esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 3 Problem Description
### Power Consumption Model  
The power consumption model used in this study refers to the simple, linear model presented by [PERSON] _et al._[3], as that model allows for the efficient evaluation of the total energy consumption of transceivers at a BS. This model considered both the output power and the input power [3]. The input power is consumed and converted to a certain output power by BS transceivers. This output power is the time-varying radiated power consumed by the BS transceivers that is then used to support user sessions so that their data rate requirements are met; herein, this power is referred to as transmission power. The input power, _i.e._, the total power required by BS transceivers, comprises the basic power and the transmission power.  
Specifically, we let \(p_{\text{trans},k}(i,j,s)\) denote the transmission power required to support active user-\(k\), where that active user is from sub-channel-\(j\) of CC-\(i\) in round \(s\). If we then suppose that the physical-layer coding scheme utilized by future systems will be sufficiently enhanced to achieve the target bit error rate (BER), then according to the analysis of [PERSON], [PERSON], and [PERSON] [12], \(p_{\text{trans},k}(i,j,s)\) can be calculated as:  
\[p_{\text{trans},k}(i,j,s)=\alpha\frac{N_{0}b_{\text{sub}}}{H_{k}(i,j,s)}(e\frac {r_{k}(i,j,s)}{b_{\text{sub}}}-1) \tag{1}\]  
Notice that in Equation (1), for round \(s\), \(r_{k}(i,j,s)\) denotes the physical-layer data rate for user-\(k\) from sub-channel-\(j\) of CC-\(i\), \(N_{0}\) denotes the noise power spectral density, \(H_{k}(i,j,s)\) indicates the channel gain betweensub-channel-\(j\) of CC-\(i\), and \(J=-1.5/\text{log}(5^{\epsilon})\), where \(\epsilon\) denotes the target constant BER. Furthermore, \(p_{\text{trans},\text{max},i}\) is used to denote the maximum transmission power allowed for CC-\(i\).
## 5 Evaluation of IDAQ System  
### Reconstruction Signal of IDAQ System  
In Section 4, it was verified that the quality of the wireless response acquired by the CAFB-based IDAQ system was excellent. In this section, the performance of the CAFB-based IDAQ system is evaluated quantitatively based on the reconstruction error in Equation (1) and the compression ratio in Equation (3). The artificial filter bank embedded in the dynamic wireless measuring system was designed with a total of six band-pass filters, a filter bandwidth of 0.6 Hz, and filters at intervals of 1.0 Hz. The design conditions were optimized to acquire the structural response of the civil structure from the model bridge for a frequency range less than 10 Hz, assuming that the representative random waveform (the El-Centro earthquake) is applied to the civil structure. Since, generally, a catastrophic situation (earthquake, gust, shock, etc.), will inevitably assume a random effect on the structural response of the civil structure, this study focused on acquiring the structural response within a frequency range under 10 Hz for the structure of the model bridge to pick a target mode assuming random loading conditions in the design. Figure 13 shows the comparison results between the raw data before applying the CAFB and the reconstruction signal after applying CAFB, for both the time and frequency domain when acquiring the dynamic response by the wireless measuring system (IDAQ system).  
Figure 11: Response using a wired measurement system: (**a**) time domain; and (**b**) frequency domain.  
Figure 12: Response using a IDAQ system based on wireless: (**a**) time domain; and (**b**) frequency domain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bending** & **Wired (Hz)** & **FE Analysis (Hz)** & **Error Ratio (\%)** & **Wireless (Hz)** & **Error Ratio (\%)** \\ \hline
1 st & 2.735 & 2.877 & 5.191 & 2.736 & 0.036 \\
2 nd & 4.005 & 4.194 & 4.719 & 4.104 & 2.471 \\
3 rd & 6.154 & 6.242 & 1.429 & 6.058 & 1.559 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the natural frequency (FE analysis and wireless vs. wired signal).  
In Figure 13, it can be observed that the reconstruction signal contained and reproduced the mode information of the raw data well enough and, as a result, it was determined that the band-pass optimizing algorithm of the artificial filter bank was effective in highlighting and expressing the target mode of interest within the frequency range of 10 Hz, achieving the goals of the initial design.
## 3 Results
### Comparison between Retrieval Uncertainty and Sampling Interval in Precipitation Estimation  
The abovementioned evaluation for each soil moisture input indicates that SM2 RAIN estimates have a relatively good agreement with rain gauge observations, although it tends to underestimate intensive precipitation and overestimate light precipitation in the central TP. Overall, SM2 RAIN exhibits an underestimation in the central TP with negative bias, which is in line with earlier findings of [PERSON] et al. [27]. This underestimation found in SM2 RAIN estimates derived from SMAP retrievals might be related to the underestimation of SMAP soil moisture [65] and the fact that precipitation is unable to drive any soil moisture variation when the soil is close to saturated. Despite this, SM2 RAIN-derived precipitation from SMAP retrievals performs better than other SM2 RAIN products in the TP. For instance, SMAP estimates yield smaller _BIAS_ (\(-\)0.26 mm/5 d, \(-\)0.16 mm/5 d, and \(-\)0.92 mm/5 d for ascending, descending, and combined orbits, respectively) and larger _TS_ (0.44, 0.44, and 0.98 for ascending, descending, and combined orbits, respectively) as compared to the ASCAT estimates (_BIAS_: 6 mm/5 d, _TS_: 0.4-0.5) [27; 32]. Thus, there is great potential for SMAP to estimate precipitation through SM2 RAIN algorithm.  
Meanwhile, the experiment conducted in this study reveals that SM2 RAIN estimates can be further improved by reducing the soil moisture retrieval uncertainty and sampling interval (as described in Sections 3.1 and 3.2), with the latter making a greater contribution. Additionally, to quantify the overall improvement introduced by both SMAP retrieval accuracy and its sampling interval, precipitation estimates from SMAP_X and OBS_Daily_X were compared as well. As shown in Figure 10, the overall uncertainties introduced by both impacts are larger than those of separate impacts in terms of _RMSE_ for ascending (2.71 mm/5 d) orbit, but opposite for descending and combined orbits (0.54 mm/5 d and 0.22 mm/5 d, respectively) (Figure 10). In general, both the impact of soil moisture retrieval uncertainty and sampling interval is more obvious for ascending data than for descending data. This may be ascribed to the fact that SMAP_D is capable of producing reasonable estimates with a smaller sampling interval, thus leaving small room for further improvement with OBS_D. The smallest improvement is observed for combined orbits. Similar to OBS_D over SMAP_D, this may be due to the fact that the joint use of ascending and descending orbits of SMAP retrievals enables more frequent soil moisture observations and thus a finer sampling interval, which allows for the capture of finer precipitation information across the day. As such, it provides a more reliable estimation of precipitation amount with SMAP_AD, and therefore leaves even less room for improvement with OBS_AD. To sum up, the smaller sampling interval is highly desirable to increase the accuracy of SM2 RAIN estimates.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 1 Introduction
### Notation  
Lowercase (uppercase) bold letters are used for vectors (matrices); \((\cdot)^{T}\), \((\cdot)^{H}\), and \([\cdot]\) denote the transpose, Hermitian, and rounding up operators, respectively; \(\langle\cdot\rangle_{N}\) denotes circular shift in modulus \(N\); \(E\{\cdot\}\) is the expected value operator. The operator \((\cdot)^{k}\) refers to the \(k\)-th OFDM symbol being considered.
###### Abstract  
This paper examines the effectiveness of _implicit_ beamforming (IBF), which enables transmission without channel state information (CSI) feedback in multi-user multiple-input multiple-output (MU-MIMO) systems with a large number of antennas. First, we explain why CSI feedback from terminal stations to the base station produces a very large overhead. A calibration technique is then introduced, which compensates for the difference between the complex amplitudes of the transmitters and receivers to facilitate CSI-feedback-free beamforming; this technique is called IBF. The efficacy of this calibration technique is demonstrated by measuring the amplitude and phase errors obtained using a 16-element array testbed and by performing a channel capacity evaluation. Finally, the throughput under IEEE802.11 ac-based massive MIMO transmission, both with and without CSI feedback, is obtained in terms of the medium access control efficiency.  
multi-user MIMO; calibration; implicit beamforming; channel state information +
Footnote †: journal: Electronics Letters
## 3 Results and Discussion
### Asphalt Mixture Performance Analysis
#### 3.2.2 Water Stability  
This study evaluates the water stability of asphalt mixtures using the immersion Marshall test and freeze-thaw splitting test. The results of the Marshall stability ratio (MSR) and tensile strength are shown in Figures 15 and 16.  
From Figure 15, it can be seen that at the same temperature, as the proportion of resin phase decreases, the leaching residual stability gradually decreases. This is because the resin phase in epoxy asphalt is greatly affected by temperature, and the reduction in the resin phase decreases the soak water Marshall stability, leading to a reduction in soak water residual stability [52; 53]. A comparison of SSEA and BJ200 found that the SSEA mixture soak residual stability is higher than BJ200, which indicates that the water stability of SSEA is higher than BJ200, reflecting the idea that the epoxy asphalt has a better ability to resist water damage than BJ200. With an increase in temperature, BJ200 soak water residual stability gradually decreases, while SSEA soak water residual stability gradually increases. SSEA-1 and SSEA-2 appear to be greater than 100% for the first time at 50 \({}^{\circ}\)C, and SSEA is greater than 100% at 60 \({}^{\circ}\)C, which is mainly because the molded SSEA Marshall specimens  
Figure 14: Rutting test results of the SSEA mixtures.  
did not reach the final curing strength after 4 h of 120 \({}^{\circ}\)C conditioning. When stored in a constant-temperature water bath, i.e., in the continued curing, moisture is lower than the amount of continuous water temperature on the epoxy asphalt maintenance process of the strength increase (compared to the water damage to the asphalt film on the aggregate surface at constant temperature), so the water-immersed residual stability appears to be greater than 100%.  
From Figure 16, it can be seen that the split strength of BJ200 after freezing and thawing decreases considerably, and the TSR is only 69.36%, while the split strengths of SSEA mixtures after freezing and thawing are 98.52%, 97.90%, 97.26%, 96.02%, and 95.06%, respectively. It can be found that with an increase in asphalt content, the freeze-thaw  
Figure 16: Freeze–thaw cycle splitting results for SSEA mixtures (TSR- freeze-thaw splitting strength ratio).  
Figure 15: Results of the immersion Marshall tests of the SSEA mixture.  
splitting ratio of SSEA mixtures show a decreasing trend, indicating that the rise of asphalt content is more sensitive to the temperature change in the field site and the ability to resist water damage decreases. Even so, the lowest TSR of the SSEA mix is 95.06%, which is still higher than that of BJ200. This indicates that the epoxy asphalt mixture has little strength loss after the freeze-thaw cycle, and its water stability is higher than that of BJ200, which can meet the requirements for Seamless expansion joints.
# Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
## 3 Results and Discussion
### Subjective Quality Assessment of Video Encoded Using VP9 Standard  
The VP9 standard, developed by Google, was the last evaluated coding technique. The VP9 codec is used, among others, on YouTube. The VP9 codec is based on an open-source license, uses the Webm container, and is basically MKV (the H.264 and H.265 codecs use the MP4 container) [40]. The degradation of the quality of the video signal encoded in the VP9 standard was assessed in a group of 30 people at home and 40 people in a laboratory. The results of the measurements made under home conditions are presented in Table 7 and graphically in Figure 11, with the results of the laboratory conditions in Table 8 and Figure 12. In addition to the MOS mean value, the tables also include the standard deviation values (S), as well as the values of the confidence interval coefficient (\(\delta\)) calculated according to the ITU BT.500 recommendation [1]. The statistical analysis of the results showed that up to a bit rate of 2000 kbps, there is no difference in the assessment of image quality with resolutions of 1920 \(\times\) 1080 and 1280 \(\times\) 720 in home measurements; for higher speeds, slight differences can be observed in favor of the image with higher resolution. However, the differences in the quality assessment are within the designated confidence interval. For both resolutions, the MOS value of 4.0 is exceeded at 3000 kbps. The quality assessment made at home for other resolutions is comparable to the H.265 standard, which is probably related to the young people's habits, as this standard is very popular, among others, on YouTube. In turn, the statistical analysis of the results obtained in the laboratory measurements showed that up to a bit rate of 1000 kbps, there is no difference in the assessment of image quality for all of the resolutions assessed. Above this bit rate, the video quality slightly depends on the resolution and, as expected, the video signal with a resolution of 1920 \(\times\) 1080 is rated the highest, for which MOS = 4 was already achieved at a bit rate of 2000 kbps. An MOS value of 4.0 was obtained for 1280 \(\times\) 720 at a bit rate of 2500 kbps and for 858 \(\times\) 480 at a bit rate of 3000 kbps. The smallest resolution, i.e., 640 \(\times\) 360, achieves the worst MOS values, but starting from 4500 kbps, the quality rating reaches the level of 4.0, just like in home measurements. Figure 10: Difference between the results of the subjective evaluation of the H.265-encoded video (\(\Delta\)MOS), conducted in the laboratory and at home, as a function of bit rate for different resolutions. Figure 11: Results of the subjective quality assessment (MOS) for the VP9-encoded video as a function of bit rate for different spatial resolutions—measurements at home.
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Skeletal Landmarks**} \\ \hline
**Point (Abbr.)** & **Acronym** & **Explanation** \\ \hline ANS & Anterior Nasal Spine & Anterior point on maxillary bone \\ \hline PNS & Posterior Nasal Spine & Posterior limit of bony palate or maxilla \\ \hline S & Sella Turcica & Midpoint of sella Turcica \\ \hline N & Nasion & Most anterior point on frontonasal suture \\ \hline Point A & Subspinale & Most concave point on anterior maxilla \\ \hline Point B & Supramentale & Most concave point on mandibular symphysis \\ \hline Me & Menton & Lowest point on mandibular symphysis \\ \hline Go & Gonion & Most posterior inferior point on angle of mandible \\  & It can also be constructed by bisecting the angle formed by \\  & intersection of mandibular plane and ramus of mandible \\ \hline MGo & Mid-Gonion & Middle point between right gonion and left gonion \\ \hline If & Infraorbital Foramen & Rlf (right); Llf (left) \\ \hline Mf & Mental Foramen & Rlf (right); Llf (left) \\ \hline S-Go & Sella-Gonion & Posterior facial height \\ \hline N-Me & Nasion–Menton & Anterior facial height \\ \hline N-Ans & Nasion–Anterior & Upper anterior facial height \\  & Nasal Spine & Lower anterior facial height \\ \hline Ans-Me & Anterior Nasal Spine–Menton & Lower anterior facial height \\ \hline N-Ans+Ans-Me & Nasion–Anterior Nasal & Total anterior facial height \\  & Spine+Anterior Nasal & Spine–Menton \\ \hline SNA & Angle between Sella/ & Antero-position of maxilla relative to \\  & Nasion plane and & upper cranial structures \\  & Nasion/A plane & \\ \hline SNB & Angle between Sella/ & Antero-position of mandible relative \\  & Nasion plane and & to upper cranial structures \\  & Nasion/B plane & \\ \hline ANB & SNA—SNB & Anteroposterior relationship of the \\  & & mandible to the maxilla \\ \hline
**Dental landmarks** & & \\ \hline
6 RIM-6 LIM & & Distance between the interproximal contact point on the \\  & & \\  & & \\  & & \\ \hline
6 RABM-6 LBM & & Distance between the mesial surface of the first right molar \\  & & and the mesial surface of the first left molar at the \\  & & alveolar bone level \\ \hline A0B0 & & Orthogonal projections on the occlusal plane of points \\  & & A and B \\ \hline AOcl & Anterior occlusal point: middle point of the line that links \\  & & upper and lower incisal points \\ \hline MAOcl & Mid-anterior occlusal point: middle point between right \\  & AOcl and left AOcl \\ \hline POcl & Posterior occlusal point: middle point of the occlusal surface of \\  & first permanent molars \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Skeletal and dental landmarks for cephalometric analysis.
## 2 RPC Model and Bundle Adjustment
### Bundle Adjustment Optimization  
Bundle adjustment, which is a computer vision technique used to jointly optimize camera parameters and 3D points, is essentially a nonlinear least-square optimization method, whose objective function is the sum of the squares of the reprojection errors (w.r.t. \(\updownarrow_{2}\)-norm) of all feature points in all images. Assuming that there are \(N\) 3D points \(\{(X_{n},Y_{n},Z_{n})\}_{n=1,2,\cdots N}\), and for \(M\) cameras, there are \(M\times N\) 2D observations \(\{(x_{mn},\,y_{mn})\}_{m=1,2,\cdots M;\,n=1,2,\cdots N}\). Actually, the number of \(N\) in the experiment is the same as the number of matching point pairs or matching point sets. The objective function of the bundle adjustment can be expressed as follows:  
\[\min_{(X,\mathbf{P})}\frac{1}{2}\sum_{n=1}^{N}\sum_{m=1}^{M}\|\mathbf{x}_{mn}-\mathbf{P}_ {m}(\mathbf{X}_{n})\|_{2}^{2}, \tag{3}\]  
where \(\mathbf{x}_{mn}=(x_{mn},y_{mn})\) represents 2D observations in the satellite images, and \(\mathbf{X}_{n}=(X_{n},Y_{n},Z_{n})\) represents 3D points. In Equation (3), \(\mathbf{P}_{m}(\cdot)\) denotes the projection functions of the satellite cameras, which are in the forms of Equations (1) and (2). Here \(\mathbf{P}_{m}(\mathbf{X}_{n})\) represents the reprojected point from the \(n\)-th 3D point projected by the \(m\)-th camera. The reprojection error is denoted by \(\mathbf{x}_{mn}-\mathbf{P}_{m}(\mathbf{X}_{n})\). Bundle adjustment solves for the optimization variable with the smallest reprojection error in the sense of Equation (3).  
As pointed out in [34], the gradient descent method, the Gauss-Newton method, and the Levenberg-Marquardt algorithm are commonly used to solve bundle adjustment optimization problems. Among the three methods, the gradient descent method is greedy and tends to increase iterations; the approximate Hessian matrix in the Gauss-Newton method may be degenerate, which leads to the non-convergence of the algorithm; the Levenberg-Marquardt algorithm is more robust compared to the Gauss-Newton method, but sometimes it converges very slowly. In this paper, the proposed method adopts an improved Levenberg-Marquardt algorithm based on trust regions, which is suitable for solving large-scale optimization problems.
###### Abstract  
Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.  
underwater acoustic sensor networks; source location privacy; passive attacks; virtual coordinate system; network coding; evolutionary game theory +
Footnote †: journal: Sensors  
0000-0001-5897-5800]Sensors