esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 5 Experiments
### Experimental Results  
We train a shadow model with the same architecture as the target model using a non-overlapping shadow dataset that has the same distribution as the target dataset. Then, we apply GLO-MIA on different GNN models. The number of perturbed graphs is set to 1000 for each dataset, which ensures a sufficient number of perturbed samples for obtaining an appropriate threshold, thereby enabling GLO-MIA to achieve higher attack accuracy, as analyzed in Section 5.4. To avoid the impact of randomness, we conduct GLO-MIA five times in all experiments and report the average results.  
We compare GLO-MIA with the gap attack, and the results are shown in Table 2. In Table 2, we record the \"train-test gap\", which represents the gap between the training and testing classification accuracy to measure the overfitting of the target model. The best attack performance for each dataset is highlighted in bold. The experimental results show that GLO-MIA is effective on different GNN models and datasets. For example, on GraphSAGE trained on the DD dataset, it is possible to achieve an attack accuracy of 0.738, which is 5.6% higher than that of the gap attack. On the ENZYMES dataset, the highest attack accuracy can reach 0.825, which is 8.5% higher than that of the gap attack. Furthermore, our experimental results show that the attack performance on the GIN models is lower than that on other GNN models in most cases. For example, the attack accuracy achieved on GIN trained on the DD dataset is 0.713, surpassing that of the gap attack by only 2.5%, which is lower than those of other models on the DD dataset, even though it performs more overfitted (train-test gap = 0.376). This phenomenon is also observed on the ENZYMES dataset. These observations may suggest that GIN may have higher inherent robustness against GLO-MIA. In contrast, GraphSAGE is relatively more vulnerable to GLO-MIA, which is likely due to its aggregation mechanism being more sensitive to feature perturbations in graphs.  
Moreover, we compare GLO-MIA to two advanced threshold-based MIAs on graph classification tasks using prediction probability vectors under the same conditions as in Table 2. Since the threshold selection strategies are different and AUC is a threshold-independent evaluation metric, we adopt AUC to assess the performance of GLO-MIA and the probability-based MIAs. The performance of these MIAs is shown in Table 3, with the best attack performance for each model-dataset combination highlighted in bold. We further visualize the results in Figure 2. From the figure, we can see that GLO-MIA achievesperformance close to that of the probability-based MIAs and, in most cases, outperforms the cross-entropy-based MIA. This further confirms the effectiveness of GLO-MIA.
## 2 System Model
### Adaptive Channel Estimation
If Equation (17) converges, the estimated channel is given by  
\[\hat{\mathbf{H}}=\text{invec}\{(\hat{\mathbf{\Lambda}})^{*}\} \tag{19}\]  
The pseudocode of Adaptive channel estimation based on the IPNLMS algorithm is presented as follows in Algorithm 1:  
```
input : \(X\), \(Y\), \(M_{\tau}\), \(N_{\
u}\), \(L\), \(N_{d}\), \(s\), \(\alpha\), \(\delta\) output: \(\mathbf{H}\) for\(n=1:(M_{\tau}-L)(N_{\
u}-2K)\)do

Perform overall Doppler compensation \(U_{l^{{}^{\prime}},k^{{}^{\prime}}}=X_{[l-l^{{}^{\prime}}]_{\text{M}},[k-k^{{}^ {\prime}}]_{\text{M}}}e^{\varphi(l^{{}^{\prime}}-lL^{{}^{\prime}}-k)}\), according to Equation (12);

Obtain the error between the output of estimated channel and the received signal by Equation (15);

Obtain the update equation by Equations (16)-(18);

end for

Obtain the channel estimation, according to Equation (19). ```  
**Algorithm 1**Pseudo codes of Adaptive channel estimation based on IPNLMS
## 5 Parametric Studies
### Effect of Cylinder Diameter (\(d\))  
The diameters of concrete cylinders used for the parametric study are from 100 mm to 550 mm at an increment of 50 mm. The variation of \(\varepsilon_{cc}\) due to the increase of concrete diameter (\(d\)) is presented in Figure 13a,b,f. It can be observed that the \(\varepsilon_{cc}\) of concrete increases up to 84.14% when \(f^{\prime}_{co}\) increases from 5 MPa to 50 MPa and d increases from 100 mm to 550 mm at constant \(E_{f}\) and \(nt\). When \(d\) increases from 100 mm to 550 mm with an increase of \(E_{f}\) from 110 GPa to 245 GPa, the decrement of 25.62% occurred in \(\varepsilon_{cc}\). Therefore, it can be visualized that at lower diameters, the effect of \(E_{f}\) on \(\varepsilon_{cc}\) is significant. Similarly, when the thickness of FRP increases from 0.15 mm to 1.05 mm at the same increase of \(d\), an increase of only 6.82% occurred. However, at lower \(d\), the effect of increasing the thickness of FRP is more significant.
## 2 Materials and Methods
### Analytical Method: Equivalent Source  
For scenarios 5 and 6, the electric field was modeled analytically using the plane-wave spectrum theory [26; 27; 28]. It represents the spatial distribution of each field component over a transverse plane as a superposition of the plane waves propagating along different directions defined by the couplet \(\mathbf{K}=\text{k}_{\text{x}}\hat{\mathbf{x}}+\text{k}_{\text{y}}\hat{\mathbf{y}}\), also called the plane wave spectrum (PWS). The PWS of an electric field phasor component \(\mathbf{E}(\mathbf{R},z_{0})\) over a plane \(\Psi\) identified by \(\text{z}=\text{z}_{0}\) and \(\mathbf{R}=\text{x}\hat{\mathbf{x}}+\text{y}\hat{\mathbf{y}}\) is expressed as:  
\[\overset{\text{\tiny{A}}}{\mathbf{E}}(\mathbf{K},\text{z}_{0})=\ \int_{\mathbf{R}}\mathbf{E}(\mathbf{R},z_{0})\text{e}^{|\mathbf{K} \cdot\mathbf{R}}\text{d}\mathbf{R}. \tag{8}\]  
The strength of this approach is its ability to represent the propagation of a complex field topography through space. The PWS over any plane parallel to \(\Psi\) located at distance l in a homogenous medium is computed by multiplying the PWS at \(\text{z}=\text{z}_{0}\) by the propagator \(\text{P}(\mathbf{K},1)=\text{e}^{-|\text{k}_{\text{z}}|}\):  
\[\overset{\text{\tiny{A}}}{\mathbf{E}}(\mathbf{K},\text{z}_{0}+\text{l})= \overset{\text{\tiny{A}}}{\mathbf{E}}(\mathbf{K},\text{z}_{0})\ \text{P}(\mathbf{K},1)\, \tag{9}\]  
where k\({}_{\text{z}}\  
For exposure case 6, the total transmitted field spectrum is given as (refer to Appendix A for more details):  
\[\mathbf{E}_{\text{tr}}^{\text{\tiny{A}}\parallel}(\mathbf{K})=\mathbf{\Pi}_{1}( \text{I}+\text{P}(\mathbf{K},2\text{d})\mathbf{\Gamma}_{1})^{-1}\mathbf{E}_{ \text{inc}}^{\text{\tiny{A}}\parallel}(\mathbf{K})\, \tag{12}\]  
where I is the identity matrix, d is the PEC-phantom separation distance, \(\mathbf{\Gamma}_{1}\) is the spectral reflection coefficients at the phantom interface given in [26]. The H-field spectrum is calculated as [28]:  
\[\mathbf{\hat{H}}_{\text{tr}(\text{m})}=\frac{1}{\mu\omega}(\mathbf{k})\times \left(\mathbf{\hat{E}}_{\text{tr}}\right). \tag{13}\]  
The spatial field components (\(\mathbf{E}\) and \(\mathbf{H}\)) are retrieved using the inverse Fourier transform of the field spectra. The TPD is calculated as [6]:  
\[\text{TPD}=\iint_{\text{A}}\text{Re}[\mathbf{E}\times\mathbf{H}^{\ast}]. \frac{\mathbf{ds}}{\text{A}}\, \tag{14}\]  
where ds is the integral variable vector with the normal direction to the integral area A on the body surface. All results are provided for an averaging area A of 1 cm\({}^{2}\) (except 2-dimensinoal TPD distributions provided in Sections 3.2 and 3.3). Note that the TPD is identical to the absorbed power density as defined in [6] and to the epithelial power density as defined by [7].
## 4 The Scheduler Architecture
### Queue Management System
#### 4.1.1 Reference Scheduling Technique
We fuse the EDF scheduling technique with the \({}^{CH}\!/_{\theta}\) policy to propose a new and improved EDF technique that takes advantage of the knowledge of deadline information as presented in Algorithm 2. In the case of the \({}^{CH}\!/_{\theta}\) mechanism, when the deadlines are known upon arrival, it allows the modification of the queue order to adequately utilize the EDF policy. ```
1: Arrival of new task \(A_{i}\)
2:\(k\) := \(w_{i}\)
3:Insert the \(A_{i}\) at the end of queue \(k\)
4:Initialize \({}^{CH}\!/_{\theta}\) service
5:\(k_{0}\) := \(\underset{k=1...K}{\operatorname*{argmax}}(\sum c_{k}\mu_{k}/\theta_{k})\)
6:\(P_{i}\) = \(\underset{A_{i}\in P_{i}^{\theta}}{\operatorname*{argmin}}(q_{j})\)
7:If (\(q_{i}<t\)) then drop \(A_{i}\)
8:else provide service to \(A_{i}\)
9:Return to 1
```  
**Algorithm 2** An Improved EDF version (I-EDF)
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
###### Abstract  
Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.  
**Dataset:** 10.5281/zenodo.1193562  
**Dataset License:** CC-BY  
**Keywords:** IoT; LPWAN; Sigfox; LoRaWAN; localization; fingerprinting
## 3 The Timepix3 Radiation Monitor
### Clustering  
The interaction of a single particle with the Timepix3 Radiation Monitor typically results in a multi-pixel experimental signature, i.e., in more than just one pixel measuring a non-zero ToT. This can be due to (i) particles arriving with a diagonal trajectory with respect to the module, hence crossing more than just one pixel, or (ii) charge carriers released by the incident particle drifting to nearby pixels before being collected by the electrodes [24; 43; 44]. In both cases, the clusters of pixels from the particle hit must be reconstructed by combining the time and space information via dedicated clustering algorithms [46; 47; 48]. For the analysis presented in this paper, a Data Processing Engine (DPE algorithm) [48] developed by Advacam within an ESA project has been used, owing to its computational efficiency for the large data sets collected (millions of particles per configuration).  
Figure 5 illustrates the time evolution of a cluster formed by the orthogonal hit of an 5.6 MeV alpha particle on the Timepix3 sensor. At first (i.e., ToA = 0 ns), the charge is released exclusively in the pixel hit by the particle (unless the hit occurs at pixel boundaries, in which case it can be distributed among a maximum of four). Subsequently, due to the charge diffusion process, the nearby pixels begin to collect a portion of the released charge, expanding the dimension of the cluster until the full size is reached (with the external pixel having a ToA that exceeds 100 ns in the case shown). The extent of this effect is determined by the charge collection time, which, in turn, is determined by the applied sensor bias and by the depth of the energy deposition [49; 50].  
Moreover, if the sensor is partially depleted (as is the case for the Timepix3 Radiation Monitor presented in this work), then the charge collection process is slow and the diffusion effects are enhanced, leading to clusters with more pixels. This feature is exploited in this work to reduce the collected energy per pixel in order to operate in the linear regime of the sensor for the large majority of incoming particles.  
When tilted at an angle, the particle tracks inside the detector cross over several pixels. In such cases, the combined effect of the diagonal trajectory of the particle and the charge diffusion process lead to the formation of elongated clusters, which can be exploited to extract information about the direction of the hit.
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 2 Related Works
### RGB-IR Re-ID Based on GAN Networks  
Another way to solve RGB-IR Re-ID problems is to use a generative adversarial network (GAN) to generate cross-modality images. This method can reduce the differences among modalities and provide data enhancement capability. Since [PERSON] et al. [32] proposed GAN, GAN-based methods have gained popularity in the field of deep learning. Additionally, GAN has been applied in RGB-IR Re-ID tasks. [PERSON] et al. [33] uses a GAN to distinguish the features of different modalities, a generator to extract shared features, and a discriminator to distinguish the unique features of each modality and different modalities to improve modal classification. [PERSON] et al. [34] uses cycleGAN to generate cross-modality images and applies the generated images at the same pixel and feature levels to reduce the differences among modalities. [PERSON] et al. [35] uses a GAN to generate cross-modality paired images and then uses modality paired images for feature extraction. [PERSON] et al. [36] combines a feature unwrapping representation with a GAN, separates ID-discriminative features and ID-excluded features, and then performs separate learning tasks. The above use of GANs to solve RGB-IR Re-ID problems involves set-level alignment, but misalignment will occur in some cases, thus reducing the performance of the network. [PERSON] et al. [37] uses a GAN to first generate paired images and align them at the instance level. Then, the paired images are input into the set-level encoder for further alignment. Moreover, the learning is easily contaminated by noisy samples and destabilized when the appearance discrepancy is large across the two modalities. All of these challenges result in less discriminative cross-modality features.  
In this field, the use of GAN to solve RGB-IR Re-ID problems involves two steps: distinguishing the characteristics of different modalities through a GAN and generating cross-modality images by producing adversarial images.These methods perform well for RGB-generated IR images but perform poorly in the one-to-many mapping of IR-generated RGB images. Additionally, background interference, noise interference, and the contextual relationships between image pairs are not considered.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 3 An Overview of LoRaWAN and SD-WAN Technologies
### LoRaWAN Technology
The AppKey is an AES-128 key that is used in the subsequent session key derivation process. As a first step, the end device sends a join-request message containing the AppEUI, DevEUI, and DevNonce. Replay attacks are prevented by using a DevNonce value, although the join-request message is not encrypted. Finally, following a successful procedure, the network server sends a join-accept message to conclude the transaction and confirm the end-to-device authentication, keeping in mind that AppKey has already been used to encrypt the message. On the other hand, by using the Activation Over-the-Air strategy, each end device performs a join procedure explicitly defined to securely connect to a specific network after obtaining all necessary cryptographic material. The joining procedure is initiated by the end device whilst providing the following information:  
* application identifier--AppEUI,
* end-device identifier--DevEUI and
* application key--AppKey. The DevEUI identifies an end device, whereas the AppEUI identifies an entity capable of processing the end-join request of the devices. The OTAA scheme is considered to be more secure than ABP [30], a fact that led us to opt for our LoRaWAN implementation.
## 4 Experiment of Geometry Processing Accuracy Evaluation  
The geometric processing method for the spaceborne SAR high-resolution sliding-spot mode based on the antenna pointing vector proposed in this paper consists of two parts: the forward process of calculating the geographic coordinates from the image coordinates of the target and the inverse process of calculating the image coordinates from the geographic coordinates. In order to effectively evaluate the effectiveness of the above methods, this section conducts two-step geometric processing experiments based on the high-resolution sliding-spot data obtained by the same SAR satellite under different pulse-width and bandwidth combinations and within different calibration fields: In the first step, the geometric inverse calculation method is used to perform the calibration process and obtain accurate geometric calibration parameters; in the second step, on the basis of high-precision geometric calibration, the geometric forward calculation method is used to perform geometric positioning and accuracy verification. The experimental data used in these two steps are not related to each other.  
The factors of spaceborne SAR geometric positioning errors always include four parts:  
One is the orbital positioning error of the SAR satellite, which leads to deviations in the range and azimuth directions. At present, most SAR satellites provide precise orbit determination products based on post-event precise ephemeris calculations, and their accuracy can reach \(cm\) level [24; 25], which is adopted in our experiment.  
Second, the error of atmospheric propagation delay leads to the deviation in slant range measurement [26]. Here, according to the NCEP global atmospheric parameters updated every six hours and the global TEC data provided by CODE, the correction values for the neutral atmospheric propagation delay and ionospheric delay are calculated, respectively, from the antenna phase center to the image center point, in order to eliminate the influence of atmospheric propagation delay for this image. The atmospheric delay correction models adopted here can be obtained as [27; 28].  
The third is the elevation error of the check points, which leads to the deviation in the range and azimuth. In our experiment, both the high-precision elevation data measured in the field and the elevation data extracted from the different scale DEM data are used to verify the influence of elevation errors on the geometric positioning accuracy.  
The fourth is the range and azimuth errors caused by the deviations in the slant time delay and azimuth time unify of the SAR payload. On the basis of stripping away the above three factors as much as possible, geometric calibration is adopted to solve these two compensation parameters.
## 3 Results and Discussion  
### Mechanical Properties of ECC  
Figure 7 shows the typical load-displacement curves of ECC under tension and bending. The curves of ECC under uniaxial tension or bending can be divided into three stages, namely, the elastic stage before cracking, the strain-hardening stage after cracking, and the final stress relaxation stage. Before the initial crack occurs in ECC, ECC is similar to an elastomer, and the load linearly varies with deformation. After the stress reaches the strength of matrix, microcracks occur in ECC, and then the strain-hardening stage begins. After the crack is formed, the tensile stress of this part will be borne by the fibers alone. The bridging stress of fibers plays a role in effectively transferring stress. In the stress relaxation stage, with the increase of deformation, the crack opening displacement gradually increases, and the overall strength of the material decreases accordingly.
## 5 Numerical Examples
### Quadruple Pendulum Problem
Although the number of dominant modes selected is different, the size of the reduced flexible bodies for the error tolerances are identical to the one we described in Equation (48). The position, velocity, and acceleration of node \(A\) in Figure 2 are plotted in Figures 5-7, respectively. For the position \(x,y,\) and \(z\) in Figure 5, the absolute error \(|\delta\mathbf{x}|\) of the accelerations are calculated as \(|\delta\mathbf{x}|=|\mathbf{x}^{ref}-\mathbf{x}^{ei}|\), where the superscripts \(ref\) and \(e^{i}\) denote the solutions from the reference model and the RFlex model generated using the error tolerance \(e^{i}\), respectively. It can be seen that the smaller error tolerance leads the smaller the absolute error of the dynamic response. It can be also found in the von Mises stress and strain in Figure 8.  
We compared the absolute error of the von Mises stress and strain for the specific time instant in Figures 9 and 10, where the selected time instant are \(t=6.04\) s and \(t=6.8\), respectively. The snapshots of the contours are selected at the arbitrary time. The absolute error of von Mises stress described in Figure 9 shows the area with a large error becoming smaller when decreasing error tolerance. In the case of the von Mises strain, comparable errors occur when the error tolerance is \(10\%\) and \(1\%\), but the RFlex of \(e^{i}=0.1\%\) gives a similar strain result to the reference model in the overall area. Figure 4: The changes of the relative eigenvalue error and its estimation with respect to the number of dominant modes for the specific modes. The error tolerance \(e^{i}\) and the mode range \(N_{m}^{i}\) are \(0.1\%\) and \(30\), respectively. Figure 5: The \(x\), \(y\), and \(z\) positions of node \(A\) and their absolute error for the error tolerance \(e^{i}=10\%,1\%\), and \(0.1\%\). (**a**) Position \(x\) of node \(A\) and its absolute error; (**b**) position \(y\) of node \(A\) and its absolute error; (**c**) position \(z\) of node \(A\) and its absolute error. Figure 6: The \(x\), \(y\), and \(z\) velocities of node \(A\) for the error tolerance \(e^{i}=10\%,1\%\), and \(0.1\%\). (**a**) Velocity \(x\) of node \(A\); (**b**) velocity \(y\) of node \(A\); (**c**) velocity \(z\) of node \(A\). Figure 7: The \(x\), \(y\), and \(z\) accelerations of node \(A\) for the error tolerance \(e^{i}=10\%,1\%\), and \(0.1\%\). (**a**) Acceleration \(x\) of node \(A\); (**b**) acceleration \(y\) of node \(A\); (**c**) acceleration \(z\) of node \(A\). Figure 8: The von Mises stress and the von Mises strain of node A for the error tolerance \(e^{i}=10\%,1\%\), and \(0.1\%\). **(a)** The von Mises stress of Node \(A\) and its absolute error; **(b)** the von Mises strain of Node \(A\) and its absolute error. Figure 9: The error contour of the von Mises stress of the different error tolerance \(e^{i}=10\%\), \(1\%\), and \(0.1\%\) for \(t=6.04\) s. **(a)**\(e^{i}=10\%\); **(b)**\(e^{i}=1\%\); **(c)**\(e^{i}=0.1\%\).
## 4 Results and Discussion  
### Experiment Environment  
In the experiments, an NVIDIA RTX 2080 GPU was used to train and evaluate the proposed model. Ubuntu-18.04 LTS was used as a base operating system, with an NVIDIA CUDA v11.3 and an NVIDIA cuDNN v8 to accelerate GPU operations. All scripts were written in python 3.8 with Pytorch v1.12.1. Every dataset was split into 80:20 for training and validation. The size of the input image was 640 \(\times\) 640 with a batch size of 16, and the training epochs were set to 1000 at each stage. The stochastic gradient descent (SGD) optimizer was used to optimize the network parameters. In training stages 1 and 2, the parameters of the network were optimized with a initial learning rate of 0.01, momentum of 0.8, and weight decay of 0.0005 every 50 epochs. In training stage 3, which is a fine-tuning stage, the initial learning rate was set to 0.001, with a momentum of 0.8 and with no weight decay. Additionally, the batch size was reduced to 4 in training stage 3.