esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Quantum computation is nowadays one of the most attractive areas of research. The main advantage of quantum computation over classical computation resides in the qubit as the quantum equivalent of the binary logical bit [1]. Among the many qubit types, superconducting qubits based on Josephson junctions (JJs) are the most promising since they can be printed on substrates like silicon electronics, retaining great scalability potential [2].  
JJs are versatile superconducting devices that can be used for many cutting edge applications such as microwave photon detection [3; 4; 5; 6; 7; 8; 9; 10], parametric amplification [11; 12; 13], and entangled photon emission [14; 15; 16]. The JJ is described formally by a fictitious phase particle trapped in a slightly anharmonic potential well. The first two energy levels can be used as a two-level system, i.e., a qubit [17].  
The most diffuse superconducting qubit is the transmon because of its simple design and solid performance. The transmon is composed of a small JJ shunted by large capacitors to minimize the charge noise [18]. The best transmon performances in terms of coherence time now approach 500 \(\upmu\)s [19]. Different designs have been proposed to exceed transmon performances, including the 0-\(\pi\) qubit, the fluxonium, or the unimon [20; 21; 22]. However, they usually have much more complex circuits or control schemes with respect to the transmon and a net superiority has not been established yet. Because of the relevance of the transmon, its design and characterization are of crucial importance in the field of quantum technologies to enable qubit-based pioneering applications.  
In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon detection applications. Notably, 3D architectures have several advantages in particular for those applications such as photon detection that do not require a large number of qubits. Dielectrics surfaces are in fact generally much lossier than bulk cavities. Al cavities reach up to 10 ms photon lifetime independent of the stored power and down to the single photon level [23]. Superconducting qubits hosted in a 3D cavity recorded coherence time \(T_{2}\) above 1 ms [24]. Moreover, superconducting microwave cavities coupled to one or more anharmonic elements in the circuit quantum electrodynamics architecture are today explored for hardware-efficient encoding of logical qubits [25].  
The paper will develop as follows: In Section 2, the transmon fabrication method and the experimental setup are described. In Section 3, we show the spectroscopic and time domain characterization of our transmon. In Section 4, we discuss the simulation framework necessary to design a transmon with the desired properties. In Section 5, as a quantum machine learning application, we report the fit to the \(u\)-quark Parton Distribution Function of the proton with the superconducting qubit device. Finally, in Section 6, we describe a new measurement protocol for a low dark-count photon detector with two qubits.
## 3 Results  
The reaction force (F) at the lower end of the tibia and the maximum value of pressure (p\({}_{\text{max}}\)) at the screw/tibia interfaces were calculated for each configuration in order to evaluate the stability of the locking system. The reaction force (F) and the maximum pressure (p\({}_{\text{max}}\)) at the bone/screw interface can in fact give useful information on implant stability. The greater the force required to cause the 0.5 mm displacement of the fractured fragment, the higher the reaction force at the distal tibia, thereby ensuring a high stability of the fixation system. As for the pressure at the screw/tibia interface, values that are too high can lead to bone damage and, consequently, to screw-loosening and therefore implant instability. Accordingly, it can be deduced that the configuration (Figure 9) with a higher value of the reaction force (F) is more stable, and the one with a lower pressure (p\({}_{\text{max}}\)) ensures reduced damage of the bone and, consequently, a higher stability of the implant.  
Figure 8: Boundary conditions: imposed displacement (**left**) and fully constrained surface (**right**).  
Figure 9: Scheme of the reaction force F.  
To obtain highly reliable numerical models, we set up the FEM models following a largely validated protocol [19; 37; 39]. Table 3 summarizes the main results obtained for each configuration analysed.  
Figure 10 reports, for each screw configuration, the calculated values of the reaction force (F).  
The difference between the highest and lowest force reaction values, in the first and third configurations respectively, is quite small. This small difference indicates, for the analysed configurations, a limited influence of the screw orientation on the calculated reaction force.  
Figure 11 shows the pressure maps at the screw/tibia interfaces.  
In this case, for the configurations analysed, it can be seen that the difference between the highest value of p\({}_{\text{max}}\) (563.48 MPa for configuration N.4) and the lowest value (384.05 MPa for configuration N.2) is about 180 MPa, showing a remarkable influence of the screw orientation. Moreover, it can be seen that the maximum value always occurs at the interface between the fragmented part of the bone (yellow area in Figure 12) and screw n.7. This may be attributed to the position of screw n.7, which is subjected to a greater load given that it is the last screw that keeps the fragmented part in contact with the remaining bone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration Number** & **Reaction Force F (N)** & **Pressure Max p\({}_{\text{max}}\) (MPa)** \\ \hline
1 & 1214.6 & 535.61 \\
2 & 1208.9 & 384.05 \\
3 & 1194.4 & 412.48 \\
4 & 1203.3 & 563.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: FEM results.  
Figure 10: Calculated reaction forces (F) at the distal tibia for the analysed configurations.
## 4 Second Step of Identification Algorithm
In the second step of identification algorithm, most NLOS BS combinations have been eliminated in first step, and the common chord intersection points corresponding to the rest of NLOS BS combinations should be outliers in all intersection points. Therefore, for \(n\) intersection point sets of discrimination result matrix, the intersection point set, which has the maximum number of elements equaling to 1, can be identified as the intersection point set corresponds to LOS BS combinations, and the BSs belonging to these LOS BS combinations are identified as LOS BS. Now, the whole proposed algorithm can be summarized as follows. 1. Construct BS combinations with every three BSs. 2. Calculate the coordinates of three intersection points of three localization circle for each BS combination. 3. Calculate the coordinate of common chord intersection point for each BS combination. 4. Estimate \(\Delta R_{est}\) using (19) and then obtain estimate of \(\Delta r_{i}\). 5.1. If there is not any BS combination satisfying (20), identify LOS BS combination with (23), if there is still no BS combination satisfying (23), we conclude that the number of LOS BS less than 3, and the whole identification procedure ends. Otherwise, the BS combination which satisfies (23) and has the least overlapping area, is identified as LOS BS combination, the BSs which belong to this LOS BS combination are identified as LOS BS, and the whole identification procedure ends. 5.2 If there is only one BS combination satisfying (20), it is identified as LOS BS combination, the BSs which belong to this LOS BS combination are identified as LOS BS, and the whole identification procedure ends. 5.3 If there are more than one BS combination satisfying (20), the BS combinations which satisfy (20) are roughly identified as LOS BS combination and enter the second step of identification algorithm, the other BS combinations are discarded as NLOS BS combination. 6. Calculate mutual distance threshold for every two common chord intersection points using (36), then obtain mutual distance threshold matrix _threshold_ with (37). 7. Calculate the distance matrix \(dis\) of all BS combinations. 8. Obtain discrimination result matrix \(resu\) using discrimination expression (39). 9. The intersection point set, which has the maximum number of elements equaling to 1, is identified as common chord intersection point set which corresponds to LOS BS combinations. 10. All BSs belong to LOS BS combination are identified as LOS BS ultimately. The flow chart of the proposed algorithm is shown in Figure 5.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 3 Results
### Holt-Winters Model  
The time series of vessel transit exhibits seasonality; therefore, the Holt-Winters model was considered, and the corresponding plot of its behavior is presented below.  
Figure 10 (top panel) does not display persistent or evident patterns, although some notable peaks may indicate outlier or extreme events, which are valid given the magnitude of the variable. The variability in the residuals is considered constant, although certain periods show greater dispersion, which may suggest some temporal instability.  
In the residual autocorrelation function (bottom left), most of the bars fall within the significance bands (dotted blue lines), indicating no significant autocorrelation in the residuals. The histogram of the residuals with a superimposed normal curve (bottom right)  
Figure 10: Forecast Using the Holt–Winters Model.  
Figure 9: Forecast Using the SARIMA (1,0,1) (0,1,1) Model.  
suggests that the residuals behaved approximately normally. There are slight deviations in the tails, but not significant enough to invalidate the model.  
Consequently, the Holt-Winters model adequately captures both the trend and seasonality of the data. This suggests that the selected Holt-Winters model is suitable for forecasting the analyzed time series.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 4 Real-World Bandwidth Testing  
The developed end-to-end group encryption workflow was validated using a self-developed multimedia server in conjunction with iOS terminal devices. The test scenario was conducted using an iPhone 11 with 64 GB of storage running OS 17. During the tests, all voice calls were encoded using the Opus codec. The test device served as the chairperson in the end-to-end group call, responsible for establishing communication with other meeting participants and mixing all voice data. We set the sampling rate to 8000 samples/second and the bit rate to 16,000 in normal conditions. When there were two participants in the  
Figure 6: Independent keys.  
meeting, network bandwidth for both incoming and outgoing data, as observed using Xcode tools, was 16.8 Kbps, with CPU usage at 5%. In low-bandwidth mode, we set the sampling rate to 2000 samples/second and the bit rate to 4096. With two participants, the bandwidth for both incoming and outgoing data was 7.2 Kbps, memory usage was 31 MB, and CPU usage remained at 5%. At this stage, the mixing mechanism was not yet engaged. When the number of participants increased to five, the results are summarized in Table 2.  
Each additional participant in the meeting increases the network bandwidth for incoming and outgoing data by 7.2 Kbps, memory usage by 3 MB, and CPU usage by 1%. Each new participant requires the chairperson to establish an additional channel for the new member. For 10 participants in the meeting, the network bandwidth for both incoming and outgoing data is 64.8 Kbps, memory usage is 55 MB, and CPU usage is 13%. These requirements must be satisfied with most commercially available mobile phones and networks when used with mobile or Wi-Fi networks. Any phone can act as the chairperson using the end-to-end group encryption developed. Those who are not the chairperson only need to satisfy a network bandwidth of 7.2 Kbps for both incoming and outgoing data. Considering that the high-orbit satellite network Thuraya provides a communication bandwidth of 16 Kbps or more, this is sufficient to meet communication needs.
## Abbreviations  
**UT**: Ultrasonic Transducer
**VNA**: Vector Network Analyzers
**PZT**: Piezoelectric Ceramic
**CW**: Continuous Wave
**NDT**: Non-Destructive Testing
**SHM**: Structural Health Monitoring
**TOF**: Time of Flight
# Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton
## 2 Materials
### Problem Analysis and Requirements  
The primary goal of evaluating exoskeletons is to assess how well they fulfill user needs across various use cases [30]. Evaluation metrics and methods in this context encompass kinematic behavior, performance, usability, workload, and ergonomics [8].  
Metrics related to behavior include degrees of freedom, kinematic structure, supportive effect and dexterity, providing insights into the exoskeleton's workspace capabilities. Performance metrics, such as task productivity, range of motion, mobility, and load capacity describe a user's ability to perform tasks while wearing an exoskeleton. Ergonomics metrics, covering aspects like muscle activity, joint angle, and interaction force, focus on the physical aspects associated with task execution. Finally, usability metrics, including usage complexity, comfort, and human-exoskeleton alignment assess cognitive load and compatibility between the exoskeleton and the user.  
Evaluation methods fall into three categories: physical model-based, human-based, and biomechanical model-based methods, each with its advantages and disadvantages [31]. Physical model-based methods stand out for their objectivity, low human life risk, high repeatability, applicability to various test conditions, and provision of standardized test procedures. For upper-body exoskeletons, evaluating their supportive effect involves measuring generated joint torques, such as in the shoulder, to reduce workload [32]. Analyzing assistive torque profiles aids in understanding how to choose an appropriate profile based on task requirements and user anthropometry, contributing to the evaluation of exoskeleton benefits [33].  
To ensure an objective and standardized test procedure, objective metrics, particularly force-based metrics are preferred over subjective and human-effected metrics. Interaction force metrics, including normal and shear forces, overall interaction force, and peak and average contact force, contribute to assessing exoskeleton ergonomics and comfort [34]. Interaction torque, derived from interaction forces, provides insights into load transfer and is typically measured at joints to calculate torque transmitted from the exoskeleton's interface to the user [30,34]. Interaction pressure, calculated from interaction force over the contact area is a critical and measurable metric impacting exoskeleton ergonomics. Metrics include maximum pressure and pressure distribution [34]. Given the complexity of anticipating shoulder motion through electromyography signals and the challenge of calculating forces in human joints, evaluating the impact of exoskeletons on joint moments, muscle forces, and joint contact forces is crucial [35,36].
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 4 Results
### Single Robot Results  
#### 4.1.1 Simulation Results  
We simulated the following scenario in the Gazebo simulator. The proposed method was applied on a single TurtleBot3 Burger to explore a 13.5 m \(\times\) 14.5 m workspace with a large wall as an inner obstacle. For SLAM, a simulated LIDAR with a 5 m laser range is utilized. The resulting path can be seen in Figure 7. The robot manages to explore the workspace covering a path distance (PD) of 51.40 m with an elapsed time (ET) of 276.68 s. It should be noted that ET in simulated experiments refers to the simulated ET.  
Figure 7: The path taken by the robot, in blue, to explore the workspace, whose boundary is denoted with a black line.
## 2 Data
### CYGNSS Data
We matched the CYGNSS dataset to the ERA5 reanalysis dataset with a time window of 1 h and a spatial window of 0.25\({}^{\circ}\). The heterogeneity of CYGNSS DDM and auxiliary parameter is apparent, as DDM is 2D image data while auxiliary parameter is 1D numerical data. Because of the heterogeneity, straightforward data fusion is not possible. Meanwhile, the low reflected signal power, high noise, and small image size characteristics of DDM make it hard to extract all the effective information using manual feature extraction methods. It will eventually affect the retrieval precision of SSH. A multimodal deep learning method is used to address these issues in this paper. We use MResNet to adaptively extract effective features from the full DDM and transform the 2D image data into 1D numerical data. Then, the extracted features and auxiliary parameters are fused as the input data for MHL-NN to retrieve the SSH. [PERSON] et al. analyzed the climate state, spatiotemporal distribution, and error characteristics of eight satellite observations of CYGNSS [35]. The results show that the inherent error of CYGNSS wind speed is about 1.8 m/s, which meets the requirements of most operational applications. However, there are inconsistencies in the wind speed measurements between different satellites; the wind speed quality obtained by CY04 satellite is slightly better than other satellites. Therefore, in this paper, measurements from CY04 satellite were selected and analyzed from 1 January 2020 to 31 March 2021.
# Flexural Strength of Composite Deck Slab with Macro Synthetic Fiber Reinforced Concrete
## 2 Mechanical Properties of MFRC
### Tensile Behavior  
According to previous studies [6; 7], macro-synthetic fibers are effective in improving the toughness and tensile strength of concrete. Accordingly, in this study, splitting and flexural tensile tests were performed to confirm the tensile strength and flexural toughness of the MFRC according to KS F 2423 [22] and KS F 2408 [23], respectively. The specimens for tensile strength test were made according to KS F 2403 [18]. Splitting and flexural tensile test specimens were manufactured in amounts of 3 and 5, respectively, by mix proportion. The flexural tensile strength test is usually a four-point loading test. However, in fiber-reinforced concrete, it is difficult to measure crack mouth opening displacement (CMOD) after the occurrence of a crack, because the crack likely will occur somewhere other than the center. Therefore, in this study, as suggested in the KS F 2408 appendix, BS EN 14651 [24] and fib Model Code 2010 [11], three-point loading tests were performed by creating a central notch.  
The results of the flexural tensile strength and splitting tensile strength tests are shown in Table 4. The fib Model Code 2010 [11] defines limit states according to CMOD. A CMOD of 0.5 mm is defined as the serviceability limit state (SLS), and a CMOD of 2.5 mm is defined as the ultimate limit state (ULS). Therefore, the flexural tensile strength was analyzed according to CMOD. Splitting tensile strength and flexural tensile strength did not show much increase with macro-synthetic fiber content. However, after the maximum flexural tensile strength of the MFRC was reached, residual stress was maintained through the tension softening effect, as shown in Figure 3. It is surmised that the macro-synthetic fibers were effective in enhancing the flexural strength.  
Figure 2: Compressive stress–strain curves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(V_{f}\) & \(f_{c}^{\prime}\) & \(\varepsilon_{0}\) & \(E_{c}\) \\  & (kg/m\({}^{3}\)) & (MPa) & (mm/mm) & (MPa) \\ \hline
1 & & 34.70 & 0.00163 & 38,912 \\
2 & 0 & 34.98 & 0.00167 & 35,457 \\
3 & & 35.74 & 0.00143 & 41,128 \\ \hline
1 & & 39.35 & 0.00164 & 42,408 \\
2 & 2.4 & 40.94 & 0.00140 & 46,054 \\
3 & & 39.89 & 0.00194 & 32,934 \\ \hline \hline \end{tabular} \(V_{f}\): macro synthetic fiber dosage, \(f_{c}^{\prime}\) compressive strength of concrete, \(\varepsilon_{0}\): strain at peak stress, \(E_{c}\): modulus of elasticity.  
\end{table}
Table 3: Concrete compressive test results.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Road Segmentation  
The direct processing of large, high-resolution orthoimagery files for entire road parcels is challenging. Images exceeding 10,000 \(\times\) 10,000 pixels are often flagged as potential decompression bombs. To address this, the script divided roads into smaller, 100 m segments. This process simplified file handling, improved image interpretability, and made the data more suitable for ML applications.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**AC** & Alternating Current \\
**AWG** & Arbitrary Waveform Generator \\
**AWGN** & Additive White Gaussian Noise \\
**BER** & Bit-Error Rate \\
**CML** & Current-Mode Logic \\
**DAC** & Digital-to-Analog Converter \\
**DC** & Direct Current \\
**EML** & Electro-Absorption Modulated Laser \\
**FEC** & Forward Error Correction \\
**FFE** & Feed-Forward Equalizer \\
**FIR** & Finite Impulse Response \\
**ISI** & Inter Symbol Interference \\
**IoT** & Internet-of-Things \\
**LUT** & Lookup Table \\
**MMF** & Multi-Mode Fiber \\
**MZM** & Mach-Zehnder-Modulator \\
**NRZ** & Non-Return to Zero \\
**PAM** & Pulse Amplitude Modulation \\
**PDF** & Probability Density Function \\
**PRBS** & Pseudo-Random Binary Sequence \\
**RLM** & Ratio of Level Mismatch \\
**RX** & Receiver \\
**SOTA** & State-of-the-Art \\
**TX** & Transmitter \\
**UI** & Unit Interval \\
**VCSEL** & Vertical-Cavity Surface-Emitting Laser \\ \end{tabular}
## 1 Introduction
The measured results demonstrate robust performance across temperature variations and mechanical tolerances, indicating suitability for mass production. Additionally, the antenna's wideband isolation properties reduce the need for adaptive cancellation algorithms in baseband processing, thereby lowering the computational overhead in the transceiver design. These practical advantages position our solution as an attractive option for 5G device manufacturers seeking to balance performance, size, and power efficiency in next-generation mobile devices. Therefore, the proposed work holds significant importance for various reasons:  
* Compact antenna design, measuring a mere 50 \(\times\) 50 \(\times\) 0.787 mm\({}^{3}\) (4.67\(\lambda_{\text{o}}\)\(\times\) 4.67\(\lambda_{\text{o}}\)\(\times\) 0.73\(\lambda_{\text{o}}\)), delivers good performance, catering to meet the needs of modern communication devices constrained by limited space. * Operates within the tri-band 28, 35, and 38 GHz, offering the promise of expedited and more efficient wireless communication solutions. * Features 34.5, 22, and 30 dB isolation between its ports, by which it effectively minimizes interference, ensuring the clarity and strength of the signals transmitted. * Achieves high gains of 9.6, 7.8, and 13.7 dBi in the tri-band, respectively, which enhances communication range and quality, facilitating improved transmission capabilities. Experimental testing has validated the antenna's performance, making it a reliable choice for upcoming 5G mmWave wireless applications. The rest of this report is structured as follows: Section 2 presents the antenna design methodology, including the geometry, substrate selection, and tri-band operation for both the single-element configuration and the four-port MIMO array, along with comprehensive simulation results. Section 3 validates the design through experimental measurements,comparing prototypes' performance with simulations. Section 4 concludes the work by summarizing its key achievements.