esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Process-based models, which were created in response to studies examining changes in environmental factors or management interventions [1,2], have the potential to reflect the nonlinear relationships between physiological processes and environmental factors. In forests, environmental factors affect the canopy structure, canopy density, biomass partitioning, and physiological processes. Intensive studies of the effects of environmental factors improve researchers' understanding of processes influencing forest growth [3].  
Among process-based models, the physiological principles predicting growth (3-PG) model is often used to estimate forest properties in fast-growing forests and forest plantations [3-7]. The 3-PG model has been used to bridge the gap between conventional empirical, mensuration-based growth and yield models, and process-based carbon balance models [8]. The 3-PG model is primarily used for forest plantations or even-aged, relatively homogeneous forests, but this type of model can also be applied to various environments and species. However, parameterization is needed for individual species [9]. _Eucalyptus globulus_ and _E. grandis_ were initially parameterized by [PERSON] [10] and [PERSON] _et al._[11]. For other species, parameter values were adjusted to fit the output based on _in situ_ measurements. [PERSON] [7] and [PERSON] _et al._[12] used the 3-PG model to predict the growth and water use of _Pinus patula_ and eucalyptus plantations. This model has been applied to analyses of forest productivity of various species in countries such as Australia, Brazil, Canada, New Zealand, South Africa, and the U.S. [3,4,13-16]. However, parameterizations are still lacking, especially for natural forests which can act as carbon sinks. To develop parameter values for natural forests, _in situ_ measurements are required.  
Forest ecosystems in middle and high latitudes of the northern hemisphere have large effects on the carbon cycle, because leaf phenological processes of forests in these regions, such as deciduous broadleaf forests, correspond to carbon sequestration. In this paper we attempted parameterization of the 3-PG model for deciduous broadleaf forests in Japan. We focused on parameters related to biomass production because biomass related to outputs as GPP provides valuable information that will aid in understanding the carbon status of the biosphere. The objective of this study was to assign standard parameter values for deciduous broadleaf forests in the 3-PG model that the original model and previous studies have not calibrated. Our results should make the 3-PG model more applicable to additional species. Moreover, sensitivity analyses were conducted to investigate the parameters with the most influence on output. Parameters identified as sensitive could then be carefully parameterized to calculate output with higher accuracy.
## 3 Design and Analysis for WPTDDM-FxLMS Algorithm  
### Decentralized Decoupling Improvement for the Secondary Path  
It can be seen from Figure 2 and Equation (8) that the traditional M-FxLMS algorithm has certain disadvantages. Assuming the system is fully decoupled, the update for each controller is only related to its corresponding error signal, which means that the non-diagonal elements in secondary path will be ignored. This simplification process is shown in Equation (9); the actual secondary path \(\mathbf{S}(n)\) first evolves into an estimated secondary path \(\mathbf{\overset{\star}{\hat{S}}}(n)\), and then the off-diagonal elements are omitted and further simplified into the matrix \(\mathbf{\overset{\star}{S}}\)\((n)\). Thus, if \(\mathbf{\overset{\star}{S}}(n)\) is used to represent the estimated value of the transfer function \(\mathbf{S}(n)\) in secondary path, the matrix \(\mathbf{\overset{\star}{\hat{S}}}(n)\) can be written as the diagonal matrix form:  
\[\mathbf{S}(n)\rightarrow\mathbf{\overset{\star}{\hat{S}}}(n)\rightarrow \mathbf{\overset{\star}{\hat{S}}}^{dm}(n)=\left[\begin{array}{cccc}\hat{S} _{11}^{dm}(n)&0&\ldots&0\\ 0&\hat{S}_{22}^{dm}(n)&\ldots&0\\ \ldots&\ldots&\ldots&\ldots\\ 0&0&\ldots&\hat{S}_{HH}^{dm}(n)\end{array}\right] \tag{9}\]  
Then, the error signal shown in Equation (4) can be updated and rewritten as  
\[e_{k}(n)=d_{k}(n)+\hat{S}_{kk}^{dm}(n)y_{k}(n) \tag{10}\]  
Figure 2: The diagram of the multi Filtered-x Least Mean Square algorithm (M-FxLMS).  
where \(\lambda_{ki}\) is a compensation factor of the coupling feedback, it can be described as  
\[\lambda_{ki}=\frac{\left|\delta_{ki}(n)\right|}{\begin{array}{c}i=1\\ i\
eq k\end{array}}\left|\delta_{ki}(n)\right| \tag{17}\]  
In Equations (16) and (17), the non-diagonal element is compensated to the main secondary path, which can effectively accelerate the system convergence speed, reduce the calculation amount, and improve the system stability. Then, substituting Equation (16) into Equation (11), the update rules of the \(k\)-th control filter can be updated as follows:  
\[\mathbf{w}_{k}(n+1)=\mathbf{w}_{k}(n)+\mu\left[\delta_{kk}(n)+\sum\limits_{ \begin{array}{c}i=1\\ i\
eq k\end{array}}^{H}\lambda_{ki}S_{ki}(n)\right]^{H}\mathbf{x}(n)e_{k}(n) \tag{18}\]  
Thus, it can be observed from the analysis of Equations (8) and (18) that it should not simply ignore the error transmission between the actuator and the non-proximity sensor. Equation (18) introduces non-diagonal elements, which is closer to the real situation. The compensation feedback factor, introduced to improve the decentralized control algorithm, has a higher identification accuracy than ignoring the non-diagonal elements and can effectively reduce the calculation amount. Moreover, only a solitary point error signal is needed, which also expands the data range of the iterative step size and improves the convergence speed.
## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks?
#### 5.4.4 Opportunities and Future Research  
Authors in several studies have devised various future work directions to enhance the performance of LoRaWAN networks.  
The author [34] suggested that existing ADR mechanisms like BADR (Blind ADR) and standard ADR do not effectively address massive packet loss in dynamic and mobile environments due to inadequate SF adjustments. They suggested a need for more sophisticated algorithms that can dynamically and accurately adjust SF based on real-time changes in the network environment. Future research could focus on developing models that better understand and predict the effects of mobility on signal degradation and network performance.  
Figure 9: Gaps identified by literature.  
Future work could focus on extensive field tests to validate the effectiveness of AI-based resource allocation strategies under diverse and unpredictable environmental conditions.  
To overcome computational overhead and complexity, there's a need for simplified algorithms that minimize computational overhead by using predictive analysis to predict potential re-transmission and maintain network performance, especially in resource-constrained environments [50]. There is a need for improved methods of online resource allocation based on application constraints, which has not yet been fully explored despite its significance in real-world deployments. The authors [52] suggested that integrating a multi-agent system for resource allocation could significantly enhance the scalability and efficiency of LoRaWAN networks by customizing and designing semi-automated ML models. Prediction analytics is also highlighted in the study [31], and the author stated that we need more robust ML models that can handle high error rates and predict network behavior more accurately. An opportunity to develop better clustering algorithms that can more effectively categorize devices based on their behavior and operating conditions is also discussed [31]. To mitigate the impact of inference and collision, the authors [51] suggested evaluating LoRaWAN's performance over distances greater than 100 m, particularly in industrial environments. There is also a need for sophisticated simulation models that integrate real-world geographical data to enhance the precision of network planning. As gaps remain in achieving optimal gateway deployment and managing transmission parameters effectively across varying urban topographies [42]. Future research could focus on refining ML algorithms and the use of real-time data [51] for better prediction and management of network behaviors and developing more dynamic and adaptive network configurations to accommodate changing urban landscapes and IoT demands. Further exploration of energy-saving technologies and methodologies to extend the life of IoT devices in the network could also provide substantial benefits in improving the efficiency of LoRaWAN deployments [30; 41]. Table 10 summarizes the key findings from the study to answer RQ4.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling
### Beam Layout
The value of \(z\) is set to 1 to represent the gain of the minor axis of the transmitting antenna. To describe the antenna envelope of an highly elliptical beam, the one-half 3 dB beamwidths in the minor and major axis of the antenna are denoted as \(\mu_{b}\) and \(\
u_{b}\), respectively. The elliptical antenna gain is simplified to the following  
\[G_{t}(\mu,\
u)=\left\{ \begin{array}{cc}G_{n}(\mu)&\
u\leq\
u_{b}\\ 0&\
u>\
u_{b}\end{array} \right., \tag{2}\]  
where \(G_{n}(\mu)\) denotes the antenna gain in the minor axis with the one-half beamwidth being \(\mu_{b}\).
# A Novel Algorithm for Local Network Alignment Based on Network Embedding
## 3 Workflow of the Proposed Algorithm  
The proposed method can be described by using the workflow reported in Figure 2. We start from two input networks which should be aligned. First, we map both networks into the embedding space using the same algorithm to embed both networks. Then, we compare the representations of all the nodes using cosine similarity in the embedding space; in this way, we can compare pairs of nodes in the embedding space, searching for those having comparable similarity values. Then, the ranked list of pairs is merged with the list of biologically similar nodes. The merging is performed by means of linear combination of the weights associated with each couple of identical pairs. The resulting list is given as input to the local aligner.  
After evaluating the similarity among nodes, we use as input a list of seed nodes derived from evaluating embeddings, and the list of seed nodes derived from biological (or contextual) information.The weights of node pairs contained in \(M_{emb}\) are merged with those contained in \(M_{w}\) following a linear combination schema, which produces a final list of mapped nodes, namely \(M_{final}\).  
We then build the local alignment using AlignMCL, an algorithm reported in [21]. As input, AlignMCL receives two networks and an initial weighted list of seed nodes, and it develops a local alignment by merging all of the input data into a single graph, called alignment graph. The Markov Clustering Algorithm (MCL) [38] is used to extract communities. During the building of the alignment graph, AlignMCL scores the links between node pairs in the alignment graph.
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Information System and Attributes
#### 2.1.1 Insertion Phase  
The Insertion Phase corresponds to the phase in the life cycle of the IC where an HT may be inserted. According to the existing HT samples, this attribute contains two classes, which are _Design_ and _Fabrication_. In the phase of IC design, considering that the design needs to be mapped to the physical circuit, developers are constrained by its function, logic, timing, and physical conditions [18]. Considering this situation, they often use third-party IP cores or standard units to economize design costs and reduce difficulty. IP cores and standard units used without explicit authorization are easy to implant with an HT [19]. In the fabrication phase, subtle changes in the mask reduce the IC performance or change its function. Attackers often implant HTs into an IC by modifying its mask. At the end of each phase, the IC designers and testers test and correct the IC. The fabrication phase follows the design phase. In the fabrication phase, it is possible to detect an HT inserted during the design phase. From this point of view, the threat of an HT being inserted in the fabrication phase is higher than the threat during the design phase.
# In-flight Wind Field Identification and Prediction of Parafoil Systems
## 4 Wind Field Prediction
### Average Wind Field Prediction Model
Substituting Equation (19) to Equation (20), and assuming that the horizontal pressure does not change with altitude, the horizontal components of average wind is obtained as  
\[\left\{ \begin{array}{l}K_{m}\frac{\mathrm{d}^{2}\overline{u}}{\mathrm{d}z^ {2}}=-f\overline{v}\\ K_{m}\frac{\mathrm{d}^{2}\overline{v}}{\mathrm{d}z^{2}}=f\overline{u}-fu_{g} \end{array} \right. \tag{22}\]  
where \(\overline{u}\) is the estimation value of \(u\), and \(\overline{v}\) is the estimation value of \(v\). According to boundary conditions  
\[ \begin{array}{l}z\rightarrow\infty,\overline{u}=u_{g},\overline{v}=0,\\ z=0,\overline{u}=0,\overline{v}=0.\end{array}  \tag{23}\]  
Through calculating the ordinary differential equation set of Equation (23), we get  
\[\left\{ \begin{array}{l}\overline{u}=u_{g}[1-e^{-z/\delta}\cos(z/\delta)]\\ \overline{v}=u_{g}e^{-z/\delta}\sin(z/\delta)\end{array} \right. \tag{24}\]  
Equation (24) is the predictive model of average wind in the generalized upper friction layer, whose value and angle can be expressed by  
\[\left\{ \begin{array}{l}|\overline{u}|=u_{g}\sqrt{1-2e^{-z/\delta}\cos(z/ \delta)+e^{-2z/\delta}}\\ \varphi=\tan^{-1}[e^{-z/\delta}\sin(z/\delta)/(1-e^{-z/\delta}\cos(z/\delta)) \end{array} \right. \tag{25}\]  
where \(\varphi\) denotes the angle between the geostrophic wind and average wind. \(\delta=\sqrt{2K_{m}/f}=z_{m}/\pi\) is Ekman elevation, and \(z_{m}\) is the approximate altitude in boundary layer.
## 2 Circuit Design
### Transistor and Bias Selection  
From the WIN\({}^{\text{TM}}\) Semiconductors technology introduction documents, the 0.25 \(\upmu\)m gate GaN technology is manufactured on 100 \(\upmu\)m SiC substrate. The verified 2 \(\times\) 125 \(\upmu\)m gate channel width transistor of the process performs saturated power density of 5 W/mm at 10 GHz; \(V_{D}\) = 28 V, and \(I_{D}\) = 100 mA/mm. In order to obtain over the desired 8 W output power, the total gate channel width of 2000 \(\upmu\)m was selected. Then, the maximum stable gain (MSG), maximum available gain (MAG), and short circuit current gain (\(I_{FE}\)) versus frequency are simulated by the 10 \(\times\) 200 \(\upmu\)m transistor. The results show the maximum  
Figure 4: The S-parameters simulation results of 400 \(\upmu\)m length bond wires with different quantities: (a) \(\mid\)S\({}_{11}\)\(\mid\); (b) \(\mid\)S\({}_{21}\)\(\mid\); and (c) \(\mid\)S\({}_{11}\)\(\mid\) (Smith chart).  
Figure 3: The S-parameters simulation results of 300 \(\upmu\)m length bond wires with different quantities: (a) \(\mid\)S\({}_{11}\)\(\mid\); (b) \(\mid\)S\({}_{21}\)\(\mid\); and (c) \(\mid\)S\({}_{11}\)\(\mid\) (Smith chart).  
frequency of oscillation (\(f_{MAX}\)) is higher than 60 GHz. The cut-off frequency (\(f_{T}\)) is higher than 20 GHz. Since the \(f_{T}\) and \(f_{MAX}\) are much higher than the desired n77 and n78 bands of 3.3-4.2 GHz, the RF operation of the 10 \(\times\) 200 \(\upmu\)m transistor was assured. In the load-pull simulation, the 10 \(\times\) 200 \(\upmu\)m transistor obtains 40 dBm output power at the 3-dB compression point (OP\({}_{\rm 3 dB}\)), as Figure 5a shows. Figure 5b presents the dynamic load line, with a V\({}_{\rm GS}\) range from 0 V to \(-\)2.6 V in steps of 0.2 V. Thus, the large-signal output performance was also assured.  
GaN HEMT usually suffers from the soft gain compression phenomenon. While the gains of other processes' transistors compress at high power levels, the gains of GaN HEMT compress very early. In order not to increase amplitude-modulation-to-amplitude-modulation (AM-AM) and amplitude-modulation-to-phase-modulation (AM-PM) distortions, an optimal bias point must be carefully determined [24]. Figure 5c showcases the large-signal transconductance (\(G_{m}\)) response for the 10 \(\times\) 200 \(\upmu\)m transistor. Notably, a gate current of 27-37 mA could cause a fairly flat \(G_{m}\) response. Thus, the gate voltage is determined by \(-\)2.47 V.
## 3 Results and Discussion
### Crop Yield vs. Distribution Uniformity  
Irrigation amounted to 320 mm during the crop cycle, and crop yield was between 14 and 18 ton ha\({}^{-1}\). The lowest yields corresponded to the initial segments (1 to 3), where uniformity was lower (regardless of the Id), whereas yield became stable at 18 ton ha\({}^{-1}\) for the remaining segments (Figure 8). Difference in yield (4 ton ha\({}^{-1}\)) is related to the lower uniformity of the initial segments, with sprinklers being spaced at 5 m, since the Id was similar in all the segments. A study involving sprinklers above the corn crop and similar spacing to that used in this work found yield losses of 2.5 ton ha\({}^{-1}\) in the central row with respect to the rows closer to the sprinklers [37]. By contrast, studies involving wheat [38] and sugar beet [40] found that yield is not always affected by reduced uniformity. This finding was attributed to an improvement in uniformity after successive irrigation events, to the interception and redistribution of water in the canopy, the redistribution of water in the soil and root development. A linear decrease in yields with respect to the increase in sprinkler spacing (spacing ranging between 1.9 and 5.7 m) was found (Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Start Time, h** & \begin{tabular}{c} **Irrigation** \\ **Time, h** \\ \end{tabular} & \begin{tabular}{c} **ETo,** \\ **mm h\({}^{-1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Wind Speed,** \\ **m s\({}^{-1}\)** \\ \end{tabular} &
\begin{tabular}{c} **Pivot Operating** \\ **Speed, \%** \\ \end{tabular} & **Re** \\ \hline
19:00 p.m. & 7.50 & 2.6 & 1.80 & 15 & 0.98 \\
21:00 p.m. & 5.80 & 7.5 & 3.17 & 25 & 0.98 \\
11:00 a.m. & 1.80 & 9.4 & 1.39 & 50 & 0.97 \\
08:00 a.m. & 0.85 & 6.1 & 3.00 & 100 & 0.98 \\ \hline \end{tabular}
\end{table}
Table 6: Losses due to droplet evaporation and drift (Re).
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 4 Upstream DFT-Spread Hybrid OFDM-DFMA PON Performance  
Utilizing the optimal ONU operating conditions identified in Section 3 and the transceiver parameters listed in Table 1, in this section, the investigations of the upstream transmission performance of a spectrally overlapped hybrid OFDM-DFMA PON incorporating the DFT-spread technique are undertaken in terms of upstream performance tolerance to limited DAC/ADC quantization bits, BER performance, maximum aggregate upstream signal transmission rate, and impact of digital filter impairments. To highlight the advantages associated with the proposed technique, the upstream transmission performances of the hybrid OFDM-DFMA PON utilizing spectrally overlapped digital orthogonal filtering are also computed, and are treated as benchmarks.
## 4 System Implementation and Experimental Results
However, the proposed cooperative human-machine evaluation framework is effective in the complex network topologies in the context of human evaluation intervention using reinforcement learning. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** \\ \hline Cooperation Type (\(\delta\) = Human Intervention Rate) & No Cooperation (Independent System) & Cooperation (\(\delta\) = 0.5) & Cooperation (\(\delta\) = 0.9) \\ \hline Average Reward & 9.3045 & 21.3144 & 23.4633 \\ \hline Q-Function Value & 12.3784 & 26.1066 & 27.1156 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of three different experiment scenarios using the proposed framework. Figure 7: Comparisons of the proposed framework with three different human intervention parameters. In addition, a quantitative empirical study was conducted from two perspectives to show that the method proposed in this study is effective. As shown in Figure 8, when learning in three different ways during the same time, the time when the pass success rate of the Artificial Intelligence (AI) soccer game reaches more than 95% was compared. The result of learning the pass of players in the AI soccer game using the method of strategically updating the reward in the form of real number through human evaluation, the method proposed in this study, shows a pass success rate of up to 96.6%. The binary number reward method achieved a pass success rate of 86.6% within the same time, and when learning using the Markov Decision Process method, a pass success rate of 62.6% was achieved during the same time. Using the method proposed in this study, strategically updating the reward in the form of a real number through human evaluation, the time required for the pass success rate of players in the AI soccer game to reach 95% was \(t=29\). In contrast, the binary number reward method took \(t=73\) to reach the 95% success rate of the pass. When learning using the Markov Decision Process method, the time it takes for the pass success rate to reach 95% is \(t=93\).
# Handover Management in 5G Vehicular Networks
## 3 Preliminaries
### Velocity and Alternative Network Monitoring  
The Cloud/Fog infrastructure continuously monitors the velocity of each vehicle by applying the methodology described in [23]. Specifically, an improved version of the Mobility State Estimation (MSE) technique defined in 3 GPP LTE Release 11 [22] was applied. This methodology categorizes the moving velocity of the vehicle as Normal, Medium, or High, by taking into consideration the number of HOs \(N_{CR_{u}}\) that the vehicle has performed during a sliding time window \(T_{CRmax}\).  
Additionally, each vehicle maintains a set \(A=\{A_{1},A_{2},\ldots,A_{n}\}\) of candidate networks based on the SINR perceived from each one of them as specified by the Cloud/Fog environment. The set \(A\) is continuously updated, since the SINR of each network is constantly changing as the vehicle moves inside the wireless network access environment. Thereafter, in the case of predictive HO, the HO initiation will be performed, while in the case of reactive HO, the network selection is immediately executed.