esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Secure Routing in Vehicular Networks  
Routing protocols [27] act as a binding force among users in ad-hoc networks. Generally, we have two routing conditions, i.e., reactive and proactive routing protocols. Typically, the proactive routing protocol requires much bandwidth because paths among interacting users are maintained even if they are out of services. AODV is one of most popular approaches as the reactive routing protocol for mobile ad hoc networks because of its on-demand feature [28], which means that a node will only perform routing operations when it wants to discover a routing path towards other nodes for communication.  
Moreover, the authors in [29] used real time traffic monitoring information with the help of GPS map and Sumo simulators as an enhanced version of AODV. [PERSON] et al. in [30] used the optimized reactive AODV routing over virtual nodes to gain some promising route results. Furthermore, some researchers used a complex cryptographic processing operation called authenticated anonymous secure routing in wireless ad hoc networks combined with AODV [17]. The authors in [31; 32] proposed reactive and multi-path routing for MANET based on fuzzy logic and Markov chain phenomena. [PERSON] et al. in [33] proposed trust-based secure routing using subjective logic. Although the authors gain some promising results in above mentioned schemes, we argue that subject logic [34] is a more powerful probabilistic tool to highlight uncertainty and suspicious factor of unknown participants. Based on the existing work, we argue that the proposed TER secure routing is also suitable in this environment. The TER using the TW algorithm efficiently searches routes and solves massive trust assessment problem with uncertainty consideration.
## 2 Materials and Methods
### Satellite Data  
To analyze the influence of vegetation on ET partitioning, Vegetation Index (VI) is applied to represent vegetation dynamics and to identify growing season. The Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product (MOD 13), which has a spatial resolution of 250 m and time resolution of sixteen days with the same period of flux data, was downloaded from National Aeronautics and Space Administration (NASA) website ([[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/))). This NDVI product is calculated from reflectance near-infrared channel and red channel using the following equation:  
\[\text{NDVI}=\frac{\rho_{nir}-\rho_{red}}{\rho_{nir}+\rho_{red}}, \tag{1}\]  
where \(\rho_{nir}\) and \(\rho_{red}\) are the reflectance of near-infrared channel and red channel, respectively.  
The NDVI of each site is the NDVI value of the pixel within which the flux site is located. In order to identify the growing season, 16-day NDVI data is then filtered and interpolated to daily NDVI data.
## 3 Results and Discussion
### The Normal Force per Grit vs. Maximum Undeformed Chip Thickness
In picture **(d)**, it is 180\(\#\). The relations between the specific grinding energy and the maximum undeformed chip thickness under three different kinds of wheels are shown in Figure 8. They indicate that grinding energy increases when \(H_{\text{max}}\) decreases. Figure 9a,b shows SEM micrographs of NO.2 experiment and Figure 10a,b shows SEM micrographs of NO.17 experiment. As we all know, the specific grinding energy includes grinding energy and friction energy [19]. Friction energy is direct proportion to the normal grinding force. Due to the decreasing of single grit normal force with dropping \(H_{\text{max}}\) while it is less than the critical depth, friction energy would decline in theory. Thus, we can get the conclusion that the increasing specific grinding energy is mainly resulted from the increasing grinding energy. From previous studies we know the removal mechanism tends to be ductile removal with the decreasing \(H_{\text{max}}\). Compared with Figure 8, Figure 10 shows that the proportion of ductile removal is great bigger. In Figure 9a, the intergranular fracture represents a significant share of removal modes. In Figure 9b, intergranular fracture mode is easy to see. In Figure 10, ductile removal and transgranular fracture play an important role. Considering that fracture of alumina grain needs more energy than crystal boundary, transgranular fracture required larger grinding energy compared with intergranular fracture, which resulted in the growing specific energy. As seen in Figure 5, the specific grinding energy tends to be flat when \(H_{\text{max}}\) is larger than approximately 4 \(\upmu\)m, which is near to the alumina grain size. That is to say, the critical depth of brittle fracture is relative to grain size. So the larger specific energy with the decreasing maximum undeformed chip thickness indicates the rising proportion of ductile removal and transgranular fraction. In Figure 11, several observations should be noted. At the same maximum undeformed chip thickness, a larger mesh size of wheels leads to a smaller specific grinding energy. The specific grinding energy fluctuates frequently as a function of \(H_{\text{max}}\), which ranges from 0.66 \(\upmu\)m to 6 \(\upmu\)m. This result is mainly owing to the maldistribution of particles and pores. Sharp changing specific grinding energy can be seen in smaller \(H_{\text{max}}\), while it tends to be gentle in larger \(H_{\text{max}}\). Thus we guess, when \(H_{\text{max}}\) is larger than 6 \(\upmu\)m, brittle fracture takes precedence. Figure 11: The specific grinding energy per grit of fused silica ceramics as a function of the maximum undeformed chip thickness with 60#, 120#, 180# wheels. Compared with Figure 11, non-crystalline ceramics show more irregular changes in specific grinding energy. From the microstructure, many pores can be seen in fused silica and scatter randomly, which results in the irregular grinding energy.
## 5 Discussion
In prospective testing over 10 years in the New Zealand CSEP testing center, EEPAS_1F significantly outperformed EEPAS_1R [29]. To construct the hybrid models, we replaced the time-varying component of each model's rate density with the average rate density of the three models, with the values of \(a_{T}\) and \(\sigma_{A}\) chosen from the trade-off line. The three models were the original one and two others formed by an arbitrary increase and decrease in \(a_{T}\) of \(\Delta=0.5\). For an increase in \(\Delta\) in \(a_{T}\), the corresponding value of \(\sigma_{A}\) on the trade-off line was found by multiplying the original \(\sigma_{A}\) by \(10^{-0.5\Delta}\). The other parameters, including \(\mu\) and \(\sigma_{T}\), remained unchanged at their values in Tables 1 and 4. Using information gain statistics, we compared the performance of the EEPAS_1F, EEPAS_0F, Hybrid_1F and Hybrid_1R models. For this, we used a test period from 2007 to 2017, during which there were 259 target earthquakes with magnitudes M > 4.95. Hybrid_1R outperformed all the other models, and EEPAS_1R was the weakest model (Figure 9). Figure 9a shows the information gain of EEPAS_1F, and Figure 9b shows that of Hybrid_1R over the other models. Both hybrid models and EEPAS_1F outperformed EEPAS_1R with 95% confidence according to the \(T\)-test [24]. This simple example of hybrid formation, even without fitting additional parameters, suggests that it might be possible to use the space-time trade-off to improve forecasting. However, much more work needs to be done to construct a formal method for optimal inclusion of the trade-off in the fitting of the EEPAS model. The temporal and spatial limitations of the catalogue are clearly among the issues to be considered. The spatial limitations \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(m_{0}\) & 2.95 * \\ \(m_{c}\) & 4.95 * \\ \(m_{u}\) & 10.05 * \\ \(b_{CR}\) & 1.16 + \\ \(a_{M}\) & 1.00 + \\ \(b_{M}\) & 1.0 * \\ \(\sigma_{M}\) & 0.32 * \\ \(a_{T}\) & 1.40 + \\ \(b_{T}\) & 0.40 * \\ \(\sigma_{T}\) & 0.23 * \\ \(b_{A}\) & 0.35 * \\ \(\sigma_{A}\) & 1.74 + \\ \(\mu\) & 0.24 + \\ \hline \hline \end{tabular}  
* Fixed. \({}^{\dagger}\) Fitted.  
\end{table} 
Table 4: EEPAS_1R model parameters for New Zealand. Figure 9: Information gain per earthquake and 95% confidence interval of the (**a**) EEPAS_1F model and (**b**) Hybrid_1R model compared with other models during the test period of 2007–2017 in the New Zealand testing region (259 target earthquakes with M > 4.95). can be resolved if a global catalogue is used, but then a higher threshold magnitude of completeness would apply. That in turn imposes further limitations. Additionally, there is evidence that the precursor time distribution is dependent on the strain rate in the vicinity of a target earthquake [17]. This dependence would have to be included in a global model. Temporal limitations can also be partly resolved by introducing a fixed lead time for all target earthquakes and then compensating for the lead time using the method described in [20].
## 3 Data and Methods
### Overview of Machine Learning Models
#### 3.5.4 Model Accuracy Verification  
To systematically evaluate the prediction accuracy and robustness of three debris flow susceptibility models, this study employs the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) to quantify model performance. Additionally, the Frequency Ratio (FR) index is utilized to validate the spatial rationality of the prediction results.  
The ROC curve is widely recognized as an effective method for assessing the accuracy of debris flow susceptibility evaluation [78]. It plots the True Positive Rate (TPR) on the vertical axis against the False Positive Rate (FPR) on the horizontal axis. The AUC value ranges from 0 to 1, with higher values indicating better model performance. The classification criteria for AUC values are as follows: excellent (0.9-1), very high (0.8-0.9), good (0.7-0.8), moderate (0.6-0.7), or poor (0.5-0.6) [79].  
The Frequency Ratio (FR) is an index used to characterize the distribution of debris flow watersheds within specific risk zones. It is calculated as the ratio of the proportion of debris flow disaster points within a given risk level to the proportion of all watershed units within the same risk level [80]. According to the literature, regions with higher susceptibility values should encompass as many disaster points as possible while occupying the smallest possible area [81]. Furthermore, the FR value should increase with higher susceptibility levels [82].
## 4 Proposed MP Clutter Suppression Algorithm Based on Joint Optimization
### The Motivation of the Proposed Joint Optimization Algorithm
A summary of the proposed joint optimization algorithm is presented in Figure 2.  
After obtaining the profile vector and the calibration vector, a two-step suppression process is performed. First, the MP clutter is canceled by utilizing the proposed joint optimization algorithm. By exploiting the estimated \(\hat{\mathbf{a}}\) and \(\hat{\mathbf{h}}\), the output at the \(l\)-th range cell is:  
\[\hat{\mathbf{\alpha}}(k,m)=\hat{\mathbf{R}}_{S}^{-1}(k,m)\Big{(}\hat{\mathbf{r}}_{Sx}(k,m)+ \hat{\mathbf{\psi}}^{\rm H}(k,m)\Big{)}, \tag{29}\]0  
Then, the STAP algorithm is used for DP clutter cancellation. Supposing the estimated ICM-CP fluctuation matrix \(\hat{\mathbf{T}}\) is obtained by prior knowledge or the alternating direction method [40], then the snapshot after MP clutter suppression and ICM-CP calibration can be expressed as \(\mathbf{y}_{l}^{\prime}=\hat{\mathbf{T}}^{-1}\mathbf{y}_{l}=\hat{\mathbf{T}}^{-1}\big{(}\mathbf{x} _{l}-\hat{\mathbf{\Gamma}}\hat{\mathbf{S}}_{l}\hat{\mathbf{a}}\big{)}\). The STAP weight vector is calculated by:  
\[\hat{\mathbf{\alpha}}(k,m)=\hat{\mathbf{R}}_{S}^{-1}(k,m)\Big{(}\hat{\mathbf{r}}_{Sx}(k,m)+ \hat{\mathbf{\psi}}^{\rm H}(k,m)\Big{)}, \tag{29}\]1  
where \(\hat{\mathbf{R}}_{u}=1/K\sum\
olimits_{k=1,k\
eq l}^{K+1}\mathbf{y}_{k}^{\prime}\mathbf{y}_ {k}^{\prime}\) and \(\mathbf{v}(\mathbf{\alpha}_{l},\mathbf{\vartheta}_{l})\) is the space-time vector of the hypothetic target. Then, the scalar output is:  
\[\hat{\mathbf{\alpha}}(k,m)=\hat{\mathbf{R}}_{S}^{-1}(k,m)\Big{(}\hat{\mathbf{r}}_{Sx}(k,m)+ \hat{\mathbf{\psi}}^{\rm H}(k,m)\Big{)}, \tag{29}\]2  
conditions are required to compute \(\hat{\eta}_{Ge}(k)\), and \(M+MN(M+N_{1})\) multiplications and \(MN(M+N_{1}-1)\) additions are required to compute \(\hat{\mathbf{h}}(k)\). Thus, \(L_{2}MN(2M+N_{1}+2)+M+MN(M+N_{1})\) multiplications and \(L_{2}M(2 MN+NN_{1}+N-3)+M+MN(M+N_{1}-1)\) additions are required. From the above analyses, the added ICM-MP error calibration step slightly increases the computational cost of the proposed algorithm.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 4 Results and Discussion
### Network without Partitions
#### 4.1.1 Number of Delivered Packets  
Every node has exactly one data packet to send per routing cycle.  
Figure 11 shows the percentage of the number of successfully delivered packets for networks consisting of 25, 50, 75 and 100 nodes. The considered networks do not have any partition initially. However, connectivity holes may be created that may or may not result in network partitioning. Our proposed scheme shows improvement in the number of delivered packets. The improvement is achieved due to our residual energy threshold policy. When a node reaches its residual energy threshold, it denies relay service to downstream nodes. This helps maintain the number of delivered packets as the data traffic is taken off the nodes with low energy and routed through high energy nodes. This enables the low energy nodes stay alive longer to send only their own data packet. (It is important to note that, besides other factors such as connectivity holes, collisions, etc., the number of delivered packets reduce with number of dying nodes as every node sends one data packet per routing cycle. Therefore, if one node dies, we have one less packet in the total number of delivered packets). Moreover, in case of denial of relay service, our rerouting mechanism extends the forwarder search area to the two-hop neighborhood, thus increasing the chances of a successful packet delivery. Furthermore, if one or more nodes cannot find any next hop node even in its two-hop neighborhood, our partition handling routine will connect it in the next routing cycle, thus maintaining a higher packet delivery ratio. Compared to PH, in NPH nodes nearer to the UW-Sink die out earlier as NPH does not follow any residual energy threshold policy. The death of nodes may result in the creation of connectivity holes which, in the absence of rerouting mechanism, results in a lower number of delivered packets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of nodes & 25, 50, 75,100 \\ Simulation Runs (Routing Cycles) & 50 \\ Energy Resource & 200 Joules \\ Minimum Energy Threshold & 20 \\ Data Packet size & 512 bytes \\ Data rate & 31.2 kbps \\ Default range & 250 m \\ Carrier Frequency & 26 khz \\ Simulation Area & \(1000\times 1000\) m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
The number of delivered packets for PH is highest for 25 nodes; however, it drops slightly due to increased contention as the number of nodes increase. Similar trend can be observed in case of NPH.
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 3 Results  
### Volume Air Fraction Calculation  
The first dataset was obtained through interpolation, based on CT scan measurements of specimens B2 and D4, to estimate the air volume fraction across all samples relative to their densities (Figure 11). The labels (e.g., A1-D4) represent density classes (A-D), with the number indicating the specimen ID within each class.  
The observed pattern clearly demonstrates a direct inverse relationship between material density and volume air fraction. Specimens classified under group A, which exhibit the highest densities, correspond to the lowest volume air fractions. In contrast, group D specimens representing the lowest density range show the highest air volume  
Figure 11: CT interpolated volume air fraction distribution of specimens across four density classes.  
Figure 10: Boundary conditions.  
fractions. This trend confirms the expected behavior, where increased porosity (and thus air content) results in lower overall material density. The consistent gradient across the density classes reinforces the reliability of the interpolation method used and supports the logical correlation between density and internal structure.
## 3 Experimental
### Liquid Chromatography and Diode Array Detector (LC-DAD)  
Detection and quantitative analyses of HF phenolic compounds were carried out using a LC-DAD method as described by [PERSON] _et al._[38]. An ProStar HPLC system (Varian Inc., Walnut Creek, CA, USA) was employed, fitted with a pump module 230, an autosampler module 410, and a ThermoSeparation diode array detector SpectroSystem UV 6000 lp (Thermo Separation, San Jose, CA, USA). Separation was obtained with a Gemini C18 column (150 \(\times\) 4.60 mm, 3 \(\upmu\)m, Phenomenex, Casalecchio di Reno, BO, Italy) using 0.2 M H\({}_{3}\)PO\({}_{4}\) (solvent A), and acetonitrile (solvent B) at a constant flow rate of 1.0 mL/min, mixed in linear gradients as follows: t = 0 A:B (85:15, v/v), reaching 60:40 (v/v) in 30 min, then 40:60 (v/v) in 10 min, and finally at 100% B until 50 min. Before each injection the LC system was stabilized for 10 min with the initial A/B ratio (85:15, v/v). The injection volume was 10 \(\upmu\)L. According to the optimal detection wavelength, the phenols analysis was performed at: 280 nm (hydroxytrosol, tyrosol, vanillic acid, oleuropein, and ligtroside derivatives), 313 nm (\(p\)-coumaric acid), and 360 nm (luteolin and apigenin). Oleuropein and ligtroside derivatives were tentatively identified by comparison with literature data [39,40]. Chromatograms and spectra were elaborated with a ChromQuest V. 2.51 data system (ThermoQuest, Rodano, Milan, Italy). Stock standard solutions were prepared in methanol and working solutions in methanol-water (80:20 v/v). The method was validated in agreement with the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidance note which describes validation of analytical methods [41] by determining linearity, limits of detection (LOD), limits of quantification (LOQ). The LODs and LOQs were used to establish the sensitivity of the method and were determined according to the equation LOD = 3.3o/S and LOQ = 10o/S, respectively (where \(\sigma\) = standard deviation of the blank, and S = slope of the calibration curve). The linearity was evaluated by preparing a standard mixture at six different concentrations, and analysing them by LC-DAD. The analyte peak areas were plotted against the corresponding concentrations, and the calibration curves were constructed by means of the least-squares method. All compounds were dosed using the calibration curve built with the respective standard, except oleuropein and ligtroside derivatives that were dosed using the oleuropein calibration curve. The correlation values were comprised between 0.9993 and 0.9999.
# Measurement of Head-Related Transfer Functions: A Review
## 3 Overview of HRTF Measurement Setups  
Section 2 describes basic measurement principles for obtaining a pair of HRTFs. However, many binaural rendering applications require HRTF datasets covering different directions and even various distances. Such a massive dataset can be measured by repeating the methods described in Section 2 by changing the position of the loudspeaker or the listener until all desired measurement positions are covered, but it may take a lot of time to complete the measurement. Over the years, various HRTF measurement setups and methods have been proposed to speed up the measurement process.  
The number and distribution of HRTF measurement points vary among different laboratories and publicly available datasets. [PERSON] et al. [27] illustrated that the angular resolution of 8\({}^{\circ}\) is sufficient to avoid audible artifacts when applying interpolation, resulting in at least 1130 HRTF pairs to be measured. A lot of studies are interested in the HRTF representation in the spherical harmonic (SH) domain, taking advantage of the spatial continuity and orthonormality of SHs over the sphere. Such a representation shows the suitability for HRTF interpolation/extrapolation [20; 21; 22; 23], binaural rendering [118], etc. [PERSON] et al. [28] compared various spatial sampling schemes (distributions of measurement points) and revealed that the IGLOO schema is the most suitable one when considering the SH transformation, and the required minimum number of HRTF pairs is 2304. [PERSON] et al. [119] further proposed a sampling schema with the consideration of practical loudspeaker arrangements.  
Most HRTF measurement systems are designed for capturing far-field HRTFs, which are distance-independent. In contrast, HRTFs are highly distance-dependent in the near-field. The HRTF spectra appear to be low-pass filtered as the sound source approaches the listener's head. Moreover, the ILDs increase noticeably for lateral sound sources as the distance decreases, while the ITDs are almost the same at various distances [16]. For 6-DoF binaural rendering applications, distance-dependent HRTF datasets are required to synthesize realistic nearby virtual sound sources. Therefore, near-field HRTFs should not only be measured for different source directions but also for various distances (see examples in [79; 114; 117; 120]), leading to a greater workload than the measurement of far-field HRTFs. The main differences between the far- and near-field measurements are the measurement distances and the sound sources used (see Section 2.4).  
Independent of far- or near-field HRTFs, multi- and single-loudspeaker setups are commonly applied for the measurement, and each setup can be used to discretely or continuously measure HRTFs.  
This section provides an overview of the state-of-the-art in HRTF measurement setups and some special measurement systems.