esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Environmental Conditions  
During the period from January 2019 to December 2022, the MAT at tower height was 16.8 \({}^{\circ}\)C in the LWS and 16.4 \({}^{\circ}\)C in the HWS; meanwhile, the average monthly TA reacheda maximum in August at 28.1 \({}^{\circ}\)C for both sites and a minimum in January at 3.6 \({}^{\circ}\)C and 4.6 \({}^{\circ}\)C in the LWS and HWS, respectively (Figure 2a). The soil temperature at both sites tended to be similar to the TA and influenced by solar radiation, with clear seasonality. The MAP was 1280 mm and 1338 mm at the LWS and HWS, respectively, and most of the rainfall occurred from June to August. The soil water content (SWC) rapidly increased and then steadily decreased, corresponding to rain events. Furthermore, the SWC in the LWS was generally higher than that in the HWS (Figure 2b). However, the wind strengths of both sites differed more than other factors, with annual average wind speed (WS) values of 2.22 and 2.82 m/s in the LWS and HWS, respectively (Figure 2d). Overall, the water and thermal conditions were similar at both locations, with the main differences between them residing in the WS and SWC values.  
Figure 2: Variations in the daily TA (**a**), SWC (**b**), photosynthetic photon flux density (PPFD) (**c**), WS (**d**), NEE (**e**), and water flux (FH\({}_{2}\)O) (**f**) in HWS (blue line) and LWS (red line) poplar forests from 2019 to 2022.
## 5 Results and Discussion
#### Analysis of Security Testing
When it comes to GPS spoofing attacks, which typically involve manipulating the GPS signals received by a device to provide false location information, these attacks target the GPS receiver hardware and are not related to the Google Maps API itself. However, any application or service that relies on GPS data, including those using the Google Maps API, can be affected by GPS spoofing if the device's GPS receiver is compromised [44; 46; 47]. In such a case, Google Maps API does not have built-in protection against GPS spoofing because it assumes that the device's location services are providing accurate data. However, with our devised MFA approach, the attackers still need to know the user PIN to totally compromise the security of payment traction. Thus, our proposed approach provides robust security against GPS spoofing attacks. Through the provided control panel, the user can still deactivate the reactive MFA, offering contingency plans for any given moment (GPS jamming and spoofing scenarios).
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 2 Related Work
### Routing in 5G  
Based on the functions of the route selection schemes in 5G networks, there are two main categories: (a) traffic offloading [9; 21; 23; 31; 32]; and (b) traffic splitting by selecting routes comprised of nodes in the femtocell layer [21; 31; 33].
## 4 Performance Evaluation
### Result Analysis of RMF-MVS + DSW
This is due to the multiple metrics considered for selection of optimal next forwarding vehicle. The multi-metric parameters are light-weight compared to the integration of REVsim for received signal strength evaluation of the candidate next forwarders. Thus, the delay of RMF-MVS + DSW is reduced compared to the 3 MRP + DSW. The average percentage delay of 3 MRP + DSW and RMF-MVS + DSW are 10.6% and 1.9%, respectively. Thus, the percentage performance improvement of the proposed scheme against the baseline scheme is 8.7%. Figure 10: Packet Loss Ratio (PLR) based on Varied Number of (**a**) Vehicles and (**b**) Data Rates Considering Dynamic Weighting Score. Figure 11b shows that RMF-MVS + DSW has a lower delay when compared to the 3 MRP + DSW. The E2 ED values were plotted alongside various data rates. The delay in the dynamic self-weighting score for the multi-metric is lower than the non-dynamic schemes, that is, 3 MRP and RMF-MVS. Delay encountered in the 3 MRP + DSW can be related to the high data rate transmitted, which causes queuing of video packets at the transmitting vehicle. Latency between the delivered data could affect the quality of the video at the decoding stage. However, the RMF-MVS + DSW outperform the 3 MRP + DSW scheme. Thus, 3 MRP + DSW and RMF-MVS + DSW schemes have 14.1% and 3.7% performance, respectively. The percentage performance improvement of RMF-MVS + DSW against 3 MRP + DSW is 10.4%. Figure 12a shows the computation delay of the proposed RMF-MVS + DSW considering different vehicle speed at a fixed number of vehicles. The number of vehicles is kept fixed at 250, while the vehicle speed is varied among10, 20,..., 50 km/h. It is observed that, the computation delay increases as the speed of the vehicles is increased. This is in connection with the intermittent disconnection among the highly mobile vehicle, thus leading to more time to computer multi-metric parameters. Similarly, Figure 12b depicts the computation delay of the RMF-MVS + DSW considering different vehicle speed with a fixed data rate at 800 kbps. The results demonstrate similar trend in terms of increase in computation delay as the speed of the vehicles are increased. This is also related to the connectivity problem in the process of multi-metric computation for next forwarding vehicle selection. Figure 11: E2 ED based on Varied Number of (**a**) Vehicles and (**b**) Data Rates Considering Dynamic Weighting Score. Figure 12: Computation Delay against Varied Number of (**a**) Mobility (Speed) at Fixed Number of Vehicles (250) and (**b**) Data Rates (800 kbps).
## 3 Analysis and Discussion
### Analysis of AFEM Setting
#### 3.3.2 Norm-2  
In neural networks, it is helpful to apply normalization before samples enter the linear transformation layer [82]. Here, as shown in Figure 5-\(M_{1}\), we use a Norm-2 conversion layer in AFEM (as shown in Figure 10). Specifically, at first, the input data are transformed into logarithmic space, which the BN [83] method is applied to here. The BN method  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Method** & **Accuracy \%** & **Increase \%** \\ \hline \(M_{0}\) & Hand-Crafted & & 87.6 & - \\  & & \((-\infty,+\infty)\) & 87.9 & 0.2 \\ \(M_{1}\) & AFEM \((a,b)\) & \((-\infty,1)\) & 88.6 & 1.1 \\  & & \((0,+\infty)\) & 89.0 & 1.6 \\  & & \((0,1)\) & 89.2 & 1.8 \\ \(M_{1}\) & AFEM (exponential) & \(e^{W_{ij}}\) & 88.3 & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of different weight constraint settings on the performance of AFEM (validation set).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{5}{c}{**Network Structure**} \\  & **1 Layer 128 Cells** & **2 Layers 32 Cells** & **2 Layers 64 Cells** & **2 Layers 128 Cells** & **3 Layers 32 Cells** & **4 Layers 32 Cells** \\ \hline MFCCs & 87.5 & 87.6 & 87.7 & 87.9 & 87.9 & 88.1 \\ MFSCs & 85.7 & 85.0 & 86.7 & 87.9 & 88.4 & 88.9 \\ \hline \hline \end{tabular} _Note:_ (1) Learning rate = 0.001, dropout ratio = 0.5; (2) input shape: MFCCs: 1024 \(\times\) 13 and MFSC: 1024 \(\times\) 40.  
\end{table}
Table 3: Effect of MFSC features on the \(M_{0}\) model’s performance (validation set).  
processes the normalized features based on the mean and variance of the current small batch sample set, calculated as follows:  
\[\hat{z}^{(l)}=\frac{z^{(l)}-\mathbb{E}\Big{[}z^{(l)}\Big{]}}{\sqrt{Var\big{(}z^{ (l)}\big{)}+\varepsilon}} \tag{8}\]  
where \(Var\big{(}z^{(l)}\big{)}\) and \(\mathbb{E}\Big{[}z^{(l)}\Big{]}\) are the variance and expectation of each dimension for \(z^{(l)}\) under the current parameters approximated by the small batch sample set, respectively. After BN, the output data is normalized using LN [84], which is different from BN as LN normalizes in the column direction of the matrix \(z^{(l)}\). Finally, an exponential function transforms the data back to the original space.  
As shown in Table 5, the contribution of each component in Norm-2 for AFEM performance is checked separately. It can be seen that the dominant factor is the log domain, which suggests that the distribution of the input features can be adjusted more effectively in the logarithmic space. In addition, BN has a significant effect on the validation accuracy of the \(M_{1}\), which makes the data centred at 0 and the weight update of the linear transformation layer no longer be biased to a specific direction, which improves the learning efficiency.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 5 General Discussion
### Do Listeners Solely Optimise Intelligibility?  
The second motivation for the present study was to determine whether listeners optimise intelligibility alone. The outcomes of both experiments strongly suggest that the answer is no. This is most clear in the quiet conditions where, apart from the centre frequency adjustment, intelligibility was high and close to ceiling levels throughout all or most of the range of adjustment, yet preference distributions were non-uniform. In quiet, listeners tended to prefer small adjustments relative to the unmodified speech.  
What is less clear is what drives preferences when intelligibility is already at ceiling. One possibility is that leaving speech close to its original form in terms of spectral balance favours naturalness. [PERSON] and [PERSON] [82] assessed how the perceived naturalness of speech is affected by several spectral distortions in quiet, finding that spectral tilt modifications degrade naturalness, especially when they are applied over the whole frequency range. The situation is somewhat different in the presence of masking noise, where any reduction in naturalness due to spectral distortion may be less perceptually salient.  
Speech quality may also play a role in listener preferences. In the present study, listeners shifted the spectral centre of gravity to higher frequencies in noise. High-frequency regions are known to include cues that contribute to the perception of sound quality. [PERSON] et al. [83] evaluated the effect of different frequency weightings on speech quality. The preferred weighting with quiet and with noise at 10 dB SNR was characterised by a flat response at frequencies below 1 kHz and a 6 dB/octave increase in the 1-4 kHz range. This high-frequency boosting led to improvements in characteristics such as brightness, clarity, and spaciousness. One study found that hearing aid users who were allowed to select their own parameters reported better sound quality than those whose parameters were selected by a clinician [67].  
Another possibility is that listeners made their choices in a way that decreased processing demands, reducing listening effort. It has been proposed that lexical retrieval is less demanding when more acoustic information is available, even when intelligibility is at ceiling, due to a reduction in the mismatch between the target speech and its mental representation [84]. A recent study by [PERSON] and [PERSON] [85] supports this speculation. They conducted a pupillometry experiment to test the impact of clear versus plain speech in noise on listening effort. Even though intelligibility was equalised for the two types of speech, pupil data suggest that clear speech requires less listening effort. One of the characteristics that differentiates clear from plain speech is greater energy in the mid frequencies, allowing more energy to escape the babble noise masker, perhaps contributing to reduced effort.
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 2 Materials and Methods
### Performance Evaluation  
For objective speech quality evaluation, the results of speech enhancement were compared using the perceptual evaluation of speech quality (PESQ), the short-time objective intelligibility (STOI), the log likelihood ratio (LLR), and the overall quality composite measure (OQCM). The PESQ scored from \(-0.5\) to \(4.5\), and the STOI scored from 0.0 to 1.0, with a higher score being an indication of better quality. The LLR scored from 0.0 to 2.0, in which a lower score is an indication of better speech quality. the OQCM was calculated using a combination of the existing objective evaluation measures to form a new measure [37], as follows:  
\[OQCM=1.594+0.805\text{-}PESQ-0.512\text{-}LLR-0.007\text{-}WSS \tag{3}\]  
The higher values of the OQCM represent a lower signal distortion and higher quality of speech. PESQ, LLR, and the weighted spectral slope (WSS) represent the perceptual evaluation of speech quality, the log likelihood ratio, and weighted slope spectral distance in each frequency band, respectively.
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 2 Description of the Rio Papaloapan Bridge (RPB)
### Finite Element Model  
The numerical model of the RPB was developed via the finite element method (FEM), using the software ANSYS(r) (V 14.0) for this purpose. The elements utilized to create the model were SHELL181 for the deck; BEAM188 for pylons, main girders, and transverse girders; and LINK180 for the stay-cables. All the dimensions, material properties, and boundary conditions were carefully considered and assigned to each element of the model, resulting in a final model of 7365 elements and 8053 nodes (see Figure 3). This model was developed with the possibility of including damage in the deck by reducing the cross-sectional area of any specific part of the deck, with different severities of damage (width and depth).  
In order to obtain the vibration responses of the RPB numerical model under different health conditions while a moving load (simulating a vehicle) crosses the bridge, a load was implemented on the deck of the model at an initial defined node, moving from node to  
Figure 1: Pictures of the RPB.  
Figure 3: FEM model of the RPB in ANSYS®.  
Figure 2: Drawing of the RPB.  
node (at a specific constant speed and in a straight line) through the deck until reaching the final node, following the route that a vehicle would take along a certain lane of the bridge. Lastly, the transient responses are obtained for the different scenarios simulated, and the corresponding data series (time vs. acceleration/displacement) are exported to be post-processed with the EWM to detect/localize damage.
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 2 Computational Method
### Aerodynamic Model
#### 2.1.1 Navier-Stokes Solver  
In the area around the rotor grid computational domain, the conservation form of the Navier-Stokes equation is established to capture the nonlinear characteristics and wake generation in the near-body zone. With the coordinate system defined on the inertial system, the equation can be expressed as  
Figure 1: Schematic diagram of the present coupling method.  
\[\frac{\partial}{\partial t}\iiint_{V}WdV+\iint_{s}FdS-\iint_{s}GdS=0 \tag{1}\]  
where \(V\) is the cell volume, and \(S\) denotes the face area. \(F\) and \(G\) are the convective (inviscid) and the viscous flux vectors, respectively, \(W\) is the vector of conserved variables and \(W=[\rho,\rho u,\rho v,\rho w,\rho E]^{\mathrm{T}}\), \(u=(u,v,w)\). \(\rho\) and \(E\) are the density and energy, respectively.  
In this paper, a dual-time stepping method is applied for the time discretization to solve the governing equation. LU-SGS (Lower-upper Symmetrtic Gauss-Seidel) [23] is adopted for sub-iteration at every pseudo-time step. The inviscid terms are calculated by the Roe scheme [24], and the Baldwin-Lomax model is employed as the turbulence model for the Reynolds Averaged Navier-Stokes (RANS) closure.
## 4 Discussion
### The Impact of XLogP3-AA on the Degradation of Pesticide Molecules  
The water solubility of compounds is an important parameter for studying the degradation of organic matter [41]. The hydrophobicity of organic compounds refers to their affinity for either water or oil when in aqueous solutions. Hydrophobic organic compounds tend to be insoluble in water; thus, their distribution and migration in the environment are often restricted, which may lead to a slower degradation rate. Additionally, hydrophobic organic compounds usually have higher chemical stability as their molecular structure is not easily attacked by water or other solvents, which can also result in a slower degradation rate in the environment. The higher the XLogP3-AA value, the greater the hydrophobicity of the organic compound. Therefore, theoretically, the lower the XLogP3-AA value, the more easily the organic compound can be degraded in water environments. As can be seen from the data in Table 3, the XLogP3-AA values of avermectin, bifenazate, etoxazole, spiromesifen, and lufenuron increase in order, which is almost the same as their degradation rate trend. Therefore, the hydrophobicity of organic compounds may also be one of the important factors causing differences in their degradation rates. In [PERSON] et al.'s study [42], it was found that under a discharge voltage of 160 kV and a treatment time of 10 min, the degradation rate of carbendazim in an aqueous solution was 89.04%. The XLogP3-AA value of carbendazim is 1.5 [43]. In [PERSON] et al.'s study [44], it was found that under a discharge voltage of 3 kV and a treatment time of 12 min, the degradation rate of aniline in the aqueous solution was 90.2%. The XLogP3-AA value of aniline is 0.9 [43]. From these two studies, it can be observed that the XLogP3-AA value of aniline is lower than that of carbendazim, indicating that aniline is relatively easier to degrade compared with carbendazim. This trend aligns with the degradation difficulty observed for the five compounds in this study. Therefore, the hydrophobicity of organic compounds may also be one of the important factors contributing to their different degradation rates.
## 7 Experimental Section
### Algorithm Properties Validation
#### 7.1.1 The Number of CHs and \(R_{a}\)  
As indicted in Figure 7, when we set a smaller value for the CHs' competition radius, there exists a greater difference between the theoretical analysis and simulation experiment results, especially in sparse scenarios. The reason is that the point coverage problem is converted to the area coverage problem when we carry on the theoretical analysis. Consequently, in sparse scenarios, if the competition radius of nodes is smaller, then the overlaps among clusters are less, or even none, which leads to the imprecise relationship between the number of CHs and \(R_{a}\) in the practical experiments. For the same reason, in the dense scenarios, no matter the size of the competition radius of nodes, the theoretical analysis results are closer to the experiment results. On the whole, according to the practical simulation results, we can conclude that our theoretical analysis is correct.  
Figure 6: Cluster heads (CHs) generated by the local energy consumption prediction-based clustering protocol (LECP-CP).  
Figure 7: The number of CHs generated with different cluster radii \(R_{a}\).
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 6 Tests, Results, and Analysis
### Response of the ROS Master Service to Different Malicious Payloads  
As outlined in the previous section, some of the test programs aimed at determining \(T_{i}\) also send to the ROS Master a (possibly partial) payload before eventually closing the connection. The server's responses, summarized in the rightmost column of Table 2, provide valuable information on how the software modules responsible for implementing the protocol layers on the server side exchange data.  
Firstly, when the test program sends either no data at all (test 1) or a complete, valid request from both the HTTP and XML-RPC viewpoints (test 7), the server does not send any response when the client closes the connection. Instead, as expected, in the same test, the server sends back a normal reply containing an indication of success at both the HTTP and XML-RPC layers upon receiving the request. This suggests that none of the software layers involved in the communication perform any action besides housekeeping upon connection closure.  
In test 2, the server replies with an HTTP error (namely, 500 Internal Server Error). This is consistent with the HTTP layer trying to parse an incomplete set of HTTP headers, in which some mandatory information is missing. In tests 3-5, the HTTP layer responds with a success indication (200 OK) because it obtains a valid set of HTTP headers, but the XML-RPC layer reports a fault because the request it obtains from the HTTP layer is incomplete or invalid. In all cases, the fault indication reflects the issue in the request. More specifically, we have the following:  
* In test 3, the XML-RPC fault indication contains the error \"no element found\" from the XML parser because empty XML-RPC requests are forbidden.
* In test 4, the parser reports a syntax error that pinpoints the invalid token present in the request.
* In test 5, the parser is unable to find the end of the dangling <params> clause.  
Interestingly, the reply the server sends in test 6 contains a success indication at both the HTTP and XML-RPC layers, although the actual requests for the length are shorter than what is declared in the HTTP Content-Length. Hence, the fact that the request is actually incomplete (a well-known fact at the HTTP layer) is not properly forwarded to the XML-RPC layer, which then proceeds to parse it as normal.  
Another important, counterintuitive conclusion that can be drawn from tests 2-6 is that, regardless of everything else, the ROS master tries to process any partial payload it may have received from a client when the client closes the connection. The reason behind this behavior has not been further investigated because it falls outside the scope of the present work but may be conducive to other kinds of attack, such as delayed or long-response DoS [6].