esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Model Improvements  
The important improvements between the previously published New Zealand model [1] and the present version are enumerated below.  
1. There is a twofold increase in the number of sites available for fitting the PTF, especially for less common soils in New Zealand (e.g., Pumice soils).
2. Inclusion of the sample depth as an explanatory variable; this is justified since sample depth is associated with changes in carbon concentration [28] and bulk density.  
Figure 9: Goodness-of-fit (GOF) measures for the 2018 logistic regression model from [1], the 2024 GAM model from this study, and the EUPTF V2 models [26], by measure. The preferred direction varies between measures; the dashed red line shows the ideal value for each of the measures.  
3. Refinements to the processing of the training data, including removal of all stony soils to a post hoc procedure, and more consistent estimation of texture classes.
4. A change from logistic regression in [1] to the GAM semi-parametric approach with a Beta response. This allows the model to adapt to the data characteristics while still adhering to the requirement for physically plausible estimates of the soil-water response and derived values (e.g., TAW).  
The structure of the models presented in this paper are such that the soil-water response is physically plausible (e.g., the values are bounded in the range [0, 100] (i.e., 0% to 100%) and monotonic with respect to tension), but the models are not physically interpretable. There are clear advantages to using a physically interpretable model (e.g., [29]), but there is a trade-off between complexity and fidelity between the measured and modeled values. Empirical models tend to have close fidelity between measured and modeled values, particularly if (as in this case) the fitting GAM methodology is semi-parametric, but the result is a very complicated model. For example, the \(-1500\) kPa model developed in this paper has 89 coefficients, most of which are associated with the smooth functions of sample depth, as well as the sand, silt, and clay content. By contrast, a physically based model may have a relatively small number of independent parameters (as few as 4-6), but the fidelity between measured and modeled values for out-of-sample data is typically worse than for the empirical approach. The choice between the two approaches depends on how important it is to the practitioner to have results that are physically interpretable.
## 2 Data and Methods
### Data
#### 2.2.2 Environmental Variable Data  
In addition to the remote sensing data, environmental variables such as elevation, slope, aspect, road proximity, temperature, and rainfall were used in this study. These variables can be categorized as climatic and topographic variables.  
A significant correlation exists between climatic factors, such as temperature and rainfall, and the spatial distribution of forest cover [71]. Rainfall data from Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) were acquired from the Farine Early Warning Systems Network (FEWS NET) dataset, accessible at a spatial resolution of 5 km ([[https://earlywarning.usgs.gov/fews](https://earlywarning.usgs.gov/fews)]([https://earlywarning.usgs.gov/fews](https://earlywarning.usgs.gov/fews)) accessed on 24 March 2023). Additionally, land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with a spatial resolution of 250 m, were utilized for temperature data. It is noteworthy that forests can influence local temperatures, including LST [72].  
Vegetation dynamics were determined using topographic factors such as elevation, slope, and aspect [73; 74], as well as road accessibility [75]. Aspects, elevations, and slopes intricately govern the spatial and temporal distribution of critical elements, including radiation, temperature, and precipitation, thereby significantly influencing species composition [76]. A digital elevation model (DEM), extracted from ALOS PALSAR with a spatial resolution of 12.5 m ([[https://search.asf.alaska.edu/#/](https://search.asf.alaska.edu/#/)]([https://search.asf.alaska.edu/#/](https://search.asf.alaska.edu/#/)) accessed on 24 March 2023), was used to generate topographic variables such as the elevation, aspect, slope, and streams of the study area. The heterogeneity of soil types gives rise to niches characterized by specific conditions, consequently influencing the distribution patterns of plants [77]. All variables underwent various pre-processing stages, which involved resampling to match the spatial resolution of PS imagery and masking to align with the study area's extent using QGIS 3.28.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods  
The proposed Position-Monitor-based Hybrid Routing Protocol (PMHRP) is a combination of the position and topology routing schemes. The proposed scheme tried to decrease the flooding messages exchange across the network by introducing the concept of PHC, unlike cluster-based routing schemes, where the cluster head is a superindentor for calculating the short paths of the whole network. As a result, the traffic load increases on the cluster head. When the PHC does not managing to find the shortest path, it just shares the geographic information of each UAV, hence decreasing the packet overhead as well as the traffic load on PHC due to the presence of significant \(z\)-axis modifications in the PMHRP 3D Free Space Model and considering the gain on receiving and the transmitting side. Furthermore, this section consists of three main parts. In the first part, a 3D Free-space model is created, and the second part describes the PHC selection mechanism. The third part describes the working mechanism of the proposed scheme.
## 4 Results and Discussion
### Displacement Variation in Specimen Particles  
During the application of load, particles in the mesoscopic view model will produce mutual misalignment and spatial position change, which causes the interior of the specimen to gradually converge to an equilibrium state. The distribution of particle displacement reflects the location and degree of force on the specimen to a certain extent. This study tracked the distribution of particle displacement under the load, as shown in Figure 11.  
Figure 11 shows that for both molding methods used, particle displacement was primarily concentrated at the top of the specimen during the molding process, and vertical displacement increased as the distance between the particles and the bottom plate increased. The overall direction of particle movement inside the specimens was downward, and displacement of upper particles was obviously greater than that of lower particles. This is because the force on the lower particles was attenuated and constrained by the bottom plate, showing the movement pattern downward and to the left and right sides. In addition, the trajectory of small particles was easily influenced by large particles; small particles moved around large particles under the action of the load.  
Measurement circles of the same size were used in both simulated specimens. Porosity inside the measurement circles was detected, and differences in porosity between the upper and lower parts of the specimens were obtained, as shown in Table 5.  
Figure 11: Comparison of skeleton particle displacement in specimens after molding. (**a**) Particle displacement of vibration specimen; (**b**) particle displacement of compaction specimen; (**c**) particle displacement of vibration specimen slice; and (**d**) particle displacement of compaction specimen slice. In the figure, E is abbreviation for exponent, for example, the number 3E0 stands for 3.  
The porosity of the upper part of the compaction molding specimen was less than that of its lower part, whereas the difference between the porosity of the upper and lower parts of the vibration molding specimen was slight. The particle distribution of the vibration molding specimen was more uniform, whereas the particles of the compaction molding specimen were obviously distributed densely at its top and sparsely in its bottom. This is because during the vibration molding process, compacted material was affected by gravity and also subjected to the vibration effect of the surface vibrator. All particles were subjected to uniform force throughout the specimen, which made it easier to achieve a dense state with relative motion and filling. In contrast, during the compaction molding process, compacted material was only subjected to the impact of the falling hammer. **Moreover, the impact force of the hammer decreased as depth increased, so that the force received by the lower particles was insufficient; the relative displacement between particles was small, and the filling effect was relatively poor.**
# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 1 Introduction
This paper utilizes the CIC-IDS dataset, widely employed for evaluating machine learning- and deep learning-based intrusion detection systems (IDSs) and encompassing diverse attack types and network traffic patterns, to effectively detect increasingly sophisticated and varied cyberattacks in network security environments. The methodology first defines source and target domains, extracting features through learning in the source domain based on shared input features between domains, which are subsequently transferred to the target domain. These features undergo fusion and fine-tuning operations. Through this process, a domain-adaptive transfer learning-based feature fusion model is proposed to classify multiple attack types in the target domain. The CIC-IDS 2017 dataset serves as the source domain for learning, with transfer learning techniques applied to transmit knowledge to the target domain based on the CSE-CIC-IDS 2018 dataset, enabling the model to acquire new features and maximize transfer learning effectiveness. In addition, binary classification and multi-class classification models are employed to fuse the extracted features, facilitating multi-perspective analysis of data characteristics and thereby enhancing detection performance. The result is a flexible intrusion detection system that effectively overcomes the limitations of previous models in detecting previously unknown attack types and mitigating dataset imbalance issues in network traffic. The structure of this paper is as follows: Section 2 analyzes existing research to highlight the distinct contributions of this study. Section 3 provides a detailed discussion of the datasets and data preprocessing techniques employed for training the AI models. Section 4 introduces the proposed transfer learning and feature fusion-based network attack-type classification model. Section 5 presents experimental results and performance analysis to validate the effectiveness of the proposed model. Finally, Section 6 concludes the study and discusses future research directions.
## 6 Conclusions  
The GNSS/INS deeply-coupled system can realize INS-aided GNSS PLLs and improve the dynamic tracking performance of the GNSS receiver. Based on the principle of the scalar-based deeply-coupled system, this paper proposes comprehensive error models and analyzes the detailed steady-state error model of the INS-aided PLLs, which can be used to guide IMU selection, parameter optimization and quantitative error analysis of INS-aided PLLs, compared to previous works of rough models for qualitative analysis. Then, the real-time deeply-coupled prototype based on a hardware platform was developed and optimized, which is unique and convincing compared to previous software receiver implementations. Finally, the tracking performances of normal PLLs and INS-aided PLLs are tested and compared through simulation and vehicle tests. The test results show that the dynamic tracking error of INS-aided PLL is much lower than that of the normal PLLs, compared with that of the normal PLLs, but its static tracking error is no more than one degree bigger, which is consistent with the proposed steady-state error model in this paper. The test results also verified that low-cost MEMS IMU performs as well as medium-grade INS in aiding PLLs. Moreover, the final navigation performance can be improved in terms of the INS-aided GNSS tracking loops. The proposed error model and the developed deeply-coupled hardware prototype in this paper can be further applied to the high-sensitivity and anti-interference GNSS receiver design and optimization under dynamic conditions.
## 3 System Model  
We consider a D2D relay-assisted cellular network, which consists of a BS, CUEs, D2D TX-RX pairs (TRs) and idle users acting as D2D RSs. Each CUE is allocated with an orthogonal uplink resource block (RB), and each RB can be reused by at most one D2D link. In this paper, we only consider the static scenario, and the moving speeds of users are not studied. Actually, the fast mobility of users has a significant impact on the network topology, mode selection and channel gains. This is a new problem and is left for future study. Nevertheless, our work is still valid for users with walking speeds. The reason is that since the optimization is performed in a slot by slot fashion, the position variations caused by the low mobility of users during channel coherence time are negligible. For instance, assuming that the moving speeds of users are an average of 1.5 m/s and the time slot for the proposed algorithm to optimize is 1 ms, therefore, after one time slot, the moving distance of the users is 0.0015 m, and this is ignorable when compared to the distance between the transmitters and the receivers. We focus on the D2D TRs, which suffer from bad channel qualities and cannot achieve direct single-hop links. To improve network coverage and throughput, D2D communication is operated in a wo-hop fashion via D2D RSs, which is shown in Figure 1. That is, a D2D TX first sends the signal to a D2D RS, and then, the RS forwards the signal to the D2D RX. In each hop, the RB of a CUE is reusedby a D2D link. We also assume that each D2D RS can serve at most one D2D TR, and each D2D TR can utilize only one RS. It is assumed that there are \(M\) D2D TRs, \(R\) D2D RSs and \(C\) CUEs in the system. D2D TXs and RXs are denoted by the sets \(\mathcal{M}_{T}=\{1,\cdots,m_{t},\cdots,M\}\) and \(\mathcal{M}_{R}=\{1,\cdots,m_{r},\cdots,M\}\), respectively. D2D RSs and CUEs (i.e., RBs) are denoted by the sets \(\mathcal{R}=\{1,\cdots,r,\cdots,R\}\) and \(\mathcal{C}\), respectively. We assume that D2D TX \(m_{t}\), D2D RS \(r\) and D2D RX \(m_{r}\) form a two-hop D2D link. The RBs reused by the first hop and second hop D2D links are denoted as \(c_{f}\) and \(c_{s}\), respectively, which also represent the \(c_{f}\)-th and \(c_{s}\)-th CUEs, \(\forall c_{f},c_{s}\in\mathcal{C}\). All the meaning of used variables are given in Table 1. For the channel model, we use the Rayleigh fading to model the small-scale fading and employ the free space propagation path-loss to model the large-scale fading [53; 54]. The free space propagation path-loss model employed in this work is an ideal model, but it does not impact the solution structure of the proposed algorithm.
## 5 Internet of Medical Things
### IoMT Systems Architecture
#### 5.3.4 Data Processing Layer  
The data layer assumes responsibility for storing and processing the data acquired by the physical layer. It comprises various components such as databases, data analytics tools, and data management systems. The data layer ensures the security, accuracy, and easy accessibility of the data for healthcare professionals.
###### Abstract  
Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm Optimization (CSO), Grey Wolf Optimization (GWO) and the proposed Chimp Optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms.  
maximum power point tracking; PV systems; cat swarm optimization; grey wolf optimization; chimp optimization algorithm
## 3 Proposed Approach  
The proposed approach consists of the use of a GA for the classifier (hyperparameter) optimization and selection of the fittest, to predict dropout in distance learning courses. Figure 1 shows the proposed solution. The following machine learning algorithms were selected to test the solution: Classic decision tree (DT), random forest (RF), multilayer perceptron (MLP), logistic regression (LG), and the meta-algorithm AdaBoost (ADA). The proposed approach was compared against the grid search method regarding hyperparameter optimization and the regular solution without hyperparameter optimization. The proposed approach uses a classification method, where several classifiers with different hyperparameters, such as DT, RF, MLP, LG, and ADA, compete against each other. In the end, the classifier and the hyperparameters with the best results are selected by a fitness function.
## 4 Incoherent FFLs
### Alternative Designs for Incoherent FFLs  
All the incoherent FFLs we have described so far are small transcriptional networks. However, different components from transcription factors (namely, RNA molecules) have been used successfully in the C1-FFL implemented in [38]. Short antisense RNA sequences are translational regulators that offer potential advantages with respect to proteins. For instance, they aid RNA conformational arrangements [64]; their structure is easily determined [65]; and they are compatible with the metabolic activity of the host cell [66], which allows fast signal transduction due their short half-life [67]. Furthermore, RNA molecules can also be employed as transcriptional regulators [68]. [PERSON] et al. [69] engineered, in a cell-free system, a pulse generator--resembling the I1-FFL--via two modes of transcription regulation based on RNAs: STAR (small transcription activating RNA) and CRISPR (clustered regularly interspaced short palindromic repeats interference--see Figure 8A). STAR (X) activates GFP (Z) expression by binding a target RNA in the 5\({}^{\prime}\) UTR (untranslated region) of Z and preventing it from folding into a terminator that would force RNA polymerase to fall off the DNA. Besides, CRISPR (a parallel X) represses GFP synthesis via a synthetic transcription factor made of a nuclease-deficient Cas9 (dCas9) in complex with two RNA sequences: the crRNA (CRISPR RNA) and the tracrRNA (transactivating RNA). It should be noted that this downregulation could have been simplified by means of a single guide RNA (sgRNA [70]) that encompasses the necessary fragments of crRNA and tracrRNA to recognize and bind both dCas9 and the target sequence on the DNA. A pulse in fluorescence arises from the different kinetics of these two regulation systems. STAR demands an RNA-RNA interaction that leads to a quick activation of fluorescence expression (a matter of few minutes). CRISPR, in contrast, requires the slow coupling of dCas9:tracrRNA with crRNA that can take up to approximately 60 min [71]. In addition, the pulse obtained in this work was in the GFP production rate rather than in the fluorescent signal itself since there is no protein degradation in the TXTL system.  
A novel design for a canonical II-FFL exploited the inhibitory action of a type II anti-CRISPR protein (AcrIIA4) on dCas9 fused to the strong activation domain VPR (dCas9-VPR) [72]. Upon binding a properly designed sgRNA, dCas9-VPR (X) activated both the expression of AcrIIA4 (Y) and GFP (Z--see Figure 8B). The fluorescence signal increased until the amount of AcrIIA4 was high enough to prevent dCas9-VPR:sgRNA from further binding Pz, i.e., the promoter upstream of the GFP gene. This I1-FFL was realized in eukaryotic cells and produced a clear pulse in fluorescence expression.
## 3 Research Methods
### Simplified Swarm Optimization Algorithm (SSO)  
This research developed an algorithm suitable for the CCHP optimization problem based on the SSO algorithm. Its biggest feature is that the adjustment of parameters is very simple, which has been shown as effective in solving many optimization problems in various fields such as data mining in medicine [38], disassembly sequencing problems [39], redundancy allocation problems (RAP) [40,41], reliability redundancy allocation problems (RRAP) [42, 43, 44, 45, 46], RAP in sensor systems [47], vehicle routing problems in the supply chain [48,49], price problems in the supply chain [48,49], optimizing sensing coverage in wireless sensor networks [51], data mining [52], airplane cockpits [53], energy and signal optimization in wireless sensor networks [54], improving UM of SSO [55], task scheduling optimization in fog computing [56], and various networks [57, 58, 59].  
The update mechanism of SSO is presented as follows, Equation (35):  
\[X_{i,j}^{G+1}=\begin{cases}g_{j}&\text{if }\rho\in[0,C_{g})\\ p_{i,j}&\text{if }\rho\in[C_{g},C_{p})\\ X_{i,j}^{\text{G}}&\text{if }\rho\in[C_{p},C_{w})\\ X&\text{if }\rho\in[C_{w},1)\end{cases} \tag{35}\]  
where  
\[X_{i,j}^{G+1}: X_{i,j}^{G+1}=\left\{X_{i,1}^{G+1},X_{i,2}^{G+1},\ldots,X_{i,N_{m }}^{G+1}\right\}\text{ represents the solution of the $j$th variable in the $i$th group of the $G+1$th iteration}.\] \[X_{i,j}^{\text{G}}: X_{i,j}^{\text{G}}=\left\{X_{i,1}^{\text{G}},X_{i,2}^{\text{G}}, \ldots,X_{i,N_{m}}^{\text{G}}\right\}\text{ represents the solution of the $j$th variable in the $i$th group of the $G$th iteration}.\] \[g_{j}:\] \[g_{i}:\] \[p_{i,j}:\] \[X_{j}^{\text{min}}\leq X\leq X_{j}^{max}\text{ represents a new randomly generated solution, whose value is between $X_{j}^{\text{min}}$ and $X_{j}^{max}$}.\] \[\rho:\] The random number randomly generated between 0 and 1.  
\[C_{g},C_{p},\] \[C_{w}:\]  
The overall update process of SSO is as follows:  
1. Randomly generate the initial feasible solution \(X_{i}^{0}\), calculate the fitness function \(F(X_{i}^{0})\), and \(i=1,2,\ldots,N_{sol}\).
2. Let the number of iterations \(G=1,p_{i}=X_{i}^{0}\), and find the best solution in \(F(X_{i}^{0})\) as the global best \(g\), and \(i=1,2,\ldots,N_{sol}\).
3. Let \(i=1\).
4. According to Equation (35), \(X_{i}^{G+1}\) is evolved, and the fitness function \(F(X_{i}^{G+1})\) is calculated.
5. If \(F(X_{i}^{G+1})\) is better than \(F(X_{i}^{G})\), then let \(X_{i}^{G+1}=X_{i}^{G+1}\); if not, then \(X_{i}^{G+1}=X_{i}^{G}\) and skip to Step 7.
6. If \(F(X_{i}^{G+1})\) is better than \(F(p_{i})\), then \(p_{i}=X_{i}^{G+1}\); if not, keep it unchanged. If \(F(X_{i}^{G+1})\) is better than \(F(g)\), then \(g=X_{i}^{G+1}\); if not, \(g\) remains unchanged.
7. If \(i=N_{sol}\), go to Step 8; if \(i<N_{sol}\), then \(l=i+1\) and return to Step 4.
8. If \(G=N_{gen}\), Stop; if \(G<N_{gen}\), then \(G=G+1\) and return to Step 3.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 2 Basic Concepts
### Uplink Transmission
\[\mathbf{W}_{D}^{ZF}=\left((\mathbf{H}^{(ul)})^{H}\mathbf{H}^{(ul)}\right)^{-1}(\mathbf{H}^{(ul )})^{H}. \tag{4}\]  
\[\mathbf{W}_{D}^{MMSE}=\left((\mathbf{H}^{(ul)})^{H}\mathbf{H}^{(ul)}+\frac{\sigma_{\eta}^{ 2}}{\sigma_{x}^{2}}\mathbf{I}_{k}\right)^{-1}(\mathbf{H}^{(ul)})^{H}. \tag{5}\]  
In (5), \(\sigma_{\eta}^{2}\), \(\sigma_{x}^{2}\), and \(\mathbf{I}_{k}\) are the variance of the noise, variance of the signal, and the \(K\times K\) identity matrix.