esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite-Time Height Control of Quadrotor UAVs
## 4 Outer-Loop Control Scheme Design  
In this paper, the finite-time super-twisting controller (17) based on the finite-time ESO (4) is designed in the height velocity inner loop (2) to compensate for the external total disturbance and track a desired height velocity signal. In this section, an integral sliding mode controller is designed to drive a quadrotor UAV in the outer loop to improve the response speed of height control. Figure 2 shows the dual closed-loop height control strategy.  
As shown in Figure 2, \(z_{d}(t)\) is the expected height, and \(e(t)\) is the error between the actual and expected heights; thus, we have  
\[e(t)=z_{d}(t)-z(t) \tag{28}\]  
The integral sliding mode surface is  
\[s_{p}(t)=e(t)+k_{1}\int_{0}^{t}e(t)dt \tag{29}\]where \(k_{1}\) is the adjustable positive parameter. Take its derivative and we have  
\[\begin{array}{ll}\dot{s}_{p}(t)&=\dot{e}(t)+k_{1}e(t)\\ &=\dot{z}_{d}(t)-\tau(t)+k_{1}e(t)\end{array} \tag{30}\]  
where \(\tau(t)\) is the integral sliding mode controller, and it is also the expected value of the inner loop. In this section, the outer-loop controller is designed as  
\[\tau(t)=\dot{z}_{d}(t)+k_{1}e(t)+k_{2}\frac{2}{\pi}\arctan(\frac{s_{p}(t)}{k_{ 3}}) \tag{31}\]  
where, \(k_{2}\) and \(k_{3}\) are the positive adjustable parameters. Considering the integral sliding mode surface and integral sliding mode controller, the Lyapunov function is  
\[V_{3}(t)=\frac{1}{2}s_{p}^{2}(t) \tag{32}\]  
Taking its derivative yields  
\[\begin{array}{ll}\dot{V}_{3}(t)&=s_{p}(t)\dot{s}_{p}(t)\\ &=s_{p}(t)(\dot{z}_{d}(t)-\tau(t)+k_{1}e(t))\\ &=-k_{2}\frac{2}{\pi}s_{p}(t)\arctan(\frac{s_{p}(t)}{k_{3}})\end{array} \tag{33}\]  
Due to the characteristics of \(\arctan(\cdot)\), the equation \(\frac{2}{\pi}s_{p}(t)\arctan(\frac{s_{p}(t)}{k_{3}})=\gamma|s_{p}(t)|\) holds, and \(0<\gamma<1\). Moreover, there is  
\[\dot{V}_{3}(t)=-k_{2}\gamma|s_{p}(t)| \tag{34}\]  
Then we have that \(\dot{V}_{3}(t)<0\) holds. Therefore, the outer-loop error \(e(t)\) eventually converges to zero, and the outer loop of the quadrotor UAV designed in this section is asymptotically stable.
## 4 Discussion  
This study aimed to assess the feasibility of using a hybrid approach that combines a boosted-CHAID technique and SVM technique with different Kernels to predict the PPV induced by quarry blasting. The applied models were analyzed regarding accuracy, error, gain performance, and input variables' importance. The models' evaluation showed that the BC-SVMRBF model achieved the best performance, which shows the efficiency of hybridizing boosted-CHAID and SVM with RBF Kernel to predict the PPV. Alternatively, the BC-SVMSIG had the weakest performance in terms of accuracy, error, and gain, which showed that this hybridization approach is not suitable for predicting the PPV.  
The finding of the present study in terms of better performance of RBF over other kernel types is in line with those of studies in other disciplines, which pointed out that the SVM model with RBF kernel has the greatest forecast capability (e.g., [91]).  
Several properties of the RBF kernel may lead to its better performance over other kernel types. These properties included its stationarity and smoothness. Besides, the RBF kernel is isotropic. Here, stationary implies that the RBF is invariant to translation. RBF's isometric property refers to the fact that in RBF, the scaling by \(\gamma\) gives a similar value in all directions.  
To support the efficiency of the proposed approaches, we applied two ANN models with two different structures, including Multilayer Perceptron (MLP) and Radial Basis Function (RBF) to the same data. The training R values of 0.858 and 0.849 were achieved for ANNMLP and ANNBF models, respectively. The results of these ANN models showed that all SVM models except SVMSIG outperformed the ANN models while were hybridized with the Boosted-CHAID method.  
Compared to the previous studies on the same dataset, this study achieved a slightly lower accuracy than that of the study by [PERSON] et al. [98]. In their study, [PERSON] et al. applied ANN and ANFIS models to five inputs parameters to achieve an R of  
Figure 9: Input variable importance for the analysis of PPV.  
0.96, while in this study, the authors applied the hybrid models on four inputs to achieve acceptable training accuracy, especially for SVM with RBF Kernel (0.95). It can therefore be concluded that the proposed models in this study are considered as reliable and sufficiently accurate in predicting the PPV induced by blasting, while retaining the advantage of reduced complexity by employing fewer input parameters.
## 3 The Radiative Transfer Equation
### RTE Solution Considerations
#### 3.2.4 Computation Implementation  
Performance Analysis  
Analyse the performance of the underwater optical communication system based on the solutions obtained from the RTE. Evaluate metrics such as SNR, BER, and link quality to assess the system's performance under different operating conditions.  
Optimisation and Design  
Use the insights gained from the RTE-based analysis to optimise system parameters such as Tx power, Rx sensitivity, modulation schemes, and coding techniques to maximise communication performance in the underwater environment.
## 3 Results
### Forest Productivity during and after the Drought and/or Warming Scenarios

Table 4: ED-2.2 model scenario settings for baseline, drought, and/or warming. with an average decrease of 31.72%, than the drought-only and warming-only scenarios in October. Relationships between Available Water, Canopy Temperature, and Forest Productivity under the Disturbance Scenarios  
We explored the relationship between soil available water and NPP under drought scenarios from July to October (Figure 4). The results showed that the mean value of soil available water from boxplots under different drought scenarios (i.e., D1, D2, and D3) gradually decreased as the drought level increase (from mild drought to severe drought). In addition, the mean value of NPP from boxplots under different drought scenarios gradually decreased with the increase in drought level. As a result, NPP and soil available moisture changed in the same direction with increasing drought severities. Figure 3: The average monthly simulated NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)] for 10 plots. The solid line represents the asymptote of NPP change, and the shadow it casts is the confidence interval. The month when the drought and/or warming scenarios occur is indicated by the shadow in the background. Black points indicate outliers. We investigated the relationship between canopy temperature and NPP under warming conditions from July to October (Figure 5). The results showed that the mean value of canopy temperature from boxplots under different warming scenarios (i.e., W1, W2, and W3) increased as warming level increase (from mild warming to severe warming). Furthermore, the mean NPP from boxplots under different warming scenarios decreased with the increase in warming level. Consequently, NPP and canopy temperature changed in the opposite direction with increasing warming severities. We also found a nonlinear relationship between NPP and canopy temperature (Figure 5). With the increase in canopy temperature, NPP showed a trend of first increasing and then decreasing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**NPP (kgC m\({}^{-2}\) yr\({}^{-1}\))**} & **Absolute Change** & **Relative Rate of** \\ \cline{2-5}  & **Baseline** & **Disturbance** & **(kgC m\({}^{-2}\) yr\({}^{-1}\))** & **Change (\%)** \\ \hline \multirow{6}{*}{October} & \multirow{6}{*}{1.034} & 1.006 (D1) & \(-\)0.028 & \(-\)2.71 \\  & & 0.894 (D2) & \(-\)0.140 & \(-\)13.54 \\  & & 0.469 (D3) & \(-\)0.565 & \(-\)54.64 \\  & & 1.032 (W1) & \(-\)0.002 & \(-\)0.19 \\  & & 1.031 (W2) & \(-\)0.003 & \(-\)0.29 \\  & & 1.031 (W3) & \(-\)0.003 & \(-\)0.29 \\  & & 0.993 (D1 + W1) & \(-\)0.041 & \(-\)3.97 \\  & & 0.796 (D2 + W2) & \(-\)0.238 & \(-\)23.02 \\  & & 0.329 (D3 + W3) & \(-\)0.705 & \(-\)68.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Absolute change and relative change rate of NPP (kgC m\({}^{-2}\) yr\({}^{-1}\)) after the drought and/or warming scenarios (October–December).
# Measurement of Head-Related Transfer Functions: A Review
## 6 Post-Processing of Measured HRTFs
### Equalization
In order to eliminate the influence of the headphone for the reproduction of binaurally synthesized sound images, a suitable compensation filter needs to be applied for each individual listener. The design of an individual compensation filter requires a successful inversion of the HpTFs taking into account the intra-individual variations caused by the repositioning of the headphones [182; 183]. A detailed overview of different HpTF equalization methods can be found in [180]. In many practical applications, the HpTF measurement and equalization can not be carried out. In this case (uncontrolled reproduction scenarios), [PERSON] et al. [184] recommended to use diffuse-field equalized headphones for the reproduction of diffuse-field equalized binaural signals (DTFs). Some commercially available dummy heads (built-in binaural microphones) are pre-equalized (e.g., KU-100 Dummy head is diffuse-field equalized) and provide the compatibility between recordings and binaural reproduction with equalized headphones.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 5 Discussion
### Comparison with Other Work  
Most current research in the field of SDN and machine learning focuses on detecting DDoS attacks with a high attack rate, as discussed in [34]. Research on low-frequency attacks is limited. Although different labelling techniques may be used, most modern machine learning techniques are primarily focused on detecting intrusions or DDoS attacks. Although most studies have focused on detecting attacks based on the volume or type of traffic [32], determining the true origin of an attacker is often challenging [37]. Most studies have focused solely on identifying IP addresses, without considering IP spoofing. Furthermore, the controller is an essential component of most existing defense mechanisms [26; 28; 29], leading to a significant increase in the network overhead.  
The current approach [44; 45; 46] to respond to attacks prioritises speed over thoroughness and disregards historical responses or attack patterns before acting. Current response mechanisms are based on applications that use a control plane to send a drop-all flow to the switch when an attack is detected [13; 45; 46]. Consequently, all traffic from the identified malicious IP addresses was blocked. This affects the host's services and may allow the adversary to bring the host down. Furthermore, this drop flow response is not reviewed until a network administrator intervenes, owing to the lack of automated rule monitoring. This results in prolonged outages and turnaround times.  
Our detection mechanism focuses on identifying low-rate timing side-channel attacks. This methodology is distinct as it does not require a minimum amount of traffic. Our employment of a machine learning model and labelling mechanism relies on previously unknown destination MAC addresses rather than IP addresses. This approach allows for the visualisation of layer two, thereby simplifying the process of identifying the device responsible for generating the timing probe. In contrast to alternative approaches, our detection method does not impose a requirement on the controller or OpenFlow switches to execute intensive operations. The response methodology utilised in this study incorporates customised flow entries based on destination IP and traffic type, rather than completely blocking traffic from a specific source IP or MAC address. With these customised flow entries, legitimate traffic is preserved and an effective response is delivered to counteract malicious activity. In contrast to alternative methods, our approach prioritises verification of the response prior to its permanent implementation. This was accomplished by constantly monitoring the response flow hits of the switch. Thus, the response flow is retained in the flow table only if it is invoked persistently. However, if the response flow remained inactive for a predetermined duration, it was automatically removed and the participant was expired.
## 2 Results and Discussion
### GRD Is Not the Prominent Binding Domain for High IQGAP-CDC42 Affinity  
#### 2.1.1 GRD Binds to CDC42 with Very Low Affinity in a Nucleotide-Independent Manner  
Different domains and fragments of the IQGAPs, including GRD1 and C794 of IQGAP1, as well as GRD2 and C795 of IQGAP2 (Figure 1A), were purified to determine their binding affinities for mGDP- and mGppNHp-bound CDC42 using fluorescence polarization. Obtained dissociation constants (K\({}_{d}\); Figure 1B) clearly show that all IQGAP constructs are able to bind CDC42 but with different affinities and preferences for the nucleotide-bound forms of CDC42. GRDs of both IQGAPs are low-affinity binders and do not discriminate between the active and the inactive states of CDC42. Similar observations were made for GRD3 and the CT of IQGAP1 (Supplementary Figure S1). In contrast, C794 and C795, encompassing in addition to both GRD and CT also the central RGCT (Figure 1A), exhibited K\({}_{\rm d}\) values of 0.6 and 0.9 uM, respectively, indicating an around 100-fold higher affinity for the GTP-bound active CDC42 as compared to CDC42 GDP (Figure 1B). This result clearly suggests that RGCT but not GRD represents a 'CDC42-specific GBD' for at least IQGAP1 and IQGAP2, by directly associating with the switch regions of CDC42 GTP. Unfortunately, our efforts to obtain IQGAP1 RGCT (aa 1276-1575) and IQGAP3 C790 (aa 841-1631) for determining their binding affinities to the members of the RHO GTPase family, including CDC42, has been remaining unsuccessful [26; 51]. Purified IQGAP1 RGCT tends to assemble into higher oligomeric or polymeric states, and, thus, is disabled in binding CDC42 [51].  
Several lines of evidence support the crucial role of RGCT rather than GRD as the IQGAP effector domain for CDC42: (i) Here we can show that proteins containing RGCT bind with a more than 100-fold affinity to CDC42 mGppNHp as compared to isolated GRD or CT (Figure 1B and Figure S1), (ii) substitution of the Serine 1443 for glutamate (a phosphomimetic mutation) drastically impaired IQGAP1 binding to CDC42 mGppNHp [5; 51]; (iii) an IQGAP1 protein, lacking the GRD (aa1122-1324), only binds CDC42 GppNHp, in contrast to IQGAP1 itself, that binds both GppNHp-bound and GDP-bound CDC42 [52]. The latter has been also demonstrated in other studies [63; 64] and support our previous [26; 51] and current findings that IQGAP domains, including GRD and CT, bind CDC42 GDP as strong as CDC42 GppNHp (Figure 1B).
## 2 Motivation and Related Work
### Related Work  
The ability of the system to restore to the original functional state, either from a partially or fully failed state, is known as _resiliency_[15]. Synchrophasor communication networks have been discussed sufficiently in the literature, such as [16; 17; 18; 19; 20; 21], to list a few. Being seminal work, however, none of these articles discussed SCNs from the perspective of resiliency analysis. [PERSON] et al., in [16], discussed the impact of routing protocols on the design and analysis of SCNs. [PERSON] et al., in [17], discussed synchrophasor communication infrastructure achieving low communication latency for synchrophasor applications. The SCN was discussed in the risk assessment paradigm in [18]. [PERSON] et al., in [19], studied the impact of delay and the approach to minimize it for synchrophasor applications. In this study, the authors computed delay and its minimization corresponding to synchrophasor data at PDCs. [PERSON] et al., in [20], proposed compressive sampling techniques for the latency analysis of synchrophasor communication systems. In [21], [PERSON] et al. proposed a strategy to optimally place communication links to improve the observability of the synchrophasor communication systems with significantly reduced latency.  
In the analytical paradigm, the reliability analysis of SCNs was comprehensively presented in [22], where the authors considered hardware and data perspectives for the reliability analysis of the SCNs. In [23], the prioritized handover was used in wireless communication infrastructure for SCNs for the reliability analysis and performance improvements of the SCNs. The communication infrastructure was proposed by [PERSON] et al. in [24] for the operator's situational awareness, where Monto-Carlo simulations were used. From the cyber-physical perspective, the situational awareness framework for the SCN was proposed in [25]. An approach for the resiliency analysis of SCNs was presented in [26], where a Monte-Carlo-based approach was utilized for the analysis. Resiliency analysis of the SCN from an SG cyber-physical system perspective was proposed in [27], where the authors utilized a simulation-based approach using QualNet 5.2. Recently, an analytical framework for the resiliency analysis of SCNs was presented in [28], in which resiliency analysis with a case study was demonstrated using QualNet 5.2. Despite a resiliency framework being proposed in [26; 27; 28], the impact of different TCP/IP protocol suite protocols was not much emphasized. To bridge such gaps in the existing work, a discrete event-based simulation framework must be utilized for network design and implementation. The present work was motivated to bridge such gaps in the literature by proposing a graph-theoretic approach to conceptualize resiliency analysis. The design, implementation, and analysis of the SCN were performed using NS-3, a discrete event-based network simulator.
# Molecular Population Genetics of Aspen Mosaic-Associated Virus in Finland and Sweden
## 2 Materials and Methods
### Primers, Detection of AsMaV by RT-PCR  
A minimum of five symptomatic leaves from the same tree were selected and neatly piled up. Then, approximately 1-2 cm\({}^{2}\) of the leaves (0.2-0.3 g) were used for RNA extraction. Total RNA was isolated according to the protocol described by [PERSON] et al. [46] and 1.5 \(\upmu\)g of the extracted RNA was applied as a template for cDNAs synthesis using random hexamers and Maxima H Minus Reverse Transcriptase kit (Thermo Scientific(tm), Waltham, MA, USA) following the protocol provided by the manufacturer. The presence of AsMaV in the collected samples was confirmed through a series of RT-PCRs employingspecific primers (Supplementary Table S2). These primers were designed to hybridize with AsMaV-RNA3 to RNA5, following established method described elsewhere [5; 47]. To design the novel primers, we utilized the RNA1 to RNA5 sequences of AsMaV isolates E55089 (GenBank accession No. LR74246, LR742462-65) as a reference. The detailed characteristics of these newly designed primers are provided in Supplementary Table S2. Notably, the isolate E55089 and its corresponding genomic molecules were used as a standard for nucleotide and amino acid positioning.
# SPEKS: Forward Private SGX-Based Public Key Encryption with Keyword Search
###### Abstract  
Public key encryption with keyword search (PEKS) enables users to search over encrypted data outsourced to an untrusted server. Unfortunately, updates to the outsourced data may incur information leakage by exploiting the previously submitted queries. Prior works addressed this issue by means of forward privacy, but most of them suffer from significant performance degradation. In this paper, we present a novel forward private PEKS scheme leveraging Software Guard Extension (SGX), a trusted execution environment provided by Intel. The proposed scheme presents substantial performance improvements over prior work. Specifically, we reduce the query processing cost from \(O(n)\) to \(O(1)\), where \(n\) is the number of encrypted data. According to our performance analysis, the overall computation time is reduced by 80% on average. Lastly, we provide a formal security definition of SGX-based forward private PEKS, as well as a rigorous security proof of the proposed scheme.  
searchable encryption; PEKS; forward privacy; trusted execution environment; SGX Article
# Electrocatalytic Nanomaterials Improve Microbial Extracellular Electron Transfer: A Review
## 3 Electrocatalytic Nanomaterials for Promoting EET
### Metal Oxides
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Nanomaterials** & **Applications** & **Performances** & **Reference** \\ \hline Transition metal oxides (TMOs) & & & \\ TiO\({}_{2}\) nanoparticles & Microbial anode & Approximate 5.1-fold increase in microbial current generation & [86] \\ TiO\({}_{2}\) nanoparticles & Biophotoelectrode & Four-fold increase in photocurrent generation of _Synechocystis_ sp. PCC 6803 & [85] \\ TiO\({}_{2}\) nanotubes & MFC anode & A maximal current density of 12.7 A m\({}^{-2}\), 190-fold higher than that of bare Ti electrode & [95] \\ TiO\({}_{2}\) nanorods & MFC anode & A maximum power density of 2576.3 \(\pm\) 33 mW m\({}^{-2}\), 2.6-fold higher than that of carbon paper electrode & [89] \\ TiO\({}_{2}\) nanosheets & MFC anode & A maximum power density of 690 mW m\({}^{-3}\), 63\% higher than that of bare carbon paper electrode & [90] \\ TiO\({}_{2}\) nanosheets & MFC anode & A maximum power density of 497 mW m\({}^{-2}\), 97\% higher than that of carbon paper electrode & [91] \\ N-doped TiO\({}_{2}\) nanosheets & MFC anode & A maximum power density of 747 mW m\({}^{-2}\), 196\% higher than that of carbon paper electrode & [94] \\ TiO\({}_{2}\) nanoclusters & MFC anode & A maximum power density of 25.9 \(\pm\) 6.2 mW m\({}^{-2}\), a record-breaking value & [92] \\ TiO\({}_{2}\)/polyaniline & MFC anode & A maximum power density of 813 mW m\({}^{-2}\), 63.6\% higher than that of TiO\({}_{2}\) electrode & [113] \\ \(\alpha\)-FeOOH nanow whiskers & Microbial anode & A maximal current density of 0.48 A m\({}^{-2}\), 60\% higher than that of carbon paper electrode & [96] \\ Fe\({}_{3}\)O\({}_{4}\) nanospheres/rGO & MFC anode & A maximum power density of 1837.4 mW m\({}^{-2}\), 2.66-fold higher than that of carbon paper electrode & [97] \\ Magnetic nanoparticles & MES modifier & An 8.5-fold increase in acetate production and 52\% decrease in methane production & [99] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of transition metal compounds as electrocatalytic nanomaterials in microbial electrocatalysis and their performances.