esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Improving the Estimation of GPP Using a Combination of SIF and CI
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression Model** & **Regression Variables** & **Regression Equation** & **R\({}^{2}\)** & **RMSE** \\ \hline \multirow{3}{*}{Linear} & SIF & \(GPP_{L}=f_{l}(SIF)=36.24\times SIF_{760}\) & 0.71 & 6.45 \\  & \(GPP_{L-CI}=f_{l}(SIF)\times g_{l}(CI_{day})\) & 0.82 & 5.71 \\  & \(g_{l}\big{(}CI_{day}\big{)}=\left(-99.78\times CI_{day}+124.19\right)\times 36.24\) & & \\ \hline \multirow{3}{*}{Hyperbolic} & SIF & \(GPP_{NL}=f_{l}(SIF)=42.6\times SIF_{760}\) & 0.82 & 3.79 \\  & \(GPP_{NL-CI}=f_{NL}(SIF)\times g_{NL}(CI_{day})\) & 0.87 & 2.92 \\ \cline{1-1}  & \(g_{NL}\big{(}CI_{day}\big{)}=-2.16\times CI_{day}+1.78\times CI_{day}+0.87\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The \(\text{SIF}_{760}\)-based GPP models with and without the inclusion of the clear-sky index (CI) based on the daily tower-based observations of the training dataset. The function \(g(CI)\) represents the influence of CI on the \(\text{SIF}_{760}\)-based GPP models. Figure 8: Relationship between CI and the ratio of measured canopy GPP (\(GPP_{meas}\)) to predicted GPP by the non-linear \(\text{SIF}_{760}\)-based GPP estimation model (\(GPP_{NL}\)). by the inclusion of the CI, resulting in a higher R\({}^{2}\) and a lower RMSE: the improvement was from R\({}^{2}\) = 0.66 and, RMSE = 7.02 mw/m\({}^{2}\)/nr/sr to R\({}^{2}\) = 0.76 and, RMSE = 6.38 mw/m\({}^{2}\)/nr/sr for the linear model, and from R\({}^{2}\) = 0.71 and, RMSE = 4.76 mw/m\({}^{2}\)/nr/sr to R\({}^{2}\) = 0.78 and, RMSE = 3.50 mw/m\({}^{2}\)/nr/sr for the nonlinear model. Therefore, although the SIF-GPP relationship is seriously disturbed by the meteorological conditions, this can be corrected for using the easily assessible CI metric.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 4 Implementation
### Transport Layer Protocol
Based on the above mechanisms, the transport layer (TL) packet is designed as shown in Figure 6.  
In the Open System Interconnect (OSI) reference model, a service primitive [25] is an operation sent by a lower-layer protocol to provide some service to an upper-layer protocol through an interface. For chunk transmission, we define the request, listen, and response primitives, and the setting of transport-related parameters, including timeout time and chunk size, which are supported through options. To support the transmission of the chunk, different packet identifiers are set, including chunk request (CREQ) packet, chunk response (CRES) packet, request (REQ) packet,  
Figure 6: Packet format of transport layer (TL) header. response (RES) packet, and chunk transmission finish (CFIN) packet. The normal transmission process is that the receiver sends a CREQ packet, the sender replies with a CRES packet; the subsequent transmission proceeds by (REQ, n \(\times\) RES) matching; the final transmission ends with the CFIN identification sent by the receiver. In the replica switching scenario, the receiver sends a CREQ packet again and fills the offset field with the location information where the current missing data starts into this packet. After receiving this CREQ packet, the new sender will check whether the local information for the transmission of the corresponding ID has been created. If not, the transmission can be continued after creating the transmission state.
## Interaction of Classical Platinum Agents with the  
**Monomeric and Dimeric Atox1 Proteins:**  
**A Molecular Dynamics Simulation Study**  
**[PERSON], [PERSON], [PERSON] * and [PERSON] ***  
Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education,  
College of Chemistry, Beijing Normal University, Beijing 100875, China;  
E-Mails: [EMAIL_ADDRESS] (X.W.); [EMAIL_ADDRESS] (C.L.)  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (G.C.); Tel.: +86-10-5880-5247 (Y.W.); Fax: +86-10-5880-2075 (Y.W.).  
_Received: 1 November 2013; in revised form: 5 December 2013 / Accepted: 12 December 2013 / Published: 20 December 2013_  
**Abstract:** We carried out molecular dynamics simulations and free energy calculations for a series of binary and ternary models of the cisplatin, transplatin and oxaliplatin agents binding to a monomeric Atox1 protein and a dimeric Atox1 protein to investigate their interaction mechanisms. All three platinum agents could respectively combine with the monomeric Atox1 protein and the dimeric Atox1 protein to form a stable binary and ternary complex due to the covalent interaction of the platinum center with the Atox1 protein. The results suggested that the extra interaction from the oxaliplatin ligand-Atox1 protein interface increases its affinity only for the OxaliPt + Atox1 model. The binding of the oxaliplatin agent to the Atox1 protein might cause larger deformation of the protein than those of the cisplatin and transplatin agents due to the larger size of the oxaliplatin ligand. However, the extra interactions to facilitate the stabilities of the ternary CisPt + 2 Atox1 and OxaliPt + 2 Atox1 models come from the \(\alpha\)1 helices and \(\alpha\)2-\(\beta\)4 loops of the Atox1 protein-Atox1 protein interface due to the cis conformation of the platinum agents. The combinations of two Atox1 proteins in an asymmetric way in the three ternary models were analyzed. These investigations might provide detailed information for understanding the interaction mechanism of the platinum agents binding to the Atox1 protein in the cytoplasm.  
**Keywords:** molecular dynamics simulations; molecular mechanics Poisson-Boltzmann surface area (MM-PBSA); Atox1 protein; cisplatin; transplaitin; oxaliplatin; interaction mechanism
## 2 Materials and Methods
### Data Analyses  
All data analyses were performed using the MATLAB numerical analysis software (MATLAB R2023a; MathWorks, Inc., Natick, MA, USA).  
The propulsion phase was identified based on the acceleration data in the forward and backward directions. The acceleration data were low-pass filtered at a cutoff frequency of 12 Hz using a fourth-order zero-lag Butterworth filter to remove random noise [23]. In addition, low-pass filtering was applied at a cutoff frequency of 4 Hz to identify the propulsion phase [24]. The point in time when the acceleration transitions from negative to positive values indicates the start of the PP, whereas from positive to negative values indicates the start of the RP (Figure 2). In this study, 10 consecutive propulsion cycle data points from the propulsion motion after passing through the 5 m point were extracted for analysis. The proportion of time that the PP occupied each cycle was calculated, and an average of 10 cycles was obtained.  
A fourth-order Butterworth bandpass filter (cutoff frequency, 20-450 Hz) was applied to the extracted raw surface EMG data to remove signal noise. Moreover, a linear envelope signal was obtained by full-wave rectification and fourth-order Butterworth low-pass filtering (cutoff frequency, 8 Hz) to smooth out the wavy lines [14]. The filtered surface EMG data of each muscle were normalized based on the maximum value during the 10 cycles and interpolated to 100 points to unify the time among cycles [25].  
Figure 1: Electrode placements. (**a**) shows the anterior and (**b**) shows the posterior of the trunk. LD: latissimus dorsi, AD: anterior deltoid, MD: middle deltoid, PD: posterior deltoid, BB: biceps brachii, TB: triceps brachii, IS: infraspinatus, PM: pectoralis major, UT: upper trapezius, SA: serratus anterior.  
Figure 2: Identification of the propulsion phase by the acceleration data. Black circles (\(\bullet\)) indicate the start of the push phase and white circles (\(\Circle\)) indicate the start of the recovery phase. This means that the period from the black circle to the white circle (the gray-colored area) represents the push phase.
## 3 Novel Framework for Wideband Cooperative Spectrum Sensing
### Novel Wideband Cooperative Spectrum Sensing Framework
Hence, the equivalent sensing time of the \(i\)-th SU is \(\tau+(i-1)t_{r}\). For example, the total duration \(\tau+3t_{r}\), and \(\tau+(K-1)t_{r}\) in the yellow region of Figure 5 denotes the equivalent sensing time of SU 4 and SU \(K\), respectively. In summation, the equivalent compressed sensing time for the \(i\)-th SU is denoted as:  
\[\Delta t_{i}=\tau+(i-1)t_{r},\,i=1,2,...,K \tag{31}\]  
The quantity of compressed samples at the \(i\)-th SU is formulated as:  
\[M_{i}=\Delta t_{i}f_{s}=(\tau+(i-1)t_{r})f_{s},\,i=1,2,...,K \tag{32}\]  
Figure 5: Novel cooperative spectrum sensing frame structure. Therefore, the equivalent compression ratio at the \(i\)-th SU is  
\[\mu_{i}=\frac{M_{i}}{N}=\frac{\Delta t_{i}f_{s}}{N}=\frac{(\tau+(i-1)t_{r})f_{s}} {N}\,,\,i=1,2,...,K \tag{33}\]  
As depicted in Figure 5, the sensing time for \(j\)-th SU is \(\tau+(j-1)t_{r}\), which does not exceeds \(\tau+Kt_{r}\). Thus, the sensing time for any SU is restricted in the open interzone of \((0,\tau+Kt_{r})\). The data transmission occurs in the closed interzone of \([\tau+Kt_{r},\,T]\). There is a good reason to believe that \(i\)-th SU transmission and the \(j\)-th SU sensing cannot happen at the same time. Thus, the signal that the \(j\)-th SU senses does not include the signal transmitted from \(i\)-th SU. Another situation to be highlighted is coincidence of the \(i\)-th SU reporting and the \(j\)-th SU sensing. According to the description in Figure 1, the \(i\)-th SU reports a binary decision (i.e., \"1\" for presence and \"0\" for absence) in the reporting link, whereas the \(j\)-th SU senses in the sensing link. In this case, they are using different frequency channel. It is true that sensing link and reporting link are independent physical channels. It can be seen clearly that each SU except SU 1 gets much longer compressed sensing time and higher compression ratio without additional time overhead compared with the general frame structure. Thus, SU spectral efficiency can be increased due to the reduced false alarm probability. In summation, it is the improvement of overall sensing performance that contributes to spectral efficiency, as demonstrated in aforementioned argument.
## 5 Conclusions  
The presented study estimated the site effect parameters for rotational and translational motions registered at two separate stations. For the IMI station, the resonance frequencies of translational and rotational motions were almost the same, which indicates that the resonance effect occurred in the same near-surface rock volume. On the other hand, in the case of the PLA station, a significant difference between the resonance frequencies was observed, which suggested different depth levels of the resonance layer. The rotational resonance effect probably occurred in the shallower volume of rock than translational due to intense fractures and the water table. These geological factors affect the depth range of the rotational motion (Figure 7). The two studied stations were independent; however, by coincidence, they were characterized with similar Vs30 parameters. This similarity influenced the final result of the total model comparison, and the comparison of single-site models produced the a-parameter values close to each other. However, these similarities cannot be taken for granted. The correction of the scaling relation showed that amplification effects influenced the scaling relation model by changing the SH wave phase velocity value.  
Conceptualization, D.N. and L.T.; methodology, D.N. and M.J.M.; software, D.N.; validation, M.J.M., G.M. and L.T.; formal analysis, G.M. and L.T.; investigation, D.N.; resources, G.M.; data curation, D.N.; writing--original draft preparation, D.N. and M.J.M.; writing--review and editing, G.M. and L.T.; visualization, D.N.; supervision, L.T.; project administration, L.T.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the University of Silesia, Institute of Earth Sciences, Research program No. WNP/INOZ/2020_ZB32.  
Not applicable.  
The data sets for this study can be found in the Central Mining Institute Repository ([[https://grss.gig.eu/repozytorium/](https://grss.gig.eu/repozytorium/)]([https://grss.gig.eu/repozytorium/](https://grss.gig.eu/repozytorium/)), accessed on 10 October 2024). The data can be obtained by contacting the Department of Geology, Geophysics and Surface Protection of the Central Mining Institute ([PERSON]: [EMAIL_ADDRESS]). More information on the R-1, EA-120, and DR-4000 instruments is available at www.eentec.com (last accessed on 7 July 2020).  
This study was funded by the project of the University of Silesia in Katowice: Pre-mining, mining and post-mining areas--space of threats and opportunities (WNP/INOZ/2020-ZB32). We would like to thank the anonymous reviewers for their valuable comments that helped to improve the quality of the original manuscript version.  
The authors declare no conflicts of interest.
## 3 Results
### Validity  
The repeated measures ANOVA resulted in a significant main effect of the variable \"gait type\" (\(p<0.001\)). The results show there was no significant effect of the variable \"measurement instrument\" (\(p=0.875\)). The ICC for correlation between the two systems for baseline measurements was indicative of good correlation, being 0.87 and 0.84 when outliers were included. Bland-Altman plots are shown in Figure 1 for all gait types, with mean differences between the two systems and limits of agreement. Data were checked for heteroscedasticity, but [PERSON]'s tau (\(\tau\)) was negative, so the data were considered homoscedastic [16], meaning the observed variance is independent of the variable mean [17]. A scatterplot is shown in Figure 2 of the mean FPA against the difference in FPA between the two systems of all gait types combined. A significant (\(p<0.001\)) regression line was fitted to the dots with the following equation: \(y=-0.79+0.17*x\) (R\({}^{2}=0.234\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**IMUs**} & \multicolumn{2}{c}{**PSW**} & \multicolumn{2}{c}{**IMUs—PSW**} \\ \hline Variable & \(\Delta\)FPA toe-out & \(\Delta\)FPA toe-in & \(\Delta\)FPA toe-out & \(\Delta\)FPA toe-in & Difference in & Difference in & \(\Delta\)FPA toe-out & \(\Delta\)FPA toe-in \\ Mean FPA \(\pm\) SD & \(14.3\pm 5.4\) & \(-15.5\pm 6.7\) & \(12.0\pm 4.8\) & \(-12.5\pm 5.1\) & \(2.3\pm 2.4\) & \(-3.0\pm 3.6\) \\ Median & \(14.4\) & \(-14.1\) & \(11.5\) & \(-11.6\) & \(2.3\) & \(-2.5\) \\ Min; max & \(4.0\); \(26.6\) & \(-41.6\); \(-7.0\) & \(2.3\); \(25.1\) & \(-30.6\); \(-4.1\) & \(-3.1\); \(7.3\) & \(-11.7\); \(2.9\) \\
95\% CI & \(12.6\); \(16.0\) & \(-17.6\); \(-13.3\) & \(10.5\); \(13.6\) & \(-14.1\); \(-10.9\) & \(1.5\); \(3.0\) & \(-4.2\); \(-1.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(\Delta\)FPA in degrees for toe-out gait and toe-in gait for both systems, and difference in \(\Delta\)FPA between the systems. \(\Delta\)FPA is calculated by subtracting the baseline FPA from the toe-out gait or toe-in gait FPA. All variables are in degrees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **IMUs** & **PSW** \\  & **Baseline Test-Retest** & **Baseline Test-Retest** \\ \hline ICC absolute agreement & 0.89 & 0.97 \\ (95\% CI) & (0.79; 0.94) & (0.95; 0.99) \\ \(p\)-value & \(p<0.001\) & \(p<0.001\) \\ SEM (\({}^{\circ}\)) & 1.6 & 0.96 \\ MDC (\({}^{\circ}\)) & 4.5 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Assessment of baseline test-retest intraclass correlation coefficients (ICC) with 95% confidence intervals, standard deviation (SD), standard error of measurement (SEM), and minimal detectable change (MDC) in degrees.  
Figure 1: Bland–Altman plots for baseline (**A**), toe–out (**B**), and toe–in (**C**) gait. Every grey dot represents one leg. Difference in FPA between IMUs (Opal sensors) and the PSW (ZenoTM Walkway) is displayed on the y–axis, and the mean FPA for Opal and ZenoTM is displayed on the x–axis. The red line represents the mean difference and the green lines the 95% limits of agreement.
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 5 DM-RS Optimization, Achieved Network Capacity and Performance Improvements  
Optimization of the DM-RS configuration was performed with two mobile operators who deployed 5G NR networks in different markets and with significant differences in deployment strategy, available spectrum assets, deployed use cases and related traffic mixes. An overview of the most relevant information about the two networks is presented in Table 1.  
In the first network which was subject to DM-RS optimization and is operating with a total of 140 MHz bandwidth of available spectrum, the dominant use case is fixed wireless access. As offered traffic, driven by traffic profile and number of users, already caused heavy congestion in some hot spots in the network, the main goal of DM-RS optimization activity was to improve capacity.  
The second network operates with more spectrum assets and a total bandwidth of 200 MHz, while the dominant use case is mobile broadband. The network is not congested, but the targeted user experience is very high, and the main goal of DM-RS optimization  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mobile** & **Use Case** & **Network Load** & **Frequency** & **Band** & **Bandwidth** \\ \hline Network \#1 & FWA + MBB & High & F1 & 3.5 GHz & 100 MHz \\  & & & F2 & 2600 MHz & 40 MHz \\ \hline Network \#2 & MBB & Moderate & F1 & 3.5 GHz & 100 MHz \\  & & & F2 & 3.4 GHz & 100 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tested networks overview: use cases, network loads and spectrum assets.  
was to improve user experience in terms of peak downlink throughputs. The results of the two DM-RS optimization campaigns will be discussed separately.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 6 Evaluation Setup
### PHM Challenge Problem
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Operating Condition A** & **Operating Condition B** & **Operating Condition C** \\ \hline Load force (N) & 4000 & 4200 & 5000 \\ \hline Speed (rpm) & 1800 & 1650 & 1500 \\ \hline Training dataset & Bearing A\_1, Bearing A\_2 & Bearing B\_1, Bearing B\_2 & Bearing C\_1, Bearing C\_2 \\ \hline Testing dataset & Bearing A\_3, Bearing A\_4, & Bearing B\_3, Bearing B\_4, & Bearing C\_3 \\  & Bearing A\_5, Bearing A\_6, & Bearing B\_5, Bearing B\_6, & Bearing A\_7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the experimental data. Figure 2: Horizontal vibration raw signal of training cases Bearing A_1, Bearing A_2, Bearing B_1, Bearing B_2, Bearing C_1, and Bearing C_2. interactions [45]. Moreover, the statistical features of various cases tend to have different noise levels depending on the degradation process. Hence, a single regression model trained on limited data is difficult to generalize for other cases.
## 3 Experiments  
### Verification of Kinematic Model of Surgical Robot  
#### 3.1.1 Accuracy Measurement Experiment for Single Joint  
To assess the accuracy of the proposed single-joint closed-loop thread-driven model, experiments were conducted on shoulder joint 1 of the minimally invasive thyroid surgery robot's manipulator arm. The camera was positioned directly above shoulder joint 1, then experiments were conducted as follows:  
1. The joint was controlled to move in 5-degree increments within a range of 0 to 40 degrees.
2. Camera images were captured at each increment to record joint positions.
3. Angle measurements were obtained using the IC Measure software.  
The angle measurement results for shoulder joint 1 using the IC Measure software are shown in Figure 8A. According to Table 3 and Figure 8C, the maximum absolute error in the motion of shoulder joint 1 is 0.79\({}^{\circ}\), with an average error of 0.51\({}^{\circ}\). Figure 8B demonstrates a strong linear correlation between the actual and theoretical motion angles of shoulder joint 1, confirming that the proposed single-joint closed-loop cable-driven model accurately describes the relationship between drive wire displacement and joint rotation.  
Figure 8: Accuracy measurement experiment for a single joint. (**A**) Motion angles of shoulder joint 1 measured using IC Measure software. (**B**) Measured motion results of shoulder joint 1. (**C**) Error analysis of the motion results of shoulder joint 1.  
Figure 7: Vision-based measurement technique for joint angles. (**a**) Measurement of joints angles using IC Measure software (2.0.0.286). (**b**) Automatic vision-based measurement method for joint angles.
## 3 Experiment Results
### Monostatic Results  
As shown in Figure 8, it should be noted that the measured 10 dB RCS reduction bandwidth is from 7.6 to 15.5 GHz, which closely matches the simulated bandwidth of 7.6 to 15.7 GHz obtained from RCS simulations (x-polarization). Moreover, this is in agreement with the phase difference simulations of the unit cell pair within the 180 \(\pm\) 30\({}^{\circ}\) frequency band. Within this frequency band, the maximum attenuation was  
Figure 6: Surface current distribution of upper and ground layers at 5.77, 10.94, and 16.7 GHz, where arrows imply current flow direction. (**a–c**) Upper layer; (**d–f**) ground layer.  
Figure 7: (**a**) Measurement system schematic. (**b**) Measuring system equipment.  
observed to be \(-\)28 dB at 10 GHz, with the RCS also reaching \(-\)20 dB at 15.1 GHz. Additionally, for oblique incidence, the monostatic RCS reduction, as shown in Figure 9, demonstrates that this metasurface maintains a stable reduction effect for incident angles ranging from 0 to 30 degrees. It can be observed that the deviation between the measured and simulated results may be attributed to the fabrication process of the proposed cell pair, which involves several rectangles with a width of 0.1 mm, which are sensitive to manufacturing tolerances. Slight misalignments or edge roughness during fabrication (Figure 5b) can alter the resonance behavior, leading to frequency shifts and amplitude variations.
# Battery Characterization via Eddy-Current Imaging with Nitrogen-Vacancy Centers in Diamond
## 5 Spatial Resolution  
One of the advantages of diamond-based sensors is their spatial resolution. For determining the spatial resolution of this battery-measurement system, we used data from one of the aluminum-made electrodes of the solid battery. As shown in Figure 7, the average cross section of 0.3 mm-width electrode is expressed by a square function, noted as the green plot, and a Gaussian-function, noted with red, was employed as a kernel to convolve the square function to fit the measured data, denoted as blue dots. All traces have been normalized for better comparison. By adjusting the full width at half-maximum of Gaussian function, we can estimate the spatial resolution of the system. We represent those results with blue. The spatial resolution of the system can be estimated form the full width at half maximum (FWHM) of the kernel and is estimated as 360 \(\pm\) 2 \(\upmu\)m. It is mostly restrained by the distance between the battery and diamond. Closer proximity could increase measurement contrast, allowing a more legible image of small samples.