esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applied Method of Instrumentation  
In this chapter, practical examples of instrumentation methods are presented to determine the main parameters or performances of engine components. The methods were implemented on the Klimov TV2-117A, a free turbine turboshaft, operated in a test bench, as shown in Figure 7. The instrumentation method was used to determine the air mass flow, the compressor total adiabatic efficiency and the turbine total adiabatic efficiency.  
In the case of the mass air flow, the engine bellmouth, shown in Figure 8, was fitted with four static pressure probes (\(p_{s}\)), and, through a differential pressure transducer in relation to the cell air pressure (\(p_{0}\)), the dynamic pressure (\(DP\)) was measured. The bellmouth diameter is 0.29 (m).  
Figure 7: TV2-117A installed on test bench.  
In the case of the compressor adiabatic total efficiency, the first step was to determine the variants of points where the instrumentation probe could be mounted, without making major auxiliary modifications. Then, taking the compressor technical possibilities of being instrumented into account, it was established that the outlet total pressure (\(p_{2,t}\)) and total temperature (\(T_{2,t}\)) can be measured at the entry of the combustion chamber's secondary flow.  
Therefore, in this case, the instrumentation was performed by removing two small mounts fixed on the combustion chamber entry section, and it was modified to transform them into instrumentation probes, in order to measure the air total pressure and total temperature [12], as presented in Figure 9.  
Figure 8: TV2-117A bellmouth instrumentation.  
Figure 9: Compressor total outlet temperature probe (**a**); compressor total outlet pressure probe (**b**); compressor total outlet temperature probe location (**c**); compressor total outlet pressure probe location (**d**).  
In the case of the turbine adiabatic total efficiency, because there are not even inspection ports on the turbine case, the turbine instrumentation was conducted by mounting the instrumentation probes [12] in the two holes at the exhaust nozzle entry section. The probes were oriented at the turbine rotor in order to measure the outlet total pressure (\(p_{4,t}\)) and the outlet total temperature (\(T_{4,t}\)), as presented in Figure 10. This was possible after small modifications of the holes were made, by mounting the adaptor to connect the instrumentation probes.  
In the case of the jet nozzle, this method will be implemented to determine the engine thrust of a Tumansky R-11F2-300, (Tumansky, in URSS (Russia)), turbojet engine, installed on the test bench, as presented in Figure 11, a particular and internal application of the Romanian National Institute of Gas Turbine Engines (COMOTI).
# A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport
## 5 Conclusions  
In this study, a deterministic methodology using smart card data and the path finding algorithm was proposed to predict ridership on new bus and subway lines. The proposed methodology was applied to actual public transport lines in Seoul, the capital of South Korea, and it was validated through the observed traffic volume of the lines recorded in the smart card data. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data. However, it was found that a more detailed consideration of subway usage preference and transfer behavior was needed in the process of traffic assignment. This study shows that it is possible to predict ridership with high accuracy using the abundant amounts of high-quality data and simple assumptions without the complex modeling of route choice and probabilistic traffic assignment.  
However, this study has some drawbacks. The proposed methodology redistributes individual passengers to the updated network considering only the demand derived from the smart card data. It does not take into account passengers who do not use smart cards, pass holders, and potential consumers who do not currently use public transport. This study also assumes that the smart card data do not contain errors. Due to missing records and fare avoidance [31], smart card data can underestimate demand. Therefore, future research considering a methodology that can supplement the fundamental limitations of the AFC system and a methodology that can estimate potential demand other than smart card data is required.  
Writing--review and editing, M.L.; visualization, I.J.; project administration, C.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (21 NSIP-B135746-05) from the National Spatial Information Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.  
Not applicable.  
The authors declare no conflict of interest.
## 7 Evaluation
### Standard Metrics  
BLEU (bilingual evaluation understudy) [40] was one of the first metrics in machine translation and remains one of the most popular. Since we use approaches originally defined for machine translation for normalization, we used the same evaluation metrics for evaluating normalization. BLEU is defined as  
\[\text{BLEU}=\min\biggl{\{}1,\frac{\text{len}_{e}}{\text{len}_{r}}\biggr{\}} \cdot\left(\prod_{i=1}^{4}\text{prec}_{i}\right)^{\frac{1}{4}}, \tag{11}\]  
where \(\text{len}_{e}\) is the length of the evaluated segment and \(\text{len}_{r}\) is the length of the reference segment. In the normalization task, in most cases, \(\text{len}_{e}=\text{len}_{r}\), as it is rare for one spoken word to have a standard form of more than one word, or for more than one spoken word to map to a single standard form. \(\text{prec}_{i}\) is the ratio between the number matched \(N\)-gramsand the number of total \(N\)-grams of order \(i\) between the reference normalization and the evaluated normalization.  
WER (word error rate) has been the standard evaluation metric for speech recognition since its inception and was later adapted for various specific tasks [41]. WER (word error rate) in its original form is defined as the Levenshtein distance between the evaluated segment and the reference:  
\[\text{WER}=\frac{S+D+I}{N}. \tag{12}\]  
\(S\) denotes the number of substituted words, \(D\) denotes the number of deleted words, and \(I\) denotes the number of inserted words. \(N\) is the length of reference. WER does not distinguish between a normalization result that has only one or a few wrong characters and a normalization result that is entirely wrong. CER (character error rate) is a more fine-grained metric, as it is defined as the Levenshtein distance at the level of characters. For the same reason, we also added the character \(N\)-gram F-score (chrF) to the evaluation metrics pool [42].
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 2 Methods
### One-vs.-One Strategy  
Multi-class semantic segmentation problems, like any other multi-class problem, can be decomposed into multiple binary semantic segmentation ones. OVO [30] is one of the most the most common strategies to decompose multi-class problems. Considering  
Figure 1: U-net architecture [40] adapted to a multi-class semantic segmentation problem, including building footprints and road networks. Note that the final \(1\times 1\) convolutional layer performs a classification into 3 classes since the background is also considered.  
a scenario with \(N\) classes, the OVO decomposition scheme divides it into \(N(N-1)/2\) binary sub-problems. At training time, only those pixels corresponding to one of the two discriminated classes are taken into account, ignoring the rest. Accordingly, pixels corresponding to ignored classes do not impact the loss function (but are considered for convolutions). In this work, given the problem statement, three binary models are learned: building-vs.-road, building-vs.-background, and road-vs.-background, as shown in Figure 2.  
Although we have decomposed a complex multi-class problem into a priori multiple easier binary ones, drawbacks exist. For each pixel in the image, the outputs of the binary models must be aggregated to build up the multi-class prediction. To simplify the notation, for a given pixel, we use \(P_{ij}\) to denote the confidence of the binary semantic segmentation model discriminating classes \(i\) and \(j\) in favor of the former one. As shown in Figure 2, each binary model contributes to the confidence of the positive class (\(i\)) with \(P_{ij}\) and to the confidence of the negative class (\(j\)) with \(1-P_{ij}\). To finally aggregate these confidence values, the weighted voting approach [41] is considered, that is, the class with the largest sum of confidence values is predicted in each pixel.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Effects of the Initial Solution  
#### 5.2.1 Speed of Convergence  
In the study conducted on R206, 100 customer points were used as an example. Initial solutions were created through random generation, random insertion, and the INNC algorithm. The population size was set at 50 with 300 iterations. Figure 4 displays the optimal iteration graph of the model. Analysis of the graph reveals that the random generation method (RGM) stabilizes around the 282 nd iteration, the random traversal insertion method (RTIM) stabilizes around the 217 th iteration, and the INNC method stabilizes as early as the 103 rd iteration. The INNC algorithm demonstrates faster convergence compared with the random generation and random traversal insertion methods. This is because the random generation method directly generates a certain number of individuals as an initialization population, and the genes of each individual are randomly generated without any specific law or order; the random traversal insertion method randomly selects an individual as a starting point in an empty population, and then randomly selects the next individual to be inserted into the population until the number of populations reaches a certain size, generating a complete path. In contrast, the INNC method is used to initialize the population, which generates the initialized population according to the characteristics and constraints of the problem, selects the customer closest to the distribution center as the starting point, then calculates the distance between the remaining customer points and the last customer point in the current path and arranges them according to the ascending method. It then determines whether the constraints are satisfied after insertion. Firstly, initializing the population in this way makes it able to directly generate feasible solutions that satisfy the constraints, making it faster to approach the optimal solution, which is conducive to the algorithm finding better solutions faster in the subsequent evolution process; secondly, under the premise of satisfying the constraints, it is able to select the customer point with the smallest insertion cost at each step, resulting in a better initial solution.  
Figure 4: _Cont._
## 2 Materials and Methods  
### Study Area  
The study area was around an artificial reef system established by the Hokkaido prefectural government in 2017, located 10 km off the south of Rishiri Island (45\({}^{\circ}\)10\({}^{\prime}\) N, 141\({}^{\circ}\)13\({}^{\prime}\) N), Hokkaido, in the northernmost part of Japan (Figure 1a). The AR system comprised 3 artificial tower reefs made of steel (10 m by 10 m, 20 m tall) in the center and 156 cube reefs made of concrete (3 \(\times\) 3 \(\times\) 3 m) sporadically deployed in the outer circular zone (Figure 1b,c). Samplings were conducted at three stations (A1, A2, and A5) in the study area (Figure 1d). A1 was located at the innermost side of the circular zone; in contrast, A2 was located at the outermost side, while A5 was a non-reef station 150 m off the outer edge of the circular zone. Detailed information on the position and bottom depth of each station is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **Latitude (N)** & **Longitude (E)** & **Water Depth (m)** \\ \hline A1 & 45\({}^{\circ}\)02\({}^{\prime}\)32.600\({}^{\prime\prime}\) & 141\({}^{\circ}\)07\({}^{\prime}\)54.368\({}^{\prime\prime}\) & 90.9 \\ A2 & 45\({}^{\circ}\)02\({}^{\prime}\)34.220\({}^{\prime\prime}\) & 141\({}^{\circ}\)07\({}^{\prime}\)55.028\({}^{\prime\prime}\) & 91.7 \\ A5 & 45\({}^{\circ}\)02\({}^{\prime}\)39.079\({}^{\prime\prime}\) & 141\({}^{\circ}\)07\({}^{\prime}\)54.848\({}^{\prime\prime}\) & 93.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The position and bottom depth of the sampling stations (A1, A2, and A5).
## 3 Results
### Target Coverage  
The results of the planning study for target coverage of the three selected techniques are shown in Table 3. All target volumes (DIL and PTV) could achieve the planning goals as expected. A visual representation of the target coverage for different parameters among the three techniques is depicted in Figure 2a,b. Figure 2: Graphics comparison of median target coverage parameters for PTV-DIL (PTV-d) (Panel a), PTV-prostate (PTV-p) (Panel b), and organ at risks (OARs) for the three considered techniques (Panel c). The greyed-out part in Panel c denotes the histograms for the bladder. Regarding PTV parameters, the comparison of median values of DVH metrics both for PTV-p and PTV-d show an increase (\(p<0.05\), Table 3) from UH-DWA to CK-95% to CK-85% in almost all considered indexes. The median CI of PTV-p is 1.00 for the three considered techniques, while a statistically significant increase (\(p<0.05\)) is observed for PTV-p in CK-85% if compared with both CK-95% and UH-DWA (0.991, 0.984, and 0.978). An example of dose distribution can be found in Figure 3; the maximum dose points are all located in the DIL.
# Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil
## 1 Introduction
[30] investigated the feasibility of the incorporation of SVM-based models with metaheuristic algorithms, and showed that the cuckoo search optimization (CSO) enhances the accuracy of the least squares support vector machine (LSSVM). It was also revealed that the ensemble of CSO-LSSVM (R\({}^{2}\) = 0.885 and RMSE = 0.082) performs more efficiently than the ANN and the regression tree (RT) for predicting the SSS. Likewise, [PERSON] et al. [7] explored the combination of particle swarm optimization (PSO) along with the support vector regression (SVR) and found that the suggested ensemble is a robust model for the design aims. This model also surpassed the benchmark models of ANN, RT, ANFIS, and multivariate adaptive regression splines. The applicability of PSO and a genetic algorithm for optimizing the ANFIS, and for approximating the clay SSS of two bridge construction projects (in Vietnam) was examined by [PERSON] et al. [31]. Their findings showed that both hybridized ANFIS networks perform more successfully than the SVR and ANN. The present study fills the gap of knowledge related to employing novel hybrid techniques for optimizing the performance of an artificial neural network toward indirect measurement of the SSS. Despite the wide application of popular optimization algorithms like the PSO and GA, the authors did not come across any previous research focusing on the robustness evaluation of recently developed algorithms like the spotted hyena optimizer (SHO) and ant lion optimization (ALO) in the aforementioned fields. Hence, these algorithms are applied to an ANN to optimize its computational weights and biases when it is utilized to predict the SSS using real-world soil information.
## 2 Data Processing
### Data Filtering and Processing  
There are duplicate values, missing values, and error values in the road section travel speed data, so we needed to filter and process the road section travel speed data according to the actual situation of the road section travel speed and data processing methods. The cumulative distribution curve of the road section travel speed showed that the travel speed cumulative frequency was larger in September than in August (see Figure 3). The data showed that a very small proportion of the travel speed data exceeded 80 km/h (speed limit) and, therefore, each month's data needed to be filtered and processed for outliers based on the threshold method and Pauta criterion [23; 24; 25]. The road section travel speed data for each month were filtered and processed at 10 min intervals, the filtering and  
Figure 2: Beijing 3 rd Ring Road location information.  
processing process of the road section travel speed data is illustrated in Figure 4, and there were 99,023 records of valid data (94% data share).  
\[\overline{v}_{T,m}=\frac{\sum\limits_{i=1}^{n}{v_{T,m,i}}}{n} \tag{1}\]  
\[v_{T,m,\sigma}=\sqrt{\frac{\sum\limits_{i=1}^{n}{\left({v_{T,m,i}-\overline{v} _{T,m}}\right)}}{n}} \tag{2}\]  
where \(\overline{v}_{T,m}\) denotes the average travel speed of road section \(m\) in time interval \(T\) (km/h), \(v_{T,m,i}\) denotes the travel speed of road section \(m\) in time interval \(T\) (km/h), \(n\) denotes the number of sample records of the travel speed of road section \(m\) in time interval \(T\), and \(v_{T,m,\sigma}\) denotes the standard deviation of the travel speed of road section \(m\) in time interval \(T\) (km/h).
## 2 Wired _vs._ Wireless  
WSN can operate in a wide range of environments and provide advantages in cost, size, power, flexibility and distributed intelligence, compared to wired ones. In a network, when a node cannot directly contact the base station, the message may be forwarded over multiple hops. By auto configuration set up, the network could continue to operate as nodes are moved, introduced or removed. Monitoring applications have been developed in medicine, agriculture, environment, military, machine/building, toys, motion tracking and many other fields. Architectures for sensor networks have been changing greatly over the last 50 years, from the analogue 4-20 mA designs to the bus and network topology of today. Bus architectures reduce wiring and required communication bandwidth. Wireless sensors further decrease wiring needs, providing new opportunities for distributed-intelligence architectures [2,10,20].  
For fieldbus architecture, the risk of cutting the bus that connects all the sensors persists. WSN eliminates all the problems arising from wires in the system. This is the most important advantage of using such technology for monitoring.  
Wireless sensor technology allows Micro-Electro-Mechanical Systems Sensors (MEMS) to be integrated with signal conditioning and radio units to form \"motes\" - all for a low cost, a small size, and with low power requirements. Available MEMS include inertial, pressure, temperature, humidity,strain-gage, and various piezo and capacitive transducers for proximity, position, velocity, acceleration and vibration measurements [20]; and according to several research works, connecting wires to these devices can be more problematic than doing it by means of wireless designs [21,22].  
Motes can form networks and co-operate according to various models and architectures. They came with miniaturized sensors mounted, that allow, in a small space (\(2.5\times 5\times 5\)cm), the gathering of data not only just about temperature, but also relative humidity, acceleration, shock and light [23].  
Another advantage for wireless sensor devices is the feasibility of installation in places where cabling is impossible, such as large concrete structures [24] or embedded within the cargo, which brings their readings closer to the true in situ properties of perishable products [25].  
Wired networks are very reliable and stable communication systems for instruments and controls. However, wireless technology promises lower installation costs than wired devices, because required cabling engineering is very costly [26].
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 4 Simulation Results
### Impact of AP Locations
#### 4.2.1 Performance of the Metric of Single Measurement  
From Figure 8, we can find that the smaller distance between the \(i\)-th real location and fourth AP generally results in the lower error bound. Based on this, we can optimize the AP locations towards the lowest error bounds.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Data Sources
#### 3.1.2 Wind Data  
In this study, we used wind data from the European Center for Medium range Weather Forecasts (ECMWF). The wind fields (wind speed and wind direction) were taken from the ECMWF operational model analyses at a spatial resolution of 0.25\({}^{\circ}\) (For the analyzed area, a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) translates to \(\approx\)23.4 \(\times\) 27.6 km\({}^{2}\)), a temporal resolution of 6 h, and an altitude of 10 m. It was shown in [11] that wind data at a 10 m altitude are an optimal choice for the task of ship-plume matching. Starting from the product version upgrade from 1.2.2 to 1.3.0, which took place on 27 March 2019, the ECMWF 10 m wind data for coinciding time are available as a support product in the TROPOMI/S5P data file.
## 3 Experimental Results and Analysis
### Evaluation Metrics  
To assess the calibration performance of the model, four traditional evaluation metrics were used: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). Smaller values of MAE, RMSE, MSE, and MAPE indicate higher accuracy of the calibration model. The corresponding formulas are as follows:  
(1) Mean Absolute Error (MAE):  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{13}\]  
(2) Root Mean Squared Error (RMSE):  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{14}\]  
(3) Mean Squared Error (MSE):  
\[\text{MSE}=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2} \tag{15}\]  
Figure 6: The correlation between pollutant monitoring data and meteorological factors.  
Mean Absolute Percentage Error (MAPE)  
\[\text{MAPE}=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right| \tag{16}\]  
In the formula, \(n\) represents the total number of calibration values, denotes the actual PM\({}_{2.5}\) values, and \(\hat{y}_{i}\) denotes the calibrated PM\({}_{2.5}\) values.