esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Risk Factor**} & \multirow{2}{*}{**Parent Nodes**} & \multicolumn{2}{c}{**Conditional Probability**} \\ \cline{3-4}  & & \(T\) & \(F\) \\ \hline \(B_{12}\) & \(B_{13}=T\) & 0.60 & 0.40 \\  & \(B_{13}=F\) & 0.01 & 0.99 \\ \hline \(B_{22}\) & \(B_{11}=T,B_{21}=T\) & 1 & 0 \\  & \(B_{11}=T,B_{21}=F\) & 1 & 0 \\  & \(B_{11}=F,B_{21}=T\) & 0.85 & 0.15 \\  & \(B_{11}=F,B_{21}=T\) & 0.10 & 0.90 \\ \hline \(B_{52}\) & \(B_{42}=T\) & 0.87 & 0.13 \\  & \(B_{42}=F\) & 0.01 & 0.09 \\ \hline \(B_{61}\) & \(B_{31}=T\) & 0.65 & 0.35 \\  & \(B_{31}=F\) & 0.01 & 0.99 \\ \hline \(B_{41}\) & \(B_{11}=T,B_{31}=T\) & 1 & 0 \\  & \(B_{11}=T,B_{31}=F\) & 0.98 & 0.02 \\  & \(B_{11}=F,B_{31}=T\) & 0.59 & 0.41 \\  & \(B_{11}=F,B_{31}=F\) & 0.01 & 0.99 \\ \hline \(B_{51}\) & \(B_{41}=T,B_{22}=T,B_{21}=T\) & 1 & 0 \\  & \(B_{41}=T,B_{22}=T,B_{21}=F\) & 0.91 & 0.09 \\  & \(B_{41}=T,B_{22}=F,B_{21}=T\) & 0.94 & 0.06 \\  & \(B_{41}=T,B_{22}=F,B_{21}=F\) & 0.96 & 0.04 \\  & \(B_{41}=F,B_{22}=T,B_{21}=T\) & 0.92 & 0.08 \\  & \(B_{41}=F,B_{22}=T,B_{21}=F\) & 0.99 & 0.01 \\  & \(B_{41}=F,B_{22}=F,B_{21}=T\) & 0.98 & 0.02 \\  & \(B_{41}=F,B_{22}=F,B_{21}=F\) & 0 & 1 \\ \hline \(D\) & \(B_{52}=T,B_{61}=T,B_{51}=T\) & 1 & 0 \\  & \(B_{52}=T,B_{61}=T,B_{51}=F\) & 0.89 & 0.11 \\  & \(B_{52}=T,B_{61}=F,B_{51}=T\) & 0.81 & 0.19 \\  & \(B_{52}=T,B_{61}=F,B_{51}=F\) & 0.93 & 0.07 \\  & \(B_{52}=F,B_{61}=T,B_{51}=T\) & 0.79 & 0.21 \\  & \(B_{52}=F,B_{61}=T,B_{51}=F\) & 0.80 & 0.20 \\  & \(B_{52}=F,B_{61}=F,B_{51}=T\) & 1 & 0 \\  & \(B_{52}=F,B_{61}=F,B_{51}=F\) & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CPT(b). The following conclusions can be drawn based on the analysis of the above inference result: (1) The main conduction path of the delayed delivery risk was \(B_{11}\to B_{41}\to B_{51}\to D\), where \(B_{11}\) represents the customer demand time change, \(B_{41}\) represents the adjustment of the production task order, \(B_{51}\) represents the delay of parts in assembly, and \(D\) represents deferred delivery to downstream customers. (2) The main reason for the delay in the delivery of the business to the downstream customers is transmitted from the customer demand change and the delayed delivery of suppliers. (3) Enterprise tasks are often adjusted, which indicates that the key node in the risk conduction path is the low degree of flexibility in the arrangement of the production schedule. Improving the flexibility of enterprise production plans will help to reduce the risk of delayed delivery to downstream customers.
## 3 Discussion
Applications of an AR navigation system in maxillofacial surgery have been extended to orthognathic surgery, tumor surgery, temporomandibular joint motion analysis, foreign body removal, osteotomy, minimally invasive biopsy, prosthetic surgery, and dental implantation [21]. One of its chief attractions is providing information on deep-tissue structures during the operation, allowing surgery to be less invasive [5]. For those reasons, orthognathic surgery is one of the most widely used fields in AR applications [19]. Recently, VR and AR have created a synergy effect in producing excellent therapeutic tools [22]. VR-based surgical simulation often combines AR navigation to guide the operation, with useful information, such as the patient's anatomy and/or preoperative planning [23]. In the future, VR and AR will develop to implement more realistic, immersive, and interactive simulations [23]. AR in the medical field is quite useful (in regards to displaying dynamic navigation), despite some software and hardware limitations [16]. Simulated images are generated faster and more realistically due to the development of computing power [24]. In the future, AR will likely serve as a navigation system for surgeries, working in symbiosis with surgeons, allowing them to perform accurate surgeries [6]. Despite our case report, VR simulation and AR navigation systems showed potential application in orthognathic surgery in this study. This means that we will be able to make surgical plans using VR for all surgeries, performing more convenient and accurate surgeries using AR navigation systems. This study has some limitations. First, this is a case report, so it is hard to evaluate the statistical analysis. Second, the equipment, such as tracking camera and computer, were common items in our office; therefore, if we use a high quality tracking system and computer, it will provide us with better results in terms of performance and accuracy.
# Refining Zero-Shot Text-to-SQL Benchmarks via Prompt Strategies with Large Language Models
## Appendix A Examples of Errors and Inconsistencies in the Original BIRD Dataset
### Inconsistent Data Representation  
Example (Data Value Mismatch--Figure A3): Question 83 involves filtering based on a 'Magnet' school indicator. The database description file specifies this field uses 'Y'/'N' values. However, the actual data in the table use '0'/'1', and the provided SQL query correctly uses T2. Magnet = 1. This inconsistency between the schema description and the actual data can mislead LLMs that rely on the description.  
Example (Misleading Naming): In some cases, table or column names mentioned in the database description documents were found to be inconsistent with the actual schema (e.g.,'set transaction' instead of'set translation'), potentially causing LLMs to fail in identifying the correct tables or columns.
###### Abstract  
In this study, the high-fidelity numerical methods are developed to investigate the tip vortex cavitation (TVC) inception and noise of underwater propellers, namely, Model-A and Model-B, which are designed to investigate the effects of sweep angle on cavitation inception and noise. In addition, the entire body of the DARPA Suboff submarine is included to consider the effects of the inflow distortion originating from the boundary layer flow of the submarine body on the cavitating flow of the propellers. The Eulerian approach consisting of Reynolds-averaged Navier-Stokes (RANS) solver and the vortex model is coupled with the Lagrangian approach using the bubble dynamics equations and the acoustic analogy for nuclei initially distributed in inlet flow. First, three-dimensional incompressible unsteady RANS simulations are performed to predict the hydrodynamic flow field driven by underwater propellers installed on a DARPA Suboff submarine body. The Scully vortex model and dissipation vortex model (DVM) are used to regenerate the tip vortex dissipated by artificial numerical damping and low grid resolution around the vortex core center, which is identified by using minimum \(\lambda_{2}\)-criterion in the swirling flow field originating from the propeller blade tip. Then, tip vortex cavitation inception is simulated by applying the bubble dynamics equations to nuclei initially distributed in the inflow region. The volume and location of each nucleus are obtained by solving the bubble dynamics equations on the flow field obtained using the Eulerian method. Finally, the cavitation noise is predicted by modeling each bubble with a point monopole source whose strength is proportional to its volume acceleration. The validity of the present numerical methods is confirmed by comparing the predicted acoustic pressure spectrum with the measured ones.  
tip vortex cavitation; underwater propeller; submarine propeller; cavitation noise; propeller cavitation noise
## 3 Geometry Models with IVC
### Geometry Models with Integrated IVCs  
#### 3.2.1 Geometry Model with Integrated Limiters  
The DEMO layout presented in Figure 1 assumes an arrangement of four toroidal segments, each of them including three limiters: IML, UL and OML [7; 8]. The latter consists of two connected limiters: OML itself and an outboard lower limiter, OLL, as shown in Figure 3. The plasma limiters have different protrusions relative to the FW surface: 70 mm, 50 mm and 40 mm for OML, UL and IML, respectively [2]. An optional simplified structure is adopted in all limiters: plasma-facing tungsten armor of 10 mm thickness, followed by a 20 mm thick homogeneous mixture of W (39.42%), CuCrZr (18.4%), Cu (9.38%), water (32.8%) and then a 30 mm thick EUROFER 97 steel layer. Behind this complex structure, a 700 mm thick shield block and finally a 30 mm thick EUROFER 97 plate are arranged to prevent a neutron leakage. The shielding block is filled with a simplified homogeneous material composed of 70% SS316L(N) steel + 30% water. The gaps of 10 mm thickness around the limiters are reserved in the models to allow their technological radial movement. A pipe forest for the cooling water is not presented in the geometry models of the limiters because this does not affect the breeding performances of the blankets and it makes the geometry models redundantly complex [9].  
Figure 3: Integration of the IML, UL, OML + OLL in one toroidal segment on 3D (**a**) and 2D (**b**) plot.
# Cross-Pol Transponder with Frequency Shifter for
## 2 Materials and Methods
### The Cross-Pol Transponder with Frequency Shifter  
As described in Reference [6], the simplest transponder consists of only two antennas and an amplifier. However, a major problem of any transponder is its possible oscillation. In particular, the isolation between the two antennas has to be higher than the gain in order to avoid the oscillation.  
A way to increase the isolation between two linearly polarized antennas is to rotate one of them by 90\({}^{\circ}\). For real antennas, the cross-polarization can provide an isolation of 20-30 dB in addition to the co-polarized isolation (see for example Reference [8]). In the specific case of the transponder described above, the cross-polarization of the antenna pointing at the radar does not affect the bistatic operation if the second receiving antenna of the radar head is cross-polarized with respect to the transmitting antenna (see Figure 2). A further advantage of the cross-polarization is that monostatic and bistatic signals have different polarizations, so possible artifacts (described in Reference [6]) due to the mixing of bistatic and monostatic signals are greatly mitigated.  
Figure 2: Monostatic/bistatic GBSAR with cross-pol transponder.  
Another effective way to avoid the oscillation of a high gain transponder is to shift the output frequency using a suitable frequency shifter. Figure 3 shows the scheme of a bistatic radar that uses a transponder with frequency shifter. The radar operates at central frequency \(f_{0}\). The transponder has its own local oscillator (LO). A mixer and a high-pass filter operates as upconverter and shift the central frequency, so the frequency of the signal transmitted from the transponder to the radar head is \(f_{0}+f_{\text{LO}}\). In the radar head, a downconverter (a mixer and a low-pass filter) brings the signal back to frequency \(f_{0}\). The two LOs have to be coherent and synchronized, so a reference signal at low frequency \(f_{\text{REF}}\) is shared with a cable. As the cable could move during the movement of the radar head along its mechanical guide, a calibration path is necessary to compensate possible phase shift of the reference signal. Moreover, the calibration provides long-term phase stability and compensates possible phase changes that can occur when the equipment is switched off and on again. Therefore, the radar head has a second transmit channel (selected by a Radio Frequency switch) that provides a calibration signal directly transmitted to the transponder. The transponder has two receiving antennas (one pointed at the target and the other one pointed at the radar). A RF switch, controlled by a Wi-Fi device, selects between the two antennas (measurement and calibration).
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
###### Abstract  
Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile(r) use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone's internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30 deg range, as suggested by the provided results.  
bluetooth; BLE; beacon; localization; RSS; asset tracking 2024 4.196-216. [[https://doi.org/10.3390/network4020010](https://doi.org/10.3390/network4020010)]([https://doi.org/10.3390/network4020010](https://doi.org/10.3390/network4020010))
# Physico-Chemical Characterization of Sesame/Rapeseed Oil Mixtures
## 3 Results
### SO/RSO Mixtures Color Characteristics  
Color measurement in the edible oil industry is essential, as it depends on pigment concentrations, the type of plant and the oil processing method [45]. CIELab color parameters of the studied oils are presented in Table 7. The given values are obtained as an average value from the measurement of 5 samples, with a standard deviation not exceeding 3%.  
The mixtures of sesame oil and rapeseed oil have high and nearly constant brightness. The \(a^{\star}\) parameter of mixtures varies in the range between 0 and 5, which means that the oil's color is close to greenish. The \(b^{\star}\) of the mixtures depends on the sesame oil and rapeseed oil concentration ratio in the mixtures. Pure rapeseed oil has the highest \(b^{\star}\) value--88, which corresponds to yellow color. It may be due to the presence of carotenoids. The sesame oil has the lowest \(b^{\star}\) value--15, which defines a pale yellow color, which is probably associated with lower carotenoid concentration [46]. The \(b^{\star}\) for the mixture of oil mixes shows a very good linear dependency on the oil concentrations (R\({}^{2}\) = 0.9904).
## 3 Experiments Evaluation
### Comparisons with Ordinary CS  
From the previous discussion we know that, if we transform the original problem Equation (1) to Equation (5), the sparse signal could also be recovered by ordinary CS. However, as we just pointed out above, if the noise not only occurs in measurement phase but also lies in data itself, Algorithm 1 can give a better estimation. Here, we address this comparison by adding relative strong noise corruption to the data in which the variance of \(\xi\) in Equation (1) is \(10^{-3}\). In addition to this noise setting, we fix the sparsity \(\rho_{s}=5\%\) and vary the CS measurement ratio \(\rho_{cs}\) from 10% to 80% to observe the performance comparison between Algorithm 1 and ordinary CS. From Figure 4 it can be easily seen that Algorithm 1 outperforms ordinary CS (CoSaMP) when data itself is also corrupted by relative strong Gaussian noise, especially when the CS measurement ratio is small.  
Figure 3: Sparse recovery performance with respect to the rank of fixed subspace.  
Figure 2: Sparse recovery performance with respect to CS measurement ratio.  
We make a more clear comparison in Figure 5. Here, we simulate a sparse signal \(s\) with sparsity \(\rho_{s}=5\%\) and take CS measurements with measurement ratio \(\rho_{cs}=20\%\). The variance of additional Gaussian noise \(\xi\) and measurement noise \(\epsilon\) are all set as \(10^{-3}\). From this noisy CS measurement setting, Figure 5 shows that the estimated sparse signal by our algorithm can exactly match the original signal at \(RelErr=2.23\times 10^{-3}\) which is approaching the data noise level \(10^{-3}\). However, ordinary CoSaMP makes a worse estimation, say \(RelErr=5.06\times 10^{-1}\), as relative strong data noise \(\xi\) would introduce more uncertainty when the algorithm tries to identify the sparse support \(\Omega\).  
Figure 4: Performance comparison between CoSaMP_subspace and ordinary CS (CoSaMP) for noisy sparse recovery problem.  
Figure 5: Noisy sparse signal recovery comparison between CoSaMP_subspace and ordinary CS (CoSaMP). Top-left is the compressive measured signal \(v\); top-right is the uncompressed signal—“dense structured signal \(+\) sparse signal”; bottom-left is the recovered sparse signal by our algorithm; bottom-right is the recovered sparse signal by ordinary CoSaMP.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 4 Transforming the Classical AES Algorithm into a Quantum-Gate-Based AES Algorithm
### Quantum Computing Programming
#### 4.3.4 Performance Evaluation  
Multiple file units (e.g., KB, MB, and GB) are used by companies. The AES reads 128 bits (16 bytes) each; hence, it randomly gives input values repeatedly to mimic the encryption of multiple files.  
AES-128 reads 16 bytes each and encrypts them, thus it becomes \"Iteration = File Size (Byte)/16 (Byte).\" Therefore, we can see the iteration according to the file size in Table 4. The experiment was conducted with AES-128 implemented in Python and Qiskit. The time unit was set to minutes. AES-128 in classical computing is implemented in Python because Qiskit consists of Python.  
Table 5 shows the performance time for each platform. The performance time showed many variances. In the case of Qiskit, it took a long time to perform as much as each file size. Therefore, we calculated the number of iterations conducted was equal to the file size, based on the time spent per iteration. However, to measure the time taken for the performance comparison would be meaningless, because quantum computing was executed on a simulation basis. The quantum resource estimation would be a fairer comparison.  
Table 6 presents the quantum resource estimation per iteration. \"Gate\" is the number of quantum gates used in the quantum circuit. \"Width\" is the number of qubits used plus the number of classical bits in the quantum circuit. \"Depth\" is the length of the critical path in the quantum circuit. \"Qubit\" is the number of qubits used in the quantum circuit. We plan to investigate a more accurate performance evaluation through quantum resource estimation analysis as an ongoing research.
## 2 Materials and Test Programs
### Test Programs
#### 2.2.2 UCS Test  
The samples prepared for UCS tests are listed in Table 3. A universal testing machine (Figure 4a) with a maximum load sensor of 100 kN, measuring error of \(\pm\)1%, and control precision of \(\pm\)0.5%, was used. The vertical constant loading displacement rate of 1 mm/min is applied to the sample until it failed. Then \(q_{\text{u}}\) could be calculated by Equation (1).  
\[q_{\text{u}}=\frac{P}{\pi R^{2}} \tag{1}\]  
where \(q_{\text{u}}\) is the value of UCS (Mpa), \(P\) is the maximum vertical loading pressure (kN), and \(R\) is the radius of the cylinder (mm).  
Figure 3: Samples molded and curing process.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Types of Tests** & **Group** & **Blend Name** & **BF Content (\%)** & **BF Length (mm)** & **Curing Time (Days)** \\ \hline \multirow{8}{*}{UCS test/ STS test} & 1 & SM0B & 0.0 & - & 3, 7, 14, 28 \\  & & SM0.1B & 0.1 & 12 & 3, 7, 14, 28 \\  & & SM0.2B & 0.2 & 12 & 3, 7, 14, 28 \\  & & SM0.3B & 0.3 & 12 & 3, 7, 14, 28 \\  & & SM0.4B & 0.4 & 12 & 3, 7, 14, 28 \\  & 2 & SM2P0B & 0.0 & - & 3, 7, 14, 28 \\  & & SM2P0.1B & 0.1 & 12 & 3, 7, 14, 28 \\  & & SM2P0.2B & 0.2 & 12 & 3, 7, 14, 28 \\  & & SM2P0.3B & 0.3 & 12 & 3, 7, 14, 28 \\  & & SM2P0.4B & 0.4 & 12 & 3, 7, 14, 28 \\ \hline \end{tabular} Note: Silty sand abbreviated to SM, P represents Portland cement, and B represents basalt fiber.  
\end{table}
Table 3: The summary of test schemes.
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 2 System Description
### Manipulator  
The manipulator has six degrees of freedom. It is composed of six 5 th-class rotary kinematic pairs. As a drive, it uses DC motors with planetary gear with a ratio from 1:30 to 1:70, depending on the axis. For each axis, an additional gear was used in the form of a harmonic drive with a gear ratio of 1:160. This allowed for high positioning accuracy and torque. Incremental encoders with a resolution of 64 pulses per revolution were used to measure the axis rotation. Taking into account the full gear ratio, the obtained values for the respective axis are from 307,200 to 716,800 pulses per revolution.  
A kinematic analysis was performed for the manipulator using the Denavit-Hartenberg notation [19]. The base coordinate system is located in the base of the robot. The coordinate system of each axis is defined relative to the coordinate system of the previous axis by a matrix of 4 \(\times\) 4 dimension. The matrix \(\mathbf{M}\) shows the general form of the transformation (1). The matrix \(\mathbf{R}\) is a 3 \(\times\) 3 dimension rotation matrix, the vector \(\mathbf{t}=[t_{x},\,t_{y},\,t_{z}]^{\text{T}}\) is a translation column vector, and \(\mathbf{0}^{T}=[0,\,0,\,0]\).  
\[\mathbf{M}=\begin{bmatrix}\mathbf{R}&\mathbf{t}\\ \mathbf{0}^{T}&1\end{bmatrix}, \tag{1}\]  
The transformation matrix is created by combining homogeneous transformations: Translations and rotations around the \(x,y,\) and \(z\) axes. Multiplying the transformation matrices provides the position and orientation of the last system with respect to the base system. The derivation of the manipulator kinematics equations is presented in Appendix A.  
The control system consists of a motherboard equipped with an STM32F407 VGT6 microcontroller and four dual servo drivers consisting of STM32F407 microcontrollers. Each controller is connected to a dual VNH5019 integrated circuit that controls the power for two DC motors and two incremental encoders. The servo drivers control the angular position with the use of PID regulators, and the motherboard carries out the manipulator kinematics algorithms. Three servo controllers operate the six main drives of the manipulator, while the fourth servo controller operates the gripper drives.  
Figure 1: Mobile robot during analog simulation mission.