esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Experimental Procedure  
The TPC chamber was evacuated over 24 h, down to rough vacuum of 10\({}^{-3}\) mbar. The chamber was then filled to 1100 mbar with N6.0 GAr (99.9999% pure). GAr was continuously flushed through the detector volume, at flow rate of approximately 5 L/min for the duration of the THGEM studies, mitigating any outgassing effects that would otherwise degrade purity. Purity, measured using the slow scintillation component--\(\tau_{2}\) lifetime--was monitored throughout, as described in Section 3.5.  
EMCCD camera settings were kept consistent throughout the duration of the experiment--operating with 4 \(\times\) 4 binning, in full frame mode, with an exposure time of 50 ms. The EMCCD gain value was set to its maximum value of 1000. In this configuration, the EMCCDs had a readout rate of 15 Hz. This rate resulted in approximately 30 alpha GAr interactions recorded in a single image. The cameras were air cooled to less than \(-60\) Celsius before recording to minimise sensor noise.  
The TPC drift field was established between the cathode, operated at a bias of \(-2.5\) kV, and the G-THGEM/THGEM bottom electrode, which was grounded. All field shaping rings were connected with a 100 M\(\Omega\) resistor chain. The THGEMs were left unbiased and grounded for a 24 h period (in order to be fully discharged) before measurements were taken. Characterisation began with a study into the dielectric charging behaviour of the G-THGEMs/THGEM. During measurements, biases were applied to the detector in a consistent manner. First, the cathode bias was applied, establishing the drift field. Next, the EMCCD was set to record data. Once the EMCCD had begun recording data, the THGEMs top electrode was biased to near their pre-established breakdown voltage (see Table 2). This pre-established breakdown voltage was determined experimentally before the 24 h discharge period, by slowly increasing the top electrode bias until THGEM discharges occurred.  
Charging was investigated, through imaging alpha GAr interactions, over a 30 min window. Care was taken to ensure that the position of the alpha source within the TPC was consistent between runs. By studying the S2 light intensity variation over time, the dielectric charging behaviour of the THGEM can be inferred. After this 30 min charging period, a THGEM bias scan characterisation study was undertaken.  
The THGEM bias was reduced from near its predetermined breakdown voltage in 50 V intervals down to 1000 V (at this point, the alpha tracks are barely discernible from the EMCCD sensor noise). For each THGEM bias, 2000 EMCCD images were recorded. Investigation of THGEM charging behaviour before the characterisation was a deliberate choice in order to maximally charge each THGEM before bias characterisation. The results of both experiments can be seen in Section 4.
# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 1 Introduction
The main contributions and objectives of the work include the following:  
* Using the information gain technique for initial feature selection to rank the features in the credit card dataset, only the top-ranked features are fed into the GA wrapper to reduce the search space and enhance the classification performance. * Secondly, the GA wrapper is employed to select the best feature subset that results in optimal classification performance, and the ELM is employed as the learning algorithm in the GA wrapper. * Additionally, this study employs the G-mean as the fitness function in the GA wrapper instead of the conventional accuracy evaluation criterion, ensuring the recognition rate of the minority samples is considered and improved. The rationale behind this approach is that the initial IG-based feature selection and ELM's ability to produce promising performance while converging faster than traditional neural networks could reduce the computational complexity of the GA and improve the classification performance. The ELM is chosen as the learning algorithm in the GA wrapper because it converges far more rapidly and achieves higher generalization performance than conventional neural networks. At the same time, its learning process is thousands of times quicker than neural networks trained via backpropagation [22]. Furthermore, for convenience, the proposed hybrid approach is called IG-GAW. It would be compared with the conventional ELM classifier, an ELM classifier with IG-based feature selection (IG-ELM), the GA wrapper (GAW), and well-performing methods in related literature. The rest of this paper is structured as follows: Section 2 presents related works, focusing on feature-selection methods in the literature. Section 3 discusses the dataset and algorithms used in this study. The proposed credit card fraud-prediction approach is introduced in Section 4. Section 5 presents the results, while Section 6 concludes the paper and provides appropriate future research directions.
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 2 Materials and Methods
### Proof of Concept  
The XoSoft Gamma prototype, Figure 1, was developed within the XoSoft European project [87], and has been extensively demonstrated in [24]. The exosuit, which generates assistive forces that are applied to the lower limbs, was developed as an open platform for rehabilitation. It was designed using a User-Centred Design approach that emphasizes testing and experience gained at each stage of the development/prototyping. The flexibility of the systems (hardware and software), components and the design of the XoSoft Gamma exosuit itself, gave it a very high degree of reconfigurability, and although initially designed for rehabilitation it was easily modified to form a daily assistive device (or in the specific case, as a resistive device), featuring high modularity and reconfigurability aspects also in the control. The modularity, reconfigurability and capacity for repurposing inherent in the design of the XoSoft exosuit means that it can support/resist actuation of different joints (i.e., hips, knees and ankles), unilaterally or bilaterally, depending on the users' needs. Moreover, the control strategy can be programmed to meet the demanded level of assistance (or resistance) requested by the user. Finally, the XoSoft exosuit garment is one-size-fits-all based on loose trousers (to allow a quick donning and doffing) with webbing elements for the fixation to the body, and actuator attachments along the lower limbs. These actuator attachments are used to install, on demand, the actuators on the body segments. One of the main defining characteristics of the XoSoft exosuit is the use of a quasi-passive actuation (QPA) principle. This means that the exosuit does not assist the user through an actuator of any kind (electric motor, pneumatic/hydraulic system, etc.), but rather uses passive elements that can store and release user generated mechanical energy. This energy storage and controlled release is regulated by an active component. The QPA has several advantages e.g., no need for a high-power actuation system, energy efficiency (giving high movement autonomy), and finally suitability for use in soft exoskeletons. The low-level controller developed for the pneumatic valves used to regulate the actuators reduces the air consumption, increasing significantly the autonomy of the system [24].
## Materials and Methods
### Organisms and Acclimation  
The experiment took place at Laboratorio Maritimo da Guia (LMG) (Cascais, Portugal) in a recirculation aquaculture system (RAS) equipped with seawater chiller systems (\(\pm\)0.1 \({}^{\circ}\)C, Frimar, Fernando Ribeiro, Lda., Portugal) connected to thermostat heaters (V2 Therm, TMC Iberia, Portugal), pH control (automatically adjusted with a Profilux(r) controlling system linked to individual pH probes; GHL, Kaiserslautern, Germany), protein skimmers (R Reef SkimPro 450, TMC Iberia, Portugal), ultraviolet (UV) disinfection (Vector 120 nano, TMC Iberia, Portugal), biological filtration (FSBF 1500F, TMC Iberia, Portugal), and chemical filtration (active charcoal).  
Juvenile (and sexually undifferentiated) _S. senegalensis_ specimens (\(n\) = 200; total length = 8.73 \(\pm\) 0.89 cm; weight = 7.63 \(\pm\) 2.08 g), reared at Portuguese Institute of Sea and Atmosphere (IPMA) aquaculture facilities (Olhao, Portugal), were transported in isolated containers with constant aeration to LMG, where they were randomly and equitably distributed in 24 tanks. Specimens were acclimated for 15 days, under the following conditions: temperature = 19 \({}^{\circ}\)C, pH = 8.0, salinity = 35%, dissolved oxygen >6 mg\(\cdot\)L\({}^{-1}\), and a 14-h/10-h light/dark photoperiod. Five days before initiating the exposure to the three studied stressors, seawater temperature and pH were slowly adjusted in tanks simulating climate change effects (increase by 1 \({}^{\circ}\)C and decrease by 0.1 pH units, per day, respectively) until reaching the experimental levels defined. Water abiotic parameters were daily monitored, using a multiparameter device (Multi 3420 SET G, WTW, Oberbayern, Germany), and adjusted whenever needed. Every two days, water exchange of about 10% (total water volume) was performed to maintain water quality. Ammonia, nitrite, and nitrate levels were monitored in each tank on a weekly basis using colorimetric test kits from Aquamerck(r). Ammonia and nitrite values were kept below detection, whereas nitrates were kept below 2.0 mg\(\cdot\)L\({}^{-1}\).
## 4 Conclusions  
In this paper, deterministic radioplanning techniques have been applied in order to fully determine the characteristics of the wireless channel for each one of the nodes of a Wireless Sensor Network in an indoor scenario. By taking into account all of the elements of the scenario, such as walls, hallways and furniture, detailed information on the topological dependence of the network layout can be obtained. This influences not only the expected values of coverage of each individual node, but also determines maximum capacity performance, due to the fact that interference levels are also dependent on the network topology. Estimations have been obtained with the aid of deterministic 3D Ray Launching code implemented in-house at Public University of Navarre and validated with a real WSN deployed in a real scenario. Parameters such as RSSI, Signal to Noise ratio and Packet Error Ratio have been compared numerically and with measurement results, showing good agreement. The consideration of factors such as topology and interference levels (given by multiple elements, such as variable user number and location) provide information which can be directly related to parameters such as coverage, capacity or energy consumption. Moreover, the results obtained by the use of 3D deterministic tools provide assessment in network planning design of confined scenarios, in which initial considerations may lead to the increase in required transmission nodes or the modification of the final locations of the full set of deployed nodes, as has been shown in this work.  
In comparison with commonplace approaches for wireless system and wireless sensor network deployment design and analysis, the use of 3D Ray Launching technique provides precise information for complex scenarios (in terms of size and number of sensors/transceivers/users), with the capability of considering site specific characteristics without the need of extensive measurement campaigns. Given the estimations in the growth of wireless sensor deployments, mainly due to the implementation of Context Aware environments, the proposed method provides a novel approach to provide initial location of nodes as well as an estimation of the total amount of nodes required, as a function of coverage limitations and capacity requirements.  
The use of deterministic planning tools in the design phases of WSN in complex indoor scenarios can lead to cost efficient network designs, in which interference levels and energy consumption levels can be minimized, whilst maximizing overall WSN capacity.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 4 Results
### Data Denoising  
In practical application, when the structure system uses the optical fiber sensor monitoring, the sensor monitoring instrument due to systematic error, Brillouin signal characteristics, and ambient environment (e.g., atmosphere, temperature, pressure, etc.) among other factors make the field monitoring data more or less mixed with noise signals inside the useful signal. The noise signal data gives a false judgment on the deformation of the monitored structure, and these noise signals should be analyzed and denoised. In order to eliminate or reduce the impact of noise on the monitoring data, the monitoring data is generally regarded as a digital signal sequence composed of several different frequencies, and the original data is denoised by an effective signal processing method. There are many methods for signal denoising, such as wavelet analysis, fast Fourier transform (FFT) and so on. The wavelet analysis method is adopted to remove the noise signal, the relatively ideal data are obtained.  
The original signal collected by BOTDR data acquisition instrument was decomposed into two layers by db5 wavelet function, the different threshold processing methods were used to process high-frequency signals of all levels, and then the noise signal was eliminated by wavelet reconstruction. The stein-based unbiased risk threshold rigrsure processing method is used [22]. According to the characteristics and applicability of soft and hard thresholds, soft thresholds are selected to process  
Figure 3: Raw data.  
high-frequency signals at all levels, the processed signals and low-frequency signals are reconstructed to obtain the denoised signals. The denoised signal is shown in Figure 4.  
Through analysis, the change rule of signals processed by Stein-based unbiased risk threshold rigrsure method is the same as the original monitoring signal. While removing most of the noise, the retaining signal is useful, it achieves the purpose of denoising the monitoring signal.
# Intelligent Total Transportation Management System for Future Smart Cities
## 2 Materials and Methods
### Hierarchical Approach to Total Transportation Management
The available information should contain data on the type of the vehicle (and of course its performance), its identification number, load, characteristics available in on-board systems, the instantaneous position of the given vehicles (for example by using the GPS positioning data), purpose, and final destination of travel
5. connecting vehicles--work together passively (using, for example, sonic or radar measurements to keep the following distance) or actively (exchanging all the available data on board) and they may even harmonize their actions (like moving in formation or using the conflict detection and resolution based on the exchanged information)
6. contract-based vehicles--may have some preferences in the transportation system (like temporary opening the bus lanes or giving them green lights as possible) served on the basis of first come, first served, and they must pay (low fees) for serving
7. priority transport--should have priority lanes/control (as for police, emergency cars, traveling of VIP persons)
8. supporting partners--starting by continuous weather forecast reporting up to emergency management organization having the highest priority. As in any classification, here, some of the categories could overlap. For example, public transportation may have enormous influence on the other classes. On the other hand, public transportation is partly a prioritized transportation because of the introduction of bus lanes and the control of the traffic lights to reduce the mass transport traveling time.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 3 Results
### Analysis of Trend  
#### 3.2.1 Trend of Drought Indices  
The Theil-Sen Median method is used to quantitatively analyze the overall trend of SHRI and TVDI during the drought (Figure 6). The pixels of drought strengthening in TVDI and SHRI are 41.8% and 55.3% in the Huanghuai region, respectively. The drought strengthening in TVDI pixels accounts for an eastern Yunnan Province (60.4%), while the drought strengthening in SHRI pixels is more scattered (47.6%). The drought strengthening in TVDI pixels and SHRI pixels in Guangdong Province is 96.1% and 68% in the east, respectively. The pixels of the same drought trends were 61.7%, 42.4%, and 65.6% in the Huanghuai region, Yunnan Province, and Guangdong Province, respectively. Yunnan Province observed the largest difference in the distribution of drought trends among the three drought events, but some regions are still experiencing the same drought process. Overall, TVDI and SHRI trends are consistent.  
Figure 5: Correlation between TVDI and SHRI in (**a**)the Huanghuai region, (**b**) Yunnan Province, (**c**) and Guangdong Province.  
Figure 6: Sen-trend of TVDI ((**a**) Huanghuai region, (**b**) Yunnan, (**c**) Guangdong) and SRHI ((**d**) Huanghuai, (**e**) Yunnan, (**f**) Guangdong) in the study area.
## 3 Results and Discussion  
### Influence of the Impeller Speed  
The droplet diameters and the width of the distribution are mainly affected by the impeller speed [1; 56]. Selected number distributions for the investigated impeller speed at \(\varphi=40\) vol.-% are exemplarily depicted in Figure 5a.  
The shape of the measured number distributions in Figure 5a show a lognormal-distribution by trend. Local maxima in number distributions occur at the class \(\overline{d}=50\)\(\upmu\)m. These droplets are too small for further breakup or coalescence in this fine dispersion. [PERSON] et al. [43] describe their CNN in combination with the presented camera and lens setup as poorly suited for small droplets, since the relative error for droplets smaller than \(d\leq 80\)\(\upmu\)m exceeds 10%. This error is insignificant for the volume distribution \(q_{3}\), since the fine dispersion has a negligible contribution to the volume distribution as depicted in Figure 5b. The volume distributions are nearly \(q_{3,(<100\)\(\upmu\)m}\approx 0\)\(\upmu\)m\({}^{-1}\) for particle classes smaller than 100 \(\upmu\)m and are normally distributed in shape, which indicates a coalescing system [3]. As expected, the droplet size in the number as well as in the volume distribution decreases with increasing impeller speed. In addition, the distribution becomes narrower with increasing energy input and thus increasing impeller speed. This trend is observed for all experiments. DSD data of all \(q_{0}\) and \(q_{3}\) is given in the Supplementary material. For a simpler visualization of multiple experiments, Figure 6 depicts the Sauter mean diameters calculated according to Equation (13). The Sauter mean diameter decreases with increasing impeller speed. The qualitative trend of the Sauter mean diameter at different impeller speeds is similar for all phase fractions, with the trend shifting towards a larger droplet diameter for higher phase fractions.  
Figure 5: (**a**) Number distribution at different rpm at \(\varphi=40\) vol.-% phase fraction; (**b**) volume distribution at different rpm at \(\varphi=40\) vol.-% phase fraction. The lines are a guide to the eye.  
All experimental data are in the typical error range. The average deviation of the Sauter mean diameter between two repetitions of an experiment (same impeller speed and phase fraction) is less than 4 \(\upmu\)m and the maximum deviation, with one exception, is less than 9 \(\upmu\)m, which confirms a high reproducibility. Small deviations are especially found for \(N\) = 250 rpm and are already discussed in the work of [PERSON] et al. [24]. The largest error with approx. 25 \(\upmu\)m is found at \(N\) = 250 rpm and \(\varphi\) = 50 vol.-% and could be caused by a slight malfunction of the CNN at those high phase fractions. All measured Sauter mean diameters and the number of measured particles are listed in the Supplementary Material in Tables S3-S48.
## 2 Related Works
### Multi-Object Tracking
#### 2.2.1 Benchmarks  
MOT-Challenge [9; 10; 24] is the most commonly used benchmark for multiple object tracking. It is one of the largest datasets for pedestrian tracking that are currently publicly available. In addition to traditional bullet cameras, the researchers from Boston University [25] introduced a new benchmark dataset of in-the-wild events for people tracking from overhead fish-eye cameras, with 14 different videos recorded in different scenes.  
However, players running on the sports field have a lot of differences compared to the pedestrian datasets mentioned above. SoccerNet-V2 [13] released 12 complete soccer games and [PERSON] et al. provided 13 hockey video clips [12] focusing on the player tracking. However, these videos are all from broadcast footage operated by professional operators, such datasets are not economically suitable for player tracking in non-professional games.  
[PERSON] et al. [14] processed a multi-views player tracking datasets, including two basketball datasets and three youth football datasets. The complexity of optical-based tracking systems increases geometrically with the number of cameras, so such data is also not suitable for our challenge's scenarios. Our previous work [15] presented a dataset focusing on basketball courts, but the area covered by the camera's perspective is small (only half a basketball court). And the scenarios involved are also relatively simple.  
The models trained based on the above datasets are not suitable for the needs of low-cost, high-efficiency, long-term and stable tracking of players. Therefore, it is necessary to establish a targeted dataset for the special circumstances of non-professional soccer games.
## 3 Experimental Validation
### Experimental Process  
A hole located at (0, 0) was drilled into the plate, and it is marked as red circular in Figure 6. Once the set-up was equipped with the air exhaust system, the air evacuated from the hole to the vacuum-to-plate adapter could simulate the leakage. Leak-generated ultrasounds spreading through the plate as guided Lamb waves were acquired by the sensors. The sensor array was placed at position A, of which the actual azimuthal direction of the leak was 9\({}^{\circ}\), and the signal sampling was started as depicted in Section 3.1. The collection of simultaneous leak signals of 8 sensors was repeated 5 times; each data series lasted 0.5 s and was split into 4 segments. Thus, a total of up to 20 segments of data at each position were gained at position A. A series of experiments was done with our array at 9 positions. The positions are marked with green marks in Figure 6. The corresponding azimuthal direction of the leak at positions B, C, D, E, F, G, H and I was 13\({}^{\circ}\), 20\({}^{\circ}\), 24\({}^{\circ}\), 31\({}^{\circ}\), 45\({}^{\circ}\), 57\({}^{\circ}\), 73\({}^{\circ}\), and 79\({}^{\circ}\), respectively. The distance from the sensor array to the leak source was maintained at R = 30 cm during the experiment. Thus, 180 segments of data were acquired in total for one leakage hole. In order to test the leakage of different holes to verify the adaptability of the method in this paper, two plates with 1.5 and 2.0 mm holes were used.  
Figure 5: A photo of the sensor array.  
Figure 6: The position of sensor array in the experiment.  
From the FWMB theory, the location of signal source can be determined by applying two sensor arrays. Thus, in the rest part of the paper, the orientation accuracy of the leak source is discussed instead of the positioning accuracy.