esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques
## 5 Modeling
### Factor Analysis Modeling  
In a final attempt to improve the classification accuracy, exploratory factor analysis [24,25] was conducted on the features and then the independent and ensemble models were re-ran with the combined factors. Exploratory factor analysis is a linear statistical method used to summarize a large set of features into smaller variables called factors. To confirm that factor analysis was indeed feasible for the given headline features, the Bartlett sphericity test and the Kaiser-Meyer-Olkin (KMO) test were used. The Bartlett sphericity test checks whether or not the features (observed variables) are intercorrelated by comparing the observed correlation matrix and the identity matrix. If the two are not the same, the test is significant. For the test of our feature set, the Chi-Square was 26,0470.01 and the \(p\)-value was 0, signifying that factor analysis is feasible. The Kaiser-Meyer-Olkin (KMO) [10] test estimates the proportion of variance among all the observed variables. KMO values range between 0 and 1 with a value of 0.6 or more indicating factor analysis is feasible. For the test of our feature set, the KMO value was 0.73, again indicating factor analysis is feasible. Next, the Kaiser criterion [26] was used to determine the number of factors. The Kaiser criterion is an analytical approach, which is based on the selection of factors that explain a more significant proportion of variance. The eigenvalue is used as an index for the variance as a portion of the total variance and it indicates how good a component is as a summary of the data. An eigenvalue of means that the factor contains the same amount of information as a single feature. Generally, an eigenvalue greater than 1 is considered a good selection criterion for a factor. The scree plot in Figure 4, which is a plot of eigenvalues
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
###### Abstract  
The radio stripe (RS) system is a practical implementation of cell-free mMIMO, in which a set of multi-antenna access points (APs) serves at the same time-frequency resources the user equipment (UE) in the network. The APs are sequentially connected in a stripe, sharing the same fronthaul link to the central processing unit. This work considers an uplink power optimization problem that aims to enhance the network spectral efficiency (SE) by considering two metrics--the max-min fairness and the max-sum rate. We employ a meta-heuristic based on the differential evolution algorithm to solve the bi-objective optimization problem. The SE performances of the full power along with the single-objective and multiple-objective scenarios are analyzed and compared for the optimal sequential linear processing detection scheme. The bi-objective approach is able to unveil the trade-offs to identify solution balancing the SE distribution resulting from the optimization of the max-min fairness and the max-sum rate objective functions.  
massive MIMO (mMIMO); cell-free (CF); radio stripe (RS); power optimization; max-min; max-sum; multi-objective (MO); differential evolution (DE) +
Footnote †: journal: Electronics
## 1 Introduction
Meanwhile, the extra redundancy caused by test data reduces the efficiency of reading, especially for the HSD flash which has relatively lower speed but needs the long-term, frequent and in-time RBER monitoring. The read speed of the flash has already been lower than MLC and TLC due to the higher storage density, so to avoid further slowing down, the RBER estimation method should not slow down or even would be better to speed up the reading process. In this paper, we proposed an improved and easy-to-implement fast RBER estimation method for HSD NAND flash utilizing true-value data comparison, which can also strengthen error performance of error control code (ECC), thereby increasing reading efficiency of the flash. The method includes an interleaving module to balance the errors from different pages, an RBER estimation module, and enhancement strategies for hard-decision and soft-decision decoding to improve error performance. The rest of the paper is organized as follows. Section 2 explains how the data modulation affects the error distribution on pages and how the interleaving module alleviates the unbalance. Section 3 describes the procedure and parameter setting of the RBER estimation. Section 4 depicts the strategies of improving error performance of ECC by using extrinsic information from the redundant true-value data. Section 5 presents a hardware implementation of the proposed method. Section 6 draws a conclusion.
## 3 Result and Discussion
### Composition of Liquid Smoke of the HDO Product
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{6}{c}{**\% Area**} \\ \cline{2-9}  & **Initial** & **Z3** & **Z5** & **Z7** & **Ni-Z3** & **Ni-Z5** & **Ni-Z7** \\ \hline Phenol & 31.61 & 32.48 & 26.27 & 16.69 & 28.41 & 30.00 & 26.70 \\ Phenol,2-methyl & 8.35 & - & 1.90 & 1.60 & 1.27 & 1.87 & 1.21 \\ Phenol,4-methyl & 8.49 & - & - & - & - & - & - \\ Guaiacol & 12.37 & 1.57 & 1.41 & 0.82 & 1.56 & 1.69 & 1.61 \\ Phenol,2,6-dimethyl & 0.72 & - & - & - & - & - & - \\ Phenol, 2-ethyl & 0.77 & - & - & - & - & - & - \\ Phenol,3,5-dimethyl & 1.92 & - & - & - & - & - & - \\ Phenol,3-ethyl & 1.45 & - & - & - & - & - & - \\
4-methylguaiacol & 7.57 & - & - & - & - & - & - \\ Phenol,2-ethyl-5-methyl & 0.69 & - & - & - & - & - & - \\ Phenol,3,4-dimethoxy & 0.15 & - & - & - & - & - & - \\
1,2-benzenediol,3-methoxy & 1.45 & 1.98 & 2.28 & 1.22 & 1.67 & 1.73 & 1.79 \\ Phenol,4-ethyl-2-methoxy & 2.51 & 0.83 & 0.80 & 0.43 & 0.71 & 0.66 & 0.63 \\
1,2-benzenediol,4-methyl & 0.87 & 4.18 & 4.86 & 2.66 & 3.39 & 3.81 & 3.58 \\ Phenol,2,6-dimethoxy & 4.13 & 7.55 & 7.78 & 4.44 & 7.67 & 7.41 & 7.63 \\
1.3-benzenediol,4-ethyl & 0.59 & 1.70 & 2.00 & 1.10 & 1.43 & 1.55 & - \\ Phenol,3-methyl & - & 2.51 & 2.24 & 1.41 & 2.75 & 2.53 & 2.56 \\ Catechol & - & 15.67 & 16.49 & 9.13 & 14.60 & 14.69 & 16.34 \\
1,2-benzenediol,3-methyl & - & 2.19 & 2.45 & 1.39 & 2.16 & 2.13 & 2.25 \\ Hydroquinone & - & 2.74 & 3.14 & 1.67 & 2.93 & 2.66 & 2.91 \\
1,4-benzenediol,2-methyl & - & 0.92 & 1.04 & 0.56 & 0.94 & 0.82 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Main components in commercial and HDO liquid smoke. Figure 9: The effect of catalyst variations on HDO products. The compound of 2-methoxy-4-methylphenol (4-methylguaiacol) was also shifted. Its initial content of 7.57% was no longer detected after HDO. The conversion of the 4-methylguaiacol compound, which went through demethylation (DME) to become 1,2-benzenedi,_A_-methyl (4-methylcathecol) and deoxygenation (DDO) to become 3-methylanisole, was reported by [PERSON] et al. [36]. 4-methylcatheolthrough DDO can be converted to phenol-3-methyl (m-cresol), and 3-methylanisolethrough DME can be converted to m-cresol. As presented in Table 9, 4-methylcathecol is seen to be increasing from 0.87% to 2.66-4.86%, and m-cresol, which initially did not exist, was detected after HDO with a content of 2.75%.
## 2 Problem Description and Model Formulation
### Map Modeling
#### 2.3.2 Grid-Based Map Representation  
To accurately simulate an automated warehouse with mobile shelves, this article employs a grid-based environment model that categorizes different types of obstacles and storage zones, which is shown in Figure 2.  
1. Black cells represent permanent obstacles, such as factory walls and pillars. AGVs cannot enter these areas;
2. Yellow cells represent occupied shelf nodes. Empty AGVs can pass through, but AGVs carrying a shelf cannot;
3. Green cells represent vacant shelf nodes, where AGVs can move freely;
4. White cells represent reserved passageway, ensuring accessibility to all shelf nodes;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Definition** & **Examples** \\ \hline Fixed or & Fixed obstacles cannot move and only affect the & Fixed: Factory walls, \\ Mixed or & connectivity of the grid they occupy; movable & pre-registered shelves. \\ Movable & obstacles can move and may affect the connectivity & Movable: Workers, forklifts, \\  & of multiple grid cells within a single timestep. & other AGVs. \\ \hline Known or & Known obstacles are registered in the central & Known: Pre-mapped factory \\ Unknown & system, with their state globally shared; Unknown & layout, online AGVs. \\ Mixed & obstacles are not registered in the central system; & Unknown: Unregistered or \\ Mixed & only nearby AGVs can detect it using their own & offline equipment, workers. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obstacles can be categorized along two dimensions.  
5. AGVs are depicted as colored icons, and their current destination is marked by a matching star icon.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 2 Theoretical of Compressed Sensing
### The Design of SVD-C-GNSS  
It can take just milliseconds to measure the range to a satellite. However, it is the delay in the initial acquisition and the time required to decode ephemeris data that makes traditional GPS receivers slow to produce the first fix. For example, to make a position determination, a receiver must identify the code and then synchronize a local replica of it for at least three satellites. Four are necessary to remove receiver timing biases and track these signals for eighteen to thirty-seconds [26].  
Given the lack of research directed towards the use of GNSS signal synchronization using fewer frequency samples, this paper expands the work described in Section 1 by attempting to investigate alternative methods of signal compression other than the traditional sparse Fourier transform. To this end the following goals are pursued:  
Figure 4: Signal processing based on compressed sensing.  
1. Improve acquisition performance of the GNSS signal by using a compressive sensing algorithm based on \(\ell_{1}\) minimization with non-restrictive isometric property RIP analysis. This method will allow finding the best anti-noise performance measurement matrix, given that it is not restricted to random Gaussian or random Bernoulli measurement matrices.
2. Develop a robust method for situations in which the receiver needs broader bandwidth to handle all types of navigation positioning signals using the non-RIP approach to compressive sensing which means the use of prior information to improve the acquisition stage.  
The performance and advantages of these techniques will be shown based on recorded intermediate frequency (IF) real signals from a GPS front-end data loggers as an input to compressive sensing, where the incoming IF signal is sampled at an appropriate sampling frequency. The signal received by the antenna would go through amplification, mixing, filtering, an analog-digital conversion in the RF front-end, and its output is the IF signal [27].  
The standard compressive theory contains three steps, including the transform sparsity of signal, the sparse signal with linear measurement, and the signal reconstruction [23].  
High sample rates lead to high power consumption, which creates a hardware power consumption issue. A solution is presented here to lower the sample rates as much as possible and sample smarter by using techniques like low-rank matrix recovery for signal processing.
# A Comprehensive Failure Risk Assessment Method of
###### Abstract
However, when the assessment system is relatively complex, the quantitative risk assessment method needs to deal with a huge amount of data, and the calculation process becomes more complicated. The comprehensive assessment method that combines the two is an organic combination of qualitative and quantitative assessment methods, taking into account the advantages of both. A typical combination of qualitative and quantitative risk assessment method is probabilistic risk assessment method (PRA) [18]. The method is mainly quantitative analysis, and the current research is focused on the probability and the consequences of failure events and the combination function of the two. In addition, some scholars have introduced the Bayesian network (BN) fusion fault tree analysis for risk assessment [19]. This method takes advantage of the fault tree's advantage in cause analysis, analyzes the severity of various events, and establishes a multi-state model of variables. It then uses the conditional probability distribution table to establish the conditional probability relationship of different variables, converts the fault tree into a Bayesian network, and then realizes quantitative risk assessment. Otherwise, the failure mode effects and criticality analysis is combined with the fault tree analysis for risk analysis [20]. This method establishes the risk priority index through FMECA to judge the failure risk level, combines the field operation failure statistical data, conducts the fault tree analysis to obtain the occurrence probability and the basic event critical importance, and then carries out the risk assessment accordingly. At present, the failure risk assessment method has made great progress in the machining center, but there are still certain limitations. In the actual machining process, a certain component in the system fails, which will spread the failure to other components through the physical connection relationship between the components and then cause the cascading failures. Therefore, the safe and reliable operation of the system is closely related to the failure propagation mechanism, but there are currently few studies on the correlation of failure modes among components, and most of them do not consider the impact of propagation risks. In addition, the topological structure of the system is the foundation for the safe and reliable operation of the system, but most of the current failure risk assessment methods ignore the influence of the topological characteristics of the system itself, which makes the evaluation results inaccurate. Therefore, in view of the current assessment model established is only a static model, real-time dynamic assessment of failure risk cannot be realized, or different failure modes are ignored for the influence of the breadth and depth of failure propagation. This paper proposes a comprehensive failure risk assessment method for a machining center component based on topology analysis.
## 4 VM-Based Intrusion Detection Mechanism
### System Components
#### 4.2.2 TCPSync  
Attack surfaces that cause memory corruption may originate from a diversity of network services ranging from L3 (e.g., ICMP) to L4 (e.g., FTP, HTTP, etc.), among which TCP-based protocols comprise a majority. Unlike other protocols, TCP is a connection-oriented, which means that each entity must maintain its own unique sequence number as an internal state for communication with each other. To the proposed system, this incurs a detection problem, since unless the same TCP state (i.e., sequence number) is used for both a target and an emulated device, the emulated device may fail to synchronize its memory with the target device.  
For instance, certain Cisco IOS products are known to have memory corruption vulnerabilities in their FTP server [21]. Since an FTP service is based on TCP, an attacker first must establish a connection to the victim to exploit this vulnerability. Therefore, to follow each step needed for the attack to reappear in the emulated device, TCP sequence number must be synchronized with that of the target device.  
TCPSync plays a role in addressing the problem of synchronizing TCP states. Residing on the path to an emulated device, this component performs transparent conversion of each mirrored TCP packet to adjust it to the consistent state of the emulated device.  
Figure 3 illustrates how TCPSync works in the system. Suppose a TCP session between a target and a sender, where an initial sequence number of the target is set to be \(y\). Because of all packets being mirrored, an emulated device also has sat up an initial sequence number \(z\) (not necessarily the same as \(y\)) during a three-way handshaking phase of this session. For every ingress TCP packet with an acknowledge number \(\text{ACK}=y+\delta\) (\(\delta\geq 0\)), TCPSync converts it to \(\text{ACK}^{\prime}=z+\delta\) and then forwards the packet to the interface of the emulated device. For all this to work, TCPSync conducts packet analysis and obtains initial sequence numbers whenever a TCP connection is being established.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 4 Simulation Results and Discussion
### Simulation Setup  
We consider a wireless sensor network of 45 sensor nodes with 10 of them being anchors and 35 being unknown nodes, placed in an area of 400 m\({}^{2}\) (i.e., 20 m \(\times\) 20 m) and its architecture is illustrated in Figure 4. The sensor nodes (anchors and unknown nodes) are randomly placed in the studied area.  
The accuracy of the studied algorithms is investigated over many environment realizations. In order to simplify the presentation of the paper and without loss of generality, we present results only for one environment test, as illustrated in Figure 4. Simulation results are consistent with other environments. 10 RSSI measurements of \(pr_{ij}\) received from sensor \(i\) (\(i=1,2,3,...,n\)) are taken at each position \(j\) (\(j=1,2,3,...,n\)) for each sigma shadowing value. This value is calculated in dBs as:  
\[pr_{ij}=pe-pl_{ij}+a_{\sigma}, \tag{36}\]  
where \(pe\) is the transmission power, \(a_{\sigma}\) is a Gaussian random variable which describes the random shadowing effects, \(pl_{ij}\) is the pathloss calculated using Equation (2). In this paper, we use parameters related to our laboratory: \(\varrho=3.23\), \(pe=20\) dBm, \(d_{0}=1\) m, \(f=2.4\) GHz. The sigma of the random variable \(a\) takes the values 0, 2, 5 to study the effect of its variation on the algorithm recovery performance and localization accuracy.  
Figure 4: Configuration of the wireless sensor network.  
Figure 3: The localization error using 10 anchors with the varying number of unknown nodes in 400 m\({}^{2}\) applying Adam for sigma shadowing = 2.
# Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value
## Abbreviations  
\begin{tabular}{l l} DEM & Digital Elevation Model \\ DSM & Digital Surface Model \\ DTM & Digital Terrain Model \\ GIS & Geographic Information System \\ PV & Photovoltaic \\ \(APT_{i,n}\) & Accumulated perception time in cell \(i\), with the observers located at node \(n\) (h/y) \\ \(APT_{i,k}\) & Accumulated perception time in cell \(i\) by all observers moving along route \(k\) (h/y) \\ \(d_{n}\) & Distance in the horizontal plane between nodes \(n\) and \(n+1\) (meters) \\ \(D_{n}\) & True distance between nodes \(n\) and \(n+1\) (meters) \\ \(d_{i,n}\) & Euclidean distance between cell \(i\) and node \(n\) (meters) \\ \(f\hbar v_{i,n}\) & Visible height factor of a PV plant placed in cell \(i\) and viewed from node \(n\) \\ \(fv_{i,n}^{b}\) & Visibility factor of a PV plant of height \(h\), placed in cell \(i\) and viewed from node \(n\) \\ \(GAPT_{i}\) & Global Accumulated Perception Time in cell \(i\) \\ \(h\) & Analysed height of the PV plant (meters) \\ \(H_{pv}\) & Maximum height of the PV plant (meters) \\ \(h_{ugg}\) & Segment length by which the height of the PV plant is increased in the visibility analysis \\ \(i\) & Geographic elemental area or cell with the possibility of housing a PV plant \\ \(Isk_{i,n}\) & Skyline index for a PV plant placed in cell \(i\) and viewed from node \(n\) \\ \(k\) & Any route considered in the study area \\ \(n\) & Observation point or node contained in route \(k\) \\ \(N_{k}\) & Total number of routes in the study area \\ \(N_{n}\) & Total number of nodes on route \(k\) \\ \(NOY_{k}\) & Annual number of potential observers moving along the route \(k\) \\ \(t_{n}\) & Average observation time when the observer moves from node \(n\) to node \(n+1\) (hours) \\ \(uc_{i,n}\) & Weighting factor calculated as a function of distance \(d_{i,n}\) \\ \(ws_{n}\) & Walking speed of the observer at node \(n\) (km/h) \\ \(\delta_{n}\) & Angle of the terrain slope in the direction of travel of the observer \\ \(\theta f_{i,n}\) & Elevation angle of the line of sight between node \(n\) and the top of the PV plant in cell \(i\) \\ \(\theta h_{i,n}\) & Elevation angle of the line of sight connecting the observer's eyes at \(n\) with the horizon, beyond cell \(i\) \\ \end{tabular}
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 4 Evaluation
### Implementation
#### 4.1.2 Software Implementation  
To obtain rich CSI data to verify the detection performance of Wi-Senser on human head motion, a total of ten volunteers were recruited for this experiment, including six adults (three men and three women) and four children (two boys and two girls), with ages ranging from 6 to 27 years old and weights between 20 kg and 65 kg. During the experiment, the transmitter and receiver were initially placed on both sides of the area where human motion was expected, with an initial relative distance of 2.5 m between them. During the three-day experiment, we asked each of the volunteers to take parts in tests 20 times per day and the duration of each test was approximately 3 min. During the testing, the volunteers rolled their head freely and randomly according to their personal sleeping habits, and one subject watched the video stream transmitted by the camera to record the length of the testing time. Therefore, Wi-Senser collected a total of 600 CSI amplitude data segments (60 times \(\times\) 10 volunteers).