esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Anomaly Detection
### The Algorithm of Network Anomaly Detection
#### 3.3.1 The Two-Phase Training Process  
The specific training process is described in the following. The reconstruction errors for the two training phases can be specified as follows.  
* **Phase 1: Autoencoder training.** Initially, the input data, represented by \(W\), is subject to compression by the encoder, symbolized as \(E\). This procedure reduces the input data into a latent space, denoted by \(Z\). Following this, each of the two decoders embarks on the task of reconstruction, with the outcomes manifesting as \(AE_{1}(W)\) and \(AE_{2}(W)\), correspondingly. The errors incurred in this reconstruction process, can hence be individually articulated as follows: \[Loss_{AE_{1}}=\big{\|}\mathcal{W}-AE_{1}(\mathcal{W})\big{\|}_{2}\] (8) \[Loss_{AE_{2}}=\big{\|}\mathcal{W}-AE_{2}(\mathcal{W})\big{\|}_{2}\] (9)
* **Phase 2: Adversarial training.** Subsequent to the initial reconstruction by the two decoders, the output derived from \(AE_{1}\) is subjected to another compression by \(E\), transitioning once more into the latent space \(Z\). This compressed output is then conveyed to \(AE_{2}\) for another round of reconstruction, resulting in the output \(AE_{2}(AE_{1}(W))\). This procedure allows \(AE_{1}\) to be conditioned in such a way as to deceive \(AE_{2}\), whereas \(AE_{2}\) is trained to differentiate between the original data and the data generated from \(AE_{1}\). Consequently, \(AE_{1}\) aspires to minimize the disparity between the original data \(W\) and the outputs from \(AE_{2}\), while \(AE_{2}\) endeavors to maximize this distinction. Within this adversarial framework, the reconstruction errors can be represented as follows: \[Loss_{AE_{1}}=+\big{\|}\mathcal{W}-AE_{2}(AE_{1}(\mathcal{W}))\big{\|}_{2}\] (10) \[Loss_{AE_{2}}=-\big{\|}\mathcal{W}-AE_{2}(AE_{1}(\mathcal{W}))\big{\|}_{2}\] (11)  
Together, these two stages form a dynamic and robust training process that allows us to effectively compress and reconstruct the data while leveraging the adversarial mechanism to refine the quality of this reconstruction.  
Summarily, both autoencoders serve dual roles. In the first phase, \(AE_{1}\) aims to minimize the reconstruction discrepancy of the original data \(W\), while in the second phase, it seeks to minimize the difference between \(W\) and the reconstructed output derived from\(AE_{2}\). Similarly, \(AE_{2}\) also endeavors to minimize the reconstruction error of \(W\) during the first phase; however, in the second phase, it strives to maximize the reconstruction error of the input data as reconstructed by \(AE_{1}\). As the training advances through multiple iterations, the reconstruction error per epoch can be articulated as follows:  
\[Loss_{AE_{1}} =\frac{1}{n}\|\mathcal{W}-AE_{1}(\mathcal{W})\|_{2}+\left(1-\frac{1 }{n}\right)\|\mathcal{W}-AE_{2}(AE_{1}(\mathcal{W}))\|_{2} \tag{12}\] \[Loss_{AE_{2}} =\frac{1}{n}\|\mathcal{W}-AE_{2}(\mathcal{W})\|_{2}-\left(1-\frac{1 }{n}\right)\|\mathcal{W}-AE_{2}(AE_{1}(\mathcal{W}))\|_{2} \tag{13}\]  
where \(n\) denotes the training epoch.
## 2 Materials and Methods
### SOS, POS, EOS Analysis in the 2001-2021 Period
#### 2.2.2 Procedure and Trend Analysis
After extracting and filtering SOS, POS and EOS, advances or delays in the cycle of grassland vegetation during the time period considered (2001-2021) were analysed. The results were then correlated with the processed weather data extracted from stations located in proximity to the study areas. Specifically, daily data of temperatures at each site were aggregated seasonally: winter (January, February and March), spring (April, May and June), summer (July, August and September) and autumn (October, November and December). Then, as well as for phenological dates, a moving average with a three-year window was performed on average seasonal values of mean temperature to smooth fluctuations and highlight temporal trends. Data were elaborated with a linear regression analysis in order to evaluate possible changes in mean seasonal temperatures over the period 2001-2021. Finally, mean temperature data were compared through a correlation analysis to phenological dates extracted from kNDVI patterns to investigate the potential impact of this factor on advances or delays in the grassland growing season. As in [38], analysis of phenological trends considered 3 different levels of significance (statistical analysis F-test): 1, 5 and 10%.
## 4 Discussion
### Effect on Ring Structure  
The graph depicted in Figure 9 provides an insight into the ring size distribution of the ligands within the glass network for the composition series. Boron and silicon, as primary network formers, exhibit different atomic sizes and coordination preferences that impact ring formation. Silicon's larger size compared to boron can cause steric hindrance, affecting the ring structure. The trend observed indicates a prominent peak around smaller ring sizes (four to six members) distributed throughout the composition series, suggesting a common structural feature across the series. The increasing boron content appears to slightly alter the ring size distribution, and the changes appear to be apparent at larger ring sizes (10-18 members). The 15B composition specifically presents an increase in the appearance of larger rings compared to the 0B and 5B compositions. This suggests that higher boron content promotes the formation of larger ring structures. Additionally, the amplitude of the peaks varies with boron content. We observe that for the 15B composition, the number of rings per base for larger rings appears to be slightly higher, thus supporting previous findings that boron substitution results in structural modification.  
Temperature and bonding characteristics also influence ring distribution. Increased boron content can lead to structural rearrangements and affect glass transition temperature (\(T_{\text{g}}\)). Boron's BO\({}_{4}\) tetrahedral structures enhance material stiffness, impacting \(T_{\text{g}}\), while SiO\({}_{2}\) reduces viscosity with modifiers, contrasting with B\({}_{2}\)O\({}_{3}\), which increases viscosity due to its transformation from sp\({}^{2}\) to sp\({}^{3}\) tetrahedra.
## 2 Engineering Background
### Installation of Surface Settlement Monitoring Points  
The monitoring points were installed by drilling holes at the designated locations using drilling tools and embedding steel bars directly into the relatively solid undisturbed strata. The depth of the embedding is not less than 1.2 m in the undisturbed soil strata and not less than 0.2 m in the disturbed soil strata. The longitudinal settlement monitoring points were set along the axis of the shield driving route. Within a range of 100 m from the starting and ending points of the shield driving section, horizontal settlement slots were set up every 5 m, while in other sections, they were set up every 10 m. The width of the settlement slot monitoring section is 45\({}^{\circ}\) to the lower deck of the tunnel roadway. The monitoring points were arranged outward from the centerline of the tunnel at intervals of 4, 4, 4, 4, 6, 6, and 10 m for each section, with a total of nine monitoring points per section. The specific arrangement is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Serial Number** & **Statum** & \(\gamma\prime\)**(kN-m-3)** & **E/MPa** & **v** & **c/kPa** & \(\varphi\)/(\({}^{\circ}\)) & **UCS/MPa** \\ \hline
1 & Mixed fill soil & 18.6 & 15.6 & 0.39 & 8 & 10 & \\
2 & Natural fill soil & 17.5 & 12.0 & 0.37 & 5 & 15.5 & \\
3 & Silty clay & 19.5 & 25.5 & 0.30 & 25 & 24.5 & \\
4 & Clay with crushed stones & 19.4 & 36.0 & 0.28 & 62 & 18 & \\
5 & Red clay & 18.7 & 28.5 & 0.33 & 43 & 17 & \\
6 & Silty clay with loess & 19.5 & 27.6 & 0.32 & 28 & 10 & \\
7 & Limestone & 25.7 & 15,900 & 0.25 & 6720 & 42 & 50 \\
8 & Moderately weathered silty sandstone & 24 & 11,100 & 0.31 & 3000 & 31 & 46.7 \\
9 & Moderately weathered fractured limestone & 25.8 & 10,690 & 0.32 & 4000 & 39 & 34.4 \\ \hline \hline \end{tabular}  
* The table includes the following parameters: \(\gamma\) for unit weight, E for elastic modulus, v for Poisson’s ratio, \(\varphi\) for the angle of internal friction, and UCS for unconfined compressive strength.  
\end{table}
Table 1: Physical and mechanical properties of geotechnical stratum.  
Figure 3: Geological profile in shield interval: (**a**) profile view; the number is the stratum number, and the yellow dotted line circle is the soil rock composite surface.and (**b**) cross-section view; the white circle is a shield tunnel.
## 3 Example Forgetting in Federated Learning
### Definition of Non-IID  
The idea of non-IID in federated learning may seem unorthodox, not the situation itself but the reason it should be considered. One of main considerations in federated learning is data privacy [1; 2; 5]. No node should have access to another node's data repository nor should it openly share its data to others. This not only distinguishes federated learning from parallel and distributed learning, it complicates many aspects of it. For example, federated learning aggregates local models because it cannot train a single centralized model directly from local datasets. Clients having computationally weak hardware would be less of a problem for we could simply transmit the data to an advanced hardware. A non-IID situation would be easily solved if the centralized control node could receive the local data distribution from each client. The problem of training on biased or skewed data could be rectified once it has the privilege to access and modify other nodes' data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MNIST** & **FMNIST** & **SVHN** & **CIFAR-10** \\ \hline Centralized & 4044 & 22,951 & 84,748 & 57,717 \\ Federated & 80,437 & 108,415 & 140,984 & 156,426 \\ \hline Increase (\%) & 1989 & 427 & 166 & 271 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Occurrence of example forgetting events.  
Considering all the obstacles from data privacy, one could even argue that federated learning is business-oriented in nature.  
The academic definition of non-IID is simply if connected clients have varying data distribution [1, 25]. Assume there is a dataset \(\{x,y\}\). For a client \(k\), the local data distribution would be \(P_{k}(x,y)\). For any other connected client \(j\), the federated learning network is non-IID if \(P_{k}(x,y)\
eq P_{j}(x,y)\). Some connected clients in the network may have the same local data distribution; however, as long as there are clients with different distributions, we can say it is still non-IID. Defining non-IID for a federated learning problem is discussing if it abstractly simulates or simplifies a plausible real life situation. Therefore, a non-IID should be convincing.  
The non-IID in our problem is a cluster-level label size imbalance. That is, clients in the same cluster share the same labels while each cluster has non-overlapping labels as shown in Figure 1. To simplify the matter, the number of total local data samples is the same for all clients. Such situations could occur due to location variations. If we are to classify flower images, flower image data collected in one area would differ from those in another area. Therefore, we could cluster nodes based on the data source location. Another possible case would be diagnosing genetic diseases. Some genetic pools would be more susceptible to some diseases.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 1 Introduction  
In recent years, there has been a great increase in the occurrence of Global Navigation Satellite System (GNSS) jamming. In conflict areas, GNSS jamming is widely deployed to hamper the operations of the opposing force. A good overview of where jamming occurs in the world is provided by the website \"GPSJam.org\". The idea behind GNSS jamming is simple; by causing interference, the jammer increases the noise received at a receiver until the receiver can no longer track the desired signals. As the received GNSS signals are extremely weak, in the order of \(-155\) dBW, a low-power interference source can impact a large area. A jammer with a power of just \(20\) mW might cause GNSS disruptions in an area with a radius of more than one kilometer, which is a surface area of more than \(3\) square kilometers. This makes GNSS jamming relatively easy to perform and cheap, and it can have a large impact, as many systems rely on GNSS not only for positioning but even more often because of the accurate timing it provides. For this reason, GNSS jamming is becoming increasingly popular among the military and other state actors, in addition to criminals and other actors. The reasons for the use of a jammer range from privacy protection for individuals to protection from GNSS-guided (cruise) missiles.  
The presence of an interference source, such as a jammer, will impact the carrier-to-noise ratio (\(C/N_{0}\)). The carrier-to-noise ratio is the ratio between the power of the carrier of the desired signal \(C\) to the power spectral density of the (thermal) noise \(N_{0}\). If the \(C/N_{0}\) is suppressed enough, the receiver can no longer track the signals and will lose its lock on these signals. Usually, for Binary Phase Shift Keying (BPSK) signals, such as the Global Positioning System (GPS) C/A code or P(Y) code signals, the threshold where the receiver will lose its lock is around \(25\) dB-Hz [1]. However, before the lock is lost, the accuracy of the receiver will deteriorate. This is shown in [2] through a practical experiment and some basiccalculations made to predict the impact on GPS C/A. Also, in [3], the effect on accuracy is shown in an experimental setup where the accuracy of commercial GPS receivers is assessed under different jamming-to-signal ratios. In the current paper, we delve deeper into the theory behind the impact of a jammer on position accuracy. The focus is on the impact of a Gaussian white-noise jammer with different bandwidths on the GPS C/A code and Galileo E1 signals. As both types of signals are sent using different modulation techniques, with GPS C/A being a BPSK signal and Galileo E1 being a Composite Binary Offset Carrier (CBOC) signal, the effects of jamming are expected to be different.  
Theory from the literature will be reviewed, after which this theory will be evaluated by comparing the theoretical results with actual measurements using a high-end GNSS signal simulator and a software-defined GNSS receiver [4].
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 3 Proposed Quality Control Method
### Population Definitions
Thus, the wind speed differences for a good sample shall be less than 2 m/s from the mean and an outlier is defined as those samples having a difference greater than 5 m/s. Finally, the filter is tested over the total population and its performance is assessed. Figure 2: Density plot of CYGNSS Level 2 retrieved winds matched to MERRA-2 reference winds used for training. Top row represents the good (left) and outlier (right) training population for \(\left(0<U_{CYG}<\)=10 m/s). The bottom row represents the good (left) and outlier (right) training population for (\(U_{CYG}\)\(>\)10 m/s).
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network
#### 3.4.1 Feature Extraction  
The deep-pruned feature network is applied to the second frame of the tracking video sequence, as shown in Figure 7.  
From Figure 7, the first layer of the feature network has \(PPCKN_{1}\) important convolutional kernels \(kernel1_{1,1},kernel_{1,2},\ldots,kernel_{1,PPCKN_{1}}\), and the corresponding feature maps are \(F_{1,1},F_{1,2},\ldots,F_{1,KN_{1}}F_{1,KN_{1}+1},F_{1,KN_{1}+2},\ldots,F_{1, PPCKN_{1}}\). Then, according to Equation (9), the global pooling \(F_{GAP}^{1}\) corresponding to \(PPCKN_{1}-KN_{1}\) unimportant feature maps in the first layer can be obtained as:  
\[F_{GAP}^{1}=GAP\left(\sum\
olimits F_{1,j}\right),\ j\in\left((KN_{1}+1,KN_{1} +2,\ldots,PPCKN_{1})\right. \tag{12}\]  
Taking \(F_{GAP}^{1}=F_{\Delta 1}\), then \(F_{i,j}\) (where \(i>1\) and \(j\in(1,2,\ldots,KN_{i})\)) and \(F_{\Delta i}\) can be obtained from Equations (13) and (14):  
\[\sum\limits_{l=1}^{KN_{i-1}}Kernel_{i,j}\star F_{(i-1),l}+Kernel _{i,j}\star F_{\Delta(i-1)}=F_{i,j} \tag{13}\] \[\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad\qquad\left.i\in(2,\ldots,CL)\right.,\ j\in(1,2,\ldots,KN_{i})\] (14) \[\sum\limits_{l=1}^{KN_{i-1}}Kernel_{\Delta i}\star F_{(i-1),l}+ Kernel_{\Delta i}\star F_{\Delta(i-1)}=F_{\Delta i}\] \[\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad\qquad\qquad\left.i\in(2,\ldots,CL)\right.\]  
From Equations (13) and (14), the output feature map of the last layer of the deep-pruned feature network is \(F_{CL,1},F_{CL,2},\ldots,F_{CL,KN_{CL}}\), which is the input of the correlation tracking filter.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## Appendix A Proofs for Section 2
Since by assumption \(A^{\prime}(v,w)=X^{\prime}(v^{\prime},w^{\prime})\), we have \(P_{s}(A^{\prime})(v,w)=P_{s}(X^{\prime})(v^{\prime},w^{\prime})\), and hence \(P(A)(v,w)=(P_{s}\circ_{T}s)(A^{\prime})(v,w)=(P_{s}\circ_{T}s)(X^{\prime})(v^ {\prime},w^{\prime})=P(X)(v^{\prime},w^{\prime})\). Clearly, in many cases it will be advantageous to consider CH-partitions whose cores contain multiple vertices. We will next show that the above approach for CH-partitions with single-node cores can be generalized to the multi-node core case. We will generalize the definitions of \(N(v,\mathcal{P}(G))\) and \(N(v,\mathcal{P}(H_{v}))\) for the case where the vertex \(v\) is replaced by a set \(U\) of vertices of \(G\). For any graph \(I\), we define \(N(U,I)=\bigcup_{v\in U}N(v,I)\). Furthermore, we define by \(H_{U}\) the subgraph of \(H\) induced by \(N(U,H)\). Suppose the sets \(\{U_{i}\mid i=1,\ldots,q\}\) are such that \(\bigcup_{i}U_{i}=V(G(A))\) and \(U_{i}\cap U_{j}=\emptyset\). In this case, we can define a CH-partition of \(G=G(A)\) consisting of \(q\) sets, where for each \(i\), \(U_{i}\) is the core and \(N(U_{i},H)\setminus U_{i}\) is the halo of \(\Pi_{i}\). The following generalizations of Lemmas A and A follow in a straightforward manner. Denote by \(H_{i}\) the subgraph \(\mathcal{P}(H_{U_{i}})\) of \(G\). Let \(v\) be a vertex of \(U_{i}\). Then \(N(v,\mathcal{P}(G))=N(v,H_{i})\)Denote by \(A_{U_{i}}\) the submatrix of \(A\) consisting of all rows and columns that correspond to vertices of \(V(H_{U_{i}})\). The following main result of this section shows that \(P(A)\) can be computed on submatrices of the Hamiltonian: this insight justifies the parallelized evaluation of a matrix polynomial. **Lemma A4**. : _For any \(v\in U_{i}\) and any \(w\in N(v,\mathcal{P}(G))\), the element of \(P(A)\) corresponding to the edge \((v,w)\) of \(\mathcal{P}(G)\) is equal to the element of \(P(A_{U_{i}})\) corresponding to the edge \((v,w)\) of \(H_{i}\)._  
In case of a QMD simulation, as outlined in Section 2, we assume that the sparsity structure of \(P(A)\) can be approximated by the one of the density matrix \(D\) from the previous QMD simulation step. For the halos we approximate \(H=\mathcal{P}(G)\) with \(G(D)\) as before, and use the current \(H\) as a substitute for the halos. This leads to the generalization of the algorithm for computing \(P(A)\) in case of multi-vertex cores given at the end of Section 2.
## 4 Simulations Results and Discussion with Performance Evaluation
### Simulations Setup  
In simulations, two different scenarios of FLMPC protocols are implemented. In both scenarios, 150 nodes are deployed in (2000 \(\times\) 2000) m\({}^{2}\) area. Extensive simulations are done to verify the compatibility of the proposed protocols. The performance parameters and network dimensions for the simulations are listed in Table 4.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection  
### Problem Formulation and Notations  
A typical SMS contains sequential words with punctuation. In English, as words are divided by blank spaces, English SMS is easy to be split, whereas in some other languages, e.g., Chinese, there are no blank spaces between words. These SMS messages have to first feed into a segmentation algorithm to extract words. In any case, each SMS text is first split or segmented into sequential words with punctuation at the very beginning.  
Not all words are suitable for NLP. Punctuation and words only for positioning do not have much semantic information. Especially for SMS, many informal words, shortened and abbreviated words, social media acronyms, and some strange character sequences often appear in SMS. Part of them is also meaningless. They are called the stop words. Therefore, these stop words and punctuation are removed from the sequential words.  
After these preprocesses, each SMS is refined to a word sequence, which is full of meaningful words. Let \(N\) denote the total number of all rest meaningful words in SMS including the duplicated ones. This set with \(N\) sequential words is the observation sequence denoted as \(\underline{X}=\{o_{1},\ o_{2},\ o_{3},\ \cdots,\ o_{N}\}\). The corresponding hidden state sequence is denoted as \(\underline{Y}=\{q_{1},\ q_{2},\ q_{3},\ \cdots,\ q_{N}\}\), satisfying the Markov property. The structure of these two sequences is represented by the directed graph in Figure 1.  
Figure 1: Directed graph of hidden Markov model.  
The HMM, denoted by \(\lambda\), can be defined by a three-tuple:  
\[\lambda=\ (\pi,\,A,\,B) \tag{1}\]  
where \(\pi\) is the initial probability distribution, \(A\) is the state transition probability matrix, and \(B\) is the observation probability distribution matrix.  
For SMS spam detection, the set of hidden states is \(S=\{s_{1},\,s_{2}\}=\{\textit{ham, spam}\}\). Thus, \(A=(a_{ij})_{2\times 2}\), where \(a_{ij}=P(q_{l+1}=s_{j}|q_{l}=s_{i})\),_for_\(i,j=1,\,2\) and \(\sum\limits_{j=1}^{2}a_{ij}=1\),_for_\(i=1,\,2\).  
Let \(W=\{w_{1},\,w_{2},\,w_{3},\,\cdots,\,w_{n}\}\) denote the set of observation states, which includes all different words in every spam and ham SMS. \(n\) is the total number of different words in both sets. Thus, \(B=(\mathsf{b}_{ij})_{2\times n}\), where \(b_{ij}=P(o_{t}=w_{j}|q_{t}=s_{i})\),_for_\(i=1,2,\,j=1,\cdots,n\).  
\(\pi\) is a \(2\times 1\) initial probability distribution over the state, \(\pi_{1}=P(q_{1}=s_{1})\), \(\pi_{2}=P(q_{1}=s_{2})\), and \(\pi_{1}+\pi_{2}=1\).  
The proposed hidden Markov model for spam detection is shown in Figure 2.