esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Load-bearing cold-formed steel (CFS) walls sheathed with double layers of gypsum plasterboard on both sides have demonstrated good fire resistance and attracted increasing interest for use in mid-rise CFS structures. As the main connection method, screw connections between CFS and gypsum sheathing play an important role in both the structural design and fire resistance of this wall system. However, studies on the mechanical behavior of screw connections with double-layer gypsum sheathing are still limited. In this study, 200 monotonic tests of screw connections with single- or double-layer gypsum sheathing at both ambient and elevated temperatures were conducted. The failure of screw connections with double-layer gypsum sheathing in shear was different from that of single-layer gypsum sheathing connections at ambient temperature, and it could be described as the breaking of the loaded sheathing edge combined with significant screw tilting and the loaded sheathing edge flexing fracture. However, the screw tilting and flexing fracture of the loaded sheathing edge gradually disappear at elevated temperatures. In addition, the influence of the loaded edge distance, double-layer sheathing and elevated temperatures is discussed in detail with clear conclusions. A unified design formula for the shear strength of screw connections with gypsum sheathing is proposed for ambient and elevated temperatures with adequate accuracy. A simplified load-displacement model with the post-peak branch is developed to evaluate the load-displacement response of screw connections with gypsum sheathing at ambient and elevated temperatures.  
cold-formed steel; screw connections; experimental investigation; elevated temperature; double-layer gypsum sheathing; design formulas +
Footnote †: journal: Applied Sciences
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 4 RF Systems  
The RF system is one of the key components in the accelerator system. Figure 14 gives a summary of the power and frequency reach of the various pulsed/CW RF sources [48]. At the frequencies of f0 = 162.5 MHz and 2 f0 = 325 MHz, the RF amplifiers that can possibly drive the RFQ and DTL tanks are based on tetrodes, klystrons and solid-state amplifiers. For tetrode-based amplifier, two Thales tetrodes TH781 [49; 50; 51; 52; 53] and TH628 [54; 54] can serve this purpose. Specially, the new generation Diacrode (TH628) presents a unique ability in either doubling the output power at a given operating frequency or doubling the frequency at a given power output. When the operation frequency is above 300 MHz, klystrons [55; 56; 49] are common solutions. However, the drawbacks are the high voltage (HV) needs with oil tank for breakdown protection, expensive modulator, and gain curve saturation at full output power. Another option as RF source for accelerators is to use RF solid-state amplifiers, which is becoming more and more popular in recent years. Solid-state amplifiers promise a relatively cost-effective power conversion from DC to RF. The modularization and standardization of a solid-state system allows relatively easy maintenance and inexpensive restoration. In contrast to a conventional tree combiner, the cavity combiner can combine the output power of all single units in one stage, therefore significantly decreasing the fabrication cost and making the RF source compact [57; 58; 59].  
Figure 14: Power and frequency of various RF sources for pulsed (**a**) and CW (**b**) operations up to 1.3 GHz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **162.5 MHz DTL** & **325 MHz DTL** \\ \hline Drift tube diameter (cm) & 15 & 9 \\ \multicolumn{1}{c}{Bore radius (cm)} & 1.2 & 1.0 \\ \multicolumn{1}{c}{Focusing lattice} & FODO & FODO \\ \multicolumn{1}{c}{PMQ Length (mm)} & 80 & 40 \\ \multicolumn{1}{c}{Quadrupole gradient (T/m)} & 18.4 \(\rightarrow\) 14.6 & 62.8 \(\rightarrow\) 43.7 \\ \multicolumn{1}{c}{Cell number} & 40 & 40 \\ \multicolumn{1}{c}{Copper power (kW)} & 380.3 & 520.3 \\ \multicolumn{1}{c}{Beam power (kW)} & 210 & 400 \\ \multicolumn{1}{c}{Averaged ZT\({}^{2}\) (M\(\Omega\)/m)} & 44.4 & 55.5 \\ \multicolumn{1}{c}{Quality factor} & 65598 & 53102 \\ \multicolumn{1}{c}{Estimated total power (kW)} & 590.3 & 920.3 \\ \multicolumn{1}{c}{Transmission (\%)} & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 2 Materials and Methods
### Modeling Method
#### 2.2.4 Model Evaluation  
In this study, model performance was assessed using the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE). The coefficient of determination quantifies the degree of correspondence between the predicted and observed values, representing the proportion of variance in the response variable that is accounted for by the predictive model; an R\({}^{2}\) value approaching 1 indicates a superior goodness of fit. RMSE measures the average discrepancy between the predicted and observed values, thereby reflecting the overall predictive accuracy; a lower RMSE denotes reduced error and more precise predictions. The mathematical formulations of R\({}^{2}\) and RMSE are provided in Equations (27) and (28), respectively.  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right)^{2}}}{ \sum\limits_{i=1}^{n}{\left({y_{i}-\overline{y}}\right)^{2}}} \tag{27}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}} \right)^{2}}}} \tag{28}\]
## 3 Study and Results on Messy Broadcasting
### Messy Broadcasting in Grid
#### 3.2.1 Model \(M_{1}\)  
The messy model \(M_{1}\) for a \(d\)-dimensional hypercube \(Q_{d}\), a special case of a \(d\)-dimensional grid, was studied in [7]. It was observed that finding \(t_{1}(Q_{d})\) is no trivial task, and the exact result for \(Q_{d}\) remains to be discovered. Thus, finding \(t_{1}(G)\) for any grid \(G\) should not fare any better. For a two-dimensional grid, however, the optimum broadcast time and scheme for model \(M_{1}\) can be calculated thanks to Lemma 1.  
Lemma 1: _In a two-dimensional grid and under any optimum broadcast scheme \(S\) originated from \(0\), if vertex \(v=(a,b)\) is informed at time-unit \(t\), then all vertices \(H_{a,b}=\{(x,y)\mid 0\leq x\leq a,\)\(0\leq y\leq b\}\) are informed by time-unit \(t\)._  
Proof: The proof is achieved by induction on the time \(t\). The claim is true for \(t=0\).  
Assume the result is true for \(t-1\) when \(t\geq 1\). Let \(v=(v_{0},v_{1})\) be a vertex that is informed at time \(t\) under \(S\). Vertex \(v\) has one or two neighbors from which it can receive the broadcast message, namely, \((v_{0}-1,v_{1})\) and \((v_{0},v_{1}-1)\). Other neighbors, if they  
Figure 3: An optimum broadcast scheme for vertex \(v=(3,2)\) in a two-dimensional infinite grid. Vertex \(v\) is informed at time-unit \((2+0+1)\times 2+(2+1+1)\times 3=18\).  
exist, are not options because, in those cases, the induction hypothesis would apply to those neighbors, indicating that \(v\) should have been informed sooner than time \(t\), which is not the case. Without the loss of generality, assume that the message is received from \((v_{0}-1,v_{1})\). Therefore, \((v_{0}-1,v_{1})\) is informed by \(t-1\) time-units. By the induction hypothesis, it follows that all the vertices of \(H_{v_{0}-1,v_{1}}\) are informed by \(t-1\) time-units. Since all the vertices \((v_{0}-1,p)\) for \(0\leq p\leq v_{1}\) are informed by \(t-1\) time-units, based on their uninformed neighbors, it follows that all the vertices \((v_{0},p)\) for \(0\leq p\leq v_{1}\) should be informed by time-unit \(t\); concluding the induction proof.  
**Theorem 2** (Two-dimensional grid, messy model \(M_{1}\)).: _Consider a grid \(G[n+1,m+1]\) with \(0\) as the originator. Under messy model \(M_{1}\), \(t_{1}(0)=n+m\). In this case, any scheme is an optimum scheme._  
Proof.: Consider the message-relaying path \(P\) from \(0\) to \(v=(n,m)\) under any broadcast scheme. Based on Lemma 1, \(P\) takes exactly \(n+m\) time-units to relay the message, as each move increments one coordinate. Once \(v\) is informed, the entire grid is informed. Therefore, \(t_{1}(0)=n+m\).
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 2 Meteorological Stations and Data
### Cleaning and Preprocessing  
For Bouzareah, each 5-min data were first verified to extract outliers or missing data. Then, the data during which the sun rises or sets were deleted because the mask effect of the environment and the no-reliable response of pyranometers at a high zenith angle (cosine effect) introduced some errors. Thus, over the 2 years, 75674 validated 5-min data were available for each parameter.  
For the Odeillo's data, an automatic quality control used in the frame of the GEOSS project (Group on Earth Observation System of System) [23] was applied. Before introducing the solar data into the machine learning process, the data were cleaned and filtered.  
For forecasting purposes, it is common to filter out the data to remove night hours and to conserve them only between sunrise and sunset. As for Bouzareah, the data near sunset and sunrise are sources of errors and a pre-processing operation was applied based on the solar elevation: Solar radiation data for which the solar elevation is lower than 10\({}^{\circ}\) were removed [15; 24]. Two years of hourly data were used in this study. After cleaning and filtering, the total number of hourly data for each solar component (GHI and DNI) was 10559 (about 60% of the data were not used (2% for outliers' data and  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Data** & **Symbol** & **Unity** \\ \hline \multicolumn{3}{c}{Measured data} \\ Global horizontal solar irradiation & GHI & Wh.m\({}^{-2}\) \\ Global tilted solar irradiation (36.8\({}^{\circ}\)) & GTI & Wh.m\({}^{-2}\) \\ Ambient temperature & T & \({}^{\circ}\)C \\ Relative humidity & H & \% \\ Wind speed & WS & m.s\({}^{-1}\) \\ Wind direction & WD & degree \\ Precipitation & Pr & mm \\ Sunshine duration & S & minutes \\ Atmospheric pressure & P & mbar \\ \hline \multicolumn{3}{c}{Calculated data} \\ Extraterrestrial Horizontal irradiation & EHI & Wh.m\({}^{-2}\) \\ Solar declination & \$ & degree \\ Zenith angle & \$ & degree \\ \hline \hline \end{tabular}
\end{table}
Table 1: Available meteorological data in Bouzareah.  
58% for sun elevation less than 10\({}^{\circ}\))). These solar data were then transformed into stationary data by a method described in Section 6.
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 3 Scan Path Planning  
### Casing Characteristics and Scanning Path Analysis  
In the automatic tracking 3D scanning measurement system, the 3D scanning probe is used as the effector of the robot. The robot controls the scanner and moves in the measurement area of the optical tracker for scanning measurement. The scanner needs to move along a straight line and adjust its posture during the scanning process. Therefore, the critical factors in determining the scanning efficiency were the total length of the scanning path and the number of scanner attitude adjustments. In this paper, the scanning object is the aeroengine casing. The overall shape is a thin-walled conical barrel, and the surface has a multi-stiffener structure. The radial diameter random-casing shell changes in height, as shown in Figure 6. According to the geometric characteristics and wall-thickness measurement requirements of the part, to achieve high-efficiency and high-quality scanning reconstruction, a circular scanning path-planning method [20; 21] was proposed.
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
## 2 Materials and Methods
### Results Validation  
The Simrad EK80 (Konsberg, Horten, Norway) is a high-precision scientific echo sounder and ADCP system designed to monitor underwater ecosystems. Simrad EK80 is capable of adjusting to six frequencies: 18 kHz, 38 kHz, 70 kHz, 120 kHz, 200 kHz, and 333 kHz. It can accurately detect various water layers and different krill, making it a common instrument for underwater resource detection [37]. In order to verify the accuracy of the buoy-collected signals and the correctness of krill identification, we utilized the Simrad EK80 scientific echosounder system installed on the \"Deep Blue\" vessel for krill resource detection during buoy signal collection. By comparing the data from the scientific echosounder with the buoy-collected data, we validated the krill detection effectiveness of the small sonar buoys.  
Figure 7a shows the sonar image after the buoy was deployed, indicating the absence of krill in the area. Figure 7b depicts the detection of krill by our vessel near the buoy around 10 a.m. on 25 January 2023, confirming the findings from the buoy's sonar. Our team conducted a thorough analysis of multiple locations, revealing a correlation between the echo data of krill in Figure 6 and the sonar images in Figure 7. Specifically, when Figure 6 showed a low echo signal, Figure 7a displayed no significant krill groups near the buoy. Conversely, a strong echo signal in Figure 6 corresponded to significant krill groups in the sonar images, as seen in Figure 7b. This comparison demonstrates the drifting sonar buoy's ability to provide clear and strong echo signals during krill detection. The echo intensity exhibits a gradual decrease, remaining low when there are no krill groups nearby. These results underscore the drifting sonar buoy's accuracy in detecting Antarctic krill groups.  
Figure 7: _Cont._
# VR Simulation and Implementation of Robotics: A Tool for Streamlining and Optimization
## 3 Theoretical Framework
### Related Works
#### 3.1.1 Simulation Model for Robotic Pick-Point Evaluation  
In 2023, the authors [PERSON], [PERSON], and [PERSON] [26] developed a study related to the simulation for robotic pick-point evaluation for a 2-F robotic gripper. Robotic bin picking systems aim to minimize human intervention by utilizing robotic arms, vision systems, grippers, and control systems to handle bulk items. However, the complexity arises when dealing with items of varying shapes, colours, weights, and mechanical properties. Robotic bin picking involves several key steps: object detection, localization, grasp detection, path planning, and object manipulation. To address the challenges in determining pick points systematically, the article explores several research questions:  
1. Can ADAMS/MATLAB (co-simulation accurately determine grip quality for specific 2-F parallel robotic grippers?
2. Is the simulation model adaptable for evaluating different 2-F robotic grippers or developing new variants?
3. How does combining analytical and empirical approaches for grasp quality assessment compare to deep learning models in overall system performance?  
While the simulation model demonstrates the ability to replicate physical systems, it relies on precise parameter settings that are challenging to verify without accurate sensors. Despite its lower accuracy compared to deep learning approaches, the simulation model provides transparency but demands longer processing time due to simulating real-world physics. Ultimately, while these analytical tools are beneficial for optimizing bin picking operations in logistics or manufacturing, they should be supplemented with quality machine vision systems for precise object detention [26].
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 4 Navigation Performance
### Real-Time Orbit Determination Accuracy
#### 4.2.1 Processing Model and Strategy
\((w_{R},\ w_{A},\ w_{C})\) represents the vector of empirical accelerations in radial (R), along-track (A), and cross-track (C), respectively, which are modeled by three first-order Gauss-Markov processes to account for the unmodeled forces [33]. The observation equation of EKF can be expressed as following:  
\[P_{K}=H_{k}\big{(}X_{k}-\hat{X}_{k}\big{)}+V_{k} \tag{8}\]  
where \(P_{K}\) is pseudorange measurement of the \(i\)th navigation satellite. \(V_{k}\) is measurement noise. The measurement matrix of \(H_{k}\) is calculated from the linearization estimation of the pseudorange observation equation, as introduced in [34], which can be described in the following:  
\[H_{k}=\left[\frac{\hat{x}_{k}-x_{i}^{s}}{\rho_{i}},\ \frac{\hat{y}_{k}-y_{i}^{s}} {\rho_{i}},\frac{\hat{z}_{k}-z_{i}^{s}}{\rho_{i}},0_{1\times 3},T_{1\times 3},0, 0_{1\times 3}\right] \tag{9}\]  
where \((x_{i}^{s},\ y_{i}^{s},\ z_{i}^{s})\) indicates the position of the \(i\)th navigation satellite, \((\hat{x}_{k},\hat{y}_{k},\hat{z}_{k})\) is the position from predicted estimate state vector \(\hat{X}_{k}\). \(T_{1\times 3}\) sets \([1,0,0]\) for GPS or \([0,1,0]\) for BDS.
# Design of Indoor Visible Light Communication PAM4 System
## 3 Key Component Design
### Frame Synchronization  
Frame synchronization is obtained by processing the frame synchronization header, which will directly affect the correctness of the subsequent PAM4 decoding results. Barker codes have good autocorrelation and cross-correlation characteristics, which can provide better synchronization performance and anti-noise ability. In order to decode the data correctly, we must determine the start time of each frame, which requires detecting the synchronization header. The detection of the frame synchronization header needs to use the \"correlation method,\" the principle of which is shown in Figure 6.  
First, the input needs to be moved to the shift register, and then, by sliding the input and bit-by-bit detection, determine whether the output value of the shift register is consistent with the standard Barker code. If it is consistent, the correlation value is increased by 1; otherwise, it is decreased by 1. Then, the Barker code frame header is determined by judging whether the final correlation value is greater than the set threshold. When the correlation value of the input signal to be detected satisfies the threshold condition, the output signal is 1; otherwise, it is 0. Finally, the frame synchronization signal is obtained, and the position value of the frame synchronization header is 1. The correlation operation in Figure 6 refers to the multiplication and addition operation. When the Barker code is related to the frame data, the output value is small, and when it is related to the frame synchronization header, the output value is large.
## 2 System Model and Channel Modeling Methods
### Channel Modeling Methods
The shot noise (with variance \({\sigma_{\text{shot}}^{2}}\)) and the thermal noise (with variance \({\sigma_{\text{thermal}}^{2}}\)) caused by sunlight and ambient light are the main noises affecting the quality of outdoor V2I-VLC, and that \({\sigma_{o}}^{2}\) is expressed as follows:  
\[{\sigma_{o}}^{2}={\sigma_{\text{shot}}^{2}}+{\sigma_{\text{thermal}}^{2}}. \tag{4}\]  
According to [33; 49], \({\sigma_{\text{shot}}^{2}}\) and \({\sigma_{\text{thermal}}^{2}}\) are shown in (5) and (6):  
\[{\sigma_{\text{shot}}^{2}}=2q\eta{P_{r}B}+2q{I_{bg}}{I_{2}}B, \tag{5}\]  
\[{\sigma_{\text{thermal}}^{2}}=\frac{8\pi kT_{R}}{G}\eta_{c}{A_{r}}{I_{2}}{B^{2} }+\frac{16\pi^{2}kT_{\text{c}}\Gamma}{g_{m}}\eta_{c}^{2}{A_{r}^{2}}{I_{3}}{B^ {3}}, \tag{6}\]  
where \(q\) is the amount of electronic charge; \(\eta\) is the photoelectric conversion efficiency of PD; \(P_{r}\) is the average received optical power; \(B\) is the system bandwidth; \(I_{bg}\) is the received background noise current; \(I_{2}\) is the noise bandwidth factor of background noise; \(k\) is the Boltzmann constant; \(T_{k}\) is the absolute temperature; \(G\) is the open-loop voltage gain; \(\eta_{c}\) is the fixed capacitance per unit area of PD; \(\Gamma\) is the field-effect transistor (FET) channel noise factor; \(g_{m}\) is the FET transconductance; \(I_{3}\) is the FET channel noise current for thermal noise.
## 2 Literature Review  
Several different solutions exist that deal with rail vehicle position on regional tracks. The vast majority of these solutions use the GNSS (Global Satellite Navigation System), balises, or odometers. Wireless information transfer is most frequently performed through mobile networks, GSM-R (Global System for Mobile--Railway), or by means of standard GSM technology.
###### Abstract  
In this study, the injection processes and spray behavior of diesel fuel (DF), rapeseed oil (RO) and their fuel blends (RO25, RO50, RO75) have been qualitatively and quantitatively analyzed to identify the differences in the injection rate and the spray parameters. The volumetric and mass injection rates, the spray penetration, the spray cone angle and the spray area under non-evaporation conditions in a nitrogen-filled chamber were analyzed. The results showed that rapeseed oil leads to a decrease in the peak injection rate due to its higher density and viscosity. Moreover, rapeseed oil and its blends with mineral diesel oil exhibited smoother rising slopes at the start of injection. The spray tip penetration was the longest for the rapeseed oil and the spray tip penetrations of the fuel blends RO25, RO50 and RO75 were arranged between the values of DF and RO. As the injection pressure increased, the differences in the spray tip penetrations diminished. Increasing the amount of rapeseed oil in fuel blends resulted in a smaller spray cone angle compared to diesel fuel. The spray area of all tested fuels increased significantly with increasing spray tip penetration, the spray area of RO was consistently lower than that of diesel fuel.  
diesel injection; injection rate; macroscopic spray characteristics; rapeseed Article  
An Experimental Study on the Injection Characteristics and the Macroscopic Spray Characteristics of Rapeseed Oil-Diesel Fuel Blends.