esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Catalytic Activity Measurements
#### 3.1.3 Role of the Nature of Support  
In Figure 3a-d the conversions obtained by different catalyst formulations (Au, Pd and Pd-Au catalysts) over the same support are compared. Slight positive effect of Pd deposition on benzene oxidation activity above 180 \({}^{\circ}\)C exhibited bimetallic sample supported on pure ceria (Figure 3a). This is the only case for improved performance of bimetallic sample. This catalyst attained 100% conversion at 220 \({}^{\circ}\)C. However, an interesting observation was lower activity of Pd/Ce in comparison with Au/Ce. The results, shown in Figure 3b allowed seeing clearly that when alumina was used as support, the activity of bimetallic formulation was due only to the presence of Pd. The degree of CBO over Pd/Al and Pd-Au/Al were the same in the temperature interval studied. Superior performance of Pd-based catalysts on CeAl and YCeAl was observed in Figure 3c,d. The comparison demonstrated significantly improved performance of Au/CeAl after addition of Pd (Figure 3c). The conversion of benzene over Pd-Au/CeAl and Au/CeAl at 180 \({}^{\circ}\)C was 80% and 20%, respectively. However, this value remained almost the same for Pd-Au/CeAl with further increase of the temperature up to 300 \({}^{\circ}\)C. The favorable role of Y-doping of CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) is evident in Figure 3d, where the activity of two monometallic and one bimetallic sample on Y-doped CeAl was compared. In this case, enhanced activity of bimetallic formulation was again observed in comparison with gold-based sample. This trend is valid for the entire investigated temperature range.  
Figure 3: Temperature dependence of CBO of different formulations, depending on the support: (**a**) over CeO\({}_{2}\); (**b**) over Al\({}_{2}\)O\({}_{3}\); (**c**) over CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) and (**d**) over Y-doped CeO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\).  
The activity of Pd-Au/YCeAl gradually increased by increasing the temperature. The conversion of benzene approached 99%, differently from Au/YCeAl that attained only 85%. However, the best performing sample was Pd/YCeAl, as it was reported above (Section 3.1.2), reflecting both the role of the nature of noble metal and ceria modification by Y\({}_{2}\)O\({}_{3}\).
# Rectangular Microstrip Array Feed Antenna for C-Band Satellite Communications: Preliminary Results
###### Abstract  
This paper proposes a rectangular array configuration of microstrip antennas combined with a parabolic reflector for C-band satellite communications. The antenna operates in the frequency range of 3.8-4.2 GHz. In particular, the proposed antenna is a 2 \(\times\) 2 feed antenna on a parabolic system. It uses a multilayer microstrip array antenna with proximity coupling and coaxial probe techniques as a feeding technique. The fabricated antenna operates at 3.8-4.4 GHz and 12.1 dBi gain at frequency 4.148 GHz. Through simulation, combining the antenna with a 2.4 m parabolic reflector results in a gain of 33.1 dBi. In conclusion, the proposed antenna configuration achieves the expected high gain and narrow beamwidth for the E plane and the H plane.  
satellite; microstrip; array antenna; reflector +
Footnote †: journal: Remote Sensing
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
Multifactor decision criteria and a load cost matrix are formulated to plan transmission paths globally from terminal nodes carrying different services to the gateway.This paper proposes the LB-OLSR protocol, aimed at addressing issues such as insufficient communication coverage, signal interference, congestion, and increased packet loss rates that may arise in traditional LoRa networks under certain complex environments. The proposed method seeks to improve network traffic distribution and enhance network balance, designing a protocol suitable for LoRa multi-hop networks that need to handle a large volume of requests while effectively utilizing resources. The remainder of this paper is structured as follows: Section 2 outlines the LoRa distributed multi-hop network model and analyzes the related issues. Section 3 presentsa connection necessity-based MPR selection algorithm to address the deficiencies of traditional MPR algorithms. Section 4 introduces the load-balancing routing optimization model and the service routing model. Section 5 details the implementation process of load-balancing routing optimization. Section 6 describes the experimental testing scenarios and the results obtained. Finally, Section 7 concludes the paper and offers insights into future work.
## 4 Learning-Based Anomaly Detection  
### Model Concept  
The overall architecture of the anomaly detection model in this work is illustrated in Figure 2. It is assumed that the flight test data, which may be collected in advance and/or being fed in a real-time manner, are not necessarily labeled. This is common in anomaly detection cases as the data can be clearly labeled only if there exists a working anomaly detector or if human investigators have looked into the data carefully. Therefore, a clustering algorithm is first used to group the data and to label them into several meaningful categories. In this work, given the uncertainty in decisions on anomaly, four categories are considered in the labeling procedure: true normal (TN), true anomaly (TA), uncertain normal (UN), and uncertain anomaly (UA). The latter two categories are introduced as there are cases in which it is not clear whether it is possible to assess if the flight behavior is normal/abnormal or not with solely the given data. Once the labeled data is secured, a 1D-CNN-based binary classifier is trained to learn the key features in determining the anomaly in the data using a set of training data. When the training is completed, the learned model is tested and verified with a separate set of data reserved for this purpose. Then, the learned model can be used as an anomaly detection scheme for both the post-flight analysis and on-line anomaly monitoring. We used the python language (version 3.5, Python Software Foundation, Wilmington, DE, USA) and the open-source library tensorflow (version 1.14.0, Google Brain, Mountain view, CA, USA) framework to implement this deep-learning network.
## 3 Results
### Principal Component Analysis and K-Means Clustering
Similarly, WBT was a key predictor in classrooms (R = 0.5809), reflecting the importance of indoor temperature in achieving thermal comfort and energy efficiency. For office spaces, WBT was the most consistent predictor, showing strong correlations in both shared (R = 0.5545) and solo offices (R = 0.5132). OHI also proved relevant in shared offices (R = 0.5163), highlighting the influence of external environmental factors. While RH had moderate correlations in shared (R = 0.4453) and solo offices (R = 0.3808), its impact was less pronounced compared to WBT and OHI. Across all room types, RH, T, and OHI consistently emerged as the most significant predictors. RH was retained in laboratories, classrooms, and offices due to its strong correlation values, highlighting its universal relevance in energy management. T played a pivotal role in classrooms and offices, reinforcing its importance in optimizing indoor temperature settings. Meanwhile, OHI contributed significantly in laboratories, computer rooms, and shared offices, emphasizing the necessity of accounting for outdoor conditions in ACU control strategies. These features collectively enhance energy efficiency while maintaining occupant comfort. By removing less significant variables such as CO and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **Laboratory Room** & **Computer Room** & \multirow{2}{*}{**Classroom**} & \multicolumn{2}{c}{**Office**} \\ \cline{4-5}  & & & & **Shared Occupancy** & **Solo Occupancy** \\ \hline CO & Removed & Removed & Removed & Removed & 0.3155 \\ CO\({}_{2}\) & Removed & Not available & Not available & Removed & Removed \\ OHI & 0.5724 & 0.5733 & 0.4067 & 0.5163 & Removed \\ RH & 0.6134 & Removed & 0.6954 & 0.4453 & 0.3808 \\ T & Removed & 0.3469 & 0.6123 & 0.5157 & 0.4753 \\ WBT & Removed & Removed & 0.5809 & 0.5545 & 0.5132 \\ TVOC & Removed & 0.6588 & Removed & Removed & Removed \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Key features (\(R\)-value) influencing ACU power consumption determined via PCA. CO\({}_{2}\), the modeling process was streamlined, improving the interpretability of the model without sacrificing accuracy. This data-driven approach allows for tailored ACU control strategies that adapt to specific room conditions while maintaining general applicability across diverse building spaces.
## 2 Numerical Analysis
### Computational Mesh, Initial and Boundary Conditions
Regarding the initial condition of the flow inside the engine cylinder, the in-cylinder flow of the ICEs is characterized by swirl, tumble, and squish. They influence the mixing quality between fuel and air. Their intensities are mainly influenced by the engine (piston) speed, piston bowl geometry, inlet port geometry, inlet valve, and inlet velocity of air or fuel-air mixture. Since squish flows are mainly formed when the piston reaches TDC, the initial flow characteristics inside the engine cylinder are characterized only by swirl and tumble. Since squish flows are mainly formed when the piston reaches TDC, the initial flow characteristics inside the engine cylinder are characterized only by swirl and tumble. Based on the structure of the piston bowl geometry (\(\omega\)-type), engine speed (720 rpm), and intake air pressure (3.5 bar) which causes the intake air velocity, as well as the guidance in the literature [26], a value of 2880 (1/min) has been set for the swirl and tumble intensities as the initial condition of the in-cylinder flow for the simulations in this study. The BCs and initial conditions for the CFD analysis were selected from the technical report of the engine and are listed in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary Condition** & **Boundary Type/Specific Condition** \\ \hline Cylinder head & Fixed wall/Temp./297 \({}^{\circ}\)C \\ Cylinder liner & Layering wall/Temp./197 \({}^{\circ}\)C \\ Piston surface & Mesh movement/Temp./297 \({}^{\circ}\)C \\ Segment-cut surfaces & Periodic \\ \hline
**Initial Conditions** & **Values** \\ \hline Temperature at IVC & 47 \({}^{\circ}\)C \\ Pressure at IVC & 3.5 bar \\ Swirl/Tumble & 2880 1/min \\ IVC & 35 CADs ABDC \\ EVO & 62 CADs BBDC \\ PSOI & 12 CADs BTDC \\ Pilot injection duration & 7.5 milliseconds (Diesel mode) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Boundary and initial conditions. Figure 2: Computational mesh with the BCs of the engine combustion chamber at 40 CADs ATDC. The energy share (ES) of the diesel pilot fuel in the total energy supplied to the engine in the DF mode is calculated by Equation (2). \[\mathrm{ES^{diesel}}=\frac{\mathrm{mDiesel\ \times\ LCV\ of\ Diesel}}{( \mathrm{mDiesel\ \times\ LCV\ of\ Diesel})+(\mathrm{mCH_{4}\times\ LCV\ of\ CH_{4}})} \tag{2}\]  
where, \(\mathrm{ES^{diesel}}\) is the ES of the diesel pilot fuel, \(\mathrm{mDiesel}\) and \(\mathrm{mCH_{4}}\) are the diesel and \(\mathrm{CH_{4}}\) masses that were supplied to one cycle, respectively. The LCV of Diesel, and LCV of \(\mathrm{CH_{4}}\) are the lower calorific value (LCV) of diesel and \(\mathrm{CH_{4}}\), respectively.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Topology-Based Routing Protocols
#### 3.3.1 Static Routing Protocols  
Static routing protocols have static and non-updatable routing tables. They are mostly used in situations with a fixed topology and no task updates. Due to the lack of fault tolerance and adaptability to dynamic environments, traditional static routing protocols have limited applications for UAV swarm systems. The following are three static routing protocols:  
(1) Load Carry and Deliver Routing (LCAD) protocol is proposed by [PERSON] et al. in [48] and is an earlier routing protocol used by the centralized communication architecture. The infrastructure passes data packets to a UAV, which then carries and delivers the data packets to its destination [49]. The goal of LCAD is to maximize network throughput while increasing security. It minimizes the number of hops to achieve batch data transmission and delay tolerance requirements. Moreover, the disadvantage of LCAD is that as the communication distance between the UAVs increases, the delay increases significantly.  
Figure 7: Classification of all routing protocols summarized in this article.  
(2) Data Centric Routing (DCR): for practical applications, a one-to-many data transmission requirement exists in multi-UAV systems. DCR works well with a \"single-group swarm Ad hoc network\", in which the gateway UAV is responsible for distributing information to other nodes [50; 51; 52]. A typical application is the \"publish-subscribe\" model, which can realize automatic connections between data publishers and subscribers, as well as collect traffic [53; 54]. Due to the minimal assistance required between UAVs, DCR is preferred when the system has a limited number of UAVs and a planned flight path.  
(3) Multilevel Hierarchical Routing (MLHR) is migrated from the mobile vehicle network, which solves the scalability problem of large-scale vehicle networks [30]. As described in the \"multi-layer swarm ad hoc network\", UAV swarms can be divided into groups based on the size and function of the UAVs. Communication networks are therefore organized as a hierarchical structure. Only gateway UAVs are responsible for G-T-G communication. The gateway UAV sends data to other UAV nodes in the group using a broadcast [17; 39]. Figure 8 depicts this pictorially. The current literature [55] has proposed the use of a clustering algorithm to select gateway UAVs based on geographical information. This routing model effectively solves the scalability problem and significantly improves the stability of the dynamic network.
## 2 Theoretical Framework and Methods
### Uncertainty Measurement Principle
Decision-makers are often limited by their own knowledge, experience, and capabilities and cannot fully consider all resilience-affecting factors. At the same time, due to the difficulty in quantifying the subjective influences of individuals, risk judgments can only be made in combination with objective realities and experiential cognition, which inevitably generates subjective, uncertain information, making the flood disaster resilience evaluation system an uncertain system. Uncertainty measure theory provides an effective solution for problems with uncertain assessment indicators. In the evaluation of flood disaster resilience, by orderly dividing the resilience levels, the confidence recognition criterion based on orderliness can more accurately reflect the actual situation compared to the maximum membership degree criterion. Furthermore, while fully utilizing the orderliness of the evaluation space, the \"non-negativity\", \"normalization\", and \"additivity\" exhibited by the indicator data perfectly align with the connotations of uncertainty measure.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 3 Materials and Methods
### Method: Image Stitching Algorithm  
Figure 2 depicts the image stitching algorithm used in this work. Image alignment and image blending are the two major components of this approach. In the image alignment phase, a feature detector is used to detect feature points, followed by a feature descriptor to extract descriptors for the detected feature points. The k-nearest neighbor (k-NN) approach is used to match each feature point. False matches may result from automatic  
Figure 1: Input datasets used in this study: (**a,b**) building dataset, (**c,d**) tree dataset, (**e,f**) classroom dataset.  
feature matching, which can lead to poor alignment; consequently, false matches must be eliminated. The M-estimator sample consensus (MSAC) algorithm is employed to eliminate outliers by reducing matching error. The goal is to collect the most feature points acceptable for transformation. The entire procedure is iterative in nature. Correct matches or inliers are those produced by reducing the matching error. The successful identification of a conjugate feature point on the second image for each corresponding feature point on the first image is defined as a correct match. These inlier points are then utilized to calculate the homography (transformation) between two images. The mathematical relationship described in Equation (1) is used to compute the homography matrix.  
\[X^{\prime}=HX,\begin{bmatrix}x^{\prime}\\ y^{\prime}\\ 1\end{bmatrix}=\begin{bmatrix}h_{0}&h_{1}&h_{2}\\ h_{3}&h_{4}&h_{5}\\ h_{6}&h_{7}&1\end{bmatrix}\begin{bmatrix}x\\ y\\ 1\end{bmatrix} \tag{1}\]  
Once homography is obtained, the target image is warped (transformed) onto the source image. After the images have been aligned, there may be noticeable seams because of differences in illumination and unintentional camera motions. As a result, image blending is performed to remove the seam.  
Table 1 shows feature detectors and descriptors, listed in the order of their development. As shown in the table, some of them are either detectors or descriptors, while others are both. Therefore, to perform a comparative assessment, the best approach is to pair each detector with each descriptor and evaluate its performance. We tested each detector-descriptor combination on a variety of datasets. For each dataset, we use the detector to detect features on the left and right images, and then the paired descriptor was used to extract features. Next, the features were matched using the k-NN matching algorithm. Finally, both the images were stitched using the algorithm described in Figure 2.  
Figure 2: Workflow adopted for image stitching.
## 2 Materials and Methods  
### Tested Material  
For this study, commercially available kefir (a fermented milk product) was used. To this kefir, powdered microalgae, specifically _Arthrospira platensis_ (Spirulina) and _Chlorella pyrenoidosis_ (Chlorella), were added. The microalgae were purchased as a dietary supplement from a local health food store. The kefir, produced by the OSM Krasnystaw (Krasnystaw, Poland) company, had a manufacturer-declared composition of milk, milk proteins, live bacterial cultures, and kefir grains. The microalgae, supplied by the Green Essence (M-Internet, Pyrzyce, Poland) company, contained solely dried Spirulina or Chlorella, without any additional ingredients. The determined nutritional values of the tested materials are presented in Table 1, with the manufacturer's declared values included in brackets. Immediately prior to the in vitro digestion experiment, the microalgae were incorporated into portions of kefir at concentrations of 0.1, 0.5, 1, and 5% (\(w/w\)). The product was mixed until it had a uniform color. The material prepared in this way was subjected to in vitro digestion to determine the amount of released vitamins, protein, phosphorus, iron, and iodine and to determine their bioavailability. In order to determine the total content of phosphorus, iron, and protein, the tested material was subjected to mineralization. For iodine determination, alkaline extraction with tetramethylammonium hydroxide (TMAH) was performed. The term \"in vitro bioavailability\" was defined as the ratio between the content of the tested compounds in the dialysate that crossed the pore barrier in the dialysis membrane and found themselves in the buffer solution during simulated in vitro digestion, relative to their total content in the starting material, expressed as % (_w/w_).
## 6 Results and Discussion
### Comparison of Data Confidentiality Violation Detection Results  
This section compares the results of the agent data confidentiality violation detection and classification using the RVQA against the detection using the CNS Searching process explained in Sections 5.1 and 5.2, respectively. The total numbers of data confidentiality violations detected by both methods throughout the CNS execution period are shown in Figure 15. At the end of the 24 h of execution, the RVQA detected a 72.45% higher number of violations as compared to the CNS Searching process. The total number of violations detected by RVQA was 137,373, while 37,851 violations  
Figure 14: Total number of published contents and the number of detected disclosed public contents versus time (hours).  
Figure 13: Total number of active accounts and the number of detected social connections versus time (hours).  
were detected by the CNS Searching process. The result shows that the implementation of the AQM framework and the RVQA process helped to detect about 70% more agent quality violations that occur during the execution of CNS as compared to the detection performed using the CNS Searching process. One of the main reasons is that more agent data confidentiality requirements and rules can be defined and implemented via the AQM framework and RVQA process. As a result, more agent data confidentiality violation conditions can be detected.
## 10 Spatial Predictions, Prediction Uncertainty, and Their Visualization
### Visualization  
Spatial predictions can be visualized using various tools, most commonly ArcGIS and QGIS. The function, _spplot_, in R is often used to plot the distribution of spatially continuous predictions [59]. The R package, _raster_, can also be used for such purpose [130]. Joint application of R and Google Earth can be used to visualize the predictions. In this study, I demonstrate how to use the latter approach along with _spplot_ to visualize the predictions as below.
## 2 Materials and Methods
### Results Based on Experiment Data  
To obtain stable stress wave signals, an electronic hammer was used to knock the sensors. In addition, all sensors were knocked several times in order to get the average value of velocity for image reconstruction. The hole defect is located at the upper left of the cross section of the wood sample, the knot defect is located at the lower right of the cross section of the wood sample, and the long crack defect is located at the center of the cross section of the wood sample. Especially, the shape of crack defects is not conducive to the acquisition of velocity signals.  
Like simulation experiments, the number of pixels in each cell is 1 pixel, and the resolution of reconstructed image is 300 \(\times\) 300 pixels (actual size is about 6.1 cm \(\times\) 6.1 cm). The actual diameter of S6 is about 35 cm, so the proportion of the reconstructed image size to the sample 6 is about 1:33. The actual diameter of S7 is about 33 cm, so the proportion of the reconstructed image size to the sample 7 is about 1:29. The actual diameter of S8 is about 28 cm, so the proportion of the reconstructed image size to the sample 8 is about 1:21. The results of the segmented rays graphs and reconstructed images with different control coefficients from the real data are shown in Figures 8 and 9.  
As shown in Figure 8, the original rays graphs are all improved significantly. Especially for sample 8, the RSEN algorithm helps to reflect the potential spatial distribution of defects. The coefficient \(c_{1}\) with a value in the range of 0.65-0.8 is suggested. The experimental results, as shown in Figure 9 (the scales of the false colors range from 0 to 1), prove that the improvement of the rays graph is beneficial to the subsequent interpolation algorithm. Hole defects and knot defects are clearly shown in reconstructed images, and even the complex crack defects can be approximately reflected. The coefficient \(c_{2}\) with a value in the range of 0.85-0.95 is suggested for the SISE algorithm.  
Figure 8: Original rays graph and segmented rays graph using the RSEN method from the real data with different control coefficients.