esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
(**a**) On car windscreen [50]. (**b**) On cactus camouflage platforms [51]. (**c**) On tree camouflage platforms [51]. Figure 2: RF spectrum and its applications [53]. For 5G applications, authors in [54; 55; 56; 57] designed antennas providing bandwidth of 34%, 28%, 11.7%, and 51%, respectively. The gain of these antennas mentioned are 2, 6.8, 15.1, and 12.4 dBi, respectively. In [58], an optimized Vivaldi antenna was designed for 5G communication, gain of 10.33 dBi, and 54% bandwidth was achieved. Some other work discusses the 5G MIMO antenna design at mm-wave band. A gain of 11 dBi in [59] and 16 dBi in [60] was achieved; however, the antennas have large dimensions. For smaller antenna dimensions, antenna provide the gain of 15 dBi, as mentioned in [61]. Another work discusses 5G MIMO antenna for IoT applications in [62], in which FBW achieved at 2.5 GHz band was 11%, 17% at 3.5 GHz band, 20% at 5.5 GHz band, 18% at 7.5 GHz, and 29% at 28 GHz (mm-wave). Antennas having a bandwidth greater than 500 MHz fall under the category of ultra-wideband (UWB) antennas. Therefore, the research showing higher values of bandwidth is suitable for ultra-wideband applications. Moreover, higher gain reflects large antenna dimensions. Optical antennas are suitable for satellite communication as they can hide the aesthetics of antennas while integrating with solar panels, commonly working at L, S, X, Ka, Ku, and W bands. For Earth observation, UHF and VHF bands are preferred, using monopole and dipole antennas providing gain less than 4 dBi, while phased array, helical, and Horn resulted in improved gain greater than 12 dBi at UHF, S, Ka, and X band, as discussed in [63]. Moreover, for gain, the available RF power budget and orbit determine that the gain and mission specifications will guide the bandwidth requirements for a particular antenna. Implantable antennas used for biomedical purposes may also use optical antennas. In [64], a quad-band implantable antenna was designed where gain between \(-\)24 to \(-\)34 dBi was achieved. In [65], a compact conformal implantable antenna was designed where gain was realized between \(-\)18 to \(-\)30 dBi. An implantable slot antenna array was designed in [66], where the gain of \(-\)26 dBi was achieved. Hence, the literature suggests that the gain of implantable antennas is negative with smaller values. Here, the gain and bandwidth are determined based on the detection, monitoring, and treatment of a certain type of disease.
## 2 Materials and Methods
### Eddy Covariance Measurements  
The EC system that was installed within our study area collected continuous measurements beginning on 25 May 2012. It consisted of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) and an open path CO\({}_{2}\)/H\({}_{2}\)O analyzer (LI7500A, LI-COR Biosciences, Lincoln, NE, USA). The anemometer measured the three-dimensional wind velocity and temperature, and the CO\({}_{2}\)/H\({}_{2}\)O analyzer monitored the CO\({}_{2}\)/H\({}_{2}\)O density. Both sensors were placed 4.5 m above the ground and separated 17 cm away from each other in the horizontal direction. They were connected to a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA) that recorded the measurements with a sampling frequency of 10 Hz. The EC measurements were processed to half-hourly averages using the EddyPro software package [50; 51].  
Measurements of air temperature and relative humidity were taken close to the EC system at seven heights (3 m, 5 m, 10 m, 15 m, 20 m, 30 m, and 40 m). All data were processed to 10 min intervals [51]. To match the EC measurements, we used measurements at a height of 5 m in this research.  
The diurnal courses of GPP (mg m\({}^{-2}\) s\({}^{-1}\)) during our experiment period were obtained by partitioning the observed net ecosystem exchange (NEE) into GPP and ecosystem respiration (R\({}_{\rm e}\)) according to [PERSON] et al. [52] and [PERSON] et al. [53].
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 2 Related Work
The introduction of multi-head self-attention and global attention mechanisms enables the model to dynamically adjust the importance of different time steps and features, thereby better handling long-range dependencies and complex nonlinear data. In contrast, some hybrid models (e.g., those based on VMD decomposition) can decompose data but lack the ability to dynamically weight temporal features. By reweighting feature channels through the SE module, the proposed method can identify and enhance the feature channels most relevant to the prediction task while suppressing redundant information. This mechanism further improves the model's noise resistance and generalization performance, particularly excelling in scenarios with high data noise or complex features. The proposed method integrates spatial feature extraction, temporal sequence modeling, and attention mechanisms into an end-to-end framework, avoiding potential information loss between modules that may occur in traditional hybrid models. For example, while the method proposed by [PERSON] et al. (2018), which combines multivariate linear regression with LSTM, can capture global trends and local details, the linear regression component may not fully adapt to highly nonlinear load data. Additionally, through multi-level feature processing and dynamic attention mechanisms, the proposed method can better adapt to different types of input data and maintain high prediction accuracy even with limited training data or complex data features. In contrast, some hybrid models (e.g., those based on BiLSTM-CNN-GWO) may face overfitting risks when dealing with small-sample data or complex nonlinear data, despite optimizing the model structure. In summary, by integrating spatial feature extraction, temporal sequence modeling, and multiple attention mechanisms, the proposed method not only inherits the advantages of hybrid models but also further optimizes feature extraction, dynamic weighting, and noise resistance, demonstrating significant improvements in adaptability, generalization capability, and prediction accuracy, thereby addressing the limitations present in existing hybrid models.
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 4 System Evaluation
### Analysis of SDR
The solution in this paper can still maintain a very high SDR under high packet loss. Therefore, it can be proved that the proposed scheme has the most robust anti-interference ability. In the real experiment, the analysis result of the relationship between video SDR and distance is as shown in Figure 5(b). With the increase of transmission distance, the packet loss rate increases. In Figure 5(b), the SDR of the replication-based scheme declines significantly with the increase of distance. In contrast, the SDR of the RLNC-based scheme and our scheme has a gentle decline, which indicates that distance only has a small influence on these two network coding schemes. The reason for this result is that the coding operation has increased the independence among packets and improved the anti-interference ability. The three schemes in our experiments all use redundant operations to improve transmission reliability, so we tested the impact of the amount of redundant data on SDR. The experimental results are shown in Figure 7. In the simulation experiments, we set the packet loss rate at 5% and the number of mobile devices at 25. From the results, we observe that the scheme proposed in this paper and the RLNC-based scheme can easily reach 1 with a small amount of redundancy. However, the SDR of the replication-based scheme only increased slightly. Figure 6: Influence of loss rate and distance on SDR. Figure 7: Influence of redundancy on SDR. It has a similar result in real experiments. The two schemes based on linear network coding can both achieve an SDR above 0.9, and with the increase of redundancy, the SDR could get close to 1. Although the SDR of the replication-based scheme increases with the increase of redundancy, the improvement rate is limited, mainly because the replication-based scheme tends to receive more repeated and invalid packets.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 3 Materials and Methods  
This section first discusses the system architecture. Then, the experimental methodology that was followed in terms of the use case and testing procedure is described.
## 3 Biosynthetic Routes to Nitrogen-Containing and Phenolic Metabolites in Cannabis
### General Phenylpropanoids and Phenolic Acids Biosynthesis  
The biosynthesis of phenolic compounds occurs via the phenylpropanol pathway, starting with phenylalanine (Figure 4). This first step regulates the flow of metabolites entering this pathway [46]. Tyrosine/phenylalanine ammonia-lysse, in some dicotyledonous organisms, and particularly monocotyledons, can catalyze the same reaction as PAL, but can also use tyrosine as a substrate [47; 48]. Otherwise, \(p\)-coumaric acid is generated by the activity of the cinnanate 4-hydroxylase. Other cinnamic acid derivatives have been identified in plants, including caffeic acid, sinapic acid, and ferulic acid [45; 46]. \(p\)-coumaroyl-CoA will serve as precursor for most phenolic compounds [46] and \(p\)-coumaric acid analogues can also serve as ligands for 4 CL.  
Figure 3: Aromatic amino acid biosynthetic pathway in plants. CM, chorismate mutase (* engaging step of the pathway). Arrow represents one biochemical reaction. PPA-AT, prephenate aminotransferase. ADT, aprogenate dehydratase. ADH, aprogenate dehydrogenase. In red: inhibition regulatory pathways. Glu, glutamate. Chorismate is converted to prephenic acid by the enzyme chorismate (E.C. 5.4.99.5). Prephenate undergoes transamination by the enzyme prephenate aminotransferase (E.C. 2.6.1.79), using glutamate, producing L-arogenate and 2-oxoglutarate. The enzyme aprogenate dehydratase (E.C. 4.2.1.91) completes the aromatic ring of the L-arogenate cycle by removing the hydroxyl and carboxylic groups attached to it, thus forming phenylalanine and generating H\({}_{2}\)O and CO\({}_{2}\). To obtain tyrosine, aprogenate is oxidized by the enzyme aprogenate dehydrogenase (E.C. 1.3.1.43) with the cofactor NAD+, producing a molecule of CO\({}_{2}\) and one of NADH.
# Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
## 1 Introduction
### Generation of Single-Anomaly Training Samples  
The BL network relies on input-output training sample pairs for functional modeling. This study employs the sample generation method from resistivity inversion proposed by ref. [45], which involves using single anomalies with varying locations across different training samples for network training. As a result, this approach imposes no restrictions on the number of anomalies prior to inversion. Figure 2 illustrates the generation of an input  
Figure 1: Configuration of the broad learning network [37; 41; 42].  
output training sample pair through the forward modeling of the gravity data. The density model is constructed with a single, colored, cube-shaped anomalous block embedded in a homogeneous background, as shown in Figure 2a. The density model is discretized into 50, 50, and 12 grid cells in the X-, Y-, and Z-directions, respectively. The size and spatial position of the anomalous block are randomly varied in each model, with the density value of the anomaly randomly selected within a range of \(-2\) to \(2\) g/cm\({}^{3}\). In particular, the coordinated rotations around the density model's center point are also performed to generate some single samples oriented in different directions. The rotation method is as follows:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\\ z^{\prime}\end{bmatrix}=\begin{bmatrix}\cos\alpha&\sin\alpha&0\\ -\sin\alpha&\cos\alpha&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}\cos\beta&0&-\sin\beta\\ 0&1&0\\ \sin\beta&0&\cos\beta\end{bmatrix}\begin{bmatrix}1&0&0\\ 0&\cos\gamma&\sin\gamma\\ 0&-\sin\gamma&\cos\gamma\end{bmatrix}\begin{bmatrix}x\\ y\\ z\end{bmatrix}, \tag{9}\]  
where \([x,\,y,\,z]^{T}\) represents the coordinates of the density model, \([x^{\prime},\,y^{\prime},\,z^{\prime}]^{T}\) is the corresponding coordinates after rotation, and the angles \(\alpha,\,\beta\), and \(\gamma\) result from rotations along the Z-, Y-, and X-axes. Utilizing the single-anomaly density model (Figure 2a) and the gravimeter deployments (Figure 2b), the surface gravity data (Figure 2c) are simulated through forward gravity modeling, resulting in the generation of an input training sample (Figure 2d).
## 3 Experimental Results and Discussion
### Load-Displacement Behaviour  
The load versus mid-span displacement curve for each specimen is presented in Figure 6 and the magnitude of deflection at ultimate load is presented in Table 4. Specim CS recorded a deflection of 7.27 mm upon reaching an ultimate load of 240 kN.
# Human Activity Recognition Using CSI Information with Nexmon
## 2 Problem Statement
### Contribution  
The following hypotheses were tested:  
1. For human activities classification, where data is collected in some controlled environment with Raspberry Pi and Asus RT-AC86U hardware, both hardware should produce relatively comparable similar results and also be consistent with or better than as published in related work [12] on evaluation with machine learning algorithms.
2. SVM can perform as well as LSTM if the data is properly denoised and a careful feature design takes place.
3. For LSTM, it is not necessary to preprocess the raw data to achieve state-of-the-art performance.  
Up to the best of our knowledge, the results obtained are the first comprehensive HAR results using the Nexmon tool. In particular:  
1. The results achieved were consistent across the three experiments, including different setups and different algorithms. The achieved classification results were relatively similar with both hardware, i.e., Raspberry Pi and Asus RT-AC86U router, in different environmental conditions and for different sets of activities.
2. The obtained results outperformed previously published results, see the remarks in Section 3 and the discussion of the results in Section 9.
3. The SVM performance is similar to LSTM. However, it requires a careful data preprocessing.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 1 Introduction
The best solution was obtained by proposing effective convex optimization methods and the augmented Lagrangian method. The simulation results show that the proposed joint optimizing scheme outperforms the previous ones in the partial computation offloading system, and the proposed algorithm is proven to be more efficient. The remainder of the article is organized as follows. The related work is introduced in Section 2. In Section 3, we describe the WPT and MEC integrated system model in details. Section 4 gives the formulation and analysis of the problem. In Section 5, we provide the implemented algorithm. We give and discuss the experimental results in Section 6. Finally, we conclude the results in this article.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods
### Method  
To distinguish IHSPAs from the background, SDGSAT-1 TIS data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. Then, an IHSPA identification model using SDGSAT-1 TIS and the combination of multi-feature extraction and SVM was proposed in this study. It mainly contains four parts: data prepossessing, multi-feature extraction, the IHSPA identification model based on SVM, and IHSPA identification/validation (as shown in Figure 2).
# Analysis of Heavy Metal Sources in Xutuan Mining Area Based on APCS-MLR and PMF Model
## 2 Materials and Methods
### Sample Collection and Analysis  
A bottle-type deep-water sampler was used to collect water samples from the water body in the study area. Nitric acid was added to the collected water samples to decrease the pH values to less than 2. The concentrations of the six heavy metal elements in the water samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS, Agilent Technologies, Santa Clara, CA, USA). On the other hand, sediment samples at the same location as the water body sampling point were collected with the Petersen grab. The collected samples were placed in polyethylene bags, air-dried, then ground evenly with an agate mortar and screened with 100 mesh to remove stones, plants, shells, and other debris. After screening, the sample was analyzed for the contents of heavy metals using the improved Tessier five-step extraction method. In total, five chemical forms were extracted step by step, namely exchangeable (F1), carbonate bound (F2), iron and manganese oxide bound (F3), organic matter bound (F4), and residual (F5) forms. The extraction solutions were analyzed by an inductively coupled plasma emission spectrometer (ICP-OES). All reagents used were of excellent purity. The standard heavy metal reserve solution was 100 \(\upmu\)g/mL. All containers were soaked in 5% dilute nitric acid overnight before use, washed three times with ultra-pure water, and then dried for use. A Perkin Elmer Avio550 ICP-OES (Shanghai, China) was used for the determination of heavy metals in sediments, and a Perkin Elmer Nexion 300X ICP-MS (PerkinElmer, Inc., Shelton, CT, USA) was used for the determination of heavy metals in water bodies.  
Figure 1: Geographic location of the study area.
[17] studied the magnetic natural convection of a hybrid nanofluid in a porous medium inside a square cavity. Their findings showed that the rate of heat transfer was decreased by increasing the magnetic field and this decrease was minimized by the value of Darcy (Da) number and boosted by the value of the Ra number and porosity (\(\varepsilon_{p}\)). The magnetic natural convection of nanofluids (H2O/copper) in a porous arc-shaped enclosure was investigated by [PERSON] et al. [18], considering Brownian motion as an important mechanism for increasing heat transfer. Their observations showed that the frequency of the convective flow had a favorable relationship with the values of the Da and Ra numbers, whereas it has an opposite relationship with the magnetic field angle of inclination and the Ha number. There have also been other worthwhile numerical investigations [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] about natural convection. In many sectors, such as mobile devices, including varied geometries and functions, traditional nanofluids, as well as their new version dubbed \"hybrid nanofluids\",are importance to thermal scientists and researchers around the world for supporting cooling processes. The aforementioned studies in the literature investigated different cavity configurations with many physical effects including the effect of magnetic field and porous media. Moreover, these studies have helped researchers in terms of the range of investigated parameters as well as the selection of innovative examined geometry. A trapezoid, which is analyzed in this paper, is one such geometry that is used for cooling purposes. This geometry is more realistic than the cavities in the rectangle. Therefore, in this paper, we investigate the natural convection heat transfer of Ag/MgO/water nanofluids in a porous cavity subjected to a constant magnetic field. The effects of diverse controlling parameters such as Da, Ha, and Ra numbers, \(\epsilon_{\text{p}}\) on flow, and heat transfer characteristics with entropy are examined.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 4 Proposed Scheme
### Trust Management  
The presence of malicious nodes in a system degrades the QoS of the network by offloading the fictitious tasks to the fog node to reserve its computing resources. In this section, a mechanism to find out the trusted offloaded tasks is discussed. TETES proposed an algorithm to evaluate the probability of trusted nodes through the information collected by all nodes in a clustering network. TETES helps fog nodes to determine the trust value of fog nodes after regular time intervals and identifies the trust value of all the offloaded tasks nodes.  
The trust level of a node is evaluated by its neighbours when they exchange their control and data packets with their neighbouring nodes. If requested data packets are received correctly against the number of requests generated. If a node \(N_{a}\) send \(K\) requests to its neighbouring node \(N_{b}\) and it receives \(J\) response files from \(N_{b}\) node. If \(J_{Req}\) are correctly received requested files and \(J_{corr}\) are corrupted files received, then trust level of node \(N_{a}\) for node \(N_{b}\) (\(T_{b}^{a}\)) is calculated as:  
\[T_{b}^{a}=\frac{J_{Req}-J_{corr}}{K} \tag{5}\]  
The probability of finding a legitimate node is evaluated on the following three parameter values.  
1. Trust status of the trust finding node about its neighbouring nodes.
2. Trust value computed by its neighbours who are in direct access to the trust finding node.
3. Trust information is collected by all other nodes in a cluster except its neighbouring nodes. All these trust finding parameters are described below.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 3 Network Model
### Node Clustering Phase  
We assume that the nodes are randomly deployed in the target area to monitor the underwater environment, and the nodes are clustered. In the initial phase, the sink nodes know the location of each node and determine the number of clusters based on the network size, and then, the target area is divided equally into several square areas. The sink node broadcasts the subregion message to all nodes, and each node determines its own cluster identifier based on its position. Nodes with the same identifier belong to the same cluster [26].  
The CHs should be selected for inter-cluster data collection and communication with the AUV. The selection of CH is carried out according to the procedure as follows. The number of optical and acoustic communication neighbors of each node in the sub  
Figure 1: Illustration of the underwater network model.  
region is first obtained, and then, the node with the highest number of optical communication neighbors and the remaining energy satisfying the energy threshold requirement is selected as the CH. Then, the above operation is repeated until all CHs in the target region are determined. Finally, a confirmation packet is sent by the sink to the designated CHs. At the end of the data collection process, all CHs are evaluated, and when the energy of the CH is less than the energy threshold, the network performs a new CH selection round.
## 2 Basic Principles
### Preprocessing
#### 2.4.2 Denoising  
One of the major problems in machine learning applications is called _overfitting_. It occurs when the model has a much higher complexity or degree of freedom with regard to the input data used for training. This leads to a perfect fit to the training data but fails when other data, i.e., testing, are considered.  
To overcome this _lack of generalization_, when other factors can be excluded (e.g., the amount of data is sufficient), _denoising_ is one of the techniques used to improve accuracy. The use of low-pass filters, convolutional filters, or model-based filters are the most common methods for reducing noise, which can mislead algorithms into learning patterns that do not refer to the process itself.  
Apart from this, adding noise can increase robustness. In [61], a _Denoising Autoencoder_ (_DAE_) was used, where noise was added to the input data, leading to an overall increase in the model's generalization ability. The most common method is to add _isotropic Gaussian noise_ to the input data [62]. Another way is to apply _masking noise_ or _salt-and-pepper noise_, which means that a certain fraction of the input data is set to zero or changed to its corresponding maximum or minimum value, respectively [62].