esa-sceva/satcom-chunk-collection huggingface dataset:
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Green Ship Technology
#### 3.2.3 Clean Energy and Power  
Low-carbon power technologies, such as liquefied natural gas (LNG) and lithium batteries, were also evaluated as a source of stable power for inland ships. For LNG, the balance between the safe and efficient use of the gas supply system and commercial cargo was addressed, and the loss of cargo space was minimized, resulting in an optimal arrangement of the LNG fuel supply system. Simulation analysis was used to optimize the system process to ensure the safety and reliability of the system. Capturing gas that evaporates naturally in the tank and the residual natural gas in the pipeline is critical to reducing overall gas loss, which would otherwise result in lower fuel efficiency and increased emissions. Process simulations were used to configure the system equipment to efficiently solve the problem of gas loss. Technology was developed for the gas supply volume, pressure, temperature, quality control, and security of the LNG supply system.  
Lithium battery power holds great potential as long as certain challenges are addressed [35]. First, battery life is greatly reduced due to the inherent inconsistency of battery cells in large-scale packs, a problem which is magnified by system temperature field differences and inconsistent current distribution [36]. Issues relevant to this problem include the field temperature distribution of marine lithium-ion battery systems, the heat generation process, heat dissipation flux demand, temperature field difference, and hot spot distribution in traditional marine battery systems under various working conditions [37]. To control battery temperature, immersion cooling technology, including the choice of the immersion media, flow channel design and optimization, and immersion convection heat exchanger [38], was evaluated. By circulating the protective liquid inside the battery system and exchanging heat with an external cooling medium, the maximum temperature rise and temperature range of the system were reduced to meet the temperature control characteristic demand during extended operation of the system.  
We also evaluated active balance control technology of the new marine battery system to improve the consistency of the discharge depth of each cell in the battery system, thereby reducing the system life decay rate. This included the analysis of a battery system energy balance strategy and an active balance device, the determination of a reasonable discharge depth range suitable for long-lasting energy marine battery systems, and the establishment of a long-lasting battery system utilization strategy. Finally, the mechanical durability of the typical structures of battery systems and the compatibility of the typical materials of battery systems with an immersion protective fluid to ensure a long battery system service life were studied.
# Bench Test and Analysis of Cleaning Parameter Optimization of 4 L-2.5 Wheat Combine Harvester
###### Abstract  
Inaccurate and untimely adjustments of cleaning parameters during the operation of wheat combine harvesters lead to high cleaning losses and impurity rates. For this reason, a self-made 4 L-2.5 threshing and cleaning experiment table was employed for cleaning parameter optimization experiments for wheat combine harvesters in this paper. The influence of the cleaning parameters on thecleaning loss and impurity rates was analyzed, and the optimum combination of cleaning parameters was predicted and verified. The contribution hierarchy of cleaning parameters to cleaning loss rate is as follows: crank speed of shale shaker > opening of chaffer > operation speed > fan speed > throttle opening. Meanwhile, the contribution hierarchy of cleaning parameters to impurity rate is as follows: operation speed > fan speed > throttle opening > crank speed of shale shaker > opening of chaffer. The predicted optimum combination of cleaning parameters, i.e., when the cleaning loss and impurity rates are both at a minimum and the feed quantity is at the maximum, is as follows: operating speed--2.2 m/s; opening of chaffer--26 mm, throttle opening--20\({}^{\circ}\); fan speed--1100 r/min; and Crank speed of shale shaker--350 r/min. With these settings, the cleaning loss rate was 1.5% and the impurity rate was 1.9%. In the validation experiment, the average cleaning loss rate was found to be 1.47%, the average impurity rate was 1.9%, and the relative error of the predicted values was 0.03% and 0.06%, respectively. Compared with the cleaning index of combine harvesters with commonly used parameters, the cleaning loss rate was reduced by 0.12% and the impurity rate was reduced by 0.19%.  
Keywords:wheat; 4 L-2.5 combine harvester; threshing and cleaning testbed; cleaning parameters optimization; response surface methodology +
Footnote †: journal: Applied Sciences
# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## 4 Methodology
### Quality Estimation and Applying of GA
The number of elite chromosomes is selected empirically to provide the best tradeoff between the convergence of evolution (yet preventing the premature convergence to suboptimal solutions) and diversity of population. The remaining 34 (of 40) chromosomes are produced by single-point crossover operations (parameter Crossover in Table 6) on pairs of chromosomes, randomly selected from the chromosomes in the mating pool. The chromosomes newly produced by crossover are mutated via single-point mutation (parameter Mutation in Table 6) with a probability of 5% (parameter Mutation ratio in Table 6). The details of the implementation of the binary selection, single-point crossover, and mutation operations are provided in [26]. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \multirow{3}{*}{Genotype} & Each classifier has its own set of parameters (as shown in Tables 4 and 5) encoded in the genotype. In addition, a fixed combination of features (pertinent to the dynamics of the accelerator pedal) is also incorporated into the genotype: [mP, aR], [mP, mR], [mR, aR], [mP, aR, mR], [mP, aR, mR, mP*aR], [mP, aR, mR, mP*mR], [mP, aR, mR, mR*aR] \\ \hline Population size & 40 individuals \\ \hline Selection & Binary Tournament \\ \hline Selection ratio & 10\% \\ \hline Elite & Best 2 individuals \\ \hline Crossover & Single-point \\ \hline Mutation & Single-point \\ \hline Mutation ratio & 5\% \\ \hline Fitness value & F-score \\ \hline Termination criteria & (\#Generations > 100) or (Fitness Value = 100\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Main parameters of GA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Allele** & **Interval of** & **Range** & **Meaning** \\ \hline \multirow{2}{*}{eta} & \multirow{2}{*}{0.002} & \multirow{2}{*}{[0, 1]} & Step size shrinkage. Controls the learning rate in update and \\  & & & prevents overfitting \\ \hline \multirow{2}{*}{gamma} & \multirow{2}{*}{0.1} & \multirow{2}{*}{[0, 100]} & Minimum loss reduction required to make a node split. Split happens \\  & & & when the resulting split gives a positive reduction in the loss function. \\ \hline \multirow{2}{*}{max\_depth} & \multirow{2}{*}{1} & \multirow{2}{*}{[1, 20]} & The maximum depth of a tree \\ \hline \multirow{2}{*}{min\_child\_weight} & \multirow{2}{*}{1} & \multirow{2}{*}{[1, 100]} & Minimum sum of weights of all observations required in a child \\ \hline \multirow{2}{*}{subsample} & \multirow{2}{*}{0.002} & \multirow{2}{*}{(0.001, 1)} & Subsample ratio of the training instance \\ \hline \multirow{2}{*}{colsample\_bytes} & \multirow{2}{*}{0.001} & \multirow{2}{*}{(0.5, 1)} & Subsample ratio of columns when constructing each tree \\ \hline n\_estimators & \multirow{2}{*}{1} & \multirow{2}{*}{[100, 500]} & The number of boosting stages to perform \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Content of chromosome of GA applied for optimizing the values of hyperparameters of XGBoost.
# Least-Squares Estimation of Tangent Space for a Triangular Mesh
## 2 Background
### Least-Squares Estimation of Rigid Transformation  
Least-squares estimation of rigid transformation between two sets of corresponding points is at the heart of our approach. It has been introduced in computer vision [8; 9; 10; 11], and it is sometimes called the absolute orientation problem [12]. In computer graphics, this method has found application in meshless deformation, involving shape matching of vertices [13], and has been further extended to encompass shape matching with oriented particles [14]. Additionally, it has been employed in as-rigid-as-possible shape deformation with vertices [15] and extended to address oriented particles [16].  
Given two sets of corresponding points, denoted by \(\bar{\mathbf{p}}_{j}\) and \(\mathbf{p}_{j}\), respectively, there exists a rigid transformation that optimally aligns two sets in a least-squares sense, as illustrated in Figure 1. This can be formulated as finding the optimal rotation matrix, \(\mathbf{R}\), that minimizes  
\[\sum w_{j}\|\mathbf{R}(\bar{\mathbf{p}}_{j}-\bar{\mathbf{c}})-(\mathbf{p}_{j} -\bar{\mathbf{c}})\|^{2}, \tag{5}\]  
where \(w_{j}\) is a weight and \((\mathbf{p}_{j}-\bar{\mathbf{c}})\) and \((\bar{\mathbf{p}}_{j}-\bar{\mathbf{c}})\) represent the relative positions of the points with respect to the weighted centers \(\mathbf{c}\) and \(\bar{\mathbf{c}}\), respectively. Here, the optimal translation is \((\bar{\mathbf{c}}-\bar{\mathbf{c}})\).  
The rotation matrix, \(\mathbf{R}\), can be obtained through the polar decomposition of the covariance matrix [11]:  
\[\mathbf{A}=\sum w_{j}(\mathbf{p}_{j}-\bar{\mathbf{c}})(\bar{\mathbf{p}}_{j}- \bar{\mathbf{c}})^{\mathrm{T}}=\mathbf{R}\mathbf{S}. \tag{6}\]  
If a point \(\mathbf{p}_{i}\) needs to be fixed, the centers \(\mathbf{c}\) and \(\bar{\mathbf{c}}\) are replaced by \(\mathbf{p}_{i}\) and \(\bar{\mathbf{p}}_{i}\), respectively, as in the as-rigid-as-possible shape deformation approach [15; 16]. We will apply this shape-matching technique to the 1-ring neighbors of each vertex, aligning them from texture space to world space.
## 5 Discussion
### Provision of Quality-of-Service (QoS)
#### 5.4.2 Request to Lower Message Periodicity  
The devices of vehicle and VRU (that are on the verge of collision) broadcast a special message requesting other nodes to lower the _periodicity_ of transmission of safety messages while maintaining their own periodicity. This leads other nodes to lower the periodicity of their message temporarily (e.g., from 10 Hz to 5 Hz for the next scheduled transmission). This allows the crash-prone devices to transmit with higher periodicity ensuring the guarantee of message delivery.
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
## 5 Damage-Equivalence Factors
#### Damage-Equivalence Factors for Total Lorry Flow
To facilitate the interpretation of the curves, in these figures are also reported the horizontal lines corresponding to \(m_{eq}=3\) and to \(m_{eq}=5\). It must be pointed out that the results mainly depend on the traffic volume, for that reason they are absolutely general and can be easily extended to other static schemes and other kind of influence lines. Investigation of the diagrams demonstrates that:  
* as expected, \(m_{eq}\) increases as the traffic flow increases;
* independently on the shape of the \(S-N\) curve, \(m_{eq}\) decreases as the span \(L\) increases;
* for details characterized by constant amplitude fatigue limit, subcase 1, Figure 14a: \(\odot\)  in the interval \(8\leq L\leq 50\) m, it results \(m_{eq}\approx 5\) for \(N_{t}=5\cdot 10^{6}\) lorries, \(m_{eq}\approx 6\) for \(N_{t}=10^{7}\) lorries, \(m_{eq}\approx 8\) for \(N_{t}=2.5\times 10^{7}\) lorries, and finally \(m_{eq}\approx 10\) for \(N_{t}=5\times 10^{7}\) lorries; \(\odot\)  when \(L\leq 5\) m, it results \(m_{eq}\approx 8\) for \(N_{t}=5\times 10^{6}\) lorries, \(m_{eq}\approx 11\) for \(N_{t}=10^{7}\) lorries and \(m_{eq}\approx 23\) for higher total flows;  
Figure 14: Inverse of the equivalent slope \(1/m_{eq}-L\) curves for various total lorry flows for bending moment at midspan of simply supported beams (case study no. 1—reference flow: \(10^{8}\) lorries): (**a**) subcase 1—\(S-N\) curves as per Figure 7a,b; (**b**) subcase 2—\(S-N\) curves as per Figure 7c,d. for \(L\geq 100\) m, the curves approach these pertaining to details lacking a constant amplitude fatigue limit, which are discussed below. * for details lacking a constant amplitude fatigue limit, subcase 2, Figure 14b:
* when \(L\leq 50\) m, if \(N_{t}\geq 2.5\cdot 10^{7}\) forries this results in \(m_{eq}\approx 5\), but \(m_{eq}\) reduces for smaller flows. More precisely, if \(N_{t}=10^{7}\) forries, it is \(m_{eq}\approx 4.9\) for \(L\leq 5\) m and \(m_{eq}\approx 4.6\) for \(8\leq L\leq 50\) m, while, if \(N_{t}=5\times 10^{6}\) forries, it is \(m_{eq}\approx 4.6\) for \(L\leq 5\) m and \(m_{eq}\approx 4.2\) for \(8\leq L\leq 50\) m;
* when \(L\geq 100\) m, this results in \(m_{eq}\approx 3.3\) if \(N_{t}=5\times 10^{6}\) lorries; \(m_{eq}\approx 3.4\) if \(N_{t}=10^{7}\); \(m_{eq}\approx 3.6\) if \(N_{t}=2.5\times 10^{7}\) lorries and, finally, \(m_{eq}\approx 3.8\) if \(N_{t}=5\times 10^{7}\) lorries. These results underline that the equivalent slope \(m_{eq}\) depends on \(L\), on the shape of the \(S-N\) curve and on the total traffic volume \(N_{t}\) and that the assumption of a constant value for the slope could lead to erroneous, even on the unsafe side, estimates of the fatigue life. Trivially, fatigue life determined assuming \(m=5\) is overestimated when \(m_{eq}>5\) and \(N_{t}>N_{t,ref}\) or when \(m_{eq}<5\) and \(N_{t}<N_{t,ref}\), underestimated otherwise. Moreover, errors are amplified when details characterized by constant amplitude fatigue limit are considered (subcase 1).
## 2 Materials and Methods
### Groundwater Numerical Simulation
#### 2.2.4 Error Analysis  
To evaluate the accuracy of the model, this study employed the root mean square error (RMSE) as the error metric. The RMSE quantifies the overall prediction bias by computing the root mean square of discrepancies between predicted water levels and actual observed water levels, where smaller values indicate higher model precision. The formula is defined as  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{(y_{i}-\hat{y}_{i})^{2}}} \tag{6}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) represent the i-th observed and predicted water levels; \(N\) denotes the total number of samples.  
For Well 2020-Taiguan2, the RMSE showed a steady decline from 0.08 in June to 0.042 in May in the following year, with consistently minimal errors. In Well 2022-Taiguan3, the RMSE decreased significantly by approximately 68.5%, dropping from 10.81 in June to 3.41 in May. For Well 2022-Taiguan4, the RMSE reduced from 17.07 to 8.29, exhibiting intermediate fluctuations but eventually stabilizing around 8 m. In Well 2022-Taiguan5, after excluding the January 2023 outlier data that deviated from the water level trend, the RMSE decreased by 37% from 7.77 in June to 4.9 in May, with minor fluctuations (Figure 10). The simulation errors across all observation wells fell within acceptable thresholds, demonstrating consistency with practical hydrological behavior.  
\begin{table}
\begin{tabular}{c c c} \hline
**Zone Number** & **K (m/d)** & **SS (1/m)** \\ \hline
1 & 1.00 \(\times\) 10\({}^{-2}\) & 6.00 \(\times\) 10\({}^{-10}\) \\
2 & 9.00 \(\times\) 10\({}^{-2}\) & 5.60 \(\times\) 10\({}^{-6}\) \\
3 & 6.50 \(\times\) 10\({}^{-2}\) & 1.00 \(\times\) 10\({}^{-12}\) \\
4 & 3.00 \(\times\) 10\({}^{-2}\) & 1.00 \(\times\) 10\({}^{-5}\) \\
5 & 2.10 \(\times\) 10\({}^{-3}\) & 4.00 \(\times\) 10\({}^{-9}\) \\
6 & 2.30 \(\times\) 10\({}^{-2}\) & 1.00 \(\times\) 10\({}^{-5}\) \\
7 & 1.90 \(\times\) 10\({}^{-2}\) & 2.00 \(\times\) 10\({}^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: Optimal hydrogeological parameters.  
Figure 10: Root mean square error (RMSE) of observed and calculated water levels: (**a**) Well 2020-Taiguan2; (**b**) Well 2022-Taiguan3; (**c**) Well 2022-Taiguan4; and (**d**) Well 2022-Taiguan5.
## 5 Acoustical Measurements  
### Equipment  
In S. Dominic of Foligno the measurements were undertaken during three separate campaigns throughout the decades using different methodologies in accordance with the technology available at the time.  
The first measurements were conducted before the refurbishment works in 1986 and then repeated with the same equipment in 1990. They were undertaken by using the following equipment:  
* Pistol shot, as the impulsive source;
* Binaural headphones (Sony DRW70C);
* Digital Audio Tape (DAT) (Aiwa St-1), as a recording receiver.  
In the laboratory the IRs were transformed digitally in. WAV files by using a digital audio board (Multi Wav Digital Pro) and then analysed by using a wave editor (Cooledit 95) in combination with a specific software (MLSSA 10.0C).  
A following survey was performed in 1994, made by using the following equipment:  
* Omnidirectional loudspeaker (LookLine);
* Binaural dummy head (Sennheiser MKE2002 set);
* Personal Computer connected to the LookLine loudspeaker and the receiver.  
A Maximum Length Sequence (MLS), produced by a MLSSA board (A2D160), was used as excitation signal, and the RIRs were obtained after the deconvolution of the deterministic sequences in time domain.  
The measurements taken after the refurbishment works and with the acoustical treatments applied were completed in 2001 and they had the following equipment:  
* Equalised omnidirectional loudspeaker (LookLine);  
Figure 3: Source and receiver positions inside S. Dominic of Foligno.  
* Binaural dummy head (Neumann KU-100);
* B-Format microphone (Soundfield MK-V);
* Personal Computer connected to the LookLine loudspeaker and to the two receivers.  
In this paper, the reference results as measured values are considered those related to the survey performed in 1994, which resulted very useful in photographing the conditions of the church before any acoustical treatment.  
In S. Dominic of Imola the acoustical measurements were undertaken with the following equipment:  
* Equalised omnidirectional loudspeaker (LookLine);
* Binaural dummy head (Neumann KU-100);
* B-Format microphone (Soundfield MK-V);
* Personal Computer connected to the LookLine loudspeaker and to the two receivers.  
The excitation signal to measure the impulse response (IR) of the room was a 20 s pre-equalized exponential sine sweep (ESS) having a frequency range set between 40 Hz and 20 kHz.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 3 Framework Design
### On-OFF Keying-Based Modulation/Demodulation
#### 3.2.2 Demodulation  
In WiFi preamble detection, we can obtain the phase offset values and count the number of stable samples every 320 samples (i.e., length of a ZigBee symbol and 16 ). If the number of stable samples equals to, there should be an extended sinusoidal waveform in this 16 microseconds. Therefore, if WiFi preamble detection finds that there exists an extended sinusoidal waveform in the received signal, it would know that the next 320 samples contain a CTC bit. The CTC bit would be determined by detecting whether there exists the extended sinusoidal waveform in the next 320 samples or not.  
Based on this modulation/demodulation approach, we can transmit 1 CTC bit within 2 ZigBee symbols while ZigBee communication transmits 4 bits within 1 ZigBee symbol. Hence, the throughput of our CTC approach should be the throughput of ZigBee, i.e., 31.25 kbps, which is 1000 times the throughput of FreeBee [8].
# Research on the High Speed of Piston Pumps Based on Rapid Erecting of Launch Vehicles
## 5 Conclusions  
In this work, an improved scheme for the problem of increased cavitation and reduced volumetric efficiency of high-pressure piston pumps at high speeds was proposed by enlarging the overflow area of the cylinder window area. A vertical dynamics model and a numerical model of the internal flow of the piston pump were developed to analyse the volumetric efficiency, flow characteristics and cavitation phenomenon of the improved axial piston pump at high speeds. The main findings are summarised as follows.  
1. The volumetric efficiency and flow rate of the improved cylinder block change slightly at low speeds but increase considerably at high speeds. The maximum speed at which the volumetric efficiency of a traditional axial piston pump remains above 90% is 1500 rpm, while the improved cylinder increases to 2100 rpm. At 2000 rpm, 2500 rpm, 3000 rpm and 3500 rpm, the volumetric efficiency is increased by 23.219%, 54.958%, 64.024% and 78.405%, respectively. The maximum flow rate of the improved piston pump rises from 1.765 kg/s to 2.295 kg/s with different speeds, an increase of 30.028%. When the speed is increased over 1500 rpm, the flow pulsation of the improved cylinder is also greatly reduced.
2. The improved block can effectively inhibit the cavitation and backflow of the piston chamber. When rotational speeds at 2000 rpm, 2500 rpm and 3000 rpm, the peak gas phase volume fractions of the improved block are reduced by 3.508%, 9.423% and 6.962%, respectively, and the peak vapour volume fractions in the piston chamber are reduced by 0.492%, 1.498% and 4.250%, respectively, while the effective transport rates are increased by 11.064%, 23.208% and 22.401%, respectively, compared to the traditional blocks. Backflow and cavitation in the piston chambers will be reduced, which will help to increase the volumetric efficiency of the piston pump, reduce flow pulsation and reduce the vibration and noise of the pump body, resulting in smoother hydraulic system performance.  
Methodology, software and writing--original draft preparation, M.H.; conceptualisation, supervision and project administration, Y.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Figure 15: Flow rate of traditional and improved piston chamber at 2000 rpm.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors acknowledge [PERSON] for his help in building numerical models.  
**Conflicts of Interest:** The authors declare no conflict of interest.