esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Simulating Gross Primary Production Using SCOPE Model  
After retrieving the time series of the forest's biochemical and structural properties, we set up the SCOPE model to simulate the half-hourly GPPSIM during the 2016 vegetation growing season. These half-hourly values were summed to daily GPPSIM values calculated in g C m\({}^{-2}\) s\({}^{-1}\). The SCOPE model inputs for GPPSIM included: (1) half-hourly meteorological variables, i.e., R\({}_{\rm in}\), R\({}_{\rm lit}\), T\({}_{\rm a}\), P, e\({}_{\rm a}\), and u (introduced in Section 3.1.2); (2) vegetation properties, i.e., C\({}_{\rm ab}\), C\({}_{\rm w}\), C\({}_{\rm dm}\), C\({}_{\rm s}\), C\({}_{\rm ca}\), \(LAI\), \(LIDF\), and \(N\) (Table 2); (3) maximum rate of carboxylation (\(V_{\rm cmax}\), in umol m\({}^{-2}\) s\({}^{-1}\)); and (4) the Bell-Berry stomatal parameter \(m\). We ran the SCOPE model with three scenarios to investigate the impact of information retrieved from the HLS data on the accuracy of GPPSIM:  
\"Fixed\" scenario: We assumed no variations in the retrieved vegetation properties during the growing season. We fixed them either at the available measured means (Table 2) or at the prior means. \(LIDF_{\rm a}\) and \(LIDF_{\rm b}\) were fixed at -0.35 and -0.15, respectively, showing the spherical characteristics of Norway spruce [54]. We further fixed \(V_{\rm cmax}=80\) umol m\({}^{-2}\) s\({}^{-1}\) and \(m=9\), which are the typical values for evergreen needle leaf species as suggested in the SCOPE model. This scenario allowed us to monitor the temporal variation in GPPSIM determined by changes in radiation and atmospheric demand.  
\"HLS info\" scenario: We used the time series of vegetation properties retrieved from the HLS data as the inputs to the SCOPE model. In this scenario, we also fixed \(V_{\rm cmax}\) and \(m\) at the typical values for evergreen needle leaf species, since these two cannot be directly retrieved from the optical reflectance [20].  
\"HLS info & var Vcmax\" scenario: We used the same time series of retrieved vegetation properties as in the HLS info scenario. Here, instead of using a fixed typical value of \(V_{\rm cmax}\), we estimated its time series from the retrieved time series of \(C_{\rm ab}\) from the HLS data. For this, we used the empirical relationship between \(V_{\rm cmax}\) [umol m\({}^{-2}\) s\({}^{-1}\)] and \(C_{\rm ab}\) [mg cm\({}^{-2}\)] proposed by [PERSON] et al. [57] for C\({}_{3}\) plants:  
\[V_{\rm cmax}=a\times C_{\rm ab}+b\, \tag{1}\]  
where \(a=2.529\) and \(b=-27.34\). This scenario allowed us to monitor the temporal variation in GPPSIM when we relied on optical domain information both directly from the retrieved vegetation properties and indirectly from the empirical relationships.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Analysis of Experimental Results  
Due to the large number of finely annotated and publicly available short video sequences in the OTB-100 dataset, it maintains a challenging nature while offering high operability and strong visual presentation, making it well-suited for visualizing detailed changes during the tracking process. In contrast, although LaSOT and UAV123 are more challenging and representative in terms of performance evaluation, their video sequences are generally longer, with frequent variations in object scale and scene content, making them less suitable for complete or efficient visual presentation within limited space. Moreover, the complex scenes in LaSOT and the aerial perspectives in UAV123 are primarily intended for the quantitative evaluation of algorithms' overall performance in complex, real-world scenarios. However, when it comes to showcasing detailed aspects--such as distinguishing objects from similar backgrounds--the typical short sequences in OTB-100 are more conducive to highlighting the fine-grained improvements achieved by the proposed method. Therefore, in order to effectively test whether the proposed algorithm has made progress in dealing with similar background clutter during the tracking period, this study carefully selected those video sequences with complicated background and similar clutter in the OTB-100 dataset, used these sequences to test the proposed algorithm, and visualized the tracking results, in order to visually observe the algorithm performance. In addition, quantitative analysis of the proposed algorithm was carried out on the OTB-100, LaSOT, and UAV123 datasets. In the test of the LaSOT dataset, on the one hand, the video sequence with background interference was tested, and the response efficiency of the algorithm in such complex scenes was deeply explored. Conversely, the algorithm presented in this paper was benchmarked against other comparable algorithms in terms of overall performance. The objective evaluation metrics utilized were accuracy and success rate. Through accurate quantitative data evaluation, the advantages and disadvantages of each algorithm were clearly identified.
## 1 Introduction  
On the basis of their low density, high modulus, high temperature strength, good flame retardancy, excellent oxidation resistance, and creep resistance, TiAl alloys have been developed into one of the most attractive materials for lightweight and heat-resistant structural parts, such as those used in aviation, aerospace, aeronautical missiles, automotive engines, and so on [1; 2; 3; 4; 5]. In recent years, there has also been great interest in porous TiAl alloys as potential engineering materials for various industrial fields [6; 7; 8; 9]. However, the traditional processing routes for preparing TiAl alloys, such as ingot metallurgy, hot forging, precision casting, powder metallurgy, and machining [10; 11; 12; 13; 14], either have long production cycles or waste quite a few materials, leading not only to high manufacturing costs, but also to difficulty in fully realizing the advantages in the preparation of parts with complex shapes. Therefore, it is of great importance to apply an appropriate technology to overcome the shortcomings mentioned above.  
Gel casting, as a near net shaping technology, was firstly developed by [PERSON] and [PERSON] in the early 1990s and has been widely applied in the ceramic industry [15; 16; 17; 18]. In such a process, a high-solid-loading slurry consisting of raw powders, solvent, and organic binder is first required.  
After the slurry is casted into a mold, the organic binder creates a macromolecular network to hold the ceramic particles together. Homogeneous green bodies with the required mechanical properties are therefore obtained. In the past few decades, a considerable amount of research has been done on the development of the ceramic gel casting technique. Along with the constant maturity of gel casting technology, the applications have been expanded to the domain of metal materials [19; 20; 21; 22].  
It is well known that the performance of titanium-based objects can be easily deteriorated by impurities resulting from residual oxygen, nitrogen, and carbon during manufacturing [23]. Due to the high stability of these impurities, such as TiO\({}_{2}\) and TiN, it is impossible to eliminate them by using titanium or its elemental powder as a raw material. Fortunately, titanium hydride can be an ideal raw material to reduce the content of these impurities. In a previous work, the powder metallurgy of TiH\({}_{2}\) and TiH\({}_{2}\)/Al was studied. Researchers, such as [PERSON] and [PERSON], also carried out impressive works on the gel casting of TiH\({}_{2}\), and their results showed that oxygen and carbon contamination could be reduced [24; 25; 26].  
In this study, a porous TiAl alloy was fabricated via non-aqueous gel casting followed by a vacuum sintering process by using TiH\({}_{2}\)/Al as raw powders. The main purpose of the present work is to further develop a simple approach for TiAl structural material production which could meet the requirements of reducing the cost and expanding application areas.
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 6 Conclusions and Future Scope  
In this paper, the concept of robust and efficient image transmission required for a sustainable smart city is realized by utilizing a hybrid combination of MIMO and OFDM systems based on two discrete transforms, i.e., FFT and DCT. The simulation results on images show a significant improvement in BER performance for both the transform when the number of receivers is increased from 1 to 2, making it a more sustainable model by decreasing the requirement of high SNR. The improvement goes up to 10 dB in terms of SNR required on switching from 1 to 2 receiving antennas. The image quality is also improving significantly, which can be accessed by visual inspection and the PSNR and SSIM values, which ensures efficient implementation of real-time monitoring methodologies critical for the smart city concept. Further, it can be observed that DCT based OFDM system performs better than FFT based OFDM. The BER improvement provided by DCT based OFDM is going up to 3 dB regarding SNR required. The same effect can be seen on the image quality also, where the PSNR and SSIM values are even more in the case of DCT based OFDM in comparison to FFT-based OFDM. Additionally, the performance of the proposed methodology over AWGN channel is better than the Rayleigh fading channel. OFDM systems augmented with MIMO provide better performance as we keep on increasing the number of antennas. Moreover, the proposed methodology can be used to transmit multimedia information, thereby making it a better contender for smart city applications.  
In the future, this work can be extended for a hybrid system of OFDM and massive MIMO to support the multimedia application for 5G users. This work can also be used to incorporate and study diverse transformation techniques instead of conventional Fourier transform to make the system more robust and efficient.  
Conceptualization, L.K., G.S.G.; Data curation, L.K., G.S.G.; Formal analysis, L.K., N.C.; Investigation, L.K., N.C.; Methodology, L.K., G.S.G., N.C. and B.-G.K.; Resources, B.-G.K.; Visualization, L.K.; Writing--original draft, L.K., G.S.G.; Writing--review & editing, L.K., G.S.G., N.C. and B.-G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
The datasets generated and/or analysed during this study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 3 Case Study and Results
### Basics of the Dataset  
The proposed ML methods enable a novel 3D BM estimation framework to be simulated in Python on a desktop computer with the Intel Xeon CPU E5-2640 dual processors and 256 GB RAM. The database is built via the collection of 3D BM in the Kansas-Missouri area in the United States. Figure 5 shows the distributions of the age and gender groups in the dataset. There are 76 males and 136 females out of the 212 participants. The age of the participants ranges from 6 up to 75. General statistics of the dataset are listed in Table 1. There are 6 body features, i.e., \"body height\", \"weight\", \"head girth\", \"arm girth\", \"sitting height\" and \"head height\", which are chosen as representatives to show the variety of the dataset based on the minimum value, maximum value, mean value and the standard deviation (std).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Feature Name** & **Min** & **Max** & **Mean** & **Std** \\ \hline Body Height (in) & 28.58 & 74.88 & 65.55 & 4.98 \\ Weight (kg) & 26.10 & 117.70 & 68.53 & 15.25 \\ Head Girth (in) & 12.09 & 25.47 & 21.79 & 1.31 \\ Arm Girth (in) & 7.09 & 23.82 & 11.49 & 1.99 \\ Sitting Height (in) & 25.59 & 39.49 & 34.59 & 2.25 \\ Head Height (in) & 7.89 & 12.33 & 9.46 & 0.71 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics of representative body features.
## 6 Conclusions and Future Work  
In this paper, we presented CENTERA, a CENTralized Trust-based Efficient Routing protocol with an appropriate Authentication scheme for wireless sensor networks (WSN), which periodically sends readings and sensed data to a powerful sink BS. CENTERA provides secure routing and a trusted network where the bad nodes are isolated and their attacks eliminated. We classified different types of bad nodes, some of which are malicious, incompetent, non-cooperative, broadcasting, outsider, and impersonating nodes that affect the routing functionality of the network.  
CENTERA utilizes the centralized approach, where the more powerful BS periodically accumulates simple counter observations from the sensor nodes and decides on the network topology and routes, and isolates the bad nodes. The BS calculates several quality metrics and two trust levels of each node and uses an effective banning system to isolate the different bad nodes from the network. Also, CENTERA uses a very efficient method to distribute the routing information to every node. The nodes forwards only through their next hop DL neighbor and forward the packets of their UL neighbors, only as indicated by the BS and drops any other packet.  
Based on its target environment, CENTERA uses a secure and efficient authentication scheme suitable for the severely constrained nodes; the authentication technique may be the symmetric key cipher RC5 in case of a safe environment under close administration, or the asymmetric key cipher PBC or IBE-ECC in case of a hostile environment with a strong attacker model.  
We simulated CENTERA using TinyOS and proved its correctness in providing secure routing information through trusted paths. Also, in CENTERA, some nodes were put on probation to observe closely while bad nodes were isolated for a specific time depending on their history, and then given another chance to try to improve.  
Our future work will focus majorly on further detailing and enhancing the BS analysis algorithm to more efficiently perform all the complex tasks it is entitled to do. We will also perform additional simulations on irregular network topologies to further analyze the effect of hop cost. In addition, we will check the inclusion of secure positioning or other positioning system to better validate nodes' neighborhood and detect colluding nodes.
## 2 Materials and Methods
### Experimental Design and Model Training  
In this study, a total of four analyses were conducted. To improve generalization under limited dataset conditions, we applied a series of data augmentation techniques to the training images, including random horizontal flipping, \(\pm 15^{\circ}\) random rotation, and brightness/contrast/saturation jittering via ColorJitter. As a result, 1428 augmented original images (856:285:287 = train-validation-test) and 1335 augmented GrabCut-Watershed images (801:267:267) were generated and included in the respective datasets (Table 1). All datasets were partitioned using a 6:2:2 ratio, in line with common practices in image-based learning tasks [33; 34; 35].  
Given the relatively limited size of the dataset, transfer learning using a CNN model pre-trained in PyTorch allows the classification model to converge relatively quickly [36].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & \multicolumn{4}{c}{**Details**} \\ \hline Original Images (Total) & Total & Stage1 & Stage 2 & Stage 3 \\  & 476 & 163 & 160 & 153 \\ \hline GrabCut Preprocessed & Total & Stage1 & Stage 2 & Stage 3 \\ Images (Total) & 445 & 139 & 171 & 135 \\ \hline Learning Rate & Train–Validation–Test & Batch Size & Epoch & Initial Learning Rate \\  & 6:2:2 & 32 & 10 & 0.01 \\ \hline The total number of & \multicolumn{2}{c}{Original Images} & \multicolumn{2}{c}{GrabCut–Watershed Images} \\ Augmented Images & 1428 & & 1335 \\ (Train–Validation–Test) & (856:285:287) & & (801:267:267) \\ \hline Learning Rate Scheduling & \multicolumn{2}{c}{Step decay: reduce by a factor of 10 every 3 epochs} \\ \hline Model & \multicolumn{2}{c}{Pre-trained ResNet50 with Fine-Tuning applied} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset used in this study.  
Figure 2: Photographs of the same crop across growth stages 1 to 3 obtained using two methods: original images and images preprocessed with GrabCut–Watershed.  
Therefore, to achieve high performance, model 1 was created by fine-tuning a pre-trained ResNet50 provided by the PyTorch library (version 2.7.0). Leveraging the pre-trained weights of ResNet50 reduces both the training time and cost on small datasets, while maintaining higher accuracy in transfer learning compared to other models such as VGG16, VGG19, and AlexNet [37; 38] (Figure 3).
## 2 Materials and Methods  
### Materials  
Commercially available cold rolled grade 75 carbon steel (CS) rebars were used as a reinforcement material and its composition provided by the manufacturer can be found in Table 1. The CS rebar samples had a diameter of 12.7 mm. Prior to the thermomechanical treatments, the samples were degreased with ethanol, cleaned with 50% HNO\({}_{3}\) solution for 20 s, rinsed with deionized water and air dried. Six heat treatments were performed to the rebars in order to obtain the different microstructure combinations that carbon steel reinforcements can develop during the event of a fire. Samples were recrystallized at 950 \({}^{\circ}\)C for 3 h and then normalizing, annealing, water and oil quenching thermal treatments were performed. For the quenched samples, one set of each was heated up to 500 \({}^{\circ}\)C for 3 h and annealed to release internal stresses from the quenching process. The whole process schematic can be seen in Figure 1a. An as-received specimen was also prepared. The results were split according to its fast and moderate cooling rate for clarity reasons.  
Figure 1: Setup used for the study: (**a**) thermomechanical treatment schematic, and (**b**) electrochemical cell.  
The synthetic FA pore solution used in the study was taken from previous works that showed the higher electrochemical influence of NaOH and KOH [24]. Its composition was 0.612 M NaOH and 2.7 mM KOH. For both chemicals, high purity pellets from Sigma Aldrich were used to prepare the electrolyte. The chloride induced pitting influence was studied by adding high purity NaCl pellets from Sigma Aldrich up to a concentration of 0.6 M NaCl.
## 2 Materials and Methods
### Field Data Collection  
The use of UAVs has been increasing in tasks such as crop management, estimating biomass, predicting yields, and monitoring plant health. UAV imagery can be used to analyze surface models, evaluate crop height and growth rate, and extract canopy temperature and digital plant counting. These data can be used as reference parameters for implementing spatially variable crop operations using a Decision Support System (DSS), enabling farmers to choose the right time to harvest crops such as rosemary for food and essential oil extraction.  
Overall, these technological advancements can provide rapid and on-demand real-time data for smart agriculture practices, benefiting farmers and businesses in the agricultural sector [16].  
The flight mission involved the strategic planning of parameters through DJI GS Pro software v2.0.17 (Table 1). These parameters encompassed various aspects such as height, speed, direction, and the acquisition details of the cameras, including the sequence of shots and the frontal and lateral overlapping.  
To mitigate potential disruptions like shadows and weeds, the flight was meticulously executed at noon, when the sun was positioned at the zenith. Prior to the flight, the entire plot underwent harrowing, and four ground control points (GCPs) were strategically positioned in the field. These GCPs were georeferenced using the GNSS (global navigation satellite system) receiver S7-G by Stonex (Milan, Italy), equipped with a Stonex geodetic antenna. This receiver utilized multiband signals from prominent GNSS satellites, including GPS, GLONASS, Galileo, and Bei Dou. Enhanced accuracy was achieved through Real-Time Kinematic (RTK) differential correction data.  
The coordinates of the GCPs were acquired in RTK (real-time kinematic positioning) mode, averaging 60 measurements. Subsequently, under favorable weather conditions characterized by clear skies and low wind speed, the flight commenced along the predefined route and waypoints.  
The multispectral images were preprocessed to create a multiband ortho-mosaic. Data processing was performed using Agisoft Metashape Professional (version 1.7.3), which generated 3D and 2D spatial data for GIS applications through photogrammetry. After image alignment, ground control points (GCPs) were added using the WGS84 geographical coordinate system (EPSG: 4326), and GCPs were identified in various photos. Image calibration was performed using the drone's brightness sensor and a white panel for radiometric calibration. The creation of a dense cloud enabled the generation of the mesh and digital elevation model. Finally, the process of orthorectification and mosaicking produced the final multiband ortho-mosaic.
_Algorithms_**2013**, \(6\), 782-804; doi:10.3390/a6040782  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Stability, Optimality and Manipulation in Matching Problems**  
**with Weighted Preferences**  
**[PERSON] \({}^{1,*}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Department of Information Engineering, University of Padova, Padova 35131, Italy;  
\({}^{2}\) Department of Mathematics, University of Padova, Padova 35121, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Computer Science, Tulane University and IHMC, New Orleans, LA 70118, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) NICTA and UNSW, Sydney, NSW 1466, Australia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39 049 827 7704.  
_Received: 9 July 2013; in revised form: 7 November 2013 / Accepted: 8 November 2013 /_  
_Published: 18 November 2013_  
**Abstract:** The stable matching problem (also known as the stable marriage problem) is a well-known problem of matching men to women, so that no man and woman, who are not married to each other, both prefer each other. Such a problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or, more generally, to any two-sided market. In the classical stable marriage problem, both men and women express a strict preference order over the members of the other sex, in a qualitative way. Here, we consider stable marriage problems with weighted preferences: each man (resp., woman) provides a score for each woman (resp., man). Such problems are more expressive than the classical stable marriage problems. Moreover, in some real-life situations, it is more natural to express scores (to model, for example, profits or costs) rather than a qualitative preference ordering. In this context, we define new notions of stability and optimality, and we provide algorithms to find marriages that are stable and/or optimal according to these notions. While expressivity greatly increases by adopting weighted preferences, we show that, in most cases, the desired solutions can be found by adapting existing algorithms for the classical stable marriage problem. We also consider the manipulability properties of the procedures that return such stable marriages. While we know that all procedures are manipulable by modifying the preference lists or by truncating them, here, we consider if manipulation can occur also by just modifying the weights while preserving the ordering and avoiding truncation. It turns out that, by addingweights, in some cases, we may increase the possibility of manipulating, and this cannot be avoided by any reasonable restriction on the weights.  
**Keywords:** stable matching; weighted preferences; manipulation