esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Relative Importance of Variables  
In estimating global GPP, the variables used in the model included monthly SIF, Land cover, latitude, longitude, DEM, month, and NIRv. We discussed the relative importance of variables and the results as shown in Figure 7. Generally, SIF was the most important variable in estimating global GPP. The other variables contributed less in estimating global GPP in this study. This conclusion fully reflects the relationship between SIF and GPP, which is strongly consistent with most research results [45; 48; 53; 54]. Other variables used in this study, such as latitude, longitude, DEM, month, etc., reflect precipitation and temperature information and are important for plant photosynthesis. SIF and NIRv provide alternative approaches to estimate global GPP [45; 70; 73], but as shown in Figure 7, SIF was far more important than NIRv. The advantages and differences of SIF and NIRv estimation of GPP need to be further studied. Although the contribution of these variables to the estimation of GPP is small, the best estimates can only be obtained by using all GPP-related variables.  
Figure 6: Differences in global GPP for all vegetation coverage types in different seasons in 2014 (green represents the difference between GPPSF and GPPMODIS, purple represents the difference between GPPSF and GPPGLASS).
# Trends of Changes in Minimum Lake Water Temperature in Poland
## 4 Discussion
The analysis of the thermal and oxygen conditions in five lakes showed an increase in water temperature in the epilimnion zone and a simultaneous decrease in the content of dissolved oxygen [48]. In addition to the aforementioned effect on hydrobiological conditions, a decrease in the amount of oxygen dissolved in water limits its self-cleaning properties [49]. Changes in temperature remain in a close relationship with water density, which in deep lakes is of key importance for shaping the stratification system. As emphasised by [PERSON] et al. [50], stratification is a factor affecting the distribution of energy, oxygen content, and the availability of nutrients, i.e., elements determining the abundance and biomass of organisms. The reported increasingly shorter terms of minimum water temperatures (characteristic of particular seasons) are of key importance for the dynamics of circulation conditions, and consequently the duration of water stratification. An increase in water temperature will contribute to, among others, the increased strength of stratification that will be dependent on the depth, surface area, and transparency [51]. [PERSON] et al. [52], analysing the case of Lake Zurich in central Europe, determined that an increase in water temperature will cause a situation in which the near-surface layer will be maintained above 4 \({}^{\circ}\)C throughout the year, contributing to a change in the system of vertical water circulation from dimitic to monomitic. Seven of the lakes analysed in the current paper also show domitic properties, and the possible change in the system of water mixing signalled above will radically affect the current course of processes that occur in them. Another important aspect for lakes in the temperate zone is the presence of ice cover [53; 54; 55]. Slower cooling of water determines a delay in the moment of development of the ice cover, and consequently a change in the ice regime. For example, an increase in minimum water temperature in Nam Co Lake recorded by [PERSON] et al. [56] was the primary cause of changes in its ice phenology and the reduction in ice duration. Additionally, in the case of lakes analysed in the paper, an evident reduction in the ice season is recorded with a simultaneous decrease in ice thickness [57]. For a dozen of the analysed lakes, they evidenced that the delay in the development of ice cover reached an average of 3.0 days/decade. On the other hand, the evident acceleration of the ice end (3.8 days/decade) creates a situation where water is not isolated from external factors, therefore accelerating and prolonging warming. The effect of this process at the turn of the winter-spring season finds confirmation in the analysed lakes characterised by an increase in minimum temperature that progresses in subsequent months.
# Accurate RANS Simulation of Wind Turbine Stall by Turbulence Coefficient Calibration
###### Abstract  
Stall, a complex phenomenon related to flow separation, is difficult to be predicted accurately. The motivation of the present study is to propose an approach to improve the simulation accuracy of Reynolds Averaged Navier-Stokes equations (RANS) for wind turbines in stall. The approach is implemented in three steps in simulations of the S809 airfoil and the NREL (National Renewable Energy Laboratory) Phase VI rotor. The similarity between airfoil and rotor simulations is firstly investigated. It is found that the primary reason for the inaccuracy of rotor simulation is not the rotational effect or the 3-D effect, but the turbulence-related problem that already exists in airfoil simulation. Secondly, a coefficient of the SST turbulence model is calibrated in airfoil simulation, ensuring the onset and development of the light stall are predicted accurately. The lift of the airfoil in the light stall, which was overestimated about 30%, is reduced to a level consistent with experimental data. Thirdly, the calibrated coefficient is applied to rotor simulation. That makes the flow patterns on the blade properly simulated and the pressure distribution of the blade, as well as the torque of the rotor, are predicted more accurately. The relative error of the predicted maximum torque is reduced from 34.4% to 3.2%. Furthermore, the procedure of calibration is applied to the MEXICO (Model Experiments in Controlled Conditions) rotor, and the predicted pressure distributions over blade sections are better than the CFD (Computational Fluid Dynamics) results from the Mexnext project. In essence, the present study provides an approach for calibrating rotor simulation using airfoil experimental data, which enhances the potential of RANS in accurate simulation of the wind turbine aerodynamic performance.  
wind turbine; stall; NREL Phase VI; S809 airfoil; MEXICO; RANS
###### Abstract  
Increasing airspace safety is an important challenge, both for unmanned aerial vehicles (UAVs) as well as manned aircraft. Future developments of collision avoidance systems are supposed to utilize information from multiple sensing systems. A compact sensing system could employ a multi-mode multi-port antenna (M\({}^{3}\)PA). Their ability to radiate multiple orthogonal patterns simultaneously makes them suitable for communication applications as well as bearing and ranging applications. Furthermore, they can be designed to flexibly originate near-omnidirectional and/or directional radiation patterns. This option of flexibility with respect to the radiation characteristic is desired for antennas integrated in collision avoidance systems. Based on the aforementioned properties, M\({}^{3}\)PAs represent a compelling option for aircraft transponders. In this paper, direction-of-arrival (DoA) estimation using an M\({}^{3}\)PA designed for aerial applications is put to the test. First, a DoA estimation scheme suitable to be employed with M\({}^{3}\)PAs is introduced. Next, the validity of the proposed method is confirmed through numerical simulations. Lastly, practical experiments are conducted in an antenna measurement chamber to verify the numerical results.  
6 (CASA X; airspace safety; direction-of-arrival estimation; experimental verification; multi-mode multi-port antennas; networked sensing systems; sense and avoid; unmanned aerial vehicles +
Footnote †: journal: Sensors
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 5 Case Study
### Calculation of Model Parameters  
According to the survey of tourists' trips, this study sets the average number of scenic spots visited by tourists per day no more than two. Then, let \(\varepsilon\) = 0.05; model parameters \(\gamma\), \(R_{m}\), and \(T_{n}\) for this case can be calculated based on Equation (10). Following the steps depicted in Figure 4, after 10 iterations, it can be found that the residual is less than 0.05, and the output \(\gamma=-1.7\). Figure 6 shows the relation between \(\varepsilon\) and the number of iterations. Then, based on Equation (10) and the value of \(R_{m}\) and \(T_{n}\), the values of parameter \(\alpha\) and \(\beta\) can be calculated. Because there are two destination zones and seven origin zones, the result values of parameters \(\alpha\) and \(\beta\) are \(\alpha_{1}\) = 10.70, \(\alpha_{2}\) = 10.81, \(\beta_{1}\) = \(-\)11.23, \(\beta_{2}\) = \(-\)8.84, \(\beta_{3}\) = \(-\)6.14, \(\beta_{4}\) = \(-\)5.92, \(\beta_{5}\) = \(-\)5.75, \(\beta_{6}\) = \(-\)7.24, \(\beta_{7}\) = \(-\)5.51.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Traffic Zone Code** & **Traffic Zone** & **Trips Volume** \\  & & **(10,000 Person/Year)** \\ \hline
1 & scenic spot 1 & 56 \\
2 & scenic spot 2 & 125 \\
3 & scenic spot 3 & 338 \\
4 & scenic spot 4 & 86 \\
5 & scenic spot 5 & 107 \\
6 & scenic spot 6 & 42 \\
7 & other scenic spots & 400 \\
8 & north railway station & 379 \\
9 & traffic zones based on the entrance/exit of roads & 595 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distributing volume of each traffic zone.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Traffic Zone Code** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
1 & 0 & 6586 & 14,709 & 24,801 & 32,110 & 37,291 & 29,416 & 40,013 & 44,567 \\
2 & 6586 & 0 & 8221 & 18,547 & 26,144 & 31,285 & 23,114 & 34,172 & 38,304 \\
3 & 14,709 & 8221 & 0 & 10,468 & 18,312 & 23,362 & 16,758 & 26,405 & 30,145 \\
4 & 24,801 & 18,547 & 10,468 & 0 & 8025 & 12,936 & 14,990 & 16,090 & 19,774 \\
5 & 32,110 & 26,144 & 18,312 & 8025 & 0 & 5186 & 19,863 & 8097 & 13,027 \\
6 & 37,291 & 31,285 & 23,362 & 12,936 & 5186 & 0 & 22,423 & 3475 & 8224 \\
7 & 29,416 & 23,114 & 16,758 & 14,990 & 19,863 & 22,423 & 0 & 25,832 & 24,850 \\
8 & 40,013 & 34,172 & 26,405 & 16,090 & 8097 & 3475 & 25,832 & 0 & 7797 \\
9 & 44,567 & 38,304 & 30,145 & 19,774 & 13,027 & 8224 & 24,850 & 7797 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trip distances between different traffic zones (unit: m).
# An IoT-Based System for Efficient Detection of Cotton Pest
## 3 Results and Discussion
### Simulation Results
#### 3.2.1 One-Hop Scenario  
In Figure 11, the arrangement of four sensing nodes, a base station (sink) node, and a mobile node with a random route is presented. The base station was placed in the center of  
Figure 10: CupCarbon nodes (**A**); sensing node (**B**); base station (sink) node (**C**); mobile node.  
Figure 9: Household detection in a controlled environment (numbers are every 10 min).  
the agricultural field and one-hop away from each sensing node and resided inside of each node's transmission range for receiving messages. The mobile node moved according to its assigned route, and if the mobile node was outside the sensing range of a sensing node, it would not be detected, and then the sensing node would send \"0\" (no pest detected) to the base station.  
The pest detection algorithm (Section 2.3) was implemented in the Cup-Carbon simulator and the test detection process included the following steps:  
* Whenever a pest entered the sensing range of a sensing node, the simulator highlighted it with yellow color as detected and sent a detection message \"1\" (pest detection) to the base station with coordinates of the highlighted region as depicted in Figure 11.
* Whenever the flying insect moved out of the sensor's reach and sensing range, it returned to the same position as it was before, as seen in Figure 12.
###### Abstract  
Manifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) and orthogonal locality preserving projection (OLPP) are two known linear manifold learning algorithms. In this study, scatter information of a distance matrix is used to construct a weight matrix with a supervised approach for the LPP and OLPP algorithms to improve classification accuracy rates. Low-dimensional data are classified with SVM and the results of the proposed method are compared with some other important existing linear manifold learning methods. Class-based enhancements and coefficients proposed for the formulization are reported visually. Furthermore, the change on weight matrices, band information, and correlation matrices with \(p\)-values are extracted and visualized to understand the effect of the proposed method. Experiments are conducted on hyperspectral imaging (HSI) with two different datasets. According to the experimental results, application of the proposed method with the LPP or OLPP algorithms outperformed traditional LPP, OLPP, neighborhood preserving embedding (NPE) and orthogonal neighborhood preserving embedding (ONPE) algorithms. Furthermore, the analytical findings on visualizations show consistency with obtained classification accuracy enhancements.  
Keywords:linear manifold learning: out-of-sample; classification; weight matrix; artificial intelligence +
Footnote †: journal: Applied
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 4 Healthcare Applications
### Implant-Tissue Interface  
The initial physical interaction of implants with surrounding tissue occurs on the implant surface. This adds another dimension to factors that affect implant failure over time, such as stress shielding due to differences in mechanical properties (compared with the surrounding tissue) and implant wear-induced cell death from movement across the implant-tissue interface [117]. Therefore, it is imperative to consider implant surface characteristics that aid in better integration and fixation to the surrounding tissue. Surface roughness of metallic bone implants is an important factor that inversely correlates with biological fixation to the surrounding bone. A study by [PERSON] et al. [118] demonstrated that 3D-printed titanium spinal implants using electron beam melting (EBM; a type of powder bed fusion technique) were superior for inducing osteoblast proliferation and calcium production on implant surface in vitro as opposed to the current standard of implant surface processing (titanium plasma spraying). They achieved this through incorporating a cancellous bone-like trabecular network on the implant surface 3D model, and subsequently achieving high-resolution fabrication using AM [118]. Nanofabrication techniques have also been used to produce porous coatings for 3D-printed implants. A study by [PERSON] et al. [119] showcased how by coating titanium implants with fibronectin nanoclusters resulted in a significant increase in integrin binding, resulting in improved implant fixation in rats. Another study by [PERSON] et al. [120] used selenium nanoclusters as a coating on titanium implants in an in vitro model. Their results showed that selenium nanoclusters had enhanced antitumor activity, while also promoting healthy bone formation on the bone surface compared to untreated implants [120].
## 2 Related Work
### Malware Detection and Classification
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Authors**} & **Types** & \multicolumn{2}{c}{**Features**} & \multirow{2}{*}{**Models**} \\  & & **Det Class** & & **Header Body** & \\ \hline
2019 & [PERSON] et al. [29] & \(\surd\) & & - & OP & Machine Learning \\
2020 & [PERSON] et al. [30] & \(\surd\) & & STC & STC & Gradient Boosting \\
2021 & [PERSON] et al. [5] & \(\surd\) & \(\surd\) & BYT & STC & Automated Pipeline \\
2021 & [PERSON] et al. [31] & & \(\surd\) & BYT & BYT & DenseNet \\
2021 & [PERSON] et al. [32] & & \(\surd\) & - & OP & RMVC \\
2022 & [PERSON] et al. [33] & & \(\surd\) & BYT & BYT & SAE \\
2022 & [PERSON] et al. [34] & & \(\surd\) & BYT & BYT & SVM \\
2022 & [PERSON] et al. [35] & \(\surd\) & & STC & - & Random Forest \\
2022 & [PERSON] et al. [2] & \(\surd\) & & API & - & Machine Learning \\
2022 & [PERSON] et al. [4] & \(\surd\) & & - & OP & Ensemble Learning \\
2022 & [PERSON] et al. [3] & \(\surd\) & & STC & - & FANN \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods of static analysis for malware on Windows. The proposed integrated learning model has significantly enhanced performance when testing a dataset consisting of real-time ransomware samples with 99.21%. A malware detection system based on static analysis was proposed by [PERSON] et al. [3]. The system is trained on pseudo labels based on unsupervised learning. The experimental findings demonstrate that the deep neural network's accuracy is 98.09% on the 15,457 PE samples, and the time efficiency and detection accuracy of the system are better than most traditional static malware detection systems. At the same time, the system reduces the risk of malware infection to the lowest. Table 1 summarizes the comparison of feature types and models employed in machine learning-based malware static detection or classification schemes. As can be seen from Table 1, the static malware detection scheme has become more and more perfect, and it has excellent detecting performance or efficiency and accuracy of classification. Researchers mainly use software PE structure information, opcode sequences, bytecode sequences, application program interfaces, and other static features to establish a malware system for static detection and classification.
# Heat and Flow Study of the Internally Finned Tubes with Different Fin Geometries
## 4 Conclusions  
The aim of this analysis is the detailed examination of the flow and heat behavior of tubes with different fins. The use of internal finds with circular, rectangular, and triangular shapes is studied and compared with the reference case of the empty tube. The analysis is carried out for different inlet fluid temperatures and different mass flow rates to study the system under different operating conditions. Below, the most critical conclusions are given:All the examined finned tubes lead to significant improvement in the heat transfer. The rectangular fins lead to 36% enhancement, circular fins to 25% enhancement, and triangular fins to 23% enhancement, approximately, compared to the reference tube. - All the examined finned tubes lead to an increase in the pressure drop. The rectangular fins lead to a 65% increase, circular fins to a 63% increase, and triangular fins to a 43% increase, approximately, compared to the reference tube. - The performance evaluation criterion (PEC) is calculated over 1 in all the finned tubes, and thus, all the examined designs lead to significant flow enhancement. More specifically, the PEC is around 1.16 for the rectangular finned tube, 1.09 for the triangular finned tube, and 1.06 for the circular finned tube. - The most suitable design is the tube with the rectangular fins; the second one is the design with the triangular fins, while the use of circular fins is the less effective design. - The pumping work demand takes very small values, which indicates that it is not an obstacle in the system operation. Moreover, the Bejan number takes values over 99%. This fact proves that the entropy is mainly generated due to the temperature increase, and the pressure drop does not generate an important amount of entropy in the system.  
E.B.: conceptualization, methodology, software, validation, formal analysis, investigation, writing--original draft preparation, writing--review and editing; P.L.: software, writing--original draft preparation; C.T.: writing--original draft preparation, supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available after request.  
The authors declare no conflict of interest.
# Greening the NetFPGA Reference Router
## 4 Frequency Control Policies for Dynamic Frequency Scaling
### Packet Loss-Aware Policy  
For both the STP and the DTP, the trade-off between power consumption and performance is mainly affected by the fixed preset thresholds. Although the DTP can outperform the STP at the cost of potentially higher power consumption, it still cannot properly cope with extremely frequent and abrupt traffic fluctuation scenarios. The operating frequency of the FSR will frequently switch as a consequence of the instantaneous traffic fluctuating abruptly and frequently. Especially in the STP implementation, these frequent fluctuations can lead to extra power consumption and may cause huge packet loss during frequency switching.  
To eliminate these deficiencies associated with threshold-based policies, a packet loss-aware policy is introduced. The only criterion of PLAP to switch to a higher frequency is when packet loss is detected. The PLAP cannot be used standalone; it can be used in conjunction with DTP to deal with frequency scaling up scenarios; the operating frequency can be switched to a higher level as soon as the processing engine detects any packet loss. This scaling up is following a level-by-level mechanism, which means that switching only appears between adjacent frequency levels regardless of the fixed preset thresholds to minimize the transition overheads for extremely fluctuating traffic scenarios. DTP controls frequency scaling down scenarios. Figure 8 presents the flowchart of the PLAP.
## 2 Conventional Uplink NOMA
### System Model
\begin{split}&\mathbf{s}_{m}^{H}\mathbf{r}=\mathbf{s}_{m}^{H} \left(\sum\limits_{k=1}^{K}\sqrt{P_{k}}e^{i\theta_{k}}t_{k}+\mathbf{n}\right) \\ &=\sum\limits_{k=1}^{K}\sqrt{P_{k}}e^{i\theta_{k}}s_{m}^{H}s_{k}x_ {k}+\mathbf{s}_{m}^{H}\mathbf{n}\\ &=L\sqrt{P_{m}}e^{i\theta_{m}}x_{m}+\sum\limits_{k=1,k+m}^{K} \sqrt{P_{k}}s_{m}^{H}s_{k}e^{i\theta_{k}}x_{k}+\mathbf{s}_{m}^{H}\mathbf{n} \end{split}  \tag{5}\]  
The first term in Equation (5) includes the signal to be decoded, the second term is the interference from other devices, and the third term is the noise. If perfect channel estimation can be performed, the channel-compensated signal can be written as follows:  
\[e^{-j\theta_{m}}\mathbf{s}_{m}^{H}\mathbf{r}=L\sqrt{P_{m}}x_{m}+\sum\limits_{ k=1,k\
eq m}^{K}\sqrt{P_{k}}s_{m}^{H}s_{k}e^{j(\theta_{k}-\theta_{m})}x_{k}+e^{-j \theta_{m}}\mathbf{s}_{m}^{H}\mathbf{n} \tag{6}\]  
Considering the QPSK-modulated signal \(x_{m}=x_{m,L}+jx_{m,Q}\), the SINR for each real signal, \(x_{m,I}\) or \(x_{m,Q}\) assuming perfect channel estimation, denoted as \(\gamma_{perfect}\), can be written as:\[\gamma_{perfect}=\frac{L^{2}P_{m}}{\rho_{L}\sum\limits_{k=1,k\
eq m}^{K}P_{k}+L \sigma^{2}} \tag{7}\]  
and thus the bit error rate (BER) before channel decoding assuming perfect channel estimation, denoted as \(p_{perfect}\), can be written as follows:  
\[p_{perfect}=Q\left(\sqrt{\frac{L^{2}P_{m}}{\rho_{L}\sum\limits_{k=1,k\
eq m}^{ K}P_{k}+L\sigma^{2}}}\right)=Q\Big{(}\sqrt{\gamma_{perfect}}\Big{)} \tag{8}\]