esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity Analysis for Gaussian-Associated Features
## 10 Concluding Remarks  
This paper has been concerned with the evaluation of the uncertainties associated with Gaussian-associated features following the GUM methodology. We have shown how sensitivity matrices necessary for a GUM uncertainty evaluation can be calculated and how the variance matrices associated with the feature parameters can be estimated for a range of complete and partial features common in engineering. Example results are given in tables that allow practitioners to estimate, _a priori_, the uncertainties associated with fitted parameters, given a proposed measurement strategy for the case in which the point-cloud variance matrix is a multiple of the identity matrix. The sensitivity matrices can be used to evaluate the uncertainties for associated features for more general point-cloud variance matrices. All the calculations are straightforward and have been implemented in a spreadsheet supporting basic matrix algebra, for example.  
This research was funded by the European Metrology Programme for Innovation and Research (EMPIR), project 17 NRM03: _Evaluating Uncertainty in Coordinate Measurement EULCoM_. The EMPIR initiative is co-funded by the European Union's Horizon 2020 research and innovation programme and the EMPIR Participating States.  
Not applicable.  
The author is grateful to EUCoM project partners (see 'Funding' above) for input into this work, in particular to [PERSON], INRIM, IT, EUCoM project coordinator, for his many insights, review comments and support. The author also thanks the (anonymous) reviewers for their helpful comments.  
The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(s|6\) & \(s|X_{6}\) & \(s|5\) & \(s|X_{5}\) & \(s|9\) & \(s|X_{9}\) \\ \hline \(x_{0}\) & 0.71 & 0.71 & 0.77 & 0.71 & 0.58 & 0.69 \\ \(y_{0}\) & 0.71 & 0.71 & 0.77 & 0.71 & 0.58 & 0.69 \\ \(z_{0}\) & 0.71 & 0.71 & 1.55 & 1.12 & 1.15 & 0.69 \\ \(r_{0}\) & 0.41 & 0.41 & 0.89 & 0.50 & 0.67 & 0.50 \\ \hline \end{tabular}
\end{table}
Table 8: Sphere datum location. Estimates of \(s(a)=\sigma_{R}u(a)\) associated with sphere parameters for three measurement strategies. The first column of two gives the uncertainties calculated using an analytic approximation based on assuming a uniform distribution of points on the sphere or hemisphere while the second column gives the uncertainties based on the actual distribution of points.
## 4 Data Communication Scheme Derivation
### The SCCP Scheme  
The block diagram for the SCCP scheme is illustrated in Figure 4d. This scheme is obtained when we adopt \(\mathbf{Q}=\mathbf{F}\). the SCCP scheme transmitter is given by  
\[\mathbf{T}_{\text{SCCP}}=\mathbf{\Psi}_{\text{t}}. \tag{28}\]  
Meaning that the transmitter only appends the CP. Consequently, the receiver matrix is expressed as  
\[\mathbf{R}_{\text{SCCP}}=\mathbf{F}^{\text{t}}\mathbf{E}\mathbf{F}\mathbf{ \Psi}_{\text{r}}, \tag{29}\]  
which comprises the CP removal matrix and the frequency domain equalization.  
Similar to Section 4.1, matrices \(\mathbf{A}_{i},i=-1,\ldots,I\), and \(\mathbf{G}\) are obtained. Moreover, using (28) and (29) results in the estimated mapped vector and the SINR for the SCCP scheme through (9) and (19), respectively.  
Moreover, if the data communication system operates without interference, then the \(k\)th symbol from the \(2N\)-length symbol block, with the vector representation given by (16), is given by  
\[\tilde{X}_{\zeta,I}[k]=X_{\zeta,I}[k]+\frac{1}{\sqrt{2N}}\sum_{i=0}^{2N-1} \text{e}^{\text{i}\frac{2\pi}{2N}ki}\Lambda_{\mathbf{\mathcal{H}}}^{-1}[i,i]V_{I} [i]. \tag{30}\]  
Consequently, the SNR associated with the \(k\)th subslot is given by  
\[\underline{\gamma}_{\text{SNR}}[k]=\frac{P_{\text{x}}[k,k]}{\frac{1}{2N}\sum _{i=0}^{2N-1}|\Lambda_{\mathbf{\mathcal{H}}}^{-1}[i,i]|^{2}P_{\text{n}}[i,i]}, \tag{31}\]  
and the corresponding nSNR is expressed as  
\[\underline{\gamma}_{\text{nSNR}}[k]=\left(\frac{1}{2N}\sum_{i=0}^{2N-1}\gamma _{\text{nSNR}}^{-1}[i]\right)^{-1}. \tag{32}\]  
Straightforwardly, we see that the expressions (31) and (32) are equal to (25) and (26), respectively. This means that SCCP and OCDM will obtain the same performance in terms of BEP and the data rate if both schemes operate free of interference. In the presence of interference, we have to consider the use of the SINR. As the SINR of both schemes differ, SCCP and OCDM attain different performances.
## 2 Tests and Methods
### Test Set-Up and Loading  
Testing was performed in the Mechanical Engineering Laboratory of the Beijing University of Technology. A hydraulic universal testing machine WE-20 was used with a maximum tonnage of 20 t. In order to apply even force across the sample, the vertical concentrated force was distributed to the four steel loading columns, which are spaced 200 mm apart. Also, four supporting columns were used to support the sample. A total of eight electrical dial indicators were used to record the deflection of the specimen during loading. The maximum deflection of the center of the wallboard was calculated from data from the 1# and 2# dial indicators; the 7# and 8# dial indicators were used to record the mid-span deflection of the specimens; the 3-6# dial indicators were used to record the corner deflection of the specimens. The specific layout of the measuring points is shown in Figure 5. Before formal loading, the specimen was pre-loaded for calibration, which was a maximum of 10% of the ultimate load calculated by the test piece. At the beginning of loading, 10% of the ultimate load was applied. When the specimen enters the yielding stage, 7% (1/15) of the ultimate load was applied until the specimen was destroyed.  
Figure 5: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete Types** & **Strength Grade of Concrete** & **Cube Compressive Strength** & **Elastic Modulus** \\ \hline FAC & C40/40.00 MPa & 40.10 MPa & 32,958 MPa \\ \hline RAC & C40/40.00 MPa & 41.15 MPa & 30,283 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of cube compressive strength of fine aggregate concrete (FAC) and RAC concrete cube samples.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Radiation Direction Map of the Antenna  
The radiation pattern of the antenna was measured in a microwave anechoic chamber. When measuring the radiation pattern of the antenna in the chamber, the designed antenna was implanted into the pork, and the other end of the coaxial cable was connected to the chamber's turntable via an SMA interface. Due to the weight of the pork block itself, the antenna cannot be directly fixed to the turntable after being implanted into the pork. Therefore, foam boards and foam cotton were used to fix the antenna. The final experimental setup is shown in Figure 11.  
The simulated and measured radiation patterns of the antenna at the 2.45 GHz frequency point are shown in Figure 12. The simulated and measured patterns in the xoz and yoz plane indicate that the maximum radiation is achieved at 180\({}^{\circ}\), with the null at 210\({}^{\circ}\). The maximum gain of the antenna is \(-\)19.92 dB. The test results show that the simulated and measured radiation patterns of the designed antenna are in good agreement, and the antenna achieves omnidirectional radiation within its operating environment. Compared to the xoz plane, the omnidirectional radiation in the yoz plane is more ideal. The antenna's omnidirectional radiation characteristic enables communication in all directions within the complex human body.  
Figure 11: Experimental setup for measuring the radiation pattern of the proposed antenna.  
Figure 10: Measured reflection coefficient of the proposed antenna in pork.  
The test results of the antenna radiation pattern show that the antenna gain is overall larger. The reason for this error may be the frequent startup of the testing equipment during the actual measurement process, leading to instability of the experimental turntable during rotation. This also results in a serrated appearance in the xoz and yoz planes, and a rightward shift in the yoz plane. Such errors are within an acceptable range.
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 4 Derivatives of the Optimal Lengths
### Derivatives of the LSL and RSR Cases  
For the CSC\({}^{+}\)case given in (13), we can trivially derive some partial derivatives of the lengths with respect to \(\alpha\) or \(\beta\). For instance, \(\partial_{\alpha}(\ell_{1}+\ell_{3})=-s\) and \(\partial_{\beta}(\ell_{1}+\ell_{3})=s\). The derivatives of \(\ell_{2}\) with respect to \(\alpha\) and \(\beta\) can be computed and simplified using (11) and collecting (12):  
\[\begin{split}\partial_{\alpha}\ell_{2}^{\text{CSC}^{+}}& =s\cos(\alpha-\theta_{S}^{\text{CSC}^{+}})-\Big{(}s\,C^{-}\cos \theta_{S}^{\text{CSC}^{+}}+(sd+\,S^{-})\sin\theta_{S}\Big{)}\partial_{\alpha} \theta_{S}^{\text{CSC}^{+}}\\ &=s\cos(\alpha-(\alpha+s\ell_{1}^{\text{CSC}^{+}}))\\ &=s\cos\ell_{1}^{\text{CSC}^{+}},\\ \partial_{\beta}\ell_{2}^{\text{CSC}^{+}}&=-s\cos( \beta-\theta_{S}^{\text{CSC}^{+}})-s\Big{(}\,C^{-}\cos\theta_{S}^{\text{CSC}^ {+}}+(sd+\,S^{-})\sin\theta_{S}^{\text{CSC}^{+}}\Big{)}\partial_{\beta}\theta _{S}^{\text{CSC}^{+}}\\ &=-s\cos(\beta-(\beta-s\ell_{3}^{\text{CSC}^{+}}))\\ &=-s\cos\ell_{3}^{\text{CSC}^{+}}.\end{split} \tag{36}\]  
Therefore, we can write the partial derivatives of the sum of the lengths using the result from (36):  
\[\begin{split}\partial_{\alpha}(\ell_{1}^{\text{CSC}^{+}}+\ell_{2 }^{\text{CSC}^{+}}+\ell_{3}^{\text{CSC}^{+}})&=s(\cos\ell_{1}^{ \text{CSC}^{+}}-1),\\ \partial_{\beta}(\ell_{1}^{\text{CSC}^{+}}+\ell_{2}^{\text{CSC}^ {+}}+\ell_{3}^{\text{CSC}^{+}})&=s(1-\cos\ell_{3}^{\text{CSC}^ {+}}).\end{split} \tag{37}\]  
To remove the dependency of \(\ell_{1}\) and \(\ell_{3}\) from (37), we substitute their expression (13), so that we can write the partial derivatives of the sum of the lengths as follows (considering \(\ell\) the total length, \(\ell=\ell_{1}+\ell_{2}+\ell_{3}\)):  
\[\begin{split}\partial_{\alpha}(\ell^{\text{CSC}^{+}})& =s(\cos(\theta_{S}^{\text{CSC}^{+}}-\alpha)-1),\\ \partial_{\beta}(\ell^{\text{CSC}^{+}})&=s(1-\cos( \beta-\theta_{S}^{\text{CSC}^{+}})).\end{split} \tag{38}\]  
The upper bound of the derivative in (37) and (38) and thus of the Lipschitz constant is 2.
## 3 Microfabrication of Suspensions of Fibers by Colloidal Self-Assembly
### Control of Fiber Properties  
#### 3.3.1 Fiber Geometry  
The fibers are characterized by a necklace-like shape: their diameter is given by the diameter of the colloidal particles as illustrated in Figure 5. As the magnetic field is applied in the direction of the channel height, if the magnetic field is applied quickly compared to the sedimentation of the colloids, the fiber length is only limited by the channel height. One can however add a sedimentation step prior to the application of the magnetic field to fabricate filaments shorter than the channel height [18]. In both cases, monodisperse fibers are obtained (see Figure 7).  
The fiber aspect ratio can be very large: typical aspect ratios (fiber length over fiber width) are 30-50, but aspect ratios as high as 265 can be obtained [19]. As fibers form as chains of spherical particles, their surface roughness is given by the particles' radius and could be modified by changing the dimension of the particles.
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 3 Methodology
### Routing Strategy
#### 3.3.1 Routing Protocol Strategy  
The main factors to be considered for routing strategies among network communication are communication among nodes, hop count, weather data reliability, and distance between vehicles.
## 10 Simulation Results and Discussion
### PAR Reduction  
We start by discussing the resulting PAR reduction in the total residential load when we use our proposed energy management algorithm. Generally, residents want to reduce their total electricity bill, while the utility is interested in providing balanced energy supply. Figure 12 clearly shows that our proposed algorithm is helpful in reducing total PAR and balancing the energy consumption by considering capacity constraint \(C_{t}\). It is also clear from Figure 12 that the K-WDO algorithm reduces the PAR by8.3% due to the optimal scheduling of home appliances in low price hours without creating congestion, while the K-PSO algorithm reduces the PAR by 6.97%.  
Figure 12: Total peak-to-average ratio (PAR) reduction comparison of the WDO and PSO algorithms for a simple household based on the TOU price signals adopted from NYISO.  
Figure 13: Electricity cost comparison of the WDO and PSO algorithms.
# The Gyrotrons as Promising Radiation Sources for THz Sensing and Imaging
## 2 Advantages of the Gyrotrons as Powerful Radiation Sources for Sensing and Imaging
### Imaging, Food Inspection, and Quality Control Using Gyrotron Radiation
Comparing the transmission and the reflection setups, it has been shown that the latter scheme provides better contrast thanks to the absence of background irradiation and suffers less from the etalon effect (which is responsible for the appearance of bright and dark interference fringes), although the width of the image is contracted due to the tilting in the configuration. One of the latest advancements in the realization of a real-time continuous-wave terahertz (THz) line-scanned imaging based on a \(1\times 240\) InGaAs Schottky barrier diode (SBD) array detector with a scan velocity of 25 cm/s, a scan line length of 12 cm, and a pixel size of \(0.5\times 0.5\) mm\({}^{2}\) has been presented in [98]. The system consists of the SBD array detector, a 200-GHz gyrotron source, a conveyor system, and several optical components such as a cylindrical lens (made of high-density polyethylene), cylindrical metal mirror, and a THz wire-grid polarizer, which improves the signal-to-noise ratio of the SBD array detector. Using this gyrotron-based imaging system, foreign substances (such as a paper clip) hidden under a cracker have been clearly detected with a spatial resolution of about 1 mm. The active real-time imaging system (ARTIS) for non-destructive food inspection developed at KERI (Korea) is illustrated in Figure 9. It uses a compact gyrotron that can operate at frequencies of either 0.2 THz or 0.4 THz respectively and irradiates objects (with a width of up to 200 mm) moving on the conveyor belt with a speed of 500 mm/s. Besides its primary application, this system is adaptable to security inspection and could be used also for the detection of dangerous objects and substances [96].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques  
FRSs pose a unique privacy concern due to their distributed nature. Since the training data are processed and stored across multiple nodes, it is more difficult to ensure data privacy than with a centralized approach. Existing research has shown that the central server can infer sensitive information from the intermediate parameters. For instance, a server can identify items the user has interacted with based on the non-zero gradients sent by the client [72]. Moreover, the server can also infer ratings from the user-uploaded gradients in two consecutive rounds [73]. To address those concerns, privacy-preserving techniques have been developed to protect user data privacy while still allowing FRSs to operate efficiently, as shown in Figure 5. In this section, we present these privacy-preserving techniques of FRSs and compare their advantages and limitations.
### Our Contributions  
The key contributions of this research are as follows:  
* We show that the throughput of existing state-of-the-art schemes (HRS [12], MRS-ST and MRS-SD [10]) reduce when relay devices have variable battery power levels.
* We propose new relay selection schemes MRS-ST-BL and MRS-SD-BL that optimize throughput of D2D communications using a socially trusted relay whenever possible. The optimization problem is formulated using social trust among users and battery power level on devices together with physical location of the devices. Social trust among users are used for filtering of devices for selection of a relay. Pareto distribution is used to model the social trust among users in the network. According to the pre-defined policy, relay devices adjust their transmission power depending upon social trust values for the source and battery power level on the devices to maximize the throughput.
* We evaluate the performance of our newly proposed relay selection schemes extensively through simulation by comparing with generic and state-of-the-art schemes. We show that our proposed schemes achieve higher throughput when devices in the network have varying battery power levels, in networks having different node densities and social trust scenarios. Additionally, we compare the average number of probes and transmission power of relay of our proposed schemes with various other schemes to show how our proposed schemes enhance throughput of D2D communications.  
This paper is an extension of our previous papers midpoint relay selection schemes (MRS-ST and MRS-SD) [9; 10]. We have significantly updated the design of relay selection by introducing battery power level of devices, which is more realistic. We have analyzed the performance of proposed schemes in various social trust scenarios and device densities by comparing with other schemes.  
The rest of this paper is organized as follows. Section 2 introduces the D2D communication system model used in this paper. It includes the network model, mobility model, social trust model, battery model and communication link model. Section 3 presents details of our proposed schemes MRS-ST-BL and MRS-SD-BL. Section 4 explains the exchange of probing messages for selection of a relay in D2D communications. Section 5 presents the extensive analysis of our proposed schemes with other state-of-the-art schemes. Section 6 presents the conclusion of this paper.
## 2 Materials and Methods
### Pipelines for Respiratory Phase Identification
#### 2.1.3 Comparing Hemidiaphragm Area Ranking  
The hemidiaphragm dome area on the projections of ranks 1, 2, and 3 was computed. When the area on the current projection (\(A_{proj}\)) is between ranks n (\(A_{n}\)) and n + 1 (\(A_{n+1}\)), the correct phase is determined to be between ranks n and n + 1. The following steps were used to calculate the area under the diaphragm dome on the projection image (see Figure 2). First, a vertical gradient was applied in the Y-axis direction on the ROI of the right hemidiaphragm, and the image was binarized based on the pixel-wise gradient intensity (Gy). Next, after defining a binary motion mask using Equation (1) in the manner described above, the search range for finding the diaphragm outline shape was limited to the motion mask obtained. Then, a second-order polynomial fit was applied to the data points within the search range. When the Euclidean distance of a point from the polynomial fit was greater than twice the standard deviation of the Y-coordinate values of the points in the same column, it was identified as an outlier and removed from the diaphragm outline candidates. Finally, the polynomial fit was refined using the remaining outline candidates, and the area under the curve fit was computed.  
Figure 2: Procedure used to compare the diaphragm area on a 2D digitally rendered radiographs (DRR) or fluoroscopy. (**a**) Comparison procedures for the hemidiaphragm area of the projection with 1 st, 2 nd, and 3 rd rankings. (**b**) Example of a binarized, dynamic pixel map used during the procedure.  
Various techniques have been employed to directly monitor the diaphragm elevation, which can be used for respiratory gating [31; 32]. For comparison purposes, the accuracy of the respiratory phase identification was also analyzed using a diaphragm-elevation method. To minimize the respiratory identification error owing to the locating error of the diaphragm using various techniques, the position of the apex of the diaphragm was manually segmented on every projection image. First, at the position of the gantry angle, the diaphragm apex position of all breathing cycles was determined. When the vertex position of the current CBCT projection was between the vertex positions of two successive respiratory phases, the current respiratory phase was determined to be between the two respiratory phases.
## 3 Experimental Approach
### Monitoring the Setpoint Events  
For the setpoint deviation events, it was necessary to use the indoor temperatures in the eight spaces as well as the outdoor temperature. In each space there were a number of points where the indoor temperature was monitored, via the 065 Air Temperature Sensors provided by Met One Instruments (accuracy \(\pm\)0.4 \({}^{\circ}\)C). In each space we averaged two such values, after making sure that their measurements were immune to solar gains and human occupancy. As explained in the Concept section, in the case of setpoint deviation events, we did not rely on energy meters, although they were available; instead, we used the calculation technique based on the actual temperature deviation from the applicable SP, while due account of the outdoor temperature was taken, according to Table 3.  
Temperature data were again conditioned to remove apparently faulty values. The resolution of the data was again that of 15 min. Outdoor temperature was read from a close-by weather station (Versailles) as well as from the building BMS. No significant difference was found between these two time series. In the end, the weather station data were used as these appeared to be slightly better in terms of data loss and service interruption.  
Besides these real-time values, a number of settings had to be entered in the module, such as the setpoint values used by facility management. These were 21 \({}^{\circ}\)C for the winter period (October to April) and 24 \({}^{\circ}\)C for the summer period (May to September). A slack of 2 \({}^{\circ}\)C was also introduced. This means that a deviation of 2 \({}^{\circ}\)C for these setpoints will be considered as normal and will not accumulate waste in the calculation.  
Multi-setpoint events could not be set because all spaces were practically never unoccupied; thus, no benefit could possibly result by lowering the applicable setpoints.
## 1 Introduction
### Literature Review
#### 1.2.3 Advanced Optical Measurement Methods  
Advanced optical techniques, such as laser Doppler vibrometry (LDV) and machine vision-based tachometry, provide high-resolution, non-contact speed measurement capabilities. LDV systems, as applied by [PERSON] et al. [24], utilize laser interference patterns to detect the surface vibrations of rotating components, achieving micron-level displacement resolution and \(\pm\)0.2% speed accuracy in laboratory settings. Machine vision methods, such as the high-speed camera system developed by [PERSON] et al. [25], track the rotational markers on shafts to enable multi-axis synchronization in hybrid powertrain testing. However, these systems have stringent environmental requirements: LDV demands stable lighting and dust-free conditions to maintain laser coherence [26; 27; 28], while vision-based systems struggle with motion blur under low illumination or rapid acceleration [29; 30]. Such limitations fundamentally restrict their deployment in harsh automotive service environments, where temperature fluctuations, vibrations, and particulate contamination are unavoidable. Compared to OBD signal extraction, optical methods lack compatibility with onboard diagnostic networks, require expensive auxiliary equipment (e.g., optical benches), and cannot provide real-time feedback for closed-loop control applications [31].