esa-sceva/satcom-chunk-collection huggingface dataset:
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
## Full Field Detection Photoacoustic Tomography
### Description of the Inverse Problem  
In order to describe the inverse problem of FFD-PAT in a compact way, we introduce some further notation. First, we define the operator  
\[\mathbf{W}_{T}\colon C_{0}^{\infty}(B(0,R))\to C_{0}^{\infty}(\mathbb{R}^{3}) \colon f\mapsto p(\,\cdot\,,T)\,, \tag{5}\]  
where \(p\) denotes the solution of (1)-(3). The operator \(\mathbf{W}_{T}\) maps the initial data \(f\) to the solution (full field) of the wave Equations (1)-(3) at the given measurement time \(T>0\). Second, we define the X-ray transform  
\[\begin{split}\mathbf{X}\colon C_{0}^{\infty}\left(\mathbb{R}^{3} \right)&\to C_{0}^{\infty}\left([0,\pi]\times\mathbb{R}^{2}\right) \\ (\mathbf{X}h)(\theta,s,z)&\coloneqq\int_{\mathbb{R} }h(s\cos(\theta)-t\sin(\theta),s\sin(\theta)+t\cos(\theta),z)\,dt\end{split} \tag{6}\]  
Note that for any fixed \(z\in\mathbb{R}\), the function \((\mathbf{X}h)(\,\cdot\,,z)\) is the Radon transform of \(h(\,\cdot\,,z)\) in the horizontal plane \(\mathbb{R}^{2}\times\{z\}\). Finally, we define the restricted X-ray transform  
\[\mathbf{X}_{R}\colon C_{0}^{\infty}\left(\mathbb{R}^{3}\right)\to C^{\infty} \left(M_{R}\right):h\mapsto(\mathbf{X}h)|_{M_{R}}\,. \tag{7}\]  
For \(|z|\leq R\), \((\mathbf{X}_{R}h)(\,\cdot\,,z)\) is the exterior Radon transform of \(h(\,\cdot\,,z)\) and consist of integrals over lines not intersecting the disc \(\{(x,y)\in\mathbb{R}^{2}\mid x^{2}+y^{2}<R^{2}-z^{2}\}\); compare Figure 2. Otherwise, \((\mathbf{X}_{R}h)(\,\cdot\,,z)\) coincides with the standard Radon transform of \(h(\,\cdot\,,z)\).  
Using the operator notation introduced above we can write the inverse problem of FFD-PAT in the form  
\[\text{Recover $f$ from data}\quad g_{R}=\mathbf{X}_{R}\mathbf{W}_{T}f+\text{ noise}\,. \tag{8}\]  
Evaluation of \(\mathbf{X}_{R}\mathbf{W}_{T}f\) is referred to as the forward problem in FFD-PAT. In this paper, we study the solution of the inverse problem (8).  
Figure 2: Horizontal cross-section of the FFD-PAT setup. Full field projections are measured over lines that do not intersect the ball \(B(0,R)\). Consequently, for any plane \(\mathbb{R}^{2}\times\{z\}\), integrals of \(p(\,\cdot\,,T)\) over those lines are given that do not intersect the disc \(D_{z}(0,R):=B(0,R)\cap(\mathbb{R}^{2}\times\{z\})\). For planes with \(|z|\leq R\), this yields the exterior problem for the Radon transform.
# Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme
## 5 Simulations and Discussion  
In this section, we validate the performance of the proposed algorithm and compare it with benchmark algorithms in different network scenarios. MPs are pre-defined carefully in the monitoring sea area according to sea state conditions and needs. Specifically, we first validate the performance in a small-scale network. Then, we show the performance of MO problem solved by [PERSON] [40] in small-scale networks and compare it with our proposed algorithm. A greedy algorithm is further presented and compared with the proposed AC-ETO algorithm. We set up multiple experiments of eleven network scenarios of different scales, as shown in Table 2. The main parameters used in the experiments are listed in Table 3. Without loss of generality, the generic cost unit (gcu) and the generic time unit (gtu) are defined to simplify the evaluation of deployment costs and network lifetime in the case studies. The impacts of \(\omega\) on the results are tested in different scenarios under various network scales, as shown in Figure 3. It can be seen that the deployment cost shows little variance, but the energy consumption may decrease significantly when \(\omega\) increases. In other experiments, the value of \(\omega\) is set to \(2\times 10^{6}\).
## 3 Methods
### Sentinel-2-Based LAI Estimation  
To compare with the estimated airborne results, Sentinel-2 satellite data were also applied. In addition to the visible and near-infrared wavelengths, the Sentinel-2 multispectral instrument which included bands in the red-edge region was found to be critical for vegetation monitoring ([PERSON], 1971). The Sentinel Application Platform (SNAP) software version 9.0.0 (European Space Agency), which was developed to work with Sentinel images, provides a scientific processor named \"Biophysical Processor\". It could give immediate retrieval results of LAI, canopy chlorophyll content, and some other biophysical variables from Sentinel-2 images ([PERSON] and [PERSON], 2018) with different canopy reflectance bands and the geometrical configuration of illumination and observation as input parameters.  
The training database was generated through the PROSAIL model ([PERSON] and [PERSON], 2018) to simulate top-of-canopy reflectance. Considering the canopy architecture, the PROSAIL model was based on the turbid medium assumption for different vegetation types. The derived LAI based on the assumption should be seen as effective LAI.  
Based on the generated training database, a back-propagation artificial neural network (ANN) was applied to retrieve these parameters from Sentinel-2 observations in SNAP. The architecture of the ANN was made up of two layers. The first layer was made up of five neurons using tangent sigmoid transfer functions, and the second one contained one neuron using linear transfer functions ([PERSON], 1971). Two steps were used to train the neural network: Firstly, a feed forward iteration was used to compute the output. Secondly, a back-propagation learning rule was applied to minimize the error between predicted results and the input values.  
In this study, Sentinel-2 satellite data on 21 June 2021 were selected corresponding to the field campaigns. The atmospheric correction of the Sentinel-2 images was performed using the Sen2 cor atmosphere correction toolbox in the SNAP software. Nine bands (B3, B4, B5, B6, B7, B8a, B11, and B12) at 20 m resolution were utilized to estimate LAI using Biophysical Processor from SNAP. In order to keep the spatial resolution the same as the field measurements, the estimated result was resampled to 30 m after calculation.
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 4 Performance Evaluation  
### Simulation Settings  
The proposed caching strategy has been implemented in ndnSIM, the official simulator of the NDN research community [16]. As representative edge domain, we consider a tree topology randomly generated with the Georgia Tech Internetwork Topology Models (GTITM) [17]. The topology includes 20 intermediate nodes and 8 leaves acting as ingress nodes. The root node connects the edge domain with a remote server acting as content producer. Because the performance assessment is focused on the edge domain, the external network is simply simulated as a link, with latency of 30 ms [18], between the root node and the server. The latency of edge links is instead uniformly distributed in the range [2; 3; 4; 5] ms.  
We consider a catalog of 15,000 contents, each one consisting of 1000 Data packets 1 Kbyte-long. A variable number of consumers attached to the leaf nodes of the topology, request contents according to the [PERSON]'s law [3], with skewness parameter \(\alpha\) set to 1. In these settings, we consider two distinct simulation scenarios.  
* In the first scenario, we assume that the total caching capacity of the edge domain, uniformly distributed among the nodes, is varying from 0.25% to 2% of the overall catalog size, similarly to the values reported in [19]. The number of consumers is set to 60.  
Figure 6: Toy example: an edge network with eight nodes.  
* In the second scenario, we assume that the cache capacity is fixed to 0.5% of the overall catalog size, whereas the number of consumers range from 20 to 70. The main simulation settings are summarized in Table 3.  
We compare the proposed model (labeled as \(PaCC\) in the plots) against the following benchmark solutions:  
* Cache everything everywhere (labeled as \(CEE\) in the plots). It is the vanilla NDN caching strategy where all the incoming Data packets are cached.
* Fixed probability-based caching (labeled as \(Prob\) in the plots). It caches incoming Data according to a fixed probability set to 0.5 [9].
* Betweenness and edge popularity caching (labeled as \(BEP\) in the plots). It implements a caching scheme based on the popularity-aware betweenness centrality metric, as in [6]. For all the noted schemes, the replacement policy is LRU. The following performance metrics are considered:
* Retrieval delay: it is computed as the average time taken by a consumer to retrieve a content.
* Number of hops: it is computed as the average number of hops traveled by the Interest packets for retrieving the corresponding Data packets.
* Exchanged NDN packets: it is the total number of Interest and Data packets transmitted by all the nodes, i.e., consumers, providers, and edge nodes, during the simulation, to retrieve the contents.  
The first two metrics capture the effectiveness of the compared schemes in caching content copies in proximity to the consumers. The last metric provides insights about the efficiency of the schemes, in terms of traffic exchanged in the network. Results are averaged over 10 runs and reported with 95% confidence intervals.
# Insights into Bending Stiffness Modeling of Elementary Flexure Hinges
## 4 Bending Stiffness Model of a Flexure Hinge
### Case Study: An Elliptical Flexure Hinge  
An example of the application of the model shown in the previous sections allows us to deepen the study in a real physical case. A flexure with a rectangular section and an elliptical profile under static loads is proposed as a case study, as shown in Figure 5. The cross-section of the flexure hinge changes its dimension with \(x\); in fact, even if its horizontal side \(l\) is constant, its height is variable according to \(2y(x)\). The smallest section in \(x=0\) has a thickness of \(t\). The major and minor semi-axes of the ellipsis in Figure 5 are \(a_{x}\) and \(a_{y}\), respectively.  
The choice of elliptical geometry allows us to give an expression to the dimensional coefficients in Equation (5), namely \(x_{0}\) and \(I_{z0}\). The former can be easily identified, in fact:  
\[x=a_{x}\widetilde{x}\qquad\text{with}\qquad\widetilde{x}\in[-1;1]. \tag{17}\]  
The latter requires more processing, following a sequence of logical steps. It starts from the expression of an elliptical profile, as shown in [15]:  
\[y(x)=a_{y}\left[\left(1+\beta_{y}\right)-\sqrt{1-\left(\frac{x}{a_{x}}\right) ^{2}}\;\right]. \tag{18}\]  
Then, Equation (18) is converted into a new function of \(\widetilde{x}\) by means of Equation (17):  
\[\widetilde{y}(\widetilde{x})=a_{y}\Big{[}\left(1+\beta_{y}\right)-\sqrt{1- \widetilde{x}^{2}}\Big{]}=y_{0}\;\widetilde{y}(\widetilde{x}), \tag{19}\]  
where  
\[\beta_{y}=\frac{t}{2a_{y}};\quad y_{0}=a_{y};\qquad\widetilde{y}(\widetilde{x })=\left(1+\beta_{y}\right)-\sqrt{1-\widetilde{x}^{2}}\;\;. \tag{20}\]  
In Equation (19) \(\widetilde{y}(\widetilde{x})\) is the positive dimensionless \(y\) coordinate of the elliptical profile. Finally, said \(I_{z}(x)=\frac{l[2y(x)]^{3}}{12}\) the moment of inertia of the cross section about the \(z\)-axis, which varies with \(x\), and using both Equations (18) and (19), it follows the required expression of \(I_{z0}\) and \(\widetilde{I}_{z}(\widetilde{x})\). Their product provides \(\widetilde{I}_{z}(\widetilde{x})\) according to Equation (5):  
\[I_{z0}=\frac{2}{3}Ia_{y}^{3};\qquad\widetilde{I}_{z}(\widetilde{x})=\left[ \left(1+\beta_{y}\right)-\sqrt{1-\widetilde{x}^{2}}\right]^{3}=\left[ \widetilde{y}(\widetilde{x})\right]^{3}\;\;. \tag{21}\]
# SepFree NMF: A Toolbox for Analyzing the Kinetics of Sequential Spectroscopic Data
## 3 Minimal Memory Effect in Sequentially Measured Spectroscopic Data
### Computation of the Minimal Memory Effect  
The minimal memory effect is computed starting with the Equation (9). In (9), the matrix \(T\) represents the \"pure\" transition probability between compounds, i.e., \(T_{ij}\) is the probability of going from compound \(i\) to compound \(j\) after a time-step \(\tau\)[5].  
If the transition matrix \(K(\tau)\) is identical to the \"pure\" transition probability matrix \(T\), then the process is totally Markovian; the compounds' kinetics do not depend on the past. In this case, the matrix \(\mathcal{S}\) is the identity matrix.  
As in the expression in (9), the matrix \(\mathcal{S}\) relates the PCCA+ projection \(K(\tau)\) to the \"pure\" transition matrix \(T\); one can think about \(\mathcal{S}\) as a measure of the difference between \(T\) and \(K\).  
In [5], the authors explain that the minimal memory effect (that they call rebinding) is proportional to the trace of the rate matrix \(Q\) related to the transition matrix as  
\[K(\tau)=\exp\tau Q. \tag{10}\]  
The entries of \(Q\) describe the transition rates between the reaction compounds.  
It has been shown in [5] that the determinant of the rate matrix in Equation (10) is given as  
\[F=\tau^{-1}[(\ln\det(\mathcal{S}))-\ln\det(T))]. \tag{11}\]  
Both \(\mathcal{S}\) and \(T\) are stochastic matrices, and their determinant cannot be larger than one. If the difference in the compounds' kinetic is not very sharp, the matrix \(T\) has a lower-valued determinant, which means a high negative logarithm. The matrix \(\mathcal{S}\) indicates the overlap between kinetic curves; in the theory of PCCA+, these are the membership functions of the dominant conformations (\(\chi\)). If the overlap is large, the determinant of \(\mathcal{S}\) reduces \(F\). That means the more the membership function overlap, the more the system is stable, or the lower \(\det(\mathcal{S})\), the higher the memory effect. The determinant of \(\mathcal{S}\), \(\det(\mathcal{S})\), is an indicator of the memory effect. Then we can compute the memory effect as  
\[\mathcal{S}=\frac{\langle H^{T},H^{T}\rangle_{\pi}}{\langle H^{T},e_{m} \rangle_{\pi}}, \tag{12}\]  
where \(e_{m}=(1,\ldots,1)^{T}\), \(e_{m}\in\mathbb{R}^{m}\), is a constant vector. It is always possible to compute the minimal memory effect, because in the PCCA+ projection, there exists a rate matrix \(Q\)[24].  
Summarizing, the minimal memory effect is high if \(\det(\mathcal{S})\) is close to zero; it is low if \(\det(\mathcal{S})\) is close to 1. The higher \(\det(\mathcal{S})\), the better the decomposition.
## 3 Materials and Methods
### Machine-Learning-Based Leaf Area Index Inversion Model
#### 3.2.2 ML-Based LAI Inversion Model  
A famous radiative transfer model, the PROSPECT + SAIL (PROSAIL) model, is used to simulate samples with both LAI and reflectance [52; 53]. The parameters of PROSAIL are shown in Table 4. Other parameters were set to the default values for all simulations: leaf structure parameter (1.75), equivalent water thickness (0.015 cm), and leaf mass per area (0.0075 g/cm\({}^{2}\)). After setting all the parameters, the PROSAIL model was used to generate 100,000 samples.  
We chose the random forest regressor (RFR) as the ML algorithm, which is an ensemble method and can model non-linear relationships between predictors and responses [54]. Random forest (RF) has been used as a classifier in remote sensing, but it is also capable of estimating vegetation parameters [55]. Compared to other ML methods, training a RF model is relatively less time-consuming and this model is robust to outliers [56]. N_estimator, max_depth, and min_samples were set to 100, 10, and 2, respectively. The flowchart of the ML-based LAI inversion model is shown in Figure 2. Generally, RTM is used to produce synthetic reflectance and _NDVI_ with different LAIs and other vegetation parameters as input and the RF is trained based on this synthetic dataset. Then the trained RF predicts satellite LAI products with real satellite reflectance and the _NDVI_ as input. All the processes were implemented based on Python with numpy and Scikit-learn packages. The PROSAIL  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Min** & **Max** \\ \hline Chlorophyll content & ug\(\cdot\)cm\({}^{-2}\) & 0 & 60 \\ Carotenoid content & ug\(\cdot\)cm\({}^{-2}\) & 0 & 40 \\ Leaf area index & m\({}^{2}\cdot\)m\({}^{-2}\) & 0 & 6 \\ Leaf inclination & - & \(-1\) & 1 \\ Solar zenith angle & \(\circ\) & 25, 30, 35, 40, 45, 50 \\ Soil brightness & - & 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings in the PROSAIL model. The first four parameters were randomly sampled based on the range between min and max values in the simulation. Solar zenith angle and soil brightness were also randomly sampled based on values as shown in the table.
## 2 System Model and Problem Formulation
### Problem Formulation  
Let \(\mathbf{A}=\{\alpha_{u,j,k},\forall u,i,k\}\) denote the associated variables, \(\mathbf{P}=\{P_{u,m,k},\forall u,m,k\}\) denote the transmit power of the buoys, \(\mathbf{V}\)=\(\{V_{u,k},\forall u,k\}\) denote the flight velocity of the UAVs, and \(\mathbf{\psi}\)=\(\{\psi_{u,k},\forall u,k\}\) denote the flight angle of the UAVs. Our goal is to minimize the completion time of the data collection-offloading missions for all UAVs by the joint optimization of \(\mathbf{A}\), \(\mathbf{P}\), \(\mathbf{V}\), and \(\mathbf{\psi}\). Then, the total mission completion time minimization problem can be formulated as  
\[(\mathrm{P1}):\min_{\mathbf{A},\mathbf{P},\mathbf{V},\mathbf{\psi}} T_{\rm{total}}\] s.t. \[\mathrm{C1}:0\leq V_{u,k}\leq V_{\rm{max}},\forall u\in\mathcal{U },k\in\mathcal{K}\] \[\mathrm{C2}:0\leq\psi_{u,k}<2\pi,\forall u\in\mathcal{U},k\in \mathcal{K}\] \[\mathrm{C3}:g_{u,m,k}\geq\overline{g}_{\rm{co}},\forall u\in \mathcal{U},m\in\mathcal{M},k\in\mathcal{K}\] \[\mathrm{C4}:g_{u,0,k}\geq\overline{g}_{\rm{of}},\forall u\in \mathcal{U},k\in\mathcal{K}\] \[\mathrm{C5}:0\leq P_{u,m,k}\leq P_{\rm{max}},\] \[\forall u\in\mathcal{U},m\in\mathcal{M},k\in\mathcal{K}\] \[\mathrm{C6}:E_{u}^{\rm{total}}\leq\bar{E}_{u},\forall u\in \mathcal{U}\] \[\mathrm{C7}:E_{m}\leq\bar{E}_{m},\forall m\in\mathcal{M}\] \[\mathrm{C8}-\mathrm{C16}:(1)-(3),(9)-(11),(14)-(16).\]  
In problem P1, C1 and C2 are the UAV maximum velocity and maximum angle constraints, respectively. C3 and C4 are the SNR constraints, where \(\overline{g}_{\rm{co}}\) and \(\overline{g}_{\rm{of}}\) are the threshold of the SNR of the buoy-UAV and UAV-OBS, respectively. C5 restricts the maximum transmit power of the buoys. C6 and C7 mean that the energy consumption of the UAVs and buoys should be smaller than their maximum energy. C8-C10 ensure that the UAVs keep apart from each other to avoid collision and do not fly beyond the target area. C11-C13 are the association constraints. C14-C16 are the data transmission constraints of the process of UAV data collection and offloading.
## 4 Discussion
The event-based method aims to statistically compute the number of labels for each event category in the sample, including drinking and non-drinking activities, and then calculate evaluation metrics based on it. Take Figure 14 as an example; after statistical analysis, the trial will obtain four labels of type C, while the quantities of other labels are all zero. Since each participant in this study needs to complete sixteen drinking activities, the completely correct result should have 16 labels for type C, with the quantities of other labels all being zero. When utilizing a support vector machine for drinking activity recognition based on multimodal signals, the event-based method recognizes an average of 15.7 labels of type C per participant, with a standard deviation of 0.56. The average quantities and standard deviations (mean \(\pm\) deviation) of other label categories are 0.25 \(\pm\) 0.54 for \(F\), 0.5 \(\pm\) 1.07 for \(F^{\prime}\), 0.05 \(\pm\) 0.22 for \(D\), and 0.4 \(\pm\) 0.66 for \(I^{\prime}\). The quantities of all other labels are zero. After calculation, a high \(F1\)-score of up to 96.5% can be achieved. Among these results, we can observe that almost all drinking activities are successfully identified, with only an average of 0.05 drinking activities excluded. In other words, only one drinking activity is misclassified as a non-drinking activity out of 320 drinking activities. The remaining cases that are not entirely accurately classified are segmented into several predicted events. Judging from the quantities of \(F\) and \(F^{\prime}\), in most cases, one true event is split into two predicted events. This issue in this part may be further improved through some post-processing methods. Figure 15: Recognition results of drinking activities with multimodal signals and single-modal signals. The multi-sensor fusion approach is only used to identify the drinking activity. There are several limitations to this work. For example, the proposed method cannot estimate the intake volume and recognize the liquid type in the container. Previous research [25] has used wrist-worn sensors and containers with built-in sensors to estimate the amount of fluid intake. In the future, we can assess the hydration status of individuals through long-term monitoring of fluid intake volume. Additionally, the acoustic signals are easily influenced by the surrounding sound. After ensuring that the multimodal fusion approach can improve the performance of drinking activity recognition, we will further explore the issue of noise removal so that the identification system can be more complete. Therefore, the processing of denoising will be researched further in the future.
###### Abstract
The dimension of the individual antenna element is only 6.8 \(\times\) 7 \(\times\) 1 mm\({}^{3}\) (47.6 mm\({}^{3}\)). The isolation value is larger than 12 dB. Therefore, the innovation points of the proposed handset antenna are wideband operation, high efficiency, and high communication capacity with minimized spacing.
## 3 Results and Analysis
### Evaluation of Cooling Energy Performance  
#### 3.1.1 OA Intakes  
Section 3.1.1 discusses the evaluation of the OA intakes by varying MLSP. Figure 4 shows the hourly OA intake rates for each case. Table 4 presents the accumulated times for maximum, partial, and minimum OA intake rates, along with the total accumulated OA intakes.  
The accumulated times for maximum OA intake rates increased progressively from Cases #1 to #7. Meanwhile, the accumulated times for minimum and partial OA intake rates decreased from Cases #1 to #7. This shift occurred because the range of OA temperatures modulated at maximum rates expanded as the MLSP was increased by 1 \({}^{\circ}\)C from Cases #1 to #7. The accumulated OA intakes from Cases #2 to #7 increased by increments of 5, 9, 17, 22, 26, and 27 m\({}^{3}\)/s, respectively, compared to Case #1. This increase was attributed to  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Simulation Cases**} & **MLSP [\({}^{\circ}\)C]** & **References** \\ \hline Case \#1 (basecase) & 18 & ASHRAE Standard 90.1-2019 \\ \hline Case \#2 & 19 & \\ \hline Case \#3 & 20 & \\ \hline Case \#4 & 21 & \\ \hline Case \#5 & 22 & \\ \hline Case \#6 & 23 & \\ \hline Case \#7 & 24 & \\ \hline \end{tabular}
\end{table}
Table 3: Simulation cases by varying MLSP.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Input Parameters**} & **Values** & **Information Source** \\ \hline \multirow{3}{*}{System} & \multirow{3}{*}{Capacity} & Air-cooled chiller & 19,240 W \\ \cline{3-4}  & & Cooling water pump & 292 W \\ \cline{3-4}  & & Variable-speed fan & 0.4 m\({}^{3}\)/s \\ \hline \multirow{3}{*}{Simulation setting} & \multirow{3}{*}{Weather data} & Daejeon, South Korea & IWEC2 \\ \cline{3-4}  & & Run period & Interseason \\ \cline{1-1} \cline{3-4}  & & Time-step & 1 min \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._the reduced frequency of minimum and partial rate periods and the enhanced duration at maximum intake rates. Consequently, as the MLSP was raised, the periods of maximum OA intake rates and, thus, the total accumulated OA intakes increased.