esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### The Responses of GPP to the Climate Changes  
Greening trends have been observed on both global and regional scales, based on emerging greening signals detected from satellites in the last decades. Earth System Modes (ESMs) also revealed a progressive increase in leaf area index (LAI) over the 21 st century [49; 50; 51]. Climate changes predominantly affect ecosystems via alterations in temperature, precipitation, and shortwave radiation patterns. In this research, notable temperature increase was observed, while precipitation and shortwave radiation exhibited spatial heterogeneity in the YRB (Figures S1-S3), which was consistent with previous studies [44]. Warming climates may extend growing seasons and strengthen photosynthesis in plants, promoting better vegetation growth. Our results indicated forests were more sensitive to temperature changes. The YRB is in a typical subtropical monsoon climate with favorable moisture conditions that are sufficient for vegetation growth. Thus, the impact of changes in precipitation and shortwave radiation on GPP was relatively weak. However, our results also showed that, for the headwaters of the Yangtze River, the predominant vegetation type is grassland, which is vulnerable and particularly sensitive to climate change. The rise in temperature, coupled with reduced precipitation and radiation, can result in a decrease in GPP. Previous studies also found grasslands in cold regions may exhibit heightened sensitivity to climate changes [52]. This was probably attributed to their typically vast soil organic carbon reserves which are at risk of breaking down [53]. Furthermore, decomposition rates generally rise with increasing temperature [54].
## 4 Experiments and Evaluation
### Key Finding
Regarding limitations, this work examined the relationship between privacy and vulnerability to MIAs in DPFL for the modeling of PA processes. The work provides the effectiveness of noise injection against MIAs, and more steps are needed toward practical realization. This includes delving deeper into the privacy-performance trade-off across various settings and techniques, looking into additional defense mechanisms beyond noise injection to address broader privacy concerns such as secure communication and secure computation, and evaluating the finding applicability to different data and domains.
## 4 Methodology
### Sequential Optimization Problems
#### 4.2.3 Reliable Node Placing Problem  
The last step in the sequential design method proposed here is to optimally place the selected node technologies on a given topology. The idea of the placing method is that the failure impact of the more vulnerable technologies should be as low as possible on the network connectivity. We remark that, when a node fails, it immediately affects its communication links; therefore, a proper network design must minimize the number of links affected by the failure of an entire set of nodes belonging to the most vulnerable technology. From Network Science theory, we know that the clustering coefficient from each technology in the network is a proper metric to assess the impact of such correlated failures on the network connectivity [43].  
With this rationale in mind, we mathematically formulated the reliable node placing problem as:  
\[T^{*}(V)=\max_{T(V)\in\mathcal{M}}\sum_{k=1}^{\kappa}\frac{\alpha_{k}}{I_{k} \;\alpha}\sum_{(u,v)\in E^{(k)}}e_{uv}^{(k)}, \tag{10}\]  
subject to:  
\[\sum_{u=1}^{u}1_{\{T(u)=k\}}=n_{k},\text{ (the number of technologies constraint)}, \tag{11}\]  
where \(k=1,2,\dots,\kappa\), \(T(V):V\to K^{\prime}\) is a mapping from \(V\) to \(K^{\prime}\) assigning to the \(u\)th node the \(T(u)=k\) technology, \(\alpha=\sum_{k\in K^{\prime}}\alpha_{k}\), and \(\mathcal{M}\) represents the search space of all possible mappings for assigning all \(\kappa\) technologies to \(n\) nodes. \(G^{(k)}=(V^{(k)},E^{(k)})\subset(V,E)\) is the network topology resulting after a failure affecting all the nodes of the \(k\)th technology, \(e_{uv}^{(k)}=1\) if \((u,v)\in E^{(k)}\) represents a working link after the failure of the \(k\)th technology, and \(e_{uv}^{(k)}=0\) represents otherwise. The number of connected components in \(G^{(k)}\) is represented by \(I_{k}\), and \(1_{\{T(u)=k\}}=1\) is the indicator function stating that the \(u\)th node belongs to the \(k\)th technology.  
We note that in the cost function Equation (10), the inner summation aims to maximize the number of working links after a failure of the \(k\)th technology, while the term \(l_{k}\) penalizes the existence of a large number of connected components after a failure. Lastly, we note also that, by introducing the number of connected components emerging after a failure in the cost function Equation (10), we aim to maximize the resilience of a data communication network according to Definitions 6 and 7.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis
### Experiment on Transmission Tower Identity Matching
#### 3.3.3 Visualization of Matching Network Results
However, the metric learning matching algorithm employed in this research demonstrated its robustness; even without retraining the model, the Euclidean distance between the newly collected image of a specific tower and its predicted counterpart remained small. This finding strongly supports the argument that regularly updating the database can maintain and enhance the model's predictive accuracy. Figure 16: Illustrative examples of retrieval outcomes derived from the matching network utilizing Inception-Resnet-v1 (d = 128) as the backbone: (**a**) the impact of online triplet generation with GPS; (**b**) the impact of online triplet generation without GPS; (**c**) impact of environmental variability on the model.
## 3 Spacecraft Dynamics
### Control Strategy
#### 3.3.1 Vibration Control: Direct Velocity Feedback (DVF)  
Substantial research work has been performed over the past decades on structural vibration reduction methods. One of the most effective yet straightforward approaches is the direct feedback control of the displacement and/or velocity measurements. When the system is stabilizable and detectable, the stability is guaranteed if the pairs of sensors and actuators are collocated and their gain matrices are positive definite [37]. As a subclass, the well-studied Direct Velocity Feedback (DVF) control implies that the output signals from velocity sensors are multiplied by gains and directly fed back to actuators. This controller was applied to space structure by [PERSON] [38] and [PERSON] et al. [39], guaranteeing that all vibration modes remained stable when the active control is in operation under perturbation of system dynamic parameters, minimizing spillover issues if a low number of modes was considered.  
When the electrodes of a piezoelectric sensor are connected to an operational amplifier, they can be regarded as short-circuited and the electric field through the piezo can be set to zero \(\varphi_{s}=0\)[34]. The input current is converted into voltage as follows:  
\[v^{s}=Ri=R\hat{Q}_{s} \tag{25}\]  
where \(R\) is the constant gain of the charge amplifier. The sensor output voltage can be fed back through an amplifier to the actuator. Hence, the definition of the actuator voltage is described hereafter  
\[\varphi_{a}=g_{k}v^{s}=g_{k}R\left(\mathbf{K}_{u\varphi}^{s}\right)^{T}\mathbf{\phi}\hat {\eta} \tag{26}\]  
where \(g_{k}\) gain of the feedback control. By substituting these expressions into the dynamic system in Section 3.1, an equivalent DVF is introduced.  
\[\mathbf{M}_{u}^{G}\hat{u}+\mathbf{C}_{u}^{*}\hat{u}+\mathbf{K}_{u}u+N_{l}=f^{G} \tag{27}\]  
with  
\[\mathbf{C}_{u}^{*}=\begin{bmatrix}0&0&0\\ 0&0&0\\ 0&0&2\mathbf{\Sigma}\mathbf{\Omega}+\mathbf{\Phi}^{T}\Big{[}g_{k}R\mathbf{K}_{u\varphi}^{a} \Big{(}\mathbf{K}_{u\varphi}^{s}\Big{)}^{T}\Big{]}\mathbf{\Phi}\end{bmatrix} \tag{28}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **P-876.A12** \\ \hline Material & PIC255 \\ Density \(\left(\text{kg}/\text{m}^{3}\right)\) & 7800 \\ Mass (g) & 10 \\ Electro-mechanical coupling coefficient \(d_{31}(V/m)\) & \(-180\times 10^{-12}\) \\ Dielectric coefficients \(\chi_{33}\) & \(1.159\times 10^{-8}\) \\ Elastic compliance constant \(s_{31}\) & \(1.6\times 10^{-10}\) \\ Voltage range \((V)\) & \([-100{-}400]\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Piezo patch sensor properties (from PI manufacturer catalogues).
# Two-Phase Deep Learning-Based EDoS Detection System
###### Abstract
The second phase detection using the LSTM algorithm is then triggered to detect an attack flow if any attacks are detected in the first phase. The second detector, called a flow detector, determines precisely which flow is abnormal. Using the period detector before the flow detector, we can know when an attack has occurred, and which data are the most critical among the long sequence of input data for the LSTM model. By doing so, we can reduce the sequence length of the LSTM input data, and can eliminate the case where the flow detector has to handle a huge amount of network flows but without abnormal flows in the network traffic. Thus, by applying the two phases of detection, our model exploits the advantage of an LSTM, while reducing the sequence length of the input data to five (the shortest length yet) and decreasing the calculation time, the delay time of the system, significantly. Our contributions in this study are as follows:  
* We proposed a novel solution using two-phase detectors to efficiently detect EDoS attacks. It is known that the solutions using an LSTM or other variants of an RNN algorithm to tackle EDoS attacks achieve higher accuracy and lower false-alarm rates than other existing hard-threshold-based solutions. However, the LSTM-based solutions require a long sequence length of input data, increasing the detection time and computational overhead for a defense system. The proposed scheme exploits the advantages of LSTM algorithms, i.e., high accuracy and low false-alarm rate, and overcomes the shortcoming in which a long sequence of input data is required. * Second, we implemented an EDoS detection and prevention system using two-phase detectors to detect common types of EDoS attacks. Whereas the existing schemes only detect if an attack is happening and warns the cloud provider to react, the implemented system detects and mitigates each abnormal flow in the network traffic. * Finally, we conducted considerable experiments to demonstrate the effectiveness of our scheme. We collected and analyzed the experimental results and made detailed comparisons with other algorithms to illustrate that the proposed scheme outperforms the others in terms of accuracy and resource consumption. The rest of this paper is organized as follows: In Section 2, we present related studies. The background knowledge is described in Section 3. In Section 4, we describe our proposedscheme in detail. Section 5 describes our testbed. The performance evaluation will be delivered in Section 6 followed by the discussion in Section 7. Finally, we provide some concluding remarks and discuss future areas of research in Section 8.
## 3 Results and Discussions
### Behavioral Characteristics of the Flap
The formation of this anti-node of the standing wave near the flap can also be confirmed for the case of \(D/L\) = 0.56 and 0.43 in pattern D, as depicted in Figures 11 and 12. The anti-node is formed on the left side (shore) for the neutral position of the flap, at D/L = 0.56 (see Figure 11), and on the right side (offshore) for the neutral position of the pendulum at D/L = 0.43 (see Figure 12). Moreover, in pattern D, the flap tends to be tilted to the left side (onshore) with the loss of the restoring force when the anti-node is on the right side (offshore) of the flap. When the anti-node is on the left side (onshore) of the flap, the flap is tilted to the right side (offshore) and halted. The position of the anti-node exhibits effects that are analogous to pattern D on patterns A and B as well. For example, the center of the rotation position of the flap is inclined leftward when the position of the anti-node is on the right side of the flap; the center of the rotation position is tilted rightward when the position of the anti-node is on the left side of the flap. Figure 9: Effects of \(h\) and \(P_{wave}\) on the behavior of the flap: (**a**) patterns C (unstable), D (loss), and E (\(P_{wave}=0\)); (**b**) patterns A (stable) and B (fluctuation). The results in the range of \(0.45<D/L<0.58\) were excluded. Figure 11: Screenshots for pattern D for the case of \(D=1.45\) m, \(h=0.51\) m, \(T=1.4\) s, and \(H_{i}=0.12\) m at (**a**) \(t/T=1/4\), (**b**) \(t/T=2/4\), (**c**) \(t/T=3/4\), and (**d**) \(t/T=4/4\). \(D/L\) was 0.56. The blue solid line denotes the water surface. Figure 10: Screenshots for pattern E for the case of \(D=1.09\) m, \(h=0.425\) m, \(T=1.4\) s, and \(H_{i}=0.08\) m at (**a**) \(t/T=1/4\), (**b**) \(t/T=2/4\), (**c**) \(t/T=3/4\), and (**d**) \(t/T=4/4\). \(D/L\) was 0.4463. Figure 13 illustrates the relationship between \(D/L\) and the reflection coefficient \(k_{r}\) from the flap. The results of patterns B and C were excluded because the reflection coefficients of patterns B and C have variability depending on the reference time. From the figure, it can be confirmed that \(k_{r}\) increases as \(D/L\) approaches 0.5, whereas \(k_{r}\) decreases rapidly when \(D/L>0.5\). The differences in \(k_{r}\) at the same position (e.g., \(D/L\approx 0.22\) for pattern A or \(D/L\approx 0.56\) for pattern D) are the effect of \(H_{l}/L\); there is a tendency that \(k_{r}\) increases as \(H_{l}/L\) increases.
# Long Term Effects of Forest Liming on the Acid-Base Budget
## 1 Introduction  
Since the late 19 th century, an increased acid input from anthropogenic activities was observed in Europe and North America [1]. Forest ecosystems were especially affected when their position was exposed and because of their large intercepting canopy surface [2]. Since the 1980s, acid atmospheric deposition has been decreasing, mainly due to reduced sulfur dioxide emissions [3, 4, 5]. However, the input of nitrogen components still remains at a high level (cf. [6]) and is currently the main source for the acid load entering the forest ecosystems [7, 8].  
In addition, centuries of intensive litter and timber harvesting contributed to large scale soil acidification [9, 10]. In Rhineland-Palatinate, which is located in southwestern Germany (Figure 1), a high percentage of forested areas are located on base-poor soils with small nutrient reserves and low buffer capacity against acidity [11]. As a result, mobilization of aluminum and heavy metals, reduction of base cation reserves, and destabilization of clay minerals could, and still can, be observed for many forest sites [12, 13, 14, 15]. To compensate for the negative consequences of the acid deposition, extensive limiting actions with dolomitic lime were performed at an early stage [16, 17]. The application of 3 to 4 tons per hectare every 10 years was recommended [2, 18].  
In 1988, three experimental sites (Figure 1) with different liming treatments were established. The base-poor forest sites were treated once to evaluate the forest management practice of liming and to investigate its effectiveness and impacts on forest ecosystems [19].  
Long term input-output element and acid-base budgets were calculated (cf. [14]) to characterize the forest ecosystems of the three experimental sites by the different processes causing the fluxes of acidity and to quantify soil acidification without and with different dosages of dolomite limestone. Therefore, we investigated if the mobilization of aluminum could be observed in the control plots without limiting, which would indicate that proton production processes exceeded the proton consumption by reaction associated with \(\mathrm{M_{b}}\) cations (=Ca, K, Mg, Na). In this case, proton consumption is carried out partially by the weathering of Al, Mn and Fe (\(\mathrm{M_{a}}\) cations) oxides causing the destabilization of clay minerals [20]. Further, important questions include did the limiting treatments counter the effects of acidification in the long term without the negative consequences of nitrate mobilization? These questions will be answered by input-output budgeting.
## 1 Introduction  
In structural vibration control, piezoelectric materials [1; 2] have been used with regularity for a few decades. The ceramic perovskite (Pb[Zr\({}_{x}\)Ti\({}_{1-x}\)X]O\({}_{3}\), \(0\leq x\leq 1\)) is a piezoelectric material transducer called a PZT patch, which is bonded onto a mechanical structure to obtain a very efficient passive damping technique using an external resonant resistive-inductive circuit known as an RL shunt. Two versions of the parallel and series RL shunt circuits are presented in [PERSON] et al. [3]. The series and parallel resonant shunts are equivalent in terms of vibration control performance, but the parallel RL shunt is less sensitive to the mistuning of the optimal resistance value. A synthetic inductor is based on a generalized impedance converter (GIC), which is well-known as a gyrator [4]. The gyrator was used instead of a real inductor in the analog circuits era [5] to implement low frequency RC filters. The drawbacks of a real inductor are related to the physical dimensions, residual resistance, and electromagnetic interferences. Synthetic inductors are also used for mechatronic applications [6; 7; 8], as in the case of resonant piezoelectric RL shunt vibration damping. The most performant and frequently used circuits to implement synthetic inductors in mechatronic applications are Riordan [9] and Antoniou [10] gyrator circuits. The RL shunt damping technique is very sensitive to resonance frequency mistuning, and adaptive circuits [9; 10] were proposed based on an additional piezoelectric sensor and a voltage-controlled synthetic inductor (VCSI). The analog VCSIs presented in the literature are based on a Junction Field Effect Transistor (JFET) [11] or photoresistive opto-isolator [12] used as a voltage-controlled resistor (VCR). These circuits controlled by an external voltage have the advantages of a wide range of resistance values but unfortunately have a nonlinear response. The vactrol (photoresistive opto-isolator) has an extended response time, a significant memory effect, and retains the advantage of optical isolation. Considering digital electronics, we can obtain equivalent behavior with previous analog VCSIs versions by using a digital potentiometer [13] as a VCR. To avoid constraining the abovementioned VCSI based on analog circuits presented in literature, two new approaches have been used to implement VCRs based on (1) an analog multiplier and (2) an operational transconductance amplifier (OTA). The best achievement of this paper is related with the linear response of the resonant frequency to the voltage control in resonant piezoelectric RL shunt vibration damping.  
This paper is organized as follows: In Section 2, the classical [PERSON] [10] synthetic inductor circuit design and simulation results are presented. In Section 3, two new circuits for a voltage-controlled synthetic inductor are simulated. The experimental results, comparative analysis, and mistuning effect of the VCSI non-linearity in RL shunt vibration damping are presented in Section 4. Finally, Section 5 concludes the paper.
## 3 Results and Discussion
Volatiles of CL-1 significantly enhanced growth of tobacco seedlings in terms of fresh weight. Studies on the role of the microbially produced volatiles dehydroaromadendrene and (+)-sativene on plant growth are also lacking. The function and exact mechanisms of action of most microbial VOCs are poorly understood. However [PERSON] _et al._[25] reported that _B. subtilis_ GB03 volatiles regulate the homeostasis of auxin and cell expansion and augment photosynthetic capacity by increasing the photosynthetic efficiency and chlorophyll content. Although the molecular process underpinning plant growth promotion are not known, the ethylene and cytokinin signaling pathways appear to be involved [10]. [PERSON] _et al._[10] also  
Figure 4: Volatiles from CL-1 by SPME/Quadrupole GC/MS/MS. Figure 3: SPME GC-MS spectrum of volatiles of CL-1. (Volatiles present at undetectable levels/low quantities were not considered in this study). suggested that the synthesis of bioactive VOCs is a strain-specific phenomenon and the exogenous application of such VOCs result in a dose-dependent stimulation of plant growth. Identification of microbial strains that release VOCs that promote plant growth is of much concern more recently. VOCs can permeate air-filled pores of soils and can travel long distances [26] and this property is an added advantage for the fungal biocontrol agents. The potential of certain fungal VOCs in ISR and also enhancing the biocontrol properties of bacterial biocontrol agents have been described [27]. Also fungal VOCs that have antagonistic properties against other microbial plant pathogens have been reported [28,29]. Studies using mutant strains that are defective in producing each of these VOCs identified in the current study will likely offer clues to the function of these compounds, and application of synthetic versions of these VOCs would confirm their role in plant growth promotion. Further research is warranted to understand whether these VOCs induce growth promotion singly or in combination.