word Embedding  
This is a text representation in which similar words have similar representations. In other words, in a coordinate system, corresponding words are placed close to each other [34; 35]. Word2 vec [36], GloVe [37] and fastText [38] are the most common word embedding models. [PERSON] et al. used Artificial Neural Networks (ANN) in a Word2 vec model. Word2 vec is based on the prediction of a word from surrounding words (Continuous Bag of Words, CBOW) or the prediction of surrounding words from a given word (Skip gram). We used word embedding and pre-trained word embedding with fastText in our study. The feature vector size was 300.
## 5 Conclusions  
1. The interaction between the FLSP and the primary lining was simplified as a pair of shear forces. The Winkler model was used to analyze the load-deformation characteristics of the FLSP under the condition of shear force. Analyzing the deformation of the FLSP under specific load conditions and the deformation of the primary lining under specific bearing capacity conditions revealed the problem that the deformation of FLSP and the primary lining was inconsistent under particular load and bearing capacities.
2. Under the two working conditions of predicted large deformation and observed large deformation, a design and optimization method for FLSPs based on the load-deformation coordination was proposed. The design method includes four parts:  
Figure 15: Vault settlement–time history curve.  
the extraction of the vault settlement control index, the simplification and stiffness calculation of FLSP, the finite element analysis of the primary lining, and the selection of FLSP.
3. For the Yulinzi Tunnel, after the design optimization method, using the passive bearing scheme, two 3.5-m-long \(\phi\)108 \(\times\) 3-millimeter FLSPs were installed, whereas, for the active bearing scheme, only one \(\phi\)108 \(\times\) 4-millimeter FLP needed to be installed when preloaded by 5 cm. The active bearing of the FLSP can significantly reduce the amount of steel required and reduce the engineering cost.  
In this study, an optimal design method for FLSPs was proposed. The design method adopts the Winkler model to consider the interaction between FLSP and the soil. Because the small deformation assumption is used in the derivation, only the elastic phase of the FLSP is considered. Next, the design method can be further improved by considering the plasticity of FLSP. The elastic-plastic solution of the deformation of FLSP can be obtained through the theoretical model, or the plasticity of FLSP can be considered through a numerical simulation. On the other hand, this study proposes the active bearing design of FLSPs, but it has not been applied in actual engineering works.  
Conceptualization, Y.W.; methodology, Y.W.; validation, C.T.; investigation, J.Z.; resources, Z.Z.; data curation, J.Z.; writing--original draft preparation, C.T.; writing--review and editing, P.X.; visualization, S.W.; project administration, Z.Z., and S.W.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Railway Corporation Science and Technology Research and Development Program, grant number 2018K019.  
Not applicable.  
All data are available from the author.  
The authors declare no conflict of interest.
# Effect of Thermal Boundary Condition on Tilting Pad Journal Bearing Behavior
## 3 Simulation Results and Discussion
### Bearing Thermal Behavior
As discussed in Figure 11a,b, 75% location of bearing pad is around the maximum bearing temperature. Figure 13 presents the film thickness distribution on each bearing pad for three cases (models no. 2, 4, and 6). The film thickness distribution is affected by pivot elastic deformation, pad tilting angle, journal position, and thermal expansion of both pad and journal as described in Equation (12). The red dot on each pad indicates the nodal position of minimum film thickness. Figure 13b shows that the minimum film thickness occurs at the axial end of bearing, whereas it happens at the axial center. It can be seen that the thermal deformation at both axial ends of the journal is greater than at the axial center, so that the minimum oil film thickness occurs at both ends in the axial direction. With the 2D bearing model, it is impossible to predict the bearing performance with the consideration in axial direction. Figure 14 shows the fluid film pressure on each pad for the simulation model no. 6 with T\({}_{\text{Lube}}\) = 80 \({}^{\circ}\)C.  
Figure 12: Temperature distribution at the axial center: (**a**) T\({}_{\rm Shaft}\) = 60 \({}^{\circ}\)C (1000 rpm); (**b**) T\({}_{\rm Shaft}\) = 60 \({}^{\circ}\)C (3600 rpm); (**c**) T\({}_{\rm Pad}\) = 60 \({}^{\circ}\)C (1000 rpm); (**d**) T\({}_{\rm Pad}\) = 60 \({}^{\circ}\)C (3600 rpm); (**e**) T\({}_{\rm Lube}\) = 60 \({}^{\circ}\)C (1000 rpm); (**f**) T\({}_{\rm Lube}\) = 60 \({}^{\circ}\)C (3600 rpm). Figure 14: Pressure distribution: model no. 6 (Pa), (T\({}_{\rm Pad}\) = 50 \({}^{\circ}\)C, T\({}_{\rm Shaft}\) = 50 \({}^{\circ}\)C, T\({}_{\rm Lube}\) = 80 \({}^{\circ}\)C). In this study, in addition to the temperature boundary condition, the change in the bearing characteristics according to the rotor spin speed was examined. In addition to the rotational speed of the journal, the performance change can be evaluated by changing the shaft radius, bearing clearance, lubricant viscosity, and load. For example, increasing the rotor spin speed increases the Sommerfeld number, so increasing the lubricant viscosity or increasing the journal radius increases the Sommerfeld number. In addition, an increase in the bearing load and a decrease in a bearing clearance increase the Sommerfeld number. Bearings with the same Sommerfeld number have the same dimensionless characteristics if the viscosity change due to bearing heat generation and the performance change due to thermal deformation are ignored. If the design variable or operating condition other than the rotor spin speed is changed, similar performance changes can be predicted according to the change of the Sommerfeld number.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 5 Results and Discussion
As argued in [29], frequent realignment based on channel coherence intervals introduces excessive overhead; instead, beam realignment should occur on the timescale of the beam coherence time to maintain efficient operation. In our model, beams are fixed with a 30\({}^{\mathrm{o}}\) beamwidth, which inherently allows for longer beam coherence times. Beam reselection is triggered only when a user exits the beam sector or when the SNR falls below a defined threshold. This design choice reduces the need for frequent physical layer realignment and is well suited for urban vehicular speeds between 30 and 50 km/h (approximately 8-13.8 m/s). Under such conditions, beam coherence times are typically on the order of seconds, allowing our algorithm sufficient temporal margin to learn and select stable beam associations. Even under higher mobility, the C-MAB framework successfully identifies long-lasting beam-IRS paths, improving robustness to short beam coherence intervals and reducing control overhead associated with frequent beam association.
## 3 Numerical Simulation Results
### Impact of the Internal Flow on the Flow Field
In conclusion, the internal flow further affects the flow field distribution in the vaneless diffuser by affecting the flow field of the vaned diffuser. A diagram of the stream line of the motor flow channel and the outlet flow channel is shown in Figure 6. In the motor flow channel, the fluid is divided into two streams due to the existence of the motor stator. A stream flows along the outer wall of the stator and the pump housing. There are some vortices that appear at the head of the flow channel due to the disturbance caused by the structural change in the inlet, and the subsequent stream line gradually becomes uniform. The other stream flows along the gap between the shaft wall and the inner wall of the stator. Due to the disturbance of the shaft wall and the bearing  
Figure 4: Velocity vector diagram of the meridional surface of the impeller and the vaned diffuser. (**a**) Tip leakage model; (**b**) tip–hub leakage model; (**c**) internal flow model. channel, the stream line in the channel is always in a fluctuating state, and eddy currents of varying degrees appear in the middle section. With the increase in the flow, the vortex moves backward, and the number of vortices decreases. In the meantime, the area of the single vortex expands.
## 1 Introduction
It acquires and analyzes digital data about workers, equipment, vehicles, and work zone conditions promptly which is not possible with current practices, such as ITCP safety planning and manual work zone monitoring. To automate this process, recent studies have created systems to track the position of workers inside construction sites utilizing different wireless technologies such as Bluetooth [10; 11], ultra-wideband (UWB) [12; 13], and radio frequency identification (RFID) [14]. Similarly, several approaches have been developed in the research community to track vehicles in construction sites using wireless technologies such as Bluetooth [15], Global Positioning System (GPS) [16], and UWB [17]. Among these wireless technologies, UWB radios are particularly promising for providing precise and feasible localization due to their impulse-shaped signals and high bandwidth. The recent generation of UWB systems can provide precise location tracking with an accuracy of up to 2-15 cm in a controlled environment without the need for extensive infrastructure [18]. These UWB technologies show great potential for automatically detecting hazardous situations in road construction sites through the accurate tracking locations of workers and equipment. Previous studies [12; 13] have shown that UWB technology has the potential to provide precise location tracking of resources in construction sites. Despite UWB radios being capable of high-accuracy localization, developing a safety monitoring system using UWB radios faces critical challenges when deployed in construction sites. A non-Line of Sight (NLOS) situation can occur when the presence of trucks, loaders, and other obstacles in the field can obstruct or deflect the signal as it travels from the transmitter to the receiver. The accuracy of the UWB localization drops when exposed to NLOS situations [19; 20; 21; 22]. Previous works on vehicle localization and pose estimation were outside of construction sites [17; 23; 24; 25; 26; 27; 28; 29] without considering the NLOS situations occurring due to construction equipment. Without a pose estimating robust against NLOS situations in construction sites, the pose of heavy construction equipment cannot be accurately determined. Therefore, our study presented in this paper attempts to develop a real-time system that can resolve the impacts of NLOS situations and accurately determine the pose of heavy construction equipment. For that, we developed a framework that reduces the impact of NLOS signals in both localization and pose estimation by proposing an NLOS error correction method before conventional TDOA location estimation. Our contributions in this paper include (1) measuring the impact of the NLOS situation caused by trucks and heavy equipment on localization and pose estimation output, and (2) developing an automated evaluation and reduction of NLOS effects on localization in a road construction environment.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 5 BER Performance Analysis of the Cooperative SCMA System  
In this section, we investigate the BER performance of the proposed downlink cooperative SCMA system with perfect channel state information by assuming ideal cooperation,i.e., the relay detects the transmit codewords without errors, under maximum likelihood (ML) multiuser detection.  
By defining \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{j}]\), according to the ML detection rule, the conditional pair-wise error probability that \(\mathbf{X}\) is transmitted while \(\mathbf{\hat{X}}=[\mathbf{\hat{x}}_{1},\mathbf{\hat{x}}_{2},\cdots,\mathbf{ \hat{x}}_{j}]\) is detected can be computed as  
\[P_{\mathrm{s}}\{\mathbf{X}\rightarrow\mathbf{\hat{X}}|\mathbf{h }\} = Q\Bigg{(}\sqrt{\frac{1}{2\sigma^{2}}\|\mathrm{diag}(\mathbf{h})( \mathbf{X}-\mathbf{\hat{X}})\|^{2}}\Bigg{)}, \tag{22}\]  
where  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{x^{2}}{2}}dt, \tag{23}\]  
is the Gaussian-Q function, which can be approximated as  
\[Q(x)\approx\frac{1}{12}e^{-\frac{1}{2}x^{2}}+\frac{1}{6}e^{-\frac{2}{3}x^{2}}, \tag{24}\]  
and \(\mathbf{X}\) consists of \(M^{l}\) possible combined cases. Note that \(\sigma^{2}\) is the average variance of the noise component of vector \(\mathbf{n}\) in (9). The noise vector \(\mathbf{n}\) in (9) consists of two parts, i.e., the noise from the direct transmission \(\mathbf{n}_{f}\) and the other from the relay transmission \(\mathbf{n}_{s}\); in this way, we define \(\sigma^{2}\) as the average of this two parts, given by  
\[\sigma^{2}=\frac{\sigma_{f}^{2}+\sigma_{\mathrm{s}}^{2}}{2}, \tag{25}\]  
From the discussions above, the pair-wise error probability in (22) can be simplified into  
\[P_{\mathrm{s}}\{\mathbf{X}\rightarrow\mathbf{\hat{X}}|\mathbf{h }\}=\frac{1}{12}e^{-\frac{|\mathrm{diag}(\mathbf{h})(\mathbf{X}-\mathbf{\hat{ X}})|^{2}}{2(\sigma_{f}^{2}+\sigma_{\mathrm{s}}^{2})}}+\frac{1}{6}e^{- \frac{2|\mathrm{diag}(\mathbf{h})(\mathbf{X}-\mathbf{\hat{X}})|^{2}}{3(\sigma_ {f}^{2}+\sigma_{\mathrm{s}}^{2})}} \tag{26}\]  
By defining \(d(\mathbf{X},\mathbf{\hat{X}})\) as the number of bits in which \(\mathbf{X}\) differs from \(\mathbf{\hat{X}}\), the BER upper bound of the downlink cooperative SCMA system can be described as  
\[P\leq\frac{1}{M!}\sum_{\mathbf{X}}\sum_{\mathbf{X}\
eq\mathbf{ \hat{X}}}d(\mathbf{X},\mathbf{\hat{X}})P_{\mathrm{s}}\{\mathbf{X}\rightarrow \mathbf{\hat{X}}|\mathbf{h}\}. \tag{27}\]
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
###### Abstract  
A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.  
Keywords:streaming processing; dynamic prediction; auto-scaling; online support vector regression; time window maximum throughput +
Footnote †: journal:
## 3 Results and Discussion
### Machine Learning Segmentation  
In machine learning segmentation, the sequence of the technique used is MGCS enhancement, CMYK_K color model, FCM segmentation, and post-processing. Table 1 displays the example of the ALO species captured in three different illuminations with the results obtained when the MGCS technique was implemented. The MGCS enhancement can standardize the illumination for the helminth images captured even though it comes in three different illuminations: normal illumination, under-exposed illumination, and over-exposed illumination. The enhanced image has shown better differentiation between the background and the helminth ovum.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Image Illumination** & **Normal Image** & **Over-Exposed Image** & **Under-Exposed Image** \\ \hline Original Image & & & \\ \hline \end{tabular}
\end{table}
Table 1: ALO image before and after implementing the MGCS technique. Scale bar = 50 \(\upmu\)m.  
Next, the K component of the CMYK_K color model and FCM segmentation were implemented on the enhanced helminth ovum image. Both of these techniques can distinguish and separate the region of interest (ROI), which is the helminth ovum from the artifact and background. The post-processing procedure, based on morphological operations such as dilation, filling holes, and noise removal, is used to obtain a clean and clear segmentation image. Figure 2 shows the resultant image achieved after the CMYK_K color model and FCM were executed to the TTO image. Through the results obtained, a clean and clear segmented TTO image was produced. However, an artifact has also been segmented, which will affect the segmentation performance.
## 6 Experimental Results and Discussion
### Strength and Stiffness Degradation  
Figure 15 illustrates peak strength and secant stiffness deterioration versus specimen drift. Peak strength is presented as half-cycle peak loads, F\({}_{\text{i}}\), normalized by the load at the effective yield point, F\({}_{\text{y,eff}}\), and analogously, secant stiffness is computed in terms of half-cycle secant stiffness, K\({}_{\text{i}}\) (with K\({}_{\text{i}}=\) F\({}_{\text{i}}/\Delta_{\text{i}}\)), normalized by the secant stiffness at the effective yield point, K\({}_{\text{y,eff}}\). As discussed in Section 6.1, similarly for all three specimens, peak strength cyclic degradation (i.e., strength loss due to repeated cycles at constant displacement level) ramped up after failure of the FRP and at a rate that increased with increasing displacement levels. Looking at the downward-loading direction, the load drop between the first two cycles at each displacement level after peak capacity amounted to: for S1A, 2% at 3% drift, 18% at 4.0% drift, and 50% at 6%; for S1B, 4% at 3.3% drift, 23% at 4.5% drift, and 41% at 6% drift; and for S5, 4% at 3.4% drift, 20% at 3.8% drift, and 26% at 4.5% drift. As shown in Figure 15, the secant stiffness degraded rapidly in both loading directions following an exponential decay. The ratio of the secant stiffness at peak capacity to the elastic secant stiffness at yield was: for S1A, 0.30 in both loading directions (peak capacity reached during the first cycle at 3.0% drift); for S1B, 0.27 downward and 0.21 upward (peak capacity reached during the first cycle at 3.3% drift); and for S5, 0.25 downward and 0.17 upward (peak capacity reached during first cycle at 3.4% drift).