esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 7 Conclusions  
A new world of technological possibilities could make its way in a very varied range of IoT sectors thanks to the integration of TSN and the well-established IEEE 802.11 technology. The most solid and promising exponent of this trend is IEEE 802.11 be, actual precursor of future WiFi 7, which should be accompanied with a well-defined and backward compatible time-sensitive operation mode to support low-latency communications.  
Although WiFi will never be able to guarantee fully deterministic communications because of its operation in license-exempt bands, there is still room to reduce the impact of all manageable causes, both internal and external, that may increase latency. On the one hand, contention with external networks may be minimized by considering dynamic spectrum access such as non-contiguous channel bonding and multi-link operation, as well as cooperative AP strategies. On the other hand, prioritization and scheduling mechanisms inside the same WLAN may provide an effective solution to reduce the latency of time-sensitive traffic in the presence of large packets from best-effort flows.  
Conceptualization, T.A. and B.B.; investigation, T.A., M.C.-Z., and B.B.; writing--original draft preparation, T.A. and M.C.-Z.; writing--review and editing, T.A. and B.B.; software, B.B. and M.C.-Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Spanish government under the projects PGC2018-099959-B-100 and TEC2016-79510-P, and by the Catalan government through the projects SGR-2017-1188 and SGR-2017-1739.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Interactive museum use case simulation. Average latency of TS packets.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 2 Signal Model
### LTE Positioning Signal Structure
#### 2.1.1 Downlink Signal  
In downlink transmission, a dedicated Positioning Reference Signal (PRS) for positioning purposes is specified. It is based on a multi-carrier waveform, which is mainly defined as an Orthogonal Frequency Division Multiplexing (OFDM) scheme. The PRS is specified in 5G standard in TS 38.211 [15]. The received signal is simulated by convolving only one OFDM PRS symbol with a multipath Channel Impulse Response (CIR). The generation of PRS includes two steps: generation of PRS sequences based on Gold sequences and the PRS mapping. The PRS sequences are Quadrature Phase Shift Keying (QPSK) modulated.
# Optimal Control for a Hydraulic Recuperation System Using Endoreversible Thermodynamics
## 2 Hydraulic Recuperation System  
The hydraulic system for mechanical energy recuperation considered here consists of the following components: a hydraulic fluid tank, a hydraulic pump/motor which is mounted to and driven by the vehicle's cardan shaft, a hydraulic bladder accumulator and the pipes connecting these components.  
The hydraulic pump/motor--or simply hydraulic unit--has the ability to operate both as a pump and as a motor, and with variable displacement. This means that a flow of hydraulic fluid can be generated using the input power from the cardan shaft, or a power output can propel the cardan shaft driven by a fluid flow, respectively. We use a displacement factor \(\gamma\) defining the ratio of current displacement to maximum displacement of the hydraulic unit, so that \(-1\leq\gamma\leq 1\) holds, to describe the operation mode. A negative displacement factor means that hydraulic unit acts as a pump.  
In this case, the hydraulic fluid is pumped from the low pressure hydraulic fluid tank to the high-pressure bladder accumulator. The inflowing hydraulic fluid within the bladder accumulator compresses the gas leading to a pressure increase. In this way, energy can be stored. The needed work is taken from the cardan shaft which decelerates the vehicle. Another useful effect of this is that the conventional disc brakes are supported and therefore have to absorb less energy resulting in reduced wear.  
If a certain amount of hydraulic fluid is within the bladder accumulator and hence there is energy stored within, the hydraulic unit can act as a motor. The high pressure causes a fluid flow in the opposite direction which propels the hydraulic motor mounted to the cardan shaft. The power carried by this fluid flow from a high to a low pressure is transferred to the cardan shaft accelerating the vehicle.  
For the detailed description of a comparable recuperation system, which includes also a pressure relief line, the reader is referred to [50].
## 3 Case Study Analysis
### Effects of Soil Parameter Spatial Variability on Failure Probability
#### 3.2.1 Effects of Spatial Variability Coefficients on Failure Probability  
Drawing from the existing research [37; 40], this study establishes the following values for the various variation coefficients and cross-correlation coefficients. The coefficient of variation for cohesion (\(Cov_{c}\)) spans from 0.1 to 0.5, the coefficient of variation for the angle of internal friction (\(Cov_{\varphi}\)) ranges between 0.05 and 0.25, and the cross-correlation coefficient between cohesion and friction angle \(\rho\)(\(c\), \(\varphi\)) ranges between \(-\)0.7 and 0.2. According to the previous studies [5], \(L_{h}\) ranges from 10 to 40 m and \(L_{\varphi}\) is within 1\(\sim\)4 m. Therefore, for the correlation lengths, three distinct combinations are considered, i.e., (\(L_{h}\) = 20 m, \(L_{\varphi}\) = 2 m), (\(L_{h}\) = 20 m, \(L_{\varphi}\) = 4 m), and (\(L_{h}\) = 40 m, \(L_{\varphi}\) = 2 m). Additionally, the parameter for the failure surface width, \(B/H\), is set at 5.0.  
Figures 5-10 illustrate the trends in \(F_{s}\) and \(P_{f}\) to variations in coefficients of variation (\(Cov_{c}\), \(Cov_{\varphi}\)), cross-correlation coefficient \(\rho\)(\(c\), \(\varphi\)), and correlation lengths (\(L_{h}\), \(L_{\varphi}\)) under spatially variable soil conditions.  
Examining the results presented in Figures 5-10, several patterns emerge. Under fixed correlation lengths, slope geometric parameters, and failure surface width, \(P_{f}\) tends to increase progressively as the spatial variability coefficients of soil parameters (\(Cov_{c}\), \(Cov_{\varphi}\), and \(\rho\)(\(c\),\(\varphi\))) intensity, while \(F_{s}\) exhibits a corresponding decline with the augmentation of these variability coefficients. Similarly, for constant soil parameter spatial variability coefficients, slope geometric parameters, and failure surface width, \(P_{f}\) increases as the correlation lengths \(L_{h}\) and \(L_{\varphi}\) increase.  
Figure 5: Variation curves of \(P_{f}\) and \(F_{s}\) with respect to \(Cov_{c}\) (different combinations of \(Cov_{\varphi}\) and \(\rho(c,\varphi)\)).  
Figure 6: Variation curves of \(P_{f}\) and \(F_{s}\) with respect to \(Cov_{c}\) (different combinations of \(L_{h}\) and \(L_{\varphi}\)).  
Figure 7: Variation curves of \(P_{f}\) and \(F_{s}\) with respect to \(Cov_{\varphi}\) (different combinations of \(Cov_{c}\) and \(\rho(c,\varphi)\)).
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 5 Discussion  
### Analysis of the Link Distance Intensity Factor  
The link distance intensity factor describes the relationship between link quality and transmission distance as a linear function. In fact, it is more likely that there is a nonlinear relationship between them. For example, according to the radio wave propagation path loss formula, the loss value is inversely proportional to the square of the distance, i.e., a doubling of the communication distance increases the link loss by 6 dB. Therefore, the link distance intensity factor \(\sigma_{2}(t)=\frac{d_{\max\hat{S_{j}}-d_{i}\hat{S_{j}}}(t)}{d_{\max\hat{S_{j}} -d_{\min\hat{S_{j}}}\hat{S_{j}}}}\) can be replaced by the signal propagation attenuation factor \(\sigma_{2}^{\prime}(t)=\frac{I_{\max\hat{S_{j}}-L_{i}\hat{S_{j}}}(t)}{I_{\max \hat{S_{j}}\hat{S_{j}}-L_{\min\hat{S_{j}}}\hat{S_{j}}}}\), wherein \(L_{\hat{S_{i}}\hat{S_{j}}}(t)\) represents the path propagation attenuation of the signal.
* When it is a microwave signal, the path propagation attenuation is \(L(\mathrm{dB})=20\log f+20\log d+32.4\), wherein \(d(\mathrm{km})\) denotes distance and \(f(\mathrm{MHz})\) denotes frequency;
* When it is a laser signal, the path propagation attenuation is \(L(\mathrm{dB})=20\log\lambda-20\log Z-261.98\), wherein \(Z(\mathrm{km})\) denotes distance and \(\lambda(\mathrm{nm})\) denotes wavelength. A more precise expression of the link distance intensity factor will be further investigated.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
###### Abstract  
Visible Light Communication (VLC) has received substantial research attention in the last decade. The vast majority of VLC focuses on the modulation of the transmitted light intensity. In this work, however, the intensity is kept constant while the polarization direction is deployed as a carrier of information. Demodulation is realized by using a differential receiver pair equipped with mutually orthogonal polarizers. An analytical expression to evaluate the Signal-to-Noise Ratio (\(SNR\)) as a function of the rotation angle of the receiver is derived. It is demonstrated that the signal quality can deteriorate heavily with receiver orientation when using a single differential receiver pair. A way to overcome this drawback using two receiver pairs is described. The analytical expression is experimentally verified through measurements with two different receiver setups. This work demonstrates the potential of polarization-based modulation in the field of VLC, where receiver rotation robustness has been achieved by means of a dedicated quadrant photodiode receiver.  
differential; polarization; Visible Light Communication; quadrant photodiode 2020
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 4 Semi-Vortices in Spinor SOC BEC with DDI and ZS
### Mobility and Effective Mass Controlled by ZS  
Besides controlling the shape of the SVS, enabling transitions between the horizontal and vertical type, Zeeman splitting also exhibits crucial effects on the mobility of the SVS. By defining the effective mass \(M_{x,y}=P_{x,y}/V_{x,y}\) where:  
\[\mathbf{P}=i\int d\mathbf{r}[(\Delta\psi_{+}^{*})\psi_{+}+(\Delta\psi_{-}^{*}) \psi_{-}] \tag{12}\]  
and \(V_{x,y}\) is the velocity of the SVS, the motion of the SVS can be studied quantitatively. Numerical results show that as in Section 3.3, the SVS can be kicked to move and undergoes different types of motion depending on the kick strength. The effective mass defined above only applies to the spiral motion (see Figure 3b), and it is found that for spiral motion, changing \(\Omega\) can switch the effective mass between positive and negative, via infinite divergence at a specific value \(\Omega=\Omega_{M=\infty}\). Typical results are displayed in Figure 8. General conclusions can be drawn from these results:  
1. For \(\Omega<\Omega_{M=\infty}\), the SVS possesses a negative effective mass, and it will move in the opposite directions of the kick. This is in accordance with results in Section 3.3, where \(\Omega=0\).  
Figure 7: Stable inverted SVS with \(\Omega<0\). (**a**) Density distribution of fundamental and vortex component of inverted SVS with \((\Omega,N)=(6,-1.82)\) (first row) and \((\Omega,N)=(10,-10.8)\) (second row); (**b**) stable threshold \(\Omega_{s}\) versus \(N\); (**c**) chemical potentials of the SVS with vortices in \(\psi_{+}\) and \(\psi_{-}\) components \((S_{+},S_{-})=(0,+1)\) and its flip counterpart \((S_{+},S_{-})=(-1,0)\) vs. \(\Omega\); (**d**) the same as in (c), except for total energy \(E\) vs. \(\Omega\)[59].  
For \(\Omega=\Omega_{M=\infty}\), the effective mass of SVS diverges to infinity, and the soliton will be pinned to move in a circular trajectory, similar to the motion shown in Panel (a) of Figure 3, which can be regarded as the immobility of the soliton.
3. For \(\Omega>\Omega_{M=\infty}\), the SVS possesses a positive effective mass, and it will move along the directions of the kick.
4. For \(\Omega\to\infty\), the effective mass approaches the total norm \(M\to N\), since it becomes a usual single-component soliton.
## 2 Theory and Methods
### Relationship between the GNSS CNR Signal and Multipath  
In theory, the GNSS receiver can not only receive the direct signal, but also can receive the indirect signal. Without a loss of generality, the signal received by the GNSS receiver can be expressed as follows [18]:  
\[S(t)=\sum_{i=0}^{n}a_{i}(t)\cos(w_{0}t+\gamma_{i})+\varepsilon(t) \tag{1}\]  
where \(S(t)\) denotes the signal received by the GNSS receiver; \(n\) is the total number of the received signals; \(w_{0}\) denotes the nominal frequency of the direct signal; \(\gamma_{i}\) is the relative phase of the \(i\)-th component; \(\varepsilon(t)\) represents noise; \(a_{i}(t)\) means the amplitude of the signal, which includes both direct and indirect signals. Thus, Equation (1) can be further expressed as follows [19]:  
\[S(t)=\sum_{i=0}^{k}a_{i}^{d}(t)\cos w_{0}t+\sum_{i=0}^{m}a_{i}^{r}(t)\cos(w_{ 0}t+\gamma_{i}) \tag{2}\]  
where \(k\) and \(m\) mean the number of direct and indirect signals, respectively; \(a_{i}^{d}(t)\) and \(a_{i}^{r}(t)\) denote the amplitude of direct and indirect signals. Considering that the strength of the GNSS signal is often denoted by CNR observation, the above Equation (2) can be simplified as follows:  
\[CNR=\sqrt{A_{d}^{2}+A_{r}^{2}+2A_{d}A_{r}\cos\varphi} \tag{3}\]  
where \(A_{d}\) is the amplitude of direct signal; \(A_{r}\) means the amplitude of indirect signal; \(\varphi\) is the phase difference between the direct and indirect signals.  
From Equation (3), it can be seen that the CNR is not only related to the direct signal, but also performs largely in tandem with the indirect signal. When the phase of the indirect signal is the same as that of the direct signal, the strength of the received signal will be enhanced. On the contrary, if the phase between the indirect signal and direct signal is inconsistent, the signal will be attenuated. This phenomenon is called the multipath effect, and the indirect signal is often denoted as a multipath signal. Thus, based on the above analysis, it can be concluded that the CNR is strongly related with the multipath. In  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surface Type** & **Reflection Coefficient** & **Attenuation Factor (dB)** \\ \hline Dry soil & 0.268 & \(-\)11.4 \\ Wet soil & 0.691 & \(-\)3.21 \\ Grassy field & 0.334 & \(-\)9.53 \\ Fresh water & 0.800 & \(-\)1.95 \\ Glass & 0.421 & \(-\)7.51 \\ Brick & 0.345 & \(-\)9.24 \\ Concrete & 0.404 & \(-\)7.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reflection coefficients and attenuation factors for common surfaces at normal incidences (elevation angle of 90\({}^{\circ}\)) on a GPS L1 frequency.  
addition, it should be pointed out that the CNR can be directly obtained from the RNIEX observation files.
## 4 Performance Analysis
### The Effect of Interval Prediction by 2D Kernel Density Estimation
As a result, the prediction model by means of the LSTM for the data of SMES with a large volatility is much less accurate, but the interval prediction by means of the 2D kernel density estimation technique helps to secure the reliability of the prediction result by modeling such variability (Figures 8 and 9).
## 4 Methodology
### Problem Formulation  
The goal of spaced repetition optimization is to schedule learning items such that the learner memorizes all items in the long-term memory, meanwhile, minimizing memory costs. In detail, given \(N\) learning items, the whole learning process consists of a learning event sequence in which a learning event \(e\) is represented as a vector \(e_{i}=(w_{i},\Delta_{i},r_{i},p_{i})\). For an incoming item \(w_{i}\), which has not been reviewed for \(\Delta_{i}\) days, the probability of the learner recalling the item successfully is equal to \(p_{i}\). The optimization algorithm has to determine the optimal interval for the item after the recalling result \(r_{i}\). In this paper, we formulate this problem into a reinforcement learning paradigm. We define each RL component as follows:  
* State space. The state space \(\mathcal{S}\) depends on the memory model. For EFC, the state space encodes the item difficulty, the time delay, and the memory strength. For DHP, the states include the difficulty, the time delay, and the recall result.
* Action space. The action space \(\mathcal{A}=\{1,2,\cdots,T\}\) consists of review intervals ranging from 1 to the maximum interval \(T\). The action \(a_{t}\in\mathcal{A}\) indicates that the current item was scheduled \(a_{t}\) days ago after its review at time \(t\).
* Observation space. Away from the internal memory states, the agent can only obtain observations \(\mathcal{O}\) including the time delay, the recall result, and the recall probability no matter the choice of memory model.
* Reward function. Following previous works [14; 15; 17], we construct the reward function using the recall probability of the current item, which is provided by the memory model \(\mathcal{M}\).  
To this end, our goal is to find the optimal policy performing the best action (optimal interval) to gain maximum rewards.  
Figure 1: The overall view of our proposed framework for spaced repetition optimization using deep reinforcement learning. The arrow represents the computation flow.
## 2 Materials and Methods
### Diagnosis Model Based on LightGBM  
A fact that cannot be ignored is that the recorder on UAV can generally store more than 20 flight data points per second, which brought us to paying more attention to the performance of the diagnostic model in processing large-scale data. When handling extremely large data dimensions, XGBoost is required to calculate the information gain of each split based on all data to consider all possibilities, but this is highly time consuming. Microsoft proposed LightGBM (Light Gradient Boosting Machine), an exceedingly fast gradient boosting framework, to improve the model training speed in processing large-scale data [28]. Applied in image recognition recently, as an example, LightGBM has been validated as an effective method for distinguishing COVID-19 from bacterial pneumonia [29]. In fault diagnosis, LightGBM was used in diagnosing shipboard medium-voltage DC power system faults [30], and rotating machinery faults [31].  
As another improved method of GBDT, LightGBM employs gradient-based one-side sampling (GOSS) to select data instances according to the weights in determining split values. To reduce the dimension of features, Exclusive Feature Bundling (EFB) technology binds some mutually exclusive features into an exclusive feature bundle, shrinking the original feature matrix as a dense matrix. A histogram-based algorithm, with efficient sparse optimization solutions (GOSS and EFB), is chosen in LightGBM to reduce the cost of calculating the gain, to speed up training and to occupy memory usage less. Furthermore, in order to obtain better accuracy, a level-wise [32] strategy is used in LightGBM, which chooses the leaf with max delta loss to grow, and limits maximum depth for prevention of overfitting.  
The purpose of our use of LightGBM is to train the diagnosis model much faster, so as to adapt to the possible real-time updating requirement of the UAV fault diagnosis model in mission, and for online raw diagnosis. The grid search approach was employed in our model to tune the hyper-parameters of LightGBM.
# Analysis of the Pre and Post-COVID-19 Lockdown Use of Smartphone Apps in Spain
## 3 Data Used  
We used data from the Cybersecurity and Confidence in Spanish Households national survey conducted during the first semester of 2020 by the National Observatory of Telecommunications and Information Society of the Spanish Ministry of Industry. We considered to be pre-lockdown the data obtained before 14 March 2020 (first confinement date in Spain), and as post-lockdown the data from that date.  
Two types of data were collected in the survey: self-reported data and scanned data. Self-reported data corresponds to the answers that participants gave to an online questionnaire. Scanned data refers to data obtained from scanning the users' smartphone upon approval by participants. A specialized software called Pinkerton (Hispasec Sistemas, Malaga, Spain. URL: [[https://hispasec.com/es/pinkerton/policy](https://hispasec.com/es/pinkerton/policy)]([https://hispasec.com/es/pinkerton/policy](https://hispasec.com/es/pinkerton/policy)) (accessed on 22 June 2021) was installed remotely in the participants' Android smartphones to search for resident malware and to collect data about the apps use, operating system, their up-to-date status, the security tools installed and the security activities of the user. The scanning software was installed, and the scanned data were collected after the participant had completed the online questionnaire. The iScan software analyzed the presence of malware using 46 antivirus engines, and used the VirusTotal tool, an online malware and virus-scanner aggregator as the base of its analysis.  
This software captured two measures: the Android operating system version and the malware incidents suffered by the smartphone. The measure of malware is the \"Risk Level\" of the smartphone. The software installed measured the risk level on a 0-3 ordinal scale, where a higher number meant a more risky/compromised computer. Antivirus engines classify malware found from weak to very dangerous. For each engine, the classification of the malware was coded from 1 (weak, low risk) to 3 (very dangerous, high risk). The risk level of a computer is the average of all risk levels of the malwares found by the 46 antivirus engines.  
To obtain the app and overall usage times, the procedure was as follows:  
* A user takes the survey
* When the quality of the survey is validated, a message is sent to the user to scan their Android device through an application designed for this purpose.
* The device records the usage time of each one of the applications on the Android smartphone using the API that reports their usage times.
* The information is sent to the server.
* The total usage time is calculated as the sum of all the applications' usage times.  
The only preprocessing made to the times of use was to \"weight\" the days of application usage time: e.g., if the scan were performed on the day 22 of the month and the month has 30 days, the adjusted usage time would be \((30/22)\times recorded\_time\).
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 3 EPB Machine Specifications  
The water tunnel project is an agricultural water transport tunnel project, and a pipe-jacking-type EPB TBM was operated in the tunnel excavation. A picture of the cutter arrangement is shown in Figure 2. The machine specifications are shown in Table 1. The tunnel diameter is 2.95 m. The daily tunnel advancement is between 2 and 8 m.  
Figure 1: Overview of the proposed disc cutter wear prediction model. The proposed 1D CNN model was trained and evaluated to obtain the best model for disc cutter wear prediction.  
Figure 2: Cutter arrangement of EPB TBM.