esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It determines a boundary (i.e., threshold), which separates between normal packet loss and packet loss due to attacks. Figure 1: Impact of upstream-node effect on false positive rate: (a) normal sending rate, (b) sending rate under packet dropping attack. Instead of building the classifier using constrained optimization problem, as in one-class SVM [27], the proposed classifier has two main characteristics: (1) it incurs less computational cost and (2) it is most appropriate for networks that are operating under quasi-stable conditions in terms of link quality. In this case, the instances that compose the normal training data set are close to each other and can be grouped into one small bounded interval. * We use four variants of the relaxed flow conservation constraint, depending on the type of monitored node. Three constraints are applied using one-hop knowledge, and the fourth one is applied while using two-hop information. To obtain the two-hop information, we propose the two-hop energy-efficient reporting scheme, which is complemented with some security mechanisms, such as authentication, encryption, filtering of ratings, and isolation. * We analyze the security of the proposed IDS as well as its resilience probability against selective routing attacks and unfair ratings. Contrary to the state-of-the art methods, we show that the relaxed flow conservation constraint prevents the upstream node effect. We also analytically and experimentally evaluate the performance of the proposed IDS-based relaxed flow conservation constraints. The rest of the paper is organized, as follows: Section 2 presents the related work. Section 3 provides the system model and problem statement. In Section 4, we justify our design choices. Section 5 describes the intrusion detection system that is based on the relaxed flow conservation constraints. In Section 6, we analyze the security of the proposed IDS against some attacks as well as its resilience probability against selective routing attacks and unfair ratings. In Section 7, we evaluate the resiliency of the relaxed flow conservation constraint against upstream-node effect and then compare it with two state-of-the-art features. The analysis of performance complexity, and node lifetime estimation are provided in Section 8. In Section 9, we present the simulation results. Finally, Section 10 concludes the paper.
## 4 Numerical Procedure and Results
### FAD Curve Based on the Volumetric Method  
To empower the reliability assessment of the steel pipe with a semi-elliptical defect in the designing, the FAD curve based on the stress intensity factor has been plotted in this paper. The expression used to construct the FAD curve is described in Equation (6) above. We refer to Table 2 in order to find out the coordinates of the operational condition.  
Then, the FAD curve is represented in Figure 7. From the diagram, the operational condition or the assessment point of coordinates \((S_{r},\ L_{r})\) is located in the safety zone, as described in the theoretical part, which means that the structure with a defect of \(\mathrm{a/t}=0.2,\ 2c/\mathrm{t}=4\) subjected to a pressure of 60 bar is safe, and no failure occurs. Moreover, the safety ratio related to the design circumstance is determined by the following rapport:  
\[f_{\mathrm{s}}\ =\ \frac{OD}{OA} \tag{12}\]  
An acceptable configuration is generally examined by comparing the calculated safety ratio with the usual value \((f_{\mathrm{s}}\ =\ 2)\)[26]. In our case, we obtained a value of \(f_{\mathrm{s}}\ =\ 4.23>f_{\mathrm{s}}\ =\ 2\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\sigma_{\mathit{eff}}\) & \(X_{\mathit{eff}}\) & K=\(\sigma_{\mathit{eff}}\sqrt{2\pi X_{\mathit{eff}}}\) & \(\sigma_{\mathit{app}}\)=\(\frac{PD}{2T}\) & \(R_{\mathit{m}}\) & \(K_{\mathit{r}}\) =\(\frac{K_{\mathit{c}}}{K_{\mathit{ic}}}\) & \(S_{\mathit{r}}\)=\(\frac{\sigma_{\mathit{app}}}{R_{\mathit{m}}}\) \\ \hline
311 & 0.17 & 10.161 & 115.92 & 440 & 0.106 & 0.263 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinates of the operational condition.  
Figure 6: Schematic of polynomial fit and gradient of \(\sigma_{\mathit{yg}}\).  
Figure 7: Failure assessment diagram to establish the operational condition.
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Research and Design
#### 2.7.4 Model Realization  
After decomposition, the high frequency components and ER will be predicted by LSTM. The realization of the LSTM model needs to consider the hidden layer, the number of neurons in each layer, the size of the training batch, the activation function and the optimizer. Too many hidden layers and neurons will increase the computer load, increase the workload of the computer, and reduce work efficiency. On the contrary, too little can easily lead to underfitting of training data, which in turn affects the prediction accuracy. In order to prevent overfitting, a dropout layer is added for each hidden layer. In addition, during the training process, the Adam optimizer is currently considered to be the best performing optimizer. See Table 3 for detailed parameter information.  
Using the decomposed low-frequency components as the input to the RF. Here, Bootstrap, max_depth and n_estimators are mainly considered. Generally speaking, if n_estimators is too small, it is easy to underfit, and if it is too large, it will be time-consuming. Therefore, it is necessary to select a moderate value for parameter tuning. Meanwhile, due to the large amount of data, max_depth needs to be set. The above pa  
Figure 8: Decomposition result of VMD measuring point deformation sequence (**(a)** EX5, **(b)** EX2).  
rameters are determined by grid search and 5-fold cross-validation. The traversal range is determined based on historical experience and manual experiments. Relevant parameters are shown in Table 4.
## 3 Improved Strategy for the Uncalibrated Visual Servo of a Hyper-Redundant Space Manipulator
### Improved Homography-Based Task Function
From Equation (7), we have \[G=K^{-1}HK=\beta I.\] (15) Taking two feature points, the coordinates of these points in \(F\) and \(F^{*}\) can be described as \[M_{i}=\beta M_{i}^{*},M_{j}=\beta M_{j}^{*}.\] (16) Obviously, regardless of in which frame this is expressed, the distance between \(P_{i}\) and \(P_{j}\) is constant. That is, \[\|M_{i}-M_{j}\|=\|M_{i}^{*}-M_{j}^{*}\|=\beta\|M_{i}^{*}-M_{j}^{*}\|.\] (17) This means that \(\beta=1\), which implies \(H=I\) and \(G=I\). Thus, \(R=I\) and \(t=0\). This new task function takes full advantage of the property that the homography matrix has eight degrees of freedom. This not only reduces the dimensions of the task function but also simplifies the state space of the visual servo system that needs to be estimated online, so the real-time performance of system is further improved. Meanwhile, this causes the total Jacobian \(J_{t}\in\mathbb{R}^{\otimes\otimes}\), which maps the task function and joint space of the hyper-redundant manipulator, to become redundant. Therefore, this redundancy can be used to finish some secondary tasks such as limiting the joint angle without a manipulator kinematic model during the servo process. Obviously, this improves the robustness of system to changes in the kinematic parameters of manipulators. **Remark 1**. : _A flaw in the inhomogeneous solution of the homography matrix is that if the true value of \(h_{9}\) is 0 or very close to 0, the estimated homography matrix becomes very unstable. In fact, this situation rarely occurs in the visual servo. In static positioning tasks, the task function generally changes near to the desired gradient direction. As long as \(\hat{h}_{9}\) is not equal to 0 at the initial position, its value will never become 0 during the servo process. In tracking tasks, the task function is always close to the desired value._
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 3 Results and Discussion
### Application Examples  
In order to verify the reliability of the developed BIS system, we carry out two kinds experiments with different samples. First experiment used a parallel RC circuit, and the second experiment used liquid solution--i.e., mixed water and milk with different concentration.
## 4 Conclusions  
The generation of multiple high-order Stokes of cascade stimulated rotational Raman scattering in compressed hydrogen pumped by \(\sim\)1.2 ps pulses at a wavelength of 1030 nm has been investigated. Pump polarization, energy, and the focusing conditions, as well as hydrogen pressure, were optimized to achieve the efficient generation of broadband cascade rotational Stokes radiation. When using the circular polarization of the pump beam, a twofold decrease in the stimulated rotational Raman scattering threshold was observed with the suppression of vibrational Stokes modes, which led to the conversion efficiency being increased to 42% at a hydrogen pressure of 5 MPa. Furthermore, the use of a white light supercontinuum seed made it possible to generate rotational Stokes combs up to the fourth order in a wide wavelength range of \(\sim\)1.1-1.4 \(\upmu\)m and reduce the threshold by another four times with an increase in conversion efficiency to 52%. Under optimal conditions, the maximum output pulse energy contained in the combs of the rotational Stokes components exceeded 3 mJ, with a spectral bandwidth sufficient for the generation of compressed to \(\sim\)14 fs transform-limited pulses at a central wavelength of 1.2 \(\upmu\)m. Although such extreme compression would require more complex techniques, using a pair of SF11 prisms, the first-order rotational Stokes pulses were compressed to 152 fs, which was approximately eight times shorter than the pump pulses.  
Figure 7: Temporal profile (solid line) of first-order rotational Stokes pulses after compression, retrieved from the SHG-FROG measurement, and the retrieved temporal phase (dashed line).  
**Author Contributions:** Conceptualization, A.M.R.; methodology, P.M. and A.M.R.; validation, A.P., P.M. and A.C.; formal analysis, A.P., P.M., A.C. and A.M.R.; investigation, A.P. and A.C.; resources, A.P., P.M., A.C. and A.M.R.; data curation, A.P. and A.C.; writing--original draft preparation, P.M. and A.P.; writing--review and editing, A.M.R. and A.P.; visualization, A.P., P.M. and A.M.R.; supervision, A.M.R.; project administration, A.M.R.; funding acquisition, A.M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Research Council of Lithuania, grant number S-MIP-23-74.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Acknowledgments:** We are grateful to [PERSON] for manufacturing the separator mirror. Two authors are grateful for NATO SPS G5734 fellowships.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Background and Related Work
### Neural Machine Translation
#### 2.1.3 NMT with Attention Mechanism  
Recently, fully attention-based NMT has shown promising performance. In particular, the attention mechanism has worked as a driving force in text feature extraction rather than having an auxiliary role. Among them, Transformer is one representative, which is a fully attention-based model from Google [15].  
Different from prior RNN- or CNN-based models, Transformer is a complete attention-based NMT model. That is, it is of self-attention with a feed-forward connection, which can be a feature extractor allowing the entire sentence to be \"read\" and modeled once. It is a common practice to stack multiple layers, which leads to an improved translation quality.  
**Formulating Self-Attention Layers.** The attention mechanism is calculated across the decoder and encoder in Equations (3) and (4):  
\[e^{ii}=a(s_{j-1},h_{i}), \tag{3}\]  
\[a_{ji}=\frac{\exp(e_{ji})}{\sum_{k=1}^{m}\exp(e_{kl})}, \tag{4}\]  
where \(e_{ji}\) is an alignment score, a is an alignment model that scores the match level of the inputs around position \(i\) and the output at position \(j\), \(s_{(j-1)}\) is the decoder hidden state of the previously generated word, and \(h_{i}\) is the encoder hidden state at position \(i\). The calculated attention vector is then used to weight the encoder hidden states to obtain a context vector as  
\[c_{j}=\sum_{i=1}^{n}a_{ji}h_{i}, \tag{5}\]  
Figure 1: A standard NMT model based on the encode-attend-decode modeling approach.  
This context vector, is fed to the decoder along with the previously generated word and its hidden state to produce a representation for generating the current word. A decoder hidden state for the current word \(s_{j}\) is computed by  
\[s_{j}=g(s_{j-1},y_{j-1},c_{j}), \tag{6}\]  
where \(g\) is an activation decoder function, \(s_{(j-1)}\) is the previous decoder hidden state, and \(y_{(j-1)}\) is the embedding of the previous word. The current decoder hidden state \(s_{j}\), the previous word embedding, and the context vector are fed to a feed-forward layer \(f\) and a softmax layer to compute a score for generating a target word as output:  
\[P(y_{j}|y_{<j},x)=softmax(f(s_{j},y_{j-1},c_{j})), \tag{7}\]
## 3 Proposed Real-Time Data Fusion Method Based on a New Distance Metric
### Data Fusion for Object Information
#### 3.1.3 Proposed Metric for Evidence Distance
\begin{bmatrix}Q(\mathrm{E},\mathrm{U})\\ Q(\mathrm{N},\mathrm{U})\end{bmatrix} . \tag{11}\]  
Let \(Q/A\) be the Schur complement of \(A\) in \(Q\), which is defined by \(C-B^{\mathrm{T}}A^{-1}B\). Then, \(Q\) is positive definite if and only if the following properties hold,  
\[C1:A\succ 0,\quad C2:Q/A=C-B^{\mathrm{T}}A^{-1}B\succ 0. \tag{12}\]  
C1 is satisfied by property of identity matrix \(A\). C2 can be expressed as  
\[1>Q(\mathrm{E},\mathrm{U})^{2}+Q(\mathrm{N},\mathrm{U})^{2}\] \[\Rightarrow 1>\left(\frac{\sum_{x\in\mathrm{E}\cap\mathrm{U}}w(x)}{\sum_{x \in\mathrm{E}\cup\mathrm{U}}w(x)}\right)^{2}+\left(\frac{\sum_{x\in\mathrm{N} \cap\mathrm{U}}w(x)}{\sum_{x\in\mathrm{N}\cup\mathrm{U}}w(x)}\right)^{2}= \left(\frac{w(\mathrm{e})}{w(\mathrm{u})}\right)^{2}+\left(\frac{w(\mathrm{n} )}{w(\mathrm{u})}\right)^{2} \tag{13}\] \[\Rightarrow (w(\mathrm{e})+w(\mathrm{n}))^{2}>w^{2}(\mathrm{e})+w^{2}( \mathrm{n}). \tag{14}\]  
The relation (13) can be derived by Definition 1 in (8) and \(\mathrm{E}\cup\mathrm{N}=\mathrm{U}\). Inequality (14) holds due to \(w(\mathrm{u})=w(\mathrm{e})+w(\mathrm{n})\), and \(w(\cdot)>0\). Thus, (10) is an induced norm [24; 46], which implies that it satisfies all of four conditions on the metric distance. We illustrate an example of the proposed distance metric in Figure 3. In both cases, Observers 1 and 2 have the same Jousselme distance given by 0.7649 due to the symmetry of confidences of \(\mathrm{E}\) and \(\mathrm{N}\). In our approach, given the weights as \(w(\mathrm{e})=2\), \(w(\mathrm{n})=1\), and \(w(\mathrm{u})=3\), we obtain new distance \(d_{k}^{12}\) for each case as 0.7147 and 0.8124, respectively. The result can be explained as follows. We assign high importance to \"existence\" by \(w(\mathrm{e})>w(\mathrm{n})\). For the case of \(m_{k}^{1}(\mathrm{E})=0.9\) and \(m_{k}^{2}(\mathrm{N})=0.6\), the level of conflict between confidences is high, but Observer 1 is highly confident of existence, i.e., \(m_{k}^{1}(\mathrm{E})=0.9\). In this case, it is regarded that two confidence vectors have a shorter distance than that by the conventional metric. Contrary to this, Observer 1 in case 2 has relatively low confidence in existence than case 1, i.e., \(m_{k}^{1}(\mathrm{E})=0.6\). In spite of the same degree of conflict, by putting more emphasis on \(\mathrm{E}\), the dissimilarity between the confidences become greater. Therefore, we obtain the distance in case 2 given by \(0.8124>0.7649\), which will affect the result of data fusion. Next, we explain how the existence of the object is determined based on the proposed distance.
# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 3 Theoretical Model
### The Proposed Object Detection Network
#### 3.2.4 Improvement of Multi-Scale Feature Fusion Network  
The fusion of multi-scale features is a key design of YOLOv5 object detection. In the neck network, given the input feature \(P_{i}^{in}\) of level \(i\), YOLOv5 converts the intermediate layer feature \(P^{td}\) from different network levels to suit the resolution of the current input feature level \(i\). Then, the feature fusion is completed using channel addition, and output feature \(P_{i}^{out}\) from level \(i\). The intermediate feature \(P^{td}\) and output feature \(P^{out}\) in the neck network are shown in Equations (9) and (10).  
\[P_{i}^{td}=Conv\big{(}P_{i}^{in}+Resize\big{(}P_{i+1}^{td}\big{)}\big{)}, \tag{9}\]  
\[P_{i}^{out}=Conv\big{(}P_{i}^{out}+Resize\big{(}P_{i-1}^{out}\big{)}\big{)}, \tag{10}\]  
where _Conv_ represents convolutional operations related to feature processing, and _Resize_ represents operations used for resolution matching.  
However, this simple fusion method fails to distinguish between feature maps and neglects the differences between them. Since feature maps of different resolutions contain different information, their importance for fusing input feature maps varies. Moreover, deep feature maps contain stronger semantic information while shallow feature maps contain stronger positional information. Therefore, building upon the framework discussed in Section 3.2.1, we incorporate the concept of a Bi-Directional Feature Pyramid Network (BiFPN) [49] and reassess the contribution of different scale features to output features. The specific structure of the feature fusion layer is illustrated in Figure 7.  
Figure 6: The architecture of C3 TR.  
The improved neck network first introduces bi-directional cross-scale connections to aggregate the input features of levels P3, P4, and P5, ensuring the integrity of the input features. Secondly, a fast normalization fusion method is designed, using a trainable parameter \(w_{i}\) as the weight for different resolution features. Through the optimization of trainable weights, the feature representation capability of the network can be further improved. The improved feature fusion formula is illustrated in Equations (11) and (12):  
\[P_{i}^{td}=Conv\Bigg{(}\frac{w_{1}P_{i}^{in}+w_{2}\textit{Resize}\big{(}P_{i+1 }^{td}\big{)}}{w_{1}+w_{2}\text{+}\epsilon}\Bigg{)}, \tag{11}\]  
\[P_{i}^{out}=Conv\Bigg{(}\frac{w_{1}^{\prime}P_{i}^{in}+w_{2}^{\prime}\textit{ Resize}\big{(}P_{i}^{td}\big{)}+w_{3}^{\prime}\textit{Resize}\big{(}P_{i-1}^{ out}\big{)}}{w_{1}+w_{2}+w_{3}+\epsilon}\Bigg{)}, \tag{12}\]  
Particularly in MMW images, where small objects may have low resolution and fewer features, weight-based feature fusion can more effectively represent small object features from different resolutions. Furthermore, the cross-scale design can address the issue of feature loss that arises from the deepening of the network, which is highly advantageous for detecting small concealed objects.
## 3 Results and Discussion  
### Cross-Linking Degree of PCS/DVB  
The Si-H bond exhibits the weaker bond energy (303.8 KJ/mol) in the structure of PCS. The cross-linking behavior of PCS can be conducted by vinyl groups in DVB by self-cross-linking through dehydrogenation. DVB itself can also undergo self-polymerization [37]. The gel content of PCS/DVB is shown in Figure 2a. No gel was left after 4 h at 120 \({}^{\circ}\)C for PCS and DVB, which indicates no cross-linking at the temperature of 120 \({}^{\circ}\)C after 4 h. It is worth noting that DVB quickly evaporates and depletes at this temperature, while the gel content of the homogeneous mixture of PCS and DVB was outstanding, suggesting a cross-linking reaction between PCS and DVB. The gel content was as high as 81.2% at the mass ratio of PCS to DVB of 1:0.5. In comparison, the gel contents of the other three were much lower than 81.2%, indicating that when the DVB content is low, the cross-linking between PCS molecular chains is linked by the DVB, while at the higher DVB content, there is a greater chance of DVB molecules meeting, leading to self-cross-linking and depletion, which result in inefficient cross-linking of PCS by DVB. Therefore, in this work, the PCS-to-DVB mass ratio was 1:0.5 for the subsequent tests.  
The FTIR spectra of DVB, PCS, and PCS/DVB are shown in Figure 2b, and detailed information is given in Table 2. For DVB, the absorption bands at 3100-3000 cm\({}^{-1}\) and 3000-2850 cm\({}^{-1}\) are attributed to the stretching of C-H in benzene and the stretching of C-H in methylene, respectively. The absorption bands at 910 cm\({}^{-1}\) -700 cm\({}^{-1}\) are attributed to the out-of-plane bending of C-H in benzene, while the absorption peak at 989 is the out-of-plane bending of C-H in vinyl. For PCS, there are three absorption peaks at 2100 cm\({}^{-1}\), 1250 cm\({}^{-1}\), and 830 cm\({}^{-1}\), which are attributed to the stretching of Si-H, the deformation of Si-CH\({}_{3}\), and the stretching of Si-C in PCS, respectively.  
In comparison with PCS and DVB, the FTIR curve of PCS/DVB contains both PCS and DVB absorption behaviors, such as the absorption peaks at 3015 cm\({}^{-1}\), 1595 cm\({}^{-1}\), 1500 cm\({}^{-1}\), 1445 cm\({}^{-1}\), 1410 cm\({}^{-1}\), and 1359 cm\({}^{-1}\) resulting from DVB, while the absorption peaks at 2100 cm\({}^{-1}\), 2150 cm\({}^{-1}\), and 830 cm\({}^{-1}\) result from PCS. It is worth noting that the C=C absorption peak at 1630 cm\({}^{-1}\) disappears, the absorption peak intensity of Si-H at 2100 cm\({}^{-1}\) and Si-CH\({}_{3}\) at 1250 cm\({}^{-1}\) decreases, and the Si-H group is depleted, which indicates cross-linking behavior between PCS and DVB.
# Ten Years of TerraSAR-X--Scientific Results
## 3 Contents of this Special Issue  
The 20 papers of this special issue cover six major disciplines. Five papers [1; 2; 3; 4; 5] deal with interferometric applications for land surface deformation mapping. A key success factor here is the high resolution, which enables the mapping of smaller landslides, of single buildings and of infrastructure.  
Another five papers [6; 7; 8; 9; 10] demonstrate the success of TerraSAR-X in the field of oceans and wetlands exploiting the high resolution and the multi-polarization capabilities of TerraSAR-X. As it turned out, not only the space segment but also the ground segment performance such as reliability and fast near real-time services contributed to the success of TerraSAR-X in maritime applications. Three papers [11; 12; 13] cover such operational aspects and the scientific and operational use of TerraSAR-X data.  
Four papers [14; 15; 16; 17] deal with the radiometric and geometric performance validation and with the new methods which made TerraSAR-X the first SAR sensor with geodetic accuracy. Two papers about mapping urban environments [18; 19] and one about the cryosphere [20] finalize the different topics covered in this special issue.  
From the 25 submissions for this special issue, 20 could be accepted and 5 had to be rejected or were withdrawn.  
Of course, this collection is only a snapshot in time and many more papers have been published in the years before, demonstrating the success of this mission. A quick search for TerraSAR-X on Google scholar delivers more than 20,000 results!