esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Semantic Segmentation-Based Tea Classification Models  
#### 3.3.1 UNet  
The UNet model is a classic semantic segmentation network that evolved from the fully convolutional network [36]. It adopts a symmetric encoder-decoder architecture and incorporates skip connections in the decoder to fuse high-level semantic information with low-level features, thereby enhancing segmentation accuracy and mitigating information loss during the process (Figure 8). Specifically, the UNet architecture is as follows: on the left side of the model are four consecutive downsampling encoder layers. Each layer undergoes two 3 \(\times\) 3 convolution operations followed by ReLU activation and then downsampling through a 2 \(\times\) 2 max-pooling layer, doubling the feature maps while halving the spatial resolution. With the increase in network depth, features of different scales are extracted, and after four consecutive downsampling steps, the model captures more abstract global features, with the receptive field gradually increasing and the spatial resolution decreasing to 1/16 of the original. In the decoding process, symmetric upsampling operations are employed. With each upsampling layer, the number of features is halved while the spatial resolution is doubled. Skip connections are utilized during upsampling by concatenating the corresponding downsampled features with the decoder features to recover local detail features lost during downsampling. After four upsampling layers, the image is restored  
Figure 7: Sample points and data distribution.  
to its original size, followed by convolution with a 1 \(\times\) 1 kernel and sigmoid activation to obtain the final image segmentation result. This architectural design enables the UNet model to exhibit excellent performance in tasks such as remote sensing image segmentation, effectively addressing issues such as inaccurate segmentation and information loss.  
In this study, three band combinations were included, with the SAR + DEM dataset as an example. The input image tile size was 224 \(\times\) 224 pixels, with 69 features. After 4 layers of downsampling operations, the number of features expanded to 512, and the spatial dimensions reduced to 14 \(\times\) 14 pixels. Subsequently, upsampling was performed to restore the original size, followed by a \(1\times 1\) convolution and sigmoid function to obtain the classification probability of tea gardens. A threshold of 0.5 was applied to obtain the classification results, which were then compared with the ground truth label data to compute the binary cross-entropy loss function. The parameters of each layer were updated using error backpropagation to complete the training process.
## 2 Results and Discussion
### Discriminative Value of the D-HS-MultiSPME/Tenax Method for Individual Human Milk Samples  
The discriminative potency of the DHS-multi SPME/Tenax method was evaluated based on the volatile profiles obtained from four individual samples of human milk, analyzed in triplicate. The PCA performed on peak areas of the detected compounds (Figure 2) displays the proximity between replicates, confirming the good repeatability of the D-HS-MultiSPME/Tenax method. In addition, the four individual milk samples are distinctly positioned on the map. Milk samples #2 and #4 are opposed on the first axis, representing 43% of the variance and positively correlated to the content of aldehydes, alcohols, and ketones. These compounds are mostly derived from the oxidation of lipids from milk [25]. Hence, this analysis suggests that milk #4 has begun to undergo lipid oxidation despite the precautions taken to collect the samples. The quantitative and qualitative composition of fatty acids in human milk strongly varies between women, depending on several causes, including genetic, make-up, food intake, stress, and lactational stage [61]. These differences can also be attributed to milk sampling, pooling or freezing conditions before storage. Besides, milk #1 and #3 are opposed on axis 2, representing 19% of the variance. This axis is positively correlated to the presence of terpen. Thus, the analysis suggests that milk #1 and #3 have the highest and the lowest level of terpen, respectively. Different diets of mothers could explain these differences in terpene levels [27]. Overall, these observations illustrate the discriminative validity of the D-HS-MultiSPME/Tenax method for small volumes (i.e., 5 mL) of individual human milk samples. In addition, the D-HS-MultiSPME/Tenax method enables the detection of 60 compounds from individual human milk samples in only one step of extraction. Representatives of all main chemical families reported in the literature on human milk are recovered by  
Figure 2: Principal component analysis performed on the peak areas of the volatile compounds detected in the milk samples of four individual donors. (**a**) Loading plot for volatile compounds (**b**) Scores plot for human milk samples.
## 4 Experimental Evaluation
### Application  
As the focus of this paper is to evaluate the application, we have implemented five neSC applications and generated their respective CPN models using the nesc2 cpn. These applications have the following characteristics: an application only containing neS statements (see Section 3.1.1), more specifically this application (named _App1_) calculates the average of five numbers; an application that has one invoke to collect the temperature, which is a typical WSN application (named _App2_); an application that uses a selection statement (see Section 3.1.2) for aggregating sensed values (named _App3_); an application that calculates the average of five sensed values (similar to _App1_), but usedtogether an iteration statement (named _App4_); and an application that collects the temperature and sends it to another sensor node (named _App5_).  
We evaluated a small piece of code of _App1_ to _App4_ and an entire application was evaluated in _App5_. These applications serve to show how the tool and proposed model are flexible in such way that they can be used to evaluate a small piece of code or an entire application. Prior to present the evaluation itself, it is worth introducing the stopping criteria adopted in the simulations.
# Detection of Rock Mass Defects Using Seismic Tomography
## 4 Application
### Data  
In the physical detection of goals, we employed three-component seismic geophones to capture seismic wave arrival times and waveforms. A total of 27 sensors were deployed in areas with higher microseismic activity and key tunnels within the tantalum-niobium mine, connected via an underground wired network to form a seismic monitoring network. This setup enabled the real-time acquisition and transmission of seismic wave signals, facilitating data analysis and early warning for monitoring personnel. The sensor deployment layout is as illustrated in Figure 9, and their precise deployment coordinates can be found in Table 1.  
Figure 8: Relationship between serial number of goals and open-pit boundary.  
Figure 9: The layout plan of sensor deployment. The sensors are denoted by black triangles, the ore body boundary is denoted by black solid lines, and the main tunnels and operational areas of the mine are denoted by the other colored solid lines.
## 2 Preliminaries
### Adopted Refined Beam Theory  
CUF assumes that the 3D displacement field (\(\mathbf{u}(x,y,z)\)) can be expressed as the generalized displacement \(\mathbf{u}_{s}\) (depending on the \(y\) coordinate) and the arbitrary functions of the section \(F_{s}(x,z)\) (depending on the \(x\) and \(z\) coordinates). Accordingly, the displacement field for 1D theories is  
\[\mathbf{u}(x,y,z)=F_{s}(x,z)\mathbf{u}_{s}(y)\qquad\qquad s=1,2,\ldots,M \tag{6}\]  
where \(M\) and \(s\) represent the number of terms used in the expansion and the sum of these terms, respectively. The \(F_{s}\) section function determines the class of the 1D CUF model. With CUF, Taylor-like (TE) and Lagrange expansion (LE) section functions can be used. There have been many studies in the literature using both extensions with CUF. For TE models, \(M\) determines the polynomial degree, and if \(M\) is equal to 1, this model calculates with first-order shear theory. As \(M\) increases, the number of terms used increases, and the effects included in the calculation increase. For LE models, the number of points determines the order of the polynomial. For example, these polynomials can be used as linear three (L3), four (L4) points, quadratic six (L6), nine (L9), and cubic sixteen (16) points in the CUF framework. The LE model has been used specifically to determine the behaviour of layered composite and FG materials [29; 39; 47; 50; 52; 53; 54]. In this study, L4 and L9 polynomials are used. In the finite element analysis, classical four-node (B4) beam elements are used along the beam axis, providing a cubic approach. The choice of this element in CUF theory does not depend on the choice of sectional functions. FEM is used to separate the beam axis along \(y\).  
Accordingly, the generalized displacement vector \(\mathbf{u}_{s}(y)\). \(N_{j}\) and \(p\) represent the \(j\)th shape function and the order of the shape functions, respectively, where \(j\) represents the sum.  
\[\mathbf{u}_{s}(y)=N_{j}(y)\mathbf{q}_{sj}\qquad\qquad j=1,2,\ldots,p+1 \tag{7}\]  
The vector of FE node parameters is given below with \(\mathbf{q}_{\mathbf{sj}}\).  
\[\mathbf{q}_{\mathbf{sj}}=\left\{q_{u_{x_{sj}}}\quad q_{u_{y_{sj}}}\quad q_{u_{ z_{sj}}}\right\}^{T} \tag{8}\]  
Details on the \(N_{j}\) shape functions can be found in [29; 50].
## 4 Performance Evaluation  
In this section, extensive computer simulations were conducted to study the performance of MRSS and dynamic MRSS (DMRSS). At first, the basic properties of MRSS were analyzed. Afterwards, the impact of approaching and leaving movements were studied. In the third simulation, the feasibility of the MRSS in a WLAN and a Mobile WiMAX [35] were examined. Then, the effect of the ping-pong effect were investigated using various mobility models. Finally, the performances of the MRSS-based handoff algorithm and DMRSS-based handoff algorithm were investigated.  
In all simulations, a log-normal shadowing model in NS2 simulator [36] and the BonnMotion node-movement generation tool [37] were used. A simple straight movement trajectory and the random waypoint mobility model were adopted to simulate a user movement trajectory. Figure 4 shows the example of the straight movement trajectory and Figure 4 shows the example of the random waypoint mobility model. In order to simulate various wireless environments, a WLAN and a Mobile WiMAX  
Figure 3: The flow chart of DMRSS-based handoff algorithm.  
environment were chosen. Table 2 shows the default parameters of the log-normal shadowing model for radio propagation in all simulations.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 4 Discussion  
The Janssen equation is most suited for predicting lateral wall-filling pressure in rigid wall cases, which is applicable to concrete silos, especially circular ones [16]. However, when using square thin steel silos, the Janssen equation predictions do not estimate the real normal pressure, as presented in the results, because it assumes that the lateral wall pressure would be uniform at all levels. However, in a thin-walled silo with a flexible wall, the wall deforms away from the solids, leading to the development of an arching mechanism (solids are transferred from the center to the corners). This will lead to a redistribution of wall pressure inside the silo. This redistribution of wall pressure will be highest in the stiffest areas of the silo (i.e., the corners), and lowest in the flexible middle section.  
As demonstrated in the results, the finite element model can effectively predict the lateral filling pressure within the square silo at any level. The model was utilized to determine the vertical load transmission for the wall and base, whether rigid or flexible. The distributed vertical loads on the flexible wall were reduced by up to 17% with varying slenderness ratios. Thus, the majority of vertical loads are directed to the base. Therefore, the steel wall thickness may be decreased for flexible wall behavior, resulting in a reduction in the structure's weight and enhanced structural efficiency. In this case, wall flexibility will be increased relatively; nevertheless, great care must be taken to ensure that wall deformations remain within codes' limits. This study's findings confirm the previous work of [PERSON] et al. [11], which reported that changes in the lateral filling pressure on the wall can make the wall thickness much thinner. This should be taken into account during the design process.  
The results of this study can be utilized by designers to update design codes. Several unstiffened numerical models with varying silo behaviors, aspect ratios, and wall thick  
Figure 18: Lateral wall pressure comparison between rigid and flexible walls, as well as Janssen. (**a**) Flat model silo \(1.5\times 1.5\times 2.5\) m; (**b**) slender model silo \(0.5\times 0.5\times 2.5\) m.  
nesses have been examined in this work. Further research should be conducted to address the influence of vertical stiffeners or ring beams in numerical models of rectangular silos to clarify their impacts on filling pressure distributions as well as wall deformations, which is beyond the scope of this research.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Damage Detection Using Multiple Pure-Mode Guided Waves
#### 3.3.1 Damage Detection Using Pure S0 Mode  
Pure S0 mode was used to detect the delamination simulated by the Teflon insert and the actual impact damage. A signal comparison between the pristine area, the delamination simulated by the Teflon insert, and the actual impact damage is shown in Figure 13.  
Strong and nondispersive wave packets of S0 mode were observed in all cases, as depicted in Figure 13a. A high signal-to-noise ratio was obtained even in the presence of damage. It was found that no obvious amplitude change was observed in the case of the Teflon insert, whereas a significant amplitude drop was clearly noted due to the presence of impact damage. The frequency spectrum were calculated by using time windowing on the acquired signals to obtain the main wave packet and remove the trailing waves. Similarly, the same phenomena were noted in the frequency spectrum, as shown in Figure 13b. Therefore, pure S0 mode was sensitive to the actual impact damage but not sensitive to the delamination simulated by the Teflon insert. This was because the impact damage, in the form of matrix cracking, fiber breakage, and interlaminar delamination, significantly reduced local stiffness. Therefore, impact damage caused a strong amplitude decrease for the S0-mode guided wave.  
Figure 12: Experimental setup for damage detection using the ABT–ABT on a 2-mm quasi-isotropic CFRP composite plate: (**a**) delamination simulated by the Teflon insert; (**b**) impact damage.
# HERAS: A Modular Matlab Tool Using Physical Optics for the Analysis of Reflector Antennas
###### Abstract  
HERAS is a tool developed in Matlab for the analysis of reflector antennas using physical optics (PO) theory. Its graphical user interface (GUI) and source code are freely available for educational and research purposes. It has the necessity of being a flexible tool to provide adaptability to system engineering requirements and can also be of interest to antenna engineers working on the design of reflector antennas. Due to the increasing demand of broadband services, satellite communications systems are becoming highly complex in order to meet connectivity requirements. To fully exploit the benefits of these systems, multidimensional optimisations are crucial, which call for an efficient estimation of the coverage characteristics. Reflector-based solutions are one of the preferred architectures for very high-throughput satellite (VHTS) systems. A detailed description of HERAS is presented in this paper which has been validated with available commercial software packages. In addition, some examples are described in which the tool has been used for the efficient estimation of VHTS systems performance.  
physical optics; reflector antennas; object-oriented programming; multi-beam antennas +
Footnote †: journal: Sensors
## 3 \(\varepsilon\)-Dependent Solvability Conditions of the Stackelberg Game (9)-(11)  
For a given \(\varepsilon>0\), let us consider the following boundary-value problem in the time-interval \([0,t_{f}]\):\[\frac{dz(t)}{dt}=A(t)z(t)-S_{\rm u}(t,\varepsilon)\lambda_{\rm u}(t)-S_{\rm v }(t)\lambda_{\rm v}(t),\quad z(0)=z_{0},\] \[\frac{d\lambda_{\rm u}(t)}{dt}=-D_{\rm u}(t)z(t)-A^{T}(t)\lambda_{ \rm u}(t)+D_{\rm v}(t)\lambda_{\rm u,v}(t),\quad\lambda_{\rm u}(t_{f})=0,\] \[\frac{d\lambda_{\rm v}(t)}{dt}=-D_{\rm v}(t)z(t)-A^{T}(t)\lambda_ {\rm v}(t),\quad\lambda_{\rm v}(t_{f})=0, \tag{17}\] \[\frac{d\lambda_{\rm u,v}(t)}{dt}=S_{\rm v}(t)\lambda_{\rm u}(t)-S _{\rm u,v}(t)\lambda_{\rm v}(t)+A(t)\lambda_{\rm u,v}(t),\quad\lambda_{\rm u,v }(0)=0,\]  
where  
\[S_{\rm u}(t,\varepsilon)=\frac{1}{\varepsilon^{2}}B_{\rm u}(t)B_{\rm u}^{T}( t)=\left(\begin{array}{cc}0&0\\ 0&\frac{1}{\varepsilon^{2}}I_{r}\end{array}\right),\] \[S_{\rm v}(t)=B_{\rm v}(t)B_{\rm v}^{T}(t),\quad S_{\rm u,v}(t)=B_ {\rm v}(t)G_{\rm u,v}(t)B_{\rm v}^{T}(t). \tag{18}\]  
Based on the results of the book [39] (Section 7.6), we directly have the following assertion.  
Proposition 2: Let Assumptions (A1)-(A6) be valid. Then, for any given \(\varepsilon>0\), the boundary-value Problem (17) has the unique solution \(\mathrm{col}\big{(}z(t,\varepsilon),\lambda_{\rm u}(t,\varepsilon),\lambda_{ \rm v}(t,\varepsilon),\lambda_{\rm u,v}(t,\varepsilon)\big{)}\). Moreover, the Stackelberg game (9)-(11) has the unique open-loop solution \(\big{(}u^{*}(t,\varepsilon),v^{*}(t,\varepsilon)\big{)}\), where  
\[u^{*}(t,\varepsilon)=-\frac{1}{\varepsilon^{2}}B_{\rm u}^{T}(t)\lambda_{\rm u }(t,\varepsilon),\quad v^{*}(t,\varepsilon)=-B_{\rm v}^{T}(t)\lambda_{\rm v}( t,\varepsilon),\quad t\in[0,t_{f}]. \tag{19}\]  
The component \(z(t,\varepsilon)\) of the solution to the boundary-value Problem (17) is the Stackelberg optimal trajectory of the game (9)-(11).
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Contributions
#### 1.5.2 Information Theoretic Quantification of Privacy, Interpretability, and Transferability  
The most important feature of our approach is that the notions of privacy, interpretability, and transferability are quantified by information theoretic measures allowing for the study and optimization of trade-offs (such as trade-off between privacy and transferability or trade-off between privacy and interpretability) in a practical manner.  
5.3 Computation of Information Theoretic Measures without Requiring the Knowledge of Data Distributions  
It is possible to derive analytical expressions for the defined measures provided that the knowledge regarding the data distributions is available. However, in practice, the data distributions are unknown, and thus, a way to approximate the defined measures is required. Therefore, a novel method that employs recently introduced membership mappings [14; 15; 16; 17; 18; 19; 20; 21; 22], is presented for approximating the defined privacy leakage, interpretability, and transferability measures. The method relies on inferring a variational Bayesian model that facilitates an analytical approximation of the information theoretic measures through variational optimization methodology. A computational algorithm is provided for practically calculating the privacy leakage, interpretability, and transferability measures. Finally, an algorithm is presented that provides  
1. Information theoretic evaluation of privacy leakage, interpretability, and transferability in a semi-supervised transfer and multi-task learning scenario;
2. An adversary model for estimating private data and for simulating privacy attacks; and
3. An interpretability model for estimating interpretable parameters and for providing an interpretation to the non-interpretable data vectors.
## 7 Discussion  
All the learning algorithms showed exceptionally better performance in the static environment (Scenario-1). The reason being is that limited link switching was needed, since \(s_{3}\) was the only fastest option. However, with a dynamic environment, where the fastest option alternates between \(s_{1}\) and \(s_{3}\), the link switching occurs at a higher rate, which is a desirable feature for HTS to be able to adapt to weather condition changes. A drawback of this mechanism is linked to an implementation issue rather than the technique itself because the dynamic flow modifications may produce packet drops when the flow rules are being modified in the switch. However, despite the additional delay involved in TCP retransmissions, the average response times were observed to improve. The performance penalty caused by the dynamic modification of flows in OpenFlow switches is a known issue [27; 28; 29]. It was experimentally verified that OpenFlow SDN hardware switches would cause 3 ms-30 ms latency due to flow rule modifications [27]. Updating of the selection probabilities with the SLA algorithm requires a certain amount of time to find the best link when the environmental conditions change. There are possibilities for non-optimal servers to be selected during the transient period due to the delay in learning. If the frequency of the changes in the environment increase, the switching between servers is also expected to increase, causing additional packet losses at the switch ports. With HSLA, the learning happens faster as the selection probabilities are adjusted in a shorter time since only two actions are available at each SLA. Another possible way to reduce these issues is through priority-based flow [30], which will be investigated in the future.