esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Identity Authentication with Auditability and Privacy
## 4 Baseline Protocol
### Limitations  
Counter requests do not reveal the services to which a given identity is authenticated. However, since the authentication counters are publicly logged and service providers submit identity information, such as a _did_ and signature, along with the counter, to the blockchain, anyone who can see the transactions on the blockchain can notice how often a user authenticates using their identity. A privacy-cautious user may not want to share this information with the public, which may be challenging, as, by design, the information on the blockchain is public. Although privacy-preserving blockchain platforms, such as Solidus [37] and zkLedger [38], which allow storage of encrypted content on-chain, have been proposed, they require trusted third-party auditors. However, such an assumption in the DID ecosystem would be against its principles of self-sovereign identity (i.e., an owner has full control of their identity). Therefore, in the next protocol, we explain how to provide auditability with a privacy guarantee over an open blockchain.
## 3 Experiments
### Comparison with State-of-the-Art Methodologies  
We also compared our proposed method with state-of-the-art methods, including the one proposed by [PERSON] et al. [43] in 2020 for prostate cancer classification using multi-channel CNNs on multi-modal MRI images. Their method takes images of three modalities--ADC, DWI, and T2--as input, and inputs each modality into a different channel. There are 11 layers of 3D convolution with a convolution kernel of 3 \(\times\) 3 \(\times\) 3, an ensemble step of 2 \(\times\) 2 \(\times\) 2, and two fully connected layers. Because the method chooses data of three modes in the experiment, we only chose the results of two modes from [PERSON] et al. [43] as input in order to balance the comparison of the experimental results. We can see that the sensitivity, specificity, and AUC values of our method were 0.14, 0.18, and 0.07 higher than those of the same two-modality image inputs, respectively. A recent  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Sensitivity** & **Specificity** & **AUC** & **CI 95\%** & **Parameters** \\ \hline [PERSON] et al., 2019 [18] & 0.636 & 0.80 & 0.723 & 0.58–0.88 & – \\ [PERSON] et al., 2020 [19] & 0.74 & 0.70 & 0.78 & – & – \\ [PERSON] et al., 2017 [17] & 0.78 & 0.83 & 0.83 & – & – \\ Our method & 0.88 & 0.88 & 0.85 & 0.85–0.87 & 44.24 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with the three cited methods used to classify the clinical severity of prostate cancer.  
study by [PERSON] et al. [44] used deep migration learning for prostate cancer classification based on multi-modal MRI images. It proposed feeding both T2 and ADC modality images into a deep migratory learning network for feature extraction and obtaining the prediction results after a fully connected layer. In the comparison experiments of [PERSON] et al. [44], they compared results using uni-modal and bi-modal image inputs. Here, for objectivity in the comparison experiments, we only selected the results of their comparison experiments with bi-modal inputs, and we found that the sensitivity, specificity, and AUC values of our model improved by 0.144, 0.08, and 0.127, respectively. [PERSON] et al. [45] proposed an approach to classifying the clinical severity of prostate cancer using migration learning on the basis of multimodal MRI; the authors mainly used migration learning and pre-trained weights obtained after training on ImageNet, and they conducted their experiments using InceptionV3. The sensitivity, specificity, and AUC values of our method were 0.1, 0.05, and 0.002 higher than those of [PERSON] et al. [45].
## 4 Verifications and Examples
### Analysis of the Permeability Coefficient Variation  
Since increasing the permeability coefficient will significantly accelerate the dissipations of EPAP and EPWP, it is imperative to explore the variations of the permeability coefficients \(k_{\text{a}}\) and \(k_{\text{w}}\) during the consolidation process. Figure 9 shows the changes in the permeability coefficients of the air and water phases with time obtained through Figure 3. Figure 9a depicts the variation of the permeability coefficient of the air phase \(k_{\text{a}}\) under exponential loading in the time domain. It is shown that \(k_{\text{a}}\) increases slightly at first and that it reduces evidently until the end of the dissipation of the EPAP when the load approaches stability. Later, \(k_{\text{a}}\) increases quickly to the initial value after a period of stability. Figure 9b depicts the variation of the permeability coefficient of the water phase \(k_{\text{w}}\) under exponential loading in the time domain. It is shown that \(k_{\text{w}}\) displays the same phenomenon as \(k_{\text{a}}\) at first. However, \(k_{\text{w}}\) will increase quickly during the intermediate period, which is different from the changing regularity of \(k_{\text{a}}\). Finally, \(k_{\text{w}}\) reduces quickly to a smaller stable one compared to the initial value.
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 4 User Pairing in NOMA System with Laser Chaos Decision-Maker (LCDM-NOMA)  
Here, we introduce the proposed user pairing method for the NOMA system with laser chaos decision-maker, which we call LCDM-NOMA hereafter. First, we explain the operating principle of the proposed method in Section 4.1. Then, we demonstrate the simulation results of the proposed method in Section 4.2.
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
## 4 Experiment and Analysis
### Tightness Analysis of Integrity Risk Evaluate Results
Moreover, a notable observation in Figure 5 is that the maximum and minimum evaluation results obtained from the NES-based integrity risk evaluation method vary significantly within the exposure interval, and the ECDF generated by the minimum evaluation results does not effectively bound the reference. This occurs primarily due to the longer duration of the exposure interval in RNP 0.1, leading to more obvious satellite geometry variation during this interval. By comparison, the proposed method can achieve a rigorous bounding for the reference risk value. This is because our method rigorously considers all epoch information when evaluating the integrity risk. Overly optimistic evaluation results pose a significant threat to the integrity of navigation services. This underscores the notable advantages of the proposed method over traditional approaches in ensuring the reliability of navigation services. In Figure 7, an illustrative example of an exposure interval at the Beijing station is selected, demonstrating fluctuations in the number of satellites. The straight line depicted in Figure 7 represents the evaluated risk value by considering geometry variation over the whole exposure interval, so it does not change with time. Since the NES-based ARAIM evaluation result only relies on the single-epoch information throughout the exposure interval, most of the integrity risk evaluation results in Figure 7 are obviously underestimated. On the contrary, the proposed method can effectively bind the reference. Based on the above analysis, we can conclude that the traditional integrity risk evaluation method may be overly optimistic during the RNP 0.1 phase. Therefore, it is necessary to quantify the impact of satellite geometry variation on the ARAIM performance. Subsequently, we utilized the proposed method to validate the worldwide integrity performance of the ARAIM. Figure 5: Integrity risk evaluation result of different methods under RNP-0.1 situation. The subfigure (**a**,**b**) indicates that \(\beta\) is 10 and 1000, respectively.
## 4 Encrypted SFC Architecture
### The Encryption Service Function  
This component is responsible for both encrypting and decrypting the data traffic in front of the SF. This functionality is realised with a data encryption application in a customised SF that we named the Encryption Service Function (EF). From a network infrastructure perspective, the EF is a copy of the SF, except for being responsible for a different network layer (the additional NSH layer). In addition to the P4 networking functionalities, the EF adopts the Software-Defined IPSec application (SD-SA) functionality that we have presented in our previous work [7]. In summary, this application has the following features:  
* We use the Linux based IP XFRM application to encrypt and decrypt IP packets and encapsulates them with an IPSec Encapsulating Security Payload (ESP) header.
* The encryption application runs inside a Linux network namespace (netns) that separates the encryption application from the P4 switch.
* IPSec Internet Key Exchange (IKE) is replaced with a new web service application that exchanges the encryption keys and the integrity keys in a separate control plane channel.
* The EF is instantiated with a set of preshared keys. These keys are used to establish a secure connection to a centralised Authentication Center (AuC) that manages the key distribution.
## 1 Introduction
Regarding classification methodologies, interest in deep learning for remote sensing image processing has increased owing to its superior classification accuracy to those of conventional machine learning models [23; 24]. Among the various deep learning models, convolutional neural networks (CNNs) have been widely applied to the supervised classification of remote sensing images [25; 26; 27; 28; 29]. CNN models can be regarded as a patch-based classifier, in that an image of interest is divided into several spatial units (patch) including multiple pixels [30; 31]. In particular, this patch-based classifier, which can account for spatial correlation information between neighboring pixels within a patch, is effective for crop classification because of its ability in considering specific spatial features, such as cultivation patterns of crops and shapes of crop parcels [27; 29; 32]. Patch-based supervised classification requires training patches that comprise a center pixel representing a specific land-cover class and its neighboring pixels. Hence, the effect of multiple pixels in training patches should be quantified accordingly because weights assigned to neighboring pixels in a patch-based classification vary according to the impurity or heterogeneity of class compositions within a training patch. Furthermore, the representativeness of training patches for a specific land-cover class has significant influence on classification performance, similar to conventional pixel-based classification [33]. Significant effort has been expended for the selection of representative training samples in pixel-based classification. For example, [PERSON] et al. [34] developed a strategy for selectingrepresentative training samples, including the optimum amount of training samples and the best balance of training samples. Variations in the degree of spatial clustering of training samples and the use of explicit spatial information were also tested for classification using machine learning models [35]. To our best knowledge, however, the effect of compositional homogeneity within a training patch on the accuracy of patch-based classifiers such as CNNs has not been fully quantified. The objective of this study is to quantitatively analyze the effect of class purity of a training patch on the performance of CNN-based classification. The class purity of a training patch refers to the degree of compositional homogeneity of classes within a training patch. Various training patches with different class purity values and sizes are first generated and then used as inputs for supervised classification using a two-dimensional CNN (2D-CNN). Quantitative indices, in particular, are newly defined to quantify both local and global variations of class homogeneity in the study area and then used to analyze the relationship between the class homogeneity of the study area and classification performance. Crop classification in two study areas with significant differences in landscape heterogeneity and spatial resolutions of input images is demonstrated to quantify and compare the effects of class purity on classification performance in patch-based classification.
## 6 On Control Messages Protection
### SeFLOOD: The Secure FLOOD
#### 6.3.2 On Evicting a Node  
In Section 5.3 we showed that a compromised node can be logically evicted from the application layer by revoking the group key and redistributing a new one to all members but the evicted one. Of course, a compromised node must be evicted at the routing layer too, as necessary. This task can be accomplished as follows. Let \(u_{c}\) be the compromised node and \(\mathcal{S}_{c}\) be the set of nodes whose respective clusters contain \(u_{c}\), _i.e._, \(\mathcal{S}_{c}=\{u|u_{c}\in\mathcal{C}_{u}\}\).  
In order to evict \(u_{c}\), the gateway GW eliminates \(u_{c}\) from the routing tables and calculates new paths among nodes. If the new network becomes partitioned, firstly the GW informs the remaining nodes to eliminate \(u_{c}\) from their neighbourhood, by triggering the Evicting Protocol (EP) and then it triggers the Network Discovery Phase to build new routing tables. Note that it is not necessary to trigger also the CKDP, because the neighbourhood of each node does not change. The Network Discovery Phase can be avoided when the network without \(u_{c}\) is not partitioned.  
During the EP phase, the GW sends an EVICT message to every node \(u\) in \(\mathcal{S}_{c}\) commanding the node to remove \(u_{c}\) from its cluster \(\mathcal{C}_{u}\).  
\(\forall u\in\mathcal{S}_{c},GW\to u:\left\langle\textsc{EVICT},u_{c},\tau_{GW }\right\rangle_{e_{u}}\)  
where \(\tau_{GW}\) is timestamp. Upon successfully receiving the EVICT message (otherwise the message is discarded), node \(u\) removes \(u_{c}\) from its cluster \(\mathcal{C}_{u}\), _i.e._, \(\mathcal{C}_{u}\leftarrow\mathcal{C}_{u}\setminus\{u_{c}\},\) generates a new cluster key \(c^{\prime}_{u}\), and sends the key to every node in \(\mathcal{C}_{u}\):  
\(\forall v\in\mathcal{C}_{u},u\to v:\left\langle\textsc{CLU}-\textsc{ KEY},u,c^{\prime}_{u},\tau_{u}\right\rangle_{\ell uv}\)
## 6 Numerical Results and Analysis
### Performance Indicators  
In our simulation experiments, we adopted four performance indicators:  
* Successful delivery ratio  
We define the Successful Delivery Ratio (\(SDR\)) as the percentage of network traffic received at destination nodes relative to the network traffic generated at sources. We use this \(SDR\) indicator to weigh the probability of successful packet transmission. A larger \(SDR\) means a higher probability of successful delivery. The \(SDR\) is computed using Formula (3):  
\[SDR=\frac{\sum_{d_{k}\in(S,D)}P_{r}}{\sum_{s_{k}\in(S,D)}P_{s}} \tag{3}\]  
* Resource redundancy degree  
We define the Resource Redundancy Degree (\(RRD\)) as the percentage of redundant network traffic relative to the network traffic generated at sources. These copied packets consist of two parts: duplicates and linearly dependent packets. This \(RRD\) indicator can be used to measure the cost of the fault-tolerant mechanism. The \(RRD\) can be computed using Formula (4):  
\[RRD=\frac{\sum\limits_{i=1}^{n}R_{packet\_useless}^{i}}{\sum\limits_{j=1}^{n}R _{packet\_emitted}^{j}} \tag{4}\]  
* End-to-end delay  
We define End-to-end delay (\(ED\)) as the sum of the latencies for every message received by the destination nodes. This \(ED\) indicator can be used to weigh the timeliness of the fault-tolerant mechanism. Because the network coding operation incurs some latency, the \(ED\) indicator consists of  
\begin{table}
\begin{tabular}{l} \hline \hline
**Parameters Value** \\ \hline Attenuation model two ray \\ Channel capacity 2 Mbit/s \\ Signal transmission range 250 m \\ Signal interference range 550 m \\ Packet size 512 Byte \\ Output queue type FIFO (First-In First-Out) \\ Cache capacity 50 packets \\ energy model generic radio energy model \\ Simulation area 1000 m \(\times\) 1000 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters Settings.  
four parts: encoding latency \(d_{encoding}\), decoding latency \(d_{decoding}\), transmission latency \(d_{transmission}\), and waiting latency \(d_{wait}\). The \(ED\) can be calculated using Formulas (5) and (6):  
\[ED=\frac{1}{n}\sum_{j=1}^{n}D_{j} \tag{5}\]  
\[D=d_{wait}+d_{transmission}+d_{encoding}+d_{decoding} \tag{6}\]  
\(\bullet\) Useful throughput ratio  
We define the Useful Throughput Ratio (\(UTR\)) as the percentage of the scale of effective network information relative to the scale of full network information received by destination nodes. We use this \(UTR\) indicator to weigh the transmission efficiency of the fault-tolerant mechanism. Therefore, a larger \(UTR\) value means more effective delivery. This \(UTR\) indicator can be computed using Formula (7):  
\[UTR=\frac{\sum_{d_{j}\in(S,D)}P_{useful}}{\sum_{d_{j}\in(S,D)}P_{received}} \tag{7}\]
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 3 Results  
### RTK Performances Based on the Xiaomi Mi 8  
#### 3.1.1 RTK Performances in Static Mode  
We tested the RTK performance in static mode on the roof of the Yifu Building of Peking University on 12 July 2019. The baseline was 15 m. We tested the single-frequency RTK and dual-frequency RTK with GPS measurements. The visible signals are shown in Figure 4a. At least two L5 signals were visible during the test. Figure 4b-d show that both single-frequency RTK and dual-frequency  
Figure 3: Close-up of tying the Xiaomi Mi 8 to our foot.  
RTK could provide fixed solutions and cm-level precision after a few minutes in static mode. The introduction of L5 measurements reduced the convergence time and improved the fixed rate.
## 2 System Model and Connection Setup
### Interference Statistics Acquisition Stage
So, during the first \(T_{a,\text{CS}}=Q_{a}T_{f}\) seconds, both the nodes can proceed with the estimation of interference autocorrelation by simply correlating the \(T_{c}\)-sampled received signal as follows:  
\[c_{z}^{(\text{CS})}[m]=\frac{1}{2K_{a,\text{CS}}-1}\sum_{p=1}^{K_{a,\text{CS}}}r _{a,\text{CS}}^{*}[p]r_{a,\text{CS}}[p+m] \tag{3}\]  
where the symbol \({}^{*}\) means conjugation and \(K_{a,\text{CS}}=Q_{a}T_{f}/T_{c}\) is the length of autocorrelation. We remark that Equation (3) generically refers to the interference acquired by any of the considered nodes, but, of course, the measured values may not be the same due to different propagation conditions (that is, \(h_{n,\mu_{1}}(t)\
eq h_{n,\mu_{2}}(t)\), as detailed in Equation (2)). As the interference changes in time, statistics must be necessarily refreshed. Therefore, during EC, a time interval \(T_{a,\text{EC}}=M_{a}T_{s}\) is reserved for autocorrelation update by using Equation (3), with \(M_{a}\) being the number of frame slots reserved for such an operation. Given \(K_{a,\text{EC}}=M_{a}T_{s}/T_{c}\) as the number of samples considered for the update, we have:  
\[c_{z}^{(\text{EC})}[m]=\frac{1}{2K_{a,\text{CS}}-1}\sum_{p=1}^{K_{a,\text{CS}} }r_{a}^{(\mu)*}[p]r_{a}^{(\mu)}[p+m] \tag{4}\]  
being \(r_{a}^{(\mu)}[p]\) defined in the vector format as:  
\[\mathbf{r}_{a}^{(\mu)}=\left[\mathbf{r}_{a}^{(\mu-1)}(K_{a}:K_{a,\text{CS}}) \ \mathbf{r}_{a}\right] \tag{5}\]  
where the superscript \((\mu)\) indicates the ordered number of updating procedure, the position \(\mathbf{r}_{a}^{(0)}=\mathbf{r}_{a,\text{CS}}\) is assumed, and \(\mathbf{r}_{a}^{(\mu-1)}(K_{a}:K_{a,\text{CS}})\) refers to the elements of the vector \(\mathbf{r}_{a}^{(\mu)}\) ranging from the \(K_{a}\)-th till to the \(K_{a,\text{CS}}\)-th one. Interference acquisition during CS may last several frames since sufficiently large statistics must be collected to reliably initialize the communication. On the other hand, interference acquisition at EC stage is performed to achieve a partial update of the statistics available from CS. So, its duration is only limited to some frame slots. A different way of defining Equation (5) is that the new acquisition of interference \(\mathbf{r}_{a}\) (that is, the sampled version of the signal already described in Equation (1) during CS stage) gathers the most recent \(K_{a}\) samples that update the vector \(\mathbf{r}_{a}^{(\mu)}\), while the oldest \(K_{a}\) ones are removed from the sequence \(\mathbf{r}_{a}^{(\mu-1)}\).
## 2 Design Method of Feedback Controller  
### Parametric Filter  
A biquad IIR filter has the basic form as follows:  
\[H(z)=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}}{a_{0}+a_{1}z^{-1}+a_{2}z^{-2}} \tag{1}\]  
The form of each filter coefficient uses the peaking or high-shelf prototypes, whose coefficients [32] are shown in Table 1 below.  
In the table,  
\[\begin{array}{l}\omega_{c}=2\pi Fc/\text{Fs},\\ \alpha=\sin(\omega_{c})/(2Q),\\ A=\sqrt{10^{(g/20)}}.\end{array} \tag{2}\]  
where Fs is the predefined sampling frequency. Among the other variables, \(Fc\) denotes the center frequency in Hz; \(Q\) represents the steepness of the central frequency and is a dimensionless number; and \(g\) denotes the gain of each filter, in dB. These three parameters can uniquely characterize a biquad IIR filter. A positive \(g\) means the filter shape is peaking or a high-shelf shape, and a negative \(g\) means notching or a low-shelf shape.  
The feedback controller is composed of cascaded biquad IIR filters:  
\[H(z)=g_{total}\prod_{n=1}^{N}H_{n}(z) \tag{3}\]  
\begin{table}
\begin{tabular}{l c c} \hline
**Coefficients** & **Peaking** & **High-Shelf** \\ \hline \(b_{2}\) & \(\frac{1-\alpha A}{1+\alpha A}\) & \(A\frac{(A+1)+(A-1)\cos(\omega_{c})-2\alpha\sqrt{A}}{(A+1)-(A-1)\cos(\omega_{c })+2\alpha\sqrt{A}}\) \\ \hline \(b_{1}\) & \(\frac{-2\cos(\omega_{c})}{1+\alpha A}\) & \(-2  
where \(H_{n}(z)\) is the nth biquad filter formed by parameter \(Fc_{n}\)\(Q_{n}\) and \(g_{n}\), and \(n=1,2,\ldots\), N; \(g_{total}\) is a real number representing the total gain of the cascade controller. This feedback controller is determined by using the following parameter vector:  
\[X=[Fc_{1},Q_{1},g_{1},\ldots Fc_{N},Q_{N},g_{N},g_{total}] \tag{4}\]