esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Attenuation Based on Wave Vector Characteristics
## 2 Principles  
### Wave Vector Estimation  
In a three-dimensional (3D) 3C seismic survey, translational motions are simultaneously recorded by one vertical (Z-) component and two horizontal (X- and Y-) components of the geophone [1]. The 3C amplitudes \(u_{Z}\), \(u_{X}\), and \(u_{Y}\) at the sample time \(t\) form a signal vector:  
\[\mathbf{U}(t)=u_{Z}(t)\mathbf{e}_{Z}+u_{X}(t)\mathbf{e}_{X}+u_{Y}(t)\mathbf{e} _{Y} \tag{1}\]where \(\mathbf{e}_{Z}\), \(\mathbf{e}_{X}\), and \(\mathbf{e}_{Y}\) are base (unit) vectors in the \(Z\)-, \(X\)-, and \(Y\)-directions, respectively. To the isotropic exploration target, when the \(X\)-component is rotated to the receiver-source (radial) direction, valid seismic waves are mainly recorded by the \(Z\)- and \(X\)-components. Thus, the vector \(\mathbf{U}\) can be written as:  
\[\mathbf{U}(t)=u_{Z}(t)\mathbf{e}_{Z}+u_{X}(t)\mathbf{e}_{X} \tag{2}\]  
At the given time \(t\), \(\mathbf{U}(t)\) is not a pure vector of a valid wave owing to the existence of different noises as ground roll, random noise, and other kinds of noise. Rather, it is a composite vector approximately expressed as:  
\[\mathbf{U}(t)=\mathbf{B}(t)+\mathbf{G}(t)+\mathbf{N}(t) \tag{3}\]  
where \(\mathbf{B}(t)\), \(\mathbf{G}(t)\) and \(\mathbf{N}(t)\) are the wave vectors of a valid signal, ground roll, and random noise together with other non-ground-roll related coherent noise, respectively, and  
\[\left\{\begin{array}{l}\mathbf{B}(t)=B(t)\mathbf{e}_{B}\\ \mathbf{G}(t)=G(t)\mathbf{e}_{G}\\ \mathbf{N}(t)=N(t)\mathbf{e}_{N}\end{array}\right. \tag{4}\]  
where \(\mathbf{e}_{B}\), \(\mathbf{e}_{G}\), and \(\mathbf{e}_{N}\) are the unit wave vectors; and \(B(t)\), \(G(t)\), and \(N(t)\) denote the wave vector moduli. Here, the non-ground-roll related coherent noise refers to the linear noise without dispersion.  
Through analysis of a 3C record using a moving window, we can derive a mean (or median) vector that characterises the polarization in the moving window [5]. The moving window size can be tailored for different applications and statistics. For instance, using a large moving window can produce the near-zero random-noise vector moduli and relatively accurate instantaneous ground-roll vectors. Using a small moving window can produce the opposite effect. Based on the wave vector characteristics of different moving windows, we consider using mean (or median) filtering to reduce the noise in 3C records.
## 2 Related Work and Methods
### Proposed Method
#### 2.4.4 Computational Complexity Analysis  
In this section, the computational complexity of the algorithm that this study proposes is analyzed.  
To construct the KNN graph in a dataset \(X\in R^{d\times n}\), the time complexity is typically calculated as \(\mathcal{O}\left(dn^{2}\right)\), where \(n\) and \(d\) are the number of samples and features of the data,respectively. For our method, we calculate similarity based on both data and features. The time complexity of constructing a KNN graph for the entire dataset is \(6\big{(}dn^{2}+nd^{2}\big{)}\).  
The optimization objective function (17) has a temporal complexity of \(6\big{(}dm^{2}+md^{2}\big{)}\), where \(m\) represents the size of the hidden layer. By the gradient descent approach, the cost of each iteration for updating \(W_{1}\), \(W_{2}\), \(b_{1}\), and \(b_{2}\) is \(6\big{(}md^{2}\big{)}\), \(6\big{(}md^{2}\big{)}\), \(6\big{(}dn+mnd+mn\big{)}\) and \(6\big{(}md\big{)}\), respectively. The entire computational complexity after t iterations is \(6\big{(}tdm^{2}+tmd^{2}\big{)}\).  
After obtaining \(W_{1}\), we need \(6\big{(}dn\big{)}\) to compute the score for each feature. Then, we sort the results, which takes a very short period.  
Therefore, the overall time complexity of DGA is \(6\big{(}dn^{2}+nd^{2}+tmd^{2}+tm^{2}d\big{)}\).
## 3 Materials and Methods
### Feature Selection Algorithm
#### 3.4.1 Wrapper Feature Selection Method  
Wrapper approaches compare the performance of several models by adding or removing variables. These processes are typically developed using the idea of the greedy search algorithm. In this method, a subset of features is first selected from the whole feature; then  
Figure 2: Box plot diagrams for the features of the MIMIC-III dataset.  
the feature set is applied to an ML algorithm. Lastly, the performance of that model is measured. This process is repeated until an optimal feature set is found. The flow chart for the wrapper selection process is shown in Figure 3.  
Forward selection, backward elimination, and bidirectional elimination are the most used techniques under wrapper feature selection. The forward selection procedure begins with one predictor and gradually adds more. In the backward elimination process, the process starts with all predictors, which are then iteratively eliminated one by one. The bidirectional elimination process is the combination of forward selection and backward elimination.  
For feature selection, three nature-based algorithms, namely the FPA, GA, and PSO, were utilized. The following are the working mechanisms of these feature selection algorithms.
## 2 The Resilient Recovery of the SN Based on Outer Synchronization
### The Resilient Recovery Measurement Based on the Outer Synchronization Error  
Resilience is the ability to accomplish the original plan in any case [39]. As shown in Figure 4, a quantifiable and time-dependent performance function is the basis for the measurement of the resilience [6; 13; 40; 41]. The dotted curve \(FT(t)\) denotes the targeted performance function of SN if not affected by disruption. The disruption deteriorates the performance to the level \(F(t_{d})\) at time \(t_{d}\). Then, the performance function of SN \(F(t)\) will be improved by recovery action and reach the targeted level \(FT(t)\) at a later time \(t_{r}\).  
Let \(R1(t)\) be the resilient recovery ratio of SN at time \(t\) (\(t>t_{d}\)). \(R1(t)\) describes the cumulative functionality that has been restored at time \(t\), normalized by the expected cumulative functionality if the SN has not been affected by disruption. \(R1(t)\) is given as Equation (6), where \(R1(t)\) is quantified by the ratio of the area with diagonal stripes \(S_{1}\) to the area of the shaded part \(S_{2}\)[6].  
\[R1(t)=\frac{\int_{t_{d}}^{t}[F(\tau)-F(t_{d})]d\tau}{\int_{t_{d}}^{t}[FT(\tau) -F(t_{d})]d\tau},\;t\geq t_{d} \tag{6}\]  
Outer synchronization error \(e_{i}\to 0\) shows that the actual status restores itself to the scheduled status. That is to say, the smaller the outer synchronization error, the better the resilient recovery.  
Figure 4: Illustration of the resilient recovery measurement.  
Total outer synchronization error of all nodes is expressed as Equation (7):  
\[E(t)=\sqrt{\sum\limits_{i=1}^{N}{\left[{(x_{i1}(t)-y_{i1}(t))}^{2}+{(x_{i2}(t)-y_{ i2}(t))}^{2}+{(x_{i3}(t)-y_{i3}(t))}^{2}}\right]} \tag{7}\]  
The outer synchronization error of all nodes \(E(t)=|F(t)-FT(t)|\) can be used to measure the resilience.  
Taking advantage of the outer synchronization error, a function for the assessment of the resilience is illustrated in Figure 5. It is a quantifiable and time-dependent performance function, also known as the resilient recovery ratio, which can be measured by \(R(t)\):  
\[R(t)=1-\frac{\int_{t_{d}}^{t}{E(\tau)d\tau}}{\int_{t_{d}}^{t}{ET(\tau)d\tau}}, \ t\geq t_{d} \tag{8}\]  
Equation (8) focuses on the resilient recovery in the range of [0, 1]. \(R(t)=0\) when \(E(t)=ET(t)\). This means that SN has not recovered from its disrupted state (there has been no \"resilience\" action); \(R(t)=1\) when \(E(t)=0\), which corresponds to the ideal case, where SN recovers to its scheduled state immediately after disruption.  
This resilient recovery quantification is capable of measuring both the magnitude and rapidity of recovery. Moreover, this measurement of resilient recovery is not memoryless, since it considers the cumulative recovery of the functionality.
# Detecting Phishing Domains Using Machine Learning
## 2 Background
### Ensemble Classification Techniques
#### 2.4.2 Boosting  
Boosting is a meta-algorithm that can be thought of as a method of model averaging [26]. It is the most popular ensemble approach, as well as one of the most effective learning concepts. This method was created for classification, but it can also be applied to regression. The original boosting algorithm created a strong learner by combining three weak learners.
## 3 Results and Discussion
### Struvite Production
The recovery performance was always close to 100% for the tests conducted with stoichiometric ratios \(n\)(P):\(n\)(N) = 1, while it generally decreased with the increase of P addition (Figure 2). These reductions were much more pronounced for the lower magnesium additions (Figure 2). Indeed, as expected, the recovery efficiency is worse when the phosphorus dosage greatly exceeds that of magnesium. Figure 1: Ammonium removal during the tests conducted by using industrial grade reagents. Anyhow, the actual recovered mass of added phosphorus was, for each tested dosage, higher than that of ammonium removed. This confirmed the coprecipitation, with struvite, of other types of insoluble compounds containing phosphorus [22; 23]. The production of these compounds, obviously, is lower for the operating conditions for which the maximum ammonium was removed. Nevertheless, the discussed results confirmed that struvite precipitation can be efficiently performed by using industrial-grade reagents. However, in order to define a more favorable procedure for the application in real treatment plants, other tests were carried out. Indeed, the great volumes of reagents (of the order of cubic meters) required in industrial processes must be dosed slowly to avoid a great formation of foams, which result from rapid pH changes. In this regard, the next experiments were performed by providing the simultaneous dosage of both Mg and P reagents with NaOH. This operating modality makes the process management easier and impedes the foam generation, by maintaining the pH at around 9. The tests were conducted on leachate volumes of 1.5 L, applying flow rates for the dosage of P, Mg, and NaOH solutions of about 5 mL/min, 15 mL/min, 10 mL/min, respectively. On the basis of the results discussed above, \(n\)(N):\(n\)(Mg):\(n\)(P) of 1:1.1:1.1, 1:1.2:1.2, and 1:1.3:1.3 were tested. The detected results showed process performances in line with those reached in previous tests. In fact, an ammonium abatements near 92% (Figure 3) and a complete phosphorus recovery (Figure 4) were reached with \(n\)(Mg):\(n\)(N) and \(n\)(P):\(n\)(N) ratios equal to 1.2. The high efficiencies indicate that the struvite formation occurs gradually as the chemicals are fed, and prove the applicability of the tested technique. The X-ray diffractogram of collected solid (Figure 5), produced by applying this dosage modality, proved the formation of struvite crystals. Indeed, the position and intensity of main peaks correspond to those of struvite standard. Anyhow, the \(n\)(N):\(n\)(Mg):\(n\)(P) molar ratios were of about 1:1.18:1.17, that, if compared with the stoichiometric values of pure struvite (1:1:1), confirm the formation of further insoluble amorphous salts of phosphorus and magnesium [22; 23]. The wet precipitate, characterized by solid of 22% (humidity of 78%), was exploited in the struvite decomposition tests. Figure 2: Phosphorus recovery during the tests conducted by using industrial grade reagents.
# Adaptive Bit-Labeling Design for Probabilistic Shaping Based on Residual Source Redundancy
## 3 Analysis and Design of Bit-Labeling  
As shown in [15], in JSCCM systems, the parity bits transferred to the bit level \(b^{0}\) are uniformly distributed. Meanwhile, the compressed bits transferred to the bit level in block \(b^{i},1\leq i\leq m-1\) can be computed by (2) as the modulo-2 sum of the source bits. Therefore, the probability distribution of the compressed bits is determined essentially by the row weight distribution of \(\mathbf{B}_{s}\) in combination with the source probability distribution \(p\) as [15; 18]  
\[p_{\mathbf{B}_{s}}^{i}(1)=\sum_{k=0,\text{odd}}^{m_{1}}\binom{w_{i}}{k}p^{k}( 1-p)^{w_{i}-k},i=1,2,\ldots,m-1 \tag{8}\]where \(w_{i}\) represents the weight of the \(i\)-th row of \(\mathbf{B}_{s}\) and \(\binom{w_{i}}{k}\) denotes the binomial coefficients. Note that \(p^{i}(1)+p^{i}(0)=1\) and \(p^{i}(1)\
eq p^{i}(0)\) for \(i=1,2,\ldots,m-1\) when \(p\
eq 0.5\), thus leading to nonuniform input symbol distributions.  
Let \(\mathcal{X}\) denote a set of \(2^{m}\)-ary QAM constellation points. After interleaving, a symbol mapper maps an \(m\)-bit vector \(\mathbf{x}=[b^{0},\ldots,b^{m-1}]\in\mathbb{F}_{2}^{m}\) onto \(X=\phi(\mathbf{x})\in\mathcal{X}\), where \(\phi(\cdot):\mathbb{F}_{2}^{m}\rightarrow\mathcal{X}\) is a symbol mapping function. Assume that the bits at the input of the modulator are independent, the symbol probabilities for transmission obtained as follows  
\[P_{\mathbf{B}_{s}}(X,\phi)=\frac{1}{2}\prod_{i=1}^{m-1}p^{i}(b^{i}), \tag{9}\]  
where \(p^{i}(u)\) represents the probability of transmitting a bit \(u\in\{0,1\}\) at bit position \(b^{i}\).  
For a given source probability \(p\) and a source code matrix \(\mathbf{B}_{s}\), the probability of every bit level can be identified. Then, the symbol probability is determined by the mapping function \(\phi(\cdot)\), which maps each length-\(m\) binary vector \([b^{0},\ldots,b^{m-1}]\) to a corresponding symbol \(X\), and thus we explicitly indicate this dependence in (9).
## 3 Materials and Methods
### Adjustment of a Process Simulation Model with Actual Real-Time Project Data
#### 3.1.1 Data Acquisition  
Simulation-based prediction adjustment requires a continuous and structured recording of the current construction progress as well as an interface between the actual real-time data of the construction project and the process simulation model.  
During tunneling, a large number of sensors record large amounts of data in order to monitor, evaluate, and control the production processes. All machine data relevant to tunneling, such as the advance speed, pressure of the thrust cylinders, or support pressure, are recorded through these sensors. Some data are visualized in real time on the control panel in order to support the operator of the TBM. Further data sources are recording shift data, data from geomonitoring, or data from tool management [32]. Shift reports are often recorded manually, but some systems already exist, which allow a direct transfer of the data to a digital data management system. In that case, the recorded data can be continuously retrieved from an online server by means of an API request. The retrieved data are then stored in an intermediate data processing system, where they are processed and evaluated for project-specific actual data.  
The recorded actual data are subsequently compared with the planned construction progress. With the help of defined comparison values, it can be determined whether the construction progress is on schedule. The key parameters are the current construction progress (construction duration and built rings) and the utilization of the TBM, as well as process-related productivity factors (e.g., maintenance, pipe extension).
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
###### Abstract  
The next generation of 5G networks is being developed to provide services with the highest Quality of Service (QoS) attributes, such as ultra-low latency, ultra-reliable communication, high data rates, and high user mobility experience. To this end, several new settings must be implemented in the mobile network architecture such as the incorporation of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), along with the shift of processes to the edge of the network. This work proposes an architecture combining the NFV and SDN concepts to provide the logic for Quality of Service (QoS) traffic detection and the logic for QoS management in next-generation mobile networks. It can be applied to the mobile backhaul and the mobile core network to work with both 5G mobile access networks or current 4G access networks, keeping backward compatibility with current mobile devices. In order to manage traffic without QoS and with QoS requirements, this work incorporates Multiprotocol Label Switching (MPLS) in the mobile data plane. A new flexible and programmable method to detect traffic with QoS requirements is also proposed, along with an Evolved Packet System (EPS)-bearer/QoS-flow creation with QoS considering all elements in the path. These goals are achieved by using proactive and reactive path setup methods to route the traffic immediately and simultaneously process it in the search for QoS requirements. Finally, a prototype is presented to prove the benefits and the viability of the proposed concepts.  
5G architecture; multiprotocol label switching; network function virtualization; QoS management; QoS traffic detection; software-defined networks +
Footnote †: journal:  
_Article_
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
###### Abstract  
Weakly supervised video anomaly detection (WS-VAD) is often addressed as a multi-instance learning problem in which a few fixed number of video segments are selected for classifier training. However, this kind of selection strategy usually leads to a biased classifier. To solve this problem, we propose a novel self-paced multiple-instance learning (SP-MIL) framework for WS-VAD. Given a pre-trained baseline model, the proposed SP-MIL can enhance its performance by adaptively selecting video segments (from easy to hard) and persistently updating the classifier. In particular, for each training epoch, the baseline classifier is firstly used to predict the anomaly score of each segment, and their pseudo-labels are generated. Then, for all segments in each video, their age parameter is estimated based on their loss values. Based on the age parameter, we can determine the self-paced learning weight (hard weight with values of 0 or 1) of each segment, which is used to select the subset of segments. Finally, the selected segments, along with their pseudo-labels, are used to update the classifier. Extensive experiments conducted on the UCF-Crime, ShanghaiTech, and XD-Violence datasets demonstrate the effectiveness of the proposed framework, outperforming state-of-the-art methods.  
Keywords:weakly supervised video anomaly detection; multiple-instance learning; self-paced learning; unbiased prediction +
Footnote †: journal: Applied sciences
## 6 Damage Detection of IASC-ASCE Structural Health Monitoring Benchmarks  
The structure shown in Figure 4 is a 4-storey building with a base plan of 2.5 by 2.5 m and a total height of 3.6 m. This structure is a model with a quarter scale model of a physical structure which was developed in the Earthquake Engineering Research Laboratory of the University of British Columbia (UBC) [24] for benchmark studies. The material characteristics of this model are as follows: hot rolled grade 300 W, Fy = 300 MPa (42.6 kpsi).  
The primary aim of the IASC-ASCE benchmarks is to provide a common platform for researchers in the field of structural health monitoring to apply different SHM methods to an identical structure and to compare their results [45]. The benchmarks comprise of two phases, e.g., Phase I and Phase II, each with simulated and experimental data. In this study, for prediction of structural damage, Phase I simulated data generated from this structure are employed. The excitation is low-level ambient wind loading at each floor in y-direction. To consider the uncertainty of environmental loads, the wind loading is modeled as filtered Gaussian white noise process passed through a sixth-order low-pass Butterworth filter with a 100 Hz cutoff [17]. The sensors are placed on each floor on the middle column of each face of the structure. Therefore, a total of 16 sensors are installed on the structure. The input signal used in this paper is the acceleration response recorded by the sensor installed on column 4. The location of these sensors is shown in Figure 5, which includes nodes 13, 22, 31, and 40. The sampling frequency is 100 Hz and the length of each recorded response is 40,000. As stated in [PERSON] and [PERSON] work [46] measured acceleration response data acquired by conventional Micromachined Microelectromechanical Systems (MEMS) sensors.  
Figure 4: ASCE benchmark structure used for health monitoring: (**a**) The analytical model [24] (**b**) Distribution of node numbering.  
The two types of Finite Element (FE) models with 12 and 120 degrees of freedom (DOF), respectively, are used to produce the time histories for pre-defined damage cases and damage patterns described in Table 2[24]. Acceleration response of damage scenarios in Table 2 is used to test the performance of the proposed model.  
It is worth noting that, the MATLAB code (MATLAB(r) R2015b-Mathworks) for the structural model was obtained from the network for earthquake engineering simulation (NEES) Database for Structural Control and Monitoring Benchmark Problems [47]. We use this source code (with predefined damage scenario of phase I) without any modification for validation of proposed algorithm. This phase deals with analysis considering shear building model. Therefore, the discrepancies, i.e., user-defined damage, between analytical model and real model were not considered. In Phase II, the freedom for user to define the type and point of damage has been included [48].