esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Monitoring Analysis of Spatiotemporal Effect  
#### 3.1.1 Horizontal Displacement of Diaphrag Wall  
The maximum horizontal displacement of the enclosure wall monitoring volume during pit excavation is shown in Figure 5. According to the on-site monitoring results, the maximum horizontal displacement of the wall of the west end well and the standard section gradually increased with the increase in the excavation depth and sequence. The maximum horizontal displacement of the wall at the west end well was close to 70 mm, and the maximum displacement of the wall at the standard section reached approximately 80 mm, both of which exceeded the allowable limit of 0.3%H specified by JGJ 120-2012. These exceedances indicated a potential risk to the stability of the foundation pit and may have adverse effects on the surrounding environment, necessitating further assessment and mitigation measures. The displacement of the diaphragm wall in the west end well exhibited significant changes as excavation approached the bottom of the pit, with deformation accelerating during the final excavation layer, indicating an increased risk of structural instability. In contrast, the maximum horizontal displacement of the diaphragm wall in the standard section demonstrated a linear correlation with excavation depth, with a maximum average displacement rate of 4.85 mm/m. After the excavation was completed, the deformation of the diaphragm wall continued to increase, highlighting the time-dependent  
\begin{table}
\begin{tabular}{c c c c} \hline
**Retaining Structure** & \(\gamma_{\text{r}}\) & **Modulus** & **Poisson/\(\
u\)** \\  & **kN/m\({}^{3}\)** & \(E\)/kPa** & **Poisson/\(\
u\)** \\ \hline Diaphrag wall & 25 & \(40\times 10^{6}\) & 0.200 \\ Crown beam & 25 & \(32\times 10^{6}\) & 0.167 \\ Concrete support & 25 & \(32\times 10^{6}\) & 0.200 \\ Steel support & 79 & \(20\times 10^{7}\) & 0.300 \\ \hline \end{tabular}
\end{table}
Table 3: Retaining structure parameters of Huaxi Park Station.  
behavior of mucky soil. These monitoring results confirm that the deformation of mucky soil progressively accumulates over time during excavation, emphasizing the need for continued monitoring and assessment to ensure long-term stability.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### Off-Line Evacuation Wayfinding Algorithms
#### 3.1.1 Cellular Automata Model-Based Algorithms  
Cellular automata models discretise a given structure into uniform \"cells\" where each cell can hold one person. This approach can precisely model the influence of an individual's physical dimensions, but is ineffective at depicting the movement speed and direction, due to the discrete spatial structure [63]. The physical conditions and the movement patterns of evacuees are normally determined by a set of local rules at each cell (one drawback is that it is relatively difficult to customise the physical attributes of each individual civilian). Since this model can effectively mimic the interactions between the environments and the pedestrians, many studies have utilised this microscopic model to simulate the pedestrian dynamics during evacuations in the last two decades. In these models, evacuees are considered as either homogeneous with identical physical attributes (e.g., gender, age, mobility, psychology) or heterogeneous individuals with different characteristics. For instance, the research in [64] utilised a homogeneous cellular automata model to investigate the exit dynamics of evacuees in a room with different numbers of exits; the arching behaviour, which is a signature of jamming that happens when the exits are overused, was observed near the exits; a \"power law behaviour\" was also found: when the exit door can evacuate more than one evacuee at a time, the evacuees will escape from the room in bursts of various sizes. In [43], a heterogeneous cellular automata model mimicked the evacuation process in a retirement house; evacuees initially belonged to three groups (middle-aged people, nursing staff and older people), and groups were also formed dynamically due to the follow-the-leader effect. In [44], grouping behaviours in evacuations were induced by introducing \"bosons\" into cells of the floor field cellular automaton [65]; bosons were placed by evacuees as markers to increase the probability for other group members to reach some particular cells. The resulting simulations indicated that the evacuation time decreased with the increasing number of groups.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### The Comprehensive Optimization of the Rotor's Key Parameters
#### 3.3.3 Brief Discussion on Different Optimization Methods  
Here, the output torque ripple optimization method and results are compared with other methods mentioned in the references, as shown in Table 4.  
1. Reference [3] analyzes an IPMSM machine which has a similar V-type magnets topology with the machine in this paper, but a slightly different pole-slot fit. Reference [9] optimizes an IPMSM machine by using asymmetric rotor topology. Overall, the torque ripple is larger than the that obtained in this paper.
2. References [5; 7; 8] present the optimization of IPMSM machines with I-type magnet location. As can be seen, the torque ripples can be significantly reduced after the optimization, which, however, are still larger than that present in this paper, except for the machine in [7]. It should be emphasized that the complex hybrid Ferrite and rear-earth permanent magnets are employed in [7] to reduce the torque ripple, which, meantime, will cause greater machine cost and much more manufacturing difficulties, and consequently degrade its potential in industrial applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Machine Type**} & \multirow{2}{*}{**Optimization Method**} & \multicolumn{2}{c}{**Torque Ripple (\%)**} \\ \cline{3-4}  & & **Original** & **Optimized** \\ \hline V-type IPMSM & & & \\ (this paper) & & & \\ \hline V-type IPMSM [15] & Pole and slot matching & 61 Nm (8.2\%) & 29.2 Nm (4.6\%) \\ \hline V-type IPMSM [19] & Asymmetrical rotor & 1.29 Nm (12.5\%) & 0.44 Nm (4.38\%) \\ \hline I-type IPMSM [16] & Rotor topology & 2.15 Nm (12.55\%) & 2.04 Nm (6.95\%) \\ \hline I-type IPMSM [17] & Rotor topology & 44.6 Nm (56.2\%) & 26.3 Nm (2.8\%) \\ \hline I-type IPMSM [18] & Computationally efficient FEA & 1.61 Nm (10.6\%) & 1.02 Nm (4.9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of optimization methods presented in references.  
Figure 12: Comparison of the torque performances when 250 Arms is applied.
## 5 Simulation Results
### Experimental Setup  
As shown in Table 3, we develop six Walker Delta constellations A-F with different orbital elements. A number of targets are generated randomly around the world according to two distributions: low and high density. The average distances between targets are around 10 km and 100 km in the high-density and low-density cases, respectively. Moreover, the observation time for each target is generated randomly between the long-term [200 s, 300 s] or short-term [40 s, 60 s], while the profit of each target takes a random integer value from 1 to 10.  
We assume that the scenario begins from the time of [1 October 2022 00:00:00 UTCG] and the horizon is set as 6 h. The VTWs between satellites and targets as well as the look angle on the three axes can be obtained directly from the software STK. The experiments are conducted on three types of texting instances: _small_, _medium_, and _large_, as shown in Table 4. There are 3 \(\times\) 16 = 48 combinations in total and we generate 10 texting cases for each combination.  
We compare the proposed PI-based distribute schedule approach, namely the PIDSM, with the existing algorithms, and the performance metrics are defined as follows:  
* \(\delta\): The average proportion of successfully assigned targets;
* \(\varphi\): The average communication times, which implies the communication burden between satellites;
* The relative percentage value (RPV): \[RPV=\mathrm{Y}_{a}/\mathrm{Y}_{best}\] (22) where \(\mathrm{Y}_{a}\) is the fitness of an algorithm for a test instance, and \(\mathrm{Y}_{best}\) is the best fitness value obtained by all algorithms for the same instance.  
To reduce randomness, each algorithm is independently run 10 times for each testing instance, then \(\delta\), \(\varphi\), best \(RPV\) (\(bRPV\)), and average \(RPV\) (\(aRPV\)) are used to evaluate each algorithm. All algorithms are coded in MATLAB 2021a and run on a PC with an Intel Core i9-9900K CPU @3.60 GHz and 32 GB of RAM in the 64-bit Windows 10 operating system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instance Type** & **Constellations** & **Task Number** & **Distribution Density** & **Observation Time** & **Combination Number** \\ \hline small & A, B & 3, 5 & low, high & long-term & \(2\times 2\times 2\times 2\times 2=16\) \\ medium & C, D & 30, 50 & low, high & long-term, & \(2\times 2\times 2\times 2\times 2=16\) \\ large & E, F & 100, 200 & low, high & long-term, & \(2\times 2\times 2\times 2\times 2=16\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter size for each instance type.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constellation** & **Altitude (km)** & **Inclination (deg)** & **Planes** & **Satellites** \\ \hline A & 6000 & 60 & 2 & 6 \\ B & 5000 & 53.8 & 3 & 9 \\ C & 3000 & 60 & 4 & 16 \\ D & 2800 & 70 & 4 & 20 \\ E & 2500 & 65 & 4 & 32 \\ F & 2000 & 60 & 5 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Satellite constellations used in the simulation.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.3 Power  
The power subsystem was designed such that it could provide continuous power to the entire vehicle for a maximum of four days, which is the maximum mission duration, and power the capsule for an additional three days, in case of an abort. These duration were discussed in the life support section. Taking this into account, the capsule and the propulsive stage are both equipped with their own separate means of power generation.  
When estimating the power requirements of the vehicle, data from other manned launch vehicles was used, such as the space shuttle and Orion. The active heating and cooling is quite minimal in the life support system, which decreased the estimated power requirements of the vehicle. The entire vehicle would have a maximum power requirement of approximately 8 kW, with the capsule on its own having a power requirement of 6 kW, in case of an abort.  
The propulsive stage is equipped with two combustion engines which consume boil-off from the main propellant tanks to generate power, making use of the Integrated Vehicle Fluids concept [21]. One engine is sufficient to power the entire vehicle, with the second engine being there for redundancy.  
For the capsule, two Proton Exchange Membrane (PEM) fuel cells [22; 23] take fuel from liquid oxygen and liquid hydrogen tanks, creating power for the capsule, as well as drinking water for the crew. Again, one fuel cell has the capacity to power the whole capsule and the other fuel cell is there for redundancy. In total, approximately 25 kg of liquid hydrogen and 201 kg of liquid oxygen is stored in the capsule for the fuel cells.
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 3 Datasets
### imATFIB Dataset  
The datasets were acquired in our in-house performed clinical study, imATFIB--IMaging-based, non-invasive diagnosis of persistent ATrial FIBrilation. The study is registered under the number NCT03584126 at clinicaltrials.gov (accessed on 22 February 2021). The study obtained ethical approval from the local Ethics committee (No. 20117/04.10.2016) and recruited subjects between 2017-2020. All subjects gave their written informed consent to participate in the study. Patients and healthy volunteers underwent cardiological evaluation using ECG and echocardiography, followed by cardiac MRI measurements.  
All subjects were imaged with a 3T whole-body MRI system (3.0T Discovery MR750w General Electric MRI scanner) using a dedicated body coil for signal reception. Cardiac MRI required synchronous cardiac gating with ECG and breath-holding techniques to overcome motion artifacts. The cardiac MRI protocol consisted of dark blood sequences (Black Blood SSFSE), FIESTA cine sequences (ALL FIESTA cine AST), and post-contrast sequences: rest perfusion (FGRE Time Course), angiography imaging (Aorta CEMRA Asset) and LGE/MDE images (2D MDE, 2D PSMDE) in three different planes: short axis, four-chamber, and two-chamber view. The MDE/LGE sequences were acquired with a time delay of 10 min after the injection of gadolinium, showing contrast between normal myocardium (dark) and abnormal myocardium (hyperenhancement). 3D HEART sequence was also acquired and used to assess coronary abnormalities. The participants were constantly monitored during the examination, and no immediate adverse effects were reported.  
Ten datasets of healthy volunteers and ten datasets of patients with atrial fibrillation were selected and the heart was manually traced by two radiologists. The results were annotated manually by a radiologist with more than 10 years of experience and cross-checked by another, senior radiologist, with more than 20 years of experience in the field. The results were exported and stored in DICOM format.  
Within the frame of our in-house performed clinical study, imATFIB, we obtained 20 cardiac MRI volumes with segmentation masks for the whole heart. The reason why the ACDC dataset was deemed a good benchmark is the similarity with the imATFIB dataset in characteristics such as resolution (\(256\times 256\) for imATFIB), inter-slice gaps and slice thickness.
## 2 Materials and Methods
### Quality Control, Read Mapping, and Transcriptome Analysis  
The raw paired end reads were trimmed and quality controlled by FASTQ preprocessor with default parameters [39]. Then clean reads were separately aligned to reference genome with orientation mode using HISAT2 software [40]. The mapped reads of each sample were assembled by StringTie in a reference-based strategy [41]. To identify differential expression genes (DEGs) between two different samples, the expression level of each gene was calculated according to the transcripts per million reads (TPM) approach. RSEM was used to quantify gene abundances [42]. Functional-enrichment analysis including GO, KEGG were performed to identify which DEGs were significantly enriched in GO terms and metabolic pathways at P-adjust \(\leq\) 0.05 compared with the whole-transcriptome background. GO functional enrichment and KEGG pathway analysis were performed by Goatools and KOBAS [43; 44].
## 3 Results
### Active Rectifier
However, dimensioning and simulation has been done by means of Mathworks-Simulink(tm) and is presented in Section 3.4. As a summary of the functions developed for the FPGA, Table 6 shows its control and monitoring algorithms. To reach the optimal commutation point of the MOSFETs, the gate-signals must be slightly advanced. The process used to reach the exact advance time consisted of simulating the system and finding the exact time by means of a test-error method. The FPGA was then programmed, and the MOSFETs' commutations were checked in the prototyped boards. Simulations were carried out with Quartus II(tm) software, observing that the desired advance on the opening signals delivered by FPGA was obtained. Thus, the lag produced by electronic devices was suppressed, and the MOSFETs' commutations were optimal. The designed active rectifier presented an efficiency higher than 98%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Control** & **Monitoring and Alarms** \\ \hline Trigger signals for the rectifier & Fuel level \\ \hline Boost synchronous converter loop & Engine RPM \\ \hline Two-way converter control loop & Cylinder overheating \\ \hline Temperature control loop & Cooling fault \\ \hline Carburettor/injection control loop & SC level of charge \\ \hline XBEET(tm) 8X 868 transmitter management & \\ \hline Pilot configuration: 433 MHz & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Control and monitoring algorithms implemented in the FPGA. Figure 15: Altera® MAX 10 FPGA-10M08 SAU169C8 GES mounted in its development board. If diodes had been used, the voltage drop would have meant a 6% reduction in bus voltage and a power loss of 6.25%, affecting the propulsion system supply. Moreover, the weight would have increased by 100 g, since a three-phase diode rectifier weighs between 150 and 180 g, in comparison with the 40-50 g of the active rectifier including the FPGA. Figure 16 shows the FPGA gate signals. The advanced signal (orange) is compared to the waveform produced by a digital signal generator. The complexity of reaching an optimal commutation can be clearly appreciated.
## 5 Computational Cost Reduction Methods
### AP Selection Methods
#### 5.1.4 Stable AP Method  
This metric is based on the behavior of APs in a time domain. The APs with the highest time variance over the AOI are excluded. In the offline phase, K APs with low time variance over the AOI are selected from L APs. In the online phase, the measured vector, V, is sorted accordingly to determine the MS's position. The stability of APs \(\zeta^{i}\) can be calculated as shown in Equation (8):  
\[\zeta^{i}=\ \frac{1}{T-1}\sum\
olimits_{l=1}^{T}{\sum\
olimits_{j=1}^{M}{(r_{j}^ {i}(t_{l})-\psi_{j}^{i})}^{2}} \tag{8}\]  
In the literature, we can find other techniques for the selection of APs, such as Bhattacharyya distance, Information Potential (IP), Information Gain (Info-Gain), Entropy Maximization, and Group Discrimination (GD) [22].
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### 5-Fold Cross-Validation  
Although the dataset we constructed is larger than that used by other researchers, it is still smaller than the common semantic segmentation dataset. Therefore, in order to prevent the over-fitting problem, we use a 5-fold cross-validation method.  
We divide the training set into five parts and select one part as the validation set, in turn, while the remaining four as the training set so that a total of five training sessions are performed. In theory, each image in the dataset will be selected as the validation set. Compared with only a small part of the images can be used as the verification set, the 5-fold cross-validation can ensure that each image in the training set will be used as the verification set, which can further verify the generalization ability of the model and prevent the randomness of the selected validation set from affecting the results.  
In order to verify the ability of the SCCD-D32 model, we choose the SCCD-16, SCCD-64, U-Net, and SegNet models for 5-fold cross-validation. The average results are shown in Table 2. M-train_loss represents the average training losses obtained from five training sessions; m-valid_loss represents the average verification losses obtained from five training sessions; the rest of the evaluation criteria are the average values of test results obtained after five training sessions. It can be seen from the table that SCCD-D64 has the lowest average training loss, and SCCD-D32 and SCCD-D64 have both the lowest average verification loss. In the test evaluation indicators, SCCD-D32 gets the highest m-Precision, m-Dice and m-IoU, and SCCD-D64 get the highest m-Recall and m-F-score. The results of the 5-fold cross-validation are basically consistent with the results obtained in Table 1. SCCD-D32 and SCCD-D64 both show better crack segmentation performance than other models, while SCCD-D32 saves a lot of Parameters and FLOPs compared to SCCD-D64. Therefore, we choose SCCD-D32 as the best model.
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 3 Results and Discussion
### The Application of ANN to Predict Water Quality under Future Climate Change and Nutrient Management
#### 3.2.2 Prediction of Future Water Quality  
Under future climate change and nutrient management scenarios, the predicted average annual Chl-a concentrations were in the range of 15-16 \(\upmu\)g L\({}^{-1}\). CYANO is the major component of total Chl-a [68], with the optimal temperature for growth above 25 \({}^{\circ}\)C [69]. In the future climate scenario, the temperature is 0.27-7.84 \({}^{\circ}\)C, which is much lower than the 25 \({}^{\circ}\)C that occurred in the summer during 2002 and 2015, so the predicted Chl-a concentrations are lower than the peak values during 2002 and 2015 in Figure 4. Phosphorus is also important for Chl-a growth [70]. In the future nutrient management scenario, phosphorus loads from Detroit and Maumee River are decreased, resulting in lower Chl-a concentrations than those during 2002 and 2015.  
Based on the guideline values for recreational utility proposed by the World Health Organization in 2003 [71], shown in Table 1, the moderate-risk category is identified as related \(>\)50 \(\upmu\)g L\({}^{-1}\) of Chl-a. The probability of adverse health effects of future 10-year water quality in WLE would be moderate. To eliminate the adverse health effects, more intense water resource management would be necessary for the future; for example, making more intense phosphorus load reduction strategies for Maumee River than the targets. Additionally, applying agricultural best management practices (BMP) on the Maumee watershed, such as cover crops and filter strips. In our future work, we will predict the water quality of Lake Erie with the application of BMP on the Maumee watershed with the ANN approach. Additionally, we could predict physical features of Lake Erie, such as water temperature, by applying the ANN approach. There are also some limitations in future work, such as the lack of continuous data required for the establishment of ANN.